Repositorio Universidad Europea del Atlántico
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Responsabilidade Civil do Advogado na Arbitragem: Comparativo com o Sistema Judicial
No full textO presente estudo tem como problema de pesquisa: “Se o advogado não é obrigatório na arbitragem, principalmente na internacional, ele deve ser responsabilizado civilmente pela sua atuação e pela perda de uma chance? Com o objetivo de demonstrar como funciona a responsabilidade civil do advogado no procedimento da arbitragem, principalmente na internacional, se tem relação com o exercício da profissão, quando o advogado atua de forma judicial e extrajudicial, em termos de ética e responsabilidade para com o seu cliente e demais envolvidos. O estudo tem como justificativa abordar o desenvolvimento do processo arbitral sob a garantia do devido processo legal, a atuação do advogado na justiça normal e nos procedimentos de arbitragem e sua responsabilidade na orientação junto ao cliente que optou pela arbitragem e como deve ser o comportamento do advogado na arbitragem. Além de oferecer maior esclarecimento sobre a importância do advogado na atuação e defesa de direitos; se apresentou o papel fundamental do advogado na atuação de procedimentos não só judiciais como também na arbitragem; se discorreu sobre a responsabilidade do advogado e como funciona o processo arbitral. A metodologia utilizada foi a pesquisa qualitativa exploratória de investigação-ação, e para sua realização foram empregados esforços para coleta e compilação de material bibliográfico, consultas em livros, revistas, legislação, dentre outros. Com esse estudo se verificou a importância da arbitragem internacional para os negócios internacionais, se constatou que o advogado tem papel fundamental na orientação e defesa de seus clientes na arbitragem internacional, e que a responsabilidade civil pátria deve ser considerada mesmo no caso de arbitragem internacional em relação aos danos pelas condutas dos advogados em relação às partes. A obra não tem o intuito de esgotar a análise a respeito, mas contribuirá para solução do problema com a criação de novos estudos teóricos, e possivelmente legislativos, novas bibliografias e aplicação prática no campo jurídico e empresarial
Prevalence and impact of long COVID-19 among patients with diabetes and cardiovascular diseases in Bangladesh
Get PDFIntroduction: Co-prevalence of long-COVID-19, cardiovascular diseases and diabetes is one of the major health challenges of the pandemic worldwide. Studies on long-COVID-19 and associated health outcomes are absent in Bangladesh. The main aim of this study was to determine the prevalence and impact of long-COVID-19 on preexisting diabetes and cardiovascular diseases (CVD) on health outcomes among patients in Bangladesh.
Methods: We collected data from 3,250 participants in Bangladesh, retrospectively. Multivariable logistic regression model was used to determine the odds ratio between independent and dependent variables. Kaplan-Meier survival curve was used to determine the cumulative survival.
Results: COVID-19 was detected among 73.4% (2,385 of 3,250) participants. Acute long-COVID-19 was detected among 28.4% (678 of 2,385) and chronic long-COVID-19 among 71.6% (1,707 of 2,385) patients. CVD and diabetes were found among 32%, and 24% patients, respectively. Mortality rate was 18% (585 of 3,250) among the participants. Co-prevalence of CVD, diabetes and COVID-19 was involved in majority of fatality (95%). Fever (97%), dry cough (87%) and loss of taste and smell (85%) were the most prevalent symptoms. Patients with co-prevalence of CVD, diabetes and COVID-19 had higher risk of fatality (OR: 3.65, 95% CI, 2.79–4.24). Co-prevalence of CVD, diabetes and chronic long-COVID-19 were detected among 11.9% patients.
Discussion: Risk of hospitalization and fatality reduced significantly among the vaccinated. This is one of the early studies on long-COVID-19 in Bangladesh
Modelo holístico para la innovación tecnológica en la pequeña empresa en Panamá
No full textSe decidió realizar esta investigación, para intentar resolver una problemática muy actual y muy real, y a la vez urgente, en relación a la innovación tecnológica y nivel de automatización en las pequeñas empresas en Panamá. Este tema es de gran relevancia en el país, al formar parte de los esfuerzos para mantenerse competitivos en el entorno tanto local como global. El enfoque de la investigación es explicativo, pues se concentra en identificar la raíz o causa del problema, para entonces así, atacarlo con la propuesta de solución ofrecida. Luego de una extensa revisión bibliográfica en torno al tema, estado del arte, análisis de datos y diagnósticos, el enfoque estuvo en las tecnologías exponenciales, por ofrecer el mayor potencial de lograr una solución más sostenible en el tiempo. Los resultados principalmente arrojan debilidades en relación a conocimientos de alfabetización digital y competencias digitales. Debido a la urgencia para dar solución a la problemática, y tomando en cuenta los vacíos existentes, la propuesta se enfoca en soluciones empaquetadas en la nube informática, que provean de todos los elementos necesarios para dar respuesta a la problemática. Todo esto deberá ir acompañado de un plan de capacitación para sacarle el mayor provecho, y situar a la pequeña empresa en un lugar de mayor competitividad
Real Word Spelling Error Detection and Correction for Urdu Language
No full textNon-word and real-word errors are generally two types of spelling errors. Non-word errors are misspelled words that are nonexistent in the lexicon while real-word errors are misspelled words that exist in the lexicon but are used out of context in a sentence. Lexicon-based lookup approach is widely used for non-word errors but it is incapable of handling real-word errors as they require contextual information. Contrary to the English language, real-word error detection and correction for low-resourced languages like Urdu is an unexplored area. This paper presents a real-word spelling error detection and correction approach for the Urdu language. We develop an extensive lexicon of 593,738 words and use this lexicon to develop a dataset for real-word errors comprising 125562 sentences and 2,552,735 words. Based on the developed lexicon and dataset, we then develop a contextual spell checker that detects and corrects real-word errors. For the real-word error detection phase, word-gram features are used along with five machine learning classifiers, achieving a precision, recall, and F1-score of 0.84,0.79, and 0.81 respectively. We also test the proposed approach with a 40% error density. For real-word error correction, the Damerau-Levenshtein distance is used along with the n-gram model for further ranking of the suggested candidate words, achieving an accuracy of up to 83.67%
Effect of reliable recovery on health care costs and productivity losses in emotional disorders
No full textDespite the high economic costs associated with emotional disorders, relatively few studies have examined the variation in costs according to whether or not the patient has achieved a reliable recovery or not. The aim of this study was to explore differences in health care costs and productivity losses between primary care patients from a previous RCT—PsicAP— with emotional symptoms who achieved a reliable recovery versus and those who did not after transdiagnostic cognitive-behavioural therapy (TD-CBT) plus treatment as usual (TAU) or TAU alone. Sociodemographic and cost data were obtained for 134 participants treated at five primary care centres in Madrid for the 12-month post-treatment period. Reliable recovery rates were higher in the patients who received TD-CBT+TAU versus TAU alone (66% versus 34%; chi-square= 13.78; df=1; p< .001). Patients who did not achieve reliable recovery incurred in more costs, especially associated with GP consultations (t=3.01; df=132; p=.003), use of emergency departments (t= 2.20; df= 132; p=.030), total health care costs (t=2.01; df=132; p=.040), and sick leaves (t=1.97; df=132; p=.048). These findings underscore the societal importance of achieving a reliable recovery in patients with emotional disorders, and further support the value of adding TD-CBT to TAU in the primary care setting
Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm
No full textInternet security is a major concern these days due to the increasing demand for information technology (IT)-based platforms and cloud computing. With its expansion, the Internet has been facing various types of attacks. Viruses, denial of service (DoS) attacks, distributed DoS (DDoS) attacks, code injection attacks, and spoofing are the most common types of attacks in the modern era. Due to the expansion of IT, the volume and severity of network attacks have been increasing lately. DoS and DDoS are the most frequently reported network traffic attacks. Traditional solutions such as intrusion detection systems and firewalls cannot detect complex DDoS and DoS attacks. With the integration of artificial intelligence-based machine learning and deep learning methods, several novel approaches have been presented for DoS and DDoS detection. In particular, deep learning models have played a crucial role in detecting DDoS attacks due to their exceptional performance. This study adopts deep learning models including recurrent neural network (RNN), long short-term memory (LSTM), and gradient recurrent unit (GRU) to detect DDoS attacks on the most recent dataset, CICDDoS2019, and a comparative analysis is conducted with the CICIDS2017 dataset. The comparative analysis contributes to the development of a competent and accurate method for detecting DDoS attacks with reduced execution time and complexity. The experimental results demonstrate that models perform equally well on the CICDDoS2019 dataset with an accuracy score of 0.99, but there is a difference in execution time, with GRU showing less execution time than those of RNN and LSTM
Can alpha‐linolenic acid be a modulator of “cytokine storm,” oxidative stress and immune response in SARS‐CoV‐2 infection?
No full textAlpha-linolenic acid (ALA) is a long-chain polyunsaturated essential fatty acid of the Ω3 series found mainly in vegetables, especially in the fatty part of oilseeds, dried fruit, berries, and legumes. It is very popular for its preventive use in several diseases: It seems to reduce the risk of the onset or decrease some phenomena related to inflammation, oxidative stress, and conditions of dysregulation of the immune response. Recent studies have confirmed these unhealthy situations also in patients with severe coronavirus disease 2019 (COVID-19). Different findings (in vitro, in vivo, and clinical ones), summarized and analyzed in this review, have showed an important role of ALA in other various non-COVID physiological and pathological situations against “cytokines storm,” chemokines secretion, oxidative stress, and dysregulation of immune cells that are also involved in the infection of the 2019 novel coronavirus. According to the effects of ALA against all the aforementioned situations (also present in patients with a severe clinical picture of severe acute respiratory syndrome-(CoV-2) infection), there may be the biologic plausibility of a prophylactic effect of this compound against COVID-19 symptoms and fatality
An Optimized Intelligent Computational Security Model for Interconnected Blockchain-IoT System & Cities
No full textBlockchain technology may provide a potential solution to the Internet of Things (IoT) security challenges by providing a decentralized and secure method for storing, managing, and sharing data. The Secure Hash Algorithm (SHA-256) hashed value of preliminary data (block) is retained in one block along with transaction data in tree form and timestamp in a chain of blocks. However, there are observations about blockchain limitations such as higher energy consumption, secure data, self-maintenance reliance, and higher cost. These constraints can be overcome by incorporating encryption algorithms into accepting blocks of data. In this paper, we propose a secure intelligent computational model for a large-scale interconnected IoT environment; an analytical modeling technique is considered for the proposed system. The proposed system takes advantage of the potential security feature of blockchain, which is considered the most appropriate secure communication system in an IoT. A computational model is built using the proposed blockchain technology to incorporate a secure and intelligent communication system. The proposed system uses the enhanced McEliece encryption approach’s potential to link the blockchain due to the faster mode of encryption and decryption process with a highly reduced number of steps
Image Watermarking Using Least Significant Bit and Canny Edge Detection
Get PDFWith the advancement in information technology, digital data stealing and duplication have become easier. Over a trillion bytes of data are generated and shared on social media through the internet in a single day, and the authenticity of digital data is currently a major problem. Cryptography and image watermarking are domains that provide multiple security services, such as authenticity, integrity, and privacy. In this paper, a digital image watermarking technique is proposed that employs the least significant bit (LSB) and canny edge detection method. The proposed method provides better security services and it is computationally less expensive, which is the demand of today’s world. The major contribution of this method is to find suitable places for watermarking embedding and provides additional watermark security by scrambling the watermark image. A digital image is divided into non-overlapping blocks, and the gradient is calculated for each block. Then convolution masks are applied to find the gradient direction and magnitude, and non-maximum suppression is applied. Finally, LSB is used to embed the watermark in the hysteresis step. Furthermore, additional security is provided by scrambling the watermark signal using our chaotic substitution box. The proposed technique is more secure because of LSB’s high payload and watermark embedding feature after a canny edge detection filter. The canny edge gradient direction and magnitude find how many bits will be embedded. To test the performance of the proposed technique, several image processing, and geometrical attacks are performed. The proposed method shows high robustness to image processing and geometrical attack
Integration of Remote-Sensing Techniques for the Preventive Conservation of Paleolithic Cave Art in the Karst of the Altamira Cave
Get PDFRock art offers traces of our most remote past and was made with mineral and organic substances in shelters, walls, or the ceilings of caves. As it is notably fragile, it is fortunate that some instances remain intact—but a variety of natural and anthropogenic factors can lead to its disappearance. Therefore, as a valuable cultural heritage, rock art requires special conservation and protection measures. Geomatic remote-sensing technologies such as 3D terrestrial laser scanning (3DTLS), drone flight, and ground-penetrating radar (GPR) allow us to generate exhaustive documentation of caves and their environment in 2D, 2.5D, and 3D. However, only its combined use with 3D geographic information systems (GIS) lets us generate new cave maps with details such as overlying layer thickness, sinkholes, fractures, joints, and detachments that also more precisely reveal interior–exterior interconnections and gaseous exchange; i.e., the state of senescence of the karst that houses the cave. Information of this kind is of great value for the research, management, conservation, monitoring, and dissemination of cave art