Eco-friendly synthesis from industrial wastewater of Fe and Cu nanoparticles over NaX zeolite and activity in 4-nitrophenol reduction

We deposited Fe and Cu over zeolite NaX (Fe/NaX and Cu/NaX) by adsorption from effluent industrial wastewater. We synthesized the zeolite NaX by the hydrothermal method. 5g of NaX completely adsorbed 350 and 380mg of Fe and Cu from the industrial wastewater, respectively, in 6h. The distribution of Fe and Cu over the NaX was uniform and amounted at 14 and 18mass%, respectively. Fe and Cu modify the morphology of the NaX zeolite: the particle size increased from 9\uce\ubcm to 10\uce\ubcm for the former and decreased to 3\uce\ubcm for the latter. Fe/NaX and Cu/NaX are less crystalline than NaX. BET analysis showed that the specific surface area decreased by 30% and 50% compared to NaX for Fe/NaX and Cu/NaX, but the ratio between meso- and micropores increased by 7 and 13 times, respectively. Fe/NaX and Cu/NaX synthesized by adsorption from industrial wastewater reduced +99% of 4-p-nitrophenol to 4-aminophenol in less than 100s, which is comparable to noble metal

Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction

This document is the Accepted Manuscript of the following article: Mohammed Chalouli, Nasr-eddine Berrached, and Mouloud Denai, ‘Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction’, Journal of Failure Analysis and Prevention, Vol. 17 (5): 1053-1066, October 2017. Under embargo. Embargo end date: 31 August 2018. The final publication is available at Springer via DOI: https://doi.org/10.1007/s11668-017-0343-y.Finding reliable condition monitoring solutions for large-scale complex systems is currently a major challenge in industrial research. Since fault diagnosis is directly related to the features of a system, there have been many research studies aimed to develop methods for the selection of the relevant features. Moreover, there are no universal features for a particular application domain such as machine diagnosis. For example, in machine bearing fault diagnosis, these features are often selected by an expert or based on previous experience. Thus, for each bearing machine type, the relevant features must be selected. This paper attempts to solve the problem of relevant features identification by building an automatic fault diagnosis process based on relevant feature selection using a data-driven approach. The proposed approach starts with the extraction of the time-domain features from the input signals. Then, a feature reduction algorithm based on cross-correlation filter is applied to reduce the time and cost of the processing. Unsupervised learning mechanism using K-means++ selects the relevant fault features based on the squared Euclidian distance between different health states. Finally, the selected features are used as inputs to a self-organizing map producing our health indicator. The proposed method is tested on roller bearing benchmark datasets.Peer reviewe

The COVID-19 Pandemic and Its Impact on Knowledge, Perception and Attitudes of Dentistry Students in Austria: A Cross-Sectional Survey

Sarra Boukhobza,1 Valentin Ritschl,2 Tanja Stamm,2 Katrin Bekes1 1Department of Pediatric Dentistry, University Clinic of Dentistry, Medical University Vienna, Vienna, Austria; 2Center for Medical Statistics, Informatics, and Intelligent Systems, Section for Outcomes Research, Medical University Vienna, Vienna, AustriaCorrespondence: Katrin BekesDepartment of Pediatric Dentistry, University Clinic of Dentistry, Medical University Vienna, Sensengasse 2a, Vienna, 1090, AustriaTel +43-1-400702801Fax +43-1-400702809Email [email protected]: Universities with dental schools are faced with complex problems during the COVID-19 pandemic. Dentistry students are at a higher risk of contracting infections, specifically COVID-19, due to direct contact with patients. The aim of this study was to assess the knowledge, perception and attitude regarding COVID-19 among dentistry students in Austria.Methods: During the first lockdown in Austria, an online survey was distributed among 165 dentistry students in their clinical term at the Medical University of Vienna. The survey contained elaborative questions on the general knowledge and attitude towards COVID-19. A special focus of the questionnaire was set on the modification of the student’s curriculum regarding infection control.Results: In total, 77 (47%) students replied; 68 questionnaires were included in the analysis. Dentistry students were found to have good general knowledge of COVID-19 during the early phase of the pandemic. Most students (89.6%) got their information regarding the COVID-19 infection from official sources; however, 58% would like to attend further lectures on COVID-19 to expand their knowledge.Discussion: The current study finds good general knowledge on COVID-19 among dental students, but some gaps regarding hygienic protocols and infection control. Students’ preferences regarding modification in the curriculum suggest practical courses and lectures as a way to close COVID-19 related knowledge gaps.Keywords: COVID-19, pandemic, infection, dentistry students, infection contro

Optimal priority and threshold assignment for fixed-priority preemption threshold scheduling

Fixed-priority preemption-threshold scheduling (FPTS) is a generalization of fixed-priority preemptive scheduling (FPPS) and fixed-priority non-preemptive scheduling (FPNS). Since FPPS and FPNS are incomparable in terms of potential schedulability, FPTS has the advantage that it can schedule any task set schedulable by FPPS or FPNS and some that are not schedulable by either. FPTS is based on the idea that each task is assigned a priority and a preemption threshold. While tasks are admitted into the system according to their priorities, they can only be preempted by tasks that have priority higher than the preemption threshold. This paper presents a new optimal priority and preemption threshold assignment (OPTA) algorithm for FPTS which in general outperforms the existing algorithms in terms of the size of the explored state-space and the total number of worst case response time calculations performed. The algorithm is based on back-tracking, i.e. it traverses the space of potential priorities and preemption thresholds, while pruning infeasible paths, and returns the first assignment deemed schedulable. We present the evaluation results where we compare the complexity of the new algorithm with the existing one. We show that the new algorithm significantly reduces the time needed to find a solution. Through a comparative evaluation, we show the improvements that can be achieved in terms of schedulability ratio by our OPTA compared to a deadline monotonic priority assignment