The human reliability of intelligent coal mine hoist operation system is affected by many factors, in order to reduce the occurrence of human error in the hoist system and improve the reliability of the system. The characteristics of phased-mission task operation of hoists is combined, the phase dependence of human cognitive errors is considered and, a new human reliability evaluation method is proposed with the help of Bayesian network (BN) model in this paper. Firstly, the phase dependence of human cognitive errors was analyzed based on the cognitive behavior model. Then the human error analysis in the hoist system was carried out, and several main performance shaping factors are selected. Secondly, BN was used to build the human reliability model of the hoist system at each stage. Finally, it is found that the phase dependence of cognitive errors has a negative impact on the human reliability of the hoist system through the case analysis. At the same time, several main performance shaping factors (PSFs)were quantitatively analyzed by using the reverse reasoning ability of BN, which proves the effectiveness of the proposed method, and provides a scientific and reasonable theoretical basis for the development of effective human error prevention measures for the operation of intelligent coal mine hoists. The number of fatal coal mine accidents and the death rate of one million tons of coal decreased from 5670 and 5.28 in 2001 to 228 and 0.059 in 2020, respectively3. Even if the adoption of digital technologies has lessened the frequency of mishaps in industrial systems, accidents brought on by human mistake are still occurring at a high rate4. This is true since incorporating new technology into the growth of intelligence also creates a number of issues5. First, the volume of information in digital systems has substantially expanded, making it more challenging to find the needed data. Second, unlike conventional coal mines, intelligent coal mine systems have both primary and secondary jobs. The number of secondary activities is significantly higher than the number of primary tasks, and secondary tasks add to the operator's workload. The development of intelligent coal mines raises the uncertainty of human relative to conventional coal mines. The probability of Human error probability (HEP) is also increased by complicated procedures6. The coal mine hoist, as the monitoring and operation center of the production material and operator transportation system of the coal mine enterprise, is an important channel to link up and down the coal mine7. Coal mine hoist as a coal mine enterprise production materials, operators transport system monitoring and operation center, is an important link between the coal mine shaft underground, the safe operation of the hoist is an important link to ensure the safe production of coal mines. During the continuous development of intelligent coal mines, the reliability of the equipment is continuously improved, and it is more necessary to focus on human reliability analysis and assessment, so as to improve the overall reliability of the system. Human reliability assessment is of great significance to improve the safety and efficiency of intelligent coal mine hoist systems as well as to prevent human error accidents8.Coal mines are a safety accident prone industry, With the development of smart coal mines, the working conditions, automation of equipment and the production environment have been greatly improved9. A large number of cognitive activities are involved during multiple phase operation tasks in intelligent coal mine hoist systems. The occurrence of cognitive mistakes is not only related to the cognitive activity associated with the present phase, but it is also influenced by the results of the cognitive activities associated with the previous phase, it is crucial to keep in mind when undertaking human reliability analysis. For instance, if the hoist operator does not observe the instrument readings, he/she may make an error. Hoist operators usually experience several phases while performing their tasks, and the human reliability of phased-mission systems (PMSs)needs to be analyzed, and the interdependence between phases, which is natural, needs to be considered. Therefore, human reliability assessment of coal mine hoist systems is more complex and requires consideration of the state dependence of the hoist operator's cognitive behavior at multiple stages.Increasingly more operators are transitioning from manual operators to machine system supervisors in the context of intelligence and digitalization10. For instance, the phase dependence of each unit and the dynamic behavior of the system are represented by the Markov chain (MC) and Petri-net (PN) model11. The second utilizes combinatorial methods such as reliability block diagrams (RBDs)12, binary decision diagrams (BDDs), and multivalued decision diagrams (MDDs)13. Large-scale PMSs have been successfully handled by combinatorial methods based on decision diagram theory and Boolean algebra. The third and more applied one utilizes the classical probabilistic graphical model BN, a type of classical probabilistic graphical model that has gained popularity among researchers for its improved ability to describe probabilities and correlations in complicated systems15. An event-tree-based BN model, for instance, was proposed by Li et al.16 and can be used to assess the risk and reliability of PMS. Zhao and Smidts17 used Bayesian networks to represent human cognitive activities and Monte Carlo simulations to account for various uncertainties in cognitive processes. Human reliability study is aided by this cutting-edge environment for cognitive modeling and simulation. Xu et al.18 created a new reliability assessment method that assesses the dependability of multistate PMSs by using data gathered from various phases. A dynamic Bayesian network and a particular expectation maximization method were combined to achieve this. Overall, BN is a method for PMS reliability assessment that works well since it can accurately depict the dependencies across phases as well as the chance correlations inside a single phase.Many different analytical modeling strategies have been put out by numerous academics to address the issue of reliance and reliability assessment in PMSs. There are three main types.The first uses Monte Carlo simulation (MCS), a state-space-based approach, to construct state transfer modelsIn the past human reliability assessment studies, HRA has been widely used in various fields as a method to assess the probability of human error to improve the safety and reliability of systems.19. They analyze how human activity affects safety and reliability systems using systems engineering and cognitive and behavioral sciences, and they forecast the likelihood of human errors based on employee actions and decisions. In the study of human reliability in the field of coal mining, Wang et al.20 analyzed the behavioral reliability of traditional mechanized coal mining operators using Monte Carlo methods. The influence of sound in the working environment on the behavioral reliability of operators was explored, but the influence of human own factors on behavioral reliability was ignored. A fuzzy Bayesian network approach was used by Qiu et al.21 to create a gas tunnel safety risk assessment model. Liu et al.22 developed the Human Factors Analysis and Classification System for Coal Mines in China based on the statistical findings of 362 significant coal mine accidents that occurred in China. He also looked into the poor safety practices of coal mine workers and the factors that influenced those behaviors. Li et al.23 identified and evaluated the risk factors of coal mine safety production using text mining and Bayesian network technology, and they clarified the six main risk elements of coal mine safety production and their connected components. Fuzzy logic and Bayesian networks were coupled by Xue et al.24 to assess three fault states in the gas monitoring network. Fuzzy logic was used to represent the fault states of the system. The results of a thorough bibliometric analysis of the HRA field conducted by Hou et al.25 revealed that classic HRA approaches are inappropriate for usage in task and system transitions as well as the current workplace. To address these developments, new HRA models must be created. According to French et al.26, workers' cognitive processes as well as other performance-influencing aspects  should be taken into account in order to further extend and improve HRA approaches.A number of HRA techniques have been created in the past to assess and forecast human errors27. In genuine operational processes, PMSs also feature the phase dependence of human cognitive errors in addition to the phase dependence of machine states. Ignoring them will inevitably cause the evaluation results' accuracy to decline11. Most of the past human factors reliability assessments about the coal mining field have been focused on traditional coal mines, and there are still some shortcomings in human factors reliability studies for smart coal mines. In particular, it will be the focus of this paper to identify and quantify the performance impact factors of human factors reliability in human–computer interaction operations in smart coal mines.In man–machine PMS, more attention has been paid to the interdependence between machine failures in the past, and less research has focused on the dependence of human cognitive errors. Also in the past human reliability studies have been more research focused on the dependence of human cognitive errors. Some researchers, for example, merely treat the operator as a machine unit without accounting for cognitive errors and the corresponding phase dependence in the operator's cognitive process, which is not representative of the actual scenarioIn summary, this paper combines the operational characteristics of intelligent coal mine hoist systems, focusing on the cognitive behaviors and cognitive error dependencies of hoist operators during operation, and proposes a new human factors reliability assessment method combined with BN. Using Bayesian networks, the PSFs and related cognitive activities of hoist operators are considered as modeling elements, and the stage dependence of human cognitive errors are also well mapped into the BN, by which a human factor reliability model of the hoist system is constructed. Subsequently, the human factor reliability of the coal mine hoist system is evaluated based on the conditional state probabilities of all identified nodes. Finally, a typical intelligent coal mine is selected to evaluate the human factor reliability of coal mine hoist system. It enriches the research related to human factor reliability assessment in human–machine interaction system of smart coal mines and provides a theoretical basis for developing effective human factor failure prevention measures for smart coal mine hoist operations.The rest of the paper is composed as follows: in "28, the relationship between human-caused errors has come to be recognized as a phenomenon. The main stages of human cognition include, observation, comprehension, planning, and execution. Errors in observation, manipulation, and understanding in the previous stage affect the likelihood of the same errors occurring in subsequent stages when similar situations arise29. Therefore, they are analyzed comprehensively in this section.In contrast to traditional single task systems, PMSs have phase dependencies between subsequent phases. Therefore, it is important to recognize and examine the phase dependencies of system units for modeling before evaluating the human reliability of coal mine hoist systems. Along with the phase dependency of machine states, phase dependence of human cognitive errors is a characteristic of PMSs. In the subject of HRA researchi is affected by the task in phase i − 1 .In terms of observation, this paper focuses mostly on gathering visual data through observation. As a result, two features, comprising (1) and, can be used to characterize the corresponding interdependencies (2). Furthermore, understanding, planning, and execution involve information processing based on the operator's cognitive-behavioral model and are therefore considered here as sharing similar phase dependence mechanisms. Therefore, the phase dependence of understanding, For instance, can also be characterized according to both aspects (3) and (4).i − 1 phase is successful: this indicates that an object at phase i − 1 was successfully detected by the operator. When a similar observation need arises in phase i, it is quite simple to capture the item again or even to forgo the observation30, lowering the possibility of errors (arrow line 1).If the observation of i − 1 phase fails: without additional cues to remind of the previous error, the operator is more likely to make the same mistake again on observing a similar object in phase i (arrow line 2)31. Conversely, with additional reminders or cues, awareness of the previous phase error will increase one's attentiveness and can inhibit the recurrence of the same error (arrow line 1).If the observation i − 1 phase is successful: It indicates that the operator is using the appropriate cognitive model and is equipped with the relevant information or rules to manage the current circumstance. When a comparable circumstance arises in phase i, the operator may produce the right understanding once more (arrow line 3).If the understanding of i − 1 phase fails; without further clues, having an incomplete cognitive model of the operator increases the likelihood that the scenario would be misunderstood again in the i phase (arrow line 4). The gap between the two phases is once again another crucial factor, even with extra cues. The operator will have the chance to complete his or her incomplete cognitive model if the time gap is reasonably long enough to enable correct understanding in the following phase (arrow line 3). Otherwise, under extreme time pressure, the operator will probably adopt a heuristic method and continue with trial and error32 (arrow line 4).If understanding the The reliance of cognitive errors between two neighboring phases can be categorized into four types, as shown in Fig. 29 is used in this research to provide a method for determining the degree of stage dependence of human cognitive errors. The decision tree (DT) proposed by SPAR-H is used in this research, as shown in Fig. The comparability of the scenarios between the two stages to enable the subsequent model generation and the subsequent estimation of the adjustment factor is a crucial prerequisite for evaluating the stage dependence of human cognitive mistakes. The decision tree (DT) suggested by Standardized plant analysis of risk-human reliability analysis (SPAR-H)It refers to the impact of an operator's failure to perform a subsequent task after failing to perform a previous task. The majority of HRA methods, notably SPAR-H, make the assumption that the probability of an operator failing or failing in the next task segment may be higher if the operator fails or fails in the task compared to when the operator succeeds. The key risk indicators may be greatly underestimated, and the ensuing results for human reliability may appear distorted, if the potential relationships between cognitive actions are not accurately portrayed. The intersection of each leaf node represents one of the four characteristics, including personnel, time, place, and cues, and each leaf node represents a level of assessed similarity. These levels are complete (CD), high (HD), moderate (MD), low (LD), and zero dependence (ZD). The impact on following cognitive processes increases with dependence level. The rules used to establish the various levels of reliance are shown on the path from the beginning to the finish in the DT. When two phases are more similar in terms of their position, their timing, and the presence of additional stimuli, there is a stronger degree of reliance between them. With regard to the aforementioned cognitive deficits in humans, the pathways in DT are compatible with and span a variety of phase dependencies. The similarity of the situation at each stage can be assessed in terms of five descriptive dependency levels. Subsequently, the independent HEP of the operator-related nodes in the later stages can be adjusted based on the assessed dependency levels. each dependency level corresponds to a specific adjustment factor α to adjust the HEP formula. Thus, the suggested modeling approach incorporates the DT for determining situation similarity.15. These can be used to encode the many types of knowledge that operators keep in their memory, or what is known as the information processing model. In order to build a human reliability assessment model for hoist systems, BN was chosen as the modeling tool because it can accurately depict the causal linkages and dependencies between factors and targets in the PMSs of hoist systems.BN are quantitative causal models consisting of a directed acyclic network and several statements about probabilities that characterize the dependencies between different PSFs and related activities. Nodes, arcs, and conditional probability tables(CPTs)are provided by BN33. A directed acyclic graph made up of a collection of nodes V = {X1, X2,⋯Xn}connected by a collection of directed edges serves as a representation of the topology. The nodes represent the interesting random variables, while the directed edges define the causal or effect linkages between them. In this study's application of BN to express the causal relationship of flaws, the directed edge between two nodes serves as an example of the causative relationship. Instead of using probability intervals between them, a particular BN is used to translate expert expertise into human error probability (HEP) values for operators. Expert knowledge is transformed into HEP values for operators rather than probability intervals between them using a customized BN. If node Xi and node Xj are connected by a directed edge, Xi is referred to as Xj 's "parent" node and is represented by the symbol Pa. (Xj). As a result, Xj is referred to as the "child" node of Xi, signifying the causal relationship between Xi and. Xj A leaf node is a node without any children, whereas a root node is a node without any parents. By allocating unconditional distributions (which can be derived from statistics or domain experts) to root nodes and CPTs to intermediate nodes34. The joint probability distribution of the entire network can be written as follows using the chain rule of BN35.Xi) denotes the parent set of node Xi.The BN consists of two parts: the qualitative topology and the quantitative CPTsThis study is broken down into three key steps as illustrated in Fig. 36. Smidts et al.37 adapted the information processing model and proposed the (IDA) Information, Decision, Action model, and divided the cognitive process into four modules: information perception, decision, execution, and mental state. However, because it was too complex for practical application, it later evolved into the Skill-Rule-Knowledge (SRK) model, which classified human cognitive behavior into three behavioral modes: skill-based, rule-based, and knowledge-based38. In recent years, some scholars, divided the cognitive behaviors of operators in digital control rooms into four stages: monitoring, state evaluation, response plan, and operation, and established a model of operators' cognitive behaviors40, this may be useful for improving the relevant human reliability analysis model.According to traditional psychology, the human cognitive process consists of three main parts: first people perceive stimuli from external information, then the received information is transmitted to the brain for processing, and finally the response activity is made, which is the classical stimulus–organism–response (S–O–R) model41, so it is difficult to fully reflect the dynamic cognitive behavior characteristics of human–machine interaction operation in intelligent coal mine hoisting machine room. Therefore, based on the analysis of human–machine interaction in intelligent coal mine hoisting machine room, considering the applicability of SRK model and the understanding of IDA model on the cyclic interaction process of cognitive functions and the relationship with mental state, this paper expands the SRK model on the basis of IDA model and adds internal and external factors that affect the cognitive process, and constructs the intelligent coal mine hoisting machine The driver's cognitive behavior model is shown in Fig. The cognitive behavior model, which is highly important in people's research on human mistake, examines the human cognitive process based on the analysis of various cognitive behaviors and cognitive functions of individuals. However, the previous cognitive behavior model is relatively simple, the interaction between the cognitive functions is not well described, and the influence of the external environment on cognition is not taken into accountThe whole cognitive process constructed by the model is driven by mental factors such as attention and memory, while each cognitive function will change the state of mental factors in real time during the execution process, and the cognitive function and mental factors are dynamic interaction process, and the cognitive process will also be influenced by the Hoist operator himself and the surrounding environment. In summary, the cognitive behavior model emphasizes the interaction between cognitive functions and mental states, the non-serialization of cognitive functions and the role of internal and external factors on the cognitive process.Human errors are simply caused by people's incorrect cognitive behavior. The cognitive processes of hoist operators and the completed work analysis were examined in the previous paper. The next stage is to investigate what leads to hoist operators' incorrect cognitive activity and sets off human errors. Human errors will also be generated by the situational environment in which the person is positioned, it is currently generally accepted that attributing human errors solely to the person's own causes is biased.43 and others, the behavior formation factors that may lead to human-caused errors are summarized by combining the characteristics of man–machine interaction operations of intelligent coal mine hoists, as shown in Table Factors that contribute to human error include at least those related to the person, task-related, man–machine interface, environment, and interactions between people in the organization. Through the study of existing behavior formation factor classification methods, such as 67 PSFs provided by THERP, 9 CPCs by CREAM, 8 PSFs by SPAR-HThe factors influencing different cognitive functions can vary, and the role of different behavioral formative factors in influencing different cognitive functions varies. Therefore, it can be assumed that different behavioral formation factors lead to different types of person-factor failures, i.e., behavioral formation factors and person-factor failure patterns are closely related. Based on the identification of the factors influencing cognitive functions, the relationship between human error patterns and behavioral formation factors was analyzed, as shown in Table Usually, as comprehensive as possible behavior formation factors are needed for humans failure identification, because only then can the causes of humans failures occur be identified more accurately, while such comprehensive behavior formation factors, which must be included at every level, are not needed for the calculation of humans failure probability. Among the commonly used human reliability analysis methods, SLM chooses six PSFs, CREAM uses nine CPCs, and HCR, HDT, and other methods choose behavior formation factors that are basically around a dozen. The reason why a small number of important behavior formation factors are usually used for human error probability calculation is mainly because when conducting quantitative analysis of human reliability, quantitative evaluation of each behavior formation factor is required, and the process of quantitative evaluation is complicated, and once the number of behavior formation factors is too many, it is likely to have the possibility of double calculation, which makes the results inaccurate instead. Interviews with seven experts were therefore conducted based on the identification of hoist operator PSFs. These experts included two university experts who had studied coal mine reliability for a long time, two hoist operators who had worked for more than 7 years, and two coal mine accident investigation experts who were primarily in charge of accident scene investigation, identification of accident elements, and determination of accident nature and direct cause. Among the PSFs in Table A BN-based technique for evaluating the human factor dependability of intelligent coal mine hoist systems is developed, which takes into account the stage dependency of human cognitive errors and the key human factor reliability PSFs of such systems in the following three steps.Ascertain each network node status. The determination of many requisite states for nodes that play a role may be necessary to help further quantitative modeling. Finding descriptive states (such as failure or success) for a specific node that is performing well or not functioning is one method advised for describing states. Degradation-like intermediate states can also be found, if necessary. To describe the operator-related nodes, this study, however, only takes into account two states.44. According to Fig. Identify the nodes' causal connections within each phase. To create a qualitative BN model for a specific phase, causal linkages between various nodes should be established. Causative development begins at the root node and travels along arcs to the intermediate and target nodes. The BN that was created can be viewed as an illustration of the cognitive-behavioral framework that the operator uses to comprehend the current circumstance. The HRA event tree can be used to determine the causal connections between operator-related nodes1Establish connections between various phases. In order to develop an integrated model, this sub-step introduces various phase dependencies, which connect the established BNs of each phase. Linking the identical nodes of all phases in a chronological order is one method for illustrating the phase interdependence of the unit-level nodes (including the cognitive behavior of the hoist operator). Additional binary nodes, also known as indicator nodes, are added to demonstrate whether the hoist operator is functioning properly at the conclusion of each phase in order to prevent too many state combinations of CPT generated by directly connected target nodes. It is simple to describe the logical relationship between the human reliability of the hoist operator completing all phases by connecting the indicator node with a directed arc between each pair of neighboring phases.The BN model qualitative structure must first be constructed using the identified nodes as a base. The BN structure can change between phases to reflect how the PMSs are configured dynamically. The following three sub-steps can be added as further divisions of this step.The three phases of lift man, lift object, and lift explosive, which correlate to phases 1, 2, and 3, are called for the target nodes for each phase. The root node represents the underlying cognitive behavioral influences, such as "Statute" and "HMI"; each phase of cognitive behavior is set as an intermediate node. Then, by linking the corresponding nodes, the phase dependencies in the human reliability system of the hoist operator can be constructed. In order to create a comprehensive qualitative intelligent coal mine hoist system human reliability BN model, the causal relationship between each phase's nodes is integrated with the phase dependencies of the nodes between various outcomes, as shown in Fig. HEPxi and NHEPxi refer to node x's independent HEP and NHEP, respectively, in the first phase.In the Bayesian network model, ascertain the prior probabilities of the root nodes and the conditional probabilities of the intermediate and leaf nodes. The probabilities can be determined in a number of different ways, such as by identifying the connections between the nodes and constructing functional relations from data from experiments or simulators, by obtaining the probabilities under various conditions through mathematical analysis of a sizable amount of previously gathered data on human-caused error, or by consulting relevant literature, manuals, or domain experts. According to this approach, all associated cognitive activities are classified as diagnostic tasks and are consequently given the NHEPs that correspond to each phase. Second, in order to evaluate the existing working conditions, PSFs must be graded for each phase. Workload flexibility, complexity, experience/training, procedures, ergonomics/human–machine interface (HMI), stress/stressful stimuli, and available time are the eight PSFs that SPAR-H offers. Each PSF has three to five rating levels and corresponding multiplier values. It is feasible to quantify the phase dependency of the node by changing the CPT in the later phases based on the CPT in the earlier phases, which is selected by domain experts based on the definition and level of the PSF employed in SPAR-H. For each phase, the remaining PSFs can be suitably set to the same value. By multiplying the associated NHEPs by the PSFs, it is possible to determine the independent HEPs of the model-related nodes. The HEP adjustment algorithm is used when many PSFs are evaluated negatively:45. In the event that the identical action from the prior phase fails, each dependency level corresponds to a particular variation of the HEP formula:In this sub-step, a further adjustment of the independent HEP is necessary to quantify the effect of the phase dependence of human cognitive errors. First, for each sort of cognitive activity, it is important to assess how closely the two adjacent phases resemble each other as they might not involve exactly the same circumstances. The comparability of events can be evaluated in terms of five descriptive dependency levels by utilizing the DT depicted in Fig. 45:Here, the adjustment factor α and the adjusted HEP at the dependent level of XD are displayed. Table i − 1 and phase i depend on MD, Table i in phase i. The same method can be used to determine the CPT of additional nodes.The logical relationship between the associated nodes in the BN model can also be used to calculate the CPT of the hoist system's human reliability-related nodes. When the individual PSF nodes have been identified, the basic reasoning behind the CPT assignment of the intermediate relevant nodes can be stated as follows: if the hoist operator's prior cognitive activity in the current phase failed or was performed incorrectly, the state of that cognitive activity node is incorrect; alternatively, its state probability is conditionally decided based on the condition of the same kind of cognitive activity in the previous phase. For instance, if phase RPi) cognitive activity and the machine system's operation are successful. Following that, the conditional probabilities listed below can be used to describe the logical relationship between the task success criteria at each phase:IN and TN stand for, respectively, the indication node and the target node (the condition of the system during the current phase). Thus, the probability of success of the indicated node in the final stage reflects the probability that the operator will maintain proper operation without human error throughout the phased-mission of the intelligent coal mine hoist system.The chain rule of CPT and BN of all identified nodes can be used to determine the joint probability distribution of the target nodes in each phase of this phase. It goes without saying that the task state of the entire human–machine system at this phases is only successful if both the operator's /2 = 0.54585, HSP = [1 + (1 − 0.0917)]/2 = 0.95415. conditional probabilities for other cases at the PSF level can be obtained in a similar way. Finally, the values of HEP and HSP obtained for various cases are brought into Table The CPT assignment procedure of the node Observed2 can be described as follows. First, the NHEP is set to 0.01 because the observed error mode is "observation error". Second, based on the SPAR-H scoring table to produce scoring Table Regarding the system's human reliability logic, the evaluation of face validity focuses on determining whether the calculation of each node, network structure, and model conditional probability is comparable to the experts' empirical knowledge. It also examines whether it accurately depicts the real hoist operation situation. Four more experts were recruited to take part in the validation and assessment of the suggested method because it was difficult for the four experts participating in determining the model a priori probability to disagree with one another. Two of the four specialists were hoist operators with expertise, while the other two were scholars working on studies related to coal mine safety management. The four experts concurred that the nodes and their connections largely matched their empirical understanding and the results of relevant research. Additionally, the model fulfills face validity since the probability distributions of the nodes and the model's parameters somewhat mimic the functionality of the real equipment.By entering evidence at its parent nodes using the formula, the forward inference analysis based on the BN model can compute the probability distribution of the integrated processor system's failure lifetime. Once all nodes' conditional probabilities are known, it is possible to estimate their state distribution in any phase using the computation procedure described above. It is also possible to assess the hoist operator's dependability during each phases. The reliability of the Hoist operators in each phase can also be evaluated. The obtained prior probabilities and conditional probabilities are fed into the already constructed Bayesian network model of human reliability through the GeNIe software. Given the prior probabilities, the reliability and humans failure probabilities of each phase of the cognitive behavior of the Hoist operator can be derived, as shown in Fig. It can be observed that the reliability of the Hoist operator gradually decreases as the task progresses. These happen as a result of the system's design and the rising HEP levels between stages. The dynamic PSFs levels can be used to explain the variations in human reliability changes in the three phases. For instance, the operator's cumulative effects of fatigue were felt in phase 3 as opposed to phase 2 due to the lengthy working hours, which caused a comparable rise in PSF levels and a more abrupt decline in system reliability. Also, the reliability of each cognitive phase decreases more from phase 2 to phase 3 than from phase 1 to phase 2. In order to compare the variability of reliability across cognitive phases, the reliability in each of the three task phases is shown in Table In the observation phase of the Hoist operator's probability of error is the lowest, the highest reliability, which is related to the advancement of coal mine intelligence, the hoist room monitoring and control devices are also gradually upgraded, man–machine interaction is more friendly, sound and light signals, functional partition layout, color highlighting, etc. can make the Hoist operator quickly find the problem, thus reducing the Hoist operator's errors in the observation phase. The execution phase has the highest probability of errors. With the increasing reliability and automation of the equipment, the cognitive tasks of the Hoist operator are increasing, and in the face of a large amount of information and audio and visual signals on the screen display, after perceiving and understanding the information, he needs to make a quick judgment, make a plan and carry out a series of operations, which may cause excessive pressure on the Hoist operator and make execution errors easily.The BN model was used to identify the root causes of failures that were caused by people. The BN established in the previous section is then evaluated based on the results of the fault diagnosis by doing a sensitivity analysis to examine the impact of changes in probability in the parent node on the child nodes. Contrary to causal reasoning, diagnostic reasoning examines the source of the outcome when the outcome is already known. In the BN model for human reliability analysis, assuming that a human failure has already occurred, one goes along the already built BN model to reverse the diagnostic reasoning to get the posterior probability of the root node and compare it with the prior probability to get the percentage change in probability, which shows which factors have a greater impact on human reliability. Therefore, assuming that a failure occurs in the lifting explosive phase, the posterior probability of its root node is reasoned and the diagnostic reasoning is shown in Fig. The posterior probability of each PFSs reliability root node is calculated by running it in the software as shown in the figure. Comparing them with the previously obtained prior probabilities, the percentage change of the posterior probabilities with respect to the prior probabilities can be obtained, as shown in Table In order to show more visually the degree of influence of the root node of each PSFs on the human reliability, the posterior probability increase of the root node of these PSFs was plotted as a bar chart, as shown in Fig. Through the Table The above facts prove that the interdependence of cognitive behaviors and human cognitive behaviors have a significant impact on the reliability of coal mine hoist operators. To address the problems of complex operational tasks, inadequate education and training, and high workload, the workflow of hoist operators should be optimized to reduce the complexity of their operational tasks and to improve the usability, operability, information recognition, and complexity of interface management tasks of the human–machine interface to reduce the workload of hoist operators. The operator's cognitive-behavioral training should also be improved, and the operation method should be standardized to aid in the operator's observation, understand, plan and execute correctly, and ensure the reliability of task completion.The development of smart coal mines has led to the digitization of hoist systems and the diversification of operator operational tasks, and in the face of the human reliability assessment problem in smart coal mine systems, most of the existing human reliability assessment methods are used in conventional coal mines and rarely manage to consider the stage dependence of human cognitive errors and how they affect their own safety performance. Therefore, the appropriateness of traditional human factors reliability assessment methods needs to be further explored. This paper is a preliminary study of human-caused reliability assessment in smart coal mines, and discusses the proposed methods and limitations of human reliability assessment in smart coal mines considering the interdependence of human cognitive errors.First, in our proposed approach, since the human factors reliability model for hoist operators is only used to quantify the HEP of operator-related nodes, it cannot change the conclusion that the interdependence of cognitive errors affects system reliability. However, the error-generating factors (which can be described by PSFs) considered in different HRA methods are more or less different, and this should also be considered when modeling coal mine hoist operators, therefore, the PSFs in the smart coal mine hoist system were identified and analyzed, and finally screened, which is more compared with the PSFS selection process in other human-causal reliability studies fit the actual situation of smart coal mine production, but the screening method relies only on expert judgment is more subjective and objective data analysis is not sufficient, and future work will make up for this.47, human emotions, and some physiological abnormalities48 have a greater impact on the reliability of human factors in coal mine production. In this study, we found through BN reasoning analysis that the complexity of tasks and the design of human–machine interface in smart coal mines more affect the human factor reliability of human in human–computer interaction system. This may be related to the change in the external environment of the smart coal mine and the gradual complexity of the operator's tasks.Secondly, in the past, the reliability research about coal mine system has focused more on the external environment and equipment on the reliability of the system. However, in the context of intelligent coal mines, the reliability of equipment is increasing and the external environment is simpler and safer, so it is more important to focus on the reliability of people in the system. In this paper, we focus on the reliability assessment of human in the system, and the results obtained are slightly different from the past studies. Past studies have concluded that environmental factorsBNs, as a directed acyclic probability map, can establish causal relationships and elucidate complex uncertainty relationships, which is beneficial for human reliability analysis. Precise inference requires access to a large amount of accurate data, which is often difficult in human reliability analysis. In this paper, in quantifying the stage dependence of cognitive errors, the decision tree proposed by SPAR-H was used in order to be able to accurately determine the situational similarity of each stage to help calculate the adjusted HEP of each node, and eventually the stage dependence of human cognitive errors between each stage was mapped to BN, making the human reliability assessment of this study more accurate.Finally, in the process of human factors reliability modeling of intelligent coal mine hoist system, data were collected for experts in related fields. The use of triangular fuzzy numbers and the application of the collected data after processing makes the determination of model parameters more reliable and accurate. However, some uncertainties with probability distribution are difficult to avoid. Future research will explore methods to further minimize the impact of uncertainty on the model results. In addition, since the cognitive processes of operators in smart coal mine hoist systems are more similar to most of the smart coal mine HMI operational tasks, the proposed human factors reliability assessment method can be adapted to other operators in smart coal mines.Human reliability assessment of coal mine systems has been a hot topic of research. However, few studies have focused on human reliability assessment in intelligent coal mine systems, especially considering the stage dependence of human cognitive errors in phased-mission system. To address this challenge, this paper first analyzes the stage dependence of cognitive errors of hoist operators and classifies them into four types based on a cognitive-behavioral model, and constructs decision trees to quantify the degree of human cognitive dependence at each stage. Then, a new reliability analysis method for hoist operators in PMS considering the stage dependence of human cognitive errors is proposed. The reliability assessment model of hoist system human is constructed with the help of BN, which is developed to represent the causal relationship between the reliability of hoist system human and each node in each stage as well as the dependence between different stages, in conjunction with the characteristics of intelligent coal mine operations. The stage dependencies of human cognitive errors are also quantified separately in the developed BN. The hoist system human-causal reliability can be evaluated based on the CPT of all identified nodes. Finally, the effectiveness of the proposed modeling approach is illustrated with a three-stage task of a typical intelligent coal mine hoist.Based on the forward and reverse directions of the BN theory, the probabilities of various influencing factors of human reliability in intelligent coal mine hoist systems can be accurately obtained. The results show that the interdependence of cognitive errors negatively affects the human factor reliability of the hoist system, and the complexity of the task of the operation, human–machine interface, education and training, and work load have a great impact on the human reliability in the smart coal mine hoist system. Therefore, certain targeted measures should be taken in advance to improve the human factor reliability of coal mine hoist systems. Although this paper studies a specific coal mine hoist operation example, the proposed method is considered applicable to other human-centered intelligent coal mine systems for human reliability assessment.


The World Health Organization declared the emergence of the novel coronavirus (SARS-CoV-2) in January 2020. To trace infection chains, Germany launched its smartphone contact tracing app, the “Corona-Warn-App” (CWA), in June 2020. In order to be successful as a tool for fighting the pandemic, a high adoption rate is required in the population. We analyse the respective factors influencing app adoption based on the health belief model (HBM) with a cross-sectional online study including 1752 participants from Germany. The study was conducted with a certified panel provider from the end of December 2020 to January 2021. This model is primarily known from evaluations of medical treatments, such as breast cancer screenings, but it was rarely applied in prior work for a health-related information system such as the CWA. Our results indicate that intrinsic and extrinsic motivation to use the CWA are the strongest drivers of app use. In contrast, technical barriers, privacy concerns and lower income are the main inhibitors. Our findings contribute to the literature on the adoption of contact tracing apps by questioning actual users and non-users of the CWA, and we provide valuable insights for policymakers regarding influences of adoption and potential user groups of disease prevention technologies in times of pandemics. Shortly after a cluster of pneumonia cases of unknown aetiology was detected in Wuhan (China) in late 2019, the World Health Organization (WHO) declared the emergence of the novel coronavirus (SARS-CoV-2/COVID-19) in January 2020 , and datWhile the HBM has been applied to COVID-Tracing apps in earlier studies ,11, bothThe health belief model was developed through a set of independently investigated research questions. It has been formulated in order to explain the widespread failure of people to participate in case-finding programs to prevent and detect asymptomatic diseases . The desMedical studies have been using HBM for decades in order to measure constructs’ association with diseases and health behaviours, such as breast cancer ,17,18, oThe HBM has also been applied to SARS-CoV-2 to explore adherence to precautionary measures and preventive behaviours  in several countries, such as Ethiopia  or ChinaPrior work on the HBM in the context of contact tracing apps can be divided into two groups. The first group of work employs HBM-based items in their survey but did not further examine the actual influence of these constructs on use . The secAgainst the backdrop of the related work and prior literature on contact tracing apps see , we intrPerceived susceptibility is taken to a large extent from Champion (1984), whereas we left out three of the original six items and replaced them with two items reflecting the infectious aspect of the disease covering the likelihood of getting infected on the job and, more generally, in daily life. Following the notion of the HBM, individuals are more likely to use disease prevention tools such as the CWA if they think that there is a high likelihood of becoming infected with SARS-CoV-2. Thus, we hypothesise:Hypothesis (H1). The perceived susceptibility (PS) of getting SARS-CoV−2 has a positive impact on the likelihood of using the CWA.Perceived severity was originally operationalised as a 12-item construct which is appropriate for diseases such as cancer (Champion 1984). We decided to split it into three distinct dimensions to cover the specifics of an infectious disease which can also harm others and can have certain social effects on social relationships and the career. Thus, we evaluate the perceived medical consequences on oneself , the perceived severity of the medical consequences when one becomes infected and endangers others, such as family members, friends and work colleagues and the perceived social consequences  that come along with infection with SARS-CoV-2, including financial and career aspects as well as risks to social relationships . The resHypothesis (H2). The perceived severity of medical consequences (PMC) on oneself when getting SARS-CoV-2 has a positive impact on the likelihood of using the CWA.Hypothesis (H3). The perceived severity of medical consequences on others (PSO) by getting SARS-CoV-2 has a positive impact on the likelihood of using the CWA.Hypothesis (H4). The perceived severity of social consequences (PSC) when getting SARS-CoV-2 has a positive impact on the likelihood of using the CWA.We changed the construct perceived benefits compared to the original construct as we needed to adapt it to disease prevention with its specific characteristics from doing self-breast exams (Champion 1984) to using the CWA. According to the HBM, the individual’s beliefs regarding the effectiveness and perceived benefits of reducing the disease threat are important factors influencing the use of disease preventives. Thus, we hypothesise:Hypothesis (H5). The perceived benefits (PB) of using the CWA have a positive impact on the likelihood of using the CWA.Perceived barriers are described as the potential negative aspects of a particular health action which impede the implementation of disease preventives. A cost-benefit analysis occurs wherein an individual weighs the action’s expected benefits with perceived barriers. Thus, it can be stated that “[…] combined levels of susceptibility and severity provide the energy or force to act and the perception of benefits (minus barriers) provide a preferred path of action” . We diffHypothesis (H6). The perceived lack of technical knowledge and understanding (PTB) of the CWA has a negative impact on the likelihood of using the CWA.Hypothesis (H7). The perceived privacy concerns (PC) related to the CWA have a negative impact on the likelihood of using the CWA.An internal or external motivational drive is necessary to produce health-promoting behaviours. The intensity of motivation needed to trigger the decision-making process to use the CWA is another important factor in the HBM next to perceived susceptibility, severity, benefits, and barriers . As cuesHypothesis (H8). The intrinsic motivation (IM) to avoid COVID-19 infections for oneself or others has a positive impact on the likelihood of using the CWA.Hypothesis (H9). The extrinsic motivation (EM) to avoid COVID-19 infections for oneself or others has a positive impact on the likelihood of using the CWA.Based on the adapted HBM and our research hypotheses, we develop the research model and describe the data collection and processing in the section.USE . We also include several control variables in order to address the issue of endogeneity due to omitted variables. Thus, we include age, gender, education, income, smartphone experience and the health condition of participants (whether they themselves or family/close relatives belong to a COVID-19 risk group (Equation (1)).Based on our research hypotheses derived from the HBM, we develop the following logistic regression model to assess the likelihood of an individual using the CWA based on the dependent observable variable CWAWe considered the ethical issues of the user study before collecting the data. The ethics board of the authors’ university provides an extensive checklist, and our study was qualified as exempt from an ethics review and therefore approved. We conducted the online study with a certified panel provider in Germany from the end of December 2020 to January 2021 (the panel provider is certified following the ISO 20252 norm). The survey was programmed with the survey software LimeSurvey (version 2.72.6)  and hostFirst, we descriptively discuss our sample and the factors of the HBM. Following this analysis, we discuss the results of the binomial logistic regression. All analyses were conducted in R (version 4.0.3).Our sample consists of 1752 participants. Following EUROSTAT 2018, participants are representative of the German population with respect to age and gender. The same diversity can be observed for income and education . Some 89Since we deliberately divided the sample into two approximately equal groups (CWA users and non-users), we need to check for statistically significant differences in the demographics between these groups. This is required in order to rule out the confounding influences of these variables on our results. We conducted a Shapiro–Wilk test for normality for all variables, and the variables are all not normally distributed. Thus, we conducted Wilcoxon rank-sum tests to analyse whether there are significant differences between CWA users and non-users. Age and gender show no differences since we deliberately sampled our participants with equal distributions with respect to age and gender. There are statistically significant differences between users and non-users of the CWA for the remaining demographics. The income is statistically significantly higher for the users compared to the non-users. However, the median is the same, which is why we argue that the absolute difference is not having a substantial confounding effect on our later analysis. The same argumentation holds for education, with a median of 4 for users and 3.5 for non-users and smartphone experience in years, with a mean 8.77 for users and 8.35 for non-users. We also calculate descriptive statistics for the used constructs. The means and standard deviations are shown in Susceptibility and the medical severity dimensions (on oneself and others), and thereby, the overall judgement on the potential threat of SARS-CoV-2, are significantly higher for users of the CWA. However, it must be noted that the perceived likelihood of getting SARS-CoV-2 (susceptibility) is not perceived as pertinent to the users of the CWA as well. In contrast, the severity related to medical consequences on oneself and on others is assessed as more critical by users, whereas non-users are indifferent. A clearer difference between the groups can be seen in the benefits of the CWA (users tend to agree that it has benefits and non-users rather disagree), the privacy concerns and the intrinsic motivation. Both groups tend to disagree that there are technical barriers and that they are extrinsically motivated . In order to assess the reliability of the constructs, we calculated polychoric correlations between the items of the respective constructs and used this matrix to conduct a factor analysis. Polychloric correlations are usually used for categorical variables . The loaWe conduct the logistic regression analysis in the following way. First, based on our finding that the constructs are valid and reliable, we calculate the mean sum scores of the constructs in order to have a single construct which is usable in the regression analyses. Second, we calculate the base model only, including the constructs of the HBM. Third, we calculated the full model with demographics with a sample size of 1571 as we needed to exclude participants who preferred not to provide their gender or income. The results of the regression analyses are shown in reduced = 1096.611 versus BICfull = 1176.289/AICreduced = 1032.298 versus AICfull = 1036.943).In order to evaluate and discuss the results of the logistic regression model, we first derive a reduced model (see Equation (2)) with all statistically significant variables and estimate whether the full model explains the dependent variable better than the reduced one based on the likelihood ratio test. We have to reject this hypothesis which indicates that the reduced model, and consequently all variables shown as significant, explain the use of the CWA with the same or better accuracy. In line with these results, the Bayesian information criterion (BIC), as well as the Akaike information criterion (AIC), are lower for the reduced model compared to the full model . Furthermore, we assessed the relative importance based on the t-values of the variables, which also confirms that all variables in the reduced model are significant . Finally, we evaluate multicollinearity, which occurs when independent variables are correlated with each other. We plot the pairwise correlations and find that intrinsic motivation and perceived benefits might pose a problem regarding collinearity  and privacy concerns (3.5 percentage point decrease in the probability of using the CWA).Interestingly, the hypotheses for none of the other threat dimensions of the HBM  can be confirmed. We even have to reject H3 (positive effect of perceived severity on others) as the marginal effect indicates that a one-unit increase in this variable decreases the probability of using the CWA by 2 percentage points if participants feel that infection with the virus harms others. This is interesting as the concept of warning others in the case of an infection is the key function of a contact tracing app.Our results indicate that the use of the German CWA app is mainly influenced by five variables. The likelihood of using the CWA is positively influenced by participants’ intrinsic motivation and extrinsic motivation. Interestingly, several variables representing threat dimensions of SARS-CoV-2 are not significant  or have relatively small effect sizes (perceived susceptibility). Perceived severity on others has a significant  negative effect. The latter finding is surprising since the mean of perceived severity on others is significantly larger for CWA-Users than for Non-CWA-Users cf. . FurtherThe three other variables with the largest effects are perceived technical barriers, privacy concerns and income. This indicates that there is a relatively high intrinsic motivation with a large effect on app use on the one hand , but this factor is attenuated by technical barriers and a lack of understanding  and privacy concerns. In addition, our results indicate that socioeconomic factors might influence app use as a lower income tends to decrease the likelihood of using the app in our sample, whereas a higher level of education increases the likelihood. These findings partially correspond to prior work showing that the older population and those in lower-income households might also face technical barriers to using the app, e.g., by having limited smartphone access or an old smartphone . This fiThese results are important for policymakers as we heard many discussions in Germany which were based on the idea that there could be a kind of trade-off between a “proper” contact tracing app and other measures such as curfews, lockdowns or social distancing. It was often argued that a contact tracing app with less focus on privacy and more functionalities could enable more freedom . HoweverAll these findings indicate that people are mainly driven by an inner motivational drive and responsibility to fight the disease, which could only be harmed by high barriers in the form of a more intrusive app collecting unnecessary amounts of data or technical barriers such as compatibility issues.This study has certain limitations. First, although we work with an observed dependent variable, the independent variables are self-reported, which could cause biases . Second, due to the contextual change of considering an infectious disease in the HBM, we had to adapt existing items  and develop new items . These are not validated, and thus do not have the same maturity as the original items in the well-established HBM and may be less reliable. However, in order to be able to report recent results about a COVID-19-related topic, we decided against validating the new items first. Third, the analysis did not include interaction effects between variables. However, we plan to conduct a second wave of the survey in order to conduct a longitudinal analysis of the changes in user behaviour of the CWA along with changing infection numbers. In the consequent analysis, interaction effects between HBM factors and demographics will be investigated in the context of a larger study. This is necessary as our study indicates a certain socio-demographic influence on the use of contact tracing apps. An analysis of interaction effects between the factors of the HBM and variables reflecting the social status of individuals (such as education or income) is a highly interesting avenue for future work as it addresses the question: to what extent can policymakers expect certain social groups to participate in pandemic-fighting tools? Such findings from IS research could even inform research on herd immunity in the context of vaccinations, which requires the majority of a society to participate in order to come back to normality. Lastly, every country had very specific types of contact tracing apps with accompanying political and social discussions. These specific features and discussions shape the perceptions of individuals in the respective countries, causing a limited generalisability of our results for contact tracing apps in other countries.We conducted a representative user study with 1752 participants in Germany on the use of the German contact tracing app (Corona-Warn-App). We sampled participants into two groups . We base our research on the health belief model, which explains why individuals engage in disease prevention behaviours. We adapted the model which was originally applied in the medical context  to infectious disease and the prevention behaviour of using a contact tracing app.We find that the probability of using the CWA is mainly positively influenced by the intrinsic and extrinsic motivation of individuals and negatively influenced by perceived technical barriers, privacy concerns, and a lower income. We contribute to the current stream of literature on the adoption of contact tracing apps, disease prevention tools in general, and the use of the HBM in the context of technologies by introducing an adapted HBM suited to investigate infectious diseases and new information systems to cope with the associated challenges of those diseases. Furthermore, we argue that our results from the IS domain can contribute to a better understanding of users and, in particular, suggest that the perceived technical barrier should be reduced, e.g., by providing further elucidations. It has to be determined in future work whether the results can be transferred to other behaviours or use of measures in the pandemic  and which factors would foster an environment for generating understanding of and participation in the measures.


A frequency spectrum segmentation methodology is proposed to extract the frequency response of circuits and systems with high resolution and low distortion over a wide frequency range. A high resolution is achieved by implementing a modified Dirichlet function (MDF) configured for multi-tone excitation signals. Low distortion is attained by limiting or avoiding spectral leakage and interference into the frequency spectrum of interest. The use of a window function allowed for further reduction in distortion by suppressing system-induced oscillations that can cause severe interference while acquiring signals. This proposed segmentation methodology with the MDF generates an interleaved frequency spectrum segment that can be used to measure the frequency response of the system and can be represented in a Bode and Nyquist plot. The ability to simulate and measure the frequency response of the circuit and system without expensive network analyzers provides good stability coverage for reliable fault detection and failure avoidance. The proposed methodology is validated with both simulation and hardware. Recent modern automobiles are equipped with electronic systems with improved capabilities and safety features, such as an advanced driver-assistance system (ADAS) and a collision avoidance system for the protection of pedestrians and people inside the car. These systems use multiple sensors, such as cameras, proximity sensors, LIDAR, RADAR, microphones, transceivers, etc. Though sensed information is processed with a reliable microprocessor, the performance of these electronic devices depends on the analog circuits with which these sensor modules are built, such as amplifiers, data converters, power management circuits, and other signal processing and conditioning circuits. These analog circuits have a definitive frequency response whose stability is typically guaranteed by their phase margin and gain margin . LimitedThe frequency response of a circuit and system is acquired by: (a) analyzing the transfer function (TF) using a linear model , (b) simSome circuits or electrical machines cannot be represented as a TF due to their complexity. Multiple methodologies have been proposed in the literature ,5,6 thatThe frequency response is characterized under LTI systems ,23,24 anThe most common method for extracting the frequency response is to apply a single-tone signal in the time domain to the input of the system under test and measure the signal transmitted to the output of the system. The amplitude ratio of the output-to-input signal provides the system gain. The phase difference between the input and output signals provides the system phase. This method tests the system one tone at a time, and frequency sweeping is required to get a frequency response from time-domain signals. Additionally, the frequency resolution depends on the number of tones swept. The proposed measurement method tests a specific number of tones at a time to decrease the measurement time. The resolution of the measurement is improved using the proposed method, where the frequency spectrum is divided into multiple segments with a high number of harmonic elements.References ,26,27 deA frequency spectrum segmentation methodology for extracting the frequency responses of circuits and systems with a high resolution and low distortion over a wide frequency range is proposed. The proposed methodology can be both simulated and measured on hardware. A high resolution is achieved by implementing a modified Dirichlet function (MDF) configured for multi-tone excitation signals. Low distortion is attained by limiting or avoiding spectral leakage and interference into the frequency spectrum of interest. The windowing technique is popularly used in the literature to reduce interference. The proposed method uses a new windowing technique, called the double-frequency Hann window, for further reduction in the signal distortion and suppression of the system-induced oscillations that can cause severe interference while acquiring signals. The proposed window function pushes the undesired harmonic components generated by the window modulation to designated frequency locations, which reduces the spectral leakage introduced by the windowing technique. The proposed segmentation methodology together with the MDF generates an interleaved frequency spectrum segment. Different spectrum segments can be obtained by configuring the MDF, and these segments can be set up in a logarithmic frequency scale to measure the spectral content in frequency decades. Overlapping the spectral content of the different segments, the entire circuit or system frequency response can be reconstructed with a relatively high resolution and low distortion, and it can be represented in a Bode and Nyquist plot after post-processing. The ability to simulate and measure the frequency response of the circuit and system without expensive network analyzers helps to obtain good performance coverage for reliable fault detection and failure avoidance.The sinc is a mathematical function widely used in DSP applications to explain the behavior of devices in the discrete-time domain. A monotonically decreasing function Time-domain signals are transformed into the frequency domain using the Fourier transform. The frequency spectrum of a sinc function is given as:Equation  in the tsinc(xt)→The signal described by Equation  helps inThe discrete-time sinc function is generated by reducing the sinc function defined in The closed-form signal definition from the convolution in Equation  in termsThe Fourier transform of the sampled sinc function of Observe that the frequency spectrum in Equation  is replialiasing ,32.The magnitude of the frequency spectrum of the practical sinc function is shown in Sharp edges are required in the frequency spectrum for data transmission applications. Multiple methods are proposed in the literature to achieve sharp edges for a sinc signal in discrete time. For instance, ,34 propoN is odd, and the second component is configured when N is even in Equation  and is given by: N is odd, where the frequency spectrum of the MDF contains the frequency components N is even, where the frequency spectrum contains the harmonic components The proposed MDF Equation . The firReferring to Equation  which isSimilar to the DF spectra, the combination of the four MDF spectra in The output signal of an LTI system As described earlier in The measurement of the frequency response of the system with one MDF requires high computational resources and becomes challenging. When the proposed technique is implemented in simulation, a high number of frequency components and a long FFT can cause the simulation to slow down and sometimes break. Similar challenges are observed during hardware implementation. Moreover, one MDF frequency measurement has a lower resolution, and it is discussed in the next section. A spectrum segmentation approach is proposed to allow the implementation of the proposed MDF technique in hardware and software.The spectrum segmentation consists of adjusting an MDF with the spectral content required to measure a frequency decade to the resolution required. The measurement of the different segments will configure the complete frequency response. The spectrum segmentation allows the achievement of a uniform resolution over all the frequency spectrum measurements. The MDF used to obtain different spectrum segments can be configured to provide a constant resolution per decade. A Bode plot is typically used for the frequency response with the frequency represented in a logarithmic scale using octave and decade segments. The proposed technique describes the segmentation of decades.The discrete-time Fourier transform (DTFT) is the Fourier transform of a discrete-time signal whose output is continuous in frequency and periodic. However, the DTFT cannot be implemented in hardware directly. Alternatively, the discrete Fourier transform (DFT), which is a sampled version of the DTFT in the frequency domain, can be used for implementation. The magnitude and phase spectra obtained from the DFT are reconstructed using discrete-frequency components known as bins. The DFT is computed using the FFT, which is an efficient computational algorithm to obtain the DFT.Frequency leakage occurs when any frequency component in the signal cannot be represented by a single bin and uses a combination between neighboring bins. Frequency leakage is one of the critical distortions in the frequency spectra obtained through the DFT. To reduce frequency leakage, the MDF signals should be configured in such way that the spectral content of any MDF components must find a bin contained in the FFT. This is limited to simulation, as an ideal sinusoidal signal with an accurate frequency cannot be generated with a waveform generator instrument. Hence, a long separation between the non-zero components in the spectral content is used to allow moderate leakage in the frequency components without causing interference to the other non-zero frequency components.The proposed spectrum segmentation depends on the resolution of the frequency response measurement. If the spectral content of the MDF has three decades of width, a resolution as low as 10 points/decade is achieved for the first decade. The second and third decades will have a resolution of 90 points/decade and 900 points/decade, respectively. Thus, significant signal information is lost for the first decade, and a precise frequency measurement can be achieved in the third decade of the spectrum segment.Consider a frequency measurement in the range from 100 Hz to 1 kHz with a resolution of 900 points/decade. The MDF bandwidth used to drive the LTI system requires 1000 frequency components, of which 900 components correspond to the desired 100–1000 Hz, 90 to 10–100 Hz, and 10 to 1–10 Hz. Therefore, the MDF should include three decades of spectral content with 1000 tones for 1 Hz bin frequency. In quations –14) is  is Ω1=2πT is the sampling time, and the sampling frequency is The above signals can be both simulated and generated using a waveform generator instrument. These continuous-time MDF signals are further required to be translated into discrete-time and are given byA plot of the discrete-time The windowing technique is a popular DSP method used to reduce spectral leakage. Multiple window functions are proposed in the literature. However, it is worth pointing out that the selection of a specific window function depends on the FFT application, and the criterion of window selection is typically a trial-and-error process. In general, the windowing technique is used when there are low-frequency components compared to the bins in the spectral content that cause leakage over the frequency spectrum.Since the MDF signal is configured to provide low leakage, the main source of the leakage is the system under test and, especially, non first-order systems. For second- or high-order systems, the low-frequency poles introduce low-frequency components into the system output signal. When the frequency of these components is lower than the bin frequency of the FFT, they leak over the frequency spectrum, and the FFT cannot represent them. This catastrophic leakage can be prevented using either a high-pass filter or the windowing technique. As it does not require additional hardware, the windowing technique is commonly used.F represents the width of the window, and The windowing technique is applied by multiplying a signal in the time domain with a specific window function. This operation is known as modulation. Single-tone window functions are ideal for this work as a multi-tone window can distort the frequency components of interest. The Hann window is a popular single-tone window, and it is defined as:The effect of the Hann and double-frequency Hann windows over the signal processing are explained by applying them to a single-tone function and are given by:When a signal is modulated by a window function, the frequency spectrum of the resulting signal, which contains the original signal, and two replicas of this component neighboring the original frequency are observed. Similar replicas are observed when these two window functions are used with the proposed MDF. The double-frequency Hann window shown in The proposed frequency response technique using MDF is verified by implementing a second-order Chebyshev low-pass filter as the device under test (DUT). The proposed MDF with the spectrum segmentation process and the windowing technique are used as a voltage excitation for the DUT. The six MDFs are configured to measure different spectrum segments. The spectral content of each MDF is configured such that the 900 points/decade resolution is achieved. The six measurements are processed using MATLAB or Octave software to obtain six spectrum segments and are used to integrate the system frequency response in the 10 Hz to 10 MHz range. The second, third, and fourth components of the proposed MDF, corresponding to the 1–10 kHz, 10–100 kHz, and 100–1000 kHz segments, are shown in The proposed double-frequency Hann window is applied to the input and output voltage signals during post-processing, and the FFT of these windowed signals is computed. Assuming that the FFT of the input segment signal is The frequency response measurement of circuits and systems is proposed using spectrum segmentation, a double Hann window, and a modified Dirichlet function. The proposed method allows the use of low-cost waveform generators, such as AWG instruments, to obtain high-resolution frequency responses with no increased test time. This method is validated with hardware measurements and simulation results. The spectrum segmentation technique reduced the memory size and time requirements to compute the FFT. The proposed technique can be embedded in critical systems, such as electric vehicles, power distribution stations, electronic device design centers, and others in which the frequency response characterization for analysis or fault detection is required for improved reliability.


The changing landscape of nursing education to competency-based education has strengthened the importance of simulation learning in the process of developing the required graduate competencies.This study aimed to develop a model that guides the implementation of simulation-based education (SBE) in under-resourced nursing education institutions in Lesotho.Four Nursing Education Institutions in Lesotho.An explanatory sequential mixed methods design was adopted. Sampling methods included stratified systematic random, purposive and systematic sampling. The total sample was 390 comprising students, nurse educators and principals. Data were collected through questionnaires, focus group discussions and in-depth unstructured individual interviews. Statistical analysis was employed for quantitative data while a grounded theory approach guided the qualitative data analysis and model development.Implementation of simulation emerged as a multilevel, multi-actor and multistage process of adopting, introducing and implementing SBE. This education takes place in a simulated environment that serves as a connecting bridge between the learning of theory in the classroom and clinical learning in real-life settings. The model generated from this study has simulation implementation as the main concept that is supported by four major concepts: (1) simulation initiation at the strategic level, (2) simulation implementation at the tactical level, (3) simulation implementation at the operational level and (4) simulation outcomes.Successful implementation of simulation requires buy-in from key stakeholders. Simulation-based education policy, competent facilitators and a well-resourced clinical skills laboratory may facilitate the development of the required competencies.The study provides guidance on how SBE can be implemented in resource-limited settings. Simulation-based education (SBE) is defined as an educational process where real clinical practice is replicated in a safe environment with the aim of enhancing students’ confidence and competence  simulation model, the practice-based simulation model, the collaborative clinical simulation model and Jeffries’ simulation model . Implementation of simulation is facing various challenges, such as limited human resources to staff the simulation laboratories, limited simulation equipment and unsuitable simulation implementation  what is the state of readiness of NEIs in Lesotho to implement SBE? (2) What are student nurses’ perspectives on SBE in Lesotho? The qualitative aspect was guided by the following research questions: (1) how is simulation based-education currently being implemented by NEIs in Lesotho? (2) How can simulation based-education be implemented effectively in resource-constrained NEIs? The model emerged from the integrated findings of the quantitative and qualitative data. According to Corbin and Straus , a modelFour NEIs that are under the Christian Health Association of Lesotho (CHAL) were research sites. These four NEIs are privately owned by various churches; they have similar programmes, and their teaching and learning is guided by a competency-based curriculum. The public NEIs were not included because their teaching and learning are still guided by a content-based curriculum; they write separate examinations, as opposed to CHAL institutions, and they have more government financial support than CHAL institutions.The study population comprised the nurse educators (NEs), the principal nurse educators (PNEs) and students from the four CHAL NEIs in Lesotho. A total of 18 NEs were from NEI 1, 15 were from NEI 2, 15 were from NEI 3 and the other 15 were from NEI 4. For the quantitative aspect, the total population sampling method was employed because the population of the NEIs was manageable. For nursing students, the population comprised second-year, third-year and midwifery students from the four selected NEIs. The first-year students were excluded because the institutions did not have first-year intake because of coronavirus disease 2019 (COVID-19) restrictions. The researchers used stratified systematic random sampling, with each level of student forming a stratum. Sample proportions were identified per institution, and from the proportions, the researchers identified the sample size using the Qualtrics sample size calculator  with the confidence level of 95% and margin of error of 5%. The recommended sample was selected from the student population, and table of random numbers were used to select every third student. In the qualitative phase, the population was made up of 28 nurse educators, 8 second year students, 8 third year students and 8 midwifery students, who were purposely selected because they have been exposed to SBE. Theoretical sampling was used when emerging categories needed to be refined until data saturation was reached as no new information emerged  were used to collect data from PNEs, NEs and students. Four FGDs were conducted for both NEs and students, culminating in a total of eight FGDs, and each FGD comprised six participants. Each FGD lasted from 45 min to 90 min. For the PNEs, in-depth face-to-face unstructured individual interviews and field notes were used to collect data, and the interviews were recorded with a voice recorder, guided by the central questions. The following central questions guided the FGDs with the nursing students:Please tell me about your experiences with simulated learning?How could your experience be improved?What do you understand is the purpose of simulated learning in nursing education?For the PNEs and the NEs, the following central questions guided the interviews:How do you currently conduct simulated learning in your institution?Tell me about your experiences as the PNE ‘or’ an educator in relation to simulated learning?How can simulated learning be improved?Please share with me your understanding of simulated learning in nursing education?Facilitative communication skills assisted the researchers to ensure that the research questions were adequately addressed  version 27  was used to code data for analysis. Descriptive statistics were used to analyse data. Qualitative data collection and analysis were conducted concurrently as the researchers were utilising grounded theory  and the Ministry of Health Lesotho . Heads of the NEIs provided permission for the study to be conducted. Full information relating to the study was provided to the participants. They were informed that participation in the study is voluntary and they can withdraw from the study at any time  have been published in other academic journals  serve as a framework for under-resourced NEIs to fully implement SBE, (2) provide guidance to the managers and administrators on how to support SBE implementation, (3) provide guidance to the nurse educators in under-resourced nursing education on how to facilitate simulation-based learning using the available resources and (4) guide NEIs in evaluating the implementation of SBE in their institutions.The simulation-based education model for under-resourced nursing education institutions in Lesotho has been purely created based on assumptions of the study results. The assumptions are: (1) SBE implementation is cyclic, (2) administrative and managerial support are crucial in SBE implementation, (3) simulation-based teaching requires detailed planning and preparation and (4) student support during simulation is essential.During the model development, SBE implementation was identified as the main concept, with four major concepts attached to it. There are four major concepts directly linked to the main concept: (1) SBE initiation at strategic level, (2) SBE implementation at tactical level, (3) SBE implementation at operational level and (4) SBE outcomes. Attached to the major concepts are subconcepts referred to as second-level concepts. The subconcepts are presented later under the definition of each major concept.The core concept of SBE implementation in this model is defined as a multilevel, multi-actor and multistage process of adopting, introducing and implementing SBE. This education is implemented in a simulated learning environment that serves as a connecting bridge between learning theory in the classroom and clinical learning in real-life settings. This is congruent with what Unver et al.  and KrisSimulation-based education initiation at the strategic level: The concept ‘SBE initiation at the strategic level’ refers to the stage of embarking on the adoption of SBE in line with the country’s agenda to transform nursing education. This takes place at the top or administrative level in the institution. It is at this stage where a shared vision and commitment is developed as well, as policy frameworks and resources are made available to support the change. This main concept of SBE initiation at strategic level includes five subconcepts: (1) shared SBE vision, (2) SBE policy, (3) funding for SBE policy implementation, (4) external change facilitator and (5) SBE champions.Shared vision refers to the presence of clear and well-communicated messages between management and the faculty on the future of SBE where all parties involved in the SBE initiation have a clear understanding of the importance of SBE. To create shared vision, the management of the institutions must allow simulation facilitators and faculty to take part while developing or revising the institutional strategic plans. On the other hand, simulation policy refers to a legal document that directs the action of leaders of institutions, faculty, students and various stakeholders involved in SBE. In this study, simulation policy must have the following aspects: simulation lab operating hours, attendance and booking, acceptable behaviour, responsibilities, physical safety, stock and equipment control, maintenance and care of simulators, which is in line with what Ross  recommenThe external change facilitator is an external expert who acts as a change agent within the institution with the responsibility to facilitate and monitor the implementation of SBE by the nursing education institution. The external change agent has to ensure that adequate capacity is built within the institution by the time the change facilitators leave the institution, as stated in Uys and Gwele . In thisFunding for SBE policy implementation is considered as ways in which the NEIs budget, mobilise and acquire funds that are necessary to implement SBE policies in the institutions. Effective policy implementation is dependent upon the availability of funding to support its implementation  teaching and learning infrastructure development, (2) curriculum and materials development and (3) human resources mobilisation and capacity building.Teaching and learning infrastructure development refers to the erection of the simulation laboratory, creating an immersive simulation environment, commissioning the newly built infrastructure and procuring adequate simulation equipment (manikins) and supplies. According to Benckendorff et al. , instituOne of the subconcepts linked to the activities at the tactical level is SBE curriculum and materials development, where the faculty in collaboration with the simulation facilitators develop SBE curriculum content to be used, explore ways in which SBE can be integrated into the curriculum and also develop materials and tools to be used. According to Riabtseva, Reynolds and Gisin , tools aIn the context of this model, human resources refer to qualified nurse educators who conduct teaching and learning in simulation and are also referred to as clinical skills laboratory coordinators ‘or’ simulation facilitators. In this study, it is recommended that facilitator–student ratio should be 1:10 as a resource-limited setting, but currently the ratio is 1:30 in almost all the NEIs. The ratio recommended by Krishnan et al  of 1:3 iSimulation-based education at the operational level: The major concept of SBE at the operational level refers to a process where plans and vision are translated into reality, and it involves two stakeholders, which are teachers and students and each has a role to play. This is where simulation teaching actually occurs. This major concept is supported by three subconcepts which are: (1) multiphase teaching and learning process, (2) the nature of the teacher and (3) the nature of the learner.The subconcept ‘multiphase teaching and learning’ refers to six cyclical phases that students need to pass through during simulation learning. Based on the assumption that learning is a transformative process, the multiphase process is important as learners grow from one stage to the next as described by Kolb . In thisIn this model, concrete experience can be demonstrated when students are involved in carrying out various skills in the simulation laboratory. Students must be offered a chance to be actively involved in carrying out nursing procedures on the manikins and the standardised patients in the simulation laboratory. Reflective observation can be demonstrated when the simulation facilitators provide a chance to the students to reflect on their simulation experience, their observations and feelings about the experience. To ensure abstract conceptualisation, after the debriefing process, the students must critically reflect on their simulation learning experiences and develop their own ideas. This is where they will explain that after the simulation experience, they are able to identify what can cause complications when performing nursing skills, such as intramuscular injection.Active experience in this model can be ensured by creating various scenarios, and students must be given the opportunity to manage their scenarios using their own decisions. Formative and summative clinical assessment can be conducted via Objective Structured Clinical Examinations (OSCEs). Various stations can be set and manned by simulation facilitators, nurse educators and preceptors. To evaluate SBE, after a simulation round, students must be taken into the reflection room in order to evaluate their simulation experience. Students must be given chance to describe the aspects they liked during that round of simulation, the challenges they met and how future simulations can be improved.In this model, during the multiphase teaching and learning, students in the simulation laboratory will move from the level where they are less competent to conduct a skill to a level where they are competent to conduct a nursing skill. This is termed zone of proximal development (ZPD) as students work in collaboration with a skilled facilitator in constructing knowledge and skill (Fani & Ghaemi The subconcept ‘the nature of the teacher’ refers to a qualified nurse educator who facilitates SBE and has various qualities such as being a facilitator, being innovative, using evidence-based teaching, understanding student diversity, providing instructional support and having mutual respect. This subconcept is important because the teacher needs to understand the characteristics of learners, including class size, diversity, disciplinary background, level and prior training, as described by Benckendorff et al. ; the teaThe nature of the learner as the subconcept that constitutes the operational level refers to nursing or midwifery students with diverse characteristics. The learner is viewed as an adult, active learner, who is self-directed with high expectations and is practical and outcome focused. Billings and Halstead  explain Simulation-based education outcomes: The major concept ‘outcome’ refers to desired perceived ‘or’ actual benefits of implementing SBE to the students, NEIs and the healthcare system. Simulation was adopted in Lesotho because almost all the nursing training institutions had inadequately equipped demonstration rooms. This was exacerbated by poor supervision of nursing students during clinical placement (NEPI ent NEPI . The benent NEPI  contend The benefit of SBE to the NEIs as a subconcept refers to the advantage gained by NEIs with limited resources who utilise SBE. The data of the study show that the NEIs can benefit from the implementation of SBE, as there are limited clinical sites and simulation offers an opportunity for clinical placement. According to Radford , NEIs haThe benefit of SBE to the healthcare system as a subconcept of outcomes is defined as the gain that the healthcare system receives when SBE is implemented by the NEIs. The study’s findings indicate that patient safety can be enhanced via simulation because students practise in a safe environment before transiting to real patients. Liaw et al.  explain According to Walker and Avant , conceptExisting capacity in the NEIs must be utilised to develop simulation polices, and the institutions must forge strategic partnerships with universities or colleges who are leaders in SBE implementation, as this will assist the NEIs to acquire affordable technical assistance and SBE trainings. As the NEIs are resource constrained, they must source funds to support SBE implementation from various potential developmental partners. The low-resource settings must take account that simulation does not only involve use of the high-fidelity manikins, but low-fidelity manikins can be utilised and scenarios can be pitched at a high cognitive level. Human resources are a challenge in SBE implementation, but faculty members with less workload can be redeployed into the simulation laboratory. As a result of challenges of time scheduling for simulation, students need to be given open access to the simulation laboratory, and this is where students can practise on their own time.This study utilised a mixed methods design, which assisted in reduction of the threat to the validity of the findings because the strengths of one method compensated for the weaknesses of the other method during data collection (Hesse-Bier & Johnson The following are considered to be the limitations of this study: the study cannot be generalised to all settings as it is restricted to low-resource settings. First-year students were not part of the study because the institutions did not have first-year intake because of COVID-19 restrictions during data collection. As a result of the fact that quantitative data were collected using online surveys, there is a likelihood that some participants may have answered the survey more than once.The WHO  advocate