Oil & Gas Value Chain and Local Content Role in Aiding Job Creation and Adding Human Resource Value to the Omani Economy

Abundance of oil in the GCC has provided countries within the region with large amount of wealth. This has enabled countries in the GCC to achieve economic progress, especially in the 21st century. As one of the oil rich countries in the GCC, Oman has been economically dependent on oil and gas as a major source of its revenues for a long time. The Omani government has recognized how dependent the economy has been on the oil sector and is implementing a number of programs to help diversify development. The implementation of the In-Country Value (ICV), together with the country’s Local Workforce Development Programme has defined a curriculum that sets the required skills and standards not only within the oil and gas industry but also within other sectors. Thus, within the local content framework, the research finds it necessary to evaluate how the oil & gas value chain and local content play a vital role in job creation for the future. Through the descriptive and exploratory research carried out in gathering primary data and compiling the secondary sources to gain support of the analysis the study has presented the following findings: Gained a comprehensive understanding of job creation and added value within the context of Oman’s economy and determine how human resource development can impact the oil and gas sector. Brought forth the gaps that are present in the development of human resources and how government initiatives can bridge the gap to the advantage of the aggregate economy. And, Evaluated the extent to which the local content policies of the government have been achieved. This includes an evaluation of the incorporation of such policies and programs within the value chains within the various spheres of the Omani economy in terms of creating and adding human resource values in different economic domains. Based on the findings the study has furthermore recommended certain necessary factors which aims to bridge the gaps identified in implementing the policies and in practicing the Human Resource Development within organizations. Human resource development should be structured by private and public sector organizations in such a way which will produce welltrained professionals who will have the knowledge to strengthen cost-effectiveness in production. The government as well as the public enterprises should develop means to thrive innovation and creativity within its workforce, besides providing financial and security benefits. The private enterprises need to provide job security to the nationals which will encourage them to prefer the sector just as they are interested in working for the government. In order to utilize the potential of its nationals to the fullest thereby adding value to the economic domain, it is important for the government to formulate policies for diversification of workforces providing women and people with disabilities equal opportunities to project their potential and skills. Keywords: In-Country Value (ICV), Local Content, Omanization, Oil and Gas Industry, Government Initiatives, Oma

Ensemble methods for instance-based Arabic language authorship attribution

The Authorship Attribution (AA) is considered as a subfield of authorship analysis and it is an important problem as the range of anonymous information increased with fast growing of internet usage worldwide. In other languages such as English, Spanish and Chinese, such issue is quite well studied. However, in Arabic language, the AA problem has received less attention from the research community due to complexity and nature of Arabic sentences. The paper presented an intensive review on previous studies for Arabic language. Based on that, this study has employed the Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS) method to choose the base classifier of the ensemble methods. In terms of attribution features, hundreds of stylometric features and distinct words using several tools have been extracted. Then, Adaboost and Bagging ensemble methods have been applied on Arabic enquires (Fatwa) dataset. The findings showed an improvement of the effectiveness of the authorship attribution task in the Arabic language

Approach to Improve Reading Skill of Students with Dyslexia

The purpose of this research is to define and explain the way of learning to read that is effective in increasing the reading abilities of students with dyslexia. The qualitative research technique that was employed in this study consisted of case study analysis. Those who participated in this research had dyslexia as their primary diagnosis. Interviews, observation, and documentation were employed as data gathering methods, with triangulation techniques being used to assess the validity of the information. Data analysis is carried out by the Miles and Huberman model, which includes data gathering, data reduction, data presentation, and final findings. Several methods were combined to create the method used for dyslexic students, including the phonic method, the syllable peeling method, and the language experience method, as well as simplification of reading practice materials and assistance with a verbal explanation of the material from a special assistant teacher for students, according to the findings. As shown by indications of early reading ability, the technique has the potential to increase early reading abilities in dyslexic pupils. Among these skills are the ability to recognize and read letters and letter sounds, as well as the ability to pronounce words correctly and to read sentences aloud and with the correct pronunciation for an extended period

Millimeter wave propagation measurements and characteristics for 5G system

In future 5G systems, the millimeter wave (mmWave) band will be used to support a large capacity for current mobile broadband. Therefore, the radio access technology (RAT) should be made available for 5G devices to help in distinct situations, for example device-to-device communications (D2D) and multi-hops. This paper presents ultra-wideband channel measurements for millimeter wave bands at 19, 28, and 38 GHz. We used an ultra-wideband channel sounder (1 GHz bandwidth) in an indoor to outdoor (I2O) environment for non-line-of-sight (NLOS) scenarios. In an NLOS environment, there is no direct path (line of sight), and all of the contributed paths are received from different physical objects by refection propagation phenomena. Hence, in this work, a directional horn antenna (high gain) was used at the transmitter, while an omnidirectional antenna was used at the receiver to collect the radio signals from all directions. The path loss and temporal dispersion were examined based on the acquired measurement data—the 5G propagation characteristics. Two different path loss models were used, namely close-in (CI) free space reference distance and alpha-beta-gamma (ABG) models. The time dispersion parameters were provided based on a mean excess delay, a root mean square (RMS) delay spread, and a maximum excess delay. The path loss exponent for this NLOS specific environment was found to be low for all of the proposed frequencies, and the RMS delay spread values were less than 30 ns for all of the measured frequencies, and the average RMS delay spread values were 19.2, 19.3, and 20.3 ns for 19, 28, and 38 GHz frequencies, respectively. Moreover, the mean excess delay values were found also at 26.1, 25.8, and 27.3 ns for 19, 28, and 38 GHz frequencies, respectively. The propagation signal through the NLOS channel at 19, 28, and 38 GHz was strong with a low delay; it is concluded that these bands are reliable for 5G systems in short-range applications

Survey of millimeter-wave propagation measurements and models in indoor environments

The millimeter-wave (mmWave) is expected to deliver a huge bandwidth to address the future demands for higher data rate transmissions. However, one of the major challenges in the mmWave band is the increase in signal loss as the operating frequency increases. This has attracted several research interests both from academia and the industry for indoor and outdoor mmWave operations. This paper focuses on the works that have been carried out in the study of the mmWave channel measurement in indoor environments. A survey of the measurement techniques, prominent path loss models, analysis of path loss and delay spread for mmWave in different indoor environments is presented. This covers the mmWave frequencies from 28 GHz to 100 GHz that have been considered in the last two decades. In addition, the possible future trends for the mmWave indoor propagation studies and measurements have been discussed. These include the critical indoor environment, the roles of artificial intelligence, channel characterization for indoor devices, reconfigurable intelligent surfaces, and mmWave for 6G systems. This survey can help engineers and researchers to plan, design, and optimize reliable 5G wireless indoor networks. It will also motivate the researchers and engineering communities towards finding a better outcome in the future trends of the mmWave indoor wireless network for 6G systems and beyond

Enhancing network design through statistical evaluation of MANET routing protocols

This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network (MANET) routing protocols: Destination Sequenced Distance Vector (DSDV), Ad hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Zone Routing Protocol (ZRP). In this paper, the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis, Mann-Whitney, and Friedman. It articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility patterns. The study is premised upon the Quality of Service (QoS) metrics of throughput, packet delivery ratio, and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network scenarios. The findings explained significant differences in the performance of different routing protocols; as a result, decisions for the selection and optimization of routing protocols can be taken effectively according to different network requirements. This paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures

Hybrid and multifaceted context-aware misbehavior detection model for vehicular ad hoc network

Vehicular Ad Hoc Networks (VANETs) have emerged mainly to improve road safety and traffic efficiency and provide user comfort. The performance of such networks’ applications relies on the availability of accurate and recent mobility-information shared among vehicles. This means that misbehaving vehicles that share false mobility information can lead to catastrophic losses of life and property. However, the current solutions proposed to detect misbehaving vehicles are not able to cope with the dynamic vehicular context and the diverse cyber-threats, leading to a decrease in detection accuracy and an increase in false alarms. This paper addresses these issues by proposing a Hybrid and Multifaceted Context-aware Misbehavior Detection model (HCA-MDS), which consists of four phases: data-collection, context-representation, context-reference construction, and misbehavior detection. Data-centric and behavioral-detection-based features are derived to represent the vehicular context. An online and timely updated context-reference model is built using unsupervised nonparametric statistical methods, namely Kalman and Hampel filters, through analyzing the temporal and spatial correlation of the consistency between mobility information to adapt to the highly dynamic vehicular context. Vehicles’ behaviors are evaluated locally and autonomously according to the consistency, plausibility, and reliability of their mobility information. The results from extensive simulations show that HCA-MDS outperforms existing solutions in increasing the detection rate by 38% and decreasing the false positive rate by 7%. These results demonstrate the effectiveness and robustness of the proposed HCA-MDS model to strengthen the security of VANET applications and protocols

Masked face recognition using deep learning: a review

A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR