The Timepix3 is a hybrid pixellated radiation detector consisting of a 256 px × 256 px radiation-sensitive matrix. Research has shown that it is susceptible to energy spectrum distortion due to temperature variations. This can lead to a relative measurement error of up to 35% in the tested temperature range of 10 °C to 70 °C. To overcome this issue, this study proposes a complex compensation method to reduce the error to less than 1%. The compensation method was tested with different radiation sources, focusing on energy peaks up to 100 keV. The results of the study showed that a general model for temperature distortion compensation could be established, where the error in the X-ray fluorescence spectrum of Lead ( Timepix3 (TPX3) is a hybrid single photon counting pixel detector with semiconductor sensors. Due to its hybrid structure, which allows various combinations of thickness and material types of the semiconducting sensor with the same electronics of the readout chip, in dependence on the desired application, it is a handy, popular, and widespread detector in several different scientific and industrial fields . TypicalFurthermore, the novel X-ray and high-energy space experiments focusing on X-rays require suitable focal detectors to record the images provided by the X-ray optics of these devices. Albeit there are several possibilities, the pixel detectors play an important and increasing role because they meet the requirements to be used on miniature satellites such as CubeSats: they are small, lightweight, energy-efficient, and less expensive than other options. However, such an application requires the detector’s working temperature range to be broad, −20 °C to 60 °C.Due to the widespread use of detectors with the TPX3 chip, it can be challenging to maintain all the standard environmental conditions for measurements. Namely, the operating temperature of the detector. The TPX3 with Silicon detection layer has been shown to be highly dependent on the operating temperature ,28.In situations where it is not possible or economically realistic to stabilise the operating temperature of the detector, it is necessary to compensate for its influence. This article deals with the possibilities and methodology of how to approach this compensation and the evaluation of the effectiveness of such compensation.This article is divided into several parts. In As part of this research, several detectors were used and subjected to a series of experiments with radiation sources and different operating temperatures. The following sections provide a description of used instruments and methods.The Timepix3 is a hybrid pixel radiation detector as the successor to the widely used Timepix detector and the next addition to the Timepix/Medipix family of chips that were developed in the CERN laboratories. Compared to the previous model Timepix , TPX3 ha2 capable of measuring a broad range of ionising particles. There are (256 × 256) single pixels and a pitch of 55 It is a square-shaped detector with a size of otal see . Each piThere are various measurement modes that suit different applications, allowing users to obtain information about various parameters of particles.The Event Counting (EC) mode is a simple counting method that records the number of particles that hits a particular pixel. In this mode, the digital output of each pixel is monitored, and the count of rising edges (above the threshold) is recorded. This mode provides basic information about the particle flux and can be used for radiation mapping and dose rate monitoring.The Time-of-Arrival (ToA) mode is a measurement mode that records the exact arrival time of incoming particles to each pixel. In this mode, the detector uses a time-stamping mechanism to record the time the particle crosses the threshold voltage level. ToA is a time indication of how much time has passed between the start of the measurement and the impact of the particle.The Time-over-Threshold (ToT) mode involves the measurement of the duration of a signal generated by a particle exceeding a specified threshold voltage level. This mode implements an indirect energy measurement method based on the Wilkinson-type ADC, where the analogue signal from each pixel is continuously monitored. Whenever the signal exceeds the threshold voltage, a timer is triggered to start counting. The timer stops when the signal, which is being discharged through a constant current, falls below the threshold voltage. After calibration, the recorded time is proportional to the energy deposited by the particle in the pixel.The Timepix3 detector can operate in one of three measurement modes  , which cThe instruments used in this work were equipped with a readout interface called MiniPIX see . It is cTwo types of radiation sources were used to obtain data for radiation energy measurements. Radiation sources of the first type use the effect of X-ray fluorescence (XRF) to generate characteristic radiation. The XRF generates precise energy lines corresponding to the target material. The lines depend on the electron orbitals that are characteristic of atoms of a given element used as the target. The materials of the targets with corresponding emission lines used in this manuscript are listed in the first five rows of Due to the possibility of polychromatic background radiation from scattered radiation in a radiation-shielded box, the overall measured energy spectrum is complex. In addition to the mentioned specific energy lines, the spectrum includes X-rays produced by the X-ray fluorescence of the materials in the shielding box, mainly Lead, as well as photons that have undergone additional scattering either in the sensor or in the X-ray box itself, detected as Compton events. The measured spectrum also includes a continuous spectrum produced by the X-ray tube.Two radionuclides Barium and Europium were used as a second type of radiation source. Their use provides sufficiently dense coverage of the energy spectrum up to 100 keV, which was chosen as the maximum reasonable value taking into account the detection efficiency of the sensors used. It should be noted that the detection efficiency of a 500 ces used .Each of these methods requires a different measurement arrangement. Therefore, two setups were prepared. To measure the energies generated by XRF, it was necessary to use an X-ray tube and a different geometrical arrangement compared to the application with radionuclides. Both setups use a holder for samples, and the whole arrangement was placed inside a radiation-shielded box. The schematic layouts are shown in All detectors were placed and thermally coupled to a Peltier plate for their thermal stabilisation at each measurement phase. The energy measurements (ToT & ToA measurement in data-driven mode) of the individual samples were performed in temperature steps of 10 °C over a temperature range of 10 °C to 70 °C. Sufficient time for thermal stabilisation of the measurement setup was kept before each measurement started.The evaluation methodology is based on the procedure presented in , where aEquation ).(1)Ge(The function E), the intensity of the error function  of the Goodness of fit and its quality are assessed using Pearson’s chi-square (Equation ), respecEquation ). It is i defines the evaluation interval, which in this case corresponds to the range of the Gaussian fit, i-th energy bin, i.The variable According to the procedure described, the position of the energy peak  and its width (relative energy resolution), are further compared and evaluated. The reference values for absolute measurement accuracy and relative error of measurement are obtained from measurements at 20 °C, the temperature at which the manufacturer calibrates the detectors.The stability of the measured energy spectrum is crucial and is achieved by maintaining a constant or known temperature. The internal thermometer on the chip should provide sufficient thermal accuracy, and the calibration of this thermometer is necessary for the proper energy shift compensation. This chapter discusses the verification of the linearity of the internal thermometer using an external reference PT1000 thermometer. The several selected temperatures in the range of 10 °C to 70 °C were chosen to be relevant to the operational temperature range of the TPX3 detector.Timepix3 detector has two places on-board to measure temperature. The first and most crucial is in the TPX3 ASIC chip, and the second is an internal thermometer of the processing microcontroller. Both integrated circuits were thermally coupled to the Peltier plate and stabilised on the desired temperature in the range from 10 °C to 70 °C and controlled by an external device until the measured temperature was stable on all three thermometers. It takes about 10 min to stabilise the plate at each point.During the thermal cycling process for spectral characterisation, temperature data was collected from all detectors. The results of the investigation indicated that all thermometers exhibited a linear progression in their readings, with a slight offset compared to the preset temperature. Therefore, the relative error of temperature measurement was noted to be between 1% and 40% depending on the specific device and the measured temperature.2) was found to be nearly 1 for all measured detectors, which indicates that linear regression is a suitable fit for the internal thermometer.A linear regression calculation was performed for the ASIC chip, resulting in the regression between 0.94 and 1.02 for all five measured detectors, as stated in the row slope in In case the estimated value of slope differs from 1 in the linear regression, the deviation from the set and measured temperature will vary if not corrected. Based on the coefficients listed in Based on the measured and processed data from all examined detectors, radiation sources, and temperature stages, the temperature dependence of the Timepix3 detectors  was veriThe results show that the energy spectrum drifts with the temperature changes. With increasing temperature, the energy spectrum nonlinearly shifts towards lower energies, this offset is more significant with increasing incident energy. Already with a 10 °C change of the detector temperature, a relative measurement error of about 4% occurs. A further change in temperature from the reference point at 20 °C, e.g., due to the influence of the environment or the detector self-heating, may cause a displacement of the energy spectrum of up to tens of kiloelectronvolts. This drift causes an error of more than 30%, in the case of heating the detector to 70 °C.As was written in the recently published paper, a simplified linear compensation  can be uT is detector temperature during measurement and m, n, o are function parameters.The examination of the measured data and subsequent processing  suggestsx represents one of the parameters m, n, o in Equation  that coIts application to the measured data yields the real energy of the incident radiation after correction for temperature distortion.The evolution of absolute measurement accuracy and relative measurement error over the complete, tested range after applying the presented correction method is shown in As part of the potential expansion of the presented compensation method for a wide range of applications without the necessity to characterise individual detectors, the possibility of parameter generalisation was also tested. Based on the tested detectors’ data, an average temperature dependence model was established and the necessary general parameters for the compensation were determined see . One detE outside the modelling set at detector temperatures of 30 °C, 40 °C and 50 °C. The resulting measured spectrum after normalisation for comparison is shown in The characteristic X-ray fluorescence of the Lead target  radionuclide with a spectral line at Due to limitations in the vacuum system and safety measures, it was possible to generate characteristic fluorescence radiation with energies up to 30 keV. Considering these restrictions and with respect to the material and thickness of the detection layer, the following XRF targets were selected: Zircon (Zr) at From The extrapolation of the above-mentioned model was tested with the same detector. A temperature compensation model was developed based on the data points that were above the freezing point (depicted as orange in This paper presents a solution to the problem of energy spectrum distortion due to temperature fluctuations in the Timepix3 detector with MiniPIX readout interface and Silicon sensor with a thickness of 500 The first part of the paper also emphasises the issues of the potential usage of an internal thermometer built into the ASIC chip, which would significantly impact the measurement and compensation capabilities.The authors propose a complex compensation method, which has been demonstrated to minimise the measurement error to no more than The possibility of creating a general model for temperature distortion compensation was also discussed. It would require testing a statistically significant number of detectors. Nevertheless, the results of the study indicate that even when applied to a small set of five, detectors, the possibility of generalisation appears to be realistic. After applying the generalised model to a detector not included in the learning set, the relative error of energy measurement in the X-ray fluorescence spectrum of Lead (The compensation method was further tested in temperatures up to The presented results, methods, and models allow the use of the TPX3 detector to be extended to a wide range of applications where cooling or temperature stabilisation of the detector is not possible or economically viable. The compensation method provides a valuable solution to a common problem in space radiation measurement and has the potential to have a significant impact on various applications.In conclusion, the authors have successfully demonstrated a solution to the problem of energy spectrum distortion due to temperature fluctuations in the Timepix3 detector with MiniPIX readout interface and Si sensor with a thickness of 500


Cai•factor (Configuration: absolute information). The analysis of the data demonstrates that the procedure is a reliable and time-efficient method for determination of absolute configuration, which gives both the assignment and a measure of confidence in the outcome, even when the spectra are imperfect. The majority of molecules tested have a high confidence score and all of these have the correct assignment.Vibrational circular dichroism (VCD) spectroscopy can generate the data required for the assignment of absolute configuration, but the spectra are hard to interpret. We have recorded VCD data for thirty pairs of small organic compounds and we use this database to validate a method for the automated analysis of VCD spectra and the assignment of absolute configuration: the The online version contains supplementary material available at 10.1186/s13321-023-00706-y. Absolute configuration is of central importance to the function and efficacy of drugs and biomolecules. Enantiomers of drug molecules usually show different efficacies at their targets. Knowledge of the structure of the active enantiomer can help in the design of improved therapeutic molecules. Similarly, the synthetic chemist may need to determine the absolute stereochemistry of an early intermediate in a synthetic sequence with the aim of making the synthesis of a complex chiral molecule as efficient as possible. X-ray crystallography  is an esAs an alternative, spectroscopic chiroptical methods, such as vibrational circular dichroism (VCD) are of growing importance and can obtain absolute stereochemical information on molecules in solution . VCD hasThe interpretation of VCD spectra is not a straightforward task, particularly if the spectra are noisy. Unlike IR and NMR spectra, there are no rules for directly interpreting VCD spectra. Therefore, a comparison between experiment and calculation must be made in order to gain comprehensible information from the data. Standard DFT computational chemistry programs, such as Jaguar  and GAUSFigure . [. [Shen, et al. developed SimIR/VCD to interpret these data . The pro.  and was . [.  used the. [. , focusse. [.  we have 2 transmission cell with a path length of 100 µm. Both IR and VCD spectra were recorded at a spectral resolution of 4 cm−1 by accumulating about 20 000 scans over six hours.The thirty enantiomeric pairs of compounds Fig.  were sel−1 or less above the ground state were all found five times or more.In order to calculate the spectra, each molecule was constructed in the Maestro molecular modelling program and a Macromodel  conformaThis process was applied to the thirty molecules in Fig. It was not clear how best to analyse the large amount of data that was now available. To investigate the many possibilities, we wrote a program, Cai, which enabled us to process this in many different ways. Cai takes the experimental and computational data as its input, and follows the instructions in a command file to compare the calculation and the experiment. The command file makes it easy to adjust the details of the calculation, including the range of frequencies that should be considered, the temperature used to calculate the Boltzmann average of multiple conformations, and other parameters. The program, which is written in Python, is available on Github: github.com/Jonathan-Goodman/cai-factor. The program’s output is a record of the calculation, the Cai•factor for the confidence in the assignment, and a graphical record of the experimental and calculated data.The accuracy of DFT vibrational frequency calculations is limited by the neglect of the anharmonicity of molecular vibrations. The results contain systematic errors, and scale factors are required to improve the match between calculated and observed VCD spectra . Small c−1. As we describe below, the experimental dataset was used to test and improve these initial values. These values can all be set as parameters in the command file.The calculated VCD spectra are generated as a series of sharp lines for each conformation, whereas experimental spectra show broad peaks. The calculated spectra were therefore transformed to more closely resemble the experimental ones by combining the peaks for the different conformations using Boltzmann weighting from the calculated energies, scaling the calculated wavenumbers, and broadening the lines. Initially, we used a scaling factor of 0.975, and a Lorentzian broadening with a half width at half maximum (HWHM) of 5 cmThe difference between the simulated and the experimental spectra were assessed using a multiplicative scoring method. Because of the possibility of sign changes arising from small changes in conformation, signals with the highest VCD intensity are not necessarily the best indicators of a compound’s absolute configuration. For each point along the experimental wavenumber axis, the calculated (c) and observed (o) values are multiplied, giving a positive outcome when they have the same sign and a negative outcome otherwise. These products are then summed (ΣCO) to give the overall outcome, either positive or negative. This sum is then scaled by the geometric mean of the sums of the squares of the calculated value at each point (ΣCC) and the observed value at each point (ΣOO).The overall result, which is a modification of the SimVCD integral , is giveThe method is illustrated in Fig. −1 energy cut-off and their spectra were combined using Boltzmann weighting and the default temperature of 300 K. The energy cut-off and the temperature are parameters which can be set in the command file but the default settings are usually appropriate. The calculation was run for the experimental spectra of the two enantiomers separately, as well as for the combined spectrum which is created by subtracting one spectrum from the other, a process which helps correct any non-linearity in the baseline for the measurements. In general, a combined spectrum, if available, gives higher confidence than a single enantiomer spectrum. If experimental data for just one enantiomer is available, a blank spectrum, can also be used to correct for the baseline variation.In addition to this graphical output, the Cai program also produces a log file with a more detailed numerical analysis. In this example, two different calculated conformations fell within the default 10 kJ molWavenumber scaling is applied to the DFT-calculated spectra in order to ensure best overlap with the experimental data. Initially, we scanned through a range of scale factors from 0.950 to 1.000, in increments of 0.005, to optimize the absolute value of the similarity factor. Having determined that the best results come towards the middle of this range, it was not necessary to explore more extreme values, and a value of 0.975 was found to be a good choice for B3LYP/6-31G; 0.980 was preferred for B3PW91/cc-pVTZ (details in SI). The Cai program calculates the similarity factor with the chosen default scaling factor (specified in the input file), and also investigates whether the match can be improved by adjusting the value.. For the example in Fig. The two input spectra are also analysed individually, and the results are reported in the log file. In this example, spectrum A has a similarity factor of 34 and spectrum B has a similarity factor of 38; both of these results give reasonable confidence in the assignment and are consistent with the combined analysis.−1 to 1600 cm−1 was appropriate in most cases, even though a wider range was available experimentally. The region below 1000 cm−1 did not add useful information to the analysis in most cases. In many molecules, there is a double-bond band above 1600 cm−1 which absorbs so strongly that the signal saturates. In the case of carbonyls the frequency is not always well calculated. This can cause a problem if the VCD signal is both intense and biphasic. This is most easily addressed by omitting the signals by restricting the range of frequencies considered, although the Cai program command file can be set to include it, if required. If experimental spectra for both enantiomers of the substrate are available, the program combines the two experimental measurements to calculate the assignment. If only one experimental spectrum is available, which is a common situation, the program can still calculate the assignment. If a spectrum of the pure solvent (“blank spectrum”) is available (solvent or racemate for example) this can also be included, as it records the baseline of the spectrometer and the cell. This is most effective when done at the same time as acquiring the experimental data of the sample. An example of the output is shown in Fig. −1 to 1150 cm−1 region where the calculation and the experiment appear to disagree.Initially, we did not know what the optimal choice of parameters would be. Therefore, we ran the program for all our experimental and calculated data and gathered the outputs into a single spreadsheet (available in the ESI) which enabled us to compare the possibilities. The wavenumber range 1000 cmThe program calculates similarity factors for spectra separately and combined, with fixed and optimised scaling factors, and distils the results into a single Cai•factor, which is the most confident of the assignments after concerns about unusual scaling factors and inconsistent data have been taken into account. Analysis of the Cai•factors for all thirty of our test molecules (details in the ESI) showed that the highest average score is reached by geometry optimization at the B3PW91/cc-pVTZ level of theory. Despite the resource-intensive calculation, we verified that the triple-zeta basis set gives the most accurate outcome out of the various methods sampled. For each method, the match scores were calculated both using single point DFT calculations on the molecular mechanics geometries, and by optimising these geometries using DFT, which is a much more computationally demanding process. The Cai•factors are compared in Fig. http://simvcd.net. Thirteen of the spectra lead to confidence on the basis of the SimVCD measure.The outcomes of SimVCD calculations are also given in Fig. Whenever possible, VCD spectra for both enantiomers of each compound were used. For cases where only a single enantiomer was available, the same method can be applied. By taking the VCD spectra of enantiomeric pairs of compounds and analysing each enantiomer separately, the case of assigning the absolute configuration of a compound where only a single enantiomer was available could be simulated.The results are also included in Fig. We tested the process by analysis of three marketed chiral drug molecules, Aprepitant, Efavirenz and Ezetimibe Fig.  These moAprepitant: A Cai•factor of 35 (fairly confident) was calculated in favour of the known stereochemistry. An optimized scale factor of 0.984 gives a Cai•factor of 40.Efavirenz: A Cai•factor of 4 (uncertain) was calculated but this rose to 47 for the known stereochemistry at a scale factor of 0.997. In order to decide whether to accept the unusual scale factor it is instructive to view the fit produced by the Cai algorithm  was calculated for the known stereochemistry. An optimized scale factor of 0.977 gives a Cai•factor of 23. We noted that in the minimum energy conformation (see ESI), the sidechain was extended, whereas the minimum energy conformation reported by Sherer [y Sherer  showed aWe have also applied the Cai•factor analysis retrospectively to 22 recent AstraZeneca molecules where we determined absolute stereochemistry using a visual method described previously . ConformWe present a new process to analyse VCD data: the Cai•factor. The procedure is straightforward to implement and has led to the automated interpretation of thirty sets of enantiomeric spectra. The process is able to give an interpretation and confidence level even for imperfect data, or when the assignment cannot be made visually. Use of the B3PW91 functional and cc-pVTZ basis set is recommended for the most reliable VCD assignments. The method has been applied to three compounds outside the original dataset returning confident assignment of absolute stereochemistry. It has also been successfully applied to more than twenty additional compounds within AstraZeneca. GitHub: github.com/Jonathan-Goodman/cai-factor.Additional file 1: Figure S2. Match of experimental and calculated VCD spectra for Efavirenz at a scale factors of (a) 0.975 and (b) 0.997.


The recent advancements in the Internet of Things have made it converge towards critical infrastructure automation, opening a new paradigm referred to as the Industrial Internet of Things (IIoT). In the IIoT, different connected devices can send huge amounts of data to other devices back and forth for a better decision-making process. In such use cases, the role of supervisory control and data acquisition (SCADA) has been studied by many researchers in recent years for robust supervisory control management. Nevertheless, for better sustainability of these applications, reliable data exchange is crucial in this domain. To ensure the privacy and integrity of the data shared between the connected devices, access control can be used as the front-line security mechanism for these systems. However, the role engineering and assignment propagation in access control is still a tedious process as its manually performed by network administrators. In this study, we explored the potential of supervised machine learning to automate role engineering for fine-grained access control in Industrial Internet of Things (IIoT) settings. We propose a mapping framework to employ a fine-tuned multilayer feedforward artificial neural network (ANN) and extreme learning machine (ELM) for role engineering in the SCADA-enabled IIoT environment to ensure privacy and user access rights to resources. For the application of machine learning, a thorough comparison between these two algorithms is also presented in terms of their effectiveness and performance. Extensive experiments demonstrated the significant performance of the proposed scheme, which is promising for future research to automate the role assignment in the IIoT domain. The Internet of Things (IoT) is rapidly expanding, bringing forth a transformation in every aspect of our everyday lives. In the IoT paradigm, many objects in the environments are interconnected in the form of a network in one way or another . The IoTThe integration of the IoT for business automation is referred to as the Industrial Internet of Things (IIoT). However, when applied to critical infrastructures, the IIoT can expose them to severe network vulnerabilities, posing a disruptive threat to society . SCADA s1.We provide a detailed analysis of the current trends, gaps, and problems in access control approaches. We present a comparative study of all contemporary access control approaches with respect to the IIoT domain. The study offers a core understanding of the requirements of modern loosely coupled critical infrastructures.2.We propose a framework for automatic role assignment problems in fine-grained access control. In existing studies, this process is manually adopted, which is a laborious and cumbersome process. By this, the fine-grained access control can achieve maximum flexibility in a time-efficient manner.3.We leveraged supervised machine learning approaches to map the SCADA-based IIoT system for this problem, which is novel and open to further research. We employed machine learning to automatically execute the role assignment and propagation in such an environment that is changing and generating complex data.4.We provide a thorough analysis of machine learning mapping to this domain with different hyperparameters and their effect to achieve maximum accuracy. A detailed discussion is provided in later sections of this paper.5.A thorough comparison is presented between MLP and ELM based on validation, test, and time effectiveness. A different number of hyperparameters were considered in the environmental setup to conduct the experimental results.In the existing literature, the roles are either assigned to users by network administrators manually or based on the attributes of the users. The assignment of roles to end objects by administrators is referred to as role engineering. The manual establishment of the roles by the network administrators is a tedious process with an impact on the efficiency of the overall process. Likewise, the situation becomes more complex in IIoT use cases due to the presence of heterogeneous devices interacting with each other and the hyperdynamic environment changing rapidly. The dynamic environment rapidly changes the characteristics of end objects in critical infrastructure applications such as metro transportation and industrial automation scenarios. In an attempt to resolve this issue, we propose a framework to automate role engineering using machine learning in access control. We propose two machine learning approaches to automatically execute the role engineering in a complex scenario with heterogeneous devices and changing environments. Following is the summary of the contributions of this proposed study:The organization of the paper is as follows: First, we discuss the preliminary concepts of SCADA and access control in the following section. The state-of-the-art access control approaches with contemporary trends and open problems are discussed in detail in the literature review followed by the problem formulation for this research study. After that, we discuss the machine learning algorithms employed in this study in the proposed solution section followed by the environment setup section. After that, we discuss the results and performance measurements in the Results Section. Following that, the conclusion and future work are provided at the end.This section provides a preliminary understanding of the core components of the IoT environment and applications of access control.The basic operation of SCADA is to gather real-time information or data and monitor equipment and processes in the critical framework, providing the connection among servers located in the field or at remote locations . Three mA traditional SCADA system consists of a central controller and a number of devices including sensors and actuators. They are widely used in industrial areas for controlling the process of the systems. It is composed of the following main components.This is the one operating the system from the organization’s premises or remotely through the Internet. The major responsibilities of the operator are to monitor the system, alert addressing, and manage important control operations.This is used to provide the interaction between the SCADA system and operator by collecting the information from the master terminal unit (MTU) and translating the commands to control properly.This is composed of computational networking and storage components that are located within the association.This transfers the information and control signals gathered from the remote terminals to the HMI, thus providing a high-level control logic in the system.This provides the service of exchanging information and commands with the MTU and transferring specific control signals to field devices such as sensors and actuators.These consist of devices that can monitor and control the process of the organization and distribute it in the organization. These devices include a number of sensors and actuators used for data collection and control actions’ execution, respectively.Access control is a process to control who can perform a particular task and which access rights a user can have on a specific resource. This access can be controlled by a subject, which can be a user, a device, or even a service. Different types of access control models have been proposed up till now, and much work is being performed in this area. According to Trusted Computer System Evaluation Criteria (TCSECs), there are two types of access control: discretionary access control (DAC) and mandatory access control (MAC) . In DAC,Although MAC is able to overcome the issues of DAC by centralizing the management, it is still not efficient enough to meet the performance requirements of complex environments . In dynaThe core concept of RBAC according to the NIST-RBAC-2000-standard  is that In ABAC, authorization policies to determine an access decision are specified using the attributes or characteristics of objects in an access event. In order to mitigate the limitations of RBAC, the authors in  proposedAnother ABAC-based model was presented in  for manaDifferent types of approaches for privacy preservation have been proposed in the field of electronic health record (EHR) systems. For example, in , the autHowever, to resolve the limitations of both ABAC and RBAC models, the authors in  proposedThe authors in  discusseIn , the autThe authors in  argued tIn this work, we considered a network hierarchy in which each layer is connected to the others using a wireless access network (WAN), as shown in The MTU contains a collection of services that are used to measure and deploy smart decisions on IoT devices. Such devices connected to the MTU using the WAN can be represented as a set t can be formally described as The environment condition in which the IoT devices can reside will directly impact the state of the device as it will be capturing the data about it. For each device n, the state of the node at time instance The approach to mapping the attribute-based access control is intuitive. At any time, for instance, t, the state of the SCADA node in the large-scale and fast-changing environment can be represented as the attributes of the nodes. The mapping of these node states to attributes can be formulated as: We can develop the hybrid context-aware access control with automated role engineering. Knowledge propagation and role assignment can be achieved by leveraging machine learning. For a machine learning model, the input is the combination of both static and dynamic attributes and the output is the decision of the model by inferring what role should be assigned to users’ attributes with a set of allowed permissions. The role of the machine learning model is to approximate a function that maps the attributes to role assignment with a set of permission. The weights for models can be learned initially by the manual role–attribute structure setups for IoT devices in the network.In this environment, different sensing nodes can capture different types of data corresponding to the environment application requirements. These captured data can be accessed by the set of users that have the access right privileges. Combining this with RBAC, the different users have different attributes that can be leveraged to determine the role of the user . In the Based on the availability of the manual user–role relationship by mathematical proof, the optimality of the automated role assignment can be guaranteed. However, the automated role assignment cannot guarantee the exposure of the system to various attacks such as denial of service, insider attack, and man in the middle. This problem can be resolved by using attribute-based encrypted systems to provide a safeguard against such attacks . In sucht. For this purpose, different machine learning classifiers can be leveraged to learn the user–role relationship and predict the roles at runtime by analyzing the context of the end device attributes. In [The key idea behind the automated role assignment is to learn the sensing data patterns and predict the end device node based on the characteristics the end device can have at any time instance utes. In , the aututes. In  by leverMultilayer perceptron (MLP) is a feedforward neural network that can be used for nonlinearly separable data. It uses three types of layers, i.e., input, hidden, and output layers. The initial weight calculation for the input layer is shown in Equation .(2)nethx is the input and w1 is the weight for the first neuron in the model. Equations netoEquation . The flonetoEquatquations  and 5)  (4)neto=C is the total cost on an output neuron at the output layer.C is the total cost. The error on the output layer can be calculated directly by using Algorithm 1: Multilayer perceptron.Forward passx and output vector y    Initialization of input data             Set for all the hidden and output neurons i at layer j    for all the hidden and output neurons i at layer j         return y     Backward passS, updated weight vector      Initialization of training samples      for each           Repeat until convergencereturn      Equations  and 7)  7) updatw1→w′=w1−N hidden nodes and with any nonlinear activation function, as shown in N nodes with a nonlinear activation function can exactly learn N unique observations of data in much less time. However, it was shown in [ELM, proposed by , is a sishown in ,44 that N is the last node of the hidden layer. The architecture of ELM with a single hidden layer is shown in The ELM architecture has only three layers: The input layer, hidden layer, and output layer. In Equation , i=1,…,NN arbitrary unique data samples N nodes and activation functions ith hidden node. Furthermore, ith hidden node with the output layer’s nodes, and The input weights and hidden layer biases can be chosen randomly if the activation functions of the hidden nodes are infinitely differentiable. The output weights can be analytically determined by simply calculating the generalized inverse (Moore–Penrose) of the hidden output matrices. For the weight calculations for N input sample with total zero error, meaning that H is called the hidden layer output matrix of the the network, and the ith column of the matrix H is the output of the ith hidden node of the network with respect to the input The standard architecture of ELM with a single hidden layer with the activation function can approximate the quations  and 9),,N input Algorithm 2: Extreme learning machine.Require: Feature data of SCADA devices and their labels: Ensure: The final relevance vector for the K pre-defined roles/permissions is ELM(y is the output vector,for input neurons Set for input neurons Set for all the hidden and output neurons i at layer jSet for all the hidden and output neurons i at layer jfor all output neurons for all output neurons Calculate Calculate the label output of classifier return The core functionality of SCADA is to capture the data from the end devices and to provide the monitory control using host controller software. To leverage the data containing the static and dynamic attributes of the end devices, this paper leveraged the above-mentioned classifiers to learn and predict the roles. These learned roles will then be propagated to overcome the challenges and limitations posed by the manual role settings by the administrators. To make use of the MLP and ELM classifiers, this paper leveraged Algorithms 1 and 2, as shown in In order to validate and test the proposed methodologies, a benchmark dataset was required for the experiments. Though many benchmark datasets are available for public use, a more concrete dataset by  was usedThe benchmark dataset contains 27,464 records of the device transactions on the network. Each record consist of a 17-dimensional feature space containing the network transaction value, including the network address, function, and command payload along with the labels. A complete feature list is presented in To test and validate the hypothesis, we used all the features of the dataset mentioned in There are 274,628 class observations in the dataset, and each class representation is shown in The model was trained using 55,035 trainable parameters for 50 epochs with the Adam optimizer using a The model fitting on the training and validation set is shown in Similarly, we evaluated the test accuracy trend of the model using the different numbers of epochs for training while keeping the batch size of 32 for each number of epochs. E represents the epoch number for each training iteration. The test accuracy against each E is shown in The model was trained for In the study , the autSelecting an appropriate number of hidden nodes is also crucial to achieve a good convergence of the model on the data. To visualize this phenomenon, we tested the performance of the ELM classifier using both the sigmoid and ReLU activation functions with a varying number of hidden nodes. The right balance between both variables can be defined as an acceptable score of accuracy with available computational resources. Further, for the given test data, ELM showed an overall accuracy of 89%, with 88% precision, 85% recall, and 86% F1-score, respectively, using 1024 hidden nodes. To obtain these results in our experiments, we used the singular-value decomposition method to find the pseudo-inverse matrix of the weights due to it being more computationally efficient than the least-squares method. The intra-class scores of precision, recall, and F1 are represented in 1.There is still an open problem of collecting data samples from SCADA system applications with feature, role, or permission tuples in real-life use cases such as smart transportation, smart healthcare, etc.2.Customized data can be further investigated using advanced tailored machine learning algorithms such as multilayer extreme learning machines and hybrid deep models to achieve robust accuracy for role engineering and propagation in fine-grained access control.3.Similarly, based on the availability of metadata and the structural topology of sensory devices, recent language models can also be employed to derive robust role engineering in this domain.4.Likewise, the role of policies in the access control model is also very crucial in this domain. As for future work, we will explore the application of machine learning to effectively map the non-conflicting policies to manage the attribute-based access control mechanism for IIoT use cases.The objective of this study was to leverage machine learning models for automated role propagation for fine-grained access control in SCADA-based IIoT use cases. However, due to the nonavailability of the benchmark dataset for access control, a benchmark dataset of the SCADA system was tailored in this study to test and validate the hypothesis of this study. We proposed a practical approach of using machine learning for automated role engineering with encrypted sensory data in SCADA-based applications. In our experiments, MLP outperformed the ELM model with more accurate results, but the convergence efficiency with respect to time was better in the ELM’s application. For future work, we would consider the following directions:


Intelligent telemedicine technology has been widely applied due to the quick development of the Internet of Things (IoT). The edge-computing scheme can be regarded as a feasible solution to reduce energy consumption and enhance the computing capabilities for the Wireless Body Area Network (WBAN). For an edge-computing-assisted intelligent telemedicine system, a two-layer network architecture composed of WBAN and Edge-Computing Network (ECN) was considered in this paper. Moreover, the age of information (AoI) was adopted to describe the time cost for the TDMA transmission mechanism in WBAN. According to the theoretical analysis, the strategy for resource allocation and data offloading in edge-computing-assisted intelligent telemedicine systems can be expressed as a system utility function optimizing problem. To maximize the system utility, an incentive mechanism based on contract theory (CT) was considered to motivate edge servers (ESs) to participate in system cooperation. To minimize the system cost, a cooperative game was developed to address the slot allocation in WBAN, while a bilateral matching game was utilized to optimize the data offloading problem in ECN. Simulation results have verified the effectiveness of the strategy proposed in terms of the system utility. The rapid popularization of IoT technology has accelerated the application over a range of industries, allowing for the change from classical informatization to automation and intelligence. Intelligent telemedicine as an IoT technology applied in the medical field is dedicated to raising the level of medical care . ConventTelemedicine technology can offer universal health monitoring services. However, the increase in the number of patients can overrun the operation system of the medical center (MC). Additionally, the delay-sensitive requirements of medical information analysis cannot usually be met by local gateway user nodes (GUNs) with poor computing capability, which restricts the advancement of the intelligent telemedicine system. As an emerging distributed computing framework, edge computing can help decrease the local latency of processing medical data packets and enhance energy efficiency by offloading data from the GUNs to ESs and avoiding medical data transmission to the MC, which usually leads to a larger latency for data processing . TherefoIn this paper, a resource allocation and data offloading strategy for edge-computing-assisted intelligent telemedicine systems was considered. To maximize the system utility, a contract mechanism was introduced to incentivize ESs to participate in system cooperation. Moreover, the whole framework for medical monitoring data updates was separated into two-layer networks: the WBAN and the ECN. For the WBAN, heterogeneous sensors share the channel through TDMA technology, and the GUNs minimize the AoI of each monitoring packet by slot allocation. For the ECN, the GUNs can choose the proper ESs for access based on the corresponding load and network conditions.An edge-computing-assisted intelligent telemedicine system was studied. Considering the requirements of the patients and the utility of the MC, the system utility was determined by the utility of the MC and the system cost;To increase the contributions of ESs to the system, an incentive mechanism based on the contract theory was introduced to motivate ESs to provide communication resources;A two-layer communication network, encompassing the WBAN and the ECN, was used to describe the edge-computing-assisted intelligent telemedicine system. For the WBAN, a cooperative game was proposed to optimize the allocation of time slots. For the ECN, a bilateral matching game was introduced to address the data offloading issue.The main contributions of this article are summarized as follows:The remaining parts of this paper are as follows. The second section introduces recent studies related to intelligent telemedicine technology. The third section introduces the system model studied in this paper. The fourth section introduces the optimization strategy to maximize system utility. In the fifth section, the performance of the strategy proposed is verified by simulation experiments. Finally, the sixth section summarizes the work of this paper.As an important component of smart wearable technology and Internet of Medical Things (IoMT), WBAN technology is considered an important way to achieve intelligent telemedicine. The WBAN is a special wireless sensor network (WSN) that surrounds the human body and consists of GUN and micro-inertial sensors distributed on the surface or inserted into the human body . NumerouThe above studies can significantly improve the energy efficiency of the system with sufficient slots. However, when the slots are insufficient to meet the requirements of all sensors, the urgency of the packets should be seriously evaluated, otherwise resulting in the poor overall performance of the system. Moreover, with the widespread application of WBAN technology in medical health monitoring scenarios, many disadvantages have emerged, such as insufficient computing resources, high energy consumption, and short operational life. Edge computing is regarded as a feasible solution to solve these problems. In , latencyMost of the reported studies have assumed that the system cost is composed of the data delay  and the energy consumption for transferring and computing the medical monitoring data, and the system utility is defined as the opposite of the system cost. In this paper, the utility of the MC is considered in the system utility for the intelligent telemedicine system. Moreover, as the generated packets usually experience a waiting delay before scheduled transmission in TDMA, the AoI is also considered in the system cost, which is different from the reported works. Additionally, a two-layer network architecture is proposed to describe the process of medical data updates. An incentive mechanism based on contract theory is considered to motivate the ESs to provide communication and computing resources to maximize the system utility. Furthermore, a cooperative game is proposed to allocate the time slots in the first layer network (the WBAN), and a bilateral matching game is adopted to solve the data offloading problem in the second layer network (the ECN).In this paper, we considered an intelligent telemedicine scenario assisted by edge computing. As depicted in The assumption is that there are Considering that each ES has a different willingness to join the intelligent telemedicine system to provide the communication resources for GUNs’ access, the MC provides the contract composed of communication resources and rewards corresponding to the willingness of ESs. ESs with different participation willingness provide different communication resources and obtain corresponding rewards from the MC. noted as :(1)θ1<θ2The utility of the MC: The ESs usually afford communication resources according to their participation willingness for GUNs’ access, which can be regarded as an auxiliary analysis to process the medical monitoring data. In order to enhance the contributions of the ESs, the MC can return the corresponding rewards to the ESs if they provide the appropriate communication resources. When the communication resource provided by the ESs for GUNs is α is the unit revenue obtained by the MC from the communication resources provided by ESs. Since there are The utility of the ES: The rewards obtained from the MC can compensate for the cost of the ESs providing communication resources. Thus, the utility function of the ES with participation willingness v(π) is the evaluation function corresponding to the reward essed as ,22:(4)UEfficient .Since medical information monitored by body sensors is time sensitive, AoI is used to describe the update speed of medical packets . In thisessed as :(5)Aij sensors monitor heart rate and blood pressure, while electroencephalographic (EEG) sensors monitor cortical activities . To reflThe MC may be made aware of the precise participation willingness of each ES after they have all signed the contracts. The MC will broadcast the contract information of all ESs to the GUNs, and after that, ESs need to provide service according to the contracts they signed.Due to the limitations of GUNs’ computing resources and energy consumption, as well as the integrity of medical monitoring data, partial data offloading was not considered in this article. To enhance spectrum efficiency, the NOMA technology is adopted for data transmission in the ECN. The data offloading rate between the GUN essed as :(11)rimGAccording to Equations (9) and (11), the delay and energy consumption for the GUN After ESs receive the collected packets, the computing resources are allocated according to the level of medical urgency. The computing resources allocated to the packets provided by the GUN The quality of experience (QoE) can be described by the timeliness of the medical data for updates and the energy consumption in the intelligent telemedicine system. Thus, the cost of monitoring data for patient In order to enhance the utility of the MC while meeting the needs of the patients, the system utility can be defined as the difference between the utility of the MC and the system cost:Our goal is to maximize the system utility in an intelligent telemedicine system, and the optimization problem can be modeled as follows:Subject to the following constraints:Since the constraints and optimization variables in the optimization problem Equation (18) are interdependent, we can rewrite it in the following form to decouple them:Based on Equation (24), the goal of maximizing the system utility can be achieved by maximizing the MC utility and minimizing the system cost, which consists of the cost for the WBAN Taking the first term in Equation (24) as the objective function, the maximization problem of the MC’s utility can be written as:Subject to the following constraints:t theory . For theSince the participation willingness of ESs is unknown to the MC, there are total Lemma 1.For any feasible contract , there is if and only if Proof of Lemma 1.See Lemma 2.The feasible contract satisfies if and only if Proof of Lemma 2.See Corollary 1.The feasible contract satisfies if and only if Based on Lemma 1, Lemma 2, and the Corollary 1, to make the optimal contract satisfy the condition that ESs with higher participation willingness will provide more communication resources while obtaining higher rewards from the MC, the following constraint can be obtained:Lemma 3.If the ESs with participation willingness satisfies the IR condition, all ESs will automatically meet the IR condition.Proof of Lemma 3.See Based on Lemma 3, To simplify the IC constraints in Equation (27), we first define that the downward IC (DIC) condition is between the ES with participation willingness +1,…,I}) . The locLemma 4.The IC conditions can be simplified to LDIC conditions and LUIC conditions, i.e.,Proof of Lemma 4.See Thus, the optimization problem in Equation (27) can be simplified as:Subject to the following constraints:The difficulty of solving the optimization problem after simplifying the redundant constraints is significantly reduced. Iterating over the constraint Equations (35) and (36), there will be:By substituting Equation (38) into Equation (34), the objective function can be expressed as:Algorithm 1 The formulation of the optimal contract based on contract theory2: Obtain the constraint Equation (30) based on Lemma1, Lemma 2, and the corollary5: Obtain the simplified optimization problem (34)Output:1: Obtain the constraints of the feasible contract based on IR condition and IC conditionThe optimal values Taking the second term in Equation (24) as the objective function, the slot allocation problem for the WBAN can be written as:Subject to the following constraints:The weight factor Moreover, to facilitate the analysis, the opposite of the cost is used as the utility function of the sensors and the WBAN:All sensors in the WBAN serve collaboratively, and any data obsolescence can cause a dramatic increase in the cost of the WBAN. Therefore, sensors are motivated to cooperate to ensure the overall utility of the WBAN. We describe the slot allocation problem for the WBAN as a cooperative game problem. In the cooperative game, each sensor competes for channel resources by adjusting its slot occupation, but each sensor aims to maximize the utility of the corresponding WBAN on the basis of ensuring its own utility.The cooperative game problem for WBAN can be formulated as:As mentioned earlier, The objective of the cooperative game for the WBAN is to maximize its utility. Thus, the game Theorem 1.The feasible utility set of the WBAN’s cooperative game has a maximum value.Proof of Theorem 1.Based on Equations (47) and (48), the set of all sensors’ utility can be expressed as:Thus, the set of the feasible utility for the WBAN can be expressed as:The set of the feasible WBAN’s utility has a maximum value if and only if the cardinality of Algorithm 2. Slot allocation based on collaborative gameInput:1: The initial maximum utility for every WBAN:Initialization:Optimization:dodothen10:             Update the unallocated slots in reverse order by removing the allocated slots11:         end if12:   end13: endOutput:A slot allocation algorithm based on collaborative game is proposed to obtain the optimal solution for the optimization problem of Equation (40). The details are shown in Algorithm 2.Taking the third term in Equation (24) as the objective function, the data offloading problem for the ECN can be written as:Subject to the following constraints:Within the ECN, the interaction between the GUNs and the ESs in the optimization problem of Equation (53) involves 0–1 variables; thus, the bipartite matching game model was introduced to solve the problem.Definition 1.For the disjoint sets of GUNs and ESs, bipartite matching needs to satisfy the following conditions: (1) ; (2) ; where and In order to measure the impact of the GUN’s selection of different ESs on the cost for the ECN, the cost generated by the GUN’s selection is defined as the selection cost, which is calculated as follows:Preference List of the GUNs: For GUNs, every GUN wants to select the ES at the lowest cost. Thus, for GUN The preference list can be acquired by sorting every row of Preference List of the ESs: From a system perspective, we are attempting to minimize the cost of the ECN caused by the different choices of the ESs as much as possible. Therefore, each ES prefers to match with the GUNs whose selection cost is much lower. For ES The preference list can be acquired by sorting every row of The preference lists offered in Equations (58) and (59) are initial values that are always modified when a stable equilibrium cannot be reached.The exact preference list is not available initially, and the preference lists between different GUNs affect each other during the matching process. Therefore, there is an externality in the bilateral matching model studied in this paper. In order to ensure the stability of the final matching result, the matching object of the established match result can be exchanged based on the gain matching, which can improve the cost of the ECN progressively.Definition 2.For the matching pair of the matching result, if change the matching object of to , where and will get the finite gain, the gain matching can be defined as Finite gain signifies that by swapping out the matching objects, the participants can lower the cost to some extent. Gain matching provides the GUNs with the opportunity to change the matching objects, but gain matching only takes effect after obtaining the permission of the ESs. For ESs, only a gain matching process that can reduce the cost for the ECN will be allowed.Definition 3.If there is no blocking pair in the matching result , it can be called stable matching.Definition 4.If the matching pair satisfies the following two conditions: (1) GUN is unmatched or there is a gain matching object of GUN ; (2) there is a gain matching object can be called a blocking pair.Algorithm 3 Data offloading strategy based on bilateral matching gameInput:Initialization:do7:     Unmatched GUNs send service requests to the ESs according to their preference list 9:     The rejected GUNs remove the ESs which reject them from their preference list10: endOptimization:dothen17:     else19:     endif21: end whileOutput:Data offloading strategy based on bilateral matching game is shown in Algorithm 3.Algorithm 4 Joint strategy for resource allocation and data offloadingInput:Algorithm 1)Optimization:do3:   Allocated slot resources based on Algorithm 24: end for5: GUNs broadcast the information of their collected medical monitoring data packets6: ESs broadcast the information of communication resources7: All GUNs execute Algorithm 3 in parallelOutput:In summary, the system utility maximization problem is split into two parts for solving: one is the utility maximization problem of the MC, i.e., the optimal contract formulation problem between the MC and the ESs, and the other is the system cost minimization problem. Specifically, the system cost minimization problem is composed of the slot allocation problem for the WBAN to minimize the cost for the WBAN and the data offloading problem for the ECN to minimize the cost for the ECN. The process of resource allocation and data offloading needs to be performed on the basis of the optimal contract signed by the ESs. Thus, a joint strategy for resource allocation and data offloading (JSRADO) based on the optimal contract which is composed of Algorithm 2 and Algorithm 3 is proposed, and the details are shown in Algorithm 4:In this section, the proposed strategy for resource allocation and data offloading is simulated and verified. In the intelligent telemedicine scenario, there are five external auxiliary ESs in a circular area with a 250 m radius, and the number of the patients is Based on ,30, the The contract mechanism based on complete information (CI) : the algStackelberg game incentive mechanism under asymmetry information scenario (SGA) : the MC Minimum Distance (MD) : all gatThe resident-oriented Gale–Shapley algorithm (RGS) : the GUNDecentralized game theoretic approach for health monitoring (DIGTAL) : a decenIn order to verify the effectiveness of the proposed strategy composed of the CT algorithm and the JSRADO algorithm, the simulation results will be compared with the following algorithms, respectively:In The changes in system utility under different system scheduling periods are shown in In this paper, an edge-computing-assisted intelligent telemedicine system consisting of the WBAN and the ECN was considered. Based on the requirements of the patients and the utility of the MC, it was assumed that system utility is jointly determined by the MC’s utility and the system cost. Considering the selfishness of ESs, the CT algorithm was first utilized to encourage ESs to provide communication resources to maximize the utility of the MC. In addition, the JSRADO algorithm was proposed to minimize the system cost, where the cooperative game and the bilateral matching game were used to address the slot allocation problem for the WBAN and the data offloading problem for the ECN, respectively. Numerical simulations demonstrated the effectiveness of the strategy composed of the CT algorithm and the JSRADO algorithm in this paper with respect to the system utility.


To develop a standardized training for charge nurses.A developmental research design divided into three parts will be undertaken.(1) A scoping review will be used to develop standardized training focusing on charge nurse skills and sub‐skills; (2) a Delphi review with nurses, managers and researchers will validate the content of the training; content validity will be assessed over sufficient rounds of review to obtain a content validity index of over 0.7 and (3) a cross‐sectional study will pilot test the training with 30 charge nurses.This study will describe the development of updated and empirically validated training to be systematically implemented in healthcare institutions and offered to charge nurses when they begin. To obta2A CN is defined as a nurse who performs a clinical‐administrative management position in a healthcare institution to support their unit manager. In Quebec (Canada), there are approximately 6630 CNs, which represent 8.47% of the total number of active nurses with the full right to practice  for the healthcare institution, as the CN may manage their workflow poorly and thus be unable to provide optimal supervision or clinical support to staff, physicians and patients  are considered in the development and delivery of training. Although, for example, Spiva et al.  describe2.3Currently, it is not standard practice for healthcare institutions to offer training to newly appointed CNs in preparation for their position. One exception is a study describing training developed and systematically delivered in an ambulatory setting, which resulted in increased CN self‐confidence and knowledge  develop a standardized training to address the skills and sub‐skills expected of the CN; (2) using a Delphi review consult a group of experts to validate the content of the training, the relevance of the suggested transfer of training and the methods proposed  to include in the organizational structure of healthcare institutions for implementation and (3) run a pilot test to validate these same elements and verify whether this training can positively influence the skills and knowledge of the CN.3.2To what extent can the skills and sub‐skills of a CN be improved through the provision of standardized training?Will CNs apply new knowledge in clinical practice if they receive transfer of training from theory to practice?Are the proposed methods of including training in the organizational structure of healthcare institutions conducive to improving its accessibility?Three research questions will be explored:3.3This study uses the clinical governance model of Brault et al. , which bOur ‘Training Model for Charge Nurses’ (TMCN) puts forward three components necessary for the development and deployment of CN training: (1) the specific skills of the position; (2) the three training inputs to consider in order to promote the transfer of training from theory to clinical practice and (3) organizational structure of healthcare institutions required to support the systematic delivery to new CNs. Each of these three components has a corresponding part of the interconnected Brault model: (1) the clinical system; (2) the clinical governance system and (3) the management system.3.3.1The first component is a part of the clinical system that is the centre of the CN's profession. In this study, the component is operationalized by the five CN skills Plourde,  which ca3.3.2This second component is a part of the clinical governance system that facilitates clinical and management initiatives that are dedicated to the quality of care, clinical excellence and institutional performance  including the design and the pilot test of the object, taking into account the data collected at each phase of the process and the existing scientific literature  that are associated with these skills?Step 2: Developing and aligning the inclusion criteria with the objective and questionsThe objective of the scoping review is to review the literature that addresses any of the five CN skills reported by Plourde . The artStep 3: Describing the planned approach to evidence searching, selection, data extraction and presentation of the evidenceThe inclusion criteria for the scoping review are in line with the objective and research questions of Step 1. Articles must be written in French or English and have been published since 2000. The selected literature will meet the scoping review criteria defined by the PICOT, which is consistent with the TMCN clinical system, and includes: the study population (P); the intervention or treatment discussed in the review (I); the comparison if any, (C); the outcomes (O) and the time period during which the data will be collected (T).assistante infirmière‐chef, assistante du supérieur immédiat, charge nurse, first‐line nurse manager, matron, ward leader, ward sister, leadership, communication, caring, problem‐solving and work environment. Also, the grey literature  will be consulted in ProQuest dissertations and thesis global, and in Google Scholar.Step 4: Searching for the evidenceA literature search will be conducted using the CINAHL, MEDLINE, Science Direct and Cairn databases. The following keywords (MeSH and associated descriptors) will be combined to create a search strategy using AND and OR Boolean operators: Step 5: Selecting the evidenceFirst, a search in the CINAHL and MEDLINE databases will be completed using the original search strategy. A research librarian and two scoping review experts will then be consulted and based on the articles retrieved, a keyword analysis will be performed to create a revised search strategy. All four databases will then be queried using this updated strategy.Step 6: Extracting the evidenceAfter eliminating duplicates and verifying the language, articles will be exported to Rayyan web platform , their skills and sub‐skills.A summary of the results will be made in relation to this study's objective, conclusions will be drawn, and potential implications of these results will be shared. The results of this study will be published in a peer‐reviewed journal and presented in scientific conferences. In addition, findings will be presented to provincial Directors of Nursing in Quebec (Canada), describing how skills training for the CN will be developed.3.4.2n = 6) and managers  who have held a position for at least 6 months on a hospital unit; (b) researchers from a university setting in Québec (n = 6), holding a position as a professor or lecturer and having expertise related to the format or content of the training. Non‐probability sampling will be used to ensure a diversity of respondents from each setting and for each expertise.Step 1: The number of experts is determined; for this study, 18 experts will be consulted Turoff, . These eStep 2: An email describing relevant experts to be recruited with pertinent details of the study will be prepared for (1) the clinical setting, where it will be sent to nursing directors; and (2) the university setting, where it will be sent to researchers (professors and lecturers).Step 3: Experts will be individually contacted and presented with information about the study, after which they will be asked if they would like to participate.Step 4: Experts who express interest will be invited to participate in the study. They will be sent information and consent forms and asked to provide written informed electronic consent.Step 5: To evaluate the training, the experts will receive a questionnaire developed by the research team and administered through LimeSurvey. Participants will evaluate: the training content; the relevance of the transfer of training and the organizational structure methods proposed to administer the training. Experts will be able to evaluate the content on a Likert scale with four response options from 1 = ‘not relevant’ to 4 = ‘very relevant without corrections’ as shorter Likert scales have produced stable findings in a previous Delphi study  analysis will be performed. A CVI up to 0.7 allows for acceptance of the content without modification, while a CVI of less than 0.7 requires modification. Response feedback is given to the team and a new round will ensue, thus the number of rounds will be determined by the responses until a CVI equal to or greater than 0.7 is achieved  working at a hospital healthcare unit in Québec (Canada). The CNs will be recruited from similar work environments such that the examples presented during training are appropriately adapted and the content will be more representative of their shared professional experiences. CNs will be recruited through non‐probability convenience sampling. To be eligible, participants will have to hold a valid Quebec Order of Nurses (OIIQ) licence, work in a hospital and occupy a CN position in any capacity. We will exclude CNs currently under competency, ethical or disciplinary review. A list of CNs will be requested from the study setting's human resources department and a recruitment poster describing the study details will be sent internally to CNs. For those who express interest, information, consent and confidentiality forms will be sent via the LimeSurvey platform. If a response to the invitation has not been received within 2 weeks, one email reminder will be sent.Step 1: The subjects and study setting will be selected. As per the TMCN, the target population for this study are CNs  as well as questions to assess each of the components of the TMCN. Examples of questions on training content include: ‘The content presented in this training is applicable to my CN position’; for the relevance of the transfer of training ‘The exercises and activities carried out will allow me to more easily transfer what I learn in training to my practice’, and for the relevance of the organizational and structural methods proposed to implement the training ‘The length was adequate’. The questionnaire will ask whether the training can strengthen CN skills, for example regarding leadership: ‘Following my participation in this training, my leadership skills have been strengthened, for example, I will be able to carry out my roles and responsibilities more effectively, and better assume my overall position’. Also, the feasibility and acceptability will be assessed with questions such as: ‘It was difficult to get my manager to release me to participate in this training’. A space to provide additional feedback will be available for each question.Step 4: Data analysis: SPSS software version 26 will be used to analyse the data. Descriptive and bivariate analyses will be done using parametric and non‐parametric tests as appropriate. These analyses will describe the needs and preferences of CNs. Content analyses will be conducted on the comments made by the participants.A cross‐sectional study will be undertaken in four steps:3.5Ethical approval for this research was obtained from the Research Ethics Board of the university where the research is taking place  and the Research Ethics Board of the hospital in this study . Ethical principles in research will be respected. All participants will be made aware of the rationale of the study, time involved, inclusion criteria and the ability to withdraw from the study at any time without consequences. At each stage of the study, all information will be transmitted to the participants so that they can ask questions. They will also be informed of the advantages of participating in the pilot test  and the disadvantages (the time required to obtain training). Participants in this study will provide written electronic free and informed consent and will sign a confidentiality form prior to their participation. Managerial approval for the release of CN participants will be sought prior to conducting the training. The confidentiality of the data collected will be ensured by assigning a numeric code to each participant. Finally, the data collected will be saved on the university's server and stored on a password‐protected computer at the university. All data will be confidentially and permanently deleted 5 years after the end of the study.3.6To ensure a rigorous research process four core elements will be put in place: (1) using a comprehensive framework to fill gaps in the scientific literature on increasing the skills of an incoming CN; (2) performing a scoping review to ensure evidence‐based data is used for training development; (3) using a Delphi review to reinforce the internal validity and scientific rigour of the training's content and (4) pilot testing the CN training in order to ensure the quality of the training with validated and evidence‐based content. The use of a rigorous process to meet the specific objectives of this research increases internal, external, construct, content and instrumental validity.4This research project will lead to the creation of standardized training specifically geared to the CN. We therefore assume that more support in terms of training for CNs will enable them to optimize their practice by better mastering their skills, as well as the general functioning of their unit and this will ultimately have an impact on staff and user satisfaction, on the quality and safety of the care and on the work environment. This study will enable CNs to increase the efficiency of their clinical‐administrative management skills within their practice. By developing strong leadership skills and working alongside their manager, CNs will be better able to achieve the institutional objectives targeted by the governance structure and strategic planning that is in place in their unit. In the context of nursing research, this study will contribute to the literature on optimization of the CN position. In addition, this project will generate evidence‐based data that will provide a solid scientific foundation for the development of future research and will strengthen expertise in this field. Thus, the field of nursing will benefit from the developed training as it will clearly define the position and inform the training and practice of CNs. In summary, this research will help fill the gap in the scientific literature for training that is comprehensive, evidence‐based, standardized, validated by experts and tested with CNs. Steps taken to test its validity will ensure that this training can be generalized to all CN working in a healthcare institution  and be integrated into their clinical governance to be offered systematically to all new CNs in Québec, Canada and internationally.This research has limitations. Training will be developed for CNs working in hospital units in Québec, Canada. It is possible that certain details of the training will be specific to the job description of CNs working in this setting and may require adjustments to the material for other settings. For CNs working outside of Québec the content will require context‐specific editing to ensure it accurately represents the roles and responsibilities of CNs in other regions. Also, healthcare institutions differ in terms of financial and human resources, culture, work systems and user characteristics; so these variables will affect the study outcome and thus render the findings not completely generalizable to other hospitals. Finally, the pilot test will require replication on a larger scale and in different work contexts to ensure its validity and generalizability.M.J., C.L., E.T.N.: Made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; M.J., C.L., E.T.N., Involved in drafting the manuscript or revising it critically for important intellectual content; M.J., C.L., E.T.N.: Given final approval of the version to be published. Each author should have participated sufficiently in the work to take public responsibility for appropriate portions of the content; M.J., C.L., E.T.N.: Agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.The authors declare no conflict of interest.This research was undertaken without patient or public involvement. They were not invited to contribute to the writing nor to the editing of this study protocol for readability or accuracy.


In the penal code, article 534 identifies  1 year” . In LebaThe start of our experience was tragic. In 2004, a trans woman who underwent successful gender affirming surgery in our clinic was killed by her children. This crime was enough to lead us to reconsider the strategies adopted in our institution to provide a plan of care for transgender patients. Autonomy: While every patient must have the right to present their self-identification in the settings of name and gender, we find ourselves obliged to insert them in the medical record upon their identity card, as requested legally and by medical insurance. However, changing your gender identity was not legally possible in Lebanon. The Court of Appeals in Beirut confirmed in 2015 for the first time the right of a transgender man to change his legal gender identity, granting him access to necessary treatment and reserving his privacy . This is Nonmaleficence: it is a principle that pushes the provider to minimize harm to patients. The assessment of the complaint is necessary to identify which treatment would be the most beneficial for each patient. Although our department can offer surgical treatment for these patients, we are still limited in Lebanon by the number of competent surgeons able to provide these operations. A multidisciplinary staff was created, based on social and psychological assistants, endocrinologists, surgeons and an ethical committee before providing any treatment for the patient. The principle of confidentiality is critically essential within the therapeutic setting to keep the trust between providers and patients. Justice: we cannot apply this principle to the majority of cases. This principle should ensure equal distribution of healthcare resources between patients. Unfortunately, all the medical insurances do not cover sexually transmitted diseases, hormonal therapy and gender affirming surgeries. Beneficence: our approach to a transgender individual is non judgmental and understanding. Our main goal is to provide a plan of care for these patients to improve their quality of life and reduce their suffering.Multiple ethical problems are assumed in the face of every transgender patient:The biggest challenge was to perform sex reassignment surgeries in our devout catholic institution with the presence of religious members on the ethical committee. When the Vatican published its first document rejecting the idea that gender is distinct from biological sex , our relWhen Elkak  reportedFinally, this article promotes our practice and conflicts in presenting our healthcare to transgender people. It is true that the approach and the behaviors of providers toward LGBT patients in Lebanon are improving in the last years  but spre


Recombination is a fundamental evolutionary force, but it is difficult to quantify because the effect of a recombination event on patterns of variation in a sample of genetic data can be hard to discern. Estimators for the recombination rate, which are usually based on the idea of integrating over the unobserved possible evolutionary histories of a sample, can therefore be noisy. Here we consider a related question: how would an estimator behave if the evolutionary history actually was observed? This would offer an upper bound on the performance of estimators used in practice. In this paper we derive an expression for the maximum likelihood estimator for the recombination rate based on a continuously observed, multi-locus, Wright–Fisher diffusion of haplotype frequencies, complementing existing work for an estimator of selection. We show that, contrary to selection, the estimator has unusual properties because the observed information matrix can explode in finite time whereupon the recombination parameter is learned without error. We also show that the recombination estimator is robust to the presence of selection in the sense that incorporating selection into the model leaves the estimator unchanged. We study the properties of the estimator by simulation and show that its distribution can be quite sensitive to the underlying mutation rates. Wright–Fisher diffusion with recombination   is non-singular, then the quadratic form in the exponent of the normal density of T, we recover the quadratic form appearing in (V(x)| in the diffusion, since we assume that the likelihood is with respect to a parametric form only for V would be trivial in this setting, since V is identifiable from the path via its quadratic variation.) See Basawa and Prakasa Rao  is singular since X, whose infinitesimal covariance matrix The matrix Assume  holds foX to take values in We consider the diffusion tituting  yields  aThe observed information matrix  is a keypression  for \docAs a check on the expression for entclass1pt{minimamentclasspt{minimaK possible alleles at the first locus, locus A, and L possible alleles at the second, locus B. The haplotype of an individual is denoted KL possible haplotypes in some agreed order into a vector of length We now turn to our main result, an expression for the MLE for the recombination parameter The model is completed by specifying the drift. Here it is of the formc(x) is immaterial, but for concreteness we will setk, j) then it mutates to haplotype  with probability In much of what follows the choice for Note the separate roles for the two components of the drift: here it is only Using the results of Sect. X solves a deterministic model. Setting Is nt}ρ^ in  a reasontly into  again shj):not mean that the estimator is consistent only in the stochastic setting. We have just seen that We can further describe the evolution of (summing  over j):ution to  is then2It is possible to repeat these calculations for a model with X(t) as the solution to a stochastic differential equation (SDE):W is a We can find an expression for the error associated with V. In the case of the covariance matrix of the Wright–Fisher diffusion, the Cholesky decomposition is given analytically by Sato  lies in the interior of Suppose that i, j, (i)X(t) lies in For some (ii)(iii)The integral Condition (iii) extracts the explosion of T.It is clear from the form of nt}IT in  that \doW, wheremutation only. More precisely we will compare P so that Z is accessible.  TracinThe conditions given in Theorem It is possible to use 0. Using , the appTo account for the possibility that It is possible to extend the above results to a general multi-locus model. The extension is straightforward and we omit many of the lengthy but straightforward calculations.j, haplotypes are of the form j and Y is In a multi-locus model of iℓ).From  and 14)\documented as in .Methods for inference of recombination can be confounded by natural selection  and , relative to other individuals, is parametrised by First consider the following: Suppose that, unknown to the investigator, the two loci are under selection—possibly a complicated type with epistatic interaction. What effect does this have on our estimator for ik at locus A, and similarly for From Eq. , \documed adjust  by definentclass1pt{minimac(x) to the drift leave the estimator (If (robust to the contribution of c(x). In the context of the Wright–Fisher diffusion we have the following result.Returning to the general inference problem of Sect. ed? From , its conrs as in , this rec(x) if and only ifFor a Wright–Fisher diffusion with drift coefficient nt}φ^ in  is robusWe determine =0,and  holds; bentclass1pt{minima(i)Estimation of mutation at locus A when there is selection at locus B,(ii)Estimation of genic selection at locus A when there is mutation at locus B;while the following problems are not robust: (iii)Estimation of recombination when there is mutation at either locus,(iv)Estimation of mutation at locus A when there is mutation at locus B,(v)Estimation of mutation at locus A when there is recombination,(vi)Estimation of mutation at locus A when there is selection at locus A,(vii)Estimation of genic selection at locus A when there is mutation at locus A;similarly for problems interchanging the two loci. We omit the straightforward calculations. We caution that in the estimation problems above, the likelihood, and thus the estimator S as in \documentfound in  and\docuatterson , up to aexact simulation of certain classes of Wright–Fisher diffusion (Jenkins and Spanò W(t) is the X can be approximated using Riemann sums constructed from the same set of gridpoints.In this section we conduct an empirical study of the properties of otion in . IntegraV(x) of Sato . If this occurs we set Using Euler–Maruyama simulation it is possible to obtain a realisation with In the following we posit a two-locus, diallelic , we expect it is possible to refine this result further; indeed we conjecture that T. This would provide an easy way to check whether In this article we have derived an expression for the maximum likelihood estimator of the recombination rate,  Theorem . AlthougAlthough Theorems by Ewens , p180.) T, finding the asymptotic behaviour of X.Because of the unusual behaviour of the estimator for Overbeck , §3.4. Hi, j) is small, since these regions are more informative for N, and behaviour near the boundaries is inappropriate when true frequencies can only be a multiple of 1/N. One might prefer to replace the estimator T], with one that integrates Finally, we observe that fundamental quantities appearing throughout this work are