Effect of testosterone and fluoxetine on aggressive behaviors of fighting fish, Betta splendens

Effects of oral administration of testosterone and fluoxetine exposure on aggressive behavior of the fighting fish, Betta splendens, were investigated. Testosterone diluted in ethanol and sprayed on pre-weighted pellet to achieve concentrations of 0, 1, 2 and 4 mg/kg of hormone in food. Two main behaviors were recorded: the time in front of mirror and duration of the gill flaring using a mirror 8 and 15 days after the start of the experiment. Then, half of the specimens in each treatment subjected to waterborne fluoxetine at a concentration of 100 µg/L for 24 hours and the behavior was recorded. After 8 days of feeding, the time in front of mirror and duration of gill flaring were not significantly different between the treatments. Duration of the gill flaring increased significantly after 15 days; however there was no significant difference for the behavior in front of the mirror. Over time the aggressive behaviors were reduced significantly after fluoxetine exposure. This study indicated that fluoxetine in the aquatic environment alters the aggressive behaviors of the fighting fish

Software Watermarking

Software watermarking is a defence technique used to prevent software piracy by embedding a signature, i.e., an identifier reliably representing the owner, in the code. When an illegal copy is made, the ownership can be claimed by extracting this identifier. The signature has to be hidden inside the program and it has to be difficult for an attacker to detect, tamper or remove it. In this paper we show how the ability of the attacker to identify the signature can be modelled in the framework of abstract interpretation as a completeness property. We view attackers as abstract interpreters that can precisely observe only the properties for which they are complete. In this setting, hiding a signature in the code corresponds to inserting it in terms of a semantic property that can be retrieved only by attackers that are complete for it. Indeed, any abstract interpreter that is not complete for the property specifying the signature cannot detect, tamper or remove it. The goal of this work is to introduce a formal framework for the modelling, at a semantic level, of software watermarking techniques and their quality features

Semifragile Speech Watermarking Based on Least Significant Bit Replacement of Line Spectral Frequencies

There are various techniques for speech watermarking based on modifying the linear prediction coefficients (LPCs); however, the estimated and modified LPCs vary from each other even without attacks. Because line spectral frequency (LSF) has less sensitivity to watermarking than LPC, watermark bits are embedded into the maximum number of LSFs by applying the least significant bit replacement (LSBR) method. To reduce the differences between estimated and modified LPCs, a checking loop is added to minimize the watermark extraction error. Experimental results show that the proposed semifragile speech watermarking method can provide high imperceptibility and that any manipulation of the watermark signal destroys the watermark bits since manipulation changes it to a random stream of bits

A review on the Current Areas of Geriatric`s Research in Iran

Introduction: Population aging is becoming a global challenge for developing countries. The aim of the present paper is to review the current literature on geriatric health and to propose possible research areas. Methods: By reviewing scientific databases, all published papers in geriatric health within the last 5 years (until 31 December, 2019) were extracted. Inclusion criteria were being about elderly health conducted on Iranian elderly population and published no longer than 5 years ago. Unrelated, foreign studies and qualitative, trend analysis, and case series were excluded. The keywords were classified into 5 macro domain of General, Biological, psychological, Social and Spiritual domains. Results: Until 31 December, 2019, 713 related studies were finally retrieved. 56.8% of keywords belonged to the Biological macro domain. Among which, neurologic disorders had the highest proportion of studies (n=108, 15.1%). The most prevalent subdomain was the “Sociological” (P=15.4%) and the most prevalent keyword was “Quality of life”. The lowest proportion of studies was belonged to Hematology and Otolaryngology (0.4% each). Among the top institutions in terms of publication output are University of Social welfare and Rehabilitation Sciences (11%), Shahid Beheshti University of Medical Sciences (9.7%), and Iran University of Medical Sciences (9%).   Conclusion: Most of the literature concerning elderly`s health in Iran has focused on biologic aspect of health. There seems to be an urgent need to develop a research roadmap to cover all aspects of research among elderlies. Various prepositions to develop and promote context-based and innovative strategies are also provided

Optimization of a Blind Speech Watermarking Technique against Amplitude Scaling

This paper presents a gain invariant speech watermarking technique based on quantization of the Lp-norm. In this scheme, first, the original speech signal is divided into different frames. Second, each frame is divided into two vectors based on odd and even indices. Third, quantization index modulation (QIM) is used to embed the watermark bits into the ratio of the Lp-norm between the odd and even indices. Finally, the Lagrange optimization technique is applied to minimize the embedding distortion. By applying a statistical analytical approach, the embedding distortion and error probability are estimated. Experimental results not only confirm the accuracy of the driven statistical analytical approach but also prove the robustness of the proposed technique against common signal processing attacks

The prevalence of urinary incontinence following radical prostatectomy and its related factors: A national registry based study

Introduction: The purpose of this paper is to evaluate the prevalence and the risk factors of urinary incontinence following radical prostatectomy in Iranian population. This study is conducted based on the available data from the National Cancer Registry. Materials and Methods: In this retrospective study, we extracted the information of all the patients with organ-confined prostate cancer who underwent radical prostatectomy from 2010 to 2014. All the patients were interviewed face to face or via telephone to collect additional data. Urinary incontinence was evaluated by a questionnaire using the definition based on pads use. The effects of risk factors were evaluated using logistic regression models. Results: The details of 13,583 registered patients with prostate cancer were collected. Overall, the prevalence of urinary incontinence was estimated as 10.5% (n=1424). It is important to mention that the highest proportion of cases with urinary incontinence belonged to the age group of 71-80 years old (n=502, 35.2%), as well as patients with elementary education (n=458, 32%) or no education at all (n=333,23.5%). Furthermore, more cases lived in urban settings (n=1159,81.7%), one-fourth of them (n=365) smoked tobacco, and nearly 11% of them reported having been diagnosed with diabetes (n=152). The odds of having urinary incontinence increased by 20% in patients who had undergone radiotherapy as part of their treatment for prostate cancer (AOR=1.20, 95%CI: 1.07,1.36). Conclusion: We estimated the prevalence of urinary incontinence after radical prostatectomy as 10.5% among prostate cancer patients. We found that having been exposed to education, having been diagnosed with diabetes, and receiving radiotherapy, are amongst the significant risk factors for urinary incontinence. We also suggested that more predictor variables should be recorded in the National Cancer Registry

Digital audio and speech watermarking based on the multiple discrete wavelets transform and singular value decomposition

The ever-increasing illegal manipulation of genuine audio products has been a dilemma for the music industry. This situation calls for immediate, yet effective, solutions to avoid further financial losses and intellectual property violations. Audio and speech watermarking has been proposed as a possible solution, since this technology embeds copyright information into audio files as a proof of their ownership. In this paper, we propose an effective, robust, and an inaudible audio and speech watermarking algorithm. The effectiveness of the algorithm has been brought by virtue of applying a cascade of two powerful mathematical transforms; the discrete wavelets transform (DWT) and the singular value decomposition (SVD). Experimental results will be presented in this paper to demonstrate the effectiveness of the proposed algorithm

BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets

The free flow of information has been accelerated by the rapid development of social media technology. There has been a significant social and psychological impact on the population due to the outbreak of Coronavirus disease (COVID-19). The COVID-19 pandemic is one of the current events being discussed on social media platforms. In order to safeguard societies from this pandemic, studying people's emotions on social media is crucial. As a result of their particular characteristics, sentiment analysis of texts like tweets remains challenging. Sentiment analysis is a powerful text analysis tool. It automatically detects and analyzes opinions and emotions from unstructured data. Texts from a wide range of sources are examined by a sentiment analysis tool, which extracts meaning from them, including emails, surveys, reviews, social media posts, and web articles. To evaluate sentiments, natural language processing (NLP) and machine learning techniques are used, which assign weights to entities, topics, themes, and categories in sentences or phrases. Machine learning tools learn how to detect sentiment without human intervention by examining examples of emotions in text. In a pandemic situation, analyzing social media texts to uncover sentimental trends can be very helpful in gaining a better understanding of society's needs and predicting future trends. We intend to study society's perception of the COVID-19 pandemic through social media using state-of-the-art BERT and Deep CNN models. The superiority of BERT models over other deep models in sentiment analysis is evident and can be concluded from the comparison of the various research studies mentioned in this article.Comment: 20 pages, 5 figure