The paper analyzes the cardiovascular parameters of patients with heart disease. The aim of this study was to predict death in a patient with cardiovascular disease based on 12 parameters, using Random Forest and Logistic Regression algorithms. Parameters were tuned for both algorithms to determine the best settings. The most significant factors in the process predicted were found using the FEATURE SELECTION method of both algorithms. By comparative analysis of the obtained results, the highest accuracy of 90% was obtained using the Random Forest Algorithm.

The Canadian Fire Weather Index system [1] has been used worldwide by many countries as classic approach in fire prediction. It represents system that account for the effects of fuel moisture and weather conditions on fire behaviour. It numerical outputs are based on calculation of four meteorological elements: air temperature, relative humidity, wind speed and precipitation in last 24h. In this paper meteorological data in combination with Canadian Fire Weather Index system (CFWI) components is used as input to predict fire occurrence using logistic regression model. As logistic regression is a supervised machine learning method it’s based on user input in the form of dataset. Dataset is collected using NASA GES DISC Giovanni web-based application in the form of daily area-averaged time series in period of 31.7.2010 to 31.7.2020, it’s analysed and pre-processed before it is used as input for logit model. CFWI components values are not imported but calculated in run-time based on pre-processed meteorological data. As a result of this research windows application was developed to assist fire managers and all those involved in studying the fire behaviour.

The development of teleoperation systems, robots, or any physical part of the system can be costly and if something goes wrong it can lead to development overdue. Precisely for these reasons, engineers and scientists today resort to the development of simulated systems before the construction of a real system. Robot Operating System (ROS) is one of the most popular solutions for robot development, manipulation, and simulation. In this paper, we present a web application for remote control of a ROS robot. The robot is controlled via a web application that is used as a virtual Joystick. Also, in this paper, an experimental work analysis of the projected system is performed. Further research possibilities include upgrading the presented web interface, adding certain motion autonomy sensors, or integrating some path planning algorithms.

Nowadays, automatic systems are using in more spheres of industry, and in this way, human intervention is avoided and used as minimally as possible. In the chicken poultry industry, the use of mother hens is transferring to automatic egg incubating systems. Such systems are helpful for the farmers to incubate the eggs automatically without the need for human intervention. These systems work by keeping the physical quantities, temperature and humidity, at the optimal level. In that way, the fetuses inside eggs are growing without the presence of the mother hen. The egg incubating systems not only improve poultry production considerably but also help in the regularity of income making, enabling the farmers to be able to get transition into possible rural entrepreneurship. This paper describes the design and implementation of a fuzzy control system for egg incubating based on IoT technology. The microcontroller is programmed to work as a fuzzy logic control system for controlling microclimate conditions in the egg incubator to keep the conditions for different eggs type optimal. Informations from the temperature and humidity sensors are sent wirelessly to the cloud. Also, the implemented egg incubating system enables automatic tracking of the remaining days until hatching chickens. In this way, remote monitoring, from any location, of microclimatic conditions inside the egg incubator is enabled. For the experimental work analysis of the implemented egg incubating system, the egg incubator is made. Based on the results of the experimental work analysis can be seen that the egg incubating system works well and that it helps with improving poultry production.

With the development of modern technology, smartphones have become a necessity for most people. Among other uses, mobile phones are increasingly being used in smart home systems. In smart homes, mobile phones are used to remotely control and monitor various systems such as simply turning on/off lights and household appliances, various monitoring systems, etc. Nowadays, sending coded messages or pressing application buttons is increasingly being avoided in process of developing smart systems. More and more frequently is used voice commands. The system which uses voice commands for remote control and monitoring smart home is described in this paper. In the implemented system, the user is able, using specific voice commands to remotely control the operation of various appliances in his home. An Android application was designed to control the implemented system. Using the designed Android application, the user controls the desired home devices with specific voice commands. Also, on the designed Android application are buttons that the user can use, in case the user’s voice is not recognized in the implemented system. For experimental work analysis, the model of the home is made with lights and different home appliances inside. The results of the experimental work analysis of the implemented system show this system is very simple to use and very efficient. Also, the latest technology for remote control and monitor smart systems is applied in the proposed smart home system.

Comprehension of speech in noise is a challenge for hearing-impaired (HI) individuals. Electroencephalography (EEG) provides a tool to investigate the effect of different levels of signal-to-noise ratio (SNR) of the speech. Most studies with EEG have focused on spectral power in well-defined frequency bands such as alpha band. In this study, we investigate how local functional connectivity, i.e. functional connectivity within a localized region of the brain, is affected by two levels of SNR. Twenty-two HI participants performed a continuous speech in noise task at two different SNRs (+3 dB and +8 dB). The local connectivity within eight regions of interest was computed by using a multivariate phase synchrony measure on EEG data. The results showed that phase synchrony increased in the parietal and frontal area as a response to increasing SNR. We contend that local connectivity measures can be used to discriminate between speech-evoked EEG responses at different SNRs.

In the present work, the influence of solution viscosity on growth kinetics and purification efficiency in layer melt crystallization was investigated. Melt crystallization experiments were conducted for three different types of aqueous sucrose solution as they are ideal solutions and a relatively wide viscosity range can be investigated with a moderate change of freezing points. The aqueous 10 wt%, 23 wt%, and 30 wt% sucrose solutions have a dynamic viscosity value of 2.01 mPas, 4.74 mPas, and 7.21 mPas at their respective freezing points of − 0.63 ◦ C, − 1.78 ◦ C, and − 2.64 ◦ C. The solution temperature distribution was predicted by computational fluid dynamics (CFD) simulations run in COMSOL Multiphysics 5.6 software. Experimental results showed that a higher solution viscosity caused a higher crystal layer impurity and lower crystal yields in static layer melt crystallization. The cooling process of different solutions predicted by a CFD heat transfer study showed that the supersaturation region is wider for less concentrated solutions as cooling proceeds more rapidly. Hence, the temperature gra-dients obtained follow the boundary layer theory, i.e., the thinner the boundary layer, the faster the heat transfer.

The limited sensitivity of existing analysis techniques at the nanometer scale makes it challenging to systematically examine the complex interactions in redox-based resistive random access memory (ReRAM) devices. To test models of oxygen movement in ReRAM devices beyond what has previously been possible, we present a new nanoscale analysis method. Harnessing the power of secondary ion mass spectrometry, the most sensitive surface analysis technique, for the first time, we observe the movement of 16 O across electrically biased SiO x ReRAM stacks. We can therefore measure bulk concentration changes in a continuous profile with unprecedented sensitivity. This reveals the nanoscale details of the reversible field-driven exchange of oxygen across the ReRAM stack. Both the reservoir-like behavior of a Mo electrode and the injection of oxygen into the surface of SiO x from the ambient are observed within one profile. The injection of oxygen is controllable through changing the porosity of the SiO x layer. Modeling of the electric fields in the ReRAM stacks is carried out which, for the first time, uses real measurements of both the interface roughness and electrode porosity. This supports our findings helping to explain how and where oxygen from ambient moisture enters devices during operation.

Pomological characteristics and consumer acceptability of four scab-resistant apple cultivars (‘Topaz’, ‘Florina’, ‘Goldstar’ and ‘Golden Orange’) and standard commercial cultivar ‘Golden Delicious’ were investigated. Consumer acceptability consisted of rating fruit samples on Likert scales measuring appearance, flavour, size, sweetness, acidity, crispiness, juiciness, skin texture and general impression. Consumers better evaluated the cultivar ‘Topaz’ sensory characteristics of flavour, juiciness, taste and general impression than other evaluated scab-resistant apple cultivars and the cultivar ‘Golden Delicious’. ‘Golden Delicious’ got good grades for appearance, size and sweetness. ‘Topaz’ also had the best pomological characteristic related to measured fruit firmness, contents of soluble solids and organic acids. It can be concluded that only the cultivar ‘Topaz’ among the scab-resistant apple cultivars achieved a good consumer assessment.

Background DIALOG+ is a patient-centred, solution-focused intervention, which aims to make routine patient-clinician meetings therapeutically effective. Existing evidence suggests that it is effective for patients with psychotic disorders in high-income countries. We tested the effectiveness of DIALOG + for patients with depressive and anxiety disorders in Bosnia and Herzegovina, a middle-income country. Methods We conducted a parallel-group, cluster randomised controlled trial of DIALOG+ in an outpatient clinic in Sarajevo. Patients inclusion criteria were: 18 years and older, a diagnosis of depressive or anxiety disorders, and low quality of life. Clinicians and their patients were randomly allocated to either the DIALOG + intervention or routine care in a 1:1 ratio. The primary outcome, quality of life, and secondary outcomes, psychiatric symptoms and objective social outcomes, were measured at 6- and 12-months by blinded assessors. Results Fifteen clinicians and 72 patients were randomised. Loss to follow-up was 12% at 6-months and 19% at 12-months. Quality of life did not significantly differ between intervention and control group after six months, but patients receiving DIALOG + had significantly better quality of life after 12 months, with a medium effect size (Cohen's d = 0.632, p = 0.007). General symptoms as well as specifically anxiety and depression symptoms were significantly lower after six and 12 months, and the objective social situation showed a statistical trend after 12 months, all in favour of the intervention group. No adverse events were reported. Limitations Delivery of the intervention was variable and COVID-19 affected 12-month follow-up assessments in both groups. Conclusion The findings suggest DIALOG + could be an effective treatment option for improving quality of life and reducing psychiatric symptoms in patients with depressive and anxiety disorders in a low-resource setting.

Catheter ablation for atrial fibrillation (AF) is one of the most commonly performed electrophysiology procedures. Despite significant advances in our understanding of AF mechanisms in the last years, ablation outcomes remain suboptimal for many patients, particularly those with persistent or long-standing AF. A possible reason is that ablation techniques mainly focus on anatomic, rather than patient-specific functional targets for ablation. The identification of such ablation targets remains challenging. The purpose of this study is to investigate a novel approach based on directed networks, which allow the automatic detection of important arrhythmia mechanisms, that can be convenient for guiding the ablation strategy. The networks are generated by processing unipolar electrograms (EGMs) collected by the catheters positioned at the different regions of the atria. Network vertices represent the locations of the recordings and edges are determined using cross-covariance time-delay estimation method. The algorithm identifies rotational activity, spreading from vertex to vertex creating a cycle. This work is a simulation study and it uses a highly detailed computational 3D model of human atria in which sustained rotor activation of the atria was achieved. Virtual electrodes were placed on the endocardial surface, and EGMs were calculated at each of these electrodes. The propagation of the electric wave fronts in the atrial myocardium during AF is very complex, so in order to properly capture wave propagation patterns, we split EGMs into multiple short time frames. Then, a specific network for each of these time frames was generated, and the cycles repeating in consecutive networks point us to the stable rotor's location. The respective atrial voltage map served as reference. By detecting a cycle between the same 3 nodes in 19 out of 58 networks, where 10 of these networks were in consecutive time frames, a stable rotor was successfully located.

The construction of smooth interpolation trajectories in different non-Euclidean spaces finds application in robotics, computer graphics, and many other engineering fields. This paper proposes a method for generating interpolation trajectories on the special orthogonal group SO(3), called the rotation group. Our method is based on a high-dimensional generalization of the Kuramoto model which is a well-known mathematical description of self-organization in large populations of coupled oscillators. We present the method through several simulations and visualize each simulation as trajectories on unit spheres S2. In addition, we applied our method to the specific problem of object rotation interpolation.

Introduction: The value of intravenous thrombolysis (IVT) in patients eligible for mechanical thrombectomy (MT) remains unclear. We hypothesized that pre‐treatment with and/or ongoing IVT may facilitate reperfusion of distal vessel occlusion after incomplete MT. We evaluated this potential association using follow‐up perfusion imaging. Methods: Retrospective observational analysis of our institution`s stroke registry included patients with incomplete reperfusion after MT, admitted between February 1, 2015 and December 8, 2020. Delayed reperfusion (DR) was defined as the absence of a persistent perfusion deficit on contrast‐enhanced perfusion imaging ⁓24h±12h after the intervention. The association between baseline parameters and the occurrence of DR was evaluated using a logistic regression analyses. To account for possible time‐dependent associations of IVT with DR, additional stratification sets were made based on different time windows between IVT start time and final angiography runs. Results: Among the 378 included patients (median age 73.5, 50.8% female), DR occurred in 226 (59.8%). Atrial fibrillation (aOR 2.53 [95% CI 1.34 ‐ 4.90]), eTICI score (aOR 3.79 [95% CI 2.71 ‐ 5.48] per TICI grade increase), and intervention‐to‐follow‐up time (aOR 1.08 [95% CI 1.04 ‐ 1.13] per hour delay) were associated with DR. Dichotomized IVT strata showed no association with DR (aOR 0.75 [95% CI 0.42 ‐ 1.33]), whereas shorter intervals between IVT start and end of the procedure showed a borderline significant association with DR (OR 2.24 [95% CI 0.98 ‐ 5.43, and OR 2.07 [95% 1.06 – 4.31], for 80 and 100 minutes respectively). Patients with DR had higher rates of functional independence (modified Rankin scale 0–2 at 90 days, DR: 63.3% vs PPD: 38.8%; p<0.01) and longer survival time (at 3 years, DR: 69.2% vs PPD: 45.8%; p = 0.001). Conclusions: There is weak evidence that IVT may favor DR after incomplete MT if the time interval between IVT administration and end of the procedure is short. In general, perfusion follow‐up imaging may constitute a suitable surrogate parameter for evaluating medical rescue strategies after incomplete MT, because a considerable proportion of patients do not experience DR, and there seems to be a close correlation with clinical outcomes.

Introduction Deep learning techniques are gaining momentum in medical research. Evidence shows that deep learning has advantages over humans in image identification and classification, such as facial image analysis in detecting people’s medical conditions. While positive findings are available, little is known about the state-of-the-art of deep learning-based facial image analysis in the medical context. For the consideration of patients’ welfare and the development of the practice, a timely understanding of the challenges and opportunities faced by research on deep-learning-based facial image analysis is needed. To address this gap, we aim to conduct a systematic review to identify the characteristics and effects of deep learning-based facial image analysis in medical research. Insights gained from this systematic review will provide a much-needed understanding of the characteristics, challenges, as well as opportunities in deep learning-based facial image analysis applied in the contexts of disease detection, diagnosis and prognosis. Methods Databases including PubMed, PsycINFO, CINAHL, IEEEXplore and Scopus will be searched for relevant studies published in English in September, 2021. Titles, abstracts and full-text articles will be screened to identify eligible articles. A manual search of the reference lists of the included articles will also be conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was adopted to guide the systematic review process. Two reviewers will independently examine the citations and select studies for inclusion. Discrepancies will be resolved by group discussions till a consensus is reached. Data will be extracted based on the research objective and selection criteria adopted in this study. Ethics and dissemination As the study is a protocol for a systematic review, ethical approval is not required. The study findings will be disseminated via peer-reviewed publications and conference presentations. PROSPERO registration number CRD42020196473.