Improvement of test methods and criteria for evaluation of resistance to flame propagation of long elements of the wiring system

The test methods for flame propagation of long elements of the electrical wiring system, in particular, cables, cable conduits and ducts, are analyzed, and differences in them are found in the test conditions and criteria for evaluating the resistance to flame propagation. Using a substrate of a wooden board covered with a layer of tissue paper with an areal density of (21±9) g/m2, adopted for testing other elements of the electrical wiring system, a cable was identified that is not resistant to flame propagation. It is proposed to use this substrate for testing the flame propagation of cables instead of a substrate made of a double layer of filter paper with a surface density of (80±15) g/m2. In one of three experiments, a cable that was not resistant to flame propagation was found based on the criterion of the presence of ignition of the substrate located under it. To reduce the risk of making an incorrect decision on compliance, it is proposed that the assessment of long elements of the wiring system be carried out according to the rules established for cable ducts, trays and ladders in EN 50085-1 and IEC 61537. For the AVVG cable with an outer diameter of 10 mm to 60 mm, when it touches the blue flame cone of 1 kW, the correlation coefficient of the dependence of the length of the charred part on the diameter was 0.969. For a distance of 100 mm between the sample and the burner along its axis, a correlation coefficient of 0.985 was obtained. It is proposed to test cables under the second condition recommended in IEC 60695-11-2. For two conduits, flame propagation was revealed when exposed to a 1 kW flame for 120 s and 240 s. However, for these pipelines, flame propagation did not occur under standard conditions of exposure to such a flame for 20 s and 25 s. To identify long elements of the wiring system that are not resistant to flame propagation, it is proposed to test them at a duration of exposure to a flame of 1 kW, established for cables in IEC 60332-1-2

Statistical Tests of Type I Error

This chapter covers tests of statistical significance that can be used to compare data across phases. These are used to determine whether observed outcomes are likely the result of an intervention or, more likely, the result of sampling error or chance. The purpose of a statistical test is to determine how likely it is that the analyst is making an incorrect decision by rejecting the null hypothesis, that there is no difference between compared phases, and accepting the alternative one, that true differences exist. A number of tests of significance are presented in this chapter: statistical process control charts (SPCs), proportion/frequency, chi-square, the conservative dual criteria (CDC), robust conservative dual criteria (RCDC), the t test, and analysis of variance (ANOVA). How and when to use each of these are also discussed, and examples are provided to illustrate each. The method for transforming autocorrelated data and merging data sets is discussed further in the context of utilizing transformed data sets to test of Type 1 error.

Analyzing Group Data

This chapter covers tests of statistical significance that can be used to compare data across phases. These are used to determine whether observed outcomes are likely the result of an intervention or, more likely, the result of chance. The purpose of a statistical test is to determine how likely it is that the analyst is making an incorrect decision by rejecting the null hypothesis and accepting the alternative one. A number of tests of significance are presented in this chapter: statistical process control charts (SPCs), proportion/frequency, chi-square, the conservative dual criteria (CDC), robust conservative dual criteria (RCDC), the t test, and analysis of variance (ANOVA). How and when to use each of these are also discussed. The method for transforming autocorrelated data and merging data sets is discussed. Once new data sets are created using the Append() function, they can be tested for Type I error using the techniques discussed in the chapter.

Linking the brain with behaviour: the neural dynamics of success and failure in goal-directed behaviour

The human brain is extremely flexible and capable of rapidly selecting relevant information in accordance with task goals. Regions of frontoparietal cortex flexibly represent relevant task information such as task rules and stimulus features when participants perform tasks successfully, but less is known about how information processing breaks down when participants make mistakes. This is important for understanding whether and when information coding recorded with neuroimaging is directly meaningful for behaviour. Here, we used magnetoencephalography (MEG) to assess the temporal dynamics of information processing, and linked neural responses with goal-directed behaviour by analysing how they changed on behavioural error. Participants performed a difficult stimulus-response task using two stimulus-response mapping rules. We used time-resolved multivariate pattern analysis to characterise the progression of information coding from perceptual information about the stimulus, cue and rule coding, and finally, motor response. Response-aligned analyses revealed a ramping up of perceptual information prior to a correct response, suggestive of internal evidence accumulation. Strikingly, when participants made a stimulus-related error, and not when they made other types of errors, patterns of activity initially reflected the stimulus presented, but later reversed, and accumulated towards a representation of the incorrect stimulus. This suggests that the patterns recorded at later timepoints reflect an internally generated stimulus representation that was used to make the (incorrect) decision. These results illustrate the orderly and overlapping temporal dynamics of information coding in perceptual decision-making and show a clear link between neural patterns in the late stages of processing and behaviour.

Factors Associated with Burnout in Clinical Year Medical Students in Terengganu, Malaysia

Many studies have shown that burnout had a detrimental effect on the well-being of medical students. In medical students, the prevalence of burnout increased as the year progressed. The Objective of the study was to determine the predictors for burnout in clinical year medical students. There were 120 clinical year medical students selected for this study using stratified random sampling. Students’ demographics, academic data, Copenhagen Burnout Inventory (CBI) and USM emotional intelligence Inventory (USMEQ-i) in Google Forms were distributed using WhatsApp application. Data include entry CGPA and whether they made the correct decision to do medicine and ever thought of dropping out. The response was in the form of ‘Yes/No’. Burnout was categorised into significant and non-significant burnout based on the mean CBI score. Each factor was categorized into two categories was analysed using Chi-Square test. Further analysis was done using multiple logistic regression. There were 105 students responded. The percentage of students categorised as having significant burnout was 41.0% (95% CI 31.5; 50.1). Incorrect decision to do medicine, ever thought of dropping out and lower emotional intelligence were significant predictors for burnout with odd ratio of 6.06, 3.06 and 3.24 respectively. As a conclusion, correct decision to do medicine and emotional intelligence were important predictors for burnout in clinical year medical students. They may be considered as important factors to be looked into during student selection interview. Keywords: Burnout, medical students, emotional intelligence

Examining the use of artificial intelligence in recruitment processes

The recruitment process is more of an issue for many businesses. The process of determining the appropriate candidate to hire is often a costly, time-consuming process. Besides, due to incorrect decision-making or lack of objectivity in hiring processes, recruitment processes may not proceed effectively. Businesses are trying to use technology in their recruitment processes to avoid these problems. Currently, many businesses use internet and software technologies to receive applications and evaluate candidates. But despite these technologies, it takes time and additional personnel costs for people to coordinate all processes. Due to these and similar situations, there has been an increase in the use of artificial intelligence technologies in recruitment processes in the world recently. The use of artificial intelligence in recruitment processes has the effect of reducing costs and decision-making errors and appears to be beneficial in saving time. In this study, the use of artificial intelligence in the recruitment processes of businesses in Turkey was examined. In this context, interviews were conducted with the human resources managers of 22 businesses. According to research results, it was understood that artificial intelligence was benefited only as an auxiliary element in recruitment processes. It has been found that businesses do not rely much on artificial intelligence in their recruitment processes, so they do not use it or partially use it.

Risk Analysis by a Probabilistic Model of the Measurement Process

The aim of the article is presentation of the testing methodology and results of examination the probabilistic model of the measurement process. The case study concerns the determination of the risk of an incorrect decision in the assessment of the compliance of products by measurement. Measurand is characterized by the generalized Rayleigh distribution. The model of the measurement process was tested in parallel mode by six risk metrics. An undesirable effect in the reconstruction building block of the model was detected, consisting in the distortion of probability distribution at the edges of the measuring range. The paper gives guidelines on how to use the model, to obtain the analytical risk assessment consistent with the results of the Monte Carlo method. The study can be useful in product quality control, test design, and fault diagnosis.

Evaluation of Strategy Based on the Three Elements

The implementation of each strategic decision involves investment of a large amount of capital, human resources and managerial efforts. An incorrect decision often means the failure of an investment. Risks of strategic decisions can be reduced if we can re-examine or identify whether the strategic decisions that have been made are consistent with the actual situation of the company and with the external environment and industry development. Based on the basic principles of strategic decision-making presented in this book, this chapter puts forward the basic logic and thinking of the evaluation of strategic decision-making from the standpoint of the three elements of strategy.

Fuzzy Bayesian Context-Aware System to Reduce Electricity Consumption

Context-awareness develops smart, intelligent IoT devices that can adapt to changing needs and act autonomously on behalf of the user. The main challenge of context-aware internet of things is to interpret the context effectively. There is an abundance of CAIOT in literature. Understanding of the meaning of the context is, however, almost ignored. Misinterpretation of context can lead to an incorrect decision that motivates to develop a system that emphasis context reasoning and decision making using the fuzzy Bayesian approach. The current investigation aims to build a context-aware IoT system using occupancy detection for energy management. The performance evaluation for the proposed system uses data collected in the tutorial room to detect occupancy. Extensive experiments highlight the utility of the proposed approach, which significantly reduces energy than the traditional ON/OFF usage pattern through customer access via mobile phone or personal computer.

Bayesian adaptive decision-theoretic designs for multi-arm multi-stage clinical trials

Multi-arm multi-stage clinical trials in which more than two drugs are simultaneously investigated provide gains over separate single- or two-arm trials. In this paper we propose a generic Bayesian adaptive decision-theoretic design for multi-arm multi-stage clinical trials with K ([Formula: see text]) arms. The basic idea is that after each stage a decision about continuation of the trial and accrual of patients for an additional stage is made on the basis of the expected reduction in loss. For this purpose, we define a loss function that incorporates the patient accrual costs as well as costs associated with an incorrect decision at the end of the trial. An attractive feature of our loss function is that its estimation is computationally undemanding, also when K > 2. We evaluate the frequentist operating characteristics for settings with a binary outcome and multiple experimental arms. We consider both the situation with and without a control arm. In a simulation study, we show that our design increases the probability of making a correct decision at the end of the trial as compared to nonadaptive designs and adaptive two-stage designs.