In accordance with the advancement in robotics and the scholarly literature, the extents of utilizing robots for autistic children are widened and could be a promising method for individual with Autism Spectrum Disorder (ASD) treatments, where the different form of robot (humanoid, non-humanoid, animal-like, toy, and kits) can be employed effectively as a support tool to augment the learning skills and rehabilitate of the individual with Autism Spectrum Disorder (ASD). Thus, the robots were exploited for ASD children in different aspects namely; modelling, teaching, and skills practicing; testing, highlighting and evaluating; providing feedback or encouragement; join Attention; eliciting social behaviours; emotion recognition and expression; imitation; vocalization; turn-taking; and diagnostic. The related literature published recently in journals and conferences is taken into account. In this paper, we review the use of robots that help in the therapy of individuals with Autism Spectrum Disorder (ASD). The articles on using robots for autistic children rehabilitation and education which reported results of experiments on a number of participants were implicated. After looking in digital libraries under this criteria, and excluding non-related, and duplicated studies, 39 studies have been found. The findings were focused mainly on the social communication skills of autistic children and how the extent of the robots mitigate their stereotyped behaviours. Deeper research is required in this area to cover all applications of robotic on autistic children in order to design feasible and low-cost robots that ensure provide high validity.

The aim of this study was to investigate the effect of the addition of different amounts of organic selenium (ALKOSEL® R397) in concentrate mixtures on the selenium content in the blood plasma and feces of ducks. The experiment was performed on 240 one-day-old ducklings (Cherry Valley) which were freely selected into 4 groups, one control K0 group, and three experimental K1, K2 and K3 groups. In two phases of feeding in fattening, ducklings were fed with two and nutritionally different concentrate feed mixtures: starter (from 1 to 14 days) and finisher (from 15 to 49 days of fattening). The control group of ducklings (K0) during feeding received food without added organic selenium in both phases of fattening. In both phases of fattening, the experimental group of ducks K1 was fed with food as well as the control group, but with the addition of 0.2 mg/kg of organic selenium, while the experimental group K2 used food with 0.4 mg/kg, and the experimental group K3 with 0.6 mg/kg of organic selenium. The addition of organic selenium to duck feed had the effect of increasing selenium content in both blood plasma and duck feces. The highest content of selenium in blood plasma and feces was determined in the group of ducks that received the highest amount of organic selenium through food during the entire experiment.

Chemical precipitation is a useful conventional process to remove heavy metals from aqueous solutions. In this work, a waste sludge from the Solway process was used as a precipitation agent for the precipitation of Cu (II), Ni (II), Pb (II) and Zn (II) ions with an initial concentration of 50 mg/L. The results of the research showed that the waste sludge from the Solway process completely removed Pb (II) ions from the solution in the pH range of 8.39 -11.74, also good efficiency was shown in other cations. The maximum precipitation efficiency for Cu (II) is 99.890% at pH 10.98, Ni (II) 99.940% at pH 11.81, Zn (II) 99.616% at 10.1. Waste sludge is proved to be a good precipitate for cation separation.

Objective COVID-19 pandemic led to major life changes. We assessed the psychological impact of COVID-19 on dental academics globally and on changes in their behaviors. Methods We invited dental academics to complete a cross-sectional, online survey from March to May 2020. The survey was based on the Theory of Planned Behavior (TPB). The survey collected data on participants’ stress levels (using the Impact of Event Scale), attitude (fears, and worries because of COVID-19 extracted by Principal Component Analysis (PCA), perceived control (resulting from training on public health emergencies), norms (country-level COVID-19 fatality rate), and personal and professional backgrounds. We used multilevel regression models to assess the association between the study outcome variables (frequent handwashing and avoidance of crowded places) and explanatory variables (stress, attitude, perceived control and norms). Results 1862 academics from 28 countries participated in the survey (response rate = 11.3%). Of those, 53.4% were female, 32.9% were <46 years old and 9.9% had severe stress. PCA extracted three main factors: fear of infection, worries because of professional responsibilities, and worries because of restricted mobility. These factors had significant dose-dependent association with stress and were significantly associated with more frequent handwashing by dental academics (B = 0.56, 0.33, and 0.34) and avoiding crowded places (B = 0.55, 0.30, and 0.28). Low country fatality rates were significantly associated with more handwashing (B = -2.82) and avoiding crowded places (B = -6.61). Training on public health emergencies was not significantly associated with behavior change (B = -0.01 and -0.11). Conclusions COVID-19 had a considerable psychological impact on dental academics. There was a direct, dose-dependent association between change in behaviors and worries but no association between these changes and training on public health emergencies. More change in behaviors was associated with lower country COVID-19 fatality rates. Fears and stresses were associated with greater adoption of preventive measures against the pandemic.

In this paper we discuss several elements of importance for securing QoS in multimedia networks. Firstly, we present the first factor, which refers to understanding the characteristics of multimedia traffic in order to define and implement the QoS requirements. Secondly, factor refers to translation between QoS parameters that implies the distribution of system and network resources, and thirdly the factor establishes the appropriate QoS architecture that can provide the required QoS guarantees for multimedia applications. We have been analyzing security-critical applications such as remote operation, which may require a guaranteed level of availability (hard QoS). There are basically two ways to secure a guaranteed QoS. The first is simply to provide a lot of resources, enough to meet the expected peak (peak) requirements with a significant security margin. This approach generously oversupplies the (over provisioning) network. We gave a detailed security analysis as features of WiMAX. More precisely, our analysis is based on the claim that its key feature of the WiMAX network is that the security layer is built into the protocol leg instead of being added later, i.e. the security layer is complex between PHY and MAC layers.

The concept of Massive Open Online Course (MOOC) brings the opportunity to adjust both the study content, and the context, based on the teaching needs. Therefore, in this paper, we present our best practices on enabling remote networking laboratories via Blackboard platform, including the Blackboard Collaborate Ultra extension, in order to efficiently react to the challenges of imminent campus closure imposed by COVID-19 breakout. We present an extensive survey as a feedback from students, which allowed us to measure and to quantify students’ experience and satisfaction with the remote teaching setup that successfully served 45 enrolled students. As the results bring the positive attitude towards practices presented in this paper, such teaching practices will foster some of the critical skills nowadays, such as collaboration, self-driven learning, and problem solving, and they can also serve as a successful example on how to efficiently cope with the limited access to traditional classroom resources within various courses.

OBJECTIVE The goal of our study was to discover and analyze possible risk factors for and possible protective factors against the occurrence of potential drug-drug interactions (pDDIs) in a hospitalized patient with community-acquired pneumonia. MATERIALS AND METHODS The central outcome was the incidence of pDDIs in patients with community-acquired pneumonia checked by Lexicomp and Micromedex interaction checkers. RESULTS The most severe pDDIs (Consider therapy modification D/Avoid combination X/Major/Contraindicated) were found in 19 (20%) and 54 (58%) patients, according to Lexicomp and Micromedex, respectively. Patients with community-acquired pneumonia who were older, smokers, and with more prescribed drugs by more than a few independent prescribers had a higher risk to experience pDDIs. Possible protective factors were longer length of hospitalization, transfer from the Emergency Department, antiarrhythmic drugs as well as an anticoagulant therapy. CONCLUSION In conclusion, community-acquired pneumonia patients with the above-mentioned factors should have their treatment more deeply monitored for pDDIs.

The rapid development of technology is directly affecting the growth and development of e-commerce shipments, especially in the Business to Customer segment. An increase in e-commerce shipments has a strong impact on the express delivery industry. In these conditions, a very significant challenge is how to organize a postal network. The problem that arises is how many postal centers, and at what locations, should be implemented in a specific geographical area in order to optimize the level of service for the users. Solving this challenge has latterly received increased attention in both industry and academia. The aim of this paper is to firstly provide a concise overview of current approaches in the process of determining the optimal location of postal centers. The second part of the paper will focus on proposing an approach that will rely on machine learning methods for clustering in defined conditions and specific geographical environment using appropriate geographic information tools for spatial data analysis and visualization.

In this paper, empirical research about Passenger Car Equivalents (PCEs) on the longitudinal downgrade of two-lane roads in Bosnia and Herzegovina has been conducted in order to determine the influence of vehicle structure under free traffic flow conditions. The research has been carried out considering the classes of vehicles at cross-sections on the downgrade of two-lane roads. As a result, the negative influence of vehicle structure under free traffic flow conditions using passenger car equivalents (PCEs) has been determined. The results show that on the downgrade of two-lane roads, the value of passenger car equivalent decreases from the level terrain to the boundary minimum value for the determined downgrade g = −3.00%, after which its value starts to increase slightly. Based on the obtained values, the models calibrated with a second-degree polynomial have been developed to determine the average value of passenger car equivalent as a function of its boundary value. The paper also compares the results obtained by the developed models with the models from the Highway Capacity Manual under free traffic flow conditions. In addition, models for the percentage values of PCE15%, PCE50% and PCE85% have been established.

This paper explores the new way of presenting one existing VR application, which was described in our previous work - Virtual Reality Experience of Sarajevo War Heritage. The goal of the application was to introduce more people with the Sarajevo siege and allow them to experience the Tunnel crossing at that time. Before this application, we made two versions, the first one for VR setup and the second for the web. In this paper, we introduce a mobile version with the same content. The challenge was to optimize the content for the mobile experience. The assets were optimized so a wider number of mobile phones with different hardware capabilities can run the application. The advantages and disadvantages of this approach are pointed out, and the limitations of the mobile application are emphasized. The memory usage and frame rate are measured for different Android devices with different operating system versions and hardware capabilities. The results show the optimized application can be run on different Android mobile devices. Nevertheless, for better user experience a higher number of frames per second is needed, which may include reducing the quality of the assets.

Clinical mistreatment and mismanagement are big issues caused by detection of too many false negative patients. Therefore, lung cancer diagnostic inaccuracy and methods to surpass it in a minimally invasive way is often the subject of research, as it is case of this study. This study focuses on the use of machine learning algorithms as a noninvasive tool to differentiate malignant pleural effusions from benign effusions. It provides performance comparisons between Adaptive neuro-fuzzy inference system (ANFIS), Support vector machine (SVM), RUS Boosted Tree (RUSBoost) and K-Nearest-Neighbor (K-NN) techniques for lung cancer detection. The proposed algorithms were chosen based on the current state of the art in the field of pulmonary diagnostics. The novelty of this work is the application of machine learning models for classification of lung cancer based on expression of tumor markers obtained from serum and pleural fluids. The performance of all models is compared and validated on data samples of 168 patients. Three classification model, SVM, RUSBoost and K-NN performed equally well, whereas underperforming model was ANFIS.

Algorithms for solving Rubik’s cube have been an active research area since the first appearance of the cube in 1974. The challenge posed when solving the cube is to choose an algorithm that solves the cube for the minimum number of steps. Many algorithms are already implemented in software, but not many are tested with modern hardware-software methodologies, such as hardware-software co-design. Here, the challenge is to take into consideration limiting factors of hardware and implement the most efficient solution. In this paper, the hardware/software co-design is used to solve the random configuration of Rubik’s cube. Two algorithms are used: the Basic algorithm and the Kociemba algorithm. The Basic algorithm is easy to understand and implement but requires many more steps to solve the cube than the Kociemba algorithm. The Kociemba algorithm requires some pre-processing tasks, such as depth-first search and pruning trees, but can solve the cube in about 25 moves. Both algorithms are implemented and tested on a custom-made robot with mechanical parts, actuators, grippers and Intel’s DE1-SoC for drive control and implementation of solving algorithms. The robot successfully solved a number of random configurations. Performances (running time, number of moves needed for solving the cube) of both algorithms are measured and compared.

Rift Valley fever (RVF) is an arboviral zoonosis that primarily affects ruminants but can also cause illness in humans. The increasing impact of RVF in Africa and Middle East and the risk of expansion to other areas such as Europe, where competent mosquitos are already established, require the implementation of efficient surveillance programs in animal populations. For that, it is pivotal to regularly assess the performance of existing diagnostic tests and to evaluate the capacity of veterinary labs of endemic and non-endemic countries to detect the infection in an accurate and timely manner. In this context, the animal virology network of the MediLabSecure project organized between October 2016 and March 2017 an external quality assessment (EQA) to evaluate the RVF diagnostic capacities of beneficiary veterinary labs. This EQA was conceived as the last step of a training curriculum that included 2 diagnostic workshops that were organized by INIA-CISA (Spain) in 2015 and 2016. Seventeen veterinary diagnostic labs from 17 countries in the Mediterranean and Black Sea regions participated in this EQA. The exercise consisted of two panels of samples for molecular and serological detection of the virus. The laboratories were also provided with positive controls and all the kits and reagents necessary to perform the recommended diagnostic techniques. All the labs were able to apply the different protocols and to provide the results on time. The performance was good in the molecular panel with 70.6% of participants reporting 100% correct results, and excellent in the serological panel with 100% correct results reported by 94.1% of the labs. This EQA provided a good overview of the RVFV diagnostic capacities of the involved labs and demonstrated that most of them were able to correctly identify the virus genome and antibodies in different animal samples.

The problem of counterfeiting diplomas in education with the advancement of digital technology is increasingly pronounced. The process of forging documents is almost always accompanied by reduced transparency in issuing the documents with no possibility to easily check the validity of the document. One of the currently very attractive and challenging technology in digitized society is the blockchain technology and all the sequential systems that have emerged based on it. One such system is the Ethereum platform, which uses blockchain technology and enables the creation of decentralized applications programmed to run on the Ethereum network. One of the Ethereum use is a Smart Contract, which allows applications to be executed online, completely autonomously without the influence of a third party, once a previously defined condition is satisfied. The objective of this research is to explore the possibility of using a Smart Contract in the process of the creation and issuance of diplomas at a higher education institution. At the end of the analysis, we provide an overview of the advantages and disadvantages of this procedure, as well as potential possibilities for its improvements. The possibilities of automation and the cost of such a process were also considered.