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The increasing popularity of intelligence systems in accounting and auditing domains has led to the interest towards drivers of blockchain technology adoption. Consequently, this empirical study inspected the antecedent constructs with direct and indirect impact on blockchain technology adoption in the accounting domain. Data were gathered from 346 accountants employed in Information technology (IT) companies, through an online survey. Structural equation modeling with Smart PLS 4 was employed for research model testing. Out of thirteen proposed direct hypotheses, twelve were accepted. Mainly, the obtained empirical results confirmed effort expectancy impacting blockchain technology adoption the most. Furthermore, the obtained results also confirmed the mediation role of effort expectancy and performance expectancy.

This study investigates how e-learning can raise the caliber of education provided in Jordanian higher education institutions. The study looks into how e-learning affects educational quality by taking into account individual demands, perceived learning value, and technological and electronic requirements. Using a questionnaire, information was gathered from professors at six Jordanian private universities in Amman. The findings underscore the significance of e-learning mandates and the moderating function of upper management backing in augmenting academic achievements. Faculty members should be empowered, and tools should be made available to maximize learning results.

Multiple sclerosis (MS) is a chronic neuro-inflammatory disease resulting in disabilities that negatively impact patients’ life quality. While current treatment options do not reverse the course of the disease, treatment using mesenchymal stromal/stem cells (MSC) is promising. There has yet to be a consensus on the type and dose of MSC to be used in MS. This work aims to study the safety and efficacy of two treatment protocols of MSCs derived from the umbilical cord (UC-MSCs) and their secretome. The study included two groups of MS patients; Group A received two intrathecal doses of UC-MSCs, and Group B received a single dose. Both groups received UC-MSCs conditioned media 3 months post-treatment. Adverse events in the form of a clinical checklist and extensive laboratory tests were performed. Whole transcriptome analysis was performed on patients’ cells at baseline and post-treatment. Results showed that all patients tolerated the cellular therapy without serious adverse events. The general disability scale improved significantly in both groups at 6 months post-treatment. Examining specific aspects of the disease revealed more parameters that improved in Group A compared to Group B patients, including a significant increase in the (CD3 + CD4 + ) expressing lymphocytes at 12 months post-treatment. In addition, better outcomes were noted regarding lesion load, cortical thickness, manual dexterity, and information processing speed. Both protocols impacted the transcriptome of treated participants with genes, transcription factors, and microRNAs (miRNAs) differentially expressed compared to baseline. Inflammation-related and antigen-presenting (HLA-B) genes were downregulated in both groups. In contrast, TNF-alpha, TAP-1, and miR142 were downregulated only in Group A. The data presented indicate that both protocols are safe. Furthermore, it suggests that administering two doses of stem cells can be more beneficial to MS patients. Larger multisite studies should be initiated to further examine similar or higher doses of MSCs.

E-learning in knowledge management was examined in this study, specifically on how it assists organizations in improving knowledge transfer and e-learning management, to increase performance and employee knowledge management. In this study, e-learning and knowledge management systems and technology were jointly implemented, and its impact on organizational performance was examined. Organizational management was also explored. The present study investigated the relationship between knowledge management (KM) and innovation performance (IP). The mediating effect of knowledge Management was deeply explored. Randomly selected managers from 57 Jordanian manufacturing companies were the study samples, and there were 470 managers involved in this study, from strategic, tactical, and operational levels. Questionnaires were used to gather data, and the questionnaire items covered the constructs of knowledge management, organizational learning (OL), knowledge-oriented leadership (KOL) and IP. A research model was proposed and was tested using structural equation modeling (SEM). The findings were as follows: KOL positively affected KM; KOL positively affected IP; OL negatively affected IP; KOL positively affected KM; OL positively affected KM; KM positively affected IP and KM mediated the relationship between KOL, OL and IP.

The effect of contextual factors namely information quality, service quality, system quality, Technological readiness, trust in applications (app) and COVID-19 health anxiety, on the intention and consequently the actual use of Mobile Payment (MP) app was examined in this study. Trust, as mediator to the relationship between technological readiness and intention to use MB-app was examined also. Data were obtained from 740 Jordanian Mobile Banking (MB) app users through an online survey. The relationship between service quality, system quality, information quality, trust in the app, COVID-19 health anxiety, Technological readiness and the intentions to use MB-app and the actual use of MB-app was empirically examined. The results showed a positive relationship between service quality, system quality, information quality, trust in the app and COVID-19 health anxiety, and the intentions to use MB-app, and in turn he actual use of MB-app, and a positive mediation of trust on the relationship between COVID-19 health anxiety and the intentions to use MB-app.

(1) Background: Natalizumab dramatically reduces relapses and MRI inflammatory activity (new lesions and enhancing lesions) in multiple sclerosis (MS). Chemical exchange saturation transfer (CEST) MRI can explore brain tissue in vivo with high resolution and sensitivity. We investigated if natalizumab can prevent microstructural tissue damage progression measured with MRI at ultra-high field (7 Tesla) over the first year of treatment. (2) Methods: In this one-year prospective longitudinal study, patients with active relapsing–remitting MS were assessed clinically and scanned at ultra-high-field MRI at the time of their first natalizumab infusion, at 6 and 12 months, with quantitative imaging aimed to detect microstructural changes in the normal-appearing white matter (NAWM), including sequences sensitive to magnetisation transfer (MT) effects from amide proton transfer (MTRAPT) and the nuclear Overhauser effect (MTRNOE). (3) Results: 12 patients were recruited, and 10 patients completed the study. The difference in the T1 relaxation times at month 6 and month 12 of natalizumab treatment was not significant, suggesting the lack of accumulation of tissue damage, while improvements were seen in MTR (MTRAPT and MTRNOE measures) at month 12, suggesting a tissue repair effect. This paralleled the expected lack of clinical and radiological worsening of conventional MRI measures of disease activity (new lesions or gadolinium-enhancing lesions). (4) Conclusion: Natalizumab prevents microstructural brain damage and has effects suggesting an improved white matter microstructure measured at ultra-high field during the first year of treatment.

The goal of this study is to develop and verify a model for successful e-Learning based on the experiences of students in the "new normal". From Jordanian universities, 550 students who have taken any e-Learning course were randomly selected. Data were collected via a survey questionnaire, and Structural Equation Modeling (SEM) was employed to test the proposed study model. The findings indicate that contactless learning and high-quality e-learning systems have a beneficial impact on student satisfaction. In addition, e-Learning cognitive involvement was found to solidify e-Learning satisfaction. Furthermore, the results show a positive and significant impact of e-Learning cognitive involvement and e-Learning satisfaction on e-Learning achievement. Also, e-Learning system quality positively affects e-Learning cognitive involvement, besides a direct impact of contactless learning quality on e-Learning cognitive involvement.

ObjectiveThis study aimed to detect possible associations between lung computed tomography (CT) findings in COVID-19 and patients' age, body weight, vital signs, and medical regimen in Jordan.MethodsThe present cross-sectional study enrolled 230 patients who tested positive for COVID-19 in Prince Hamza Hospital in Jordan. Demographic data, as well as major lung CT scan findings, were obtained from the hospital records of the COVID-19 patients.ResultsThe main observed major lung changes among the enrolled COVID-19 patients included ground-glass opacification in 47 (20.4%) patients and consolidation in 22 (9.6%) patients. A higher percentage of patients with major lung changes (24%) was observed among patients above 60 years old, while (50%) of patients with no changes in their lung findings were in the age group of 18–29 years old. Results obtained from the present study showed that only patients with major CT lung changes (9.7%) were prescribed more than three antibiotics. Additionally, 41.6 % of patients with major lung CT scan changes had either dry (31.0%) or productive (10.6%) cough at admission.ConclusionSeveral factors have been identified by this study for their ability to predict lung changes. Early assessment of these predictors could help provide a prompt intervention that may enhance health outcomes and reduce the risk for further lung changes.

Window-windcatchers, a passive ventilation method, have been shown to improve ventilation and enhance thermal comfort. Preliminary characterization of a novel window-windcatcher has been undertaken in a previous work, but no relationship had been identified between the actual ventilation rate (Qact), the wind velocity (VTw) and crucial design parameters such as the fins angle (ϴ)). In this paper, the relationship that quantifies how the window-windcatcher’s performance depends on VTw and ϴ was determined. Additionally, for the first time, the ventilation performance of the window-windcatcher was optimized by studying the effects of ϴ and the fins-wall distance (DW−f) through a Computational Fluid Dynamics parametric study (ANSYS)|. In this optimization approach, the angle ϴ and the distance DW−f corresponding to the maximum actual-to-required ventilation rate were found to be 80° and 45 cm, respectively. The actual ventilation rate increased by approximately 13.2% compared with the baseline design of the windcatcher (ϴ and DW−f equal to 40° and 45 cm, respectively); this corresponds to an increase of approximately 8.6% in the actual-to-required ventilation rate, according to the ASHRAE standards.

The present research evaluates how E-learning environment, E-learning adoption, Digital readiness, and Students attitudes towards E-learning, affect Academic achievement. The study focuses on a much-neglected cultural context, Gulf Cooperation Council countries (GCC), since Student's readiness as well as institutions and professors' endowments greatly varied within countries and among universities. The study further incorporates Instructors attitudes and evaluates the mediation effect of Academic engagement on Academic achievement. The methodology relies on Partial Least Squares structural equation modelling (PLS-SEM). The research findings emphasize the role of E-learning environment, Digital readiness, Academic engagement, students as well as instructors E-learning attitude as the decisive factors that determine students' Academic achievement. This implies that institutions who adapt to a changing environment by aligning students and instructors' goals to develop a positive and supportive E-learning environment, will foment Academic engagement and promote students' Academic achievement.

Previous studies on window-windcatchers have shown their effectiveness in capturing the prevailing wind and redirecting it into a building, increasing the actual-to-required ventilation ratio by 9%, above what is required by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). However, the effect of implementing the proposed system on energy performance, energy costs, and thermal comfort has not been studied. Therefore, here, we investigate and test the implementation of the window-windcatcher on a typical residential building, using a validated DesignBuilder model. Compared to the base case (no window-windcatcher), the total annual energy consumption of the entire building (Etot,b), and consequently the cost, is reduced by approximately 23.3% (i.e., from 18,143 kWh/year to 13,911 kWh/year) when using the window-windcatcher. The total annual reduction in thermal discomfort hours is estimated to be 290 h, which corresponds to an average monthly reduction of approximately 24 h.

Recent evidence available in the literature has highlighted that the high-energy consumption rate associated with air conditioning leads to the undesired “overcooling” condition in arid-climate regions. To this end, this study quantified the effects of increasing the cooling setpoint temperature on reducing energy consumption and CO2 emissions to mitigate overcooling. DesignBuilder software was used to simulate the performance of a generic building operating under the currently adopted ASHRAE HVAC criteria. It was found that increasing the cooling setpoint temperature by 1 °C will increase the operative temperature by approximately 0.25 °C and reduce the annual cooling electricity consumption required for each 1 m2 of an occupied area by approximately 8 kWh/year. This accounts for a reduction of 8% in cooling energy consumption compared to the ASHRAE cooling setpoint (i.e., t_s = 26 °C) and a reduction in the annual CO2 emission rate to roughly 4.8 kg/m2 °C. The largest reduction in cooling energy consumption and CO2 emissions was found to occur in October, with reduced rates of approximately–1.3 kWh/m2 °C and −0.8 kg/m2 °C, respectively.

Developing an accurate energy prediction model is one of the most efficient methods to explain building energy efficiency. Such a prediction is a way to conserve energy and help make better design decisions in order to reduce energy consumption. To date, many studies have built models for predicting the building energy consumption of existing buildings, but few studies have considered energy efficiency at the early design stage with the possible variations of the parameters that might affect the overall energy consumption. In this study, supervised machine learning algorithms were used with parametric design tools to address energy usage in the early design decision stages due to its high speed, accuracy, and capacity to handle nonlinear interactions between variables. Additionally, the Grasshopper plugin for Rhino was used as a parametric design tool, and the honeybee plugin was used to anticipate energy usage for various architectural considerations of residential units in Jordan. The outcomes of the honeybee model were used to predict energy consumption using ANN and SVM as machine learning algorithms. The findings showed that the ANN model with two hidden layers had the highest accuracy for training and testing (0.972%). Therefore, this model provides an efficient, accurate way to predict the energy consumption for residential buildings. [ABSTRACT FROM AUTHOR]

Chemical modification of the surface of viruses, both the interior and the exterior, imparts new functionalities, that have potential applications in nanomedicine. In this study, we developed novel virus-based nanomaterials as a contrast agent for computed tomography (CT) imaging in vitro. The gold-coated cowpea mosaic virus (Au-CPMV) particles were generated by the electrostatic adsorption of positively charged electrolyte on the virus capsid with the subsequent incubation and reduction of anionic gold complexes. Au-CPMV particles as a CT contrast agent offer a fast scan time (less than 2 min), low cost, and biocompatibility and allow for high-resolution imaging with ca. 150 Hounsfield units (HU). The Au-CPMV surface was further modified allowing for the incorporation of targeting molecules of specific cell types.

We investigate and test the effectiveness of a novel window windcatcher device (WWC), as a means of improving natural ventilation in buildings. Using ANSYS CFX, the performance of the window-windcatcher is compared to a control case (no window-windcatcher), in three different geographic locations (Cardiff, Doha and Amman) which are representative of three different types of atmospheric conditions. The proposed window-windcatcher has been shown to improve both thermal comfort and indoor air quality by increasing the actual-to-required ventilation ratio by up to 9% compared to the control case as per the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standards. In addition, the locations with minimum velocities have been identified. Those locations correspond to the regions with a lower infection risk of spreading airborne viruses such as SARS-CoV-2, which is responsible for the COVID-19 pandemic.

Arabic sentiment analysis is a process that aims to extract the subjective opinions of different users about different subjects since these opinions and sentiments are used to recognize their perspectives and judgments in a particular domain. Few research studies addressed semantic-oriented approaches for Arabic sentiment analysis based on domain ontologies and features’ importance. In this paper, we built a semantic orientation approach for calculating overall polarity from the Arabic subjective texts based on built domain ontology and the available sentiment lexicon. We used the ontology concepts to extract and weight the semantic domain features by considering their levels in the ontology tree and their frequencies in the dataset to compute the overall polarity of a given textual review based on the importance of each domain feature. For evaluation, an Arabic dataset from the hotels’ domain was selected to build the domain ontology and to test the proposed approach. The overall accuracy and f-measure reach 79.20% and 78.75%, respectively. Results showed that the approach outperformed the other semantic orientation approaches, and it is an appealing approach to be used for Arabic sentiment analysis.

Problem statement: Malaysian public hospitals apply an electronic medical record (EMR) system. However, health-care professionals are facing several difficulties in trusting the EMR system in Malaysia. Studies that have addressed EMR acceptance, particularly its application in the context of privacy and security concerns on the basis of a multi-criteria perspective in Malaysian hospitals, are still lacking. Objective: This research proposed a new model testing the individual, security, and privacy factors affecting EMR acceptance and the role of trust as a mediator. The suggested model was developed on the basis of multiple perspectives targeting the health-care professionals in Malaysia. Methodology: A conceptual model was developed on the basis of unified theory of acceptance and use of technology UTAUT2 to explore the relationship between individual, privacy, and security factors and the acceptance of the EMR system and the role of trust as a mediating construct. A total of 375 usable questionnaires were collected from the study population. Structural equation modeling was employed to analyze data and test the fit of the model. Findings: Two constructs, namely, security and individual, have positive influences on EMR acceptance and use. The privacy construct shows a negative effect on EMR acceptance and use. Trust mediates the effects of security, privacy, and individual factors partially, which, in turn, influences the behavioral intention to use and adopt EMR.

This paper considers the self-localization of a tethered drone without using a cable-tension force sensor in GPS-denied environments. The original problem is converted to a state-estimation problem, where the cable-tension force and the three-dimensional position of the drone with respect to a ground platform are estimated using an extended Kalman filter (EKF). The proposed approach uses the data reported by the onboard electric motors (i.e., the pulse width modulation (PWM) signals), accelerometers, gyroscopes, and altimeter, embedded in the commercial-of-the-shelf (COTS) inertial measurement units (IMU). A system-identification experiment was conducted to determine the model that computes the drone thrust force using the PWM signals. The proposed approach was compared with an existing work that assumes known cable-tension force. Simulation results show that the proposed approach produces estimates with less than 0.3-m errors when the actual cable-tension force is greater than 1 N.