Confidence Intervals for Parallel Systems with Covariates

Exact confidence intervals for regression models with censored data are often not tractable, and hence approximate intervals are derived. The most common method of obtaining these approximate intervals is based on the asymptotic normal distribution of the maximum likelihood estimator. These intervals are easy to compute and they are used in most computer statistical packages. However, these intervals have some limitations. When the sample size is small or even moderate they tend to be anticonservative and have asymmetric upper and lower tail probabilities. An alternative method based on the asymptotics of the maximum likelihood estimator is to construct intervals from the inverted likelihood ratio tests. The performance of these intervals is investigated for the regression models based on parallel systems with covariates, and with randomly right censored data for finite samples. The simulation results show that the intervals based on the inverted likelihood ratio test have better performance. They have coverage probability that is close to the nominal one, and have nearly symmetric upper and lowel tail probabilities

Real-Time QoS Monitoring and Anomaly Detection on Microservice-based Applications in Cloud-Edge Infrastructure

Ph. D. Thesis.Microservices have emerged as a new approach for developing and deploying cloud applications that require higher levels of agility, scale, and reliability. A microservicebased cloud application architecture advocates decomposition of monolithic application components into independent software components called \microservices". As the independent microservices can be developed, deployed, and updated independently of each other, it leads to complex run-time performance monitoring and management challenges. The deployment environment for microservices in multi-cloud environments is very complex as there are numerous components running in heterogeneous environments (VM/container) and communicating frequently with each other using REST-based/REST-less APIs. In some cases, multiple components can also be executed inside a VM/container making any failure or anomaly detection very complicated. It is necessary to monitor the performance variation of all the service components to detect any reason for failure. Microservice and container architecture allows to design loose-coupled services and run them in a lightweight runtime environment for more e cient scaling. Thus, containerbased microservice deployment is now the standard model for hosting cloud applications across industries. Despite the strongest scalability characteristic of this model which opens the doors for further optimizations in both application structure and performance, such characteristic adds an additional level of complexity to monitoring application performance. Performance monitoring system can lead to severe application outages if it is not able to successfully and quickly detecting failures and localizing their causes. Machine learning-based techniques have been applied to detect anomalies in microservice-based cloud-based applications. The existing research works used di erent tracking algorithms to search the root cause if anomaly observed behaviour. However, linking the observed failures of an application with their root causes by the use of these techniques is still an open research problem. Osmotic computing is a new IoT application programming paradigm that's driven by the signi cant increase in resource capacity/capability at the network edge, along with support for data transfer protocols that enable such resources to interact more seamlessly with cloud-based services. Much of the di culty in Quality of Service (QoS) and performance monitoring of IoT applications in an osmotic computing environment is due to the massive scale and heterogeneity (IoT + edge + cloud) of computing environments. To handle monitoring and anomaly detection of microservices in cloud and edge datacenters, this thesis presents multilateral research towards monitoring and anomaly detection on microservice-based applications performance in cloud-edge infrastructure. The key contributions of this thesis are as following: • It introduces a novel system, Multi-microservices Multi-virtualization Multicloud monitoring (M3 ) that provides a holistic approach to monitor the performance of microservice-based application stacks deployed across multiple cloud data centers. • A framework forMonitoring, Anomaly Detection and Localization System (MADLS) which utilizes a simpli ed approach that depends on commonly available metrics o ering a simpli ed deployment environment for the developer. • Developing a uni ed monitoring model for cloud-edge that provides an IoT application administrator with detailed QoS information related to microservices deployed across cloud and edge datacenters.Royal Embassy of Saudi Arabia Cultural Bureau in London, government of Saudi Arabi

Exploring the tolerance of Iraqi wheat varieties: Evaluating seed germination and early growth of six Iraqi wheat varieties under salinity stress

Abiotic stresses reduce the production of crops by 50% which significantly affects the food security globally. Plant growth and development are affected by salinity stress, Salt stress affects about 19.5% of irrigated lands and 2.1% of drylands which is expected to rise in the future. Wheat Triticum aestivum is classified as one of the most significant crop globally besides maize and rice which significantly contribute as a part of daily calories and proteins and it ranked first for its values in domestication and staple food. The purpose of the study was to assess how well various wheat genotypes tolerated salinity under various salinity concentrations, and the varieties were (Iba99, Hadbaa, Hashmiaa, Al-Rasheed, Sham, and Rabiaa). Different NaCl concentrations were used (50, 100, 150, and 200 mM) and Measurements were made on germination %, shoot length, fresh weight, and dried weight. Iba99, Sham and Rabiaa were the best varieties where the seed germination was 100% and other varieties differed slightly (Hadbaa 40, Hashmiaa 80 and Al-Rasheed 60%). The growth parameters results demonstrated that all the shoot lengths and fresh and dry weights were affected by the salinity stress and the correlation was inverse. It was decreased with the NaCl concentration increase. Rabiaa and Iba99 were the more tolerant and demonstrated high growth under salinity whereas Sham showed lowest growth under salinity

3D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A review

Three-dimensional (3D) point cloud analysis has become one of the attractive subjects in realistic imaging and machine visions due to its simplicity, flexibility and powerful capacity of visualization. Actually, the representation of scenes and buildings using 3D shapes and formats leveraged many applications among which automatic driving, scenes and objects reconstruction, etc. Nevertheless, working with this emerging type of data has been a challenging task for objects representation, scenes recognition, segmentation, and reconstruction. In this regard, a significant effort has recently been devoted to developing novel strategies, using different techniques such as deep learning models. To that end, we present in this paper a comprehensive review of existing tasks on 3D point cloud: a well-defined taxonomy of existing techniques is performed based on the nature of the adopted algorithms, application scenarios, and main objectives. Various tasks performed on 3D point could data are investigated, including objects and scenes detection, recognition, segmentation and reconstruction. In addition, we introduce a list of used datasets, we discuss respective evaluation metrics and we compare the performance of existing solutions to better inform the state-of-the-art and identify their limitations and strengths. Lastly, we elaborate on current challenges facing the subject of technology and future trends attracting considerable interest, which could be a starting point for upcoming research studie

Bioefficacy of selected insecticides on late stage instars of bagworm, metisa plana (walker) / Mohammad Daniel Mat …[et al.]

Metisa plana is a significant oil palm pest in South East Asia and are well recognized for its devastating impact on oil palms in Malaysia. Due to the high intensity of the M. plana assault on oil palm plantation in the peninsular, Malaysia. The use of insecticides has become a famous means of controlling M. plana infestation when the economic threshold is reached. Three selected insecticides are Cypermethrin, Flubendiamide, and Bacillus thuringiensis tested for their toxicity toward M. plana. Late-stage instar of M. plana was collected at Felda Serting Hilir 4, Bahau, to be tested on three different pesticides, including one control treatment. For each treatment, five replications were exposed to the selected chemical by using the leaf dip bioassay method. The mortality of M. plana was recorded for eight consecutive days. Results showed that both Cypermethrin and Flubendiamide could achieve a 100% mortality rate within four days while it takes eight days for Bacillus thuringiensis and control treatment. Further study should be done in the field to obtain more accurate results when exposed to natural conditions and the environmen

Compact V-Shaped MIMO Antenna For LTE And 5G Communications

A new small V-shaped MIMO antenna with dimensions of the antenna 21 × 24 × 0.8 mm within the bands of (4.4-4.9) and (5.15-5.925) GHz was designed, and the fabrication and measurement outcomes derived from the use of the MIMO prototype revealed that the fractal MIMO antenna. The small and simple fractal antenna demonstrated high isolation of less than -18.5 dB and envelope correlation coefficient less than 0.05. These attributes are suitable for mobile, which is being introduced into Japanese markets

Multiple model predictive control of nonlinear pH neutralization system

In this paper the control of nonlinear systems using linear models is studied. The control strategy utilizes a piecewise linear description of the process, considered the model bank. The model bank is then combined at each sampling interval, through the application of a Bayesian weight calculator, to render a single linear model describing the system. The linear model is used in a model predictive control (MPC) setting to render the optimal control move. The performance of the setup is simulated for a pH neutralization process, which demonstrates a good following of setpoint changes and quick reduction of oscillations

Compact V-Shaped MIMO Antenna For LTE And 5G Applications

A compact sierpinski MIMO antenna was designed in the bands of 4.4 - 4.9 GHz and 5.15 - 5.925 GHz.The proposed dimension of the antenna were 21 × 24 × 0.8 mm, and was fabricated with inexpensive FR4 substrate with a thickness of 0.8 mm, a dielectric constant of 4.3, and 0.035-mm thick copper lining. The fabrication and measurement outcomes derived from the MIMO prototypes revealed that the proposed MIMO antennas are better in terms of size, isolation and the ECC. These attributes are suitable for LTE and 5G smartphone applications, which are being introduced into Chinese and Japanese markets

Compact Mimo Slots Antenna Design With Different Bands And High Isolation For 5G Smartphone Applications

In this paper, two elements of the multi-input multi-output (MIMO) antenna had been used to study the five (3.1-3.55GHz and 3.7-4.2GHz), (3.4-4.7 GHz), (3.4-3.8GHz) and (3.6-4.2GHz) 5G bands of smartphone applications that is to be introduced to the respective US, Korea, (Europe and China) and Japan markets. With a proposed dimension of 26 × 46 × 0.8 mm3, the medium-structured and small-sized MIMO antenna was not only found to have demonstrated a high degree of isolation and efficiency, it had also exhibited a lower level of envelope correlation coefficient and return loss, which are well-suited for the 5G bands application. From the fabrication of an inexpensive FR4 substrate with a 0.8 mm thickness level, a loss tangent of 0.035 and a dielectric constant of 4.3, the proposed MIMO antennas that had been simulated under the five different band coverage were discovered to have demonstrated a respective isolation level of about 14dB, 12dB, 21.5dB, 19dB and 20dB under a -10dB impendence bandwidth. In the measurement and fabrication outcomes that were derived from the use of the prototype MIMO in the (3.4-3.8) band of the Europe and Chinese markets, the proposed MIMO was thus found to have produced a better performance in terms of efficiency, isolation, and envelope correlation coefficient (ECC)