Search results for: neural perception.

Authors: Amal Machtalay

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In this survey, we aim to review the rapidly growing literature on numerical methods to solve different forms of mean field problems, namely mean field games (MFG), mean field controls (MFC), potential MFGs, and master equations, as well as their corresponding recent applications. Here, we distinguish two families of numerical methods: iterative methods based on mesh generation and those called mesh-free, normally related to neural networking and learning frameworks.

Keywords: mean-field games, numerical schemes, partial differential equations, complex systems, machine learning

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Authors: M. Pawlaczyk-Łuszczyńska, T. Wszołek, A. Dudarewicz, P. Małecki, M. Kłaczyński, A. Bortkiewicz

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Human tolerance to infrasound is defined by the hearing threshold. Infrasound that cannot be heard (or felt) is not annoying and is not thought to have any other adverse or health effects. Recent research has largely confirmed earlier findings. ISO 7196:1995 recommends the use of G-weighted characteristics for the assessment of infrasound. There is a strong correlation between G-weighted SPL and annoyance perception. The aim of this study was to propose exposure limits for infrasound from wind turbines. However, only a few countries have set limits for infrasound. These limits are usually no higher than 85-92 dBG, and none of them are specific to wind turbines. Over the years, a number of studies have been carried out to determine hearing thresholds below 20 Hz. It has been recognized that 10% of young people would be able to perceive 10 Hz at around 90 dB, and it has also been found that the difference in median hearing thresholds between young adults aged around 20 years and older adults aged over 60 years is around 10 dB, irrespective of frequency. This shows that older people (up to about 60 years of age) retain good hearing in the low frequency range, while their sensitivity to higher frequencies is often significantly reduced. In terms of exposure limits for infrasound, the average hearing threshold corresponds to a tone with a G-weighted SPL of about 96 dBG. In contrast, infrasound at Lp,G levels below 85-90 dBG is usually inaudible. The individual hearing threshold can, therefore be 10-15 dB lower than the average threshold, so the recommended limits for environmental infrasound could be 75 dBG or 80 dBG. It is worth noting that the G86 curve has been taken as the threshold of auditory perception of infrasound reached by 90-95% of the population, so the G75 and G80 curves can be taken as the criterion curve for wind turbine infrasound. Finally, two assessment methods and corresponding exposure limit values have been proposed for wind turbine infrasound, i.e. method I - based on G-weighted sound pressure level measurements and method II - based on frequency analysis in 1/3-octave bands in the frequency range 4-20 Hz. Separate limit values have been set for outdoor living areas in the open countryside (Area A) and for noise sensitive areas (Area B). In the case of Method I, infrasound limit values of 80 dBG (for areas A) and 75 dBG (for areas B) have been proposed, while in the case of Method II - criterion curves G80 and G75 have been chosen (for areas A and B, respectively).

Keywords: infrasound, exposure limit, hearing thresholds, wind turbines

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Authors: Jared Abuga, Wesley Too

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Medical error is the third leading cause of death in US, with approximately 98,000 deaths occurring every year as a result of medical errors. The world financial burden of medication errors is roughly USD 42 billion. Medication errors may lead to at least one death daily and injure roughly 1.3 million people every year. Medical error reporting is essential in creating a culture of accountability in our healthcare system. Studies have shown that attitudes and practice of healthcare workers in reporting medical errors showed that the major factors in under-reporting of errors included work stress and fear of medico-legal consequences due to the disclosure of error. Further, the majority believed that increase in reporting medical errors would contribute to a better system. Most hospitals depend on nurses to discover medication errors because they are considered to be the sources of these errors, as contributors or mere observers, consequently, the nurse’s perception of medication errors and what needs to be done is a vital feature to reducing incidences of medication errors. We sought to explore knowledge among nurses on medical errors and factors affecting or hindering reporting of medical errors among nurses working at the emergency unit, KNH. Critical care nurses are faced with many barriers to completing incident reports on medication errors. One of these barriers which contribute to underreporting is a lack of education and/or knowledge regarding medication errors and the reporting process. This study, therefore, sought to determine the availability and the use of reporting systems for medical errors in critical care unity. It also sought to establish nurses’ perception regarding medical errors and reporting and document factors facilitating timely identification and reporting of medical errors in critical care settings. Methods: The study used cross-section study design to collect data from 76 critical care nurses from Kenyatta Teaching & Research National Referral Hospital, Kenya. Data analysis and results is ongoing. By October 2022, we will have analysis, results, discussions, and recommendations of the study for purposes of the conference in 2023

Keywords: errors, medical, kenya, nurses, safety

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Authors: Dila Ram Bhandari

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Throughout the 20th century, new governments were created where identities such as ethnic, religious, linguistic, caste, communal, tribal, and others played a part in the development of constitutions and the legal system of victim and criminal justice. Acute issues with extremism, poverty, environmental degradation, cybercrimes, human rights violations, crime against, and victimization of both individuals and groups have recently plagued South Asian nations. Everyday massive number of crimes are steadfast, these frequent crimes have made the lives of common citizens restless. Crimes are one of the major threats to society and also for civilization. Crime is a bone of contention that can create a societal disturbance. The old-style crime solving practices are unable to live up to the requirement of existing crime situations. Crime analysis is one of the most important activities of the majority of intelligent and law enforcement organizations all over the world. The South Asia region lacks such a regional coordination mechanism, unlike central Asia of Asia Pacific regions, to facilitate criminal intelligence sharing and operational coordination related to organized crime, including illicit drug trafficking and money laundering. There have been numerous conversations in recent years about using data mining technology to combat crime and terrorism. The Data Detective program from Sentient as a software company, uses data mining techniques to support the police (Sentient, 2017). The goals of this internship are to test out several predictive model solutions and choose the most effective and promising one. First, extensive literature reviews on data mining, crime analysis, and crime data mining were conducted. Sentient offered a 7-year archive of crime statistics that were daily aggregated to produce a univariate dataset. Moreover, a daily incidence type aggregation was performed to produce a multivariate dataset. Each solution's forecast period lasted seven days. Statistical models and neural network models were the two main groups into which the experiments were split. For the crime data, neural networks fared better than statistical models. This study gives a general review of the applied statistics and neural network models. A detailed image of each model's performance on the available data and generalizability is provided by a comparative analysis of all the models on a comparable dataset. Obviously, the studies demonstrated that, in comparison to other models, Gated Recurrent Units (GRU) produced greater prediction. The crime records of 2005-2019 which was collected from Nepal Police headquarter and analysed by R programming. In conclusion, gated recurrent unit implementation could give benefit to police in predicting crime. Hence, time series analysis using GRU could be a prospective additional feature in Data Detective.

Keywords: time series analysis, forecasting, ARIMA, machine learning

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Authors: Theresa Slump, Paula Neumeister, Katharina Feldker, Carina Y. Heitmann, Thomas Straube

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A characteristic feature of post-traumatic stress disorder (PTSD) is the automatic processing of disorder-specific stimuli that expresses itself in intrusive symptoms such as intense physical and psychological reactions to trauma-associated stimuli. That automatic processing plays an essential role in the development and maintenance of symptoms. The aim of our study was, therefore, to investigate the behavioral and neural correlates of automatic processing of trauma-related stimuli in PTSD. Although interpersonal traumatization is a form of traumatization that often occurs, it has not yet been sufficiently studied. That is why, in our study, we focused on patients suffering from interpersonal traumatization. While previous imaging studies on PTSD mainly used faces, words, or generally negative visual stimuli, our study presented complex trauma-related and neutral visual scenes. We examined 19 female subjects suffering from PTSD and examined 19 healthy women as a control group. All subjects did a geometric comparison task while lying in a functional-magnetic-resonance-imaging (fMRI) scanner. Trauma-related scenes and neutral visual scenes that were not relevant to the task were presented while the subjects were doing the task. Regarding the behavioral level, there were not any significant differences between the task performance of the two groups. Regarding the neural level, the PTSD patients showed significant hyperactivation of the hippocampus for task-irrelevant trauma-related stimuli versus neutral stimuli when compared with healthy control subjects. Connectivity analyses revealed altered connectivity between the hippocampus and other anxiety-related areas in PTSD patients, too. Overall, those findings suggest that fear-related areas are involved in PTSD patients' processing of trauma-related stimuli even if the stimuli that were used in the study were task-irrelevant.

Keywords: post-traumatic stress disorder, automatic processing, hippocampus, functional magnetic resonance imaging

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Authors: Francois Brambati, Daniele Ruscio, Federica Biassoni, Rebecca Hueting, Alessandra Tedeschi

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This research, in the frame of social acceptance of renewable energies and community-based production and consumption models, aims at (1) supporting a data-driven approachable to dealing with climate change and (2) identifying & quantifying the psycho-sociological dimensions and factors that could support the transition from a technology-driven approach to a consumer-driven approach throughout the emerging “prosumer business models.” In addition to the existing Social Acceptance dimensions, this research tries to identify a purely individual psychological fourth dimension to understand processes and factors underling individual acceptance and adoption of renewable energy business models, realizing a Prosumer Acceptance Index. Questionnaire data collection has been performed throughout an online survey platform, combining standardized and ad-hoc questions adapted for the research purposes. To identify the main factors (individual/social) influencing the relation with renewable energy technology (RET) adoption, a Factorial Analysis has been conducted to identify the latent variables that are related to each other, revealing 5 latent psychological factors: Factor 1. Concern about environmental issues: global environmental issues awareness, strong beliefs and pro-environmental attitudes rising concern on environmental issues. Factor 2. Interest in energy sharing: attentiveness to solutions for local community’s collective consumption, to reduce individual environmental impact, sustainably improve the local community, and sell extra energy to the general electricity grid. Factor 3. Concern on climate change: environmental issues consequences on climate change awareness, especially on a global scale level, developing pro-environmental attitudes on global climate change course and sensitivity about behaviours aimed at mitigating such human impact. Factor 4. Social influence: social support seeking from peers. With RET, advice from significant others is looked for internalizing common perceived social norms of the national/geographical region. Factor 5. Impact on bill cost: inclination to adopt a RET when economic incentives from the behaviour perception affect the decision-making process could result in less expensive or unvaried bills. Linear regression has been conducted to identify and quantify the factors that could better predict behavioural intention to become a prosumer. An overall scale measuring “acceptance of a renewable energy solution” was used as the dependent variable, allowing us to quantify the five factors that contribute to measuring: awareness of environmental issues and climate change; environmental attitudes; social influence; and environmental risk perception. Three variables can significantly measure and predict the scores of the “Acceptance in becoming a prosumer” ad hoc scale. Variable 1. Attitude: the agreement to specific environmental issues and global climate change issues of concerns and evaluations towards a behavioural intention. Variable 2. Economic incentive: the perceived behavioural control and its related environmental risk perception, in terms of perceived short-term benefits and long-term costs, both part of the decision-making process as expected outcomes of the behaviour itself. Variable 3. Age: despite fewer economic possibilities, younger adults seem to be more sensitive to environmental dimensions and issues as opposed to older adults. This research can facilitate policymakers and relevant stakeholders to better understand which relevant psycho-sociological factors are intervening in these processes and what and how specifically target when proposing change towards sustainable energy production and consumption.

Keywords: behavioural intention, environmental risk perception, prosumer, renewable energy technology, social acceptance

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Authors: Forouzan Salehi Fergeni

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Converting the movement intents of a person into commands for action employing brain signals like electroencephalogram signals is a brain-computer interface (BCI) system. When left or right-hand motions are imagined, different patterns of brain activity appear, which can be employed as BCI signals for control. To make better the brain-computer interface (BCI) structures, effective and accurate techniques for increasing the classifying precision of motor imagery (MI) based on electroencephalography (EEG) are greatly needed. Subject dependency and non-stationary are two features of EEG signals. So, EEG signals must be effectively processed before being used in BCI applications. In the present study, after applying an 8 to 30 band-pass filter, a car spatial filter is rendered for the purpose of denoising, and then, a method of analysis of variance is used to select more appropriate and informative channels from a category of a large number of different channels. After ordering channels based on their efficiencies, a sequential forward channel selection is employed to choose just a few reliable ones. Features from two domains of time and wavelet are extracted and shortlisted with the help of a statistical technique, namely the t-test. Finally, the selected features are classified with different machine learning and neural network classifiers being k-nearest neighbor, Probabilistic neural network, support-vector-machine, Extreme learning machine, decision tree, Multi-layer perceptron, and linear discriminant analysis with the purpose of comparing their performance in this application. Utilizing a ten-fold cross-validation approach, tests are performed on a motor imagery dataset found in the BCI competition III. Outcomes demonstrated that the SVM classifier got the greatest classification precision of 97% when compared to the other available approaches. The entire investigative findings confirm that the suggested framework is reliable and computationally effective for the construction of BCI systems and surpasses the existing methods.

Keywords: brain-computer interface, channel selection, motor imagery, support-vector-machine

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Authors: Sunil Chandra, Himanshu Rawat, Vikas Gusain, Triparna Barman

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Forest fires are a recurrent phenomenon in the Himalayan region owing to the presence of vulnerable forest types, topographical gradients, climatic weather conditions, and anthropogenic pressure. The present study focuses on the identification of forest fire-affected areas in a small part of the West Himalayan region using a differential normalized burnt ratio method and spectral unmixing methods. The study area has a rugged terrain with the presence of sub-tropical pine forest, montane temperate forest, and sub-alpine forest and scrub. The major reason for fires in this region is anthropogenic in nature, with the practice of human-induced fires for getting fresh leaves, scaring wild animals to protect agricultural crops, grazing practices within reserved forests, and igniting fires for cooking and other reasons. The fires caused by the above reasons affect a large area on the ground, necessitating its precise estimation for further management and policy making. In the present study, two approaches have been used for carrying out a burnt area analysis. The first approach followed for burnt area analysis uses a differenced normalized burnt ratio (dNBR) index approach that uses the burnt ratio values generated using the Short-Wave Infrared (SWIR) band and Near Infrared (NIR) bands of the Sentinel-2 image. The results of the dNBR have been compared with the outputs of the spectral mixing methods. It has been found that the dNBR is able to create good results in fire-affected areas having homogenous forest stratum and with slope degree <5 degrees. However, in a rugged terrain where the landscape is largely influenced by the topographical variations, vegetation types, tree density, the results may be largely influenced by the effects of topography, complexity in tree composition, fuel load composition, and soil moisture. Hence, such variations in the factors influencing burnt area assessment may not be effectively carried out using a dNBR approach which is commonly followed for burnt area assessment over a large area. Hence, another approach that has been attempted in the present study utilizes a spectral mixing method where the individual pixel is tested before assigning an information class to it. The method uses a neural network approach utilizing Sentinel-2 bands. The training and testing data are generated from the Sentinel-2 data and the national field inventory, which is further used for generating outputs using ML tools. The analysis of the results indicates that the fire-affected regions and their severity can be better estimated using spectral unmixing methods, which have the capability to resolve the noise in the data and can classify the individual pixel to the precise burnt/unburnt class.

Keywords: categorical data, log linear modeling, neural network, shifting cultivation

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Authors: A. Afrashi

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Introduction: Synaesthesia is a term denoting the perception or description of the perception of one sense modality in terms of another. In literature, synaesthesia refers to a technique adopted by writers to present ideas, characters or places in such a manner that they appeal to more than one sense like hearing, seeing, smell etc. at a given time. In everyday language too we find many examples of synaesthesia. We commonly hear phrases like ‘loud colors’, ‘frozen silence’ and ‘warm colors’, ‘bitter cold’ etc. Empirical cognitive studies have proved that synaesthetic representations both in literature and everyday languages are constrained ie. they do not map randomly among sensory domains. From the beginning of the 20th century Synaesthesia has been a research domain both in literature and structural linguistics. However the exploration of cognitive mechanisms motivating synaesthesia, have made it an important topic in 21st century cognitive linguistics and literary studies. Synaesthetic metaphors are linguistic representations of those mental mechanisms, the study of which reveals invaluable facts about perception, cognition and conceptualization. According to the main tenets of cognitive approach to language and literature, unified and similar cognitive mechanisms are active both in everyday language and literature, and synaesthesia is one of those cognitive mechanisms. Main objective of the present research is to answer the following questions: What types of sense transfers are accessible in Persian synaesthetic metaphors. How are these types of sense transfers cognitively explained. What are the results of cross-linguistic comparative study of synaestetic metaphors based on the existing observations? Methodology: The present research employs a cognitive - corpus based method, and the theoretical framework adopted to analyze linguistic synaesthesia is the contemporary theory of metaphor, where conceptual metaphor is the result of systemic mappings across cognitive domains. Persian Language Data- base (PLDB) in the Institute for Humanities and Cultural Studies which consists mainly of Persian modern prose, is searched for synaesthetic metaphors. Then for each metaphorical structure, the source and target domains are determined. Then sense transfers are identified and the types of synaesthetic metaphors recognized. Findings: Persian synaesthetic metaphors conform to the hierarchical distribution principle, according to which transfers tend to go from touch to taste to smell to sound and to sight, not vice versa. In other words mapping from more accessible or basic concepts onto less accessible or less basic ones seems more natural. Furthermore the most frequent target domain in Persian synaesthetic metaphors is sound. Certain characteristics of Persian synaesthetic metaphors are comparable with existing related researches carried on English, French, Hungarian and Chinese synaesthetic metaphors. Conclusion: Cognitive corpus based approaches to linguistic synaesthesia, are applicable to stylistics and literary criticism and this recent research domain is an efficient approach to study cross linguistic variations to find out which of the five senses is dominant cross linguistically and cross culturally as the target domain in metaphorical mappings , and so forth receiving dominance in conceptualizations.

Keywords: cognitive semantics, conceptual metaphor, synaesthesia, corpus based approach

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Authors: Othmane Bouferroum, Malika Boudraa

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In this study, a dual exponential model for CV formant transition is derived from locus theory of speech perception. Then, an algorithm for automatic vowel and consonant’s target formant frequency detection is developed and tested on real speech. The results show that vowels and consonants are detected through transitions rather than their small stable portions. Also, vowel reduction is clearly observed in our data. These results are confirmed by the observations made in perceptual experiments in the literature.

Keywords: acoustic invariance, coarticulation, formant transition, locus equation

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Authors: Bruna Lima Gomes, Vanessa Caroline De Barros Bonato, Luciano Freschi, Eduardo Purgatto

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Plant hormones are long known to be tightly associated with fruit development and are involved in controlling various aspects of fruit ripening. For fleshy fruits, ripening is characterized for changes in texture, color, aroma and other parameters that markedly contribute to its quality. Ethylene is one of the major players regulating the ripening-related processes, but emerging evidences suggest that auxin is also part of this dynamic control. Thus, the aim of this study was providing new insights into the auxin role during ripening and the hormonal interplay between auxin and ethylene. For that, tomato fruits (Micro-Tom) were collected at mature green stage and separated in four groups: one for indole-3-acetic acid (IAA) treatment, one for ethylene, one for a combination of IAA and ethylene, and one for control. Hormone solution was injected through the stylar apex, while mock samples were injected with buffer only. For ethylene treatments, fruits were exposed to gaseous hormone. Then, fruits were left to ripen under standard conditions and to assess ripening development, hue angle was reported as color indicator and ethylene production was measured by gas chromatography. The transcript levels of three ripening-related ethylene receptors (LeETR3, LeETR4 and LeETR6) were evaluated by RT-qPCR. Results showed that ethylene treatment induced ripening, stimulated ethylene production, accelerated color changes and induced receptor expression, as expected. Nonetheless, auxin treatment showed the opposite effect once fruits remained green for longer time than control group and ethylene perception has changed, taking account the reduced levels of receptor transcripts. Further, treatment with both hormones revealed that auxin effect in delaying ripening was predominant, even with higher levels of ethylene. Altogether, the data suggest that auxin modulates several aspects of the tomato fruit ripening modifying the ethylene perception. The knowledge about hormonal control of fruit development will help design new strategies for effective manipulation of ripening regarding fruit quality and brings a new level of complexity on fruit ripening regulation.

Keywords: ethylene, auxin, fruit ripening, hormonal crosstalk

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Authors: Sonda Jallouli, Omar Hammouda, Imen Ben Dhia, Salma Sakka, Chokri Mhiri, Mohamed Habib Elleuch, Abedlmoneem Yahia, Sameh Ghroubi

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Multiple sclerosis (MS) is characterized by gender differences with affecting women two to four times more than men, but the disease progression is faster and more severe in men. Fatigue represents one of the most frequent and disabling symptoms related to MS. Results of previous studies regarding gender differences in fatigue perception in MS persons are contradictory. Besides, fatigue has been shown to affect negatively postural balance and functional mobility in MS persons. However, no study has taken into account gender differences in the response of these physical parameters to a fatiguing protocol in MS persons. Given the reduction of autonomy due to the alteration of these parameters induced by fatigue and the importance of gender differences in postural balance training programs in fatigued men and women with MS, the aim of this study was to investigate the effect of gender difference on unipedal postural balance and functional mobility after performing a fatiguing task in MS adults. Methods: Eleven women (30.29 ± 7.99 years) and seven men (30.91 ± 8.19 years) with relapsing-remitting MS performed a fatiguing protocol: three sets of the 5×sit to stand test (5-STST), six-minute walk test (6MWT) followed by three sets of the 5-STST. Unipedal balance, functional mobility, and fatigue perception were measured prefatigue (T0) and post fatigue (T3) using a clinical unipedal balance test, timed up and go test (TUGT), and analogic visual scale of fatigue (VASF), respectively. Heart rate (HR) and rate of perceived exertion (RPE) were recorded before, during and after the fatiguing task. Results: Compared to women, men showed an impairment of unipedal balance on the dominant leg (p<0.001, d=0.52) and mobility (p<0.001, d=3) via reducing unipedal stance time and increasing duration of TUGT execution, respectively. No gender differences were observed in 6MWT, 5-STST, HR, RPE and VASF scores. Conclusion: Fatiguing protocol negatively affected unipedal postural balance and mobility only in men. These gender differences were inconclusive but can be taken into account in postural balance rehabilitation programs for persons with MS.

Keywords: functional mobility, fatiguing exercises, multiple sclerosis, sex differences, unipedal balance

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Authors: Oksana Koval, Stephen Nabareseh

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Talent attraction is identified as a top priority between 2015 – 2020 for Shared Service Centers (SSCs) based on an industry-wide studies. Due to market dynamics and the structure of labour force, shared service industries in Eastern and Central Europe strive for qualified graduates with appropriate and unique skills to occupy such job places. The inbuilt interest and course prescriptions undertaken by prospective job seekers determine whether SSCs will eventually admit such professionals. This paper assesses students’ overall perception of careers in the shared services industry and further diagnosis gender impact and influence on the job preferences among students. Questionnaires were distributed among students in the Czech Republic universities using an online mode. Respondents vary by study year, gender, age, course of study, and work preferences. A total of 1283 student responses has been analyzed using Stata data analytics software. It was discovered that over 70% of respondents who are aware of SSCs are quite ignorant of the job opportunities offered by the centers. While majority of respondents are interested in support positions (e.g. procurement specialist, planning specialist, human resource specialist, process improvement specialist and payroll specialist, etc.), around a third of respondents (32.8 percent) will decline a job offer from SSCs. The analysis also revealed that males are more likely than females to seek careers in international companies, hence, tend to be more favorable towards shared service jobs. Females, however, have stronger preferences towards marketing and PR jobs. The research results provide insights into the job aspirations of students interviewed. The findings provide a huge resource for recruitment agencies and shared service industries to renew and redirect their search for talents into SSCs. Based on the fact that great portion of respondents are planning to start their career within 6-12 months, the research provides important highlights for the talent attraction and recruitment strategies in the industry and provides a curriculum direction in academia.

Keywords: Czech Republic labour market, gender, talent attraction, shared service centers, students

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Authors: A. M. Khourshid, I. Sabry

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Friction Stir Welding (FSW), a solid state joining technique, is widely being used for joining Al alloys for aerospace, marine automotive and many other applications of commercial importance. FSW were carried out using a vertical milling machine on Al 5083 alloy pipe. These pipe sections are relatively small in diameter, 5mm, and relatively thin walled, 2 mm. In this study, 5083 aluminum alloy pipe were welded as similar alloy joints using (FSW) process in order to investigate mechanical and microstructural properties .rotation speed 1400 r.p.m and weld speed 10,40,70 mm/min. In order to investigate the effect of welding speeds on mechanical properties, metallographic and mechanical tests were carried out on the welded areas. Vickers hardness profile and tensile tests of the joints as a metallurgical feasibility of friction stir welding for joining Al 6061 aluminum alloy welding was performed on pipe with different thickness 2, 3 and 4 mm,five rotational speeds (485,710,910,1120 and 1400) rpm and a traverse speed (4, 8 and 10)mm/min was applied. This work focuses on two methods such as artificial neural networks using software (pythia) and response surface methodology (RSM) to predict the tensile strength, the percentage of elongation and hardness of friction stir welded 6061 aluminum alloy. An artificial neural network (ANN) model was developed for the analysis of the friction stir welding parameters of 6061 pipe. The tensile strength, the percentage of elongation and hardness of weld joints were predicted by taking the parameters Tool rotation speed, material thickness and travel speed as a function. A comparison was made between measured and predicted data. Response surface methodology (RSM) also developed and the values obtained for the response Tensile strengths, the percentage of elongation and hardness are compared with measured values. The effect of FSW process parameter on mechanical properties of 6061 aluminum alloy has been analyzed in detail.

Keywords: friction stir welding (FSW), al alloys, mechanical properties, microstructure

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Authors: Vladimir V. Iakovlev, Erin T. Carey, John Steege

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The use of polypropylene mesh devices for Pelvic Organ Prolapse (POP) spread rapidly during the last decade, yet our knowledge of the mesh-tissue interaction is far from complete. We aimed to perform a thorough pathological examination of explanted POP meshes and describe findings that may explain mechanisms of complications resulting in product excision. We report a spectrum of important findings, including nerve ingrowth, mesh deformation, involvement of detrusor muscle with neural ganglia, and polypropylene degradation. Analysis of these findings may improve and guide future treatment strategies.

Keywords: transvaginal, mesh, nerves, polypropylene degradation

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Authors: C. Slim

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This paper presents an integrated model, which hybridized data envelopment analysis (DEA) and support vector machine (SVM) together, to class countries according to their efficiency and performance. This model takes into account aspects of multi-dimensional indicators, decision-making hierarchy and relativity of measurement. Starting from a set of indicators of performance as exhaustive as possible, a process of successive aggregations has been developed to attain an overall evaluation of a country’s competitiveness.

Keywords: Artificial Neural Networks (ANN), Support vector machine (SVM), Data Envelopment Analysis (DEA), Aggregations, indicators of performance

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Ankit Shah, R. K. Shrivastava

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Flood and drought are two main features of hydrology which affect the human life. Floods are natural disasters which cause millions of rupees’ worth of damage each year in India and the whole world. Flood causes destruction in form of life and property. An accurate estimate of the flood damage potential is a key element to an effective, nationwide flood damage abatement program. Also, the increase in demand of water due to increase in population, industrial and agricultural growth, has let us know that though being a renewable resource it cannot be taken for granted. We have to optimize the use of water according to circumstances and conditions and need to harness it which can be done by construction of hydraulic structures. For their safe and proper functioning of hydraulic structures, we need to predict the flood magnitude and its impact. Hydraulic structures play a key role in harnessing and optimization of flood water which in turn results in safe and maximum use of water available. Mainly hydraulic structures are constructed on ungauged sites. There are two methods by which we can estimate flood viz. generation of Unit Hydrographs and Flood Frequency Analysis. In this study, Regional Flood Frequency Analysis has been employed. There are many methods for estimating the ‘Regional Flood Frequency Analysis’ viz. Index Flood Method. National Environmental and Research Council (NERC Methods), Multiple Regression Method, etc. However, none of the methods can be considered universal for every situation and location. The Narmada basin is located in Central India. It is drained by most of the tributaries, most of which are ungauged. Therefore it is very difficult to estimate flood on these tributaries and in the main river. As mentioned above Artificial Neural Network (ANN)s and Multiple Regression Method is used for determination of Regional flood Frequency. The annual peak flood data of 20 sites gauging sites of Narmada Basin is used in the present study to determine the Regional Flood relationships. Homogeneity of the considered sites is determined by using the Index Flood Method. Flood relationships obtained by both the methods are compared with each other, and it is found that ANN is more reliable than Multiple Regression Method for the present study area.

Keywords: artificial neural network, index flood method, multi layer perceptrons, multiple regression, Narmada basin, regional flood frequency

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Authors: Abdulmalek Ahmed

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Formation pressure is the main function that affects drilling operation economically and efficiently. Knowing the pore pressure and the parameters that affect it will help to reduce the cost of drilling process. Many empirical models reported in the literature were used to calculate the formation pressure based on different parameters. Some of these models used only drilling parameters to estimate pore pressure. Other models predicted the formation pressure based on log data. All of these models required different trends such as normal or abnormal to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the formation pressure by only one method or a maximum of two methods of AI. The objective of this research is to predict the pore pressure based on both drilling parameters and log data namely; weight on bit, rotary speed, rate of penetration, mud weight, bulk density, porosity and delta sonic time. A real field data is used to predict the formation pressure using five different artificial intelligence (AI) methods such as; artificial neural networks (ANN), radial basis function (RBF), fuzzy logic (FL), support vector machine (SVM) and functional networks (FN). All AI tools were compared with different empirical models. AI methods estimated the formation pressure by a high accuracy (high correlation coefficient and low average absolute percentage error) and outperformed all previous. The advantage of the new technique is its simplicity, which represented from its estimation of pore pressure without the need of different trends as compared to other models which require a two different trend (normal or abnormal pressure). Moreover, by comparing the AI tools with each other, the results indicate that SVM has the advantage of pore pressure prediction by its fast processing speed and high performance (a high correlation coefficient of 0.997 and a low average absolute percentage error of 0.14%). In the end, a new empirical correlation for formation pressure was developed using ANN method that can estimate pore pressure with a high precision (correlation coefficient of 0.998 and average absolute percentage error of 0.17%).

Keywords: Artificial Intelligence (AI), Formation pressure, Artificial Neural Networks (ANN), Fuzzy Logic (FL), Support Vector Machine (SVM), Functional Networks (FN), Radial Basis Function (RBF)

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Authors: Kanda Janyam, Piyaporn Vijit

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Background: As a consequence of sustained declines in fertility and mortality during the last three decades of the 20th century, Thailand faces a rapidly growing population of older persons. This demographic change directly affect Thailand workforce. Therefore, the study of health promotion behaviour of the older employees will benefit the employers as they can then develop the preparation for promoting well-being in older persons. Purpose: The current study aims to investigate health promotion behaviour and factors related to health promotion behaviour of older employees in factory. Methodology: The research instrument was questionnaire on health promotion behaviour and semi-structured interviews. The questionnaire was launched with 326 employees aged between 45-59 years in three factories in Songkhla Province, southern Thailand. The data collection started in December 2011. The data were analysed with mean, standard deviation, and correlation. Results: The results revealed that overall health promotion behaviour of the older employees in factory was at a high level. Moreover, when considered by aspect, it was found that their responsibility for health, nutrition, success in life, interpersonal relationship were at a high level while stress management, and exercise were at a moderate level. The results from correlation analysis indicated that the overall health promotion behaviour was positively related to knowledge of health promotion behaviour, attitude toward health promotion behaviour, health perception, the policy of health promotion, participation in health promotion activities, convenience in obtaining health promotion services, health resources, advice from people supporting health, and information received from the media. In addition, the results of the interviews with four key informants helped to confirm the factors related to health promotion behaviour of older employees in factory. Therefore, health promotion for elderly employees in factory is likely to be successful, if the support is given to the four health promotion factors that are divided into: leading factors consisting of attitude toward health promotion behaviour, and health perception, and supporting factors consisting of advice from other people, and information on health from various media. Practical implications: The results of the study identified the factors related to health promotion behaviour of older employees in factory. Such information will benefit employers as they can then develop specific strategies to increase their staffs’ well-being and, hence, presumably enhance the organization productivity.

Keywords: health promotion behavior, older, employee, factory

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Authors: Bharath Anantharamaiah, Tomas Bouda, Elke Deckers, Stijn Jonckheere, Wim Desmet, Juan J. Garcia

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Automotive tyres are gaining importance recently in terms of their noise emission, not only with respect to reduction in noise, but also their perception and detection. Tyres exhibit a mechanical noise generation mechanism up to 1 kHz. However, owing to the fact that tyre is a composite of several materials, it has been difficult to model it using finite elements to predict noise at high frequencies. The currently available FE models have a reliability of about 500 Hz, the limit which, however, is not enough to perceive the roughness or sharpness of noise from tyre. These noise components are important in order to alert pedestrians on the street about passing by slow, especially electric vehicles. In order to model tyre noise behaviour up to 1 kHz, its dynamic behaviour must be accurately developed up to a 1 kHz limit using finite elements. Materials play a vital role in modelling the dynamic tyre behaviour precisely. Since tyre is a composition of several components, their precise definition in finite element simulations is necessary. However, during the tyre manufacturing process, these components are subjected to various pressures and temperatures, due to which these properties could change. Hence, material definitions are better described based on the tyre responses. In this work, the hyperelasticity of tyre component rubbers is calibrated, using the design of experiments technique from the tyre characteristic responses that are measured on a stiffness measurement machine. The viscoelasticity of rubbers are defined by the Prony series for rubbers, which are determined from the loss factor relationship between the loss and storage moduli, assuming that the rubbers are excited within the linear viscoelasticity ranges. These values of loss factor are measured and theoretically expressed as a function of rubber shore hardness or hyperelasticities. From the results of the work, there exists a good correlation between test and simulation vibrational transfer function up to 1 kHz. The model also allows flexibility, i.e., the frequency limit can also be extended, if required, by calibrating the Prony parameters of rubbers corresponding to the frequency of interest. As future work, these tyre models are used for noise generation at high frequencies and thus for tyre noise perception.

Keywords: tyre dynamics, rubber materials, prony series, hyperelasticity

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Authors: Carolina Zambrana, Grover Zurita

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The food loss production that occurs in deficient agricultural production is one of the major problems worldwide. This puts the population's food security and the efficiency of farming investments at risk. It is to be expected that this food security will be achieved with the own and efficient production of each country. It will have an impact on the well-being of its population and, thus, also on food sovereignty. The production losses in quantity and quality occur due to the lack of efficient detection of diseases at an early stage. It is very difficult to solve the agriculture efficiency using traditional methods since it takes a long time to be carried out due to detection imprecision of the main diseases, especially when the production areas are extensive. Therefore, the main objective of this research study is to perform a systematic literature review, of the latest five years, of Precision Agriculture (PA) to be able to understand the state of the art of the set of new technologies, procedures, and optimization processes with Artificial Intelligence (AI). This study will focus on Corns, Potatoes, and Rice diagnostic diseases. The extensive literature review will be performed on Elsevier, Scopus, and IEEE databases. In addition, this research will focus on advanced digital imaging processing and the development of software and hardware for PA. The convolution neural network will be handling special attention due to its outstanding diagnostic results. Moreover, the studied data will be incorporated with artificial intelligence algorithms for the automatic diagnosis of crop quality. Finally, precision agriculture with technology applied to the agricultural sector allows the land to be exploited efficiently. This system requires sensors, drones, data acquisition cards, and global positioning systems. This research seeks to merge different areas of science, control engineering, electronics, digital image processing, and artificial intelligence for the development, in the near future, of a low-cost image measurement system that allows the optimization of crops with AI.

Keywords: precision agriculture, convolutional neural network, deep learning, artificial intelligence

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Authors: Soumya Mishra, Elena Neiterman

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Aligned with the growing interest in the impact of the pandemic on academic experiences of university students, this study aimed to examine the challenges Canadian undergraduate students experienced during the university closures due to COVID-19. Using qualitative methodological approach, the study utilized semi-structured interviews conducted with 20 undergraduate students enrolled in an Ontario university to explore their thoughts and experience regarding online learning during the peak of the COVID-19 pandemic, from January 2021 to March 2021. The interviews yielded four major themes with the following associated subthemes: Personal Challenges Associated with Adapting to the Pandemic (Change in the Type of Stress Experienced, Unique Impact on Certain Groups of Students, Decreased Motivation, Crucial Role of Resilience), Social Challenges Associated with Adapting to the Pandemic (Increased Loneliness, Challenges Faced while Communicating, Perception of Group work, Role of Living Conditions), Challenges associated with Accessing University Resources (Crucial Role of Professors, Perception of Virtual Events, Importance of Physical Spaces). Overall, the analysis showed that the COVID-19 pandemic fostered resilience and psychological flexibility amongst all students. However, the mental health and social wellbeing of students deteriorated during the COVID-19 pandemic and they reported experiencing chronic stress, anxiety and loneliness. International students, first year and final year students experienced a unique set of challenges. It was hard for participants in our study to make strong new connections with their classmates and maintain existing friendships with their peers. The importance of professors in facilitating learning was amplified in the online environment due to the lack of in-person interaction with other students. Despite these challenges, most participants reported that they received high grades during online learning. The findings from this study could be helpful for organizations and individuals working towards fostering the wellbeing of undergraduate students. They can also help in making post-secondary institutions more resilient to future emergencies by creating contingency plans regarding online instructions and risk management techniques.

Keywords: Canadian, COVID-19, university students, wellbeing

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Authors: Sreekanth Reddy Ballarapu

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The perception of the whole process of teaching and learning is undergoing a drastic and radical change. More and more student-centered, pragmatic, and flexible approaches are gradually replacing teacher-centered lecturing and structural-syllabus instruction. The issue of teaching English as a Foreign language is no exception in this regard. The traditional Present-Practice-Produce (P-P-P) method of teaching English is overtaken by Task-Based Teaching which is a subsidiary branch of Communicative Language Teaching. At this juncture this article strongly tries to convey that - Task-based learning, has an advantage over other traditional methods of teaching. All teachers of English must try to customize their texts into productive tasks, apply them, and evaluate the students as well as themselves. Task Based Learning is a double edged tool which can enhance the performance of both the teacher and the taught. The sample for this case study is a class of 35 students from Semester III - Network branch at TVTC College of Technology, Adhum - Kingdom of Saudi Arabia. The students are high school passed out and aged between 19-21years.For the present study the prescribed textbook Technical English 1 by David Bonamy was used and a number of language tasks were chalked out during the pre- task stage and the learners were made to participate voluntarily and actively. The Action Research methodology was adopted within the dual framework of Communicative Language Teaching and Task-Based Learning. The different tools such as questionnaires, feedback and interviews were used to collect data. This study provides information about various techniques of Communicative Language Teaching and Task Based Learning and focuses primarily on the advantages of using a Task Based Learning approach. This article presents in detail the objectives of the study, the planning and implementation of the action research, the challenges encountered during the execution of the plan, and the pedagogical outcome of this project. These research findings serve two purposes: first, it evaluates the effectiveness of Task Based Learning and, second, it empowers the teacher's professionalism in designing and implementing the tasks. In the end, the possibility of scope for further research is presented in brief.

Keywords: action research, communicative language teaching, task based learning, perception

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Authors: Joane Adeclas, Yoonyoung Kim, Taekyun Hur

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This study investigates individual’s intrinsic motivation to practice their leisure activities, as well as, how the cultural environment may influence their motivation to practice their activities. Focused on the positive psychology, the present study proposed redefinition of leisure activities considering two factors. First, leisure activities could be as any activities that provide pleasure or meaning to individuals. Second, they can be practiced alone or in groups. In fact, based on this definition, a four-dimensional model of leisure activities was developed, to measure individual’s perception of their leisure experience, based on four factors that are: personal pleasure, social pleasure, personal meaning and social meaning. Furthermore, recent studies have argued that leisure activities can be interpreted and understood differently across cultures. Therefore, the present study proposed to examine the possible role of the cultural context of individual’s leisure practices. To do so, two cultural groups (Koreans vs. French) were compared in terms of the four-dimensional model of leisure activities. Three hundred Koreans and three hundred French participants were asked to answer an online survey about their leisure activities. Participants had to respond to questions related to several aspects of leisure practices as followed: the reason why their practice their leisure activities, the reason why they fail to practice their leisure, and their obsession relate to their leisure activities. Factor analyses based on participant’s responses proposed a moderate fit of the four-dimensional model of leisure activities. Furthermore, significant cultural differences were also found. As a result, the cultural context seems to influence the reason why individuals practice their leisure activities based on our model. In fact, Koreans explained more than French, the practice of their leisure activities with social-pleasurable reasons. At a contrary, French explained more than Koreans, the practice of their leisure activities with social-meaningful reasons. The two cultural groups also significantly differ on their perception of failure. The results showed that French participants used more meaningful social factors to explain why they failed to practice their leisure activities than did Koreans participants. Finally, Koreans and French significantly differed regarding their obsession on their leisure activities. In general, French tend to have more obsession than Koreans about their leisure activities. Those results validated the four-dimensional model of leisure, as well as, the cultural differences in leisure practices. However, further studies are needed to validate this model at an individual and cultural level.

Keywords: culture, leisure, meaning, pleasure

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Authors: Banu Hatice Gürcüm, Pınar Arslan, Mahmut Yalçın

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Fashion designers create lots of dresses, suits, shoes, and other clothing and accessories, which are purchased every year by consumers. Fashion trends, sketches of designs, accessories affect the apparel goods, but colors make the finishing touches to an outfit. In all fields of apparel men's, women's, and children's wear, including casual wear, suits, sportswear, formal wear, outerwear, maternity, and intimate apparel, color sells. Thus, specialization in color in apparel is a basic concern each season. The perception of color is the key to sales for every sector in textile business. Mechanism of color perception, cognition in brain and color emotion are unique subjects, which scientists have been investigating for many years. The parameters of color may not be corresponding to visual scales since human emotions induced by color are completely subjective. However, with a very few exception each manufacturer concern their top selling colors for each season through seasonal sales reports of apparel companies. This paper examines sensory and instrumental methods for quantifying color of fabrics and investigates the relationship between fabric color and sale numbers. 5 top selling colors for each season from 10 leading apparel companies in the same segment are taken. The compilation is based according to the sales of the companies for 5 to 10 years. The research’s main concern is the corelation with the magnitude of seasonal color selling figures and the CIE chromaticity coordinates. The colors are chosen from the globally accepted Pantone Textile Color System and the three-dimentional measurement system CIE L*a*b* (CIELAB) is used, L* representing the degree of lightness of color, a* the degree of color ranging from magenta to green, and b* the degree of color ranging from blue to yellow. The objective of this paper is to demonstrate the feasibility of relating color perceptance to a laboratory instrument yielding measurements in the CIELAB system. Our approach is to obtain a total of a hundred reference fabrics to be measured on a laboratory spectrophotometer calibrated to the CIELAB color system. Relationships between the CIE tristimulus (X, Y, Z) and CIELAB (L*, a*, b*) are examined and are reported herein.

Keywords: CIELAB, CIE tristimulus, color preference, fashion

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Authors: Sabina-Adriana Floria, Marius Gavrilescu, Florin Leon, Silvia Curteanu, Costel Anton

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Neuroevolution is a subfield of artificial intelligence used to solve various problems in different application areas. Specifically, neuroevolution is a technique that applies biologically inspired methods to generate neural network architectures and optimize their parameters automatically. In this paper, we use different biologically inspired optimization algorithms in an ensemble strategy with the aim of training multilayer perceptron neural networks, resulting in regression models used to simulate the industrial chemical process of obtaining bricks from silicone-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. In addition, the initial conditions that were taken into account during the design and commissioning of the installation can change over time, which leads to the need to add new mixes to adjust the operating conditions for the desired purpose, e.g., material properties and energy saving. The present approach follows the study by simulation of a process of obtaining bricks from silicone-based materials, i.e., the modeling and optimization of the process. Optimization aims to determine the working conditions that minimize the emissions represented by nitrogen monoxide. We first use a search procedure to find the best values for the parameters of various biologically inspired optimization algorithms. Then, we propose an adaptive ensemble strategy that uses only a subset of the best algorithms identified in the search stage. The adaptive ensemble strategy combines the results of selected algorithms and automatically assigns more processing capacity to the more efficient algorithms. Their efficiency may also vary at different stages of the optimization process. In a given ensemble iteration, the most efficient algorithms aim to maintain good convergence, while the less efficient algorithms can improve population diversity. The proposed adaptive ensemble strategy outperforms the individual optimizers and the non-adaptive ensemble strategy in convergence speed, and the obtained results provide lower error values.

Keywords: optimization, biologically inspired algorithm, neuroevolution, ensembles, bricks, emission minimization

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Authors: Syed Romat Ali Shah, Bilal Shoaib, Saleem Akhtar, Munib Ahmad, Shahan Sadiqui

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Classification is a data mining technique, which is recognizedon Machine Learning (ML) algorithm. It is used to classifythe individual articlein a knownofinformation into a set of predefinemodules or group. Web mining is also a portion of that sympathetic of data mining methods. The main purpose of this paper to analysis and compare the performance of Naïve Bayse Algorithm, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN)and Support Vector Machine (SVM). This paper consists of different ML algorithm and their advantages and disadvantages and also define research issues.

Keywords: Data Mining, Web Mining, classification, ML Algorithms

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Authors: Pinn Tsin Isabel Yee

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Students are increasingly turning towards online learning materials to supplement their education. One such approach would be the gamified student engagement platforms (GSEPs) to instill a new learning culture. Data was collected from closed-ended questions via content analysis techniques. About 81.8% of college students from the Monash University Foundation Year agreed that GSEPs (Quizizz) was an effective tool for learning. Approximately 85.5% of students disagreed that games were a waste of time. GSEPs were highly effective among students to facilitate the learning process.

Keywords: engagement, gamified, Quizizz, technology

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Authors: Anna Bjällmark, Victoria Persson, Bodil Karlsson, May Bazzi

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Introduction: Interventional radiology (IR) is a continuously growing discipline that allows minimally invasive treatments of various medical conditions. The IR environment is, in several ways, comparable to the complex and accident-prone operation room (OR) environment. This implies that the IR environment may also be associated with various types of risks related to the work process and communication in the team. Patient safety is a central aspect of healthcare and involves the prevention and reduction of adverse events related to patient care. To maintain patient safety, it is crucial to build a safety culture where the staff are encouraged to report events and incidents that may have affected patient safety. It is also important to continuously evaluate the staff´s attitudes to patient safety. Despite the increasing number of IR procedures, research on the staff´s view regarding patients is lacking. Therefore, the main aim of the study was to describe and compare the IR team members' attitudes to patient safety. The secondary aim was to evaluate whether the WHO safety checklist was routinely used for IR procedures. Methods: An electronic survey was distributed to 25 interventional units in Sweden. The target population was the staff working in the IR team, i.e., physicians, radiographers, nurses, and assistant nurses. A modified version of the Safety Attitudes Questionnaire (SAQ) was used. Responses from 19 of 25 IR units (44 radiographers, 18 physicians, 5 assistant nurses, and 1 nurse) were received. The respondents rated their level of agreement for 27 items related to safety culture on a five-point Likert scale ranging from “Disagree strongly” to “Agree strongly.” Data were analyzed statistically using SPSS. The percentage of positive responses (PPR) was calculated by taking the percentage of respondents who got a scale score of 75 or higher. The respondents rated which corresponded to response options “Agree slightly” or “Agree strongly”. Thus, average scores ≥ 75% were classified as “positive” and average scores < 75% were classified as “non-positive”. Findings: The results indicated that the IR team had the highest factor scores and the highest percentages of positive responses in relation to job satisfaction (90/94%), followed by teamwork climate (85/92%). In contrast, stress recognition received the lowest ratings (54/25%). Attitudes related to these factors were relatively consistent between different professions, with only a few significant differences noted (Factor score: p=0.039 for job satisfaction, p=0.050 for working conditions. Percentage of positive responses: p=0.027 for perception of management). Radiographers tended to report slightly lower values compared to other professions for these factors (p<0.05). The respondents reported that the WHO safety checklist was not routinely used at their IR unit but acknowledged its importance for patient safety. Conclusion: This study reported high scores concerning job satisfaction and teamwork climate but lower scores concerning perception of management and stress recognition indicating that the latter are areas of improvement. Attitudes remained relatively consistent among the professions, but the radiographers reported slightly lower values in terms of job satisfaction and perception of the management. The WHO safety checklist was considered important for patient safety.

Keywords: interventional radiology, patient safety, safety attitudes questionnaire, WHO safety checklist

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