The adoption of bitcoins technology: The difference between perceived future expectation and intention to use bitcoins: Does social influence matter?

Bitcoin is a decentralized system that tries to become a solution to the shortcomings of fiat and gold-based currencies. Considering its newness, the adoption level of bitcoin is yet understood. Hence, several variables are proposed in this work in examining user perceptions regarding performance expectancy, effort expectancy, trust, adoption risk, decentralization and social influence interplay, with the context of user’s future expectation and behavioral intentions to use bitcoins. Data were gathered from 293 completed questionnaire and analised using AMOS 18. The outcomes prove the sound predictability of the proposed model regarding user’s future expectations and intentions toward bitcoins. All hypotheses were supported, they were significantly affecting the dependent variables. Social influence was found as the highest predictor of behavioral intention to negatively utilize bitcoins. The significant impact of social influence, adoption risk and effort expectancy which affect behavioral intention to use bitcoins the most, are demonstrated in this study. Bitcoins should thus, present an effective, feasible and personalized program which will assist efficient usage among users. Additionally, the impacts of social influence, adoption risk and perceived trust on behavioral intention to utilize new technology were compared, and their direct path was tested together, for the first time in this context

Hybrid feature selection method based on particle swarm optimization and adaptive local search method

Machine learning has been expansively examined with data classification as the most popularly researched subject. The accurateness of prediction is impacted by the data provided to the classification algorithm. Meanwhile, utilizing a large amount of data may incur costs especially in data collection and preprocessing. Studies on feature selection were mainly to establish techniques that can decrease the number of utilized features (attributes) in classification, also using data that generate accurate prediction is important. Hence, a particle swarm optimization (PSO) algorithm is suggested in the current article for selecting the ideal set of features. PSO algorithm showed to be superior in different domains in exploring the search space and local search algorithms are good in exploiting the search regions. Thus, we propose the hybridized PSO algorithm with an adaptive local search technique which works based on the current PSO search state and used for accepting the candidate solution. Having this combination balances the local intensification as well as the global diversification of the searching process. Hence, the suggested algorithm surpasses the original PSO algorithm and other comparable approaches, in terms of performance

Агрессивная форма ювенильной макромастии

ПОДРОСТКИМОЛОЧНЫЕ ЖЕЛЕЗЫ ЧЕЛОВЕКА /АНОМАЛБОЛЕЗНИ РЕДКИЕМАКРОМАСТИЯГИПЕРТРОФИЯМОЛОЧНОЙ ЖЕЛЕЗЫ ПЛАСТИКАХИРУРГИЧЕСКИЕ ОПЕРАЦИИ ВОССТАНОВИТЕЛЬНЫЕМОЛОЧНОЙ ЖЕЛЕЗЫ БОЛЕЗНИ /ТЕР /ХИ

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Strategy for Effective Awareness

<p><strong>Abstract:</strong> With the modern technologies business is evolving and the use of technology become a necessity. But with each technology there is a security and one of the main concerns for companies is awareness. Even if you have the top security controls, user's awareness most be effective to strengthen the security controls.  Users understanding for the awareness is always different and interesting challenge for any information security analyst. How to know its effectiveness? and how to measure? or even how to provide the right awareness? we will introduce the four WWWW that will lead towards effective, sustainable and sloid awareness. Who are the audience? What is there level of cyber security? What is their role in company business? What is the historical information about cyber security program? These questions are critical and in this technical paper we will discuss its value.</p><p><strong>Keywords:</strong> Information security, Human factor, Cybersecurity, Analysis. </p><p><strong>Title:</strong> Strategy for Effective Awareness</p><p><strong>Author:</strong> Bayan Almulhim, Rami Alghamdi</p><p><strong>International Journal of Computer Science and Information Technology Research</strong></p><p><strong>ISSN 2348-1196 (print), ISSN 2348-120X (online)</strong></p><p><strong>Vol. 11, Issue 4, October 2023 - December 2023</strong></p><p><strong>Page No: 53-54</strong></p><p><strong>Research Publish Journals</strong></p><p><strong>Website: www.researchpublish.com</strong></p><p><strong>Published Date: 21-November-2023</strong></p><p><strong>DOI: </strong><a href="https://doi.org/10.5281/zenodo.10166242"><strong>https://doi.org/10.5281/zenodo.10166242</strong></a></p><p><strong>Paper Download Link (Source)</strong></p><p><a href="https://www.researchpublish.com/papers/strategy-for-effective-awareness"><strong>https://www.researchpublish.com/papers/strategy-for-effective-awareness</strong></a></p&gt

Important Criteria for Asymptotic Properties of Nonlinear Differential Equations

In this article, we prove some new oscillation theorems for fourth-order differential equations. New oscillation results are established that complement related contributions to the subject. We use the Riccati technique and the integral averaging technique to prove our results. As proof of the effectiveness of the new criteria, we offer more than one practical example

Raptor: Large Scale Analysis of Big Raster and Vector Data

With the increase in amount of remote sensing data, there have been efforts to efficiently process it to help ecologists and geographers answer queries. However, they often need to process this data in combination with vector data, for example, city boundaries. Existing efforts require one dataset to be converted to the other representation, which is extremely inefficient for large datasets. In this demonstration, we focus on the zonal statistics problem, which computes the statistics over a raster layer for each polygon in a vector layer. We demonstrate three approaches, vector-based, raster-based, and raptor-based approaches. The latter is a recent effort of combining raster and vector data without a need of any conversion. This demo will allow users to run their own queries in any of the three methods and observe the differences in their performance depending on different raster and vector dataset sizes

Identifying the key characteristics, trends, and seasonality of pedestrian traffic injury at a major trauma center in Saudi Arabia: a registry-based retrospective cohort study, 2017–2022

Abstract Background Pedestrian traffic injuries are a rising public health concern worldwide. In rapidly urbanizing countries like Saudi Arabia, these injuries account for a considerable proportion of trauma cases and represent a challenge for healthcare systems. The study aims to analyze the key characteristics, seasonality, and outcomes of pedestrian traffic injuries in Riyadh, Saudi Arabia. Methods This study was a retrospective cohort analysis of all pedestrian traffic injuries presented to King Saud Medical City, Riyadh, and included in the Saudi Trauma Registry (STAR) database between August 1, 2017, and December 31, 2022. The analysis of metric and nominal variables was reported as mean (standard deviation, SD) or median (interquartile range, IQR) and frequencies (%), respectively. A logistic regression analysis was performed to examine the influence of patients’ pre-hospital vitals and key characteristics on arrival at the ED on the need for mechanical ventilation and in-hospital mortality. Results During the study period, 1062 pedestrian-injured patients were included in the analysis, mostly males (89.45%) with a mean (SD) age of 33.44 (17.92) years. One-third (35.88%) of the patients were Saudi nationals. Two-thirds (67.04%) of the injuries occurred from 6 p.m. until 6 a.m. Compared to other years, a smaller % of injury events (13.28%) were noticed during the COVID-19 pandemic (2020). Half (50.19%) of the patients were transported to the emergency department by the Red Crescent ambulance, and 19.68% required intubation and mechanical ventilation. Most of the patients (87.85%) were discharged home after completion of treatment, and our cohort had a 4.89% overall mortality. The logistic regression analysis showed the influence of patients’ pre-hospital vitals and key characteristics on arrival at the ED on the need for mechanical ventilation (Chi2 = 161.95, p < 0.001) and in-hospital mortality (Chi2 = 63.78, p < 0.001) as a whole significant. Conclusion This study details the demographic, temporal, and clinical trends of pedestrian traffic injuries at a major Saudi trauma center. Identifying high-risk individuals and injury timing is crucial for resource allocation, targeting road safety interventions like public awareness campaigns and regulatory reforms, and improving prehospital care and patient outcomes