The use of digital teaching resources became widespread and very helpful during the COVID‐19 pandemic as an alternative to a traditional course with cadavers. Technologies such as augmented reality (AR), virtual reality (VR), 3D models, video lectures and other online resources enable three‐dimensional visualization of the anatomical structures and allow students to learn more interactively. The aim of this study was to compare students' performance in the traditional anatomical courses in teaching neuroanatomy and technology‐based learning methods such as video lectures, 3D models and 3D printed specimens. Four groups of first‐year students of Veterinary Faculty established for the practical classes during the academic year 2021/2022 took part in this research. The total number of students participating in this research was 72. Each group attended separately the theoretical lecture with a demonstration based on a different technique; the control group used formalized specimens, while the three experimental groups used video lectures, 3D models and 3D printed specimens, respectively. Subsequently, all groups completed the same questionnaire testing their short‐term memory of the neuroanatomical structures. After four weeks students were tested for their long‐term memory of the neuroanatomy lecture with the follow‐up test containing an identical list of questions. The test scores using video lectures and 3D printed models were significantly higher compared with the group that learned in the traditional way. This study suggests that alternative approaches such as technology‐based digital methods can facilitate memorization of anatomical terms and structures in a more interactive and sensory engaging way of learning.

Dripping rainfall simulators are important instruments in soil research. However, a large number of non-standardized simulators have been developed, making it difficult to combine and compare the results of different studies in which they were used. To overcome this problem, it is necessary to become familiar with the design and performances of the current rainfall simulators. A search has been conducted for scientific papers describing dripping rainfall simulators (DRS) and papers that are thematically related to the soil research using DRS. Simulator design analysis was performed integrally, for simulators with more than one dripper (DRS>1) and with one dripper (DRS=1). Descriptive and numerical data were extracted from the papers and sorted by proposed categories, according to which the types and subtypes of used simulators are determined. The six groups of elements that simulators could consist of have been determined, as well their characteristics, representation and statistical analyses of the available numerical parameters. The characteristics of simulators are analyzed and presented, facilitating the selection of simulators for future research. Description of future simulators in accordance to the basic groups of simulator elements should provide all data necessary for their easier replication and provide a step closer to the reduction of design diversification and standardization of rainfall simulators intended for soil research.

Wastewater-based epidemiology (WBE) is based on the analysis of human metabolic excretion products (biomarkers) of xenobiotics in wastewater, to gain information about various lifestyle and health aspects of a population in an evidence-based manner. Due to the complex wastewater matrix and trace level occurrence of human biomarkers in the sewage network, it is crucial to have sensitive analytical procedures available. Additionally, to improve the value of WBE as a complementary epidemiological source, there is increasing pressure on the analysis of more compounds, more locations, and more samples. A high-throughput method based on 96-well Oasis MCX solid phase extraction (SPE), requiring less influent wastewater (2 mL), was developed in accordance with the European Medicine Agency guidelines. Validation was successful for 28 parent drug and metabolites of antidepressants, opioids and drugs of abuse. The selection of biomarkers and quantification limit was chosen to be relevant for WBE and was predominantly 10 ng/L or below. The final method was successfully applied to 24h composite samples of October 2019 (n=27), obtained from urban wastewater treatment plant Leuven (Belgium).

The human population is growing every year and naturally so is the need for resources. The most essential resource, water, is in danger of scarcity, both from pollution and increased use. The aim of this study is to reduce the usage of drinkable water in the agriculture sector with the use of Artificial Intelligence, specifically for green areas of undemanding flora (grass in front of buildings, houses, etc.). Conventional ways of irrigation for these green areas are human-operated, regulated by a scheduled timer, sensor directed, or some combination of those. Sensor-directed irrigation with the help of humans has proven to be efficient. This study will show how artificial intelligence replaces sensors and human labor. Using soil moisture sensors, and weather station data (rainfall, humidity, wind strength, wind direction, temperature), the artificial neural network is trained first to show with which data soil moisture data correlates the most, and after that with the data collected for one month is trained to know what is the relative moisture of soil based on current weather station data, so we can set the trigger for the irrigation system to start irrigating the fields. With this study, the need for human labor in means of controlling irrigation and sensor maintenance will be cut out, so a much cheaper and more efficient model for irrigation is achieved.

Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is rare, poorly understood, with heterogeneous characteristics resulting in difficult diagnosis. We aimed to systematically review evidence of soluble markers in peripheral blood or bronchoalveolar lavage fluid (BALF) as biomarkers in SSc-ILD. Method Five databases were screened for observational or interventional, peer-reviewed studies in adults published between January 2000 and September 2021 that assessed levels of biomarkers in peripheral blood or BALF of SSc-ILD patients compared with healthy controls. Qualitative assessment was performed using Critical Appraisal Skills Programme (CASP) checklists. Standardised mean difference (SMD) in biomarkers were combined in random-effects meta-analyses where multiple independent studies reported quantitative data. Results 768 published studies were identified; 38 articles were included in the qualitative synthesis. Thirteen studies were included in the meta-analyses representing three biomarkers: KL6, SP-D and IL-8. Greater IL-8 levels were associated with SSc-ILD in both peripheral blood and BALF, overall SMD 0.88 (95% CI 0.61 to 1.15; I2=1%). Greater levels of SP-D and KL-6 were both estimated in SSc-ILD peripheral blood compared with healthy controls, at an SMD of 1.78 (95% CI 1.50 to 2.17; I2=8%) and 1.66 (95% CI 1.17 to 2.14; I2=76%), respectively. Conclusion We provide robust evidence that KL-6, SP-D and IL-8 have the potential to serve as reliable biomarkers in blood/BALF for supporting the diagnosis of SSc-ILD. However, while several other biomarkers have been proposed, the evidence of their independent value in diagnosis and prognosis is currently lacking and needs further investigation. PROSPERO registration number CRD42021282452.

We consider the problem of maximum-likelihood estimation in linear models represented by factor graphs and solved via the Gaussian belief propagation algorithm. Motivated by massive Internet of Things (IoT) networks and edge computing, we set the above problem in a clustered scenario, where the factor graph is divided into clusters and assigned for processing in a distributed fashion across a number of edge computing nodes. For these scenarios, we show that an alternating Gaussian belief propagation (AGBP) algorithm that alternates between inter- and intracluster iterations, demonstrates superior performance in terms of convergence properties compared to the existing solutions in the literature. We present a comprehensive framework and introduce appropriate metrics to analyze the AGBP algorithm across a wide range of linear models characterized by symmetric and nonsymmetric, square, and rectangular matrices. We extend the analysis to the case of dynamic linear models by introducing the dynamic arrival of new data over time. Using a combination of analytical and extensive numerical results, we show the efficiency and scalability of the AGBP algorithm, making it a suitable solution for large-scale inference in massive IoT networks.

We use simplicial complexes to model simple games as well as weighted voting games where certain coalitions are considered impossible. Topological characterizations of various ideas from simple games are provided, as are the expressions for Banzhaf and Shapley-Shubik power indices for weighted games. We calculate the indices in several examples of weighted voting games with unfeasible coalitions, including the U.S. Electoral College and the Parliament of Bosnia-Herzegovina.

We use simplicial complexes to model simple games as well as weighted voting games where certain coalitions are considered impossible. Topological characterizations of various ideas from simple games are provided, as are the expressions for Banzhaf and Shapley-Shubik power indices for weighted games. We calculate the indices in several examples of weighted voting games with unfeasible coalitions, including the U.S. Electoral College and the Parliament of Bosnia-Herzegovina.

This paper presents an overview of advances in estimation of the biofouling state of fish cages as a part of the HEKTOR (Heterogeneous Autonomous Robotic System in Viticulture and Mariculture) project. Firstly, the developed framework for biofouling estimation is shown and explained in brief. A method using k-means clustering for labeling images of fish cages is outlined. It is followed by results of machine learning approaches for automatic inferring of semantic meaning of pixels in an image trained on a recorded dataset using said outlined method. Furthermore, a brief overview and results of contour detection on images classified by trained machine learning models are given. Moreover, a method of feature-based monocular camera distance estimation constrained by assumptions of the viewing angle is presented. All mentioned methods and algorithms fit together in order to produce an estimation of how biofouled the observed net is. The successfulness of the estimation depends on the viewing conditions while filming the cages. In good conditions the results are satisfactory and could be used by industrial fisheries in place of human labour. All the developed algorithms are fast enough so that the entire process from start to finish takes less than 1 second.

Global urbanization is advancing, and with it, the densification of cities. Due to increased sealing of open spaces and the re-densification of existing urban settings, green spaces in the city are becoming scarcer. At the same time, greening within the urban fabric is known for its positive effects on the environment and decisively counteracts the urban heat effect. This study deals with the benefits of green façades for the environment as a cooling measure. Two façade greening systems, one trough and one cassette system, consisting of curtain wall elements with a basic metal structure, installed at a south-facing outdoor wall of a school building in Vienna, Austria, were taken under metrological examination. In order to evaluate the cooling effect caused by evapotranspiration, the amount of water evaporated was calculated using the difference of inflow and outflow. Furthermore, the surface temperatures of the greened and non-greened walls were measured to display the influence of the interaction of shading and evapotranspiration on the surrounding microclimate. The investigated vertical greening system with an area of 58 m2 has an average evaporation capacity of 101.38 L per day in the summer. The maximum surface temperature difference was measured to be 11.6 °C.

Palliative care is an approach to the comprehensive care of a patient suffering from a chronic, incurable disease when the curative methods of treatment have been exhausted.

When developing products or performing experimental research studies, the simulation of physical or logical systems is of great importance for evaluation and verification purposes. For research-, and development-related distributed control systems, there is a need to simulate common physical environments with separate interconnected modules independently controlled, and orchestrated using standardized network communication protocols.The simulation environment presented in this paper is a bespoke solution precisely for these conditions, based on the Modular Automation design strategy. It allows easy configuration and combination of simple modules into complex production processes, with support for individual low-level control of modules, as well as recipe-orchestration for high-level coordination. The use of the environment is exemplified in a configuration of a modular ice-cream factory, used for cybersecurity-related research.

Peer violence and school safety have always been relevant topics in society. After the tragedy that occurred at the “Vladislav Ribnikar” Elementary School in Belgrade, these issues have gained special significance and received great public attention. The loss of innocent children’s lives has prompted a reassessment of past practices in schools regarding school safety. The aim of this paper is to analyze problems related to peer violence in the Republic of Srpska. Subsequently, the authors address the issue of school safety in a broader context and, finally, discuss the justification for educating students in the field of security culture in primary and secondary schools in the Republic of Srpska.