FOSSBot: An Open Source and Open Design Educational Robot

In the last few years, the interest in the use of robots in STEM education has risen. However, their main drawback is the high cost, which makes it almost impossible for schools to have one robot per student. Another drawback is the proprietary nature of commercial solutions, which limits the ability to expand or adapt the robot to educational needs. Different robot kit versions, which have different electronics and programming interfaces and target different age groups, make the decision of educators on which robot to use in STEM education even more complicated. In this work, we propose a new low-cost 3D-printable and unified software-based solution that can cover the needs of all age groups, from kindergarten children to university students. The solution is driven by open source and open hardware ideas, with which, we believe we will help educators in their work. We provide detail on the 3D-printable robot parts and its list of electronics that allow for a wide range of educational activities to be supported, and explain its flexible software stack that supports four different operating modes. The modes cover the needs of users that do not know or want to program the robot, users that prefer block-based programming and less or more experienced programmers who want to take full control of the robot. The robot implements the principles of continuous integration and deployment and allows for easy updates to the latest software version through its web-based administration panel. Though, in its first steps of development and testing, the proposed robot has a huge potential, due to its open nature and the community of students, researchers and educators, that potential has kept growing. A pilot at selected schools, a performance evaluation of various technical aspects and a comparison with state-of-the-art platforms will soon follow

Χορήγηση εισπνεόμενων αντιβιοτικών στους βαρέως πάσχοντες

ΕΙΣΑΓΩΓΗ Η υπερκατανάλωση αντιβιοτικών έχει συμβάλλει στην ανάπτυξη μηχανισμών αντοχής από τα μικρόβια σε αντιμικροβιακούς παράγοντες με αποτέλεσμα δυσκολία στην θεραπευτική αντιμετώπιση και συχνά θεραπευτικά αδιέξοδα σε λοιμώξεις από ανθεκτικά και πολυανθεκτικά στελέχη. Η πνευμονία που σχετίζεται με τον αναπνευστήρα (Ventilator associated pneumonia, VAP) επιπλέκει την κλινική πορεία ασθενών που ευρίσκονται υπό μηχανικό αερισμό, συνοδεύεται από υψηλή νοσηρότητα, θνητότητα και αυξημένο κόστος νοσηλείας. Οφείλεται κυρίως σε πολυανθεκτικά Gram αρνητικά μικρόβια με επακόλουθη δυσκολία στην θεραπευτική αντιμετώπιση. Η χορήγηση εισπνεόμενων αντιβιοτικών, γνωστή για την αντιμετώπιση των ασθενών που πάσχουν από κυστική ίνωση ή βρογχεκτασίες , έχει αναβιώσει τα τελευταία χρόνια λόγω του φαινομένου της πολυαντοχής και υπάρχει έντονο ερευνητικό ενδιαφέρον. Νεότερες θεραπευτικές στρατηγικές εξετάζουν την χορήγηση εισπνεόμενων αντιβιοτικών για το πλεονέκτημα της άμεσης πρόσβασής τους στο πνευμονικό παρέγχυμα και αύξησης της απόδοσης της δράσης τους. ΣΚΟΠΟΣ ΤΗΣ ΜΕΛΕΤΗΣ Σκοπός της παρούσας ανασκόπησης είναι να παρουσιαστεί η υπάρχουσα βιβλιογραφική γνώση που αναφέρεται στην χορήγηση εισπνεόμενων αντιβιοτικών για την θεραπεία πνευμονίας οφειλόμενης σε πολυανθεκτικά παθογόνα. ΜΕΘΟΔΟΣ Πρόκειται για μια βιβλιογραφική ανασκόπηση (narrative review). Διεξήχθη συστηματική αναζήτηση σε βάσεις δεδομένων, συμπεριλαμβανομένων των PubMed και Google Scholar με τις ακόλουθες λέξεις-κλειδιά: Colistin methanesulfonate, Colistin, Nebulizers, Clinical efficacy, Toxicity, Multidrug-resistant, Ventilator associated pneumonia, Aerosolized, Inhaled, Antibiotics ΑΠΟΤΕΛΕΣΜΑΤΑ Η χορήγηση των εισπνεόμενων αντιβιοτικών, και ειδικά της κολιστίνης που έχει μελετηθεί εκτενέστερα, φαίνεται πως επιτυγχάνει καλύτερες συγκεντρώσεις του αντιβιοτικού στην εστία της λοίμωξης σε ασθενείς με πνευμονία οφειλόμενη στον αναπνευστήρα, με ταυτόχρονη μείωση της συστηματικής τοξικότητας. Η επίτευξη αποτελεσματικής νεφελοποίησης των αντιβιοτικών, εξαρτάται από συγκεκριμένους παράγοντες όπως το μέγεθος των αερολυμένων σωματιδίων, το φάρμακο, η συσκευή νεφελοποίησης, η τεχνική νεφελοποίησης, το κύκλωμα εξαερισμού και τον ασθενή και πρέπει να βελτιστοποιούνται σε κάθε χορήγηση. ΣΥΜΠΕΡΑΣΜΑΤΑ Η χορήγηση εισπνεόμενων αντιβιοτικών για τη θεραπεία πνευμονίας οφειλόμενης σε πολυανθεκτικά Gram (-) βακτήρια παρουσιάζει πλεονεκτήματα όσον αφορά την φαρμακοκινητική στο πνευμονικό παρέγχυμα, την ασφάλεια, την απουσία συστηματικής τοξικότητας και την κλινική έκβαση. Περαιτέρω μελέτες αναμένεται να διευκρινίσουν περαιτέρω θέματα δοσολογίας, ασφάλειας και θεραπευτικής αποτελεσματικότητας.INTRODUCTION The overuse of antibiotics has contributed in the development of multidrug-resistant strains to different antimicrobial agents. As a result antimicrobial treatments often become ineffective to difficult to treat pathogens . Ventilator associated pneumonia (VAP) is a common complication in patients under mechanical ventilation and is associated with high morbidity, mortality and increased hospitalization costs. This is caused mainly by multi-drug resistant Gram(-) microbes which are difficult to treat. The use of inhaled antibiotics, already known from patients with cystic fibrosis or bronchiectasis, has renewed the interest of the medical community in the last decade due to phenomenon of antimicrobial resistance. New therapeutic strategies focus to the use of nebulized antibiotics for better lung penetration and increased effectiveness. AIM OF THE REVIEW The aim of this review is to present the existing literature on the administration of inhaled antibiotics for the treatment of pneumonia caused by MDR pathogens. METHOD This is a narrative review. A systematic search was conducted across multiple databases, including PubMed and Google Scholar with the following keywords: Colistin methanesulfonate; Colistin; Nebulizers; Clinical efficacy; Toxicity; Multidrug-resistant; Ventilator associated pneumonia; Aerosolized; Inhaled; Antibiotics. RESULTS The use of nebulized antibiotics, especially colistin that has been studied more extensively in patients with VAP, seems to manage higher concentrations of the antibiotic in the infected lung parenchyma , while reduce systemic toxicity. In order to achieve an effective nebulization of the antibiotic, a number of factors including nebulized particle size, drug, nebulizer, nebulization technique, ventilator as well as patient’s characteristics, must be taken into account and been optimized in each case CONCLUSIONS The use of nebulized antibiotics for the treatment of VAP caused by multidrug-resistant pathogens seems beneficial concerning the pharmacokinetics into the lung parenchyma, safety, the clinical outcome and the absence of systemic toxicity. Further studies need to elucidate the optimal dosage of the antibiotics, safety and therapeutic efficacy

A direct adverse effect of smoking

Smoking has been accounted for numerous adverse effects. We report a direct effect of smoking in a 73-year-old patient, a heavy smoker who presented to the emergency department with a 48-h history of productive cough and fever. Chest x-ray and chest CT revealed right lung infiltrates; however, they were not suggestive of the diagnosis, which was established through flexible bronchoscopy. The specific procedure concurrently contributed to the treatment of the patient

A Portable, Optical Scanning System for Large Field of View, High Resolution Imaging of Biological Specimens

Large field-of-view (FOV), high resolution imaging of biological specimens isa challenging task, requiring sophisticated and bulky optical systems. Such systems cannot be used for diagnosing or monitoring a disease at the point-of-care. To address this need, we developeda portable, optical system that can image—with a 2.88 μm resolution—large areas (6 mm × 40 mm) from various biological samples by performing scanning in one direction. This is achieved through the use of a microfabricated, mini-lens array. We demonstrated that our system can detect single cells from a smear blood test and thus validating our vision for its use at the point-of-care

A model for predicting room occupancy based on motion sensor data

When designing a large scale IoT ecosystem, it is important to provide economical solutions at all levels, from sensors and actuators to the software used for analytics and orchestration. It is of equal importance to provide non-intrusive solutions that do not violate users' privacy, but above all, it is important to guarantee the accuracy and integrity of the provided solution. In this work, we present a research prototype solution that has been developed as part of an ongoing project called (EM)3. The project involves IoT sensors and actuators, realtime data analytics modules and cutting edge recommendation algorithms in an ecosystem that improves energy efficiency in office buildings. The main concept of the (EM)3 is to recommend energy saving actions at the right moment to the right user. At the core of the (EM)3 vision is to detect when is the right moment for an energy saving action and sensors play a vital role in this. This article focuses on the model that predicts room occupancy using only data from a motion sensor. The predictions of the model, are used to trigger automations and notifications that turn-off office devices (e.g. air conditioning, lights, monitors, etc.) as soon as the office becomes empty, or a few minutes before this happens, in order to further promote efficient energy consumption habits. The evaluation of the model, using data from a camera sensor for validation, demonstrates a very low error rate and a very short delay on the detection of when the room is actually empty. 2020 IEEE.ACKNOWLEDGMENT This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Using big data and federated learning for generating energy efficiency recommendations

Internet of Things (IoT) devices are becoming popular solutions for smart home and office environments and contribute the most to energy efficiency. The most common implementation of such solutions relies on smart home systems that are hosted on the cloud. They collect data from a multitude of sensors, process it in real-time on the cloud and deliver immediate actions to sets of actuators that are installed locally. In this work, we present the (EM)3 project (Consumer Engagement towards Energy Saving Behaviour by Means of Exploiting Micro Moments and Mobile Recommendation Systems), which combines data collection, information abstraction, timed recommendations for energy saving actions and automations that promote energy saving in a household or office setup. The advantage of the (EM)3 project is that each room or office setup is controlled locally on an edge device, thus removing the need to share data to the cloud. The current article details on the data and information processing aspects of the (EM)3 solution, which efficiently handles thousands of sensor events on a daily basis and provides useful analytics and recommendations to the end user to support habit change. It also demonstrates the scalability of the solution by simulating a simple scenario of distributed data collection and processing on the edge nodes, which takes advantage of federated learning in order to adapt to the needs of multiple users without exposing their privacy. 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Real-time personalised energy saving recommendations

The increased consumption of energy worldwide has boosted the interest of people for energy-efficient solutions at every level of daily life, from goods production and transportation to the use of household and office appliances. This gave rise to monitoring applications that monitor the daily user interaction with the electrical and electronic appliances, detect unnecessary or extensive usage and recommend corrective actions. In this direction, this work presents the anatomy of the Consumer Engagement Towards Energy Saving Behavior by means of Exploiting Micro Moments and Mobile Recommendation Systems (EM)3 recommendation engine, which supports household and office users with real-time personalized recommendations for avoiding unnecessary energy consumption and reducing the overall household (or office) energy footprint. The recommendation engine is based on a set of sensors that monitor energy usage, room occupancy, and environmental conditions inside and outside the living space, and a set of actuators that allow the remote control of devices, (e.g. on and off actions, set to eco or standby mode, etc.). The innovating feature of this recommendation engine is that it puts the human in the loop of energy efficiency by recommending actions at the right moment, in real-time, with user approval and rejection options. In addition, it provides savings related facts in order to increase the persuasiveness of the recommendations. Initial results show that users respond positively to personalized recommendations and are further persuaded when specific types of facts are chosen. 2020 IEEE.ACKNOWLEDGMENT This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Data Analytics, Automations, and Micro-Moment Based Recommendations for Energy Efficiency

Energy conservation is a critical task for domestic households and office buildings, mainly because of the shortage of energy resources and the uprising contemporary environmental issues. The development of an IoT ecosystem that monitors energy consumption habits and timely recommends actions to promote energy efficiency can be beneficial for the mainstream. In this work, we present the EM3 project, which combines data collection, information abstraction, timed recommendations for saving actions and automations that promote energy saving in a household or office setup. The article focuses on the data and information processing aspects of the EM3 solution, which efficiently handles thousands of sensor events on a daily basis and provides useful analytics and recommendations to the end user to support habit change. 2020 IEEE.This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

A Micro-Moment System for Domestic Energy Efficiency Analysis

Domestic user behavior is a crucial factor guiding overall power consumption, necessitating the development of systems that analyze and help shape energy-efficient behavior. Therefore, the most important step in the process is the collection and understanding of highly detailed domestic consumption data. This article presents an appliance-based energy data collection and analysis system for energy efficiency applications. It leverages the concept of micro-moments, which are short-timed and energy-based events that form the overall energy behavior of the end user. The system comprises sensing modules for recording energy consumption, occupancy, temperature, humidity, and luminosity storing recordings on a database server. Sensing parameters were tested in terms of connection stability and measurement accuracy. A four-week contextual appliance-level dataset has been collected from research cubicles. Collected data were also classified into corresponding micro-moments with a variety of classifiers including ensemble decision trees and deep learning, achieving high stability and accuracy of 99%. Further, the micro-moment usage efficiency is calculated to quantify the efficiency of usage at the appliance level. 2021 IEEE.Manuscript received November 4, 2019; revised March 23, 2020; accepted April 22, 2020. Date of publication June 9, 2020; date of current version March 9, 2021. This article was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. (Corresponding author: Abdullah Alsalemi.) Abdullah Alsalemi, Yassine Himeur, and Faycal Bensaali are with the Department of Electrical Engineering, Qatar University, 2713 Doha, Qatar (e-mail: [email protected]; [email protected]; [email protected]).Scopu