Geobacillus stearothermophilus as an indicator. It presents two contradictory situations: on the one hand, the application of a high-temperature treatment to a low acid food with G. stearothermophilus spores causes their inactivation, reaching food safety and stability from a microbiological point of view. On the other hand, high temperatures favour the appearance of acrylamide. In this way, the two objectives  are opposed. In this work, we analyse the effects of high-temperature thermal treatments  in food from two perspectives: microbiological safety/stability and acrylamide production. After analysing both objectives simultaneously, it is concluded that, contrary to what is expected, heat treatments at higher temperatures result in lower acrylamide production for the same level of microbial inactivation. This is due to the different dynamics and sensitivities of the processes at high temperatures. These results, as well as the presented methodology, can be a basis of analysis for decision makers to design heat treatments that ensure food safety while minimizing the amount of acrylamide (or other harmful substances) produced.In food processes, optimizing processing parameters is crucial to ensure food safety, maximize food quality, and minimize the formation of potentially toxigenic compounds. This research focuses on the simultaneous impacts that severe heat treatments applied to food may have on the formation of harmful chemicals and on microbiological safety. The case studies analysed consider the appearance/synthesis of acrylamide after a sterilization heat treatment for two different foods: pureed potato and prune juice, using  Different conflicting objectives often arise in many food processes . Finding the optimal solutions which balance among all the existing objectives is not an easy task due to the complexity of the mathematical models describing such processes ,2,3,4. TOne of the most important methods of food preservation in the food industry is thermal processing. Historically, the focus was on optimizing heat treatments to improve the processes related to microbial destruction, nutrients retention, cooking values and loss of quality . The maiIn the case of baby food, the recommendations for acrylamide levels are more restrictive than in other types of food, and a maximum allowed amount of 30 μg/kg is set . DiffereFrom a quality vs. microbiological point of view, heat stability of heat-labile quality factors presents a higher z-value than those typical of bacteria. Then, high-temperature short-time (HTST) processes are less deleterious to food quality while ensuring microbial food safety and stability , althougGeobacillus stearothermophilus, a Gram-positive, thermophilic, and spore-forming bacterium with an optimal growth temperature around 55 °C. The spores are very heat-resistant and usually survive canning and sterilization operations. Furthermore, it has been detected in different foods such as canned vegetables, ready-to-eat meals containing meat, fruit preparations, or dehydrated ingredients [Clostridium botulinum or Bacillus cereus, have not been considered here, as their inactivation is generally not a problem within the range of temperatures considered in this study which give rise to significant amounts of formed acrylamide.The microorganism considered here is redients . Other rGeobacillus stearothermophilus spores are used to validate heat sterilization processes [As rocesses ,32,33,34In this work, we have simulated and analysed the dynamics of the two objectives in a thermal inactivation operation. The aim is to use mathematical models to determine the conditions where the amount of formed acrylamide is minimum while ensuring microbial inactivation. To the authors’ knowledge, no previous work was published facing the inactivation of microorganisms and acrylamide production.This study analyses the dynamics of two processes associated with the application of severe heat treatments in some foods: microbial inactivation and acrylamide production. In principle, higher temperatures ensure a safer operation from the microbiological point of view, but can also induce a higher acrylamide production. We have applied this analysis to two practical cases of the food industry (potato puree and prune juice) where different time/temperature treatments are simulated to assess the inactivation of a thermo-resistant microorganism and the acrylamide formation. The microorganism is first characterized see  and thenGeobacillus stearothermophilus within the literature . ,38. GeobThe influence of treatment temperature iability ,40. Geobacillus stearothermophilus were collected from the literature (Web of Science database) as described in [To estimate the model parameters, several D-values (n = 113) for thermal inactivation of ribed in . A temperibed in ,48,49,50The estimated model parameters are ended by . Monte CTo quantify the acrylamide formation, the multi-response kinetics in a glucose-asparagine reaction at high temperatures (120–200 °C) proposed by  were useProduct X). Study [T values in this study , we consider that the same logarithmic relationship applies in our case. The estimated kinetic constant values were used in the system of ordinary differential equations that quantifies the amount of acrylamide formed for a specific time and temperature, shown in Equation (3).Glucose and asparagine react to form a Schiff base. Fructose is formed by glucose isomerization, and it also reacts with asparagine to form the Schiff base. At the same time, the Schiff base is degraded into melanoidins and acrylamide, whereas acrylamide is degraded into unknown species (named ). Study  calculatred potato was chosen, as it produces the highest amount of acrylamide [One of the solutions to the system of differential equations is the acrylamide concentration formed for a certain heat treatment , which is the second objective in our formulation. In this second objective, apart from the heat treatment conditions, that is, the time and temperature variables, it is necessary to set the initial amounts of glucose, fructose, and asparagine. Two foods were selected: potato puree  and prune juice . For each of the two selected foods, initial concentrations are shown in rylamide .This section analyses the results of the simulation of isothermal heat treatments with temperatures between 120 and 135 °C for two food matrices, pureed potatoes and prune juice. On the one hand, to quantify inactivation, a Monte Carlo simulation approach is used, as explained in On the other hand, acrylamide production is reported in the units of measurement parisons .Regarding the first objective , On the other hand, the impact of the heat treatment on acrylamide formation is represented in log10S≥6 . In otheIn any case, this mono-objective analysis confirms that both objectives counter each other, thus a balance in the operating parameters must be achieved. As an expected conclusion of this analysis, the increase in temperature or processing time in thermal treatments favours the inactivation of the microorganism and disfavours  acrylamide formation. However, the sensitivity of both responses to changes in temperature or time is significantly different.As previously discussed, both objectives conflict. Therefore, to analyse the influence of the temperature on both objectives simultaneously, the inactivation rates and the amount of formed acrylamide for different treatment temperatures are evaluated. Simulated inactivation curves for the ranges of temperature and time considered are represented in It is observed that both for the potato puree A and theThe dependence of both objectives on time and temperature calls for a multi-objective optimization approach where the aim would be to find the pairs  that provide the Pareto front of optimal solutions . However, the application of such an approach led to a set of Pareto solutions which consisted of the maximum temperature tested (135 °C) and different processing times (data not shown). This behaviour can be explained by the different dynamics of the objectives analysed above: an increase in temperature drastically reduces the processing time needed to achieve While this is a perfectly valid mathematical result, it is not so useful from the engineering point of view if we want to check the effects of other temperatures over the objectives, which can be critical in the case of considering additional objectives. For this reason, we eliminated the temperature as a decision variable and fixed it to some discrete values within the tested range, analysing the evolution of both objectives for each of the discrete temperatures over time. The highest temperature tested  caused the lowest amount of formed acrylamide at any level of probability as treatment time was short. On the other hand, for the lowest temperature  the highest amount of acrylamide is formed. This reaffirms the results obtained in the previous section and also allows us to broaden the perspective of the problem, as it can be observed that increasing the probability of reaching the target microbial inactivation promotes the acrylamide formation, although with a very low sensitivity, except in the area of probability close to 100%, where the acrylamide formation tends to rise more significantly. The differences between foods A,B are oFor the lowest temperature (120 °C) the duration of the treatment exceeds 23 min for 0S≥6=90% . On the To increase To assist the decision-making process,  (μg/kg) , that waIn Decision makers can use plots such as To complement these design steps, the duration of treatment should be calculated. For that purpose, Geobacillus stearothermophilus in two particular foods  that we have characterized with the Bigelow model. The acrylamide formation has been modelled with the Maillard equation. The analysis of the dynamics of both processes reveals that, to ensure a certain level of microbial inactivation, heat treatments at higher temperatures lead to decreased acrylamide formation, similar to the behaviour of quality components. This is due to the processes’ different sensitivities to temperature. While microbial inactivation is very sensitive , acrylamide formation is not. The methodology presented here can be used by decision makers to design heat treatments when food safety objectives are faced, maximizing inactivation and minimizing the amount of acrylamide (or other target substances). As the selected foods can be used as ingredients in baby foods, the obtained outcomes were compared with the EFSA’s recommendations about maximum acrylamide concentrations in baby food. Even at the highest temperatures, where the least amount of acrylamide is formed due to the short processing times to ensure the microbial inactivation, the expected acrylamide amounts are very close to the maximum EFSA’s recommendation, therefore it should be taken into account when using them in infant formulations.We have analysed the balance between microbial inactivation and acrylamide formation in the case where a thermoresistant microorganism can be present in food in which acrylamide can be formed due to high temperature thermal treatments. As a case study, we have chosen the inactivation of The methodology presented here can be a basis to re-design processes where food safety and acrylamide formation are important issues. It can be used to make decisions when there are unexpected process variables deviations . It can also be extended to other processes and microorganisms, and in future work we plan to include other conflicting objectives depending on temperature and time, such as quality or cost. This future work will also be addressed to experimentally validate the conclusions obtained here to refine the model fitting to other conditions and to check the effects of different food matrixes.


This paper reports the development of a new composite material as a matching medium for medical microwave diagnostic systems, where maximizing the microwave energy that penetrates the interrogated tissue is critical for improving the quality of the diagnostic images. The proposed material has several advantages over what is commonly used in microwave diagnostic systems: it is semi-flexible and rigid, and it can maximize microwave energy coupling by matching the tissue’s dielectric constant without introducing high loss. The developed matching medium is a mirocomposite of barium titanate filler in polydimethylsiloxane (PDMS) in different weight-based mixing ratios. Dielectric properties of the material are measured using a Keysight open-ended coaxial slim probe from 0.5 to 10 GHz. To avoid systematic errors, a full dielectric properties calibration is performed before measurements of sample materials. Furthermore, the repeatability of the measurements and the homogeneity of the sample of interest are considered. Finally, to evaluate the proposed matching medium, its impact on a printed monopole antenna is studied. We demonstrate that the permittivity of the investigated mixtures can be increased in a controlled manner to reach values that have been previously shown to be optimal for medical microwave imaging (MWI) such as stroke and breast cancer diagnostic applications. As a result, the material is a good candidate for supporting antenna arrays designed for portable MWI scanners in applications such as stroke detection. The utilization of non-ionizing electromagnetic (EM) waves at microwave frequencies for medical imaging is a technology that has been widely investigated in the last years. Microwave imaging (MWI) systems are portable, cost-effective as well as safe due to the usage of non-ionizing radiation. Therefore they can be used as an alternative or complementary modality for existing medical imaging systems such as magnetic resonance imaging (MRI) and computed tomography (CT) ,2,3 withThe impact of matching media on the diagnostic capabilities of medical MWI systems has been demonstrated in various studies ,11,12,13For stroke detection applications, the influence of a matching medium for a microwave stroke diagnosis was investigated in . Multi-lBeyond stroke and breast cancer detection, the impact of the matching medium’s conductivity on a biomedical microwave tomography system designed for limb imaging was studied in . A mixtuConsidering the dielectric mismatch between air and human tissue, a matching medium is typically used to couple the EM energy to the body. This helps to minimize the reflections and improve the transmission to the body-air interface. Immersing the antenna array inside a lossy matching medium has additional advantages such as broadening the antenna bandwidth and reducing unwanted reflections from the imaging tank . On the The open-ended coaxial probe is the most common technique to measure the dielectric properties of a candidate matching medium. The coaxial probe method employs a truncated coaxial transmission line which propagates the EM field along its axis. The reflections happen where the EM field faces an impedance mismatch between the probe and the sample material. The reflections are then measured by the VNA and the material’s dielectric properties are calculated .This paper focuses on the development and characterization of a new material as a matching medium for MWI systems. The proposed material is a micro-composite of barium titanate bm1a1We can calculate the true reflection coefficient at the A-Aane from  as:(2)Γ=During the calibration, BaTiOn method ,22. Its at 1 kHz . It is rat 1 kHz  that purg probes .Polydimethylsiloxane (PDMS) is a widely used silicon-based organic polymer. It is optically clear, inert, non-toxic, and non-flammable as well as biocompatible and gas-permeable. Unmodified PDMS is hydrophobic, however surface treatment with oxygen plasma can make the surface temporarily hydrophilic . AqueousTo prepare the matching medium samples, we mixed BaTiOittivity .We now describe the steps involved in the process of making the composite. First, we measure the weight of the BaTiOFive different mixture ratios of the proposed matching media were prepared and the dielectric properties measurements were performed following the methods described in The measurement results for different samples are depicted in In another study , four ditimal in . TherefoThe experimental process for assessing the repeatability of our measurements is summarized in As it is shown in The material properties must be consistent through all the sample to ensure a homogeneous matching medium. To assess the homogeneity of the sample, five different points were selected on the MUT5, and measurements were performed at each point. The probe’s pressure was adjusted to be equal on all the selected points. The results and the positions of selected points are depicted in We observe that the results are almost identical. The small differences between measurements is mainly due to experimental errors from the probe pressure on different points.To evaluate the effect of the matching medium on an antenna used in microwave imaging, we measured the reflection of a printed monopole antenna backed by our composite. The antenna has been used previously in a microwave tomography system, where it was immersed in a lossy glycerol-water mixture . To remoThe results are depicted in As shown in  imaging . Moreove imaging .In this paper, we have developed a new coupling medium for MWI systems in medical diagnosis. As previously pointed out, the presence of a proper matching medium in a microwave-based medical imaging system reduces the reflection signals and increases the transmitted power to the human tissue which causes higher quality of the reconstructed image and thus the accuracy of the diagnostic system. Many papers have studied the influence of coupling medium on MWI systems in different applications such as brain stroke and breast tumor detection. To sum up, findings from different studies ,11,12,13We believe that the proposed matching medium and its method of preparation has many important advantages. Firstly, we are able to obtain various dielectric constant values from 2 to 11 by a proper mixing ratio of BaTiOOur future work will focus on studying this material in more detail. In particular, continuing from our experiments which demonstrated that it is feasible to immerse a MWI antenna into the proposed medium and enhance its reflection coefficient, we aim to examine the performance of an antenna array constructed with this material and compare imaging results with those of a prototype immersed in glycerol-water mixtures . Applica


The information defined in medical health data is researched based on machine learning-related algorithms. Also, this paper used random forest and other related algorithms to perform health data training and fitting. Research shows that the algorithm proposed in the paper can improve the progress of health data classification. The algorithm can provide technical support for the improvement of medical data classification. With the development of China's medical industry, the medical market has become more and more complicated. Establishing a sound medical credit system is one of the important means to regulate the medical market. The lack of standard measures for participants in China's medical market has led to frequent breaches of trust, such as registration breaches . This seThis paper studies the decision tree and random forest algorithm. Since the random forest algorithm is based on the ensemble learning idea, it effectively avoids noise in the training data set, so there will be no overfitting phenomenon. The simulation results also show that this method performs better than logistic regression and K-nearest algorithm in identifying dishonest behaviours . This haIn recent years, decision trees have been one of the most widely used algorithms in machine learning. Compared with the neural network algorithm decision tree, it has the characteristics of flexibility and strong interpretability. Flexibility embodied in the decision tree can prune the tree structure according to the wishes of the algorithm designer and improve the algorithm's performance. Interpretability is embodied in the decision-making standard with extremely high confidence when each root node of the decision tree makes a decision. The decision tree is composed of three parts: directed edges of internal nodes and leaf nodes. The basic process is shown in Ci using different feature dimensions A. When the classification tree is classified, different classification tree algorithms are based on different node classification standards [It can be seen from tandards . Figure D is as follows:First, define information entropy. The definition of information entropy of data set A(D) for D:Different features have conditional information entropy Info The information gain of the feature at this time ispk as the probability that the current sample is category k. At this time, the Gini coefficient can be defined according to the probability distribution:Academically, different types of decision trees use different node decision criteria. This article also uses the Gini coefficient as the decision criterion . The decD, the Gini coefficient of the sample can be written asWhen there is a sample set D is divided into different subsets D1 and D2 according to feature A:The sample set The Gini coefficient divided according to feature A isIn actual engineering applications, the ability of a single decision tree is limited. The noise and outliers in the training data will cause overfitting of the decision tree, which will seriously affect the accuracy of the decision tree to classify unknown data. Therefore, pruning operations are required after the decision tree is generated. To avoid overfitting, random forest (RF) can also be used . The estT during random sampling. The extraction is divided into k rounds. After k rounds of extraction, k training sets can be obtained [It can be seen from obtained . The prok decision trees. During the training process, the decision tree does not need to be pruned. After the training is completed, k weak classifiers can be obtained. Then, assign the same weight to different classifiers to get a strong classifier [After randomly generating the training set, the decision tree is generated. K training sets can train assifier . To meash1(x), h2(x), ⋯, hN(x)}, there is a sample set {X, Y}. Its distribution is X, Y:For the classifier {The interval function of the random forest algorithm can be denoted as var(mr), and its upper bound can be given by the following formula:s represents the classification strength of a single decision tree, and P represents the correlation between decision trees. The above formula shows that the interval function of the random forest has an upper limit. This upper limit can be lowered when the strength of a single classification tree is increased, and the correlation between each tree is reduced.This article investigates the three parties of patients, hospitals, and medical companies. Then, from the patients' perspective, random forest algorithms identify dishonest behaviours to build a healthy medical platform. First of all, this article obtained data sets related to citizen credit from public data sets . In the When dealing with outliers, mainly eliminate data items that are seriously inconsistent with logic. The method used in the article is Turkey's algorithm. This method can define the data of 1.5 times the 4th quartile range as outliers according to the distribution characteristics of the data and eliminate them. The data normalization process uses the following formula:After the data are normalized, the value of the data itself will not cause an offset to the evaluation result, and the evaluation result only depends on the influence of the data attribute. The final step of preprocessing is to perform correlation analysis on the data. We eliminate the more relevant attributes in the data to reduce the dimensionality of the input data under the premise of ensuring the classification accuracy, thereby improving the efficiency of model training and classification.In the random forest, it is necessary to reasonably set the relevant parameters of the random forest algorithm according to the feature vector dimension and the data dimension. This paper analyzes the error of random forest under different parameters . The errM). It can be seen that when ntry reaches 1000, the model error drops to a stable level. Figures In addition to mtry and ntry, the important parameters of random forests also include classes. In the subsequent model training and testing, the value of each parameter is shown in k-neighbor algorithm (k-NN) for comparison. The recognition accuracy of each algorithm is shown in After setting the parameters of the model, we trained and tested the model. To better identify the efficiency of the batch model in the medical dishonesty behavior, this paper uses logistic regression (LR) and k-NNLR. The accuracy rate is 1.4% and 1.3% higher than that of k-NN and LR, respectively. The true negative rate has dropped to a certain extent. It can be seen that the RF algorithm has better performance when recognizing unpredictable medical behaviours.In This article is based on the idea of machine learning and data mining to research medical and health data. The article establishes a prevention and monitoring model based on the random forest algorithm. This article focuses on the input features used in related medical models. In addition to combining the historical medical information of medical participants, we also introduce the patient's social credit status, which can effectively compensate for the behavior identification of medical record personnel and the prevention of dishonesty. The random forest algorithm used in this article can avoid the overfitting phenomenon in the training process and improve the prediction accuracy. The content of this article has certain practical significance for the behavioral norms of medical market participants.


The scanning electron microscope (SEM) is widely used in the analysis and research of materials, including fracture analysis, microstructure morphology, and nanomaterial analysis. With the rapid development of materials science and computer vision technology, the level of detection technology is constantly improving. In this paper, the deep learning method is used to intelligently identify microcracks in the microscopic morphology of SEM image. A deep learning model based on image level is selected to reduce the interference of other complex microscopic topography, and a detection method with dense continuous bounding boxes suitable for SEM images is proposed. The dense and continuous bounding boxes were used to obtain the local features of the cracks and rotating the bounding boxes to reduce the feature differences between the bounding boxes. Finally, the bounding boxes with filled regression were used to highlight the microcrack detection effect. The results show that the detection accuracy of our approach reached 71.12%, and the highest mIOU reached 64.13%. Also, microcracks in different magnifications and in different backgrounds were detected successfully. The scanning electron microscope (SEM) is a large precision instrument used for high-resolution microarea analysis. It has the characteristics of large depth of field, high resolution, intuitive imaging, strong stereo perception, and wide magnification range, and the sample to be tested can be rotated and tilted in a three-dimensional space –4. In adThe conventional method manually changes the magnification of the SEM image to find the region of interest in the microscopic morphology . AlthougDeep learning is derived from machine learning and has outstanding performance in computer vision and natural language processing . In the At present, deep learning algorithms are applied in the investigation of macrocracks has achieved phased results, but there are relatively few studies on microcracks in micromorphology. In order to further explore the intelligent detection of microcracks in the microtopography, this paper employs a dense rotating bounding box based on the deep learning model to intelligently detect microcracks in the microtopography. SEM images are grayscale images; the brightness and contrast information is diverse due to the influence of imaging technology. Inspired by the literature , 30, 32,The workflow of our approach is shown in The direction of fatigue crack growth is mostly presented as a complex curve, due to specimens being affected by load form, alternating stress, surface state, chemical composition, and inclusions. Each bounding box corresponds to a complete detection target in traditional target detection methods. Limited by the diversity of crack shapes, it is not conducive to the extraction of effective features if a bounding box is used to express the characteristic information of the crack as a whole. Therefore, the bounding boxes express only the local area of the crack and express the complete crack information through dense and continuous labeling. Each label box contains the local information of the crack trajectory, but there are different degrees of difference in the information. It is found that the crack information in the conventional bounding box labeling may become irregular line segments, by comparing Figures Y can be calculated as X + F(X) using the residual connection, which means that the output Y can learn the features from the input X .Among them, x1, y1) and . d is the length of the diagonal of the bounding box, calculated by Most of crack colors in the SEM image are black or gray-black. The densely arranged bounding box is not conducive for people to observe the detection result if the predicted bounding box is directly returned to the crack. This paper proposes a method to modify the regression bounding box to further highlight the detection results in the SEM image. As shown in μm to 20 μm, and all the cracks caused by fatigue experiments. After data expansion processing, such as cropping and rotating the cracked area, 120 images are obtained. The interpolation algorithm was used to adjust the size of all images to adjust the size of all images to 512 × 512 at first. Then, we randomly selected 80 pictures as the training dataset, and 20 pictures as the verification dataset and test dataset. Our algorithm was run on the PyTorch deep learning framework with CUDA10.0 and cudnn7.6.4. All experiments use an iterative training strategy and Adam optimizer and set the total number of iterations to 5000. We set the initial learning rate to 0.8e − 4 and attenuate the learning rate by ten times after 3200 and 4200 iterations.The experimental equipment includes desktop computers (an Intel(R) Core(TM) i7-9700 CPU @ 3.00GHz,GPU NVIDIA GeForce RTX 2080S, 16GB of RAM, and Windows10-64 bit) and the high-vacuum scanning electron microscope produced by American FEI company, as shown in P), average intersection ratio (mIOU), F1 score, and average angle loss (mAng) will be used as the evaluation indicators of model performance in this paper, calculated as follows:Accuracy (A is the real bounding box area, B is the predicted bounding box area, and mIOU represents the average intersection ratio between the predicted bounding box and the real bounding box.Among them, TP is a positive sample with correct prediction, FP is a positive sample with incorrect prediction, and FN is a negative sample with incorrect prediction. Precision refers to the proportion of positive samples in the prediction result, and mAng represents the average error between the predicted angle and the true angle. The dataset with rotation annotation and conventional annotation was used to conduct experiments. Figures The conventional labeling methods are prone to missed detections at local cracks, as shown by the white marks in the first column of The imaging resolution change continuously. And the image background, illumination, and crack characteristics also change when scanning electron microscopy searches for damaged areas. In order to verify the feasibility of our method, This paper is compared our approach with four different published methods which include SSD , RetinaNA crack detection method with dense rotating bounding boxes based on the deep learning model was proposed to realize the intelligent recognition of microcracks in SEM images in this paper. Firstly, dense and continuous bounding boxes were used to mark along the microcrack growth trajectory to reduce the learning of background features by the deep learning model. Secondly, the rotation was used angle to reduce the difference in the local characteristics of the cracks in each bounding frame. Our model consists of a ResNet feature extractor and an anchorless frame-based CenterNet algorithm, which adds angle prediction to high-dimensional feature maps. The shape of the bounding box was modified and filled in color to optimize the detection effect of cracks in SEM images. The results show that the detection accuracy of our method reaches 71.12%, which can be used for SEM crack image detection with different magnifications. The regression state of rotation was used to successfully distinguish the local gap area of the crack. This paper successfully combined the deep learning method with the crack SEM image and found that it has great development potential in computer materials through experiments. In the future, we will consider how to embed deep learning algorithms into SEM to realize real-time, traceable, and more intelligent detection of microcracks.


With the continued development and rapid growth of wearable technologies, PPG has become increasingly common in everyday consumer devices such as smartphones and watches. There is, however, minimal knowledge on the effect of the contact pressure exerted by the sensor device on the PPG signal and how it might affect its morphology and the parameters being calculated. This study explores a controlled in vitro study to investigate the effect of continually applied contact pressure on PPG signals  and 17 morphological PPG features) from an artificial tissue-vessel phantom across a range of simulated blood pressure values. This experiment confirmed that for reflectance PPG signal measurements for a given anatomical model, there exists an optimum sensor contact pressure (between 35.1 mmHg and 48.1 mmHg). Statistical analysis shows that temporal morphological features are less affected by contact pressure, lending credit to the hypothesis that for some physiological parameters, such as heart rate and respiration rate, the contact pressure of the sensor is of little significance, whereas the amplitude and geometric features can show significant change, and care must be taken when using morphological analysis for parameters such as SpO2 and assessing autonomic responses. Photoplethysmography (PPG) is an optical measurement technique used primarily for detecting volumetric changes of pulsatile blood flow in vascular tissue. It takes advantage of the fact that light is absorbed by body tissues, such as skin, fat, bone, and blood, in different amounts for any given wavelength of light. During the systolic phase of the heart, when blood is pumped from the pulmonary circulation to the systemic circulation via the left atrium and left ventricle of the heart, there is a momentary increase in the volume of blood in arteries, and the arteries expand. This increase in volume causes more light to be absorbed, resulting in less light being transmitted through the tissue. Utilising suitable sensors and instrumentation, these changes in light intensity can be detected and recorded, and the “pulses” detected can be used for anything from simple heart rate calculations to assessing cardiovascular health .There are two modes of sensors used in PPG, transmission and reflectance. In transmission mode, the light source and detector are placed opposite one another inside a finger clip, and light is transmitted through the finger tissue. This is the most common mode seen mainly in pulse oximeters. The second type of mode is reflectance, where the source and detector are placed next to each other, and light is backscattered within the tissue. This mode is most commonly found in wearable devices, watches and armbands, or incorporated into mobile technology. Irrespective of the type of sensor used, the acquired PPG signal exhibits a quasi-periodic pattern consisting of an arterial pulse wave for each heartbeat. The morphology of this arterial pulse is known to be influenced by several physiological variations such as heart rate, heart rhythm, stroke volume, arterial stiffness, blood pressure, respiration, and the autonomic nervous system. This responsivity of the PPG signal to various physiological processes has helped researchers derive various diagnostic markers for vascular ageing and arterial compliance, large arterial stiffness, hypertension risk stratification, total peripheral resistance, atrial fibrillation, stress, endothelial dysfunction, gingivitis, apnoea, and autonomic nervous system (ANS) responses .However, various other factors that are not physiological, such as sensor geometry, skin-sensor interface, contact pressure exerted by the sensor clip, photodiode sensitivity, ambient light, and measurement site, can also exhibit morphological changes in the PPG signal. In particular, the contact force or contact pressure (CP), the pressure exerted by a PPG sensor on the measurement location, is thought to influence the quality and morphology of the PPG signal significantly. Simple morphological features such as the systolic peak , pulse width, pulse area, and the relative AC/DC amplitudes of red and infrared signals  are thought to be adversely affected by an increase in sensor contract pressure . In the How much is the degree of change in PPG signal features that an increase in sensor CP can create?What are the PPG signal features that are most affected?Is the change in a particular PPG signal feature significant enough to create misinterpretations when deriving diagnostic indices?As can be seen from the table, the effect of CP on the PPG signal acquired or the parameters estimated from the PPG signal is significant. Following these research paths still leaves a number of research questions viable for investigation. These are:These questions and the question of the potential behaviour of arteries located under the PPG sensor when applied to wearable devices has not been investigated . Other fAll the above factors have led to a growing consensus that external factors that can affect the PPG signal quality and morphology, such as contact pressure, need to be further studied rigorously to help standardise PPG sensor design, which will, in turn, facilitate ideal PPG signal acquisition. This will also help standardise PPG measurements so that they are useful in not only standard monitoring (pulse oximetry and heart rate) but also for future and emerging applications such as Pulse Rate Variability (PRV) or Pulse Wave Velocity (PWV) measurements to assess cardiovascular health. As a first step towards discovering the optimal PPG sensor design and conditions, an in vitro investigation was carried out using a relatively simple vessel-tissue phantom with similar mechanical properties to human anatomy. The developed in vitro vessel-tissue phantom with a pulsatile fluidic flow was able to maintain and replicate various blood pressure states whilst under controlled and measurable compressive loads, and was simultaneously able to record PPG signals directly at the site of compression. Through this effort, we aim to determine what sensor-contact pressure is optimum to obtain PPG signals and explore which morphological PPG features are least or most affected by increasing contact pressure.2, incorporating an integrated optoelectronic sensor , was placed atop the phantom, directly above the vessel; this was connected to a custom and dedicated PPG monitoring system [A vessel-tissue phantom was constructed using a previously described method with a clear silicone elastomer  with an altered catalyst ratio to better mimic the softness of human tissue . The vesg system ,14. The To investigate the effect of varying contact pressure on the PPG signal quality and how it may also affect the PPG SNR, morphology, and its features, a blood-mimicking fluid (distilled water and India ink solution) was continuously pulsed through the phantom via a branching circuit from a simplified human arterial network. The pulses were timed at 60 BPM (1 Hz) using a custom bell-pulse profile. Four blood pressure states were implemented by varying the amount of resistance on a clamp simulating total peripheral resistance (TPR). These pressures equated to the ranges for hypotensive, normotensive, stage 1 hypertensive, and stage 2 hypertensive conditions, and were confirmed by reading the pressure at the input of the phantom for 30 s prior to the main experiment. The systolic blood pressure (SBP), diastolic blood pressure (DPB), and mean arterial pressure (MAP) produced are reported for each state in −1, starting just above the phantom surface. The sensor was withdrawn once it had reached the depth of the vessel (3 mm), ensuring that the vessel had been completely occluded. PPG signals were recorded simultaneously with the load cell voltage and repeated 3 times.On confirmation that the correct pressure-state had been established, the linear actuator descended the PPG sensor into the phantom at a rate of 0.03 mms® , using a 4th order Infinite Impulse Response (IIR) low-pass zero-phase filter with a cut-off at 12 Hz. To preserve fine detail in the temporal domain for the PPG feature analysis, the signals were not resampled.All signals were sampled at 1 kHz and were processed offline in MATLAB®.Unlike Electrocardiology, where the electrocardiogram (ECG) can be assessed quantifiably for quality, the PPG signal is highly dependent on the sensor technology used, the PPG signal bandwidth, and the sensor location monitored. As a result, there is no consensus on what constitutes adequate PPG signal quality, and instead, it has been discussed that assessing the PPG signal must be done separately for basic quality, then diagnostic quality . Basic PAll signals obtained from the in vitro setup were segmented to analyse the portions of data in which the sensor was in contact with the tissue phantom and in which there was still pulsatile activity. These portions were automatically segmented applying the following process.First, the signals were segmented from the location in which the load cell voltage was positive to the location of maximum contact pressure. Then, the envelope of the rectified PPG signal was obtained using a low-pass FIR filter with a cut-off frequency of 0.5 Hz and order of 5 times the sampling rate. The resulting envelope signal was also differentiated and then filtered using a low-pass FIR filter with 0.1 Hz cut-off frequency and order of 10 times the sampling rate. The initial point of the signal of interest was identified as the location in which the normalised first derivative of the envelope crossed the normalised envelope. Finally, the endpoint of the signal of interest was determined as the first onset of the envelope signal occurring after the minimum of the derivative of the envelope.From the segmented signals, the cardiac cycles were automatically detected using the algorithm proposed by Li et al. . Since iFrom each identified cardiac cycle, 17 PPG morphological features were extracted. These features are illustrated in The aim of this analysis is to identify those morphological features of the PPG signal that are less affected by contact force. Therefore, the relationship between the contact force and the behaviour of each feature was assessed using Spearman correlation coefficients. By this analysis, the features with coefficients closer to zero will be those that are less affected by the sensor contact force, since a correlation coefficient closer to zero indicates a low correlation, and this would mean that the trend of the magnitude of a given feature does not have a similar trend to the changes in contact pressure.In total, there were 24 recorded PPG signals  with increasing contact force split into 3 repeated recordings for each blood pressure state. It can be observed in the example signal recording in In line with the observed behaviour of the features, it is observed here that the time-related features (highlighted in blue) are less correlated to contact force when compared to amplitude-related features (highlighted in yellow). Features F1 (cycle duration), F9 (x-coordinate of the centroid of the systolic phase of the pulse), F11 (x-coordinate of the centroid of the diastolic phase of the pulse), F13 (pulse width), and F14 (rise time) showed the lower correlations to contact force when measured from infrared signals, whereas F1, F11, F13, F14, and F15 (decay time) were the features with lower correlations to contact force when extracted from red PPG signals. There was no observed difference across or between the BP states either, showing that the blood pressure state in this setup did not play a role in either the observed time-related or amplitude-related features.From The quality and morphology of a PPG signal are known to be affected by several external factors. One such factor that is known to play a significant role is the sensor contact force or pressure. Several researchers including Teng and Zhang ; GrabovsIn an attempt to understand the above, in this work, we investigated the effect of sensor contact pressure on an in vitro vessel-tissue phantom which mimics the properties of human tissue. The phantom was built such that the PPG sensor can be placed directly (3 mm deep) above the vessel, facilitating the investigation of our research questions.The nature of in vitro investigation, however, is limiting in that some interesting physiological phenomena and anatomical features are difficult or, at present, impossible to replicate. In favour of the simplified model presented, however, it does allow the investigation of simple external stimuli, such as contact pressure, without complicating the response with factors outside our control, such as vasodilation/contraction or age-related vessel compliance. The results of the in vitro experiment can therefore be used to further inform or compare results from future in vivo studies.It has been shown in previous studies that there exists optimum sensor contact force/pressure and the reported physiological reading when compared to another or gold standard recordings ,6,7,8,9.The study by Grabvoskis et al.  was of pWhilst it is observed that the red PPG signals have lower SNR generally than the infrared signals, this can be explained by the fact that the infrared PPG monitoring in this phantom may be more robust either due to the sensor component geometry, the type of ink used or a combination of the two. It is also interesting to note that this relationship seems to reverse during the stage 2 hypertensive experiments and might be explained by some mechanical property of the phantom, though further analysis, and more rigorous experiments and a review of the phantom construction may be needed to confirm this. To test the hypothesis that this is related to a mechanical property, a new in vivo experimentation protocol should be developed to investigate whether the PPG signal can be used to directly infer a mechanical condition of anatomy, such as vessel compliance.It has been shown that the morphological features of the PPG are affected by the contact force induced by the sensor to the tissue ,5,9. TheInterestingly, the variation among the repeated experiments for each measurement was less notorious for features extracted from red signals. This could have two different explanations; the red signals are less affected by noise in the in vitro setup and are more robust due to the type of dye used (India ink which is absorbed more by the red light than the infrared light in accordance with the complementary colour chart), or the features extracted from red PPG signals tend to be less affected by noise. Although it is not possible to certainly pinpoint the reason from this current study, further studies should aim to explain these differences, and they should take into account that the red features might be more repeatable in the face of different contact forces or in scenarios in which the contact force cannot be controlled. Furthermore, the behaviour of an arterial blood vessel located directly underneath the PPG sensor can be derived from this investigation. Physiologically, arteries have two important functions . The firThis study has shown conclusively that in an in vitro environment, where there is significant effort to mimic relevant human anatomy and physiological state, the pressure exerted by the PPG probe (approximately between 10 and about 50 mmHg) does not significantly affect the ability to detect and measure certain PPG morphological features. It has also shown that there does exist a point at which the pressure exerted by the PPG probe on tissue is optimal (between 35.1 mmHg and 48.1 mmHg); moreover, the pressures reported in this experiment are in line and of similar values to those already reported in the literature. Rigorous controlled in vitro experiments utilising customised tissue phantoms applied in the field of Photoplethysmography could pave the way in exploring further PPG related research.


This paper presents an OSA patient interactive monitoring system based on the Beidou system. This system allows OSA patients to get timely rescue when they become sleepy outside. Because the Beidou position marker has an interactive function, it can reduce the anxiety of the patient while waiting for the rescue. At the same time, if a friend helps the OSA patients to call the doctor, the friend can also report the patient's condition in time. This system uses the popular IoT framework. At the bottom is the data acquisition layer, which uses wearable sensors to collect vital signs from patients, with a focus on ECG and SpO2 signals. The middle layer is the network layer that transmits the collected physiological signals to the Beidou indicator using the Bluetooth Low Energy (BLE) protocol. The top layer is the application layer, and the application layer uses the mature rescue interactive platform of Beidou. The Beidou system was developed by China itself, the main coverage of the satellite is in Asia, and is equipped with a high-density ground-based augmentation system. Therefore, the Beidou model improves the positioning accuracy and is equipped with a special communication satellite, which increases the short message interaction function. Therefore, patients can report disease progression in time while waiting for a rescue. After our simulation test, the effectiveness of the OSA patient rescue monitoring system based on the Beidou system and the positioning accuracy of OSA patients have been greatly improved. Especially when OSA patients work outdoors, the cell phone base station signal coverage is relatively weak. The satellite signal is well-covered, plus the SMS function of the Beidou indicator. Therefore, the system can be used to provide timely patient progress and provide data support for the medical rescue team to provide a more accurate rescue plan. After a comparative trial, the rescue rate of OSA patients using the detection device of this system was increased by 15 percentage points compared with the rescue rate using only GPS satellite phones. Sleep respiratory diseases include obstructive sleep apnea (OSA) and central nervous system sleep apnea (CSA), etc., the research shows that 80% of the patients with sleep apnea syndrome have snoring phenomena . Long teThere are ~5,000 standard sleep laboratories in the world, and most sleep laboratories use polysomnography (PSG) as the gold standard for detecting sleep disorders . PSG canDue to problems such as OSA not being effectively solved by surgery, and sleep correction using CPAP causing discomfort to patients with sleep disorders, the data suggests that 80% of the patients with sleep disorders have not been effectively diagnosed or treated in time. There is therefore a need for an Internet of Things (IoT) framework system to monitor OSA patients. This system enables OSA patients to be rescued in time when they are drowsy outdoors. Because the Beidou position marker has an interactive function, it can reduce the anxiety of the patient while waiting for rescue. At the same time, if a friend helps an OSA patient to call a doctor, the friend can also report the patient's condition in time.The system uses a popular IoT framework. At the bottom is the data acquisition layer, which uses wearable sensors to collect vital signs from patients, with a focus on ECG and SpO2 signals. The middle layer transmits the collected physiological signals to the network layer of the Beidou indicator using the Bluetooth Low Energy (BLE) protocol. The top layer is the application layer, and the application layer uses the mature Beidou rescue interactive platform. Since the GPS indicator does not include a communication satellite, it does not have an SMS function. OSA patients can only passively wait for rescue. In addition, due to the lack of satellites in Asia and the insufficient density of ground-enhanced systems, OSA patients have large positioning errors. The Beidou system is developed by China itself, the main coverage of the satellite is in Asia, and is equipped with a high-density ground enhancement system. Therefore, the Beidou model improves the positioning accuracy and is equipped with a special communication satellite, which increases the short message interaction function. Therefore, patients can report disease progression in a timely manner while waiting for a rescue.After our simulation test, the effectiveness of the OSA patient rescue monitoring system based on the Internet of Things framework and the positioning accuracy of OSA patients have been greatly improved. Especially when OSA patients work outdoors, the signal coverage of cell phone base stations is relatively weak. The satellite signal is well-covered, plus the SMS function of the Beidou indicator. As a result, the system can be used to provide timely patient progression and provide data support to the medical rescue team to provide a more accurate rescue plan. After a comparative test, the rescue rate of OSA patients using the system's detection equipment increased by 15 percentage points compared to the rescue rate using only GPS satellite phones.Beidou system. Section Experimental study summarizes and suggests the direction for future improvements.The rest of this paper is structured as follows. Part 2 presents the latest literature on this area of research. Part 3 presents the methods and simulation results of a sleep disorder monitoring system based on the At present, the golden standard for the diagnosis of sleep apnea is polysomnography (PSG) . HoweverIn the research of detecting sleep dyspnea based on ECG signal, the methods of OSA detection of ECG signal were studied by the University of Wisconsin, Taiwan Jiaotong University and Peking University –18. At pThe Beidou satellite navigation system provides positioning, navigation, and timing services , which aThe above research proves the feasibility of using machine learning to study the assistance positioning system for OSA patients. However, most of the above studies are based on small sample data sets and lack large-scale clinical applications. In addition, too many features may be selected in the feature engineering, resulting in too many calculations. Or there are too few functions selected, resulting in low classification accuracy. In addition, when selecting a deep learning classification method, manually setting parameters is too inefficient. At the same time, it may overtrain and lead to test overfitting and, because most research methods are only based on small data sets, in practical applications, the generalization ability of this model is low. Therefore, this topic aims to use machine learning algorithms on the cloud platform to standardize rescue positioning data from different devices.The hardware component of the IOT-based sleep disorder monitoring system consists of three parts. The bottom layer is the hardware terminal of the indicator mark, the middle layer is the transmission medium, and the upper layer is the application platform. The beacon terminal includes an ARM main control board, a 4th generation mobile communication module, a Beidou communication module, a Bluetooth communication module, an antenna interface module, and a power management module. In addition, the sides of the housing are provided. There are data transmission interfaces, SOS buttons, and charging ports. There are several sealing rings and fixing screws at the bottom of the housing. However, when a sleep disorder patient is working outdoors, when the drowsiness state occurs, the SOS button can be used to send the help information to the Beidou satellite. Through the auxiliary ground-based augmentation system, Beidou Satellite sends the current status and position coordinates of the sleep-disabled patients to the Beidou rescue platform. The information released by the rescue platform enables the rescue team to quickly and accurately find the location to be rescued. As shown in The whole system is based on ARM main control board, 4th generation mobile communication module, Beidou communication module, Bluetooth communication module, antenna interface module and power management module. In addition, the sides of the housing are provided. There are data transmission interfaces, SOS buttons and charging ports. There are several sealing rings and fixing screws at the bottom of the housing. However, when a sleep disorder patient is working outdoors, when the drowsiness state occurs, the SOS button can be used to send the help information to the Beidou satellite. The Beidou second-generation passive positioning and Beidou active short message communication are combined. The location information of OSA patients can be obtained through the Beidou module, while OSA patients can send disease information through Android mobile phone software operation. The emergency call function is provided in an emergency, and the OSA patient can send his own location information and simple condition information, thereby realizing the emergency rescue function of the OSA patient monitoring system based on the IOT framework.The STM32 development board communicates with the Beidou module by serial port. It uses the command of RNSS format and uses BDGGA to get its own location information and stores it in the cache. When a mobile phone is connected with the terminal through Bluetooth, the data is forwarded to the mobile phone if the mobile phone makes a data request. The mobile phone is transformed into latitude and longitude coordinates through the received Beidou position information and displayed.OSA patients can use the Android phone to send AT commands to initialize the Beidou indicator, and obtain their own location information through BLE communication. The latest rescue information and the latest location information obtained through the refresh function. The OSA can also provide a special message input function through the Beidou indicator, input the condition information through the mobile phone and forward it to the terminal, and then the condition message and location information can be sent to the Beidou rescue platform by the Beidou indicator.It is very necessary for the mobile terminal to communicate with the terminal using Bluetooth to maintain complete communication. The mobile phone terminal obtains whether the communication is normal by sending instructions, and the terminal receives the correct return value by continuously sending test information, and providing an LED flicker function display.By setting the timer, the function of obtaining the position can be used automatically, and the acquired position information can be sent periodically. The B4 function can be started by triggering the button B3 automatic function to stop the automatic function.The main consideration is to obtain location information, and how to send information for help and other aspects. In the event of a problem, the rescue party can get the message in time, and the suitor can also get the necessary information anytime and anywhere. The interactive function of the display object is mainly realized by Bluetooth module and STM32 main control board.The default encoding mode for Android phones is utf-8, which is used for serial communication to receive and send gdb32 encoded Chinese data. When you need to convert an object to a String object, the encoding system may produce inconsistencies, so the server also encodes and converts characters when performing transceiver control. The Server still does not stop when the Activity application is paused, so receiving information should not stop when the application is not in use at this time. Activity needs to bind to Server .Terminal function service flow, as shown in The Beidou indicator uses the STM32F103RBT chip development board. The code running on the beacon is compiled on the mdk4.9 platform and downloaded using the CooCox development tool. Android phones use android stdio as the development platform for the Android sdk version. The entire mark is shown in The Beidou indicator uses a switch control to control the switch of the Bluetooth module. If Bluetooth is turned on, you can choose to connect to the Beidou.Send success messages and location for success as shown in Logistic regression is a statistical tool based technique to deal with machine learning problems. We evaluated the results using logistic regression. Logistic regression works with sigmoid function. the accuracy of the LR model is very high (90%). In particular, LR models using all 10 features show better accuracy than other existing models using RQA functionality with an accuracy rate of 90%.Most importantly, models using LR are still the best in terms of prediction accuracy, precision, recall, and F-measure.The results of scatter plot from logistic regression is shown in A customized fitting model is shown in *exp(b * x)]. The coefficients of the model are a = 430.5, and b = 0.0005877. The R-square value: 0.8975 and root mean squared error is 439.5. The Gauss fitting model is shown in f (x) = a1*exp[−((x–b1)/c1)2]. The coefficients of the model are a1 = 3855, b1 = 4005, and c1 = 2310. The R-square value: 0.9795 and root mean squared error is 196.6.The motivation of the current work is to locate and rescue patients with OSA. Whether the positioning is accurate or not is actually a problem of binary classification of rescue targets. We use logistic regression methods to create classification models. The test result data set is divided into training and test data. Among them, 70% of the data is used for training the model, and 30% of the data is used for the test results. The simulation results confirmed that logistic regression can be used to classify the rescue positioning results of patients with sleep disorders. The positioning accuracy of this system is 90%.The OSA sleep disorder patient monitoring system is based on the IoT framework structure. The entire system provides real-time disease reporting information, precise location information, and physician assistance information for OSA patients. The terminal hardware is realized by the Beidou indicator module, the 4G mobile communication module, and the BLE communication module. Through the secondary development of the Android mobile phone SDK development kit, it is convenient for users to operate. The special operation of shielding the Beidou indicator is made to make this OSA patient monitoring and rescue system easier to integrate into the existing hospital emergency system. The system currently only provides the Android mobile phone client as the input and receiving end of the message, which is convenient for the user, who is able to interact with the doctor in real time through the smart phone. The OSA monitoring system has self-healing capabilities and is extremely robust, providing system reliability. This system test shows that the OSA patient system we developed has lower power consumption and a simpler hardware composition. In addition, our OSA patient monitoring and rescue system has a stable and simple operation while meeting design requirements. In short, our system is an innovative application of IoT technology in modern medicine, with a particular focus on the effective management of OSA patients. In the future, we will explore how to further improve system performance by integrating more sensors to monitor OSA patients in a more comprehensive manner.The simulation results of this research show that the use of machine learning techniques (such as logistic regression) is very suitable for building a classifier for the accuracy of the rescue positioning of patients with sleep disorders. LR has the best performance in all aspects of prediction accuracy, accuracy, recall, and prediction.These high-accuracy results will encourage researchers to more rigorously study the accuracy of sleep disorder rescue positioning data, and use logistic regression for binary output in more practical classification problems. In the next phase or research, we plan to use deep learning, convolutional neural networks, and transfer the learning to the classification of sleep disorder rescue positioning accuracy. In addition, it aims to collect data from hospitals and use machine learning algorithms to evaluate and predict the accuracy of sleep disorder rescue positioning. It is possible to manufacture devices with IOT to monitor sleep abnormalities in real time.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.CL, CX, and DM conceived the study, designed the experiments, analyzed the data, and wrote the whole manuscript. MB and LM provided the preprocessed data. ZZ, LS, and RY carried out experiments. HQ helped to analyze the data and experiments result. YS revised the manuscript.This work was supported by the Fujian Provincial Scholarship Council, the National Science Foundation of China (No. 61773124), the Fujian Social Science Foundation (FJ2021BF029), the Fujian Natural Science Foundation (2018J01796), the Fujian Science and Technology Department Foundation (2019I1009), Project of Youth Foundation of Fujian Education Department (JAT191104), the Foundation of Fuzhou University Zhicheng College (KJ20190001), and the Foundation of National College Student Innovation and Entrepreneurship Project (ZJ1934). In part by The Open Fund of Provincial Key Laboratory of Eco-Industrial Green Technology-Wuyi College.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.


Intensive care units (ICUs) experienced a surge in patient cases during the COVID-19 pandemic. Demand was managed by redeploying healthcare workers (HCWs) and restructuring facilities. The rate of ICU admissions has subsided in many regions, with the redeployed workforce and facilities returning to usual functions. Previous literature has focused on the escalation of ICUs, limited research exists on de-escalation. This study aimed to identify the supportive and operational strategies used for the flexible de-escalation of ICUs in the context of COVID-19.The systematic review was developed by searching eight databases in April and November 2021. Papers discussing the return of redeployed staff and facilities and the training, wellbeing, and operational strategies were included. Excluded papers were non-English and unrelated to ICU de-escalation. Quality was assessed using the mixed methods appraisal tool (MMAT) and authority, accuracy, coverage, objectivity, date, and significance (AACODS) checklist, findings were developed using narrative synthesis and thematic analysis.Fifteen papers were included from six countries covering wellbeing and training themes encompassing; time off, psychological follow-up, gratitude, identification of training needs, missed training catch-up, and continuation of ICU and disaster management training. Operational themes included management of rotas, retainment of staff, division of ICU facilities, leadership changes, traffic light systems, and preparation for re-expansion.The review provided an overview of the landscape of de-escalation strategies that have taken place in six countries. Limited empirical evidence was available that evaluated the effectiveness of such strategies. Empirical and evaluative research from a larger array of countries is needed to be able to make global recommendations on ICU de-escalation practices. Previous research has explored the expansion of intensive care units (ICUs) during the pandemic, to address the increase in patient demand, and the strategies for the redeployment of staff, including the integration of training and wellbeing support. This review searched eight databases of peer-reviewed and grey literature in April and November 2021, with no limitations in relation to geographic location, but limited to articles published in English. Detailed search criteria were developed around the phrases ‘COVID-19′, ‘de-escalation’, ‘ICU response’, ‘redeployment’ and ‘healthcare workforce’, and the quality of included articles was assessed using the mixed methods appraisal tool (MMAT) and authority, accuracy, coverage, objectivity, date, and significance (AACODS) checklist.To our knowledge, this is the first systematic review of the evidence on the de-escalation of ICUs in the context of the COVID-19 pandemic. The key operational approach in relation to maintaining flexibility for future surges was to use a traffic light or phased return system for both the workforce and the facilities, as it would allow for a quick return to redeployment, if needed. The key supportive strategies have focused on the wellbeing and the training needs of the returning redeployed workforce, which included ensuring that staff received time off to rest and recuperate. These strategies also entailed monitoring and supporting the long-term mental health of staff ensuring staff received recognition and gratitude for their service; identifying training needs in the trainee healthcare workforce and catching up on any missed training. The most relevant training strategy in relation to preparation for future surges of COVID-19 was to continue with ICU and disaster preparedness training and practices.This review has served as a first step to map the available evidence on the strategies that are currently being used for the de-escalation of ICUs in healthcare settings from six countries. To enhance the field of planning for de-escalation, further evidence should be collected from a wider range of healthcare settings globally. This would enable the further sharing of experiences, allowing healthcare leaders and policymakers to identify strategies that could be adapted to their local setting.Alt-text: Unlabelled box,,14Just over 200 million cases and 4.4 million deaths had been reported worldwide due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by August 2021.,36The first peak of COVID-19 cases and ICU admissions has come and gone for numerous countries. By August 2020 many countries had got through the first peak of ICU admissions whilst suffering considerably, and faced the prospect of future surges.The purpose of this systematic review was to systematically review the strategies that have been used to de-escalate the ICU response and return redeployed staff and facilities to their usual role. The review focuses on the strategies to provide wellbeing and training support, and the operational strategies to manage the return of the workforce and facilities in contexts that are shaped with the potential need to escalate services due to future surges. Identifying these strategies is an important starting point to promote research into the evaluation of reported strategies globally. Once evaluation research is available it would then be possible to make de-escalation recommendations for recurrent surges of COVID-19.The review was guided by the following research questions: What are the mechanisms developed to support the wellbeing of the healthcare workforce following redeployment periods? What are the training strategies recommended to support healthcare staff as they return from redeployment? What operational strategies have been suggested to manage the return of the healthcare workforce from redeployed areas? What operational strategies have been established to manage the de-escalation of intensive care facilities?,The review was designed as a systematic review capable of capturing the emerging evidence on de-escalation strategies of ICUs following COVID-19 surges. The review was developed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement and following the systematic review guide shared by Tricco et al.,Following the creation of the research questions, an initial list of search items was developed using the PICO framework.The eligibility criteria can be found in The search results were imported to Rayyan.Following the title and abstract screening, the remaining publications that met the inclusion criteria were imported into an Excel document and the full text was screened for eligibility. The publications identified during the review were also added to the full text screening process. The second reviewer cross-checked 10% of exclusions, using the same percentage agreement rate threshold (agreement rate 89% for first wave and 100% for the second wave of screening), and any discrepancies were discussed until resolved. The references of the final list of included publications were also screened to identify any additional relevant articles.The data from the included publications were extracted by using a data extraction form developed in Excel using a pre-defined list based on themes identified in the screening process (see Appendix 2). The extracted study details included information on study design, study population, study setting, and study methodology. The main experiences collected from the publications included wellbeing support initiatives, training strategies, and operational strategies for the workforce and the ICU facilities. Additional data on limitations were also collected.,,The mixed methods appraisal tool (MMAT) was used for qualitative, quantitative, and mixed methods studies to assess their quality, reliability, and relevance.Narrative synthesis was conducted based on the methodology used by Popay et al.The screening and selection process can be seen in During the data extraction process, an in-depth review of the 29 papers resulted in 14 papers being excluded as the content was related to broad hospital responses of de-escalation rather than ICU specific responses or was related to existing staff in ICUs rather than redeployed staff. The screening of references of the final 15 publications did not result in additional publications for inclusion.,,,,n = 6), the USA (n = 4), Singapore (n = 2), China (n = 1), Iran (n = 1), and Australia (n = 1).A summary of the key characteristics of the included papers can be found in 22An overview of the quality assessment scores can also be found in The key findings from the articles included in the review could be organised across the four categories presented in ,,A common strategy implemented to improve the wellbeing of the returning redeployed staff was to provide time off to rest and recuperate.A concern among hospitals were the long-term mental health effects of working in ICUs during the pandemic. Panayiotou et al.,,A key experience outlined in three of the papers was that redeployed trainees should have their missed training resumed once returned to usual roles.HCWs not only required further training in their usual fields, but some requested further training in the ICU field. Schneiderl.Many healthcare settings acknowledged that future surges of COVID-19 and other epidemics are expected, so recommended the training of staff in disaster management should be continued following redeployment to allow for flexibility for future threats. This was recommended by Leng et al.,,,,Many of the hospitals facilitated the return of the redeployed staff to their usual departments through updates to rotas.In addition to adapting rotas to allow the return to usual roles, both Price et al.,Some of the papers illustrated a step-down or traffic light approach to the de-escalation of ICUs.,Some of the papers documented similar approaches to the traffic light system to manage the return of redeployed staff, for de-escalating the ICU facilities.A common theme in the management of ICU facilities is that some of the healthcare settings were wary of future COVID-19 surges. Shaparin et al.This review has identified the supportive and operational strategies that have been implemented and documented to date to manage the de-escalation of ICUs in certain countries where COVID-19 admissions have experienced a decline.The key supportive strategies focussed on the wellbeing and the training needs of the returning redeployed workforce. Wellbeing mechanisms concentrated on ensuring that the staff received time off to rest and recuperate; they also entailed monitoring and supporting the long-term mental health of staff; and ensuring the staff received recognition and gratitude for their service. The most obvious training strategies were to identify training needs of the trainee HCWs and to catch up on any missed training. The most relevant training strategy in relation to preparation for future surges of COVID-19 was to continue with ICU and disaster preparedness training and practices.De-escalation strategies also included operational approaches to manage the return of the workforce and facilities. The key operational approach in relation to maintaining flexibility for future surges was the plan to use a traffic light or phased return system for both the workforce and the facilities, as it would allow for a quick return to redeployment if needed. Maintaining oversight of ICU supplies, ringfencing ICU beds, and preparing non-COVID-19 wards for closure were other plans to ramp up ICU capacity, if needed. Other operational strategies included updating staff rotas to return staff to usual roles or to maintain a portion of the workforce to facilitate vacations and to deal with the backlog of critical care needs. It was also documented that ICUs were separated into COVID-19-positive and -negative beds to focus on care needs outside of COVID-19. In terms of management, it was suggested that control should be returned from a central overarching body to the specific centre managing the COVID-19 cases.The research included in this review focused mainly on the de-escalation of redeployed HCWs in comparison to the de-escalation of facilities. This contrasts the heavy reporting of strategies used to escalate facilities during the COVID-19 surge.8,Similarities exist between the wellbeing strategies that have been described in this review and strategies that have been implemented following other emergencies. In the aftermath of Hurricane Katrina, plans were made to ensure mental health services were available to nurses, due to the adverse emotional outcomes they had reported.,,Research by Chang et al. and Shiao et al. found an increase in nurse resignations and staff turnover following the SARS outbreak due ongoing feelings of stress.,,The plans to ensure that ICU and disaster preparedness training and practices continue is not a novel idea. Following the SARS epidemic, research by Thomas et al. and Lam et al. concluded that training in disaster management and emergency preparedness should continue to prepare HCWs for future epidemics.,,Maintaining flexibility in the operational management of staff and facilities to adapt to future ICU demands has been recommended previously following the SARS outbreak and numerous influenza outbreaks.There is a difference between the previous contexts described above and the context of the COVID-19 pandemic. This pandemic is unique in the ongoing emergence of new waves of infection across the world. The new waves of infections have meant that the healthcare workforce have had to be agile with de-escalating and re-escalating the ICU response in terms of staffing, restructuring facilities, and ensuring staff receive the necessary training and wellbeing support to cope with the recurrent demands on ICU facilities. This highlights the pressing need to develop strategies of de-escalation that are flexible for re-escalation, which this review has attempted to identify.The primary limitation of the review is the lack of representativeness as most of the included articles were from the UK and the US. There were no reports from Africa, Latin America, Europe, and most of Asia. The reason for the limited geographical scope could be due to limiting articles to those published in English. Other explanations for the limited de-escalation literature from LMICs could be because healthcare settings in these regions are still dealing with high numbers of hospitalisations  or have not had time and resources to develop reports and peer-reviewed articles on de-escalation. The strategies discussed in the review are, therefore, not representative of all healthcare settings globally and may not be feasible in different regions of the globe.This review set out to identify ICU de-escalation strategies from empirical data and from grey literature. However, limited empirical data were identified and the majority of data were based on recounts from hospital departments on the de-escalation strategies implemented locally.This review has served as a first step to map the available evidence on the strategies that are currently being used for the de-escalation of ICUs. To further enhance the field of planning for de-escalation, strategies in the form of guidelines, case studies and plans that have been implemented, should be collected from a wider range of settings. This would enable the sharing of experiences across the globe and the identification of what works, allowing healthcare leaders and policymakers to identify strategies that could be adapted to their settings.SEC led the study design, data collection, data analysis, and data interpretation, and contributed to the drafting and revision of the manuscript. CV-P led the study conception, drafting and revision of the manuscript, and contributed to the study design, data analysis, and data interpretation. GC contributed to the study conception and data analysis. All authors were responsible for the raw data associated with the study. CVP and SC made the decision to submit the manuscript for publication.Datasets utilized in this study can be accessed upon reasonable request. All datasets within the study are available within the article or supplementary material.None declared.


In modular robotics modules can be reconfigured to change the morphology of the robot, making it able to adapt to specific tasks. However, optimizing both the body and control of such robots is a difficult challenge due to the intricate relationship between fine-tuning control and morphological changes that can invalidate such optimizations. These challenges can trap many optimization algorithms in local optima, halting progress towards better solutions. To solve this challenge we compare three different Evolutionary Algorithms on their capacity to optimize high performing and diverse morphologies and controllers in modular robotics. We compare two objective-based search algorithms, with and without a diversity promoting objective, with a Quality Diversity algorithm—MAP-Elites. The results show that MAP-Elites is capable of evolving the highest performing solutions in addition to generating the largest morphological diversity. Further, MAP-Elites is superior at regaining performance when transferring the population to new and more difficult environments. By analyzing genealogical ancestry we show that MAP-Elites produces more diverse and higher performing stepping stones than the two other objective-based search algorithms. The experiments transitioning the populations to new environments show the utility of morphological diversity, while the analysis of stepping stones show a strong correlation between diversity of ancestry and maximum performance on the locomotion task. Together, these results demonstrate the suitability of MAP-elites for the challenging task of morphology-control search for modular robots, and shed light on the algorithm’s capability of generating stepping stones for reaching high-performing solutions. Contemporary research in robotics commonly investigates how to adapt the controllers of robots when exposed to damage or changing environments. These studies usually consider robots with a fixed morphology. In modular robotics, morphological adaptation is achieved through the reconfiguration of modules . With thmodular robots due to the connection between controller optimization and morphology. In one relevant study, The field of Evolutionary Robotics (ER) approaches this challenge by applying Evolutionary Algorithms (EAs) to design and adapt both control and morphology of robots. EAs have been successfully applied to modular robot design and control  althoughOvercoming challenges in modular robotics require optimization algorithms that can evolve high performing solutions while retaining morphological diversity to avoid premature convergence in the resulting deceptive fitness landscapes. While little research exists so far on this challenge in the context of modular robotics, proposed approaches include a custom constructive approach , morpholRecent advances in ER are based on promoting phenotypic diversity in the search process. One simple but powerful way to achieve this is by making phenotypic difference in the current population an additional objective to maximize . This muQD algorithms differentiate solutions based on phenotypic properties, also called behavioral descriptors, which dictate the inclusion into an archive. The most popular variants of QD algorithms are Novelty Search with Local Competition (NSLC) , which uintermediate step to a final solution  Remove a module. The tree is traversed adding all modules, except the root, into a list of candidates to remove. A module is randomly selected from the candidates before being removed along with any existing children. 3) Rotate a module. The two modules in use both support rotation around its connection axis and mutation will randomly select a new orientation in 90° increments. Note that only one of the three possible changes can occur per morphological mutation. These mutations cannot alter the type of a module.The morphological encoding supports mutation- and crossover-operators. When mutating the morphology three possibilities exist: 1) For crossover, a branch exchange is implemented. For both parent morphologies the tree is traversed adding all modules, except the root, to a list of candidates. A random candidate is selected from both morphologies before being exchanged. The candidate module, including its children, from the first morphology, is inserted into the place of the candidate from the second morphology and vice versa.Lastly, the morphology is limited to a maximum size, t is the time since the controller was initialized, oi is the amplitude offset. The output of the controller, i.e., the maximum and minimum values of The control systems of the joint modules are based on a decentralized wave pattern generator . Each jop is the individual parameter of the controller and σ is the magnitude of the noise. The magnitude, σ, is scaled for each parameter so that a global mutation rate can be used for the controller, the scaling is defined by the range of each parameter detailed in bounce-back restriction function  . This EAus works  to avoidus works  to usingcuriosity of solutions in the repertoire (The last EA used represents the QD paradigm and is the MAP-Elites algorithm . Centralpertoire . CuriosiTo ensure a balanced comparison, the mutation parameters of each algorithm were optimized in advance in a parameter sweep (parameters detailed in The environment where the robots must move in shape the search space of our experiments. With a flat terrain, an evolutionary run might lead to a smooth progression due to the absence of obstacles/deceptive traps. To see whether all the approaches perform the same when changing the environment, we use three different environments: A flat terrain, a raised platform with a single wall, and a circular terrain where circular walls ripple outwards .U test (To begin analysing the performance of the three search algorithms, we will start by looking at the best fitness obtained by any single solution in the population. The best fitness is plotted in U test  between U test , shows tSince it is not only the fitness, or quality, of the solutions that we are interested in, it is informative to project the population of solutions into a repertoire using the morphological descriptors as axes. This projection gives an overview of the population as a whole and it is possible to visualize where in the morphological space the best solutions are found. The projection also enables us to visualize the quality-diversity trade-off, which shows that not all solutions, or morphologies, can obtain the same fitness. For the objective based search algorithms the projections are created by inserting solutions after each generation, accumulating solutions as the search progresses. In Although the middle row of precision or reliability since both of these metrics decrease as new low fitness solutions are added due to the lower average performance, which disadvantages search algorithms that generate diversity. A Mann-Whitney U test demonstrates that the differences between all three search algorithms, after the last evaluations, for both plots in To summarize the projections in To get an impression of the evolved morphologies, we selected the three best runs from each search algorithm and extracted the single best solution. The solutions are shown in To better understand the value of diversity in our modular robotics scenario we created two new environments, with different obstacle profiles as shown in To highlight the difference between the evolved populations in the different environmental settings, we projected the best-found solutions for the different morphological descriptions in Up until now the focus has been on the quality and diversity of the evolved populations, however, the previous graphs have not been able to show why the search algorithms evolve differently. We will therefore analyze the genealogical history of solutions to better understand how the solutions evolved and formed stepping stones. ancestry of solutions in the population. This summary is different from the data in As shown in To illustrate these genealogical statistics we selected a random run and plotted the ancestry of the best solution found during evolution. R2 of 0.9084, which indicates that the model fit the data quite well, coefficients can be found in 2 To verify if the model fit with the maximum fitness of To investigate a possible connection between the QD properties of the ancestry, as shown in The results for the locomotion task, in the default flat terrain, shows that QDSA produced the highest performing solutions for this problem . For theWith respect to morphological diversity we showed that explicitly promoting diversity through either objective- or QD-based search algorithms improved the assortment of morphologies. However, QDSA produces more diversity compared to MOFD, demonstrating the advantage of QD algorithms for this task. Coupled together, it is likely that the diversity produced in QDSA together with the selection pressure to improve in promising areas of the search landscape led to higher overall performance, underscoring the utility of diversity in EAs. The effect of this could potentially explain the slower rise in performance seen on in When transitioning to different environments we showed that QDSA is able to gain significantly higher performance compared to the two other approaches. However, all approaches achieved equal fitness when seeded with the population of QDSA at the time of transition. The seeding also led to all three search algorithms finding good solutions in the same area of the search space . This shThe analysis of genealogical ancestry revealed several interesting aspects about how the population of solutions evolve for the three different search algorithms. Based on the number of ancestors and the age of individuals in the population , it can diverse stepping stones, as proposed by Based on the QD metrics of the ancestry, shown in We showed how QDSA is an effective method for evolving both the morphology and control of modular robots for performance, diversification, and transfer to new environments. While the implementation of QDSA is promising for evolving both the morphology and control of modular robots, there are additional challenges related to how different selection methods could be further improved in QDSA. The modular robotics approach furthermore allows us to expand our module inventory to incorporate various types of other structural, sensing and actuator modules. This possible extension will convolute the search space further and possibly benefit more from QDSA than other algorithms. Experiments should also be conducted with different modular robotics systems to verify the results reported here for different modules, genotype-to-phenotype mappings, and tasks. One challenge with our setup is that different robotic blueprints can map to the same morphological features, which might be a problem as it can constrain the type of solutions found to particular robot morphologies. Therefore, additional morphological features could be implemented to create a multi-dimensional map that could lead to better and more unique solutions.Optimizing both the morphology and controller for modular robots is challenging due to the large and unknown search space. As the amount of exploration vs. exploitation to be used for optimization strategies is usually determined by the complexity of the agent and the environment—the ruggedness of the resulting fitness landscape—we compared three evolutionary algorithms to determine how each performs on this challenging search space. The results showed that the QDSA approach based on MAP-Elites produced higher performing and more morphologically diverse solutions compared to the SOFO and MOFD approaches which were based on an objective-only EA and a diversity-augmented multi-objective EA, respectively. The importance of the diversity produced by MAP-Elites is corroborated through the successful transferal of evolved robots to two additional, and more difficult, terrain types. The genealogical ancestry produced by each algorithm furthermore indicated that MAP-Elites found more diverse and high performing stepping stones, shedding new light on how the algorithm achieves high-performing final solutions. The added pressure for diversification in the two morphological dimensions of the MAP-Elites-based approach bolsters how useful it can be for evolving both the morphology and control of modular robots.