Blockchain for UAE Organizations: Insights from CIOs with Opportunities and Challenges

© 2018 IEEE. A blockchain is a distributed ledger and the underlying technology of the Bitcoin cryptocurrency, allowing it to operate in a decentralized fashion with no intermediaries such as financial institutions. The use of blockchain, however, will be far beyond the financial sector; smart contracts, for example would allow business and legal agreements to be stored and executed online. It is anticipated that blockchain will do to middle- and back-office functions what the Internet and the Web have done to the front-office - automate functions. In this paper, we investigate what Chief Information Officers (CIOs) think of blockchain and how they plan to utilize it for their organizations. We conducted a survey that was completed by 25 CIOs in the United Arab Emirates and found mixed feelings about the technology and barriers for use. In this paper, we also discuss opportunities and challenges in adopting blockchain

Port exteriorization appendectomy in children: An alternative to the conventional laparoscopic technique?

Introduction Laparoscopic appendectomy is usually performed using an  intracorporeal approach. The conventional procedure uses three ports. The port exteriorization appendectomy uses two trocars to perform the entire procedure and  can be considered an efficient alternative to the conventional approach, especially  in case of nonavailability of adequate material. We report our experience using port exteriorization appendectomy with the aim of evaluating this technique and  determining its feasibility for all grades of appendicitis.Patients and methods Between May 2013 and January 2014, 193 appendectomies  were performed in our department; in 50 cases (26%), a port exteriorization appendectomy was performed. Technical challenges, complications, and  postoperative recovery were determined and analyzed.Conclusion Port exteriorization appendectomy can beconsidered a safe and  economical approach to perform  pediatric appendectomy when conditions are  favorable. It allows minimizing minimally invasive surgery even further, enabling a low level of invasiveness and resulting in postoperative pain.Keywords: appendectomy, children, laparoscopy, port exteriorizatio

Multi-Attribute Monitoring for Anomaly Detection: a Reinforcement Learning Approach based on Unsupervised Reward

International audienceThis paper proposes a new method to solve the monitoring and anomaly detection problems of Low-power Internet of Things (IoT) devices. However, their performances are constrained by limited processing, memory, and communication, usually using battery-powered energy. Polling driven mechanisms for monitoring the security, performance, and quality of service of these networks should be efficient and with low overhead, which makes it particularly challenging. The present work proposes the design of a novel method based on a Deep Reinforcement Learning (DRL) algorithm coupled with an Unsupervised Learning reward technique to build a pooling monitoring of IoT networks. This combination makes the network more secure and optimizes predictions of the DRL agent in adaptive environments

Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey

International audienceNowadays, many research studies and industrial investigations have allowed the integration of the Internet of Things (IoT) in current and future networking applications by deploying a diversity of wireless-enabled devices ranging from smartphones, wearables, to sensors, drones, and connected vehicles. The growing number of IoT devices, the increasing complexity of IoT systems, and the large volume of generated data have made the monitoring and management of these networks extremely difficult. Numerous research papers have applied Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) techniques to overcome these difficulties by building IoT systems with effective and dynamic decision-making mechanisms, dealing with incomplete information related to their environments. The paper first reviews pre-existing surveys covering the application of RL and DRL techniques in IoT communication technologies and networking. The paper then analyzes the research papers that apply these techniques in wireless IoT to resolve issues related to routing, scheduling, resource allocation, dynamic spectrum access, energy, mobility, and caching. Finally, a discussion of the proposed approaches and their limits is followed by the identification of open issues to establish grounds for future research directions proposal

Leveraging Reinforcement Learning for Adaptive Monitoring of Low-Power IoT Networks

International audienceLow-power Internet of Things (IoT) networks are widely deployed in various environments with resource constrained devices, making their states monitoring particularly challenging. In this paper, we propose an adaptive monitoring mechanism for low-power IoT devices, by using a reinforcement learning (RL) method to automatically adapt the polling frequencies of the collected attributes. Our goal is to minimize the number of monitoring packets while keeping accurate and timely detection of threshold crossings associated to supervised attributes. We study the various RL parameter settings under different monitoring attribute behaviors using OpenAi Gym simulator. We implement the RL based adaptive polling in Contiki OS and we evaluate its performance using Cooja simulator. Our results show that our approach converges to optimal polling frequencies and outperforms static periodic notification-based methods by reducing the number of monitoring packets, with a percentage of correctly detected threshold crossings exceeding 80%

Conservative management of post-appendicectomy intra-abdominal abscesses

<p>Abstract</p> <p>Purpose</p> <p>Appendicitis is the most common abdominal inflammatory process in children which were sometimes followed by complications including intra-abdominal abscess. This later needs classically a surgical drainage. We evaluated the efficacy of antibiotic treatment and surgical drainage.</p> <p>Methods</p> <p>Hospital records of children treated in our unit for intra-abdominal post appendectomy abscesses over a 6 years period were reviewed retrospectively.</p> <p>Results</p> <p>This study investigates a series of 14 children from 2 to 13 years of age with one or many abscesses after appendectomy, treated between 2002 and 2007. Seven underwent surgery and the others were treated with triple antibiotherapy. The two groups were comparable.</p> <p>For the 7 patients who receive medical treatment alone, it was considered efficient in 6 cases (85%) with clinical, biological and radiological recovery of the abscess. There was one failure (14%). The duration of hospitalization from the day of diagnosis of intra-abdominal abscess was approximately 10.28 days (range 7 to 14 days). In the other group, the efficacy of treatment was considered satisfactory in all cases. The duration of hospitalization was about 13 days (range: 9 to 20).</p> <p>Conclusion</p> <p>Compared to surgical drainage, antibiotic management of intra-abdominal abscesses was a no invasive treatment with shorter hospitalization.</p

Comparison of three copromicroscopic methods to assess albendazole efficacy against soil-transmitted helminth infections in school-aged children on Pemba Island

Background The diagnostic accuracy of three faecal egg count techniques (Kato-Katz, McMaster and FLOTAC) to assess albendazole efficacy against soil-transmitted helminth (STH) infections was compared. Methods The study is registered with Current Controlled Trials [identifier: ISRCTN90088840]. During September-November 2009, 304 school-aged children on Pemba Island, Tanzania, were screened and those infected with Ascaris lumbricoides, hookworm or Trichuris trichiura were treated with a single dose of albendazole (400 mg). Twenty-one days post-treatment, children provided a single stool sample which was examined using the same diagnostic methods. All stool samples were divided into two aliquots and one was fixed in 5% formalin and examined using FLOTAC and McMaster approximately 6 months after collection. Results Using fresh stool samples, comparable prevalences were demonstrated for the three methods at baseline (90-92.2% for T. trichiura, 41.1-52.8% for hookworm, 32.9-37.2% for A. lumbricoides); FLOTAC was the most sensitive method at baseline and follow-up. Albendazole showed high cure rate (CR) against A.lumbricoides (90-97%), moderate CR against hookworm (63-72%) and very low CR against T.trichiura (6-9%), regardless of the technique used. Egg counts (eggs per gram) at baseline were similar for A. lumbricoides and for hookworm among the three methods, and higher using McMaster and Kato-Katz compared with FLOTAC for T. trichiura. All methods were similar for hookworm and A. lumbricoides egg reduction rate (ERR) estimation, but Kato-Katz indicated a significantly higher ERR than McMaster and FLOTAC for T. trichiura. Preserved stool samples revealed consistently lower FECs at baseline and follow-up for all STHs. Conclusion Further development and validation of standard protocols for anthelminthic drug efficacy evaluation must be pursue

A multi-patch use of the habitat: testing the First-Passage Time analysis on roe deer paths

A heterogeneous environment includes several levels of resource aggregation. Individuals do not respond in the same way to this heterogeneity depending on the scale at which they perceive it, and develop different foraging tactics accordingly. The development of methods to analyse animal movements has enabled the study of foraging tactics at several scales. Nevertheless, applied to large vertebrates, these methods have generally been used at large scales, such as for migration trips or the study of marine patches several kilometres large. In this study, we applied a recent method, the First-Passage Time analysis, based on a measure of the foraging effort along the path, to a much finer scale, i.e. under 500 meters. We used 30 daily paths of highly sedentary roe deer females. We modified the initial method, developed by Fauchald and Tveraa (2003), to detect a multi-patch use of the habitat. First-Passage Time analysis results showed that most of the female roe deer exploited their home range as a patchy resource, ranging from 1 to 5 areas of intensive use in their home range. These areas were identified as the most attractive sites within the roe deer female home range. Moreover, this method allowed us to rank the attractive areas according to the time spent in each area. Coupled with habitat selection analysis to identify what makes these areas attractive, the First-Passage Time analysis should offer a suitable tool for landscape ecology and management

Phenotypic but not genetically predicted heart rate variability associated with all-cause mortality

Low heart rate variability (HRV) has been widely reported as a predictor for increased mortality. However, the molecular mechanisms are poorly understood. Therefore, this study aimed to identify novel genetic loci associated with HRV and assess the association of phenotypic HRV and genetically predicted HRV with mortality. In a GWAS of 46,075 European ancestry individuals from UK biobank, we identified 17 independent genome-wide significant genetic variants in 16 loci associated with HRV traits. Notably, eight of these loci (RNF220, GNB4, LINCR-002, KLHL3/HNRNPA0, CHRM2, KCNJ5, MED13L, and C160rf72) have not been reported previously. In a prospective phenotypic relationship between HRV and mortality during a median follow-up of seven years, individuals with lower HRV had higher risk of dying from any cause. Genetically predicted HRV, as determined by the genetic risk scores, was not associated with mortality. To the best of our knowledge, the findings provide novel biological insights into the mechanisms underlying HRV. These results also underline the role of the cardiac autonomic nervous system, as indexed by HRV, in predicting mortality.</p

Propuesta de Implementación de Técnicas de Inteligencia Artificial en el Proceso Enseñanza-Aprendizaje para Fortalecer las Competencias Profesionales en los Estudiantes de la Carrera de Ingeniería en Sistemas Computacionales del TecNM Campus Minatitlán

Para fortalecer las competencias profesionales de los estudiantes en el TecNM campus Minatitlán, el presente artículo propone implementar técnicas de inteligencia artificial, que ayuden durante el proceso enseñanza-aprendizaje para adquirir habilidades que podrán desempeñar en su ámbito laboral, correspondiendo a su perfil de egreso como Ingenieros en Sistemas Computacionales, ante las exigencias de nuevas herramientas tecnológicas con tendencia al futuro