Adaptation of handover parameters in ultra-dense networks has always been one of the key issues in optimizing network performance. Aiming at the optimization goal of effective handover ratio, this paper proposes a deep Q-learning (DQN) method that dynamically selects handover parameters according to wireless signal fading conditions. This approach seeks good backward compatibility. In order to enhance the efficiency and performance of the DQN method, Long Short Term Memory (LSTM) is used to build a digital twin and assist the DQN algorithm to achieve a more efficient search. Simulation experiments prove that the enhanced method has a faster convergence speed than the ordinary DQN method, and at the same time, achieves an average effective handover ratio increase of 2.7%. Moreover, in different wireless signal fading intervals, the method proposed in this paper has achieved better performance. In recent years, the number of mobile users around the world has exploded, and so has the data traffic. A study found that the number of mobile terminal devices worldwide could reach tens of billions by 2023 . At the Dense deployment of small base stations could shorten distances as well as greatly improve link quality between mobile stations and base stations, improve signal coverage, and increase system capacity . HoweverAt present, there have been a lot of research on cell handover, mainly by optimizing the trigger threshold and trigger time to improve the handover performance. The authors in  proposedIn order to better optimize the handover problem, the handover failure rate and the handover ping-pong rate need to be considered jointly. The authors in  proposedBased on joint optimization, system adaptive optimization is an important research direction to dynamically achieve the optimal performance in the actual communication process. The authors in  proposedThe motivation of this paper is to establish a handover adaptive optimization method with strong backward compatibility. This paper regards reinforcement learning module as an independent module in relation to the handover process. Thus, the information interaction between the two needs to be reduced as much as possible. Therefore, in this paper, signal fading conditions are regarded as the basis for optimization. Furthermore, in this paper’s context, the optimization module is not supposed to participate in the handover process but only gives parameter suggestions according to signal fading conditions.Considering different conditions of signal fading, a real-time handover parameter selection method using a DQN is proposed. The method can be added to the actual communication system as an additional module without changing the handover process and has better backward compatibility. Good convergence performance is obtained through the validation of simulation platform.An enhanced DQN method based on digital twin is proposed. RSRP data that can trigger different TTT parameters in the actual system are collected, and used as the input of digital twins to predict the reward value under the assumed handover parameters. The virtual rewards from digital twin act on the DQN together with the actual system reward values. Compared with the ordinary DQN, it has faster convergence speed, and the final convergence effect is also better and more stable.Digital twin with LSTM network as the core is established. The RSRP temporal series between UE and each base station before triggering the handover event is used as LSTM input to predict whether the handover failure and ping-pong occur. Unbiased estimation of handover failure rate and ping-pong rate based on this method is proposed. With the aid of LSTM-based digital twin, the enhanced DQN achieves 2.7% higher effective handover rate than the ordinary DQN, and 10.9% higher than random parameter strategy.This paper proposes a DQN handover parameter optimization scheme based on LSTM-assisted digital twins. A DQN handover parameter selection method for different wireless signal fading conditions is established, an LSTM-assisted digital twin is utilized to enhance performance by improving system efficiency and convergence effect. The main contributions of this paper are summarized as follows:The following paper is organized as follows. In a mobile communication network, in order to ensure the communication quality of the mobile terminal UE, instant handover to a base station with better link quality is an effective guarantee for its link quality. Especially with the increase of the communication frequency band, the coverage distance becomes smaller and the density of the base station is also increasing to ensure the coverage quality. The mobility management of UDN is also one of the key requirements for determining the system capacity. Usually handover events can be triggered by the relative signal strength relationship between the serving base station and neighboring base stations. For example, in the A3 event , which iHandover failure refers to the unexpected interruption of the handover process due to various reasons, such as weak link quality or strong interference . The hanHandover ping-pong is an event where a UE is handed over to a new base station and then is handed over back to the original base station in less than a certain period of time . The han3 events , increas3 events .Neither handover failure or handover ping-pong is the expected effective handover. So the effective handover rate is defined as:Wireless signal fading is one of the important factors affecting communication systems and it also has a great impact on handover performance . Under dAssuming that when the fading condition changes, which is described as fluctuation in both time and space domain in , the hanSince the fading standard deviation is a continuous value, it is difficult for training samples to traverse all situations, so it is necessary to find a suitable decision modeling method. Compared with methods such as interpolation and polynomial fitting, the Q-learning method based on deep neural network is expected to have better results .Although there is some research on the DQN method for handover parameter optimization, it has the following disadvantages: either the parameter selection is not optimized for signal fading conditions, or there are too many protocol procedures that need to be modified, resulting in poor backward compatibility. The purpose of this paper is to establish a DQN while considering signal fading characteristics with good backward compatibility. The DQN realizes the best action selection given by the Markov decision process (MDP) . It mode formula :(8)at^=at + 1 is calculated as:In order to obtain a suitable Q-network, it is necessary to interact with the real environment and obtain the corresponding reward value of an action. In this paper, after the action It should be noted that the DQN in this paper can be deployed on the terminal side in order to ensure that the difference of signal conditions of each UE is taken into account.Algorithm 1: DQN AlgorithmInitialize for  do Update standard deviation of signal strength sd; if ; Update Q-network with loss calculated as in (10);end forInitialize Q-network;This paper now exhibits the basic DQN algorithm.sd is the standard deviation of signal fading, which can be obtained by measuring resource blocks in the time-frequency domain, and Where The advantages of algorithm 1 in this paper are as follows. The signal fading condition is considered, specifically, when the signal fading is in a low range, the Q-network tends to adopt a more aggressive handover parameter strategy, or otherwise a conservative handover parameter strategy; this DQN module does not participate in the whole handover process, but just fetches the handover event statistics to give parameter suggestions, in other words, is independent from traditional mobile communication systems, which does not change the existing A3 handover process, resulting in strong backward compatibility.Algorithm 1 also has the disadvantages of most DQN methods, that is, when the action space to be predicted is large, the convergence effect would degrade; the DQN in this paper utilizes continuous value of signal fading standard deviation as input, which puts forward higher requirements to the generalization ability of the Q-network. This may take a long time to train in order to achieve better prediction performance. In the following, an LSTM-based digital twin enhancement strategy is proposed to improve the convergence efficiency and performance of the DQN.Digital twin is a virtual representation of the physical world, which continuously interacts with the physical world and predicts and supplements various behaviors of the physical world . The appThis paper also utilizes an Artificial Intelligence (AI) method to create a copy of the real world to help predict real-world handover events. In general, the real world is only able to provide limited feedback, which in this paper’s context, is the real-time rewards for the selected handover parameters. However, some data for other handover parameters, such as RSRP, could also be collected at the same time. Digital twin is proposed to utilize these data to predict rewards.Specifically, after a communication system selects a set of A3 handover parameters, the handover behavior must satisfy the TTT window length. In other words, when a handover is triggered, the A3 buffer has retained the RSRP value of TTT consecutive measurement periods, which also includes potential A3 events that can be triggered with a lower TTT value, as shown in This proposed digital twin is also considered to be deployed on the terminal side. This premise ensures that the approach in this paper is UE-specific, that is, data collection and processing, virtual event prediction as well as handover parameter decision are completed at the terminal side.Through the digital twin based on the LSTM network, the potential A3 events could also be taken advantage of. Specifically, after the potential A3 event is triggered, the RSRPs of each base station at several historical-measurement periods are fed to the digital twin to obtain the handover failure rate and the ping-pong rate prediction for the corresponding handover parameter. Then, its reward value is calculated and fed back to the Q-network. The digital twin is able to take advantage of the RSRP values that would have been difficult to utilize and enrich rewards. The specific construction method of the digital twin is explained in Algorithm 2: DTe-DQN AlgorithmInitialize for  do Update standard deviation of signal strength sd; if ; Update Q-network as in (10); for  do  Collect RSRP sequential values triggered by A3 parameter   Predict HO failure rate failurep and HO pingpong rate pingpongp via LSTM-based digital twin;  Calculate reward r as in (9);  Update Q-network with loss calculated as in (10); end for end forInitialize Q-network;The algorithm of Digital twin enhanced DQN (DTe-DQN) is as follows.j-th element in Where By predicting the virtual handover reward value from the digital twin, algorithm 2 is able to obtain more diverse reward information within a limited data collection period. The diversity of rewards is reflected that under the same signal fading condition, reward values for different handover parameters can be obtained. Diverse rewards can greatly improve the search efficiency in the DQN training process, thereby alleviating the convergence difficulties caused by large-scale action space and continuous value input. Note that if the real-world reward of a feedback cycle were repeatedly used to update the Q-network, the performance would not improve. This is because this does not introduce more effective exploration, but repeats old experience. Instead, the repeated training might lead to a worse local optimum.It should also be noted that both the ordinary DQN algorithm and DTe-DQN algorithm have low requirements on the computational resources or efficiency of the actual system. In each iteration, statistics of several handover events are collected for the iterative process and the act of gathering itself might take a long time. Although this process could be accelerated by sharing handover information between multiple UEs, it is not possible to take less than the time it takes that most UEs perform a handover, i.e., in the order of seconds. Therefore, the efficiency of the algorithms is mainly limited by the real-time feedback rather than computational resources or power where the algorithms are deployed.The LSTM-based digital twin proposed in this paper includes the following stages: real-world data collection, LSTM-network prediction, and unbiased estimation of handover failure rate and ping-pong rate, as shown in In the first stage, the digital twin reads data from the A3 buffer. When some candidate base stations in the A3 buffer satisfy the Formula (1) for a certain length In the second stage, the prediction task of the LSTM network is to predict whether the current handover is successful and whether ping-pong handover occurs according to the RSRP series. The RSRP series is expected to reflect the user’s relative signal strength trend with each base station, so machine learning can be used to extract possible future events. LSTM has the ability to process temporal series data ; the autLSTM is a special kind of recurrent neural network (RNN), mainly to alleviate the problem of gradient disappearance and gradient explosion during long sequence training . Only ont needs to receive information from the LSTM layer at ht−1 and deliver information to the LSTM layer at ht+1. The LSTM network is now in place, using historical RSRPs to predict whether handover failure or ping-pong occurs.The LSTM network in this paper is constructed as shown in Xi-th measurement. In the third stage, after establishing an LSTM network that predicts a single handover failure or handover ping-pong event, this paper proposes an unbiased estimation method for the failure rate and ping-pong rate in the digital twin. Taking handover failure as an example, if LSTM prediction is not deployed, the number of handover failures observed in actual system under the This is an estimation method when statistics from an actual system can be collected. However, when using LSTM to predict a single event, uncertainty would be introduced because its true positive probability Proposition 1:With LSTM-predicting events, the unbiased estimation of Proof.If the handover failure were predicted to occur, it could be a correct prediction of the event occurring or a false prediction of the event not occurring. So the probability of LSTM predicted handover failure is calculated as:The number of LSTM predicted failures obeys the Bernoulli distribution Since estimator (14) is the unbiased estimator of Calculate the expectation of Formula (12) substituting Formula (15) and the following equation can be derived:Since the expectation of the estimator (12) for the parameter Corollary 1:The variance of unbiased estimation of (12) is expressed as:Proof:Due to Bernoulli distribution Get the variance of Formula (12) and substitute (18) to derive the following equation:Corollary 1 is proved.The variance of unbiased estimation in actual system is expressed as:Compared with (20), corollary 1 indicates that after the prediction of LSTM, the prediction error of LSTM results in an increase in the estimation variance, thereby reducing the estimation performance. On the other hand, as the number of samples increases, the estimation variance is able to decrease steadily. In the LSTM-based digital twin proposed in this paper, the predicted handover failure rate and ping-pong rate correspond to more aggressive handover parameters with smaller TTT value, which brings much more potential triggers than the real physical-world system. Therefore, more sample data are available, compensating for the performance penalty introduced by LSTM. In general, the accuracy of unbiased estimation based on digital twin compared to that based on data collected in the physical world is mainly determined by the following variables: controllable Similarly, the unbiased estimation of handover ping-pong rate based on LSTM prediction can also be expressed as:Three steps are required to implement the digital twin. Firstly, a dataset needs to be collected for the LSTM network. The dataset should include historical RSRPs and whether handover failure or ping-pong occurred accordingly, which is collected in the actual communication network. The dataset should also be divided into a training set, a validation set and a test set. Second, the LSTM network is trained with the training and validation set and then evaluated with the test set. The true positive and true negative probabilities are derived. Thirdly, the digital twin is deployed. After the preparation of the previous two steps, the A3 buffer reading, LSTM real-time prediction and unbiased estimation are online and provide virtual rewards for handover parameter optimization.A simulation platform has been built to illustrate the proposed method which realizes the air interface communication and handover process of the cellular system, as well as implements the two DQN algorithms proposed in this paper. Data set for LSTM training and validating is first gathered. The true positive and true negative possibilities of LSTM-predicting events achieve 91.64% and 87.96% for handover failure and 92.19% and 91.98% for handover ping-pong, respectively. In addition, the number of samples entering the digital twin is at least 1.4 times that of the physical world. Thus, the unbiased estimation variance of the digital twin is at most 1.12 times (for failure prediction) and 1.01 times (for ping-pong prediction) that of the physical world. It can be considered that the estimation accuracy of digital twins is at the same level as that of the physical world.Since this paper is optimized for different signal fading conditions, the performances of different fading intervals are also the focus of this paper. We contribute this to the joint optimization reward and the generalization ability of the Q-network. This paper designs reward function with the aim of maximizing effective handover rate. Thus, the overall reward has been increased, even though there is little improvement in handover failure rate of high fading interval. Furthermore, the strong generalization ability of the Q-network enables digging out better parameter decision strategy on maximizing reward.In UDN, handover faces severe problems such as large interference and high ping -pong rate. This paper starts with the optimization problem of effective handover rate and proposes a dynamic DQN algorithm based on signal fading condition. This method has a good backward compatibility and can provide parameter optimization suggestions for the mobile communication system as an independent module. In order to improve the efficiency and performance of the ordinary DQN method, this paper proposes a digital-twin-enhanced DQN based on the LSTM network. Taking advantage of potential handover parameters before actual handover is triggered and corresponding RSRP sequences, handover failure rate, ping-pong rate, and corresponding reward value are estimated in LSTM-based digital twin. The proposed digital twin could increase the exploration efficiency of the DQN algorithm. The simulation results show that the two DQN methods mentioned in this article have significantly promoted effective handover rate, while the DTe-DQN has better convergence efficiency and robustness than the DQN, and eventually could achieve higher effective handover rate.


Nanoparticles play an essential role in biomedical applications. A most promising area in nanomedicine is drug targeting which is done with the aid of magnetized nanoparticles. In this study, the hemodynamics of hybrid nanofluid flow with gold and copper nanoparticles suspended in it is investigated. This research primarily focuses on magnetic drug delivery which is propagated through a tapered stenosed artery under three situations, including converging, diverging, and non-tapering arteries. To explore the rheological characteristics of blood, a Sutterby fluid, which is a non-Newtonian fluid, is postulated. The energy equation also incorporates the effects of the magnetic field and joule heating, as well as the viscous dissipation function. Lubrication theory provides a mathematical framework for model formulation. The hypothesized modeling is simplified to a set of nonlinear differential equations that are then solved using a perturbation method up to the second order of approximation. Graphs are used to describe the outcomes of different evolving parameters. The Sutterby fluid parameter opposes the flow negligibly, whereas the Hartmann number and thermal Grashof number strengthen the flow field. Copper nanoparticles (in the absence of gold nanoparticles) are observed to deplete the thermal profile substantially more than gold nanoparticles. Nevertheless, the thermal profile is enhanced by the presence of both nanoparticles (hybrid nanofluids). For greater values of the Sutterby fluid parameter, the wall shear stress has been observed to rise considerably, whereas the inverse is true for the Hartmann number and the thermal Grashof number. The present results have been improved to give significant information for biomedical scientists who are striving to study blood flow in stenosis situations, as well as for those who will find the knowledge valuable in the treatment of different diseases. Nanotechnology is a significant issue in contemporary science. Scientists, chemists, medics, and engineers may use nanotechnology to operate on cellular and molecular levels, bringing considerably advanced technology to health care and life sciences. The word nano is derived from the Greek word nanos, which means small. According to the ASTM ; an international 2006 report, nanoparticles are particles with two or more dimensions and a size ranging from 1–100 nm [The existence of an external magnetic field on nanoparticles is also significant in biological research, material science, biotechnology, engineering, and environmental sciences ,4,5. MagWhen compared to other types of metallic nanoparticles, gold and copper nanoparticles were shown to be the least harmful and most effective agents for the transportation of drugs and for hyperthermia agents . Due to In light of the extensive uses of nanofluids in diverse sectors, several authors have investigated their behavior under a variety of conditions and geometrical shapes. For instance, Ardahaie et al.  analyzedFollowing the detailed analysis of the literature, it was discovered that little focus was given to the blood flow under the suspension of copper and gold nanoparticles, with a particular focus on magnetic drug delivery. Magnetic nanoparticles have a vital role in the treatment of a variety of disorders, including cancer. The proposed nanoparticles are also helpful to treat diabetic patients. Drug targeting has attracted a lot of attention in recent years as one of the most developed technologies. Magnetized nanoparticles, in conjunction with an extrinsic magnetic field and magnetized implants, allow particles to be delivered to the precisely desired spot, fixed at the local site while the medicine is released, and act locally. Transporting drugs to a designated place may eliminate adverse effects and reduce the necessary dose. Gold and copper nanoparticles have excellent bio-compatibility and economic viability; therefore, it has multitudinous engineering applications in medical science. A catheterized tapered artery is considered, which is filled with non-Newtonian Sutterby fluid with copper and gold nanoparticles under the presence of the extrinsic magnetic field. Lubrication theory and perturbation approaches are used to obtain the solutions for the nonlinear differential equations. The results are discussed with the help of graphs for all the emerging parameters. The obtained findings will be helpful for the experimental work on the heat transfer with Sutterby magnetized hybrid nanofluid flows. The present results have been improved to give significant information for biomedical scientists who are striving to study blood flow in stenosis situations, as well as for those who will find the knowledge valuable in the treatment of different diseases.r is located in the radial direction, z is located along the flow direction and Consider a catheterized tapered artery having length Sutterby fluid model which is defined below [B is the material constant, The equations governing the hybrid nanofluid flow can then be expressed as below :(2)∇⋅V˜=ed below :(4)ζ=μhnThe temperature equation with viscous dissipation and joule heating functions reads as follows:Using the lubrication technique, the hypothesized governing equation may be modeled. To achieve the suggested model in dimensionless form, the scaled variables are thus specified as follows:We construct the following set of nonlinear couple differential equations with respective corresponding boundary conditions by substituting the aforementioned Equation (6) into the governing equations (ignoring the And boundary conditions areAnd in Equation (7), And the remaining expression are defined as:To obtain the solutions of Equation (7), we will employ homotopy perturbation method . This apDefining the expansionThe following set of differential equations is what we get at zeroth order:The solutions of the above both equations can be written asWe acquire the subsequent set at first order:The solutions of the above both equations can be found asWe present the following set of differential equations of second order:The solutions of the above both equations can be found asThe approximate series solutions can be written asQ) can be computed with the help of following expressionThe flux (The impedance expression may be determined using the preceding Equation (29), thus we obtainThis section is dedicated to the discussion of the graphical plots of results for all the parameters appearing in the governing equations. Particularly, the results are evaluated for velocity mechanism, temperature profile, impedance profile, wall shear stress, and trapping mechanism. These results are presented for three different cases including converging tapered artery, diverging tapered artery and non-tapered artery see . The numThe thermal profile variation for each of the emerging elements is shown in Trapping is a major influencing factor in the flow of nanofluids that can be examined by observing the trajectories. Trapping is the creation of internally moving free eddies surrounded by streamlines in the blood. This mechanism is very significant in biology since it assists in the production of blood clots and the spread of pathogens. (i)The Sutterby fluid parameter opposes the flow negligibly, whereas the Hartmann number and thermal Grashof number strengthen the flow field. In addition, the thermal Grashof number and the Hartmann number exhibit a decreasing tendency closer to the walls.(ii)It is also observed that the addition of gold nanoparticles (mono nanofluids) results in a greater magnitude of velocity than copper nanofluids.(iii)The thermal profile exhibits a diminishing trend owing to greater values of the Sutterby fluid parameter, whereas a rising trend is detected due to the significant impact of the magnetic field, Brinkman number, and thermal Grashof number.(iv)Copper nanoparticles (in the absence of gold nanoparticles) are observed to deplete the thermal profile substantially more than gold nanoparticles. Nevertheless, the thermal profile is enhanced by the presence of both nanoparticles (hybrid nanofluids).(v)It is observed that the impedance profile has a dual pattern for various values of the Sutterby fluid parameter, but the thermal Grashof number and magnetic field exhibit a uniformly declining tendency.(vi)When the impact of both nanoparticles rises, the impedance profile grows while the amplitude of the impedance profile for mono nanofluids decreases.(vii)For greater values of the Sutterby fluid parameter, the wall shear stress has been observed to rise considerably, whereas the inverse is true for the Hartmann number and the thermal Grashof number.(viii)The trapping mechanism demonstrates that the fluid parameters influence the size and frequency of the bolus. However, for other parameters, the trapped bolus manifested for certain values, although for converging and non-tapered arteries, it did not.In this paper, we analyzed the hemodynamics of hybrid nanofluid flow, with a specific emphasis on magnetic drug targeting. In the presence of gold and copper nanoparticles, flow is considered to be propagating in the tapering stenosed artery. Because blood acts as non-Newtonian in nature, the non-Newtonian Sutterby fluid model is supposed to be a base fluid. To comprehend the rheology of the hybrid nanofluid flow, several impacts, such as magnetic field, Joule heating, and viscous dissipation, are taken into consideration in the mathematical modeling. To model and solve the stipulated nonlinear differential equations, the lubrication technique and perturbation method are utilized. The primary findings of the research context are outlined below:


Remarkable improvement in the perovskite solar cell efficiency from 3.8% in 2009 to 25.5% today has not been a cakewalk. The credit goes to various device fabrication and designing techniques employed by the researchers worldwide. Even after tremendous research in the field, phenomena such as ion migration, phase segregation, and spectral instability are not clearly understood to date. One of the widely used techniques for the mitigation of ion migration is to reduce the defect density by fabricating the high-quality perovskite thin films. Therefore, understanding and controlling the perovskite crystallization and growth have become inevitably crucial. Some of the latest methods attracting attention are controlling perovskite film morphology by modulating the coating substrate temperature, antisolvent treatment, and solvent engineering. Here, the latest techniques of morphology optimization are discussed, focusing on the process of nucleation and growth. It can be noted that during the process of nucleation, the supersaturation stage can be induced faster by modifying the chemical potential of the system. The tailoring of Gibbs free energy and, hence, the chemical potential using the highly utilized techniques is summarized in this minireview. The thermodynamics of the crystal growth, design, and orientation by changing several parameters is highlighted. Hybrid halide perovskites (HHPs) have appeared in a supporting role as visible light sensitizers  in the sDue to the interdependence of material properties, they are entangled in a greatly complex way. Major complexity arises due to ion migration which is the main reason for the degradation in perovskites. One of the highly used strategies to minimize ion migration is the reducing defect density in the crystal structure by fabricating the high-quality perovskite thin films. In this review, several reports focusing on understanding and controlling the crystallization process to achieve some best quality perovskite films are discussed. The perovskite film morphology is a key factor in deciding the carrier lifetime, the carrier diffusion lengths, and ultimately the device performance . For theIn the last decade, a vast amount of research to maximize the efficiency of the HHP-based devices is based on the optimization of the hybrid perovskite crystal design, size, and growth orientation. A plethora of strategies and methods have been used to obtain uniform, pinhole-free, full coverage, and smooth perovskite layers. These methods involve changing the perovskite precursor solution concentration and temperature, optimizing the coating parameters such as the spin speed and time, tuning the annealing temperature of thin films. In addition to these methods, new techniques such as composition  and therIn any solution processable technique, such as spin coating, crystal growth can be considered as a two-step process; the first step is the nucleus formation (nucleation), and the second step is the growth of nuclei into larger crystals. The nucleation of crystals can be of two types: homogeneous and heterogeneous nucleation. Homogeneous nucleation occurs spontaneously without any external assistance within the solution. However, heterogeneous nucleation requires an external site to occur . Both of these types of nucleation can occur in the solution on the colloidal particle sites. The produced nuclei can assemble into bulk clusters and grow into exaggerated film morphology when dropped on a substrate, leading to incompact films with incomplete coverage and a large number of defects. Therefore, nucleation on the substrate is the ideal approach for a high-quality perovskite film.Nucleus formation on the substrate is initiated when the solution is supersaturated and the concentration of the precursor solution is sufficiently higher than its solubility. The solution in the supersaturated state is prerequisite to yield pure perovskite phases. This supersaturation stage can be achieved sooner by preconditioning the precursor or the coating substrate. One of the commonly used techniques to assist this process is the temperature treatment. The input of thermal energy facilitates a faster nucleation process, leading to uniform perovskite thin films. In addition, the intermediate phases or the perovskite precursor solvate (solvent–solute interaction) significantly influences the final perovskite film formation. These always occur in the thin film during the processing methods such as antisolvent treatment or solvent engineering.Nucleation and growth are best understood in terms of chemical potential (µ), which in the simplest term is the energy released or absorbed by the addition  of a particle into (from) the system. At a constant pressure and constant temperature, which is the archetypical lab conditions for solution processing, the chemical potential (µ) can be calculated from a thermodynamic potential called Gibbs free energy (G).The process of nucleation and growth can be tuned by regulating the chemical potential of the thermodynamic system. There are several thin film processing methods by which the chemical potential of the nucleic site can be altered. Few of the latest techniques being followed these days include the variation in the substrate or solvent temperature and variation in the rate of solvent extraction at the time of spin coating or by changing the vapor pressure of the ink, which have to be used in order to fabricate perovskite films. Here, we focused on how the crystallization kinetics is modulated and the crystals are designed to yield a high-quality perovskite thin film by changing the substrate temperature before film fabrication, antisolvent treatment of the film during spin coating, and solvent engineering of the precursor solution.3) films coated on substrates with different temperatures were examined. It was noted that preheating the substrate affects the degree of surface-induced nucleation. With the increase in temperature, the number of nucleation events increased, leading to a larger number of smaller crystals and hence better coverage. In another study, Zheng et al. demonstrated the thermally induced Volmer–Weber growth mechanism during the hot casting of methylammonium lead iodide (MAPbI3) perovskite films (2) and methylammonium chloride (MACl) solution onto the substrate maintained at temperature of up to 180°C followed by a subsequent spin coating of the precursor. They have attributed that the excess solvent present on the substrate at a temperature above the crystallization temperature for the formation of the perovskite phase allows the prolonged growth of the perovskite crystals and hence yielding large crystalline grains.We have already discussed earlier that the formation of perovskite crystals is a two-step process: nucleation and growth. In addition, during the complete process, the temperature plays a very significant role. The chemical potential (μ) and, hence, Gibbs free energy (G) of the system can be significantly tuned by modulating the temperature of the process. The temperature of the system can be changed either during nucleation or during growth. The fabrication of the perovskite thin films using spin coating involves mainly two steps. The first is spin coating the precursor solution onto the substrate (nucleation) and annealing the coated substrate at an optimized temperature (growth). In this article, we focused on how the morphology of the perovskite thin film can be tuned by playing with the process of nucleation of the perovskite crystals. The process of nucleation can be controlled either by changing the temperature of the precursor solution before spin coating or preheating the substrate. The technique of preheating the substrate before coating the perovskite layer has been explored recently. It is one of the latest advanced strategies used to modulate the mechanism of crystal growth and, hence, the crystal design. There have been few studies on the effect of substrate temperature on the morphology of the perovskite film. Among some of the earlier studies is a report by te films . In thiste films . Figure m. Above Tm, ΔG > 0 is not favorable for spontaneous transformation, whereas below Tm, ΔG < 0 favors spontaneous phase transformation. In this study, the perovskite film was coated on the substrates preheated to five temperatures below Tm, (Tm for MAPbI3 = 120°C).In 2018, Srivastava et al. described the underlying theory of crystallization thermodynamics altered by the preheating temperature which leads to a significant change in the film morphology . In a siI) is given by the product of the number of critical sized particles (N*) and the frequency at which they become supercritical, i.e., the frequency at which they cross the interface to join the product phase (ν′), as given by the equation  according to this equation is shown in m, which agrees with the observed results. It was found that the perovskite film coated on the substrate with a preheating temperature of 100°C has an optimum morphology for the high open-circuit voltage and uniform charge and ion diffusion across the interface. Moreover, the dependence of the growth rate (U) with the temperature reveals that the growth of the nucleated particles is maximum at a temperature just below the maximum nucleation temperature. Hence, in this study, the films were annealed at the temperature of 100°C for 10 min. This study suggests that the mechanism of fast nucleation followed by delayed growth is the best method to obtain high-quality perovskite films  and organic methylammonium iodide (MAI) precursors , the conversion of MAI with PbI2 is inhibited, leading to the formation of the unreacted amorphous film. Therefore, tailoring of the optimum substrate temperature for both spin coating  can work as an antisolvent, given that it is miscible with the solvent of the perovskite ink; at the same time, perovskite precursors are expected to show poor miscibility with the antisolvent. The antisolvent treatment method helps the perovskite ink to reach the supersaturation state and form the nucleus sites. Formation of nuclei is governed by the variation in Gibbs free energy (G). For homogeneous nucleation, change in the Gibbs free energy is given by the following equation ΔG=ΔGS+Δmated as ΔG∗=16ΠγTo achieve the system with stable nuclei for further growth, the critical Gibbs free energy and critical radius of the nuclei need to be reduced. This requires a decrease in the surface energy between the supersaturated solution and the crystalline surface. The addition of additives such as benzylamine (PMA) in a/an solvent/antisolvent is known to decrease the surface energy (γ) and help in achieving uniform, compact, and small grain-sized perovskite films suitable for the high-performance light-emitting diodes (LEDs) .max corresponds to the equalization of chemical potentials of liquid and nucleus sites. Now, the role of the antisolvent treatment is to increase the chemical potential of the nucleus site and cause a positive gradient in the µ-n curve, which is the characteristic signature of Frank–van der Merwe (FM) growth. Thus, by tuning between the Volmer–Weber (VW) and FM growth, we controlled the crystallization dynamics to achieve the required film morphology. The volume of the antisolvent, dripping delay in antisolvent treatment, and the rate of pouring of the antisolvent can tune the perovskite film morphology by controlling the chemical potential of the nucleus sites. Kumar et el. used antisolvent dripping delay to achieve textured film morphology  or dissolving FAI. The strong coordination of tBuOH:EA makes this possible  . Althougpossible . Antisolpossible .via bypassing the intermediate phase (CH3NH3I–PbI2–DMSO)  or o-dichlorobenzene (o-CB) with a larger dipole moment, as compared to toluene, can induce the liquid to a crystalline phase transformation instantaneously I2–DMSO)  (Jung etI2–DMSO) . The int2. The role of IA formation is investigated in MASnI3-based perovskite films, and it has been observed that by using DMSO (strongly coordinating solvent) as the solvent forms more stable IAs as compared to DMF. Since DMF has a lower coordination affinity, lower boiling point, and higher vapor pressure as compared to DMSO, using DMF in the perovskite film preparation immediately forms the perovskite structure resulting in a black-colored film, whereas in case of DMSO, a gradual yellow to black color change occurs while annealing the films, which indicates that DMSO shows a higher stability of IAs as compared to IAs formed by DMF solvent. Formation of the new IAs from different solvents  has been confirmed, using the XRD and FTIR measurements. The formation of IAs is helpful in controlling the crystal size, crystallization rate, and ultimately the morphology of the perovskite films. Stable IA formation leads to a much compact, uniform, and large grain-sized perovskite films. Cai et al. investigated the effect of the solvent ratio in a binary solvent system. They found that 20–40% of DMF or GBL mixture in DMSO results in an optimum morphology (DMF (Dimethylformamide), DMSO (Dimethylsulfoxide), DMA , GBL (γ-butyrolactone), and NMP (N-methyl-2-pyrrolidone) are the most commonly used solvents in making good quality perovskite films for the high-performance perovskite solar cells and light-emitting diode. All of them are aprotic, high boiling point, and low vapor pressure solvents, and they are only useful in making full coverage, pinhole-free compact film when used along with some additional treatments such as antisolvent treatment or gas-assisted crystallization . The chorphology   of 19.2°, 22.4°, 21.6°, and 47.4°, respectively. The surface coverage for the controlled samples (without any solvent additive) was found to be around 20–30% only. Surface coverage for EA and CB additives was improved to more than 95%  stoichiometry.Solvent additives can be used to optimize the surface energy of the solvent, and thus, according to than 95% . They fovia solvent extraction by an antisolvent treatment. Preheating the substrate can affect surface-induced nucleation and control the crystal growth. It has been observed that the formation of uniform pinhole-free perovskite films can be realized by elevating the substrate temperature close to the equilibrium melting point (Tm). However, only those substrate temperatures below Tm are thermodynamically favorable. The temperature-dependent growth rate reveals that the growth of the nucleated particles is maximum when the substrate temperature is kept closer to Tm. The chemical potential of the nucleic site can be tuned in order to tune between the FM and VW growth mode with the help of tuning the delay of the antisolvent treatment. This helped us to tune the perovskite film morphology for the application in the high-performance devices. Also, the rate of antisolvent dripping plays a significant role in improving the crystallization process; a slow rate of antisolvent dripping is found to result in better quality perovskite films and solar cells. Apart from this, the solvent boiling point, coordination affinity, and dipole strength also play a significant role in deciding the ultimate film morphology, defect density, and charge transfer resistance. Solvents with a higher coordination affinity such as DMSO help to slow down the crystal growth rate and achieve the larger sized grains suitable for the perovskite solar cells. An optimized ratio of the low and high coordination affinity solvent can help to control the grain size and film morphology in accordance with the application.In summary, we have reviewed the latest, advanced strategies to control the perovskite crystal growth in fabricating the high-quality hybrid halide perovskite thin films, suitable for optoelectronic devices. It has been found that the key parameter deciding the nucleation and subsequent growth of the nucleated particles is the Gibbs free energy. We mainly focused on the three most popular strategies, namely, substrate temperature treatment, antisolvent treatment, and cosolvent engineering. We found that these techniques are found to be highly effective in controlling the film morphology and obtaining the high-performance perovskite solar cells and perovskite light-emitting diodes. The condition for the nucleus formation is to achieve the supersaturation of the perovskite precursor solution, either by heat treatment or


The aim of this study was to show a case of a Class III subdivision adult patient treated with clear aligners (CA) and the extraction of a lower bicuspid. A 19-year-old male with a class III canine and molar relationship on the right side and a deviation of lower dental midline to the left asked for an aesthetic treatment. He refused orthognathic surgical procedures, so he was offered a camouflage orthodontic treatment with the extraction of lower right first premolar to achieve a canine Class I relationship and to center the lower midline. The treatment was performed with clear aligners and the use of Class III elastics to maintain distal anchorage on the right side during the canine distalization. At the end of the treatment, the occlusal objectives were achieved. The management of Class III malocclusion has always been a challenge for clinicians . DependiIn recent years, clear aligners have become very popular for orthodontic treatment in adult patients, considering that they are more aesthetic, more comfortable and easier to manage in terms of both emergencies and oral hygiene as compared to braces ,14. TodaThe present case report describes the results obtained in an adult patient with a Class III subdivision malocclusion and dental midline deviation, treated with clear aligners and the extraction of a lower bicuspid.A 19-year-old man presented for observation with the chief complaint of crowded lower teeth. Extraoral examination showed a Class III skeletal pattern with a nasolabial angle increased and flat cheekbone contouring, showing a slight maxillary hypoplasia; increased lower facial height; and a slight deviation of the chin to the left . IntraorThe primary treatment objectives were to establish a canine Class I relationship on the right side, to center the lower dental midline with the upper one, to correct the mandibular crowding, and to achieve a normal overjet and overbite.As a first option, a surgical treatment plan was proposed to the patient to solve mandibular asymmetry and to improve maxillary retrusion and simultaneously achieve occlusal objectives while normalizing the position of the incisors: a maxillary Le Fort I advancement combined with a mandibular bilateral sagittal split osteotomy (BSSO) setback, which involved the extraction of the two mandibular third molars. The patient refused the surgical procedures, so he was presented with orthodontic camouflage treatment. The patient accepted this option and asked for an aesthetic treatment. Hence, the first treatment plan included the use of clear aligners and the extraction of a lower bicuspid (4.4), with the use of Class III elastics on the right side only during the night. Another treatment option was the unilateral distalization of the fourth quadrant teeth by using skeletal anchorage and with the extraction of the lower right third molar (4.8). However, this procedure was longer and less predictable, so the patient chose the treatment with the extraction of the premolar.The lower right bicuspid (4.4) was extracted one week before the patient received the first two aligners, with the aim of taking advantage of the regional acceleratory phenomenon (RAP) . A couplAt the end of the first treatment phase (after 15 months), an open bite tendency and reduced overjet (due to the increased Bolton index) were present . The finThe post-treatment records demonstrated that all the treatment objectives were achieved. Extraoral photographs showed the improvement in facial aesthetics both in the frontal and lateral view, although, obviously, the skeletal deviation of the chin remains unchanged . IntraorThe purpose of this study is to show a Class III subdivision case with lower midline deviation treated with a premolar extraction and clear aligners. Class III asymmetrical malocclusion has always been a biomechanical challenge for orthodontists. When large dentoskeletal discrepancies are present, a combination of orthodontics and orthognathic surgery remains the treatment of choice . If the In addition, the patient had asked for an aesthetic treatment and had warned that he would have to spend a few months working abroad. The literature showed that clear aligners are more aesthetic, more comfortable, allow better oral hygiene and have fewer emergency visits compared to fixed appliances ,28,29. FIn this case, clear aligners also provided advantages in terms of orthodontic biomechanics. Indeed, the vertical control of the posterior teeth due to the posterior bite-block effect from the aligners’ constant occlusal coverage  was ableAnother advantage of clear aligners therapy was to be able to create a fully digital workflow with the help of three-dimensional technology in diagnosis, planning, and assessing the impact of therapy on the soft tissues of the face and in evaluating the results in progress ,38. The The limitation of this study is that it is only a case report. Hence, more scientific literature concerning Class III subdivision treatment with the use of clear aligners and an isolated extraction of a bicuspid in the lower arch is needed to improve and validate this technique. More widely, further research in borderline Class III cases treated with clear aligners is necessary to learn more about this issue.This case report demonstrated a successful approach to treating Class III subdivision malocclusion in an adult patient using clear aligners. The extraction of a lower bicuspid and Class III elastic on one side only at night led to a satisfactory occlusal and aesthetic correction, with higher predictability and reduced time compared to unilateral molar distalization. An extractive therapy with clear aligners could be considered a valid alternative in Class III non-growing patients who refuse orthognathic surgery and choose a treatment aimed at dentoalveolar compensation.


Upscaling is a significant part of implementing industrial research in a large-scale market. In some cases, this means constructing a simulated factory version before establishing an in situ pilot-scale plant to predict and increase any chances of feasibility. A black soldier fly larvae fatty acid extraction factory may find its merit in the proper utilization of the increasingly explored potential of insect larvae. The factory predicted and assessed the feasibility of black soldier fly larvae under three different feed varieties. Fatty acids would be the final product, with several waste products and by-products.Black soldier flies have been studied as an alternative animal feed. On the other hand, they could be used to yield an abundance of fatty acids. Their omnivorous diet and low space requirements allow for the mass breeding of black soldier flies, using widely available food wastes as feedstock. This study simulates the industrial upscaling of an extraction process for black soldier fly larvae using SuperPro™ 9.5 simulation software. This software contains an extensive material library that regulated physical data for the chemical composition of the larvae and the products. It also bundled several types of bioreactors utilized in bioprocessing. The scheduling of the plant was aided by SchedulePro, which allows for the generation of batch durations and Gantt charts. Four fatty acids were chosen as the main revenue source, with simulated proteins assigned as by-products of the plant. Ash and cellulose were the wastes of the plant, and were separated through multiple filters. The plants were later assessed for their economic feasibility. The kitchen waste plant was the most profitable, and the control variable was the only unprofitable plant. These results may have been impacted by the waste content found in the control variable and the abundance of revenue products in the kitchen-waste-fed larvae. In recent years, the utilization of deliberately bred insects has piqued the interest of the academic world. Insects have been investigated thoroughly for their benefits in medicinal applications, animal feed, and even pest control in commercial agriculture. One insect, in particular, has attracted the interest of researchers—the black soldier fly. This highly adaptable , fast-brBesides proteins and fatty acids, black soldier fly larvae also produce ash and fiber wastes during their metabolism . The conIn recent years, black soldier fly larvae have been used for the small-scale waste treatment of organic materials such as rice straw, fishery wastes, fecal sludge, and industrial food wastes . The demThe fatty acids found in black soldier fly larvae have known benefits in medicine, food, oleo chemistry, and biochemistry, with an increasing demand locally in Taiwan and globally ,8. In MeEstablishing a plant usually requires a concise schematic of the unit processes involved in the production, from its feedstock to the commercial product . HoweverThe simulation was conducted using SuperPro™ 9.5 . It was used with cooperation with Misri Gozan from University of Indonesia in Indonesia as the academic license owner. The tools were taken from parts of the software library and sized accordingly.The plant was simulated under a long-term batch system. The operation time was set to 7920 h annually. The deliberate choice of operating time was to account for some downtime that occasionally occurs for upgrades or equipment maintenance.The components required for this simulation to run are presented in The input materials of the plant—in this case, fly larvae—had multiple compositions programmed through the customization of the SuperPro™ library and presented as a summary in The alcalase enzyme is a protease enzyme that enables the induction of proteolysis. In the context of proteases, alcalase is considered a “serine endopeptidase”, which provides information about the catalytic structure of the enzyme, which is known for its classical catalytic triad of amino acids, with serine being one of them . This enThe alcalase enzyme’s effect on black soldier fly larvae was assessed by Assis et al.  on a labThe unit processes performed here followed the unit library provided by the SuperPro™ simulator software. The labelling followed the default automated unit labelling system built into the software; the final product may differ in labelling, but the overall functions typically remained the same. The details regarding the unit processes can be read in The whole factory can be split into three main sections : the mecEach unit within the simulation was customized according to the throughput and composition of each step of the batch’s input stream. The software automated the labelling of each unit with built-in numberings. The two most common parameters in all units were temperature and pressure. The factory did not operate under a vacuum, and the air was considered to be a mixture within the simulation library. The details regarding the processes utilized are available in The plant parts consisted of the flow of raw material and intermediary substances in multiple forms. The expectations regarding what should be found in a certain flow for the whole simulation are listed in The waste streams were split into wastes 1 and wastes 2 from the order of placement within the factory. Wastes 1 assigned towards the first filtration unit and waste 2 assigned to second filtration unit. The Reusable Water Streams were split in the same manner, focusing on centrifugation unit and distillation tower respectively. Streams labelled as Impure products were vaporized fatty acids needing condensation to be in a packageable formThe calculated contents of the flow were recorded similarly to Plant consumables are any material inputs that impact the process by manipulating environmental parameters instead of being involved in the processes themselves . These cThe authors constructed a plant process flow using SuperPro™ simulation software. The plant itself contained four major revenue flows and three major waste flows. The revenue flows consisted of lauric acid, stearic acid, myristic acid, and palmitic acid. The ash waste and cellulose waste outputs comprised the main waste products of the facility, along with a freeze-drying exhaust waste output. A secondary revenue was established on the protein output flow, which utilized the default protein properties provided by the built-in library. The Plant was operated under a pseudo-continuous mode of operation, allowing for an active period of 330 days as shown in The plant batch was conducted using several inter-dependent processes within the plant. These interdependent processes often used the output of a prior process as the input of its succeeding process. Some processes also worked in parallel, which means the process was semi-continuous, with the output moving into the subsequent processing unit. Most of the processes shown in the Gantt chart were set within the constraints of the software as seen in Many processes were set to one hour to suit a typical workday of 8 h, with the data visualization in The material balance report can be broken down into the summary and the stream details . The sumThe pie charts that comprised The generated income was from the lauric acid, stearic acid, palmitic acid, and myristic acid . These fThe waste product was programmed to be a cost-inducing component, as the plant did not possess any waste treatment installation. A possible third-party entity handled any form of waste. This is in stark contrast to some factories that center their profitability on a built-in waste treatment system, which eliminates the need for a third-party waste treatment facility . Thus, wThe fiber-rich spent-grain fly larvae diet may have caused a reduction in fatty acid revenue components. Due to an inverse ratio between the chemical makeup of fly larvae, the presence of fatty acids and fiber is competitive. This contrasts kitchen-waste-fed larvae or animal-waste-based feed for the larvae .The authors deemed the two filters installed within the factory necessary, yet they could pose issues. Both the waste separation outputs contained fatty acids, which may have reduced the potential profits, as some of the revenue material was removed as waste by the passive indiscrete filter membrane.The fatty acids were programmed with a fixed amount of costs, so the researchers deemed the price changes to be unimpactful to the overall profitability of the factory. It was also set under the assumption that animal-based fatty acids would not experience any issues becoming accepted within the Taiwanese market.The cost within the factory was hiked immensely by the distillation segment, owing to the multiple towers required to separate the purified forms of the fatty acids, all of which also required a significant energy input to operate. This was surprisingly in line with previous fractionation tower research . FollowiThe factory payment system can be assessed using a cash flow analysis, and in that case, the modelling followed a 20-year payment period. In most cases, a factory will start off as unprofitable before returning the capital after at least 10 years. The economics were based on a 6% flat tax rate. Factory profitability can be measured by assessing IRR and NPV . The intIt can be seen from Several waste products could be addressed better, particularly cellulose and ash, which both have established methods of waste treatment. Implementing a more discrete membrane for the filter may also be beneficial for reducing the potential wastage from carried fatty acids, which reduced profitability. In addition, the alcalase enzyme could be reused with a limited lifespan if the enzyme was introduced in an immobilized or encapsulated form.The factory simulation deemed the extraction and refining of fly larvae fatty acids to be feasible through engineering and economical viewpoints. There are certain considerations to be made regarding the waste treatment of the plant, as cellulose-based wastes may add a lot of additional costs to the factory’s capital requirements.


This work presents the application of a methodology to measure domain expert trust and workload, elicit feedback, and understand the technological usability and impact when a machine learning assistant is introduced into contingency analysis for real-time power grid simulation. The goal of this framework is to rapidly collect and analyze a broad variety of human factors data in order to accelerate the development and evaluation loop for deploying machine learning applications. We describe our methodology and analysis, and we discuss insights gained from a pilot participant about the current usability state of an early technology readiness level (TRL) artificial neural network (ANN) recommender. The field of power systems represents an exemplar for domains that struggle to incorporate new technologies. Challenges with control room work culture tend to stifle technological innovation and prevent the evolution of technology Kennedy, . To an eMachine learning advances can make it possible to aid operators and engineers in critical action decisions, particularly when time pressure is involved. A notable implementation of machine assistance is accurate forecasting of total system load as well as solar and wind expectations for generation scheduling and energy market pricing  is a process used to identify potential violations of equipment and operational limits due to unexpected loss of any single piece of physical equipment. Performing this analysis allows operators to identify the best course(s) of action to keep the power grid operational. Operators currently rely on predetermined procedures and their own experience to resolve contingencies in the power grid. This study evaluates the readiness and usability of a recommendation system called ACAT . This system makes use of an artificial neural network (ANN) to recommend contingency mitigation actions to operators in order to quickly address potential violations.To emphasize the importance of operator workflows, human expertise, and how advancement on the HRL scale can facilitate TRL advancement, our research team considered an early stage TRL algorithm that was designed to provide recommendations to control room operators and engineers for mitigating potential power system violations. The disconnect between technology developers and end-users is a living obstacle in getting well-meaning technology in the hands of mission-critical personnel that have to work with these proposed tools and systems on a daily basis. Our work aims to provide an example of how a human factors study can be conducted with an end-user to provide actionable feedback to developers. We seek to accelerate the development and evaluation loop inherent to these development cycles by rapidly collecting information from domain experts that addresses these operational procedures. This includes the acquisition of objective, subjective, and qualitative feedback measures to address cognitive load, trust, human readiness, and the technological readiness, usability, and impact of a novel system. The captured data are aggregated and analyzed to report back on the current state of the system under evaluation.In particular, we contribute:A methodology that measures domain expert trust, cognitive load, and technological usability of recommender systems, with the goal of rapidly acquiring and analyzing objective, subjective, and qualitative measures.Results of the application of this methodology to an early-stage tool designed to provide contingency mitigation recommendations to power grid operators.Insights gained from observing user interactions under individual scenario conditions and difficulties.The methodology applied here provide a framework for developers and human factors experts evaluating recommender systems, with a particular focus on how humans respond to and rely upon the provided recommendations. Researchers can then apply this methodology when integrating those systems into a workflow. The methodology proposed should help to identifying limitations and shortcomings in the system or challenges in its integration into human operations. This will support improvement of the system under development and accelerate the integration of the system into operations.In this section, we discuss existing work in three topics related to this research. We briefly review common barriers to technology adoption (Section 2.1), we provide an overview of common terminology and discuss active research in human-machine trust (Section 2.2), and we describe the usage of heart rate variability as a proxy for understanding cognitive load (Section 2.3).From the many barriers to the adoption of new technology identified by Rogers , we focuCharacteristics that are inherent to the technology itself often are often the result of flaws with the system, including unreliability, failures, malfunctions, and low accuracy , the user can develop additional trust in the system . HRV measures variation in time between heartbeats, or more precisely the variation in recorded intervals between R spikes, the peaks of an electrocardiogram wave. Measuring this variability in heart rate consistently shows that when mental workload is increased, HRV decreases , subjective (Section 3.3), and qualitative (Section 3.4) feedback from a participant. To capture the data needed to interpret heart rate variability (HRV), participants are fitted with a Zephyr bioharnessSDNN: Measured in milliseconds, this is the standard deviation of N-N interbeat intervals that have been filtered and artifacts have been removed.RMSSD: Measured in milliseconds, this is the root mean square of successive R-R interval differences. R-R intervals are interbeat intervals between all successive heartbeats.LFn: This is the ratio of the low frequency power (0.04–0.15 Hz) in the ECG frequency-domain to the total power.HFn: This is the ratio of the low frequency power (0.15–0.4 Hz) in the ECG frequency-domain to the total power.LF/HF ratio: This is the ratio of LF power to HF power.There are a number of reviews that compare and contrast these metrics as measures of mental workload  to 5 (Strongly Agree).After each trial, participants receive a modified version of Madsen and Gregor's Trust Questionnaire . This self-report measure assesses a person's workload on six dimensions  and the software (Section 4.2) available to the participant, summarize the experimental procedures (Section 4.3), provide a reference overview of the scenarios used in the experiment (Section 4.4), and present an example scenario that is representative of the contingencies that the participant is resolving in this study (Section 4.5).The experiment was conducted in the Electricity Infrastructure Operations Center (EIOC) West Control Room at Pacific Northwest National Laboratory (PNNL), shown in The participant in this study made use of two software systems during the experiment. Participant interactions were performed within PowerWorld Simulator, a commonly-used program within control room environments for exploring the effects of changes to the power grid. ACAT, the system under review in this study, is a recommendation system intended to direct operators toward optimal solutions for resolving contingencies.PowerWorld SimulatorFor the experiment, the participant was given three PowerWorld displays seen in  containiAdditionally, a custom logging script was written to record the actions taken by the participant in PowerWorld over the course of the experiment. This log file contained records of the particular piece of equipment operated by the participant , new setting values for the equipment, and timestamps logging when the participant updated the power flow solution and contingency analysis results.ACAT is the technology under evaluation in this study. At the time of this study, ACAT has a Technology Readiness Level (TRL) of 3  due to the fact that it had been trained to consider line and transformer outages on a single power system model with a fixed topology Mankins, . A TRL oSolutions to new contingencies fed into the model are generated via the corrective action algorithm. This algorithm uses an iterative process to compare known stable system states to the load and generation profile under evaluation, developing a mitigation procedure to transition from a state with a violation to one without. This mitigation procedure is validated by the ANN to identify any potential follow-up violations that may result from applying the solution. Following this validation step, a sequence of control actions is then displayed to the operator as a recommended procedure for resolving the contingency. Recommendations generated by ACAT take the form of text on a large monitor in the control room, and they identify substations on the 118-bus system and what actions  should be taken. The recommendations span the general class of actions that human operators can take as corrective actions. Operators are not required to act on these recommendations but can.Rather than collecting a team of participants to fill the collection of control room roles in a normal operating environment , this research focused on the interaction between a single participant and the ACAT recommender. We made this determination to maintain focus on interactions with the ACAT recommender, allowing us to better measure performance, trust, and the operator's real-time opinions about the quality of the tool and its recommendations during trials when ACAT was present. During each trial, the participant was responsible for identifying the contingency analysis violation, determining the proper mitigating actions, and implementing those actions in the PowerWorld simulation. The workflow deployed in this experiment is modeled off of actual workflows for power system engineers. The general steps include presentation of a scenario, an exploratory solving phase, a final solution implementation, and completing a post-scenario survey.Two adjacent consoles were configured with two instances of the PowerWorld power system simulation software. The left console was set up as the simulated real-world Supervisory Control and Data Acquisition (SCADA) interface and one-line diagram. The right console was configured as a “study-mode” session, with two monitors containing tabular displays and one-line diagram from ACAT and PowerWorld. In the performance results presented in Section 5.2, these are labeled as “SCADA” and “study-mode” respectively. A paper copy of the power system operations manual was also available for the participant to consult at all times during the experiment. This operations manual contains instructions for resolving common contingencies in the grid and evaluating the severity of violations, as well as finding use in training sessions.The experiment took place over the course of three partial-day sessions. At the beginning of each day, the participant was fitted with the Zephyr bioharness. The first day included a training session, so that the participant could gain exposure to the layout of the room and consoles, had time to work with the IEEE 118 Bus network that was used in the scenariosFollowing the training session, the participant was presented with scenarios of varying difficulty (described in Section 4.4) and asked to resolve the contingencies in each scenario. Trials alternated between solving a scenario with and without the presence of an ACAT recommendation. Each of the scenarios that we created were tested under both experimental conditions, but the order of the trials was designed so that the participant did not see a scenario twice on the same day. Following each trial, the participant completed a copy of the survey appropriate for the trial condition (see Section 3.3 for details of the differences).In this study we are particularly interested in how the human responds to and relies on (or not) ACAT recommendations. We only evaluate ACAT recommendations to the extent that it can provide insight for what the human operator does in a given scenario. In cases where ACAT's recommendations were not perceived as appropriate by the participant, we attempt to capture the human's perception of ACAT rather than explaining ACAT's technical process.For this evaluation, we constructed a collection of scenarios based on the IEEE 118 Bus network. Some of these scenarios were set aside for use only in the training session. Seven scenarios were used in the experimental trials, which we judge to have three levels of difficulty based on expert power system engineer feedback. Readers less familiar with power systems should take away that scenarios were designed with varying difficulties to resolve contingencies without an aid. We include the technical aspects for the interested reader. The scenario numbers are included in the sections that follow for easy reference to the difficulty of each scenario.NOA LIGHTLOAD-HIGHVOLT: An easy scenario that is initialized with light load throughout the grid, but the loss of a 138kV line between substationsE1. WOA LIGHTLOAD-HIGHVOLT: An easy scenario that is initialized with light load throughout the grid, but the loss of a 345kV line between BEQUIN-1 and BREED-1 begins to cause high voltages in the West region because of an open-ended line.E2. WOA TANNRS-SORENS: An easy scenario centered around the large interchange between the North and West regions. The loss of a 345kV line between TANNRS-1 and SORENS-1 causes power imported by the West region to now come exclusively through long 138kV tie lines, with some lines encountering thermal limits.E3. SOA KANAWH-CABINC: A medium scenario beginning with low generation in the southeastern portion of the South region. The loss of a 345kV-138kV transformer between KANAWH-1 and CABINC causes an overload in the South region.M4. NOA EASTLI-MUSKNG: A medium scenario centered around the large interchange between the North and West regions. The loss of a 345kV line between EASTLI-1 and MUSKNG-1 risks voltage collapse due to power imported by the West region now coming exclusively through long 138kV tie lines.M5. WOA OLIVE IROL 2: A medium scenario centered around the large interchange between the North and West regions. The loss of a 345kV-138kV transfomer from OLIVE-1 to OLIVE-2 causes voltage collapse throughout the West region.M6. SOA CA SOLUTION FAILURE: A hard scenario centered around the large interchange between the North and South regions. The loss of a 345kV line between EASTLI-1 and MUSKNG-1 could lead to a possible system blackout due to angle instability along the tie lines connecting the West and South regions.H7. The experiment was designed to replicate the naturalistic decision making processes used by power system operators first documented by Greitzer et al.  and GreiOperators will refer to the guidelines in their operations manual to evaluate the severity of a particular violation, and the required timeline for which the contingency must be mitigated. To resolve CA violations, operators use a sequential decision process that involves projecting the impact of a particular contingency, tracing the root cause of the violation, and identifying available equipment near source of the problem. The decision process typically follows a series of yes/no questions that operators ask themselves while resolving the CA violation.The typical process used by operators for resolving undervoltage violations can be illustrated by tracing a decision tree for a sample violation. A snapshot of the PowerWorld interface for the particular scenario is shown in The operator will examine the system and identify the source of the problem is the large load at HOLSTO substation without any voltage support (“Root Cause Location” in Q: Under 0.92 per unit (pu)? A: Yes.Q: Nearby capacitor or reactors? A: No.Q: Nearby generators? A: The closest is at GLEN L substation  and a summary of the qualitative feedback collected about ACAT (Section 5.4). In Section 6, we explore the details of individual scenarios and trials to better understand how interactions with ACAT change over time.ECG data was measured to assess cognitive workload experienced by the participant. We included a training session to allow for learning effects and familiarity of working with ACAT to stabilize. Given this training, we assume that cognitive load is a measure of both scenario difficulty and additional burden (or not) of working with ACAT, and not changing over the course of the experiment. As shown in Two metrics were most expressive across the scenarios, SDNN and LF/HF. A manual clustering was performed on the scatter plot of absolute metric values with respect to SDNN  against LF/HF (ratio of low-frequency power to high-frequency power) to understand where there was corroborating evidence for higher workload among the metrics. Here we included both no ACAT and ACAT trials together. When SDNN is low and LF/HF is high then there is evidence from both metrics that workload is high. We found four clusters in this analysis :: WOA OLIVE IROL 2 , NOA LIGHTLOAD-HIGHVOLT , SOA KANAWH-CABINC : SOA CA SOLUTION FAILURE , WOA OLIVE IROL 2 , NOA EASTLI-MUSKNG : WOA TANNRS-SORENS , NOA EASTLI-MUSKNG , WOA LIGHTLOAD-HIGHVOLT : WOA TANNRS-SORENS , SOA CA SOLUTION FAILURE The third and fourth clusters indicate higher cognitive load trials than the first two clusters. Noticeably, Cluster 3 only contains ACAT trials and Cluster 4 contains the hard scenario in the ACAT condition. This scatter plot also highlights that trials where ACAT was used (marked with an ‘x' in the figure), corresponding in most cases to either lower SDNN or higher LF/HF.As a measure of performance, we note that completion times tended to decrease throughout the experiment despite changes in difficulty and counter balanced ACAT/No-ACAT trials. This may be an indicator that the participant was still optimizing a strategy for resolving contingencies generally. We note that the participant did not have previous exposure to the 118 bus system, the ACAT tool, or the procedures manual prior to the experiment. The participant did complete a 1-day training session during the first day of the experiment, where he familiarized himself with the ACAT algorithm and 118 bus system.Completion times cannot be completely explained by learning effects. The scenario, WOA OLIVE IROL 2 (M6), is a scenario where the participant was faster with ACAT although ACAT was used first. In a second scenario, SOA KANAWH-CABINC (M4), the participant was slower with ACAT with this trial being the second time the participant was in this scenario. When scenarios are separated out by difficulty and by ACAT use, there is an ordering where more difficult scenarios required longer completion times see . GeneralAction penalties were computed according to the formula provided in Anderson et al. . This meFor most of the scenarios, system penalties  scenario, the participant implemented a better solution on the SCADA console than the study-mode console for the run without ACAT. However, with ACAT the system penalties (SCADA and study-mode) were both zero.Unexpected penalty results are seen in scenario WOA OLIVE IROL 2 (M6). While the penalties for trials without ACAT are comparable across study-mode and SCADA, the trials with ACAT exhibit a higher penalty for the SCADA solution than the study-mode. In study-mode, the participant resolves the contingency with a penalty of zero, but when switching to the SCADA console, the system penalty increases to 78.99. Upon further examination, the participant did not implement the study-mode solution and instead believed fewer actions (not switching a reactor) would resolve the contingency. The participant had high confidence this single action would resolve the contingency.M = 1.73 (SD = 0.31) to M = 2.29 (SD = 0.48), one point lower than their in Operations Procedures that ranged from M = 3.00 (SD = 1.77) to M = 3.33 (SD = 0.00)  .The only scenarios tested that did not reveal consistently lower trust in ACAT across constructs were NOA EASTLI-MUSKNG (M5) and WOA OLIVE IROL 2 (M6). For the NOA EASTLI-MUSKNG (M5) scenario, Cognitive-Based Trust  in ACAT was comparable to Cognitive-Based Trust in Operations Procedures. In contrast, Emotion-Based Trust  was considerably lower for ACAT when compared to operations procedures . When raM = 2.00) for this scenario (M = 2.00) when compared to the average Faith rating across all other scenarios (M = 3.00). Survey data did not provide any additional insights into the participant's lack of faith in the procedures during the WOA OLIVE IROL 2 (M6) scenario. The participant did not identify any portions of the procedures that led to a lack of faith during this scenario.For the WOA OLIVE IROL 2 (M6) scenario, trust in the Operation Procedures was higher for all constructs except Faith. Faith in both ACAT and Operation Procedures was low (scenario . Faith iM = 1.29 to M = 1.86). Physical demand was non-existent (M = 0.00) and perceived performance received the highest possible rating for all scenarios (M = 5.00). On average we found very little difference in perceived workload between conditions  and not increasing power generation often enough. The participant was able to resolve all scenarios without load shedding, despite the ACAT recommendations. The load shedding steps also were not accompanied with other information that an operator would need to know, such as reactive power output. Other common mitigation steps such as line switching and bringing offline power plant units into service were not provided as recommended actions by ACAT.In this section, we explore some of the individual scenarios, obtaining insights regarding participant interactions with the system under varying scenario conditions and difficulties.The SOA KANAWH-CABINC scenario serves as a representative example for a case where the positives of the ACAT recommender outweigh the negatives. In this medium-difficulty scenario, the loss of a transformer has led to some line overloads, low power generation in a substantial region of the grid, and low voltages in some distant buses. Mitigating this contingency requires a participant to redispatch multiple generation units which keeping line thermal lines in mind. This scenario was encountered near the middle of both trial days, and the trial without access to the ACAT recommender was encountered prior to the trial with ACAT assistance.The HRV data indicate that the participant had approximately the same cognitive workload in both trials, with slightly higher workload during the first trial without ACAT assistance. Although ACAT assistance was provided after the participant had already seen this scenario, the similar cognitive workload in the ACAT condition is backed up in the survey data. The participant showed consistently lower trust in ACAT than in the operational procedures, and the TLX ratings were higher when the participant was working with ACAT. The participant also had a higher completion time in the ACAT condition, and also performed more system checks to confirm the ACAT recommendation. Despite this higher workload, higher effort, and lack of trust in ACAT, the performance results showed that ACAT was helpful, with lower penalties in both study mode and in the SCADA console solution during the ACAT condition.Overall, this shows that ACAT objectively helped the participant to mitigate the contingency, supporting the technical correctness of the tool. However, the lower trust and higher workload outcomes in the ACAT condition demonstrates the lack of human readiness of the tool. The participant's opinion of the tool had already stabilized in both cognitive and emotional trust constructs, and the assistance of the tool did not affect those opinions.Following the training sessions, the first scenario encountered by the participant is NOA EASTLI-MUSKNG, a medium-difficulty scenario in which there is a risk of voltage collapse at multiple substations because of power flow across very long tie lines. Mitigating this contingency requires a participant to redispatch multiple generation units while also keeping in mind transfer path limits and power balance. This trial is also performed with access to the ACAT recommender in the first condition, with the second condition run on the next day with only procedures available. This setup provides an opportunity to understand early participant trust in the system.Indeed, the survey results showed this scenario to be a trust outlier when compared to the other scenarios. The cognitive components of trust were nearly equal between the ACAT and No-ACAT trials , which stands in contrast to the other scenarios in which the No-ACAT trials were consistently higher that the ACAT trials. The emotional components of trust displayed the opposite pattern; these values were minimal in this scenario, with a wide difference between the ACAT and No-ACAT trials. In the other scenarios, the emotional components of trust were still consistently lower for the ACAT trials, but the gap between ACAT and No-ACAT was smaller.These results show the evolution of trust in the ACAT system, where the cognitive components of trust  decreased whereas the emotional components of trust  increased across the early trials. Both evolution trends were rapid, with both cognitive and emotional trust in ACAT stabilizing across the later trials .WOA TANNRS-SORENS was another of the early scenarios encountered, appearing as the third scenario. This also represents another scenario in which the participant has access to the ACAT recommender first, with the No-ACAT trial encountered on the next day. This scenario is judged to be easy in difficulty, recovering from a loss of a 345 kV line which causes a few line thermal overloads elsewhere in the grid. Mitigating this contingency also requires a small number of redispatched generation units while also keeping in mind the line thermal limits.The performance results of this scenario show that the action penalties were substantially lower in the ACAT condition than in the No-ACAT condition . This stSome indications of this lack of trust are seen in the performance results. While the HRV data shows comparable cognitive load, the participant spent more time mitigating the contingency in the ACAT condition and also performed more system checks to evaluate the quality of the recommendation. The ACAT solution also required more steps to implement, resulting in a higher discrete action penalty score in this condition.The SOA CA SOLUTION FAILURE scenario is unique in this collection of experimental trials, both because of its overall difficulty and because the PowerWorld solver was unable to converge to a contingency analysis solution, leaving the participant without that system and therefore fully dependent on the operational procedures and on ACAT when it was available. In this scenario, the loss of a 345 kV line has lead to angle instability in the tie lines connecting the WOA and SOA regions, and the power system is possibly one contingency away from total blackout. Should similar circumstances occur in the real power grid rather than in a simulation, the looming blackout and lack of a contingency analysis tool solution can easily lead to operator panic as they respond to a time-sensitive issue.The participant encountered the experimental condition with ACAT assistance first and was able to mitigate the contingency. The performance values were roughly the same, with the ACAT trial demonstrating slightly higher action penalties and equal system penalties, but with more system checks performed in the No-ACAT trial. The HRV data was also similar between the two experimental conditions, indicative of the high cognitive load required when exploring and solving this challenge. The TLX survey results were also approximately identical, with only the Effort dimension differing (one point higher for ACAT).Despite these similarities in workload and performance, the participant rated their trust in the operational procedures much higher than their trust in ACAT across all trust components . This reWhile ACAT is a low TRL algorithm, there is value in early stage engagement from end users, in this case, from control room operators and engineers. By studying the performance of ACAT with a representative user, we can identify shortcomings in both the technical reliability and human usability of the tool. Overall, we found ACAT to be underperformant across our testing scenarios. In many cases, this underperformance fell in the usability category (as seen in the trust survey results), but there were also technical limitations of ACAT identified in the debrief. Because it required more time to interpret ACAT results than to implement the solution without ACAT, the participant avoided using the system in many of the scenarios where the system recommendations were available. Both technical and usability issues led to participant disuse of the tool in multiple scenarios, where personal expertise and formal operational procedures were preferred for decision-making.As noted in the overarching results in Section 5, system penalties were typically low in both the ACAT and No-ACAT conditions, indicating contingency mitigation solutions of similar quality in both conditions. This was true regardless of whether the participant encountered the ACAT or No-ACAT trial first. Action penalties presented somewhat more variability in scale, with some trials showing better performance with ACAT and others showing better performance without the recommender.Results suggest it may not be enough to simply improve system transparency as a technique for building trust. Two key points that the participant identified for improvement are the quality of ACAT recommendations and the interpretability of those recommendations. Because ACAT recommendations did not match common operational procedures and were too aggressive in load shedding, the operator was hesitant to implement those recommendations. Similarly, the challenge of interpreting bus numbers rather than substation names added unnecessary cognitive load to the trials. The identification of these challenges with the system provides actionable feedback for developers working on the next version of ACAT.The lower initial ACAT emotional trust ratings seen in the NOA EASTLI-MUSKNG (M5) scenario are likely related to its position as the first scenario encountered in the overall experiment. There is some empirical evidence and theoretical support for this explanation. Lowery et al. found the construct Faith lagged behind other constructs as trust in a new system increased with exposure Lowry et al. . In addiLastly, the construct Understandability showed the greatest variability across all scenarios. A potential explanation for the lower ACAT condition mean for this item may be the focus on the utility of ACAT in the prompt provided during the experiment. Perhaps the participant is distinguishing between understanding the system  and understanding how the system can provide value to the decision-making task (Item 2).One concern with the design of this study is the introduction of learning effects, in which the participant recalls a previous solution or a previous sequence of operations that are unique to a scenario, therefore enabling a more rapid solution in the second trial with that scenario. Our results showed that while completion times did generally decrease over the course of the experiment, there is no correlation between penalty scores and trial order. Further, we found that many scenarios exhibit different penalty scores, indicative of different strategies being pursued and different solutions being generated in the ACAT and No-ACAT trials.The participant also reported not feeling comfortable with trusting ACAT recommendations because he did not have sufficient exposure to the system in advance, and so did not have a good understanding of its reliability. Without that comfort, operators will have a tendency to avoid using the tool regardless of its quality. The participant also noted a lack of familiarity with the operational procedures. Since many contingency violations are resolved based on operator knowledge, additional training with both the grid and ACAT are both necessary in future experiments.This work presents an evaluation of ACAT, an early TRL recommendation system designed to aid power system operators in their decision-making process for contingency analysis and maintaining an operational power grid. To perform this evaluation, we apply a methodology that captures objective, subjective, and qualitative measures of both technical accuracy and the readiness of human operators to employ the system regularly. These measures enable us to quickly evaluate the impacts of including machine learning into an operator's existing workflow on several dimensions simultaneously. The method supports diagnosing and identifying key areas during the development process for successful integration when deployed into the field. We measure performance and workload in two experimental conditions, one in which the ACAT recommender is available and another in which the operator only has access to fixed procedures.In addition to compiling overall performance results across all trials, we performed a detailed exploration of individual scenarios to better understand the nuances of how the participant interacted with ACAT. In our experiment, we identified several key issues with the ACAT system, ranging from usability concerns  to mismatches between the system recommendations and current operational procedures . Communicating these technical and usability challenges to developers will further enhance both the TRL and HRL of ACAT in the next release of the tool.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Institutional Review Board (IRB) at Pacific Northwest National Laboratory (PNNL). The patients/participants provided their written informed consent to participate in this study.JW, BJ, AA, DC, and CF contributed to the conception and design of the study. AA was on-site during the study and handled much of the data collection. All authors contributed to data analysis, and interpretation, writing to the manuscript, and approved the submitted version.The research described in this paper is part of the MARS Initiative at Pacific Northwest National Laboratory. It was conducted under the Laboratory Directed Research and Development Program at PNNL, a Multiprogram National Laboratory operated by Battelle Memorial Institute for the U.S. Department of Energy under Contract DE-AC05-76RL01830.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.