Naser Hamdi

An Intelligent Healthcare Management System: A New Approach in Work-order Prioritization for Medical Equipment Maintenance Requests

The effective maintenance management of medical technology influences the quality of care delivered and the profitability of healthcare facilities. Medical equipment maintenance in Jordan lacks an objective prioritization system; consequently, the system is not sensitive to the impact of equipment downtime on patient morbidity and mortality. The current work presents a novel software system (EQUIMEDCOMP) that is designed to achieve valuable improvements in the maintenance management of medical technology. This work-order prioritization model sorts medical maintenance requests by calculating a priority index for each request. Model performance was assessed by utilizing maintenance requests from several Jordanian hospitals. The system proved highly efficient in minimizing equipment downtime based on healthcare delivery capacity, and, consequently, patient outcome. Additionally, a preventive maintenance optimization module and an equipment quality control system are incorporated. The system is, therefore, expected to improve the reliability of medical equipment and significantly improve safety and cost-efficiency.

Adsorption of Phenol on Different Activated Carbons Prepared from Date Pits

The potential use of different activated carbons (ACs) prepared from dates pits and phosphoric acid for the removal of phenol from aqueous solutions was investigated. Date pits were converted into five different types of activated carbons by air and phosphoric acid activation. The specific surface area (BET) of the prepared ACs varied from 794 m2/g, for the phosphoric acid:date pit ratio of 5:1, to 1707 m2/g for a ratio of 2:1. Batch adsorption experiments revealed that the adsorption of phenol varied among all of the prepared ACs, where the 2:1 AC showed the highest uptake. Equilibrium pH studies showed that the phenol removal was pH dependent and the maximum phenol uptake occurred at an equilibrium pH of 3.0. Dynamics studies indicated that the initial uptake of phenol on 2:1 AC at pH 4 was rapid, where 80% of the maximum uptake was achieved during the first 30 minutes; both surface adsorption and intraparticle diffusion were involved in the adsorption process and the data followed the pseudo second-order reaction. The equilibrium adsorption data of phenol on 2:1 AC at solution pH 3 was best described by the Redlich-Peterson, Sips, and Langmuir models.

A Comparison Study on Machine Learning Algorithms Utilized in P300-based BCI

This study addresses Brain-Computer Interface (BCI) systems meant to permit communication for those who are severely locked-in. The current study attempts to evaluate and compare the efficiency of different translating algorithms. The setup used in this study detects the elicited P300 evoked potential in response to six different stimuli. Performance is evaluated in terms of error rates, bit-rates and runtimes for four different translating algorithms; Bayesian Linear Disciminant Analysis (BLDA), Linear Discriminant Analysis (LDA), Perceptron Batch (PB), and nonlinear Support Vector Machines (SVMs) were used to train the classifier whilst an N-fold cross validation procedure was used to test each algorithm. A communication channel based on Electroencephalography (EEG) is made possible using various machine learning algorithms and advanced pattern recognition techniques. All algorithms converged to 100% accuracy for seven of the eight subjects. While all methods obtained fairly good results, BLDA and PB were superior in terms of runtimes, where the average runtimes for BLDA and PB were 13 ± 2 and 15.6 ± 6 seconds, respectively. In terms of bit-rates, BLDA obtained the highest average value (22 ± 12 bits/minute), where the average bit-rate for all subjects, all sessions, and all algorithms was 18.76 ± 10 bits/minute.

The prospects for sustainable biodiesel production in Jordan

This article addresses the feasibility of commercial biodiesel production in Jordan as an alternative fuel source. The study suggests that the most effective method of producing biodiesel in a country like Jordan could be based on large-scale microalgae cultivation. Since many strains of microalgae exist, it would first be necessary to identify the species most appropriate for growth in the Jordanian climate, and, second, to develop an affordable cultivation media based on locally-available ingredients. Because of the tremendous positive impact the success of this project would have on the local and national economy, the Jordanian government should also consider the establishment an algae biotechnology research center focusing on production of recombinant algal strains that grow rapidly and yield greater amounts of lipids. The southern part of Jordan, near Aqaba, provides a warm climate year-round, plentiful sea water, and a wealth of naturally-available minerals for enrichment of the cultivation media. A suitable bioreactor design is necessary for the continuous cultivation of the algae; the bioreactor should be coupled with a chemical/biochemical reaction stage for the large-scale transesterification required for conversion of the oil into biodiesel. Finally, it would also be advantageous to set up a carbon-neutral biodiesel-based electric generation power plant close by to supply power to neighboring cities while carbon emissions serve as a source of CO2 for the microalgal production cycle. Microalgae-derived biodiesel would serve as a renewable and nearly carbon-neutral source of energy. This paper addresses the key steps in the production cycle that would optimize the oil yield and biodiesel production capacity from microalgae.

An electrochemical dopamine sensor based on a flexible conductive film of Single-Wall Carbon NanoTubes (SWCNT) on pet

This study investigates the potential application of a non-conventional substrate, a polyethylene terephthalate (PET) film treated with single-wall carbon nanotubes (SWCNT), as a dopamine sensor. The flexible and conductive SWCNT/PET substrate proved to be a sensitive dopamine sensor; it exhibited an excellent S/N ratio, good resistance to fouling, and rapid response characteristics. This approach may potentially be combined with screen-printing technologies for the production of conductive and flexible micro-electrode arrays with a multitude of biomedical sensing and point-of-care diagnostic applications.Copyright