Effective recommendations on healthy food choice need to be personalized and sent out on a large scale. In this paper, we present a model of automatic message selection tailored on the characteristics of the recipient and focused on the reduction of red meat consumption. This model is obtained through the collaboration between social psychologists and artificial intelligence experts. Starting from selected psychosocial models on food choices and the framing effects of recommendation messages, we involved a sample of Italian participants in an experiment in which they: (a) filled out a first questionnaire, which was aimed at detecting the psychosocial antecedents of the intention to eat red/processed meat; (b) read messages differing as to the framing of the hypothetical consequences of reducing  versus not reducing  red/processed meat consumption; (c) filled out a second questionnaire, which was aimed at detecting participants’ reaction to the messages, as well as any changes in their intention to consume red/processed meat. Data collected were then employed to learn both the structure and the parameters of a Graphical Causal Model (GCM) based on a Dynamic Bayesian Network (DBN), aimed to predicting the potential effects of message delivery from the observation of the psychosocial antecedents. Such probabilistic predictor is intended as the basis for developing automated interactions strategies using Deep Reinforcement Learning (DRL) techniques. Discussion focuses on how to develop automatic interaction strategies able to foster mindful eating, thanks to (a) considering the psychosocial characteristics of the people involved; (b) sending messages tailored on these characteristics; (c) adapting interaction strategies according to people’s reactions. Red/Processed Meat Consumption (from now on RPMC) has substantial effects on people’s health, such as an increased likelihood of developing various cancer and type 2 diabetes . For thiIs it possible to think of a communication on healthy eating that is effective, personalized and at the same time easily addressed to many people? Collaboration between social psychology and Artificial Intelligence (AI) can help achieve this goal. On the one hand, social psychology has developed consolidated models of the psychological factors that underlie food choices. On the other hand, AI can, starting from psychosocial models, assess their predictive capacity, as well as simulate their application to larger populations.In this paper, we present an example of how, through the collaboration between social psychology and AI, it is possible to develop personalized communication strategies which, precisely because such, have a better chance of being effective than the standard and one-sided communication that often characterizes the functioning of apps and other digital devices supporting people in their food choices.We involved a sample of Italian participants in an experiment consisting of three steps. First, they filled out a questionnaire aimed at detecting the psychosocial antecedents of their intention to eat red/processed meat. Second, they read messages differing as to the framing of the hypothetical consequences of reducing  versus not reducing  RPMC . Four different subgroups of the sample were randomly assigned to one of the four different framing conditions. Finally, all participants filled out a second questionnaire, which was aimed at detecting participants’ reaction to the messages they read, as well as any changes in their intention about RPMC. Data collected were then employed to learn both the structure and the parameters of a Graphical Causal Model (GCM) based on a Dynamic Bayesian Network (DBN), which was used for predicting the potential effects of message delivery from the observation of the psychosocial antecedents. Such probabilistic predictor is intended as the basis for developing automated interactions strategies using Deep Reinforcement Learning (DRL) techniques by which an artificial neural network can be trained toward a rapid estimate of the psychosocial antecedents and the automatic selection of the most effective message, according to the characteristics of the recipient.In the remainder of this introduction, we will first examine the psychosocial models we referred to in the present research. Then, we will illustrate the reasons that led us to consider the application of Bayesian causal models as opportune and fruitful for the development of a personalized communication strategy.valence framing, that is, of messages framed either in terms of the gain deriving from following the proposed recommendations or the loss deriving from not following them  gain messages state that following the recommendation will have a desirable consequence ; (b) non-loss messages state that following the recommendation will avoid having an undesirable consequence ; (c) non-gain messages state that not following the recommendation will imply missing the opportunity to have a desirable consequence ; (d) loss messages state that not following the recommendation will have an undesirable consequence .Past research has demonstrated that the persuasive effect of communication largely depends on how message recommendations are framed. Message framing refers to the fact that a given content can be presented in different, although objectively equivalent, versions . In the ing them . The resing them . In thisSo far, some studies have considered the differential effects of gain, non-loss, non-gain, and loss messages on the promotion of physical activity  and adheTo move further in the comprehension of the dimensions that may underlie the effects of recommendation messages regarding RPMC, in the present study we considered not only TPB dimensions, but also several other psychological dimensions that previous research has shown to be related to RPMC and/or to the effectiveness of messages aimed at reducing the intention to eat red/processed meat. If research usually tests the influence of few dimensions at a time, thanks to the application of Bayesian causal models and clustering techniques, we were able to consider a considerable number of dimensions all together. We intended to predict which dimensions and which combinations of dimensions would lead to a change in the intention to eat red/processed meat, depending on the specific type of message received.regulatory focus. According to the Regulatory Focus Theory  to being affected by a harmful and severe disease  how severe you think is to develop gastro-intestinal diseases - intend less to act an unhealthy food choice  and that not eating it is beneficial  tend to process nutritional messages more systematically and thereafter show lower intention to choose unhealthy food  collecting information about the interactant using a modicum of questions; (b) selecting the intervention that is expected to be most effective, given the information acquired. The training process of DRL, however, involves reproducing a huge number of episodes, each like a match in a game, in which the machine can explore several alternatives and observe their effects.Overall, the purpose of our research is using the GCM as the basis for a wider system using DRL to generate automated policies for personalized interactions. The overview and the technical details of the DRL-based system can be found in In such scenario, a GCM-based probabilistic predictor acts as a simulator of the interactants, that is, by producing (stochastic) responses to machine actions. There are two further advantages in using a GCM as the basis for DRL. First, the probabilistic predictor can provide an effective and computationally manageable representation of the psychosocial models adopted. Second, the GCM can guarantee an explanation of the DRL policy, once the training process has been completed, in a way that the DNN alone could not provide .Starting from the above, in the present study we developed and tested a model to select messages focused on the reduction of RPMC and tailored on the characteristics of the recipient. In consideration of the type of analysis chosen, based on prediction and not on explanation, we did not formulate specific hypotheses, but some research questions (RQ) aimed at discovering whether the results of the predictive analysis would have validated the relevance of the psychosocial constructs described above in influencing the change of intention after exposure to messages. We therefore asked ourselves the following five main RQ:RQ1 Does reading the recommendation messages used in the study (directly and/or indirectly) predict intention change regarding RPMC?RQ2 Which psychosocial dimensions have a direct influence on intention change after exposure to recommendation messages?RQ3 Which psychosocial dimensions have an indirect influence on intention change, and through the mediation of which message-related dimensions?RQ4 Are there differences in the influence of the four message frames  on intention change depending on the characteristics of the receivers and, if so, what differences?A-priori Sample Size Calculator for Structural Equation Models created by p-value = 0.05; statistical power level = 0.80) and 299 participants to test the moderation of the four levels of past adherence to the MeDiet . However, we opted to increase our sample size, following Jackson’s recommendation (N = 124), and those who weekly ate less than 3 portions of red and processed meat (N = 96) were eliminated from the sample. Then, participants were randomly assigned to one of four different experimental conditions . One week after the completion of the first questionnaire, they were required to read eight messages focused on the health consequences of RPMC and framed according to the experimental condition they had been assigned to. Immediately after reading the messages, participants filled in a second questionnaire (Time 2-T2), assessing their evaluation of the messages, as well as their future intention to eat red/processed meat. The dependent variable Delta intention was then calculated as the difference between Intention measured at Time 2 and Intention measured at Time 1. Participants who did not fully or accurately complete both questionnaires were excluded (N = 70). Therefore, the final sample was composed by 545 participants , distributed in the four message conditions as follows: gain message condition N = 134; non-loss message condition N = 134; non-gain message condition N = 136; loss message condition N = 141.The present study received ethical approval from the Catholic University of the Sacred Heart (Milan). Using the endation  to have In the first questionnaire, participants answered a series of questions measuring the relevant psychosocial dimensions focused on in the study on 7-point Likert scales.Attitude toward reduced RPMC was measured using a semantic differential scale with eight items . Such curve is also called Received Operating Characteristic (ROC). Given that the target variable in our case had five categorical values, in the present study the multiclass version of AUC  and predictive effectiveness is tested for participant d alone. The procedure was repeated for all participants in D and the resulting success ratio was computed. Given that, in general, in-sample performances are better than out-of-sample ones, the metrics adopted in this study was defined as:In-sample mAUC was estimated by learning probabilities from the dataset and then applying these same probabilities to each of the individuals in the dataset. Out-of-sample mAUC was estimated 1 method : one parmi), the automated structure elicitation procedure was based on a preliminary screening to select the most promising candidate structures. In such screening, all random variables in the model were considered in sequence, starting from the target node and proceeding backward along the guidelines imposed by DBN modeling. In each step, the best parent sets for each random variable were selected according to their H is the entropy:where conditional entropy is defined as:and the 2, p can be construed as the empirical probability distribution, estimated as frequency ratios in the dataset.In all the above equationsmi). By assumption, some of Time 1 dimensions may have influence on Time 2 dimensions. Finally, Time 1 dimensions, message framing, and Time 2 dimensions all have a direct influence on the measurable outcome Y, which in this study is related to the target variable Delta intention.By design, any model in the class of DBNs of reference for this study can be construed as a GCM. direct path M → Y and another indirect path M → T2 → Y. The aim is identifying the causal effect of messages M onto the estimated outcome Y. From T1 represents the adjustment set  we are interested in can be estimated as:where m is the expected value of the outcome Y given potential interventions do(m) and the observations T1o. In the case of GCM structures described in In other words, under the principles of GCMs, the APE of a message framing T1=T1o. In this case, APE can be estimated as:For the sake of simplicity, in evaluating the results presented below we assumed that all Time 1 dimensions were observed, so that Y was measured by a utility function intended to estimate to what extent the probability distribution over the target random variable was skewed in the desired direction. Formally:In our study, the outcome Z is the target random variable Delta intention and where the weighting function w assigns the values 2, 1, 0, -1, -2 to the categorical values high-positive, low-positive, neutral, low-negative, high-negative, respectively.where T1. In the model presented in T1 variables that can assume 3 categorical values each. Therefore, the simulated population created contained 38=6561 individuals. For each individual, we computed the APE of each message framing m. In this way, we could associate each individual to a four-dimensional numerical vector:Interpreting a graphical model, be it a GCM or a DBN, with all probability parameters learnt from data, is difficult. A completely defined GCM describes a joint probability distribution over the entire set of random variables in which the influences on each variable by other ones are represented as numerical definitions of conditional probabilities. Nonetheless, such distributed representations must be considered as one, when analyzing information conveyed by GCM. To make the potential effects described more readable, we created a simulated population including one individual per each possible configuration of values for the categorical variables in me represents the estimated APE of message framing m. In this way, each 4D vector can be construed as the soft clustering profile of the corresponding individual. Clustering diagrams can be traced by assuming the Euclidean distance among 4D vectors as a dissimilarity measure among individuals.where each value mean effect, intended as the average of the four values; (B) the best effect, intended as the pivot in each vector; (C) delta effect, intended as the difference between the pivot and the mean message effect. In these versions, the coldest color (deep blue) represents minimal values while the warmest color (bright red) represents maximal ones. Color scales are identical in , with deep blue representing negative values, while in diagram (C) deep blue represents zero. As it can be seen, individuals in which the effects are more pronounced, either positively or negatively, tend to occupy spatial positions on the border of the main cluster.gain, non-loss, non-gain, and loss respectively. The prototype of indifferent receptors, namely those individuals that may be receptive to messages but are not particularly affected by message framing was selected by considering the 2% quantile of the lowest delta effects. Oppositive prototypical receptors, namely those who are expected to react negatively to messages, regardless of framing, were identified by selecting the 2% quantile of the lowest mean effects.Using the soft clustering profile, we also extracted a few relevant prototypes in the joint probability distribution. By selecting the 2% quantile of the highest best message effects, we identified the prototypical receptors for each the four message framings We employed the results of the above soft clustering to answer our RQ4 regarding which combinations of psychological antecedents would lead participants to prefer a given message frame rather than another, changing their intention to eat red/processed meat accordingly. We first distinguished participants according to their high, medium, or low intention to eat red/processed meat at Time 1, that is, before being exposed to the messages. We then considered two other relevant and direct antecedents of Delta intention after message exposure, namely, prevention focus and the perceived severity attributed to the probability of developing diseases. The results of the analysis showed that different combinations of these three dimensions led to identify seven prototypes, who are presented in In the case of strong meat eaters, namely, participants with a high intention to eat red/processed meat, two main prototypes emerged:Strong meat-eater 1. They want to eat a lot of meat, but at the same time they are prevention-oriented and consider health diseases as very severe. They are more persuaded by loss messages. Therefore, they are convinced by messages stressing the negative health consequences deriving from eating too much meat.- Strong meat-eater 2. They want to eat a lot of meat, are prevention-oriented but not especially worried by diseases. These participants tend to be equally persuaded by all messages. They may therefore be considered indifferent as regards frame. This result suggests that prevention-oriented participants who are not especially scared by diseases can follow a health recommendation independent from how it is formulated.- In the case of medium meat-eaters, namely, participants with a medium intention to eat red/processed meat, we obtained three prototypes:Medium meat-eater 1. They have an intermediate level of prevention focus but consider diseases as very severe. They are especially persuaded by gain messages. This finding means that informing on gaining positive health outcomes is effective for people who do not considerably intend to eat meat, perceive health diseases as severe but are not strongly prevention oriented.- Medium meat-eater 2. They have a low level of prevention focus, but they consider diseases as very dangerous. They are persuaded by non-gain messages. This finding suggests that providing information on missing out on positive health outcomes is effective for low prevention-oriented people who are more sensitive to losing positive health consequences, as they fear getting sick.- Medium meat-eater 3. They have a low level of prevention focus and do not consider diseases as dangerous. They are more persuaded by non-loss messages. This finding means that informing on avoiding negative health outcomes is effective for people who do not considerably intend to eat meat and prefer a growth health goal that allows them to avoid negative consequences.- Finally, in the case of low meat-eaters, namely, participants with a low intention to eat red/processed meat before the intervention, we identified two prototypes:Low meat-eater 1. They are strongly prevention-oriented and consider health diseases as very severe. They are persuaded by non-loss messages. Therefore, individuals who already had a low intention to eat meat, were strongly prevention-oriented and scared by diseases prefer to receive information about what negative consequences they will avoid if they pursue their healthy intention.- Low meat-eater 2. They are not prevention-oriented and consider diseases as not so severe. These individuals are oppositive, that is, they are not persuaded by any messages. This result suggests that individuals who already have a low intention to eat meat, have a low prevention focus and are not scared by diseases are unlikely to be convinced to further reduce their RPMC by health recommendations, independent on how they are framed.- Thanks to the integration of theoretical models developed in the field of social psychology and the possibilities of probabilistic calculation allowed by AI, in this study we have developed a procedure to personalize messages aimed at favoring the reduction of RPMC, and thereby make them more effective. We identified psychological factors that play an important role in bringing about a different RPMC change after being exposed to differently framed messages. What we found constitutes an advancement of our knowledge both in terms of matching between message framing and message target in the field of dietary recommendations and in how AI can contribute to the development of increasingly effective communication strategies, extensible through the network to a very large number of people.The analysis of the GCM obtained through the simulated population supported our expectation that message exposure would predict intention change both directly and through the mediation of systematic processing. Consistent with previous research , when thdirect predictors of intention change. One of them was prevention focus. Interestingly, prevention focus had a direct link with intention change, while this was not the case for promotion focus, which had only a link mediated by systematic processing. This evidence suggests that prevention-focused people  are influenced by the health-related messages on red/processed meat reduction regardless of how deeply they processed the messages  style in messages aimed at supporting behavior change . As compWhat emerged from our study offers some suggestions for the development of effective communication campaigns on RPMC reduction. First, the opportunity to develop and use prefactual messages is further supported by our data. At first glance, prefactual messages may seem less effective than more direct messages, as they are less strong from the illocutionary point of view. However, these messages, precisely because of their being indirect and therefore less overtly persuasive, often result very effective . Second,Our research has some limitations, related to some characteristics of the sample used and the procedure followed in data collection. The sample was not fully representative of the population and replicating the survey with another sample would allow us to subject the model that emerged from our analysis to further validation. As for the research procedure, people read the eight messages all together and replied about their intention to consume red/processed meat immediately thereafter. Further studies that involve sending multiple messages over a period of two or more weeks could be useful to further corroborate the results of the predictive model . MoreoveBeyond the afore mentioned limits, what has been achieved in this research opens the way to further steps in the implementation of an automated predictor. In keeping with our preliminary experiments, an artificial neural network can be trained using DRL techniques to guide interactions toward a rapid estimate of the psychosocial antecedents of intention change, as well as the automatic selection of the most effective messages, according to the characteristics of the recipient. This leads to the possibility to adapt interaction strategies according to people’s reactions and through a continuous learning process.The goal of this path of integration between social psychology and AI is to develop communication on food choices that is on the one hand more and more personalized and in tune with the needs and resources of each individual, on the other hand aimed at an increasing number of people. The entire process can be based on consolidated theoretical models, as well as on monitoring and supervising machine learning, thus maintaining the high levels of transparency and explainability required of an AI truly at the service of the human.https://bitbucket.org/unipv_cvmlab/framing-and-tailoring-prefactual-messages-to-reduce-rpmc/.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The studies involving human participants were reviewed and approved by the Ethics Committee of the Catholic University of the Sacred Heart – Department of Psychology. The patients/participants provided their written informed consent to participate in this study.PC proposed the research questions, supervised the conception, research design, interpretation of the data, and also thoroughly revised this manuscript as regards contents and style. VC proposed the research questions, planned the research design, and took responsibility for the data collection. MP proposed the research questions, analyzed the data, authored all software procedures, and supervised the interpretation of the results. All authors contributed to the article and approved the submitted version.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.


The novel sensing technology airborne passive bistatic radar (PBR) has the problem of being affecting by multipath components in the reference signal. Due to the movement of the receiving platform, different multipath components contain different Doppler frequencies. When the contaminated reference signal is used for space–time adaptive processing (STAP), the power spectrum of the spatial–temporal clutter is broadened. This can cause a series of problems, such as affecting the performance of clutter estimation and suppression, increasing the blind area of target detection, and causing the phenomenon of target self-cancellation. To solve this problem, the authors of this paper propose a novel algorithm based on sparse Bayesian learning (SBL) for direct clutter estimation and multipath clutter suppression. The specific process is as follows. Firstly, the space–time clutter is expressed in the form of covariance matrix vectors. Secondly, the multipath cost is decorrelated in the covariance matrix vectors. Thirdly, the modeling error is reduced by alternating iteration, resulting in a space–time clutter covariance matrix without multipath components. Simulation results showed that this method can effectively estimate and suppress clutter when the reference signal is contaminated. Having the advantages of both passive bistatic radar (PBR) and airborne radar, airborne passive bistatic radar has aroused extensive research interest from research institutions and scholars as a new type of sensing technology ,2,3. As In contrast to ground-based radar, due to the movement of the receiving platform, the clutter received by airborne PBR does not concentrate at zero Doppler. Therefore, the ground-based clutter cancellation algorithm is not applicable. Space–time adaptive processing (STAP) is usually used to solve the Doppler-broadened clutter problem in airborne radar ,11,12. TThe abovementioned STAP methods are designed for conventional airborne radar but are also applicable to airborne PBR radar. In recent years, some STAP methods have been proposed for airborne PBR. The authors of  designedAirborne PBR systems, like traditional airborne radars, use monitoring antennas to receive echo signals from their monitoring areas. However, since airborne PBR does not have a local reference signal like active radar, a separate reference antenna pointing to the non-cooperative transmit source is also required. The reference signal received by the reference antenna and the echo signal are used for clutter cancellation and matched filtering. However, in practical applications, because the civil transmit source used by PBR radar is often in a complex environment, when the reference antenna is used to receive the reference signal, it also receives other scattering points in the environment  that refract multipath signals, thus causing the received reference signal to become a contaminated reference signal.Multipath reference signals cause a series of problems due to the clutter cancellation and target detection of airborne PBR. First of all, multipath clutter in the reference signal matched with the spatial–temporal clutter in the echo signal expand the clutter power spectrum. When the target echoes are close to the multipath clutter region, STAP suppresses the target echoes and the target self-cancellation phenomenon occurs. As a consequence, the performance of STAP is seriously degraded, and when the multipath and target Doppler are close, the target self-cancellation phenomenon occurs; at the same time, when the multipath component in the reference signal matches the target component in the echo signal, a false target situation occurs. Therefore, it is necessary to design an effective algorithm that allows airborne PBR to effectively suppress spatial–temporal clutter and detect targets when the reference signal is contaminated. The blind equalization algorithm in ,27 was pAiming to solve the contaminated reference signal problem of airborne PBR, the authors of this paper propose a clutter suppression method based on the sparse Bayesian learning (SBL) methods . FirstlyThe remainder of this paper is organized as follows. In this section, we use formulas to analyze the impact of a contaminated reference signal. The working status of a typical airborne PBR detection system can be expressed as shown in The reference antenna receives not only a direct wave signal from a non-cooperative emission source but also the multipath signal reflected by the strong point. Therefore, the signal received by the reference channel can be expressed as:The signal model received by the echo channel can be expressed as:i-th point, and the model can be expressed as:The components of an airborne PBR system, as analyzed below, receive the reflection model signal from the Therefore, the spatial–temporal clutter echo at the 1st distance unit, that is q-th range unit can be expressed as:The target echo signal in the In actual space–time adaptive processing, the distance unit signal near the distance unit to be detected is generally used as the training sample of clutter to determine whether there is a target in the unit. Therefore, by using the echo signal in the H0 state and an impure reference signal for matched filtering, the following can be obtained:l-th distance unit to be detected is expressed as :Therefore, the clutter component of the In space–time adaptive processing, it is necessary to obtain the clutter covariance matrix of the current distance unit, which cannot be known in advance in practical processing. Generally, the clutter covariance matrix can be estimated from L distance units near the distance unit to be detected; the process can be expressed as:Then, the weight of space–time adaptive cancellation is obtained by using the clutter covariance matrix:It can be seen that the obtained adaptive clutter cancellation weight This section explains the proposed algorithm through five subsections. Before analyzing the multipath component, this subsection first proposes the observation model of direct path clutter. As in traditional airborne radar, the clutter of a certain range unit received by the array can be expressed as:In conventional SR space–time adaptive processing algorithms, spatial–temporal clutter is usually estimated by constructing an over-complete sparse recovery dictionary. The general spatial–temporal guidance dictionary is obtained by discretizing the angle Doppler plane. The entire normalized space plane uniformly extends the space frequency axis and the Doppler axis into essed as :(14)Xdc= follows :(15)Φ=sfAccording to the analysis in This can be deformed to obtain:Given T snapshots, the covariance matrix of the spatial–temporal clutter of the current range unit can be expressed as:According to formulas (16)–(18), the covariance moment The m-th column vector Due to the presence of multipath clutter components, the elements in the matrix The estimation error By substituting the decorrelation matrix In Equation (20), Make In the previous subsections, the spatial–temporal clutter covariance sparse model was derived. In this subsection, the covariance of only pure direct clutter is reconstructed by sparse Bayesian learning.The covariance vector estimation error  that is :(25)ξ¯m∼Suppose that The conditional probability density function of the spatial–temporal covariance The posterior probability density function of ectively :(29)μU¯=The likelihood function of The estimation of The objective function is constructed according to the maximum likelihood criterion, and the quantity related to the Find the partial derivative about q + 1, In iteration According to Equation (35), when the algorithm iteration is close to convergence, a large number of elements in The calculated To set the spatial–temporal clutter covariance vector, one needs to know the variance The algorithms proposed in this section are summarized in In this section, five sets of simulation experiments are used to verify the effectiveness of the algorithm proposed in this paper and its advantages compared to existing algorithms. The experiments in This experiment evaluated the performance of the proposed algorithm by comparing the spatial–temporal clutter spectra obtained by the proposed algorithm and three other existing algorithms: the Full-STAP, Sparse Recovery (SR-STAP), and cascade cancellation STAP (CM-STAP) algorithms. Among them, the Full-STAP algorithm is representative of the conventional STAP algorithm or its dimensionality reduction and rank reduction methods; and the SR-STAP algorithm is representative of the sparse STAP algorithm; the first step of the cascade cancellation of the STAP algorithm uses the RLS algorithm, and the second step uses the Full-STAP algorithm. In addition, for fair comparison performance, the SR method, the CM-STAP method, and the method proposed by this paper all divide the spatial–temporal grid into 60*60. The settings of other major parameters are shown in At the same time, in order to show that the advantages of the algorithm proposed in this article are more obvious, the experimental environment was divided into scenes 1 and 2 according to the state of the reference multipath signal; scene 1 represents an environment with a small number of multipaths in the reference signal and a high intensity, and scene 2 represents an environment in which the multipath in the reference signal is caused by dense and strong reflection points, with a large number and a lower intensity than those of scene 1. The four abovementioned algorithms were used to process scenes 1 and 2 to obtain the spatial–temporal clutter spectra shown in Since the first step of the cascade cancellation algorithm uses the RLS adaptive cancellation algorithm to perform iterative operations, a certain number of training samples are required to make the RLS algorithm enter a steady state such that when the number of multipaths increases and the intensity divisions are denser, it reduces convergence speed and affects suppression performance; however, since the proposed algorithm uses decorrelation in the clutter covariance matrix, it is not affected by the number of multipaths. Therefore, in scene 2 see c,d, the In order to further verify the multipath clutter suppression performance of the proposed method, this experiment simulated the relationship between the improvement factor (IF) of the signal-to-noise ratio and the normalized Doppler frequency of the target. IF can be obtained via Equation (38):In our experimental simulations with different algorithms, the quantitative analysis was accompanied by an increase in the number of multipaths, which affected the clutter suppression performance of the algorithm. The number of multipaths varied from 0 to 80. For the quantity value of each multipath, the SINR loss value was recorded as the average value of 200 iterations.As shown in In this experiment simulation with different algorithms, we quantitatively analyzed the influence of the algorithm’s clutter suppression performance with the increase of multipath intensity. The number of multipaths was fixed, and the intensity of the multipath changed from –10 to –50 dB; the SINR loss value corresponding to each multipath strength value was the average result of 200 experiments.As shown in In this experiment, the target detection performance in the presence of multipath clutter was verified via simulations. For the convenience of discussion, this part of the simulation was set as scene 1, and the target was set at the Doppler position where multipath clutter existed at the 84th range unit. As such, the results in the range dimension without STAP algorithm and after processing with the abovementioned four algorithms can be compared.As can be seen from Aiming to solve the problem of degraded clutter suppression performance due to the contamination of the reference signal in airborne PBR, a novel algorithm of multipath clutter suppression based on sparse Bayesian learning was proposed in this paper.Firstly, the spatial–temporal clutter snapshot with multipath is expressed in the form of covariance, and then the multipath components are decorrelated in the covariance vector. Finally, the cost function is established, the modeling error is eliminated by the EM algorithm, and the direct path spatial–temporal clutter covariance matrix without multipath components is iteratively obtained to suppress multipath clutter.The simulation showed that the proposed algorithm could achieve better performance than existing algorithms regarding the space–time clutter spectrum and the signal-to-noise ratio improvement factor; the algorithm could better maintain stable performance than existing algorithms when the number and strength of multipath increased; and when the target to be detected and the multipath clutter had the same Doppler characteristics, the proposed algorithm showed better target detection performance.


The ankle joint of a powered ankle–foot orthosis (PAFO) is a prominent component, as it must withstand the dynamic loading conditions during its service time, while delivering all the functional requirements such as reducing the metabolic effort during walking, minimizing the stress on the user’s joint, and improving the gait stability of the impaired subjects. More often, the life of an AFO is limited by the performance of its joint; hence, a careful design consideration and material selection are required to increase the AFO’s service life. In the present work, a compact AFO joint was designed based on a worm gear mechanism with steel and brass counterparts due to the fact of its large torque transfer capability in a single stage, enabling a compact joint. Further, it provided an added advantage of self-locking due to the large friction that prevents backdrive, which is beneficial for drop-foot recovery. The design was verified using nonlinear finite element analysis for maximum torque situations at the ankle joint during normal walking. The results indicate stress levels within its design performance; however, it is recommended to select high-grade structural steel for the ankle shaft as the highest stresses in AFO were located on it. A great deal of work is in progress on wearable orthotics, yet lightweight, portable, and efficiently designed products are not available on the commercial market. Hence, lightweight wearable orthotics for assistive motor functions have gained considerable popularity and research interest in recent years, which is quite evident from the numerous literature ,2,3. AnkNumerous types of powering mechanism have been employed for the plantar flexion such as electric actuators in a series with a spring element (SEA: series elastic actuator) that uses the motors for powering flexion ,11; pneuIn the present work, a compact worm gear mechanism-based ankle flexion powering design is proposed. The worm gear was used due to the fact of its ability for large torque multiplication in a single stage  along wiThe contents of the article are structured in the following order: (a) the design of the orthosis, (b) the methodology implemented for design verification, and (c) the test results. The The design of the AFO structure including the flexion mechanism was performed using SOLIDWORKS 3D modeling software package. The overall system weight of the orthosis, including the motor, was measured at approximately 1.1 kg, which is less than the prescribed weight limit for an effective assistive PAFO.Compact ankle flexion powering mechanism;Structural design to minimize and/or withstand the ankle torque;Material selection for optimum strength to the overall orthosis weight.The important factors to consider for the design of an ankle orthosis joint are:An ankle joint complex with a worm gear mechanism in a speed reducing configuration, as shown in The AFO structure, comprising an upright frame and a foot frame, was fabricated using a sufficiently thick sheet metal design. Being positioned parallel to the sagittal plane, the orthosis frame provides the stiffness needed for powering ankle flexion while retaining the flexibility about the joint for moderate out-of-plane movements. In addition, the flexible upright frame design, which allows a certain degree of elastic deflection, helps to minimize the stress on the ankle joint of both the user and the orthosis, in particular, and the overall AFO structure, in general.The AFO joint was constructed about a stainless-steel shaft that was firmly locked onto the square hole of the foot frame and fastened to it firmly on the medial end; while the shaft running through a flanged bearing mounted on the upright frame, it held the worm wheel on the posterior end. The upright frame was a fixed member of the AFO being strapped to the leg, whereas the foot frame moved about the ankle joint and was firmly strapped to the foot. The motor drove the worm wheel through a worm screw, powering the ankle flexion during walking. An exploded view detailing the internal components of the ankle joint and the structure of the orthosis is shown in Proper material selection to meet the specific functional requirement is very crucial for an optimum design performance. As the frame constitutes most of the AFO’s gross weight, a structural-grade aluminum alloy was selected to a deliver high strength-to-weight ratio; whereas the components at the ankle joint, such as the joint shaft and linkages, being subjected to considerably higher stress levels, were made of ASTM A36 structural steel. Regarding the worm gears, it is customary to select a hard material, mostly surface-hardened steel, for the worm screw and a softer material, such as brass/bronze, for its mating counterpart to minimize the friction and wear on the worm tooth. The materials selected for the orthosis and their properties considered for the analysis are listed in A mechanical structure must always be thoroughly tested for its design feasibility and worthiness and, hence, the developed 3D model was exported to the ANSYS software package to perform an FE analysis. The AFO joint in the present analysis was tested for its durability against the torque exerted by a user about the ankle joint during normal walking. First, the applied torque was theoretically estimated with added reference to the literature, and then the stress analysis of the AFO ankle joint was performed for the calculated torque values to evaluate its design stability and performance characteristics.M is a person’s mass, g is the acceleration due to the fact of gravity, and a is the vertical acceleration of the CM. The GRF for a person weighing 80 kg while considering an average walking acceleration of 0.7 m/s2 will be 80 (9.81 + 0.7) = 840 N. However, the entire force may not be applied on the foot due to the weight being distributed among both legs and considering the inertia depending on the speed of walking. Thus, considering 80% of the total force applied on the foot, we obtain a force of 672 N, for which the horizontal and vertical components of the forces can be calculated with respect to the foot reference planes. By knowing the force components acting about the sagittal plane at the center of pressure (CoP) during a specific phase of walking and the distance of the CoP from the ankle joint, as shown in xF and zF are components of the force along the x- and z-directions, respectively. Whereas dx and dz are the distance of the CoPs from the ankle joint measured along x- and z-axes, respectively.The AFO experiences the ground reaction force (GRF) during walking, which is the force exerted by the ground on the foot as it makes contact with it due to the gravitational attraction. While walking, the GRF provides a measure of the impact that a body experiences when the foot makes contacts with the ground. For a static condition, the GRF corresponds to the person’s weight, but while in motion, the GRF increases due to the acceleration of the body. For normal walking, the GRF is the sum of the person’s weight and the acceleration of the center of mass (CM) as shown in Equation (1) .(1)GRF=The applied load or torque about the ankle joint has a variable nature that can be attributed to the variance in the type of foot contact with the ground at ankle angles corresponding to the different phases of the walking gait. Accordingly, heel-strike and toe-off, as indicated in Nonlinear analysis was considered for the AFO ankle joint using the Newton–Raphson method, and the mesh model was created using pure penalty contact formulation while considering the coefficient of friction between the mating gears.For both cases, the material model, as shown in nF is the contact force, nk is the contact stiffness, and px is the penetration depth. To ensure accurate results, the penetration depth should be as small as possible, which depends on the magnitude of the contact stiffness.To ensure contact compatibility so that the contacting bodies, such as gears at the ankle joint, did not interpenetrate, a pure penalty contact formulation, as shown in Equation (3), was used .(3)Fn=kMoreover, a frictional contact formulation was defined with 0.2 as the coefficient of friction, μ for the mating worm and worm wheel set. The value was selected for a maximum friction case; however, the coefficient of friction of a material is difficult to evaluate and depends on various factors such as sliding speed, gear specifications , and the lubrication conditions. Normally, between a steel worm screw and a brass/bronze worm wheel, the value of μ is approximately 0.15 . FurtherThe analysis was conducted based on the Newton–Raphson (N–R) method as a solution to the nonlinear finite element equation. The N–R iteration is commonly used for nonlinear static structural analysis of a multiple degrees of freedom system.K] is the stiffness matrix, {U} is the displacement vector of unknown degrees of freedom, and {aF} is the applied load. If the stiffness matrix itself is a function of the displacement vector, then the Equation (4) is termed a nonlinear equation; then, N–R iteration applied to obtain the root of the nonlinear equation may be written as:iTK] is the tangent stiffness matrix, i represents the current iteration; {iu} is the displacement vector; {inrF} is the restoring force vector calculated from element stresses. In the iteration process, {Δiu} is calculated from Equation (5) with the known element stiffness matrix and element stresses; next, the calculated {Δiu} is added to {iu} to get the next approximation {i+u1}. This process is repeated till we obtain the convergence as shown in The governing equation for a finite element analysis may be written as:The ANSYS software package was used for performing the FE analysis and simulation. The complete analysis of the AFO joint model took approximately 5 h for each case. The applied boundary conditions and the results of the static structural analysis for both the heel-strike and toe-off phases are presented below. A torque of approximately 40 Nm expected to be generated about a human ankle joint, as calculated previously during the heel-strike phase, was taken as the applied torque input for the AFO structure. The torque was acting in the clockwise direction due to the PF torque about the ankle joint. By keeping the remaining structure fixed against rotation, as shown in The results, as seen in For a toe-off phase, as shown in Even during the toe-off phase, a similar scenario was observed with a maximum average stress of approximately 130 MPa at the ankle joint shaft as shown in The analysis results obtained for the orthosis are useful for validating the preliminary design of the ankle joint, understanding the suitability of material selection for the components, reviewing the mechanism employed for ankle flexion, and identifying the crucial components for a possible design modification.For both cases, it was observed that the maximum stress region was confined within the ankle joint shaft in an orthosis and, hence, due considerations will be made during the prototype design to minimize the stress concentration about the ankle shaft and to consider optimizing the shaft diameter based on the ankle joint kinetics. The current structure, however, minimizes the stress on the joints due to the fact of its flexible design, allowing for large elastic deformation, will include a serial spring element in the second prototype design for enhanced ankle flexibility; this is expected to further reduce the stress on the ankle shaft. The design and results from the present study will serve as a reference for those considering the worm gear mechanism as a means for powering the ankle flexion. Further studies based on fatigue analysis as a second phase verification on the orthosis and the ankle shaft, in particular, will be considered, as the components are subjected to cyclic stresses during walking. Although moderate stress levels are induced on the ankle joint, the interaction of multiple components at the joint may diminish the fatigue life of the ankle shaft; the minimum suitable diameter and the material selection and processing considerations will be included for the study. With satisfactory test results, a test model is being developed based on the current design that uses a surface-hardened worm screw, ground and mated with a relatively soft worm wheel made of phosphorous bronze; thus, much lower stress levels may be expected for the actual test setup at the mating gear part. The ankle shaft will be made of high-grade structural steel to withstand high stress and fatigue loading. The developed orthosis will be tested for its assistive ability in enhancing/regaining the motor functions intended to help the elderly or persons suffering from diminished ankle flexion performance. A simple, yet efficient flexion powering joint design was suggested in the present work for an AFO. The proposed design for powering the plantar flexion was based on a worm gear mechanism, which is advantageous due to the fact of its large torque transmission capability with self-locking capability. Moreover, the design was subjected to a nonlinear finite element analysis based on the maximum torque generated during the heel-strike and toe-off phases of a gait during normal walking; the ankle torques generated during normal walking were calculated to be 40 Nm CW and 120 Nm CCW during the heel-strike and toe-off phases, respectively. The structures were subjected to safe stress levels indicating a structurally feasible design, wherein the maximum stress values of 75 MPa and 130 MPa were observed at the ankle joint shaft during nonlinear FEA analysis for the heel-strike and toe-off phases, respectively. The orthosis design was structurally verified, and a test prototype based on the current flexion powering design will be fabricated and tested, a high-grade structural steel is recommended for the ankle joint shaft, with carburized and ground steel worm screw, mating with a phosphorous bronze worm wheel for better service life expectancy from an AFO. Structural design modifications of the orthosis are considered for the future work by including a serial spring element to further minimize the stress on the joint and improve the stability.


The need to monitor specific areas for different applications requires high spatial and temporal resolution. This need has led to the proliferation of ad hoc systems on board nanosatellites, drones, etc. These systems require low cost, low power consumption, and low weight. The work we present follows this trend. Specifically, this article evaluates a method to determine the cloud map from the images provided by a simple bi-spectral infrared camera within the framework of JEM-EUSO (The Joint Experiment Missions-Extrem Universe Space Observatory). This program involves different experiments whose aim is determining properties of Ultra-High Energy Cosmic Ray (UHECR) via the detection of atmospheric fluorescence light. Since some of those projects use UV instruments on board space platforms, they require knowledge of the cloudiness state in the FoV of the instrument. For that reason, some systems will include an infrared (IR) camera. This study presents a test to generate a binary cloudiness mask (CM) over the ocean, employing bi-spectral IR data. The database is created from Moderate-Resolution Imaging Spectroradiometer (MODIS) data (bands 31 and 32). The CM is based on a split-window algorithm. It uses an estimation of the brightness temperature calculated from a statistical study of an IR images database along with an ancillary sea surface temperature. This statistical procedure to obtain the estimate of the brightness temperature is one of the novel contributions of this work. The difference between the measured and estimation of the brightness temperature determines whether a pixel is cover or clear. That classification requires defining several thresholds which depend on the scenarios. The procedure for determining those thresholds is also novel. Then, the results of the algorithm are compared with the MODIS CM. The agreement is above 90%. The performance of the proposed CM is similar to that of other studies. The validation also shows that cloud edges concentrate the vast majority of discrepancies with the MODIS CM. The relatively high accuracy of the algorithm is a relevant result for the JEM-EUSO program. Further work will combine the proposed algorithm with complementary studies in the framework of JEM-EUSO to reinforce the CM above the cloud edges. The detection of clouds with airborne instruments is critical when studying the meteorology and climate of the Earth. However, there are other applications where auxiliary systems for cloud detection and characterization are needed, which is the case that concerns us.Thermography is one of the main techniques for measuring the temperature of things remotely. It is based on InfraRed (IR) cameras to characterize the relationship between the object temperature and the IR energy it emits. Although the first applications of IR cameras were military (World War II), their use spread to many fields since the 1960s. However, the direct application of IR cameras to some applications did not obtain the expected results. Frequently, the lack of knowledge of the physical phenomena involved in the emission, propagation, and measurement of IR radiation led to inappropriate use of commercial IR cameras or prevented a correct interpretation of the images.To solve a new technological or environmental problem, it is necessary to follow an adequate procedure. The method should begin with a radiometric and spectral characterization of the problem. Afterward, the IR instruments can be developed or adapted according to the previous characterization. Finally, to solve the specific problem, it is necessary to develop specific algorithms that retrieve the physical information from the data provided by the IR instrument: combustion measurements ,2 forestNowadays, technology has evolved significantly, and there are extremely high- performance hyperspectral IR systems. Many of these systems are embedded in satellites and can provide very precise information on many phenomena globally.However, the most current trend in the space sector is moving towards developing small satellites, nanosatellites, or CubeSats. At present, many environmental and technological problems do not require the high spectral resolution that current technology offers. However, they do require simple systems with low cost, low mass, and low energy consumption. In this framework, it makes sense to recover bi-spectral thermography systems, which are simpler and cheaper than sophisticated multispectral systems. The spectral and radiometric characterization of the problem should be the basis to simplify the devices. This alternative approach facilitates observation at higher spatial and temporal resolution and addresses monitoring problems in specific areas. This is also the case of some instruments using ancillary devices to provide complementary but essential information.Precisely, the work that we present here aims to develop and validate an algorithm to detect clouds from the information provided by a simple bi-spectral IR camera. In the Joint Experiments Missions- Extreme Universe Space Observatory (JEM-EUSO), a secondary instrument  will carry out the detection of the clouds. However, the relevance of the results we present here goes beyond the JEM-EUSO missions. New instruments for mini and nanosatellite constellations, mainly focused on communications and Earth observation, could use this algorithm.E > 5 × 10The objective of the international JEM-EUSO program is to observe Ultra High-Energy Cosmic Rays (UHECRs) to explain the origin and nature of such particles . When a  program ,8.However, the presence of clouds between the EAS and the UV telescope may interfere with the EAS measurement. It may also lead to a misinterpretation of the observations. Then, for this type of application, an auxiliary instrument that maps the state of the clouds when a UHECR occurs is essential.Today there are many weather satellites with instruments capable of determining the state of the sky with great accuracy ,10,11. Mst 1970s . Even thst 1970s . The Spi970s [−1 . Logical970s [−1 ,17,18.However, current weather satellites do not have the temporal and spatial resolution necessary to ensure simultaneous information to JEM-EUSO main instrument. In addition, the high cost, weight, power consumption, and dimensions of those high-performance spectroradiometers make them non-viable for auxiliary purposes of JEM-EUSO missions.For this reason, the JEM-EUSO instrument and some of the pathfinders include in their payload a bi-spectral camera in the thermal infrared (TIR) spectral region ,20,21,22Reference  includesThe JEM-EUSO camera has its bands located around 11 and 12 se bands . It is aConcerning the data analysis, most of the cloud detection methods use radiometric and multispectral single-pixel tests, which rely on selecting different thresholds ,25,26 orThe use of radiometric methods focused on cloud detection is widespread and exploits the information of those multiple bands. The Cloudiness Mask (CM) used by MODIS uses 11 spectral tests for 19 different bands . The cloAt this moment, the most innovative methods are learning-based techniques such as a machine or deep learning ,33,34,35Since the JEM-EUSO IR cameras have slight differences in their designs , it would be necessary to readapt and retrain the learning-based methods for the different pathfinders. In addition, the IR systems onboard pathfinders do not provide enough data to train those methods due to the short duration and trajectory of the flights. For this reason, we have developed a radiometric algorithm that can be more suitable in the frame of the JEM-EUSO missions.However, the bi-spectral concept of JEM-EUSO IR cameras adds strong restrictions to the available tests when determining the CM for this system. Nevertheless, it is still possible to retrieve information about cloud coverage using those bands. Moreover, the conditions where an EAS can be measured make the development of the CM easier. JEM-EUSO program will detect the EAS just when the UV background noise is low enough. Clouds, then, only have to be detected during night-time and mainly over oceans. We have extended the study to day-time images to interpret the results better and make this new CM more applicable.The goal of the study we present is to design and evaluate a CM test to determine the presence of clouds in the Field of View (FoV) of the JEM-EUSO telescope, over oceans and during night-time. Although we have also studied the performance of a gross test based only on the brightness temperature measured at 11 The main idea of the methodology we propose is to establish a relationship between the ue as in  to deterThe use of two adjacent bands has been used before in other CM algorithms ,25,36. TAlthough there are some pathfinders, including IR cameras, for example, the EUSO-SPB II , the IR cameras have not provided enough images . For thaAs JEM-EUSO does not retrieve the SST, a global SST model is needed. In this study, the NOAA 1/4y OISST)  providesIn this section we will describe the data used to design and validate the SWCM. Afterwards, the theoretical basis of the SWCM is summarized and the SWCM is defined. The last subsection details the methodology to optimize the thresholds that characterize the SWCM.Since the CM will be applied in the future to JEM-EUSO systems, it must be based on the data that provide those systems. Although the spectral characteristics of the different JEM-EUSO IR cameras can be slightly different, the bands of the IR reference instrument are centred at In this work, we use daily Collection 6.1 level 2 MODIS images  combinedCollection 6.1 level 2 from MODIS (MYD06) is a product that contains processed information about the optical properties of the clouds and other processed MODIS products (as the MODIS CM).MODIS products have been widely used as ground truth to evaluate new CM algorithms due to his great confidence ,39. A coThe similarity between JEM-EUSO IR camera bands, its global coverage, and its high precision; make MODIS a great choice to calculate and validate the SWCM.The data fields taken from MODIS include However, although MODIS provides a CM with a resolution of 1 km2 CM . The valThe CF product is immediately derived from the MODIS As SST input, we use the daily OISST. Numerous algorithms use this kind of OISST products as input, e.g., MODIS uses a weekly OISST to calculate initial radiances. The OISST is correlated with the MODIS data fields using the geolocation that both products provide. Since the resolution of the SST model is worse than that of MODIS images, we have applied a linear interpolation to correlate MODIS and OISST products.Since the final JEM-EUSO instrument is thought to be on board the International Space Station (ISS), the trajectory does not include polar latitudes larger than 60The main idea of the methodology we propose is to establish a relationship between the Due to the close relationship between the SST and the  sources . On the  sources  uses simA, C and D are constants to be determined.The combination of ,44,45 leIn this work, we also propose the use of an ancillary SST global model together with the radiative information from a bi-spectral system to estimate the In the next section, we explain how the SWCM uses the difference between the real and estimated To obtain the SWCM, we define a new parameter, If the radiative properties of the atmosphere remained the same, clear-sky pixels would satisfy Equation  determinSelecting the MODIS images from the method group.Separating the images for two different regions: tropical and midlatitude.Fitting each subset of clear-sky pixels to the Equation  to obtaiThe method used to determine the coefficients consists of:Determining Determining Determining a ground truth CM to compare with the SWCM.Grouping the pixels of the images for the different scenarios .Scanning different Selecting the optimum The method used to determine the thresholds consist of:Although other works base the calculation of the coefficients on direct radiative models, in this work, we determine the coefficients of Equation  empiricaA total of 4006 midlatitude , which gives an idea of the SWCM performance in real situations where mixed pixels are always present. A transformation of the CF into a CM using the h, in this paper, h has also been selected based on skill scores measures.In principle, the h parameter can be chosen at the user’s convenience and depends on the final application. To avoid an arbitrary selection of clr and cld. By computing ROC curves for different h values, we can choose the optimal h threshold. h values. The optimal h parameter is 40% since it is the one that maximizes the area under the curve.In this framework, the best response to the cloud mask determines the threshold. The Receiver Operational Characteristics (ROC) curves are one of the most common methods to evaluate that response . ROC curIn addition, we introduce the Pure Cloud Mask (PCM) to evaluate the SWCM capacity to discriminate totally clear and totally cover-sky pixels. To achieve that, the PCM excludes all pixels with a We can determine the optimal The udy ones  but the e values .Compared with the Since the  the KSS .The results of this work have been split into two subsections: The CM coefficients have been calculated for the tropical and midlatitude models and are summarized in We have calculated the optimal value The validation procedure consists of applying the SWCM to a set of 3800 MODIS images. The results are compared with those of the PCM and RCM described in the previous section . The twoThe validation set contains images of each month of the 2018 year during the day and night-time, including latitudes between 0 and Around 95 × 10a, b, c, and d), the When the ground truth is the RCM, the As an example Since the data include pixels during the day and night-time, The results show no significant differences between the day and night time skill scores, excluding results in midlatitude when RCM is used as ground truth, with better results during day-time with a As commented in Although some skill scores remain almost stable .The lower As seen in the As expected, the comparison with the PCM ground truth, which evaluates the performance of the SWCM to classify clear and cloudy pixels, gives more satisfactory skill scores than the comparison with the RCM ground truth, which better reflect the overall skill of the SWCM including mixed pixels. For the PCM, the In general, we can say that the results are very encouraging since the or night . The difThe olutions  . Nevert average ) the effclr for which there is a clear difference between day and night. This exception could be related to the well-known difficulty of identifying clear pixels when the underlying surface is at low temperature, and there is no good contrast with clouds, which are also cold. This poor contrast between the cold SST and the clouds could also explain the seasonal variation in the midlatitude region observed in the validation section  CM  is an alIn , an operEven though the SWCM uses only one test based on two TIR bands, its global Concerning the When analyzing the Other works that show similar found in .To summarize, the performance of the SWCM is similar to the one of the other CMs based on multispectral or spatial tests, even though the SWCM is a one-test CM based on only two spectral bands.The main objective of this new SWCM is to provide the JEM-EUSO program with a cloudiness map in the interest regions. For this reason, we have only focused on areas over oceans because the UV principal instrument will keep clear of populated areas to avoid the associated UV light pollution.Our proposal is a simple cloud mask test based on a split window algorithm. The inputs of the SWCM are the BTs in two spectral bands in the thermal infrared region , the SWCM could be applied to all the JEM-EUSO pathfinders. The spectral bands (centering and width) are very similar to each other and very similar to the MODIS spectral bands. For this reason, the results obtained by applying SWCM to MODIS data can be extrapolated to the IR systems of the JEM-EUSO missions and other systems with similar spectral bands. The proximity between the bands and their coincidence with an atmospheric window also favor extrapolation. Finally, the SWCM uses brightness temperature data, which is calibrated and does not depend heavily on the sensor used.Since the JEM-EUSO instrument is not already in orbit, we have used MODIS data. Precisely, MODIS bands #31 and #32 are very similar to those of the IR JEM-EUSO Camera. MODIS data have allowed us to develop the SWCM and validate it without crossing data between different instruments and, therefore, without introducing instrumental effects in the evaluation of the CM performance. However, in the future, these instrumental differences will have to be studied.The SWCM results are very good when applied to totally cloudy or clear pixels, which implies that the proposed algorithm is an appropriate classifier. Comparing the results using PCM and RCM reveals that the main discrepancies are related to partially cloudy pixels at the cloud edges. However, they have been minimized by optimizing the h threshold. However, as the spatial resolution of JEM-EUSO (about 0.75 × 0.75 kmclr for midlatitude regions during the winter months. These results could be improved by conducting specific regression fits and calculating new coefficients A, C, and D for that period in midlatitude regions. Moreover, if the JEM-EUSO mission detected an EAS, local coefficients could be calculated through the radiative Transfer Equation [The lower performance corresponds to the Equation , using vEquation .The scope of our study has been determined by comparing SWCM with other relevant studies. The comparison between the skill scores  allows uNevertheless, in the future, it is expected to integrate this algorithm in a more elaborated CM algorithm, also using other complementary tests. Actually, other works based on spatial analysis , deep leIt is also important to emphasize the relevance of our results in the field of future remote sensing sensors. The great advantage of the proposed SWCM algorithm is its easy hardware implementation. It requires defining only two spectral bands by using band-pass interferential filters on two arrays of the same detector material. The use of only one type of material means a big simplification in terms of electronics and data acquisition and management.Finally, we would highlight that this algorithm could also be applied to data of plenty of satellites, both operative and non-operative since the use of the TIR bands has been widespread since the second half of the last century. Moreover, the concept proposed in this article can be easily transferred to nanosatellites and CubeSats constellations devoted to Earth observation, following the current trend of development of simple and low cost, mass, and energy-consumption systems. The possibility of monitoring the presence of clouds at high resolution and in specific areas with simple systems will allow providing complementary and valuable information for numerous environmental and technological applications.


Ion channels allow for the passage of ions across biological membranes, which is essential for the functioning of a cell. In pore loop channels the selectivity filter (SF) is a conserved sequence that forms a constriction with multiple ion binding sites. It is becoming increasingly clear that there are several conformations and dynamic states of the SF in cation channels. Here we outline specific modes of structural plasticity observed in the SFs of various pore loop channels: disorder, asymmetry, and collapse. We summarize the multiple atomic structures with varying SF conformations as well as asymmetric and more dynamic states that were discovered recently using structural biology, spectroscopic, and computational methods. Overall, we discuss here that structural plasticity within the SF is a key molecular determinant of ion channel gating behavior. The ability of ion channels to permit ion flux across biological membranes is essential for the functioning of a cell. There are a wide array of diseases associated with ion channel dysfunction that are collectively termed channelopathies including epilepsy, cardiac arrhythmia, deafness, asthma, and cancer . Pore loThe SF can act as a gate and modulate the electrophysiological behavior of the ion channel. One regulatory mechanism is C-type inactivation that was first discovered in voltage-gated potassium channels, where prolonged activation of the channel by an external stimulus leads to a non-conductive conformation of the SF. In the model potassium channel KcsA high external potassium concentrations are able to slow C-type inactivation, which highlights the link between the SF and C-type inactivation .In recent years there has been a strong increase in research demonstrating the dynamic nature of the SF in various channels. Commonly used structural biology techniques, such as X-ray crystallography or cryogenic electron microscopy (cryo-EM), lead to static snapshots of biological processes where the dynamics can easily be overlooked. Molecular dynamics (MD) simulations complement experimental approaches by obtaining information on the dynamics of previously determined structures . HoweverWe will discuss several modes of structural plasticity for the SF region of potassium-selective and non-selective cation channels; specifically collapse, asymmetry and disorder. These discoveries highlight recent developments in the field and indicate the importance of SF plasticity in pore loop channel function.The first glimpse of structural plasticity of the SF came from crystal structures of the conductive and collapsed conformations of KcsA with high and low potassium concentrations, respectively . The conMutations of E71 in the pore helix of KcsA change the equilibrium between the conductive and collapsed SF conformations. The E71A mutant was crystallized in two distinct conformations, one similar to wild-type KcsA and one with several flipped residues . The fliA recent study using MD simulations of a homology model of the Shaker channel also displayed a stable constricted SF conformation that was allosterically regulated . This coAll SF conformations described so far assume fourfold symmetry across the tetramer assembly, but this is not necessarily the case. A functional example of an asymmetric SF conformation was found in the non-selective channel NaK with the unique SF sequence TVGDGN. Using ssNMR we discovered that the channel adopts two distinct SF conformations, stabilized by either potassium or sodium ions . This diP2 channels. These channels consist of two dimers that form a pseudo-tetramer that can have different SF sequences, often with a tyrosine to phenylalanine substitution [T(V/I)GFG] in one or both of the SF sequences. The primary gating of KP2 channels occurs at the SF  channel is a pore loop channel that functions as a fast-inactivating potassium-selective channel, which has a phenylalanine instead of a tyrosine in its SF sequence (SVGFG). The cryo-EM structure of the open state of the channel revealed that the sidechain of the phenylalanine was slightly shifted compared to other potassium-selective channels, and this was proposed to be linked to the fast inactivation of the hERG channel . A recenThe previously discussed non-selective channel NaK can be made potassium-selective by two point mutations in the SF sequence from TVGDGN to TVGYGD or TVGFGD . We haveAnother example of SF disorder comes from recent work using FRET (Förster resonance energy transfer) in the potassium-selective channel KirBac1.1. A decrease in the concentration of potassium ions changes the SF from a rigid high-FRET state to a more dilated and dynamic state with medium and low-FRET signals . This inKcsA displays three different gating modes within the wild-type channel which can be mimicked by a set of three mutants for E71 . Solid-sMicrosecond-timescale MD simulations of the hERG channel show SF instability . This isIt has become increasingly clear in recent years that structural data alone does not give enough information to describe ion channel functioning. Recent research has shown that a lack of dynamic information has limited the mechanistic understanding of ion channels. We have summarized all examples of structural plasticity of the SF in the pore loop channel family, focusing on the potassium- and non-selective cation channels. The SF structures include various constricted, stable conformations as well as asymmetric or even disordered states see . RecentlIt was already known that the SF is one of the structural elements responsible for gating in many pore loop channels. The balance between conductive, asymmetric, collapsed or disordered SF states is very important for the specific electrophysiological profile of a channel. The various constricted states of the SF, both collapsed and asymmetric, with a narrowed ion pore all show stabilization by either water molecules or a residue sidechain behind the SF. Interestingly, it seems that the constriction of the conduction pathway can be found at either the S2 or S0 ion binding site of the SF, where the constriction at the S0 site appears to be linked to an asymmetric state of the channel. It is unclear if all these different modes lead to the same behavior of C-type inactivation.It is particularly challenging to obtain high-resolution structural information on the SF of ion channels by methods such as X-ray crystallography or cryo-EM when this is a dynamic region. MD simulations and NMR can provide additional information taking structural plasticity into account which aids the further understanding of ion channel functioning. An emerging theory links ion selectivity of the channel and SF disorder. It was first shown by us that multiple SF conformations are essential for non-selective ion conduction in NaK, with a symmetric state responsible for potassium and an asymmetric state responsible for sodium conduction . The disKH wrote the original draft and prepared the figures. All authors contributed to the writing of the manuscript.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.


The literature examining healthcare-associated infections (HAI) points to two main problems in conforming to infection prevention and control (IPC) guidelines among healthcare professionals (HP). One is the discrepancy between HPs’ behavioral intentions and their implementation in practice. The other refers to how HPs maintain these practices after the intervention stage ends. The method proposed in this study seeks to address both these issues by using the Positive Peviance (PD) approach to focus on the dissemination stage of interventions. The study seeks to offer a method for disseminating 27 PD practices to 135 HPs, among them nurses, nurse assistants and physicians, so as to help them maintain IPC guidelines, offer feedback on the dissemination process and examine the impact of the dissemination stage on changes in their behavior.The theoretical model underlying this qualitative research was the Recognition-Primed dDecision (RPD) model, which we implemented in the field of healthcare-associated infections (HAIs). Moreover, we used the Discovery & Action Dialogue (DAD) and Think Aloud (TA) techniques to describe the methodological development of simulations for HPs. Feedback from the HP demonstrators underwent content analysis, while descriptive statistics were used to characterize behavioral changes.HPs’ information processing regarding infection prevention shifts from peripheral/automatic processing to intuition and analytical/central processing, turning PD practices into positive norms. The HPs personally experienced finding a solution and made repeated corrections until they overcame the barriers. Most of the HPs (69.4%) reported that the practices were fully implemented, together with additional practices.Implementation of the dissemination stage indicates that in order for HPs to integrate and assimilate practices that are not in the official guidelines, merely observing simulations is not sufficient. Rather, each staff member must personally carry out the procedures. Nosocomial infections, also known as health-care associated infections (HAIs) and hospital-acquired infections, constitute one of the most critical and investigated issues in public health worldwide , 2. DespNumerous and diverse programs have been designed throughout the world to reduce the rate of HAIs, but there is still uncertainty regarding the effectiveness of each specific strategy relative to others or the effectiveness of a number of combined strategies –7. A sysEven though HPs working in public settings are aware of the importance of maintaining infection prevention and control (IPC) guidelines, here too, as in other fields, there are still significant discrepancies between intentions and actual behaviors , 14. SomThe PD approach is an innovative behavioral approach to solving complex problems . The approach addresses two key parameters that emerge from the literature: 1) the need to find solutions from within the community’s existing resources, and 2) the need to empower HPs by identifying individuals who behave in exceptionally positive ways. These individuals  serve as role models by virtue of the fact they have developed successful solutions and strategies for dealing with problems without resorting to additional resources unavailable to fellow members of their community . In the Implementing the PD approach has the potential to reduce the gap between intentions and actual behavior reported in the literature, increase the rates of HP compliance with infection prevention rules, and reduce infection and mortality rates .The PD methodology consists of four basic steps carried out by members of a community.Step 1: Identify “positive deviants,” i.e., individuals who consistently demonstrate exceptionally high performance in an area of interest.Step 2: Study these individuals in depth using qualitative methods to generate hypotheses about practices that enable organizations to achieve top performance.Step 3: Test hypotheses statistically in larger representative samples of individuals.Step 4: Work in partnership with key stakeholders, including potential adopters, to disseminate the evidence about newly characterized best practices.This article focuses on Step 4 of the PD approach, namely the dissemination of new practices. Disseminating practices among other HPs depends on how they assimilate their new knowledge. The process of internalization and assimilation can be explained by the Recognition-Primed Decision (RPD) model , 18, whiThe RPD model encompasses two steps individuals must adopt in making decisions. First, they must recognize which course of action makes sense. After that, they must evaluate this course of action by imagining whether the actions resulting from the decision make sense. In this decision-making process, experience plays a major role.HPs must move from one complex task to another. In such a reality, identifying specific situations that require conforming to IPC guidelines presents a challenge. The literature offers a number of techniques that can help HPs complete their behavioral intentions of maintaining IPC guidelines. For example, the study by Fuller et al.  attempteWhile using TA, individuals verbalize how they are using available information to generate a solution to a problem. Unlike other techniques for gathering verbal data, TA entails no interruptions or suggestive prompts or questions. The TA participant is encouraged to concentrate on the tasks being performed while verbalizing a continuous stream of thoughts and avoiding interpretation or explanations . TA can The TA process can help nurse educators teach nursing students how to identify and correct reasoning that is not up to par and show them scenarios that might arise during their clinical assignments. The educators can use the TA approach to help promote clinical reasoning strategies, such as hypothesizing, judging, and inductive and deductive logic. Nursing students can be evaluated on how they comprehend and verbalize what is taught in classroom lectures and how they connect scientific facts with health-related outcomes in order to identify problems and concerns. Not only must nurses be able to use diagnostic reasoning, they must also be flexible, knowledgeable, and capable of reflecting on approaches to clinical work while developing reasoning skills.Other studies suggest that physicians undergo a similar cognitive process in making clinical decisions –25. ChanThe current study is based on a large-scale study conducted in 2017–2019 in three Israeli hospitals and focuses on the information dissemination stage of PD practices (Stage 4) in reducing healthcare-associated infections (HAIs). To the best our knowledge, no studies have investigated how HPs assimilate new behavioral practices demonstrated by PDs who maintain IPC guidelines. We also found nothing in the literature examining the work of HPs who use RPD to prevent acquired infections. This article describes an application of the model and suggests methodological developments in the context of maintaining IPC guidelines.The first goal of the research is to offer a method for disseminating PD practices for maintaining IPC guidelines to other HPs. The method is based on a number of tools that have been used in other fields, as specified below. The second research goal is to examine feedback from the demonstrators regarding the proposed method. The third goal is to examine the impact of PD intervention on HPs’ reported behavioral changes in maintaining IPC guidelines.This article focuses on Step 4 of the PD approach, namely the dissemination of practices. A necessary condition for disseminating practices among other HPs depends on how they assimilate their new knowledge. We used the TA technique to explain the learning process and the RPD model to illustrate how the participants process the information.RPD served as the theoretical model underlying this research. The model focuses on decision-making under circumstances of stress and intense conditions. Thus it is suitable for the medical field, where HPs are required to find effective solutions. In this study we outlined the methodology of the theoretical model by using various tools that facilitate its use and accommodated it to the issue of reducing HAIs and maintaining IPC guidelines. The research model illustrated in Fig. We used a technique that integrated TA and retrospection, wherein TA protocols or behavioral observations during a session are used to obtain a retrospective protocol of any pauses in the TA session or fragments that were incomprehensible, incomplete, or odd and introspective. The TA method then uses more direct data for validation  and is sThe study was conducted at three hospitals in Israel: Hadassah Ein Kerem Medical Center, Bnei Zion Medical Center, and Rambam Medical Center. Five departments/units participated: one Medical Intensive Care Unit (MICU), two internal medicine departments and two orthopedic departments.The study included 135 HPs who participated in the dissemination stage; 27 of them served as the demonstrators , while three of them  are mentioned here for the first time.The 27 practices documented by video included 13 of the PD behavioral practices along the care continuum: (1) changing a surgical dressing; (2) removal of protective clothing when leaving an isolation room, and carrying out hand hygiene ; (3) takThree of the 27 practices documented by video require maintaining sterility and using sterile equipment in addition to maintaining hygiene: (1) changing a dressing attached to a central line; (2) inserting a urine catheter; and (3) suctioning a respirated patient.The procedures were performed in the hospital in their natural setting. In most cases, the facilitator/demonstrator was a medical staff member specializing in the procedure, while another HP who was a member of the research team filmed and documented the procedure. In some cases the facilitator guided, filmed and documented the procedure using a smartphone. Each HP worked on one of the 27 practices, and each of them conformed to the integrated three-stage model, as follows:In the first stage, we asked each of the 27 HP demonstrators to demonstrate one procedure/practice. Prior to the demonstration, the PD facilitator explained the approach and performed the procedure. During the demonstration and filming, the facilitator applied the DAD method, asking the demonstrator to find solutions to situations for which there are no clear guidelines. In accordance with the TA method, the demonstrators were told to speak out loud about their problems and deliberations, while explaining their actions step-by-step. At any point when demonstrators stopped, the facilitator asked why they stopped and how they thought to continue. It is important to note that every time the demonstrators criticized themselves , they repeated the action again. In effect, they repeated the action again and again several times, making corrections until finding the optimal solution/frame.In the second stage, which took place about 1 month after filming the procedure, we showed the demonstrators the videos again and interviewed them about the influence and effectiveness of the procedure, using the TA introspection prompting technique. We chose a time period of 1 month to allow the demonstrators to gain perspective about the process.In the third stage, we asked all HP observers to rate each practice by indicating the extent to which it had changed their behavior and motivated them to implement the practice, on the following scale: 1 - I did not implement it; 2 - I implemented it partially; 3 - I implemented it fully; 4 - I implemented it fully and added my own practices.The materials underwent content analysis as follows:In the first stage we analyzed the transcripts of the texts spoken during the filming, including the correction stages, until the speakers found solutions during the medical procedure. We then constructed the following index of criteria for analyzing the processes: (1) identifying the gray area on the continuum of actions (defining the problem); (2) corrections made by the demonstrator; (3) quotes from the demonstrator’s correction process; and (4) thematic analysis of the demonstrator’s evaluation of the process.The second stage entailed thematic analysis [analysis  of the 2The thematic analysis was conducted independently by two researchers to ensure reliability. These researchers identified relevant themes and sub-themes concerning participation in the research model. The analysis began by reading through the transcripts for general and potential meanings. Then, each researcher created an initial coding structure based on the descriptive coding by coding units of text as themes and labeling them with a phrase related to the participant’s account. Next, the researchers conducted a joint analysis that entailed consolidating and prioritizing the independently identified themes, yielding the initial thematic framework of the analysis.In the third stage, the behavioral changes reported by the HPs were analyzed using descriptive statistics. We pooled all practices identified at the three hospitals and assessed the proportion of participants who rated each of the implementation levels.The validity of each action performed by the demonstrator to prevent contamination was scientifically approved by the two HPs present at the event. In addition, an external HP from the infection control unit viewed the videos and approved the final results.The interviews and videos were analyzed by three different researchers. These analyses were then compared, and agreed-up themes were identified. In the very few cases where there was disagreement, we went back to the transcripts and the videos.Table Table General evaluation of the model of the proposed method: The demonstrators emphasized the applicability and accessibility of the model to other HPs in raising awareness.HP information processing by the analytical route: The demonstrators emphasized the quick and effective assimilation of practices facilitated by using the model.Contribution of filming: (1) promotes assimilation of practices more effectively than merely reading a procedure; (2) actively involves the staff; (3) empowers and enhances sense of professionalism.Bringing up difficulties along the care continuum and filling in the gray area: The demonstrators noted that during the practice demo, they were able to address and find solutions to the problems that arose during the demo, whereas in day-to-day work they failed to notice “problems.”Contribution of TA: The TA technique in which demonstrators are asked to speak aloud caused them to plan and think about every step they took during the demo and encouraged a sense of security and sharing among the observers.The following is a brief explanation of the five key themes emerging from the demonstrators’ feedback:Figure The model proposed in the present study overcomes two discrepancies noted in the introduction regarding the maintenance of IPC guidelines: 1) the gap between intentions and practical implementation, and 2) the problem of maintaining behaviors after the intervention stage, namely assimilation and practical maintenance without external oversight . As we nReay and Rankin  claim thUnlike studies that examine analytical versus intuitive decision-making , we chosIn other words, our findings show that in processing information concerning infection prevention, HPs move from peripheral/automatic processing to intuitive and analytical/central processing. The HPs’ personal experience in finding a solution while verbalizing their considerations using the TA technique and repeatedly correcting themselves until they overcome the barriers encountered shifted the issue of infection prevention from the automatic peripheral to the central analytical route. This increases the likelihood that the demonstrators will continue to maintain IPC guidelines while performing this procedure in the future. In addition, documenting the procedures by videotaping with smartphones, a readily available device, raises the likelihood that other HPs will watch the filmed procedures. Indeed, this is what happened based on our interviews with the demonstrators and the other HPs who observed the simulation. The interviewees reported that they incorporated the demonstrated practice into their routine work, and 69.4% of them reported that they changed their behavior in line with the PD intervention.In addition, note that a growing body of research has been conducted using Video-Reflexive Ethnography (VRE) , 35 to aMoreover, while the literature describes how the PD method identifies exceptional people who find solutions to complex situations along the care continuum and disseminate their practices to the rest of the staff members, exactly how this transfer process takes place was not discussed. The method introduced in the current study points out the different stages in the transformation process, thus motivating other HPs to be as creative as the PDs and transforming the desirable behavior from a deviance to a norm. This is because the HPs who participated in the PD study not only implemented the practice, but added to it during the intervention process, and some even offered their own tips. Therefore, the proposed model raises the likelihood that HPs will not only maintain hygiene in the practices they watched or experienced, but will also apply the approach and the skills they acquired to other procedures along the care continuum. Follow-up studies in the future can examine whether the HPs who participated in the demonstration in the dissemination stage implemented the approach in their ongoing work.The research limitation is that the study examined and evaluated the PD intervention in specific hospitals. Therefore, further research is needed to explore the impact of this methodological model at other hospitals. In addition, beyond the research and evaluation stage, the assimilation and maintenance of the behavior change should be studied and evaluated over time.The implementation of the dissemination stage indicates that in order for HPs to integrate and assimilate practices that are not in the official guidelines, they must carry out the procedures themselves. It is important to note that implementation of this methodology does not require special resources on the part of the hospital system. The procedures can be demonstrated and practiced at different times in the hospital wards. Smartphones can be used for documentation and filming, and all the instructions can be delivered by the HPs themselves.