Prospective Cohort Study of the Kinetics of Specific Antibodies to SARS-CoV-2 Infection and to Four SARS-CoV-2 Vaccines Available in Serbia, and Vaccine Effectiveness: A 3-Month Interim Report

Real-life data on the performance of vaccines against SARS-CoV-2 are still limited. We here present the rates of detection and levels of antibodies specific for the SARS-CoV-2 spike protein RBD (receptor binding domain) elicited by four vaccines available in Serbia, including BNT-162b2 (BioNTech/Pfizer), BBIBP-CorV (Sinopharm), Gam-COVID-Vac (Gamaleya Research Institute) and ChAdOx1-S (AstraZeneca), compared with those after documented COVID-19, at 6 weeks and 3 months post first vaccine dose or post-infection. Six weeks post first vaccine dose, specific IgG antibodies were detected in 100% of individuals fully vaccinated with BNT-162b2 (n = 100) and Gam-COVID-Vac (n = 12) and in 81.7% of BBIBP-CorV recipients (n = 148), while one dose of ChAdOx1-S (n = 24) induced specific antibodies in 75%. Antibody levels elicited by BNT-162b2 were higher, while those elicited by BBIBP-CorV were lower, than after SARS-CoV-2 infection. By 3 months post-vaccination, antibody levels decreased but remained ≥20-fold above the cut-off in BNT-162b2 but not in BBIBP-CorV recipients, when an additional 30% were seronegative. For all vaccines, antibody levels were higher in individuals with past COVID-19 than in naïve individuals. A total of twelve new infections occurred within the first 3 months post-vaccination, eight after the first dose of BNT-162b2 and ChAdOx1-S (one each) and BBIBP-CorV (six), and four after full vaccination with BBIBP-CorV, but none required hospitalization

Recognition of students` activity during the lecture utilizing the Internet of things

Predmet ovog istraživanja je praćenje aktivnosti studenata, odnosno prepoznavanje paterna iz okruženja i njihova prezentacija posredstvom tehnologije Interneta inteligentnih uređaja. Glavna hipoteza od koje se polazi i koja je dokazana u okviru ove doktorske disertacije je da se primenom tehnologije Internetа inteligentnih uređаjа u nаstаvi može poboljšаti efikаsnost nаstаvnog procesа kroz reаlizаciju sistemа zа prаćenje аktivnosti studenаtа koji u gotovo realnom vremenu omogućava analizu parametara iz okruženja i prezentaciju obrađenih rezultata. Kako bi se sagledao broj tehnika neophodnih za realizaciju pomenutih procesa kao i da bi se opravdala potreba date studije, urađen je pregled relevantnih istraživanja na polju računarski i društvenih nauka. Pregledom je obuhvaćeno poređenje i komparativna analiza platformi pametnih učionica koje pre svega predstavljaju platforme u kojima će zaživeti nova sveobuhvatna kompjuterska rešenja sposobna da prepoznaju sociološke kontekste u momentu pojavljivanja. Društvene nauke su vrlo bitne da bi se razumela sama pozadina procesa koji se klasifikuju i prate, tako da je pregledom društvenih nauka, pre svega socioloških signala, zaokružen pregled relevantnih bibliografskih izvora i data smernica dalje moguće realizacije sistema za praćenje aktivnosti. Pregledom utvrđeni su potencijalni parametri i algoritmi koje je moguće analizirati i upotrebiti za analizu a potom je predstavljena metodologija koja je korišćena u istraživanju za sve faze studije. Pre definisanja algoritama koji se mogu koristiti i postavke arhitekture objašnjeni su zahtevi sistema koje je neophodno ispuniti da bi prepoznavanje paterna u učionici moglo efikasno obavljati. Prepoznavanje paterna se obavlja metodom mašinskog učenja za koji je preduslov postojanje klasifikatora baziranom na određenom setu podataka. Simulacija sistema je urađena pre implementacije korišćenjem seta podataka koja nisu korišćeni u procesu treniranja. Pri simulaciji sistem je pokazao prosečnu tačnost od 92.2%.This paper proposes novel method for detecting students’ attention by utilizing Internet of Things and machine learning algorithms. The main hypotesis that has been proven in this PhD thesis is that utilization of the Internet of Things in the education can increase teaching efficiency by implementing system for detecting students’ attention that enables environmental parameters analysis and presentation of processed results. In order to provide further insight required for realization of above mentioned processes as well as to justify the need for performing featuring study, a survey on relevant computer and social science researches is done. The survey includes comparative analysis of smart classroom platforms that represents a medium for these new algorithms to rise and identificate sociological contexts in a moment of appearance. The sociological sciences are very important as they help us understand a background of sociological processes being monitored and classified; thus survey of sociological sciences and social signals above all are given to finalize a survey of relevant researches and inline a direction of further realization of the proposed system. In addition, survey has also inlined potential parameters and algorithms that can be used, followed with a methodology description for all phases of the research. Then, requirements of such system are analyzed and important features required for detection identified as well as sociological factors that influence these features. Pattern classification is done by levaraging a machine learning method that requires classificator based on a certain dataset. Before implementation, system simulation is done on a dataset which is not used in the process of training. During simulation system have shown avarage accuracy of 92.2%. After the simulation, the system was implemented and its performance evaluated by comparing a real-time annotator (i.e. the students’ feedback) with the system output during the lectures. The average accuracy of the system evaluated for three different groups of students was 81.9%

Recognition of students` activity during the lecture utilizing the Internet of things

Predmet ovog istraživanja je praćenje aktivnosti studenata, odnosno prepoznavanje paterna iz okruženja i njihova prezentacija posredstvom tehnologije Interneta inteligentnih uređaja. Glavna hipoteza od koje se polazi i koja je dokazana u okviru ove doktorske disertacije je da se primenom tehnologije Internetа inteligentnih uređаjа u nаstаvi može poboljšаti efikаsnost nаstаvnog procesа kroz reаlizаciju sistemа zа prаćenje аktivnosti studenаtа koji u gotovo realnom vremenu omogućava analizu parametara iz okruženja i prezentaciju obrađenih rezultata. Kako bi se sagledao broj tehnika neophodnih za realizaciju pomenutih procesa kao i da bi se opravdala potreba date studije, urađen je pregled relevantnih istraživanja na polju računarski i društvenih nauka. Pregledom je obuhvaćeno poređenje i komparativna analiza platformi pametnih učionica koje pre svega predstavljaju platforme u kojima će zaživeti nova sveobuhvatna kompjuterska rešenja sposobna da prepoznaju sociološke kontekste u momentu pojavljivanja. Društvene nauke su vrlo bitne da bi se razumela sama pozadina procesa koji se klasifikuju i prate, tako da je pregledom društvenih nauka, pre svega socioloških signala, zaokružen pregled relevantnih bibliografskih izvora i data smernica dalje moguće realizacije sistema za praćenje aktivnosti. Pregledom utvrđeni su potencijalni parametri i algoritmi koje je moguće analizirati i upotrebiti za analizu a potom je predstavljena metodologija koja je korišćena u istraživanju za sve faze studije. Pre definisanja algoritama koji se mogu koristiti i postavke arhitekture objašnjeni su zahtevi sistema koje je neophodno ispuniti da bi prepoznavanje paterna u učionici moglo efikasno obavljati. Prepoznavanje paterna se obavlja metodom mašinskog učenja za koji je preduslov postojanje klasifikatora baziranom na određenom setu podataka. Simulacija sistema je urađena pre implementacije korišćenjem seta podataka koja nisu korišćeni u procesu treniranja. Pri simulaciji sistem je pokazao prosečnu tačnost od 92.2%.This paper proposes novel method for detecting students’ attention by utilizing Internet of Things and machine learning algorithms. The main hypotesis that has been proven in this PhD thesis is that utilization of the Internet of Things in the education can increase teaching efficiency by implementing system for detecting students’ attention that enables environmental parameters analysis and presentation of processed results. In order to provide further insight required for realization of above mentioned processes as well as to justify the need for performing featuring study, a survey on relevant computer and social science researches is done. The survey includes comparative analysis of smart classroom platforms that represents a medium for these new algorithms to rise and identificate sociological contexts in a moment of appearance. The sociological sciences are very important as they help us understand a background of sociological processes being monitored and classified; thus survey of sociological sciences and social signals above all are given to finalize a survey of relevant researches and inline a direction of further realization of the proposed system. In addition, survey has also inlined potential parameters and algorithms that can be used, followed with a methodology description for all phases of the research. Then, requirements of such system are analyzed and important features required for detection identified as well as sociological factors that influence these features. Pattern classification is done by levaraging a machine learning method that requires classificator based on a certain dataset. Before implementation, system simulation is done on a dataset which is not used in the process of training. During simulation system have shown avarage accuracy of 92.2%. After the simulation, the system was implemented and its performance evaluated by comparing a real-time annotator (i.e. the students’ feedback) with the system output during the lectures. The average accuracy of the system evaluated for three different groups of students was 81.9%

Recognition of students` activity during the lecture utilizing the Internet of things

Predmet ovog istraživanja je praćenje aktivnosti studenata, odnosno prepoznavanje paterna iz okruženja i njihova prezentacija posredstvom tehnologije Interneta inteligentnih uređaja. Glavna hipoteza od koje se polazi i koja je dokazana u okviru ove doktorske disertacije je da se primenom tehnologije Internetа inteligentnih uređаjа u nаstаvi može poboljšаti efikаsnost nаstаvnog procesа kroz reаlizаciju sistemа zа prаćenje аktivnosti studenаtа koji u gotovo realnom vremenu omogućava analizu parametara iz okruženja i prezentaciju obrađenih rezultata. Kako bi se sagledao broj tehnika neophodnih za realizaciju pomenutih procesa kao i da bi se opravdala potreba date studije, urađen je pregled relevantnih istraživanja na polju računarski i društvenih nauka. Pregledom je obuhvaćeno poređenje i komparativna analiza platformi pametnih učionica koje pre svega predstavljaju platforme u kojima će zaživeti nova sveobuhvatna kompjuterska rešenja sposobna da prepoznaju sociološke kontekste u momentu pojavljivanja. Društvene nauke su vrlo bitne da bi se razumela sama pozadina procesa koji se klasifikuju i prate, tako da je pregledom društvenih nauka, pre svega socioloških signala, zaokružen pregled relevantnih bibliografskih izvora i data smernica dalje moguće realizacije sistema za praćenje aktivnosti. Pregledom utvrđeni su potencijalni parametri i algoritmi koje je moguće analizirati i upotrebiti za analizu a potom je predstavljena metodologija koja je korišćena u istraživanju za sve faze studije. Pre definisanja algoritama koji se mogu koristiti i postavke arhitekture objašnjeni su zahtevi sistema koje je neophodno ispuniti da bi prepoznavanje paterna u učionici moglo efikasno obavljati. Prepoznavanje paterna se obavlja metodom mašinskog učenja za koji je preduslov postojanje klasifikatora baziranom na određenom setu podataka. Simulacija sistema je urađena pre implementacije korišćenjem seta podataka koja nisu korišćeni u procesu treniranja. Pri simulaciji sistem je pokazao prosečnu tačnost od 92.2%.This paper proposes novel method for detecting students’ attention by utilizing Internet of Things and machine learning algorithms. The main hypotesis that has been proven in this PhD thesis is that utilization of the Internet of Things in the education can increase teaching efficiency by implementing system for detecting students’ attention that enables environmental parameters analysis and presentation of processed results. In order to provide further insight required for realization of above mentioned processes as well as to justify the need for performing featuring study, a survey on relevant computer and social science researches is done. The survey includes comparative analysis of smart classroom platforms that represents a medium for these new algorithms to rise and identificate sociological contexts in a moment of appearance. The sociological sciences are very important as they help us understand a background of sociological processes being monitored and classified; thus survey of sociological sciences and social signals above all are given to finalize a survey of relevant researches and inline a direction of further realization of the proposed system. In addition, survey has also inlined potential parameters and algorithms that can be used, followed with a methodology description for all phases of the research. Then, requirements of such system are analyzed and important features required for detection identified as well as sociological factors that influence these features. Pattern classification is done by levaraging a machine learning method that requires classificator based on a certain dataset. Before implementation, system simulation is done on a dataset which is not used in the process of training. During simulation system have shown avarage accuracy of 92.2%. After the simulation, the system was implemented and its performance evaluated by comparing a real-time annotator (i.e. the students’ feedback) with the system output during the lectures. The average accuracy of the system evaluated for three different groups of students was 81.9%

Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing

Because the number of air quality measurement stations governed by a public authority is limited, many methodologies have been developed in order to integrate low-cost sensors and to improve the spatial density of air quality measurements. However, at the large-scale level, the integration of a huge number of sensors brings many challenges. The volume, velocity and processing requirements regarding the management of the sensor life cycle and the operation of system services overcome the capabilities of the centralized cloud model. In this paper, we present the methodology and the architectural framework for building large-scale sensing infrastructure for air quality monitoring applicable in urban scenarios. The proposed tiered architectural solution based on the adopted fog computing model is capable of handling the processing requirements of a large-scale application, while at the same time sustaining real-time performance. Furthermore, the proposed methodology introduces the collection of methods for the management of edge-tier node operation through different phases of the node life cycle, including the methods for node commission, provision, fault detection and recovery. The related sensor-side processing is encapsulated in the form of microservices that reside on the different tiers of system architecture. The operation of system microservices and their collaboration was verified through the presented experimental case study

Surface composition and structure of Ni-Cr sputtered coatings exposed in air at room temperature

Nichrome coatings have been deposited using the d.c. sputtering of the target (80% Ni-20% Cr wt.) by Ar+ ions at a working pressure of 10(-1) Pa and at room temperature. After the air exposure of samples at room temperature, their phase composition and structure was studied by X-ray diffraction, while the surface elemental composition was determined by low energy ion scattering (LEIS) and Auger electron spectroscopy (AES). AES has been also used for the bulk composition analysis. There is a significant difference between the compositions of the surface and of the bulk. Surface segregation of chromium was observed using both LEIS and AES, and it was found that the chromium concentration increases with that of oxygen adsorbed at the surface during the air exposure. The nichrome surface composition is qualitatively the same as in the case of its exposure to O-2 at room temperature, but significantly different from that of the thermally annealed nichrome to 400 degrees C in air. (c) 2006 Elsevier B.V. All rights reserved.22nd International Conference on Atomic Collisions in Solids, Jul 21-26, 2006, Tech Univ Berlin, Berlin, German