Performance of Vertically Stacked Horizontal Si Nanowires Transistors: A 3D Monte Carlo / 2D Poisson Schrodinger Simulation Study

In this paper we present a simulation study of 5nm vertically stacked lateral nanowires transistor (NWTs). The study is based on calibration of drift-diffusion results against a Poisson-Schrodinger simulations for density-gradient quantum corrections, and against ensemble Monte Carlo simulations to calibrate carrier transport. As a result of these calibrated results, we have established a link between channel strain and the device performance. Additionally, we have compared the current flow in a single, double and triple vertically stacked lateral NWTs

A Structured Approach to Insider Threat Monitoring for Offensive Security Teams

In many countries, government agencies resort to third parties to acquire security services of many kinds, including Red Team operations to test the effectiveness of own defenses mechanisms. Absolute trust is a key requirement, lest a potentially devastating finding be exploited by a treacherous Red Team against the same entity which commissioned the operation, or sold to its adversaries. In our endeavour as a joint private-academic initiative to address this peculiar market, we observed that a structured approach to this issue is much less common than we would have expected. In this work, we outline the process we are devising to offer customers a verified environment, but integrating it with an evidence-based proof of their correct behavior during the operation, striving to solve the “Quis custodiet ipsos custodes” struggle in an offensive setting

Computer Vision Techniques for Quality Assessment of Dates

Computer vision (CV) is a technique in which the image of an object is obtained through any imaging system and the image is analyzed to characterize the quality of the object objectively. The advancement in electronics has made this technique utilized in various fields. In North America, food industry is one of the top ten industries utilizing CV technique for quality monitoring. However, CV technique is not much utilized in the food industries in Asia. Dates is an important fruit crop in Oman and many other Arab countries. The quality assessment of dates during handling and processing are mainly carried out through manual inspection method. But this method has many challenges such as the efficiency of a worker, subjectivity, and so on. There are lots of opportunities to utilize CV technique for measuring and monitoring various quality aspects of dates. Through an Open Research Grant program funded by The Research Council (TRC), Oman, potential of CV technique for various internal and external qualities of dates was determined. This paper describes the efficiency of CV systems for variety identification, surface crack detection, texture and hardness determination

Towards the Creation of Interdisciplinary Consumer-Oriented Security Metrics

Information systems are evolving: IoT devices and Cyber-physical systems (CPS) impact on the security of assets and people in the real world. Old cybersecurity approaches, which focused on seeing humans 'as a problem', could be substitute by new paradigms of seeing humans 'as a solution'. Therefore, consumers awareness will be one of the building blocks, as well as initiative that aim to create a set of standardized security metrics that can evaluate the security of systems. In order to do that, researchers need to study which are the essential factors that our future metrics should focus on. In this paper we analyzed this problem over CPS while assuming the consumer perspective. We summarize the state of the art in security metrics and advocate the need for a research effort aimed at taking the field to a new level of formal soundness and practical usability by considering interdisciplinary implications on cybersecurity

Poster: Continual Network Learning

We make a case for in-network Continual Learning as a solution for seamless adaptation to evolving network conditions without forgetting past experiences. We propose implementing Active Learning-based selective data filtering in the data plane, allowing for data-efficient continual updates. We explore relevant challenges and propose future research directions

Complete Chloroplast Genome Sequence of Omani Lime (Citrus aurantiifolia) and Comparative Analysis within the Rosids

The genus Citrus contains many economically important fruits that are grown worldwide for their high nutritional and medicinal value. Due to frequent hybridizations among species and cultivars, the exact number of natural species and the taxonomic relationships within this genus are unclear. To compare the differences between the Citrus chloroplast genomes and to develop useful genetic markers, we used a reference-assisted approach to assemble the complete chloroplast genome of Omani lime (C. aurantiifolia). The complete C. aurantiifolia chloroplast genome is 159,893 bp in length; the organization and gene content are similar to most of the rosids lineages characterized to date. Through comparison with the sweet orange (C. sinensis) chloroplast genome, we identified three intergenic regions and 94 simple sequence repeats (SSRs) that are potentially informative markers with resolution for interspecific relationships. These markers can be utilized to better understand the origin of cultivated Citrus. A comparison among 72 species belonging to 10 families of representative rosids lineages also provides new insights into their chloroplast genome evolution

Biotic and Abiotic Stresses of Major Fruit Crops in Oman: A Review

Oman is located in an arid region of the world that is characterized by adverse climatic conditions, including heat and drought. In recent years, it has also been affected by climate turbulence and the occurrence of severe weather, such as cyclones and heat/cold waves affecting large agricultural areas of the country. Fruit cultivation area represents 31% of the total cultivated area (97,239.58 ha) in the country. However, the production share is only 17% of the total crop production in the country (2.6 million tons). About 90% of the fruit cultivation area is dominated by date palm, banana, lime, and mango. In addition to the abiotic stresses, such as drought, heat, and salinity, major fruit crops have declined in recent years due to various biotic stressors, primarily insect pests, and diseases. For several decades, the date palm has suffered from the Dubas bug and in recent years from Red Palm Weevil. Lime has been infected with Witch’s Broom Disease of Lime (WBDL) caused by ‘Candidatus Phytoplasma aurantifolia’ that has led to the decline of production to 25% from its peak in the nineties. Banana is Oman`s second-largest fruit crop in production and export. It has also been the subject of studies due to losses incurred by farmers during pre-and post-harvest stages, in addition to several pests and diseases that affect bananas in Oman. Mango is another major fruit crop that is primarily cultivated in northern Oman. Severe infection with mango decline has led to the eradication of mango orchards from many regions of Oman, particularly in Batinah Coast, where increased salinity has led to a decline in mango yield. Research conducted in Oman has investigated several aspects of these challenges. This review paper summarizes the outcome from studies conducted in the country and proposes directions towards resolving current and future challenges to the fruit industry

Clinical outcomes of COVID-19 in hemodialysis patients

BackgroundThe coronavirus disease 2019 (COVID-19) is known for its effects on the respiratory system. Three years after the pandemic morbid and mortal consequences, growing evidence is showing that the disease also has adverse outcomes and complications on additional organs including the kidneys. This study aims at investigating the effects of COVID-19 on hemodialysis patients receiving services at Palestine Medical Complex (PMC) kidney dialysis department, and to identify mortality related risk factors.MethodsIn April 2022, data was collected using the electronic medical records system for the dialysis department at PMC. The study included all PMC hemodialysis patients that were infected with COVID-19 between January 2020–April 2022. The collected data included patient demographics, clinical features, laboratory tests, dialysis frequency and the disease outcome.ResultsThe results showed that the patients’ outcomes and dialysis frequency were impacted by their blood urea nitrogen (BUN), serum creatinine (SCr) and calcium levels. About one third of the study population died after being infected with COVID-19. The frequency of dialysis was also affected by the presence of comorbidities like hypertension, diabetes mellitus (DM) and myocardial infarction (MI).ConclusionThis study found that there was a high mortality rate within the hemodialysis patients infected with COVID-19. Having comorbidities affected the frequency of dialysis following COVID-19 infection. Dialysis patients should be protected from infections such as COVID-19 and their comorbidities should be monitored and kept under control as much as possible

Probabilistic seismic hazard maps for the sultanate of Oman

This study presents the results of the first probabilistic seismic hazard assessment (PSHA) in the framework of logic tree for Oman. The earthquake catalogue was homogenized, declustered, and used to define seismotectonic source model that characterizes the seismicity of Oman. Two seismic source models were used in the current study; the first consists of 26 seismic source zones, while the second is expressing the alternative view that seismicity is uniform along the entire Makran and Zagros zones. The recurrence parameters for all the seismogenic zones were determined using the doubly bounded exponential distribution except the zones of Makran, which were modelled using the characteristic distribution. Maximum earthquakes were determined and the horizontal ground accelerations in terms of geometric mean were calculated using ground-motion prediction relationships developed based upon seismic data obtained from active tectonic environments similar to those surrounding Oman. The alternative seismotectonic source models, maximum magnitude, and ground-motion prediction relationships were weighted and used to account for the epistemic uncertainty. Hazard maps at rock sites were produced for 5 % damped spectral acceleration (SA) values at 0.1, 0.2, 0.3, 1.0 and 2.0 s spectral periods as well as peak ground acceleration (PGA) for return periods of 475 and 2,475 years. The highest hazard is found in Khasab City with maximum SA at 0.2 s spectral period reaching 243 and 397 cm/s[superscript 2] for return periods 475 and 2,475 years, respectively. The sensitivity analysis reveals that the choice of seismic source model and the ground-motion prediction equation influences the results most.Oman Ministerial Cabinet (project number 22409017

Quantifying the need for supervised machine learning in conducting live forensic analysis of emergent configurations (ECO) in IoT environments

© 2020 The Author(s) Machine learning has been shown as a promising approach to mine larger datasets, such as those that comprise data from a broad range of Internet of Things devices, across complex environment(s) to solve different problems. This paper surveys existing literature on the potential of using supervised classical machine learning techniques, such as K-Nearest Neigbour, Support Vector Machines, Naive Bayes and Random Forest algorithms, in performing live digital forensics for different IoT configurations. There are also a number of challenges associated with the use of machine learning techniques, as discussed in this paper