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Povzetek: The turning process is a widely used cutting operation in industry. Any optimization of this process can significantly improve product quality, streamline costs, or reduce unwanted events. With automatic monitoring of turning tools, we can reduce costs, increase efficiency, and decrease the number of undesirable events that occur during machining (scrap, tool breakage, etc.). In single-piece or small-batch production, tool wear is monitored by the machine operator; however, such wear assessment is left to subjective judgment and requires intervention in the process. The presented solution eliminates this problem with automated monitoring of the cutting tool’s condition. An IR camera was used for process monitoring, which also captures the thermographic state. The camera was properly protected and mounted right next to the turning tool, enabling close-up observation of the machining. During the experiment, constant cutting parameters were set for turning the workpiece (low-alloy steel designated 1.7225, i.e.,42CrMo4) without the use of coolant. Using turning inserts with varying levels of wear, a database of more than 6,000 images was created during the turning process. With a convolutional neural network (CNN), a model was developed to predict wear and damage to the cutting tool. Based on the captured thermographic image during turning, the model automatically determines the cutting tool’s condition (no wear, minor wear, severe wear).The achieved classification accuracy was 99.55%, confirming the suitability of the proposed method. Such a system enables immediate action in the event of tool wear or breakage, regardless of the operator’s knowledge and training.
Ključne besede: deep learning, tool condition monitoring, turning, tool wear
Objavljeno v DiRROS: 03.02.2026; Ogledov: 57; Prenosov: 36
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Ključne besede: biochemistry, proteins, synthetic biology, machine learning
Objavljeno v DiRROS: 03.02.2026; Ogledov: 102; Prenosov: 57
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Povzetek: Introduction: There has been little research on systematic training of new employees in perioperative nursing. The aim of the present study was to establish whether the attitude of the employees in perioperative nursing towards the training of new employees in the workplace is connected to their education in perioperative nursing, workload, work competence or attitude of new employees in perioperative nursing. Methods: A quantitative exploratory research was performed in March 2017 and data was collected by means of a structured survey questionnaire. Perioperative nursing staff working in a selected Slovenian hospital's operating theatres, who are trained well enough to work independently (n = 69) and have at least one year of working experience, were questioned to obtain a purposive sample. Descriptive statistics, the Pearson correlation coefficient and factor analysis are shown. Results: Employees in perioperative nursing gladly transmit their knowledge and experience (x = 4.79), growing professionally as they do so ( = 4.63). They see the training of new employees as their personal challenge (x = 4.17). The knowledge of the respondents was shown to be a factor that is marginally positively associated (r = 0.278, p = 0.021) with the attitude to training new employees in the workplace. The training that experienced employees in perioperative nursing have received, their competencies, workload, and their opinion of the work of the new employees, are not connected to the attitudes they posses when training these new employees in the workplace. Discussion and conclusion: The study indicates the need to establish definined criteria relating to the employment of candidates, and may contribute toward the future design of a systematic training course in perioperative nursing.
Ključne besede: competency assessment, development of professional competencies, professional attitude, knowledge transfer, situational learning, workplace learning
Objavljeno v DiRROS: 28.01.2026; Ogledov: 156; Prenosov: 118
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Povzetek: Introduction: The Covid-19 epidemic has significantly compromised the practical training of nursing students. While in nursing, the use of simulation is not new, virtual simulation or virtual patients represent relatively new educational modalities. The aim of this literature review was to examine the most recent empirical evidence on the efficacy or effectiveness of using virtual simulation or virtual patients in nursing education around the world.Methods: Scholarly articles published between 2016 and 2021 in the CINAHL, MEDLINE, ERIC and COBIB bibliographic databases were reviewed. The review included articles which focused on student nurses using virtual simulation or virtual patients as a method of learning rather than as a way of assessing students' knowledge acquired through a different learning method. A thematic analysis was used to synthesise the results. Results: Twelve studies were included in the review, most of which were conducted in developed countries. The results showed that the use of virtual simulation or virtual patients has a positive effect on the acquisition of cognitive and affective knowledge, practical implementation of interventions, assessment of self-efficacy and competence, and student satisfaction.Discussion and conclusion: In situations where clinical training is not possible for nursing students, the use of virtual simulation or virtual patients can replace the clinical setting for the purposes of practising clinical decisions, but it cannot replace the clinical education and experience students obtain when working with actual patients.
Ključne besede: clinical decisions, students, nursing, simulated learning environment, critical thinking
Objavljeno v DiRROS: 28.01.2026; Ogledov: 145; Prenosov: 58
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Povzetek: Mapping forest areas affected by bark beetle infestation using remote sensing imagery is crucial for effective hazard management and risk assessment. This study evaluates the potential of Sentinel-2 satellite image time series (SITS) in combination with in-situ felling data to detect bark beetle infestation in coniferous forests in Pokljuka, Slovenia. The analysis uses the CuSum method, all Sentinel-2 spectral bands and key spectral indices such as NDVI and NBSI to identify changes and areas of forest loss in the period 2017–2021. The resulting geospatial dataset, which integrates these remote sensing results with field data, serves as a basis for further analyses using advanced machine and deep learning methods and various remote sensing data such as hyperspectral datasets. In addition, we found that the most useful bands for detecting the loss of alpine coniferous forests are SWIR (B11, B12), Red (B04) and Red-Edge (B05) as well as the two spectral in dices used, NDVI and NBSI.
Ključne besede: Norway Spruce, CUSUM, Pokljuka, Slovenia, deep learning dataset
Objavljeno v DiRROS: 21.01.2026; Ogledov: 86; Prenosov: 62
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