Determining the Best Plan to Launch the Saudi Virtual University

This paper presents a project for the development of a virtual university in Saudi Arabia, associated challenges, and, remaining questions that need answering to ensure successful implementation and adoption of the university

Bayesian based intrusion detection system

AbstractIn this paper an intrusion detection system is developed using Bayesian probability. The system developed is a naive Bayesian classifier that is used to identify possible intrusions. The system is trained a priori using a subset of the KDD dataset. The trained classifier is then tested using a larger subset of KDD dataset. The Bayesian classifier was able to detect intrusion with a superior detection rate

Pushing the boundaries of photoconductive sampling in solids

The advent of laser-based optical tools featuring few-cycle pulses with durations of less than a hundred femtoseconds in the late 1980s enabled scientists to initiate and observe the evolution of chemical reactions. This powerful approach combined the interactions of light and matter and unleashed an unprecedented metrology concept that tracks the interactions of atoms and molecules in their natural timescales. Electron wavepacket dynamics take place in the attosecond range, a thousand times faster than molecules. In optical terms, such durations typically last less than the half-cycle duration of optical fields. Consequently, the investigation of such electronic processes necessitates measurement techniques capable of resolving the oscillations of the electric field of light. The primary objective of this thesis is to develop and advance novel field characterisation techniques based on photoconductive sampling. The first portion of this thesis addresses broadband field characterisation based on nonlinear photoconductive sampling. A theoretical analysis of current formation and localisation in solids is presented, prompting the fabrication of a heterostructured sample with the aim of enhancing the magnitude of the signal obtained from the measurement technique. A thorough proof-of-principle experiment is performed, whereby a significant enhancement in signal magnitude is established. As a consequence of signal improvement, the heterostructured sample reaches the desired stability regime earlier than its traditional bulk counterparts. Moreover, the performance of the heterostructured sample for field characterisation is compared to fused silica and benchmarked against the well-established technique of electro-optic sampling. These results pave the way towards field sampling in low pulse energy systems. The following section details broadband field characterisation based on linear photoconductive sampling by employing tailored pulses from a waveform synthe- siser. Visible-ultraviolet pulses are utilised to inject carriers in a common semi- conductive material (gallium phosphide), enabling the complete characterisation of a mid-infrared test field. Furthermore, the technique is validated against electro-optic sampling. When compared to electro-optic sampling, the response function of linear photoconductive sampling is concerned with the intensity envelope of the gating field, relaxing the strict requisites on the temporal phase of the gate. The demonstrated results represent a significant achievement in extending field sampling techniques beyond 100 THz and towards the visible range. Finally, a machine learning-based algorithm for denoising waveforms obtained from a laboratory setting is developed and implemented. The algorithm is based on a one-dimensional convolutional neural network, ideal for processing data presented on an evenly spaced grid. The model is compared with well-established methodologies, namely denoising via the fast Fourier transform and wavelet analysis and exhibits excellent performance, extending the repertoire of tools typically used for combating noise. The field characterisation methodologies presented in this thesis pave the way towards accessible and cost-effective field sampling techniques, enabling researchers to study field-induced electron dynamics in matter and usher in ultrafast optoelectronic signal processing towards the PHz range. In general, the field characterisation techniques presented occupy a small footprint, and the measurements take place in ambient air conditions, facilitating their integration in existing experimental infrastructures. With the aid of AI-accelerator chips, the machine learning tool developed in this thesis can be implemented during laboratory measurements as a concurrent denoising technique

A novel coarse-grained molecular dynamics method for the accurate prediction of helix-helix interactions in GPCRs

This thesis describes a novel computational method developed to identify and characterise points of protein-protein interaction between two G protein-coupled receptors (GPCRs). An ensemble-based coarse-grained molecular dynamics (eCG-MD) approach was applied to GPCR oligomers with experimentally-determined contact interfaces (adenosine A2A receptor, rhodopsin, CXCR4 and β1AR). Error analysis was used to determine 1) the number of replicas in an ensemble and 2) the simulation time for each replica that were needed to obtain convergence with experimental results. Error analysis also enabled identification of non-interacting regions. This novel method yielded calculations of distance between rhodopsin, CXCR4 and β1AR transmembrane domains reported to form contact points in homodimers that correlated well with the corresponding measurements obtained from the structural data, demonstrating an ability to predict contact interfaces computationally. The method gave distance measurements between residues shown to be involved in oligomerisation of the fifth transmembrane domain from the adenosine A2A receptor that were in very good agreement with the existing biophysical data. Further, the method provided information about the nature of the contact interface that could not be determined experimentally. This CG-MD method was then used as a high-throughput screen to identify novel sites of interaction in the adenosine A2A receptor, informing the design of future experimental work. Experimental methods to investigate interactions are also described in this thesis. These were less successful in identifying contact points, however, the present computational method will enable novel interaction points between GPCRs to be predicted and tested experimentally using assays of ligand binding and receptor signaling. In conclusion, this work provides an accurate, reproducible and reliable method for determining the specific points of interaction between GPCR dimers. The eCG-MD method discriminates between residues in TM helices that form specific interactions and residues that are in close proximity but do not interact

An Ensemble-Based Protocol for the Computational Prediction of Helix-Helix Interactions in G Protein-Coupled Receptors using Coarse-Grained Molecular Dynamics

The accurate identification of the specific points of interaction between G protein-coupled receptor (GPCR) oligomers is essential for the design of receptor ligands targeting oligomeric receptor targets. A coarse-grained molecular dynamics computer simulation approach would provide a compelling means of identifying these specific protein–protein interactions and could be applied both for known oligomers of interest and as a high-throughput screen to identify novel oligomeric targets. However, to be effective, this in silico modeling must provide accurate, precise, and reproducible information. This has been achieved recently in numerous biological systems using an ensemble-based all-atom molecular dynamics approach. In this study, we describe an equivalent methodology for ensemble-based coarse-grained simulations. We report the performance of this method when applied to four different GPCRs known to oligomerize using error analysis to determine the ensemble size and individual replica simulation time required. Our measurements of distance between residues shown to be involved in oligomerization of the fifth transmembrane domain from the adenosine A2A receptor are in very good agreement with the existing biophysical data and provide information about the nature of the contact interface that cannot be determined experimentally. Calculations of distance between rhodopsin, CXCR4, and β1AR transmembrane domains reported to form contact points in homodimers correlate well with the corresponding measurements obtained from experimental structural data, providing an ability to predict contact interfaces computationally. Interestingly, error analysis enables identification of noninteracting regions. Our results confirm that GPCR interactions can be reliably predicted using this novel methodology

Artificial intelligence (AI) competencies for organizational performance : A B2B marketing capabilities perspective

The deployment of Artificial Intelligence (AI) has been accelerating in several fields over the past few years, with much focus placed on its potential in Business-to-Business (B2B) marketing. Early reports highlight promising benefits of AI in B2B marketing such as offering important insights into customer behaviors, identifying critical market insight, and streamlining operational inefficiencies. Nevertheless, there is a lack of understanding concerning how organizations should structure their AI competencies for B2B marketing, and how these ultimately influence organizational performance. Drawing on AI competencies and B2B marketing literature, this study develops a conceptual research model that explores the effect that AI competencies have on B2B marketing capabilities, and in turn on organizational performance. The proposed research model is tested using 155 survey responses from European companies and analyzed using partial least squares structural equation modeling. The results highlight the mechanisms through which AI competencies influence B2B marketing capabilities, as well as how the later impact organizational performance.© 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).fi=vertaisarvioitu|en=peerReviewed

A Multiple Classifiers Broadcast Protocol for VANET

Many types of artificial intelligent machines have been used for decision making purposes. In VANET broadcast protocols, vehicles must decide the received messages are to be rebroadcast or not. Several attributes such as sender-to-receiver distance, sender-receiver speed difference, number of neighboring vehicles, as well as vehicle’s movement direction are important measures to take the broadcast decision. As the relationships of attributes to the broadcast decision cannot be mathematically defined, the use of a classifier-based artificial intelligence may approximately predict the relationships of all the incorporated attributes to such a decision. As the decision is based on prediction, the use of multiple classifiers in decision making may increase accuracy. Therefore, this research employs a combined-classifiers at an abstract level to provide firmer broadcast decisions on VANET. Our research results justify that the performance of our combined multiple-classifiers outperformed a single-classifier scheme. The multi-classifiers scheme contributes to an average increase of 2.5% in reachability compared to that of the efficient counter–based scheme (ECS). The combined multi-classifiers scheme also improves the saving in rebroadcast tries by 38.9%

On the joint a-numerical radius of operators and related inequalities

In this paper, we study p-tuples of bounded linear operators on a complex Hilbert space with adjoint operators defined with respect to a non-zero positive operator A. Our main objective is to investigate the joint A-numerical radius of the p-tuple.We established several upper bounds for it, some of which extend and improve upon a previous work of the second author. Additionally, we provide several sharp inequalities involving the classical A-numerical radius and the A-seminorm of semi-Hilbert space operators as applications of our results

Targeted nonviral gene therapy in prostate cancer

Prostate cancer is the second most widespread cancer in men worldwide. Treatment choices are limited to prostatectomy, hormonal and radiotherapy that commonly have deleterious side effects and vary in their efficacy, depending on the stage of the disease. Among novel experimental strategies, gene therapy holds great promise for the treatment of prostate cancer. However, its use is currently limited by the lack of delivery systems able to selectively deliver the therapeutic genes to the tumors after intravenous administration without major drawbacks. To remediate to this problem, a wide range of non-viral delivery approaches have been developed to specifically deliver DNA-based therapeutic agents to their site of action. This review provides an overview of the various non-viral delivery strategies and gene therapy concepts used to deliver therapeutic DNA to prostate cancer cells, and focuses on the recent therapeutic advances being made so far