Search

Coronavirus disease 2019 (COVID-19) is a respiratory illness that leads to severe acute respiratory syndrome and various cardiorespiratory complications, contributing to morbidity and mortality. Entropy analysis has demonstrated its ability to monitor physiological states and system dynamics during health and disease. The main objective of the study is to extract information about cardiorespiratory control by conducting a complexity analysis of OSV signals using scale-based entropy measures following a two-month timeframe after recovery. This prospective study collected data from subjects meeting specific criteria, using a Beurer PO-80 pulse oximeter to measure oxygen saturation (SpO2) and pulse rate. Excluding individuals with a history of pulmonary/cardiovascular issues, the study analyzed 88 recordings from 44 subjects (26 men, 18 women, mean age 45.34 ± 14.40) during COVID-19 and two months post-recovery. Data preprocessing and scale-based entropy analysis were applied to assess OSV signals. The study found a significant difference in mean OSV during illness (95.08 ± 0.15) compared to post-recovery (95.59 ± 1.03), indicating reduced cardiorespiratory dynamism during COVID-19. Multiscale entropy analyses (MSE, MPE, MFE) confirmed lower entropy values during illness across all time scales, particularly at higher scales. Notably, the maximum distinction between illness and recovery phases was seen at specific time scales and similarity criteria for each entropy measure, showing statistically significant differences. The study demonstrates that the loss of complexity in OSV signals, quantified using scale-based entropy measures, has the potential to detect malfunctioning of cardiorespiratory control in COVID-19 patients. This finding suggests that OSV signals could serve as a valuable indicator for assessing the cardiorespiratory status of COVID-19 patients and monitoring their recovery progress. [ABSTRACT FROM AUTHOR]

Cloud computing technologies can play an essential role in public organisations and companies while it reduces the cost of using information technology services. It allows users to access the service anytime and anywhere, with paying for what they use. In developing countries, such as Saudi Arabia, the cloud computing is still not extensively adopted, compared to countries in the west. In order to encourage the adoption of cloud services, it is considerable to understand an important and particular complications regarding to cloud computing is the potential and perceived security risks and benefits posed by implementing such technology. This paper investigates the critical security factors that influence the decision to adopt cloud computing by Saudi government agencies. A framework was proposed for three categories, Social Factors category, Cloud Security Risks Category and Perceived Cloud Security Benefits that includes well-known cloud security features. The framework factors were identified by critically reviewing studies found in the literature together with factors from the industrial standards within the context of Saudi Arabia. An experiment study was conducted in five government agencies in Saudi Arabia by interview and questionnaire with experts in order to improve and confirm the framework. All the factors in the proposed framework were found to be statistically significant. An additional factor identified was Failure of client side encryption. Moreover, they suggested including this factor as a potential risk under Security Risk Factors Category. The initial framework was updated based on the expert reviews and questionnaires. The results were analysed via one-sample t -test with the data integrity analysed via Cronbach’s alpha. The outcome indicated the majority of cloud security adoption framework categories were statistically significant. Potential future study directions and contributions are discussed. [ABSTRACT FROM AUTHOR]

Saudi universities have at their disposal a huge number of low cost IT resources to aid in teaching, research and learning. By migrating to cloud services, Saudi universities will be moving data and programs from local servers to the internet, thereby providing users with the ability to access and share information at any time from multiple devices. The migration to cloud-based IT resources is not yet widespread in Saudi universities due to several challenges including security, legal policies and implementation. At present, there is lack of research and guidance for Saudi universities on how to overcome these challenges and how contextual factors can influence the successful migration to the educational clouds. This research presents a framework for the successful migration to cloud technology in the Saudi Arabian universities. In this research, a set of key critical success factors (CSFs) were identified by synthesizing components from studies concerned with the migration of cloud for higher education and factors identified from the successful implementation of WBL (Web Based Learning) and ERP (Enterprise Resource Planning) on higher education in Saudi Arabia. Based on this knowledge, the proposed framework was evaluated via expert review and a survey by IT specialists from the Saudi universities. The initial CSFs were updated based on the expert reviews and the results were analysed. Based on the findings at this stage, additional CSFs were added to the framework as suggested by the experts. Subsequently, in order to confirm the reviewed CSFs, additional investigation via a structured online questionnaire was conducted and the outcome was analysed via one-sample t -test with the data integrity analysed via Cronbach’s alpha. The outcome indicated the majority of CSFs to be statistically significant except the Physical Location CSF. Potential future study and contributions are discussed. [ABSTRACT FROM AUTHOR]

• Real-time determination of adaptive estimates of router traffic-rate demand. • Predict the evolution of the estimated demand within 5-min intervals using M-MRSP. • Monitoring tools could utilize the results to predict one 5-minute interval ahead. • The results were proven to maintain the same statistical characteristics. Smart grid data traffic behaves in a similar way to computer network data traffic and they are vulnerable to the same security risks. This paper presents a methodology for the real-time determination of adaptive estimates of router traffic-rate demand every five-minutes, as well as for the evolution of the estimated demand starting from zero during a five-minute interval using a Modified Mean Reverting Stochastic Process (M-MRSP). The determination of real-time adaptive estimates is based on an autoregressive model (AR(n)), which uses a window-size of past real-time router traffic-rate data with coefficients determined by a Kalman Filter (KF). The benefit of this technique is that potential monitoring tools could be provided with future knowledge of one 5-minute interval ahead. The methodology simulations show that the range of RMS error between the KF prediction and the internet service provider (ISP) measurements is of the order of 0.03, whereas the range of RMS error between the KF prediction and the M-MRSP values is of the order of 0.07. The synthetic time-series is a combination of M-MRSP and KF methodologies and maintains all the characteristics of a real traffic-rate time-series, such as non-stationarity, non-normality, long-range dependency (LRD), self-similarity and multimodality. [ABSTRACT FROM AUTHOR]

• Level 1 and Level 3 features are integrated to detect live and spoof fingerprints. • WLC extracts ridge contours at both global and local level. • Perceived magnitude with improved weber law effectively extracts Level 3 features. • Modified Scharr operator is used to extract low level details of fingerprints. • The weighting function is proposed to interpolate the sparsity in Level 3 features. Fingerprint-based biometric systems are designed to authenticate and provide authorized access to the users in various security applications. However, such systems can be jeopardized by different kinds of presentation and spoof attacks by using different spoof materials. In this paper, an improved feature descriptor named as Quantized Fundamental Fingerprint Features (Q-FFF) is proposed for live fingerprint detection. The proposed feature descriptor integrates Level 1 and Level 3 features of fingerprints. Level 3 features of fingerprints such as ridge contours are extracted by the weighted linear combination (WLC) of magnitude of perceived stimuli and local contrast information in spatial domain. The novel magnitude of perceived stimuli with modified Weber law is proposed to compute the perceived stimuli of initial pixel intensities. The weights are explicitly determined by sigmoid function to interpolate the sparse information. Level 1 features such as ridge orientation is computed by retaining only the significant frequency components in frequency domain. Both ridge contours and ridge orientation are explicitly quantized into predefined intervals. The quantized feature sets are then integrated to populate a 2-D histogram. Performance evaluation of Q-FFF is determined on the three standard datasets of LivDet competition i.e. LivDet 2011, 2013 and 2015. Results comparison showed a reduction of Average Error Rates (AER) to 5.28, 2.0 and 4.62 on LivDet 2011, LivDet 2013 and LivDet 2015, respectively. [ABSTRACT FROM AUTHOR]