The aim of the article was to address the relevance of cultural capital and relative risk aversion theories for educational outcomes and aspirations of upper secondary school students in Croatia, and to examine whether these approaches are compatible in explaining educational inequalities. Linear and logistic regressions were used to analyze data from 2106 students in their last year of upper secondary school (16-18 years old). Both constructs, cultural capital and relative risk aversion, had positive effects on school performance, enrolment of gymnasium instead of vocational school and aspirations for higher education. The analyses indicated that cultural factors frequently associated with school performance should not be separated from more direct status concerns shaping educational decisions. Rather than seeing them as unrelated, these mechanisms may both be seen as constitutive of students' educational dispositions. KEYWORDS: cultural capital; relative risk aversion; educational inequality; upper secondary education; aspirations for higher education APSTRAKT: Cilj ove studije bio je da se sagleda značaj teorija kulturnog kapitala i izbegavanja relativnog rizika za obrazovne ishode i aspiracije učenika srednjih škola u Hrvatskoj, te da se ispita da li su ovi pristupi kompatibilni u objašnjavanju nejednakosti u obrazovanju. Podaci 2106 učenika u njihovoj poslednjoj godini srednje škole (starost od 16-18 godina) analizirani su linearnom i logističkom regresijom. Oba konstrukta, kulturni kapital i izbegavanje relativnog rizika,

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The growth of the Internet of Things (IoT) continues to be rapid, making it an essential part of information technology. As a result, IoT devices must be able to handle data collection, machine-to-machine (M2M) communication, and preprocessing of data, while also considering cost, processing power, and energy consumption. This paper introduces a system for device indoor localization that uses variations in the strength of the wireless signal. The proposed system addresses logistics use cases in which it is imperative to achieve reliable end-to-end delivery, such as pharmaceutic delivery, delivery of confidential documents and court exhibits, and even food, since the same is introduced into human organism and presents a potential risk of terrorist or other attack. This work proposes a concept based on low-power and low-cost LoRaWAN based system that utilizes a Machine Learning technique based on Neural Networks to achieve high accuracy in device indoor localization by measuring the signal strength of a beacon device. Furthermore, using signal strength measurements, that is, RSSI and SNR captured by LoRaWAN gateways, it is possible to estimate the location of the device point with an accuracy of up to 98.8%.

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