A growing body of scientific studies has been published to inform responses to the ongoing coronavirus pandemic, and some have claimed that cigarette smoking has a beneficial or mixed effect on the prevention and treatment of COVID-19. The presentation of such findings, unfortunately, has created an infodemic. This study integrated the theory of planned behavior and the health belief model and incorporated findings on addiction from the medical literature to predict cessation intention and support for tobacco control measures in the context of the COVID-19 infodemic. The study found that cessation intention partially mediated the effect of perceived severity and fully mediated the effects of perceived benefits, self-efficacy, and addiction on support for control measures. In addition, a positively-valenced message of the effect of smoking on the prevention and treatment of COVID-19 vs. a mixedly-valenced message was significant in predicting cessation intention, and the positively-valenced message of smoking indirectly predicted support for tobacco control measures. Perceived susceptibility, barriers, and subjective norms, however, exerted neither direct nor indirect effects on the two outcome variables. Scientists worldwide have been working to find risk factors and therapeutics for COVID-19 since SARS-CoV-2 was identified. Therefore, while the effect of cigarette smoking on COVID-19 has been widely studied, unfortunately, the conclusions have been mixed and even contradictory. This trend is unfortunate because the dissemination of confusing findings through media reporting, although not intended to be harmful by the media, has not only undermined years of public health efforts to curb tobacco use but also resulted in chaos during the pandemic .Numerous studies have examined why people do not give up cigarette smoking even though tobacco use is a major risk factor for more than 20 different types or subtypes of cancer . The detAlthough all of the abovementioned theories have gained currency in their own right, they were essentially derived from expectancy-value theory (EVT) –24. EVT Several theories or models that build upon the expectancy-value theory elaborate general expectancies in the TRA and the TPB into specific beliefs. They include the health belief model (HBM)  and its The HBM hypothesizes that health behaviors are influenced by four kinds of health beliefs, i.e., perceived susceptibility (PSUS), perceived severity (PSES), perceived benefits (PBEN), and perceived barriers (PBAR), as well as cues to actions (CTA) . The HBMPSUS, PSES, PBEN, and PBAR refer to four specific beliefs that fall into the category of outcome expectancies. Numerous studies have consistently found that a higher level of risk appraisal of the outcome (PSUS and PSES), a higher level of PBEN and a lower level of PBAR to avoiding the outcome are associated with a higher level of likelihood of compliance with recommended health practices , 32. PSUH1a: People who perceive stronger susceptibility to becoming ill due to smoking are more likely to quit smoking.H1b: People who perceive a stronger severity of the consequences of smoking are more likely to quit smoking.H1c: People who perceive stronger benefits of smoking are less likely to quit smoking.H1d: People who perceive stronger barriers to not smoking are less likely to quit smoking.CTA can be either internal  or external  to people of concern, and they trigger readiness to adopt a certain health behavior , 40. TheH2: The higher the internal CTA are, the more likely people are to quit smoking.The external cues that people encounter are more varied than internal cues . Some sNotwithstanding the paramount importance, the actual impact of mixed findings with respect to the relationship between smoking and COVID-19 on cessation intention has not yet been empirically examined. A research question is hence raised below:RQ1: Are there differences in the effects of three types of message valence  and the control condition on cessation intention and tobacco control measures?As mentioned above, the HBM was derived from EVT but differs from EVT primarily with regard to specific behavioral beliefs replacing the general beliefs. In addition, compared to the more general EVT model , the original HBM ignores subjective norms and self-efficacy (SEF).Originally proposed in social learning theory, self-efficacy is a “belief in one's capabilities and effectiveness to organize and execute the courses of action required in performing specific tasks” , 58–60. SEF has been hypothesized to positively affect behavioral intentions and has received empirical support in numerous antismoking studies –64. ConsH3: The higher SEF is, the higher smoking cessation intention is.The TRA and the TPB underscore the importance of perceived social norms in affecting intention. Sullivan et al.  includedH4: The higher subjective norms are, the higher smoking cessation intention is.According to the IM, the effect of smoking or nicotine addiction on cessation intention may be at best close to that of habit if addiction is considered a kind of past habitual behavior . HoweverH5: The higher smoking addiction is, the less likely people are to quit smoking.Although numerous factors influencing support for government control of smoking have been examined –87, therH6a: There is a positive relationship between cessation intention and support for tobacco control measures.H6b: There is a positive relationship between PSUS to becoming ill due to smoking and support for tobacco control measures.H6c: There is a positive relationship between the PSES of smoking consequences and support for tobacco control measures.H6d: There is a negative relationship between the PBEN of smoking and support for tobacco control measures.H6e: There is a positive relationship between PBAR to not smoking and support for tobacco control measures.H6f: The higher subjective norms are, the more likely people are to support tobacco control measures.H6g: The higher self-efficacy is, the more likely people are to support tobacco control measures.H6h: The higher smoking addiction is, the less likely people are to support tobacco control measures.H7: The associations between the predictors (shown in H6b through H6h) and support for tobacco control measures are partially mediated by cessation intention .There is one additional research question regarding the effects of external cues to action.RQ2: Are there differences in the direct and indirect effects of the three types of message valences  and the control condition on support for tobacco control measures?The participants were recruited through a paid research panel hosted by the online survey platform “Questionnaire Star” in China in September 2020. All the participants were required to be smokers according to the clinical guidelines of the CDC . We exclAt the beginning of the questionnaire, a consent form was presented to the participants. If the participants agreed to the terms and conditions, they could subsequently begin to answer questions. However, the questionnaire was terminated immediately if they objected to the consent form. The online questionnaire proceeded page by page, and the participants could not return to a page once they had progressed beyond it. The questionnaire consisted of the following sections: a pretest (demographics were inquired), the treatment (stimulus), a manipulation check, and a posttest . After the participants submitted the questionnaire, they were debriefed regarding the veracity of stimuli and were advised to consult with the WHO guidelines, which were accessible through a link provided to information about the relationship between smoking and COVID-19.https://ndownloader.figshare.com/files/28524306?private_link=04d6bc1646c1eb6f7278).The study manipulated external CTA, specifically, the valence of the relationship between smoking and COVID-19. There were three treatment groups based on the message valences received, i.e., that smoking is beneficial to prevention and the treatment of COVID-19 , that smoking increases one's risk of contracting COVID-19 and worsens the severity of COVID-19 , and that smoking may have both negative and positive effects on the risk of infection and treatment of COVID-19 . A total of 600 subjects were randomly assigned to the three groups. An additional 100 subjects were also randomly assigned to a control group in which no stimulus was presented. The four groups were dummy coded for three predictors, with the mixedly-valenced message group as the reference category, because we aim to examine the effect of COVID-19 Infodemic on smoking cessation. The stimuli are stored at the online appendix  test of sampling adequacy and Bartlett's test of sphericity were conducted for all scales. The results were above the recommended cut-off values .Regarding the RQs, the positively-valenced message vs. the mixedly-valenced message was significant in predicting cessation intention healthy behaviors As mentioned above, cessation intention partially mediated the effect of perceived severity and fully mediated the effects of perceived benefits, self-efficacy, and addiction on support for control measures. A perception of the severity of smoking consequences determines both cessation intention and support for punitive measures against smoking. Therefore, focusing on and publicizing the severity of smoking consequences are crucial to both induce people to quit smoking and garner their support for stricter regulations against tobacco use. Furthermore, the perceived benefits of smoking, self-efficacy, and addiction contributed to cessation intention in the hypothesized directions, and these predictors affected support for tobacco control measures through the mediator, i.e., cessation intention. Perceived benefits  increasPerceived susceptibility (H1a), barriers (H1d), and subjective norms (H4) exerted neither direct nor indirect effects on the outcome variables. However, perceived severity not only predicted cessation intention but also directly and indirectly predicted support for control measures. Compared to susceptibility, perceived severity promotes individuals' complete knowledge of smoking hazards [see ] and priBesides, Janz and Becker  differenThe nonsignificant effect of subjective norms on cessation intention is consistent with previous studies . This fInternal CTA (H2) did not have significant associations with the outcome variables. Examining the responses to the internal CTA scale, we found that the nonsignificant effects may be attributed to the little variation among the indicators. On average, the majority of the participants  did not believe that they had any health problems related to smoking, which led to optimistic bias . The reThe experimental findings indicate the simple fact that scientific studies are important to the wellbeing of our society. More people have been increasing their tobacco use due to distress and other mental problems caused by the pandemic , and conThe findings of the study have additional practical implications. The nonsignificant role of subjective norms indicates that the stigmatization of smokers  may not be an effective strategy and may even backfire, as smokers discredit social influence in their decision to continue smoking. Antismoking efforts are a collective endeavor in a civic society, which means we need to not only publicize the knowledge using concrete examples to promote beliefs about smoking hazards (severity) and the benefits of quitting and support self-control (self-efficacy) over addiction but also generate popular support for the enforcement of stricter control measures.This study integrated the TPB and the HBM and incorporated findings on addiction from the medical literature to predict cessation intention see . FurtherThe study has limitations. First of all, the measures on the smoking cessation intention and the support for control measures are collected soon after the subjects were primed with the various stimuli. This raises the concern that the effects of stimuli may well fade away in a longer time horizon, but such a limitation regarding external validity is shared by most experimental research .In addition, the respondents were recruited in China, which has the largest smoking population in the world , 124, isPrevious studies , 126–128The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethical Committee of Medical School, Shenzhen University. The patients/participants provided their written informed consent to participate in this study.GF was responsible for research design, analysis, and write-up of the manuscript. SZ was responsible for data collection and literature review. XZ was responsible for the revision of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Increasing attention has been devoted in recent years to in situ sensing and monitoring of the electron beam melting process, ranging from seminal methods based on infrared imaging to novel methods based on backscattered electron detection. However, the range of available in situ monitoring capabilities and solutions is still quite limited compared to the wide number of studies and industrial toolkits in laser-based additive manufacturing processes. Some methods that are already industrially available in laser powder bed fusion systems, such as in situ detection of recoating errors, have not yet been investigated and tested in electron beam melting. Motivated by the attempt to fill this gap, we present a novel in situ monitoring methodology that can be easily implemented in industrial electron beam melting machines. The method is aimed at identifying local inhomogeneity and irregularities in the powder bed by means of layerwise image acquisition and processing, with no external illumination source apart from the light emitted by the hot material underneath the currently recoated layer. The results show that the proposed approach is suitable to detect powder bed anomalies, while also highlighting the link between the severity of in situ detected errors and the severity of resulting defects in the additively manufactured part. The quality and performance of high-value-added parts produced via powder bed fusion (PBF) processes, either laser- or electron beam-based, are known to be affected by a large variety of factors. Several sources of poor quality can be predicted and tackled by properly selecting, controlling, and optimizing powder properties, process parameters, build design choices, and machine calibration settings. However, a variety of other events and process dynamics that are difficult or impossible to model and predict may lead to stochastic variations and detrimental effects on the final quality and conformity of the part. Because of this, a whole body of scientific literature and industrial research has been devoted to in situ measurement and monitoring methods suitable to detect the onset of process defects and the origination of unstable process conditions, exploiting sensor data acquired during the process. Recent studies reviewed the literature devoted to in situ monitoring of PBF and summarized the most recent advances and achievements in this field ,2.One category of methods involves the in situ measurement and characterization of layer properties, exploiting so-called “powder bed cameras”, i.e., off-axially mounted cameras that acquire one or more pictures on a layer-by-layer basis. Images captured just after the powder recoating operation allow the detection of possible anomalies in the powder bed, such as incomplete spreading, recoater hopping or streaking patterns, contaminations caused by debris or deposited spatters, and superelevated edges ,4,5. EveThe capability to detect local inhomogeneity in the powder bed has been explored mainly  in L-PBF, whereas almost no study has been devoted to electron beam PBF . One main reason is the different nature of powder bed images acquired in L-PBF compared to those acquired in EB-PBF. An example is shown in The nature of power bed images like the one in Another approach involves the use of so-called “electronic imaging”, i.e., an imaging approach that relies on backscattered and secondary electrons generated when the electron beam scans the build area, similarly to the principle adopted in scanning electron microscopy. Wong et al.  demonstrOur study presents an automated powder bed monitoring methodology applicable to EB-PBF, which relies on a camera and no external illumination source (apart from the light emitted by the hot surface of the build). One main advantage is the ease of adoption on industrial EB-PBF machines, possibly exploiting the already installed hardware equipment. Another advantage compared to fringe projection regards the lower dimensionality of layerwise images compared to actual topographic measurements, which makes data collection and storage more efficient. Furthermore, the method requires no extra time for powder bed measurement, unlike other techniques mentioned above.The proposed method is aimed at bridging the gap between consolidated powder bed monitoring methods in L-PBF and the current lack of such methods in EB-PBF. It combines different image processing steps to isolate possible contaminations of the powder bed, as well as powder spread errors caused by a damaged recoater. The present study extends the preliminary idea presented in  and demoA scheme of the major steps involved in the proposed approach is shown in The proposed sensing setup for pre- and post-recoating image acquisition is presented in The proposed sensing setup takes advantage of the viewport on the top of the build chamber, which is available in industrial EB-PBF systems. An example is shown in ® Type 100 HN rolling film and a 60 × 60 × 5.5 mm lead glass to shield the X-ray emissions. The protective film is shifted once per layer before image acquisition. A high-spatial-resolution camera equipped with a sensor in the visible range was used  recoating passes are usually applied to obtain a homogeneous powder bed. A picture can be taken at the end of each recoater pass. In this study, we advocate the use of the image acquired after the last recoating pass only, as it is the one capturing the actual layer condition before the successive processing phase. This image is saved and used as the post-recoating image, i.e., the second input for our proposed approach.6Al4V specimens with an Arcam A2 system , using default process parameters provided by the machine vendor. A Ti6Al4V extra low interstitial (ELI) powder with particle size in the range 45–106 µm was used.The experimentation presented in this study was carried out by producing TiAs shown in the example in Generally speaking, three kinds of anomalies may occur, leading to different patterns in the image. The first consists of a local lack of powder. Depositing no powder or an insufficient amount of powder on a surface that appears bright because of its high temperature results in a bright area in the post-recoating image. In this area, pixel intensities are expected to be equal or very close to pixel intensities observed in the same area in the pre-recoating image, because of the small amount of powder deposited on top of it. The second type of anomaly consists of an excess of powder. In this case, an area where the powder layer is thicker results in a darker region compared to other areas of the same image, because of a thick layer of cold powder. If excessive powder is deposited over a previously melted region, pixel intensities will experience a higher reduction compared to pixels belonging to properly recoated areas. The third type of anomaly consists of a contamination of the powder bed, i.e., the presence of debris. This kind of anomaly is expected to yield an effect in post-recoating images that is analogous to the one of a local excess of powder, as the debris will cover and mask the underneath material. Therefore, by combining and comparing the spatial pixel intensity information enclosed in pre- and post-recoating images, all types of anomalies may be detected.Correction of the image perspective error. This operation can be applied during the camera calibration stage. The reader is referred to  for an oImage filtering, applied to both input images in each layer. The aim is to reduce the noise of the image, smoothing pixel-wise intensity variations. A high image noise is caused by the lack of an external illumination source, which imposes the adoption of a sufficient integration time combined with a sufficient sensor sensitivity enhancement. Is this study, we advocate the use of a median filter applied to both pre- and post-recoating images. It is a nonlinear filter that is known to be quite effective for a simultaneous reduction in image noise and preservation of contours . The pixImage transformation. Let The first step consists of three sequential image processing operations:The rationale for this transformation is the following: It is worth noticing locations where The transformed image, Relying on the fact that extreme values of pixel intensity Different ways to define such a transfer function can be envisaged. The methodology proposed in this study is described below. A sensitivity analysis with respect to the choice and definition of this function is discussed in The image By transforming the image The last step consists of defining an alarm rule to automatically signal groups of pixels belonging to the ′=0.0027 .This section presents a real case study where powder recoating errors were induced on purpose in order to evaluate the performance of the proposed approach.Two different types of parts were included within the 210 × 210 mm build area, as shown in Low-severity damage: only one tooth along one row was manually warped;Mid-severity damage: two teeth per row were removed;High-severity damage: 2 two per row were removed, and the neighboring teeth were manually warped.The powder recoating errors were induced as shown in After the process, two parallelepiped specimens affected by mid- and high-severity powder bed discontinuities were inspected by means of X-ray computed tomography (CT). A North Star Imaging X25 micro X-ray CT system  was used, and the measurements were performed with a voxel size of 7.62 µm.In situ powder bed images were acquired by means of the setup described in 6Al4V powder were used. The build composition was different, consisting of lattice structures only. The aim was to highlight that the proposed method is quite insensitive with respect to the shape of parts produced in the build, enabling the use of builds different from the one that is currently monitored for training purposes.In order to determine the control limit, Other examples of anomaly detection result in other layers of the same build are shown in In order to determine the performance of the proposed approach in terms of false alarms, the signaled areas in all monitored layers were visually inspected by a human expert. The following metrics were computed: (1) overall percentage of pixels where false alarms where signaled, (2) average area of connected components consisting of pixels where false alarms where signaled, and (3) maximum area of those connected components. The percentage of false alarms was computed as the overall percentage of pixels in the A further analysis was performed to investigate the effect of powder recoating discontinuities on the quality of manufactured part. Due to the powder bed discontinuity, the specimens exhibited both a swelling on the top surface , i.e., aThanks to its ability identify in situ anomalies responsible for such severe defects in the part, the proposed approach can allow the operator to stop the process, preventing a waste of material and resources in the production of scraps.In order to evaluate the performance of the proposed approach in the presence of different build geometries and powder bed discontinuities that were not induced on purpose, the methodology was tested during the production of other parts with the same EB-PBF machine and with the same experimental settings mentioned in the previous sub-sections.One example is shown in In order to determine the influence of the transfer function, function b and a sfunction c.By using a linear function, the pixel intensity drops from 1 to 0 linearly, instead of following a sigmoid pattern. This transfer function can be defined as follows:In this case, increasing Another alternative is the step function. This corresponds to an image binarization operation, as the output is an image where pixel intensities can be either 0 or 1. It can be viewed as a dual-threshold binarization operation, where pixel intensities are set to 0 if their original intensities are in the range An example of anomaly detection in one layer resulting from the choice of different transfer functions, Regarding the sigmoid function, a smaller value of A similar result was also obtained for the linear function. In this case, the range of values of A quite different result was achieved by using the step function. As the output of the transfer function The proposed method highlighted the suitability of in situ powder bed monitoring in EB-PBF using a high-spatial-resolution camera and no additional illumination source. Unlike other in situ sensing configurations proposed in the literature, this approach requires no extra time to perform the measurement, and it can rely on sensing equipment that is already available in industrial EB-PBF systems.A preliminary investigation of the suitability of this methodology was discussed in , where aThe presented results highlight that the method is quite effective in detecting not only macroscopic discontinuities in the powder bed, but also small and local contaminations. As shown in In this study, the anomalies inserted on purpose by damaging the powder recoating system produced a local excess of powder. In principle, the proposed approach is designed to also detect a lack of powder defects, but this kind of anomaly requires additional experimentation to be induced and tested. It is also worth pointing out that material remelting may heal the effects of powder bed anomalies. If an anomalous powder recoating occurs in one single layer (or a few consecutive layers), the effect on the quality of the part may be healed by consecutive remelting operations. If the anomaly persists for more consecutive layers, instead, the probability of inducing volumetric defects and/or geometrical distortions in the part is higher. From this perspective, the proposed approach may be combined with more conservative alarm rules that consider a minimum number of consecutive layers where a powder bed anomaly must be detected in a given location before an alarm is signaled.As shown in In the presented approach, an off-axially mounted camera equipped with a sensor in the visible range and no optical filter was adopted. Nevertheless, possible extensions and improvements of the proposed approach can be explored by using a camera equipped with a near-infrared filter, to reduce the spectrum bandwidth and the dynamic range, and/or an external illumination source, to enhance the quality of the image and improve the characterization of the powder bed surface pattern.Powder bed inhomogeneity is known to be a primary source of errors and defects in PBF processes, as it directly influences the volumetric energy density provided to the material with consequent local variations in the melting and solidification mechanisms. The automated detection of powder bed anomalies and discontinuities has been extensively investigated in L-PBF, and almost all industrial systems are equipped with powder bed cameras to this aim. Such capability is still lacking in EB-PBF, because of the different challenges imposed by the EB-PBF process on the use on in situ imaging. This study aimed to fill this gap with a first demonstration of the suitability of powder bed imaging combined with image processing and analysis techniques to automatically detect powder recoating errors in EB-PBF that have a detrimental effect on the final product quality.The method is open to different possible extensions and improvements, in terms of both image processing algorithms and image acquisition equipment, including the use of ad hoc illumination sources or a selective use of the radiation spectrum to enhance the characterization of the powder bed surface. As a future development, powder bed monitoring in EBM can be further extended by exploiting different sensing methods to detect and characterize other phenomena of interest, such as surface powder oxidation ,34, whic


Piezoelectric sensors can be embedded in carbon fibre-reinforced plastics (CFRP) for continuous measurement of acoustic emissions (AE) without the sensor being exposed or disrupting hydro- or aerodynamics. Insights into the sensitivity of the embedded sensor are essential for accurate identification of AE sources. Embedded sensors are considered to evoke additional modes of degradation into the composite laminate, accompanied by additional AE. Hence, to monitor CFRPs with embedded sensors, identification of this type of AE is of interest. This study (i) assesses experimentally the performance of embedded sensors for AE measurements, and (ii) investigates AE that emanates from embedded sensor-related degradation. CFRP specimens have been manufactured with and without embedded sensors and tested under four-point bending. AE signals have been recorded by the embedded sensor and two reference surface-bonded sensors. Sensitivity of the embedded sensor has been assessed by comparing centroid frequencies of AE measured using two sizes of embedded sensors. For identification of embedded sensor-induced AE, a hierarchical clustering approach has been implemented based on waveform similarity. It has been confirmed that both types of embedded sensors (7 mm and 20 mm diameter) can measure AE during specimen degradation and final failure. The 7 mm sensor showed higher sensitivity in the 350–450 kHz frequency range. The 20 mm sensor and the reference surface-bounded sensors predominately featured high sensitivity in ranges of 200–300 kHz and 150–350 kHz, respectively. The clustering procedure revealed a type of AE that seems unique to the region of the embedded sensor when under combined in-plane tension and out-of-plane shear stress. The layered nature of fibre-reinforced plastic (FRP) materials allows the incorporation of sensors, such as piezoelectric wafer active sensors (PWAS)  or fibreFunctioning of the embedded PWAS can be assessed through static capacitance measurements ,17, impeEmbedding PWAS in a composite structure may also influence structural integrity and possibly, damage mechanisms ,16,22,23Classification methods have been employed to relate AE to damage mechanisms in baseline FRPs . TypicalThis research is aimed at providing insight into (i) qualitative assessment of the response of embedded piezoelectric sensors to damage-induced AE in CFRPs, and (ii) identification of clusters of AE that can be related to the possible damage induced by sensor embedment. To be able to assess these, CFRP beam specimens are manufactured by prepreg materials in three categories: no PWAS, smaller PWAS embedded, and larger PWAS embedded. Experimental sensitivity assessment of the embedded sensor mainly revolves around mirroring the response of the small PWAS to that of the large PWAS during four-point bending of CFRP specimens up to failure. Concurrently, surface-mounted sensors monitored the AE behaviour in all specimens. A clustering method based on waveform similarity is employed to discern AE that can be related to a damage mechanism related to the embedment of PWAS.The manufacture of the specimens, the experimental procedure, and the clustering method are explained in 7,0°]s symmetrical cross-ply lay-up. On predefined locations along the midline of the panel (in the length and width directions) and from the 21st to 25th laminae, drop-shaped cut-outs were made to accommodate the PWAS. The sensor lead wire was embedded between the 22nd and 23rd laminae. A close-up view of the 22nd lamina including embedded sensor is given in The specimens under investigation are coupons made from AS4/8552 unidirectional prepreg CFRP panels stacked in a The PWAS is made of PZ27 soft composite PZT material  poled inSensor lead wiring, made from bifilar urethane-enamelled copper with a 0.15 mm diameter, was soldered to the PWAS using silver solder (S-Sn95Ag4Cu1). To prevent the wiring from getting damaged at the edge of the laminate due to the prepreg curing procedure, SMC style connectors were located at the laminate edge.The conductivity of CFRP demands the PWAS and connector to be electrically insulated. The PWAS is laminated between two single layers of woven GFRP  resulting in a total sensor thickness varying between 0.7 mm and 1.2 mm. After curing, the sensor assembly was trimmed to shape. No processing of the GFRP surface has taken place. The connector was placed at one of the specimen ends during embedding between two layers of polyimide tape. Prior to and after embedding, the static capacitance of the sensor was evaluated to assess its post-manufacturing integrity.In total 25 specimens were manufactured, among in which 18 PWAS were embedded.Non-destructive and destructive tests have been carried out to assess the sensitivity and performance of the embedded sensor in AE measurement of damage in CFRP laminate. In a preliminary non-destructive test, the performance of the embedded PWAS is assessed by exciting the specimen at least five times with a Hsu-Nielsen source at a distance of 60 mm from the specimen centreline using a mechanical pencil with 0.5 mm H lead. At the top centre of the specimen, a reference R15I-AST sensor was also mounted using adhesive putty as couplant. The setup can be seen in T). The different specimen types are visualised in T specimens were tested to determine the ability of the embedded PWAS to measure damage-related AE. The embedded sensor is placed in the centre of the specimens, facilitating that the four-point bending creates a uniform strain field over the sensor region. A schematic of the four-point bending set-up can be seen in In the destructive tests, four types of specimens were subject to a four-point bending loading. Next to four baseline specimens (named N), two specimens with small embedded PWAS on the compressively loaded side of bending set-up (S), two specimens with large embedded PWAS loaded on the compressive side (L), as well as two specimens with small embedded PWAS placed on the tensile side of the bending setup  ,38. In Elength N .(3)AICnThe normalisation from Equation (2) yields a cross-correlation parameter By comparing all waveforms to each other, a matrix is acquired containing the similarity indices. Waveforms are grouped through average linkage, using the difference in mutual dissimilarity indices as a measure of inter-cluster distance . The resIn order to compare the sensitivity of the different piezoelectric sensors in use in the experiment, Hsu-Nielsen tests with the procedure described in When comparing the embedded sensors, it stands out that up to 300 kHz, results are fairly similar. Beyond 380 kHz, sensitivity of the larger sensor declines. This can be partly attributed to an aperture effect, as the sensor integrates displacement over the contact area. For wavelengths that are increasingly smaller than the sensor diameter, the full wavelengths enclosed by the contact area increasingly cancel out.For the same experiments (under the same conditions), this decreasing trend is also visible in the R15I-AST sensors from 350 kHz. Furthermore, the embedded sensors seem to be more sensitive than the R15I-AST to lower frequencies (<80 kHz), which can be partly attributed to the heavier reliance of the latter on resonant behaviour.During the experiments outlined in For the specimens tested, it stands out the embedded sensor is able to receive AE signals up to failure, either when loaded under compression or in tension. This may be seen in In the first stage of testing it was noted that the embedded sensor tended to pick up extensive amounts of continuous noise. Based on the preliminary tests from Due to their early and repeating appearance (burst repetition frequency around 50 kHz), in the wave forms, these signals are considered noise. In a second batch of experiments it was identified that the continuous noise related to a metal-to-CFRP contact in the four-point bending setup. After insulating the aluminium loading tabs with polyester film tape, no such behaviour occurred anymore. Therefore this effect is not directly attributed to possible triboelectricity effects due to the external wiring . FurtherMeasures were taken to prevent the noise from interfering with the analysis. In assessing the performance and features of the embedded AE sensor, a waveform similarity approach as described in For specimen L3, a total of 598 waveforms measured by the embedded sensor between 162 s and 242 s after the start of loading were used as a reference and compared to all 3939 waveforms from the embedded sensor. The resulting similarity matrix with the waveforms in chronological order is shown in The figure shows that waveforms In In the identification of waveforms related to embedded sensor-induced damage, measurements from the embedded sensor were not included in the analysis to prevent possible influence of damage to the sensor during the experiments. Only waveforms measured near-simultaneously  this damage on the compressive side is protracted up to near the fourth lamina before gross failing in the tension side, including delamination and fibre breakage in three of the specimens. In one of the specimens of type N, failure occurred solely on the compressive side close to the loading tabs. For specimens failing on the tension side, this location was between 8 mm and 21 mm from the centre of the specimen.In specimens where a sensor was embedded on the compressive side (S and L type specimens) of the beam final failure occurred on the compressive side in an instantaneous manner reaching from the top surface to the laminae that contained the embedded sensor. Generally the location of rupture seemed to coincide with the interface between the CFRP and the embedded sensor. In one specimen however, failure was near the centre. The rupture can be considered as a combination between delamination and compressive fibre failure. Furthermore, the sensors did fail together with the GFRP insulation. This can be regarded as an extra damage mechanism, due to a combined in-plane compression and out-of plane shear stress (related to the bending setup).T) the initial damage on the compressive side did not extend beyond the top laminae. At increasing loads, acute failure occurred on the tension side of the specimens, approximately 4.5 mm to 8 mm off-centre up to the laminae with embedded sensors. Similar to the s and L specimens, degradation modes relate to delamination and tensile fibre failure. Failure of the sensor itself and insulation is caused by combined in-plane tension and out-of plane shear stress.Like in the baseline specimens, for the specimens with sensors embedded on the tension side of the beam  it appears that the recorded energy is larger than with the other specimens throughout the testing. This can be seen in T specimens being higher than in other specimens . When considering specimen ST5, high-energy AE around 5000 kN is observed, followed by lower-energy AE. This reminisces of the observations from Xiao et al. [From the figure it can be noted that in baseline (N) specimens, energy-rich AE started to occur from 6000 N onwards and increased in energy around the failure load of the specimens. For specimens with large embedded sensors on the compression side of the specimen (L2 and L3), energy content seems to be fairly comparable to the baseline specimens. For their counterparts with smaller PWAS, AE in specimens S2 and S9 seem to commence relatively early, around 1–1.5 kN. For specimens with embedded PWAS on the tensile side  do not influence the identification process, the threshold is selected such that major clusters contain waveforms from multiple specimens of the same type. It should be noted this selection relies on the assumption that the dissimilarity between different damage mechanisms is larger than the dissimilarity between multiple specimens given the same damage mechanism.For a dissimilarity threshold of 0.65, in total 154 clusters were formed, with the bulk of waveforms (92%) found within 8 clusters. T specimens most distinct is the abundance of cluster 109, including 325 waveforms uniformly divided over the two specimens. In the other specimens the occurrence is almost negligible . The high repeatability of the cluster possibly allows attribution to damage mechanisms related to the specific embedding procedure and the combined in-plane tension stress and out-of-plane shear stress that is experienced in ST.From the table and In T, 7 mm diameter), a cluster (cluster 126) is noted that appears to be specific to this type of specimen. This suggests the presence of a possible damage mechanism related to the embedded sensor experiencing combined in-plane tension stress and out-of-plane shear stress.To summarise, clusters are formed using waveform dissimilarity. This clustering generally represents AE behaviour that is repeatable over different specimens with the same embedded sensor type. Furthermore AE cluster distribution in specimens with embedded sensor is compared to that in the baseline specimens. It may be noted that specimens with a large  sensor embedded on the compressive side of the bending setup exhibit comparable AE behaviour to that of the baseline specimens. Conversely in specimens with smaller sensors embedded on the same side  AE tends to be relatively dissimilar in general, but clusters with high frequency content seem to occur earlier than in baseline specimens. For specimens with the embedded sensor loaded in tension  were compared in terms of performance and sensitivity. Typically, embedded PWAS were able to acquire AE up to final failure. In the measurement of damage-related AE from a flexural strength test, the smaller embedded PWAS (7 mm diameter) recorded AE with a centroid frequency typically ranging between 350 kHz and 450 kHz. For the larger embedded PWAS (20 mm diameter) centroid frequencies were determined to be generally between 200 kHz and 300 kHz. In all cases, the reference R15I-AST sensors measured centroid frequencies between 150 kHz and 350 kHz. The variation in sensitivity between the larger and smaller embedded PWAS is partly attributed to an aperture effect by taking into account the wavelength of the propagating wave of interest. This implies that embedded PWAS dimensions and FRP wave propagation properties should be carefully assessed in the design and analysis of an FRP structure with embedded PWAS.A waveform similarity approach was used for defining AE clusters and in the processing of noise. It was shown using average linkage and a dissimilarity threshold that clusters of AE can be formed that are overarching different specimens while being specific enough to accentuate the effects found in different specimen types. This confirms the assertion that the dissimilarity of AE between different types of specimens is larger than the dissimilarity between multiple same-type specimens. As is seen in T11 that recorded up to gross failure. Given the inaccessibility of the location of damage for detailed inspection during the test, further validation related to the degradation mode is needed.From the combination of waveform similarity and AE energy assessment it may be concluded that the AE behaviour of the two specimens with embedded PWAS on the tensile side of the bending load are distinctly different from the baseline and other specimens. The identified AE phenomena are envisioned to be associated to a tensile cohesion-related failure, since the strength of the GFRP-resin-CFRP interface is subject to large uncertainty. Tensile fracture of the PWAS is not expected as a likely source given the low stiffness of GFRP and resin insulation, as confirmed by the embedded PWAS in specimen SBetween baseline specimens and specimens with embedded PWAS on the compressive side under the bending load, no clear relation in terms of AE can be established. Although visually the damage mechanisms in these specimens are different compared to baseline specimens, this did not translate to sufficiently consistent trends in AE.From a wider perspective, the ability to relate AE to embedded PWAS-affiliated and non-affiliated degradation mechanisms allows for the monitoring of a structure with embedded PWAS with respect to the health of the structure. Further, the monitoring for embedded PWAS-induced degradation facilitates the development of design criteria for structures with embedded PWAS. In this regard, the observation that AE from PWAS-induced damage is distinguishable from other sources, prior to specimen gross failure and depending on loading direction, is of value. It is considered that this work will also pave the way for more widespread use of embedded PWAS, such as in larger-scale FRP structures with embedded arrays or distributed configurations.This research can be seen as a preliminary step towards assessing the influence of embedded PWAS size and location on their measurement capabilities and on the structural integrity of the host structure. As such, it is acknowledged that the topics and outcomes presented in this research can be elaborated upon, by means of extended experiments and numerical simulations. Measuring different wave modes with different wave lengths and the effect on the sensitivity of embedded PWAS deserves further research. Evaluation with respect to the sensitivity of the embedded PWAS is envisioned on different host materials, such as GFRP and sandwich structures. The clustering method used may be compared to other clustering procedures that are used typically in the classification of FRP damage mechanisms. In this context, the respective qualities of the formed clusters may be assessed. Moreover, it is recognised that increasing the population size of the specimens will help drawing more accurate and definite conclusions on the formation of AE clusters related to embedded PWAS-induced damage. Also, this will allow specimens in the destructive testing to be stopped prematurely so that they can be inspected microscopically for the initiation of damage mechanisms. The premature inspection will facilitate attributing clusters to damage mechanisms. In a further stage, a database of clusters of waveforms with identified damage mechanisms may be used to quantify the nature and extent of damage in similar structures. The features of AE signals from identified damage mechanisms can also be used to develop possible damage indices to better estimate the remaining load bearing capacity or remaining lifetime.(1)The size of the embedded PWAS has an effect on the frequency content of the measurement of AE signals; Damage-related AE signals in CFRP specimens were measured with a centroid frequency between 350 kHz and 450 kHz by the 7 mm diameter embedded PWAS, while for the 20 mm embedded PWAS the range was between 200 kHz and 300 kHz. For comparison, no change in frequency content in the AE signals was measured by a reference surface-mounted sensor for the different specimens.(2)Damage mechanisms in the CFRP laminates due to embedded PWAS when placed in the tension side in the bending experiment exhibited AE that were reasonably distinguishable from other sources of AE. This was confirmed when assessing waveform similarity and AE energy in specimens with and without embedded sensors. When loaded under compression, no major additional degradation AE due to the embedment process was identified.Using data from destructive four-point bending tests performed on thick CFRP specimens with embedded PWAS the performance of the PWAS in terms of endurance and spectral sensitivity for AE measurement was investigated. Furthermore, a waveform similarity assessment method was employed to identify clusters of AE signals that relate to embedded PWAS-induced failure mechanisms. It was found that:


This paper presents a discrete event simulation model to support decision-making for the short-term planning of hospital resource needs, especially Intensive Care Unit (ICU) beds, to cope with outbreaks, such as the COVID-19 pandemic. Given its purpose as a short-term forecasting tool, the simulation model requires an accurate representation of the current system state and high fidelity in mimicking the system dynamics from that state. The two main components of the simulation model are the stochastic modeling of patient admission and patient flow processes. The patient arrival process is modelled using a Gompertz growth model, which enables the representation of the exponential growth caused by the initial spread of the virus, followed by a period of maximum arrival rate and then a decreasing phase until the wave subsides. We conducted an empirical study concluding that the Gompertz model provides a better fit to pandemic-related data  and has superior prediction capacity than other sigmoid models based on Richards, Logistic, and Stannard functions. Patient flow modelling considers different pathways and dynamic length of stay estimation in several healthcare stages using patient-level data. We report on the application of the simulation model in two Autonomous Regions of Spain (Navarre and La Rioja) during the two COVID-19 waves experienced in 2020. The simulation model was employed on a daily basis to inform the regional logistic health care planning team, who programmed the ward and ICU beds based on the resulting predictions. Regularly updated information on the COVID-19 outbreak is also available on the websites of the European Centre for Disease Prevention and Control (ECDC), the European Commission (EC), and the World Health Organization (WHO). This outbreak has brought changes in health care delivery, and in hospital systems stretched by the sudden increase in demand. The treatment of COVID-19 patients requires dedicated resources, material, and personnel. The pandemic has had a particularly intense impact on Intensive Care Units (ICU), which require highly specialized personnel and costly technical apparatus. Accurate planning requires accurate prediction of resource needs. In addition, hospitalized COVID-19 patients need to be isolated from other types of patients, which makes advance preparation of wards necessary. Therefore, the management of both ICU and ward beds for COVID-19 patients benefits from accurate short-term demand forecasting. Other resource needs, such as personnel requirements, can be calculated from bed demand numbers. Usually, the hospitalization bed is still widely used as a hospital (ICU) management parameter both at the strategic and operational levels.The COVID-19 pandemic presents a major global health threat. Since the outbreak in China in early December 2019, more than 180 million confirmed cases, and close to four million deaths from COVID-19 infection , variation in the patient hospital length of stay (LoS) pattern and censored data. This paper presents a Discrete Event Simulation (DES) model combining dynamic forecasting to predict (simulate) new patient arrivals and the reproduction of patient flow patterns and designed to address all the issues just raised. The simulation process yields future resource-use scenarios to inform the health authorities of future needs and give them time to plan. In fact, the results of the simulation model were used on a daily basis during the successive waves of the pandemic (from March 2020 to May 2021) by local governments of two Spanish regions and by the Spanish Ministry of Health as a health resources planning instrument. Therefore, the main feature of the simulation model presented here is its capacity to reproduce the evolution of the health system from its current state, in a non-stationary and changing environment, thus providing a useful forecasting tool.A method to simulate patient arrival times based on Population Growth (PG) models. These are better suited for the prediction of hospitalization (and positive cases) series than other mathematical alternatives such as SIR-type models, which require detailed knowledge of the spread of the disease throughout the population and the estimation of many parameters. PG models produce S-shape curves able to represent the evolution of pandemic variables, such as positive cases and hospitalizations, from beginning to end of the outbreak.A statistical analysis of the accuracy of four different PG models in simulating and forecasting the spread of the pandemic.The representation of the current state of the health system based on a set of state variables and a dynamic and adaptive statistical analysis of patient flow and hospital LoS.The combination of all elements in a DES model flexible enough to recreate scenarios based on stochastic models fitted to data (data-driven prediction), scenarios defined by expert judgment, and a mixture of both.In practical terms, this paper also shows how operations research can contribute to a rapid respond to a healthcare crisis by reporting on a successful real-world application of the simulation model to support a decision-making process of crucial importance to the health of patients in two Autonomous Regions of Spain (Navarre and La Rioja).The main contribution of this paper is our proposal for a new simulation framework enabling short-term (from days to a few weeks) prediction of critical resource needs for the care of COVID-19 patients and our account of its use by health authorities during the COVID-19 pandemic waves. The simulation framework can be adapted for application in potential future outbreaks. To achieve this main contribution our research includes:The rest of the paper is organized as follows. Section Simulation is one of the most suitable analytical tools for the analysis of complex systems, such as healthcare systems, as reflected in numerous specialist articles describing the use of simulation models for decision-making in the healthcare context. DES has been used to model and analyze all aspects of logistics management in healthcare, particularly the improvement of patient flow management, bed-planning, waiting list management, health service design, medical staff scheduling, etc. For reviews of the use of simulation models in healthcare, see Brailsford et al. ; Günal aThe ultimate goal of these models is to match resource availability with demand in order to provide high-quality patient care while maintaining adequate human and technological resource provision. Some of the problems analyzed in this framework are patient flow  , exposed (E), infective (I), and recovered (R). The model depends on epidemiological parameters such as the infection rate (the number of people that an infective person infects per day), the disease latent time (the lag between contact with an infected person and the appearance of symptoms), the recovery rate, and the death rate. The basic SEIR model has been extended to categories such as the protected (P) and the quarantined people (Q) , which is a non-stationary process highly dependent on the number of positive (active) cases in the population. A compartmentalized epidemiological model, such as the SEIR model, enables analysis of the spread of the disease throughout a population. It models transition dynamics between four different states of a population: susceptible . The results included in Appendix The second statistical analysis is designed to test the short-medium term predictive capacity of the PG models. For each of the 20 countries used in the first statistical analysis, the data up to the day on which cases exceed 25%, 40%, and 65% of the total cases registered at the end of the pandemic wave are used to predict the cases for the following successive 5, 10 and 15 day horizons. Thus, nine prediction exercises are performed for each country and each PG model. The results included in Appendix Our statistical analysis supports the use of the Gompertz for modelling series of cumulative hospitalizations. The parameters of the original equation of this model, presented above, are more suited to mathematical than biological interpretation, like most equations describing sigmoidal growth curves. Therefore, before using it in our modeling, some transformation will aid interpretation of the curve. Zwietering et al.  rewrite Suppose we are at pandemic day Once the curve Fit the Gompertz curve to cumulative hospitalization data series (e.g. by using the least squares method). Record the estimated parameter vector Simulate a parameter vectorCalculate the expected number of arrivalsSimulate the number of hospitalizations, for each day Repeat steps 2–4 as many times as necessary for the different hospitalization sequences to be simulated.The simulation procedure is summarized in the following four steps:This simulation procedure takes into account both variability due to uncertainty in the estimation of the Gompertz parameters and variability in hospital arrival numbers around the mean. Figure Each parameter vector mentclass2pt{minimmentclass2pt{minimThis section focuses on modelling patient flow through the health system. First, we describe the possible patient pathways through the hospital, and then explain how the LoS of each patient is modelled.Polymerase chain reaction (PCR) test), etc. Depending on the severity of his/her condition, the person is admitted to the health care system as a COVID-19 patient, either in a hospital ward or directly in the ICU.COVID-19 patients can access the health system in a variety of ways: following diagnosis with COVID-19 in a primary healthcare facility, hospital emergency department, or nursing home; or after undergoing a SARS-CoV-2 test control -file, which we assume to have taken place at the end of the ized Law . The simThe discharge time of each ICU patient A patient admitted to a ward for A hospital trajectory is simulated for each patient Similarly, for each ward patient Once discharge times and transfer times between ward and ICU have been simulated for each hospitalized patient, and recorded in the event calendar  the DES model is ready to advance the simulation clock from time zero to the minimum of the times recorded in the event calendar. The state variables and calendar events are then updated accordingly and the main loop of Fig. The fitted Gompertz curve forecasts daily patient arrivals, which can be uniformly distributed over the following 24 h or according to a non-stationary pattern when, for example, arrivals drop significantly overnight.The DES model works by generating patient arrivals, discharges, and transfers causing variations in ward and ICU occupancy levels, which are recorded by statistical counters. The simulation model includes two sources of randomness: the number of patient arrivals , and patients’ LoS. Therefore, each time the DES model is run departing from the current situation of the healthcare system (generating randomness based on a different seed) the ward and ICU bed requirements differ. Figure th percentile (P5), 50th percentile (P50), and 95th percentile (P95) are plotted on a graph as confidence bands for future resource needs. Figure The DES simulator generates percentile data, which are stored in an Excel file. The 5The methodology introduced in the previous Sections, implemented in software, has been used by the Governments of the Spanish Autonomous Regions of Navarre and La Rioja to support bed planning in their hospitals during the two pandemic waves experienced to date. We briefly describe how the virus has affected these two regions globally, then explain the stochastic modelling of the hospitalized patients and their pathway through the hospital, and then present the predictions obtained by the DES model at different times. We conclude this Section with some observations and tips for the practical use of this forecasting tool.Navarre and La Rioja are two Regions of northern Spain with populations of about 650,000 and 350,000, respectively, more than half concentrated around the capitals (Pamplona and Logroño). With this population distribution, Navarre Health Services have a main hospital in Pamplona, with a bed capacity of more than 1000, and two secondary hospitals in two of the most populated cities (Estella and Tudela) bringing total bed capacity to 1,466 ward beds and 45 ICU beds. La Rioja has a main hospital in Logroño with 630 hospital beds and 21 ICU beds and a secondary 80-bed hospital in Calahorra. Both regions have the possibility of increasing bed numbers if necessary.Navarre and La Rioja figure among the five Spanish autonomous regions with the highest cumulative COVID-19 rates during both waves of the pandemic, according to data collected by the Governments of Navarre and La Rioja. Daily numbers of new admissions have important implications for hospital management teams. Figure As the pandemic spreads, the data load increases, making it possible to improve the simulation model. Since March 16, 2020, the arrival pattern is calculated from the hospital admission series. Figure Both ward and ICU lengths of stay are estimated daily, as explained in Sect. 0. Ward lengths of stay . Taking this estimation as a simulation input, bed demand rises exponentially to figures much higher than the entire regional population. This is not a realistic estimation even in the worst case scenario of the entire population being infected. Nevertheless, in this case, the estimation would be valid for as many days as the exponential growth holds, and, as more data is collected, the accuracy of the upper asymptote estimate increases.However, to avoid unlimited exponential growth, and improve the accuracy of the estimates at the beginning of a new pandemic wave, we recommend conducting a mixed estimation of the Gompertz parameters, combining an estimate based on expert opinion for one parameter with statistical fit estimates for the other two. Specifically, experts are able to estimate total hospitalizations based on the population incidence rate scaled by a hospitalization factor. For example, at the beginning of the first wave of the pandemic, Navarre Health Administration professionals guessed that 1% of the population would catch the virus (based on flu incidence), and 40% of the cases would require hospitalization . Using values ranging around these estimates, we could run the simulation model to obtain possible hospitalization scenarios throughout the entire wave. These predictions overestimated total hospitalizations by the end of the first wave by only 30%. At the beginning of the second wave, the initial predicted maximum can be the value observed in the first wave or a percentage of it. However, as soon as enough data are available for an accurate parameter estimation, the simulation model should be completely data-driven.A similar problem arises when estimating the parameters of the LoS probability distributions at the onset of a new wave. When insufficient data prevents the statistical estimation of all parameters, the simulation model has to be flexible enough to allow manual parameter input. We recommend the use of the triangular distribution to represent the LoS for different hospital status levels. The triangular distribution family is a popular choice for the estimation of task completion times because it embodies the idea of the ‘three-point estimation’ where subjective judgment is used to estimate a minimum, a ‘best guess’, and a maximum value of the variable of interest Law . ExpertsHealthcare systems are overburdened as high demand for healthcare services from COVID-19 patients places strains on ICU capacity and creates excessive workloads for healthcare professionals. Accurate predictions of patient care resource needs are essential to advanced resource planning which can ease pressure on the system and relieve stress among hospital staff. Accurate predictions optimize response times and thus help to save lives.Under normal circumstances, managers cope with demand surges through resource contingency plans based on predictions made about one week in advance. In this paper, we have developed a DES model to predict hospital resource needs, particularly in terms of ward and ICU beds. The simulation model is fed with new hospitalization predictions generated by a PG model. The Gompertz growth model was selected following an analysis of the fit and forecasting properties of four PG models: Logistic, Richards, Stannard, and Gompertz. Forecasting improvements could be achieved using an ensemble of these models, but such an exercise is beyond the scope of this paper and remains for future research. Forecasting accuracy can be improved by including other factors affecting resource consumption, such as age and the Adjusted Morbidity Group (AMG), in LoS stochastic models.The structural simplicity of the simulation model makes it appropriate for general use, i.e., it can be adapted to estimate bed needs in any geographic area. The growth model requires only three parameter estimates, which can be obtained directly from the observed data. Easy online parameter estimation is one of the advantages of this model over other complex models, such as the SIR type.It is worth mentioning the strength of simulation models in this context of uncertainty, that is, their capability to run what-if scenarios enabling decision-makers to explore the consequences of different policy choices, such as the spatial allocation and quantity of additional healthcare resources required by COVID-19 patients in a context of uncertain demand. The simulation model is data-driven, patient arrivals and lengths of stay can be estimated from data, but it also has the flexibility of allowing the use of simulation from user-determined input to explore additional scenarios.A distinct technical/methodological feature of the simulation model is its focus on the transition period of the health system rather than the stationary state as is usual in simulation studies or on transition periods following regeneration points. This simulated transition period is unique, given that the outbreak has no regeneration points. Therefore, accurate representation of the initial health system status is paramount. The simulation of remaining LoS per hospitalization has shown to be a key point to the smooth projection of health system dynamics and the process of linking them (and mixing them) with the new dynamics obtained from simulated new patient arrivals and lengths of stay. However, the simulation of the remaining LoS depends on the amount of information known about hospitalized patients. In this paper, we have considered patient-level information (exact admission and discharge dates). In cases where only aggregated hospital-level information is available, that is, daily numbers of admissions and discharges, an estimated admission date per patient at time zero of the simulation is required.The simulation model can be extended to include non-COVID-19 ICU and ward bed utilization. This extension would enable the creation of hospital scenarios on which to test the effects of decisions involving other hospital areas, such as a reduction in elective surgeries to free more beds for COVID-19 patients during epidemic waves. The ultimate purpose is to create a learning tool by developing an interactive simulation model to enable the inclusion of patients from all types of pathways , where bed management decisions are made by the program user.The methodology presented in the previous Sections was implemented in software using the Python programming language. It takes input from a data file containing a record of six variables including sex and age of patient and four dates representing the hospital and ICU admission and discharge times for each patient admitted to the hospital so far. The dates for patients still awaiting discharge or ICU admission are left blank. These four dates enable estimation of all the LoS probability distributions and branching probabilities. The additional age and sex data enable segmentation of the patient population. The functionalities and manner of use of the software evolved through time and from one pandemic wave to another. Initially (from March 2020 to June 2020), the simulation model was computationally implemented on its own, such that a daily manual statistical analysis was required to fit probability distributions. Throughout this period, the predictions and reports were drawn up by the research group and sent to the hospital’s COVID-19 logistics manager. Only one regional government used the results of our simulation model during this period. The statistical analysis was automatized and integrated in the software during the summer of 2020. In addition, the user interface, output analysis and automatic reports generation were also implemented. Then, from October 2020 and throughout the second pandemic wave, the analyses performed by local government health administration personnel (Govts. of Navarre and La Rioja), under the supervision of the research group. From December 2020, and throughout the third and fourth waves, the health administration analysts worked almost autonomously. During the last period  the research group also assisted the Spanish Health Ministry by providing predictions for each of the 17 Autonomous Communities in Spain.www.unavarra.es/quphs) has been cooperating for more than 10 years in the solving of real problems surrounding health service improvements. Problem analysis is always addressed through multidisciplinary teamwork involving academics (engineers and mathematicians) and health service personnel .Multidisciplinary teamwork and a background of cooperation with health managers. The research group q-UPHS  planning is based on 10-bed modules. Prior knowledge of bed needs therefore facilitates resource planning.Rapid response. Five days after the original request, the group presented the simulation model and the initial results (predictions) for validation by the region’s hospital and healthcare logistics managers.Joint development of the model. Decision-makers were involved in the development of the model and maintained continuous communication with the research team.Continuous improvement of the computer application. Suggestions made by health managers and a user-friendly software interface were implemented to free users gradually from the need for supervision by the research team.Joint monitoring of the results. Quality assessment and critical analysis of the predictions were performed jointly by the research team and health managers.After 15 months’ cooperation with health authorities, we have reached the conclusion that the success of this operations-research support system for decision-makers in difficult pandemic times is due to the following factors:Thus, the simulation paradigm presented in this paper is suitable for the realistic representation of health service processes, which makes it more credible and easier to understand by the managers who will have to rely on the results in their decision-making. From March 2020 until the moment of writing this paper (end of June 2021), the simulation model has been used daily to predict hospital resource needs in the Spanish regions of Navarre and La Rioja, and all other Spanish Communities from January, 2021. We found that the involvement and continuous improvement suggestions of the hospital logistics manager in the development of the simulation model has been crucial for obtaining a user-centered simulator and a practical forecasting tool to enable daily updates of data from the hospital administration’s information system.


The estimation of average treatment effect (ATE) as a causal parameter is carried out in two steps, where in the first step, the treatment and outcome are modeled to incorporate the potential confounders, and in the second step, the predictions are inserted into the ATE estimators such as the augmented inverse probability weighting (AIPW) estimator. Due to the concerns regarding the non-linear or unknown relationships between confounders and the treatment and outcome, there has been interest in applying non-parametric methods such as machine learning (ML) algorithms instead. Some of the literature proposes to use two separate neural networks (NNs) where there is no regularization on the network’s parameters except the stochastic gradient descent (SGD) in the NN’s optimization. Our simulations indicate that the AIPW estimator suffers extensively if no regularization is utilized. We propose the normalization of AIPW (referred to as nAIPW) which can be helpful in some scenarios. nAIPW, provably, has the same properties as AIPW, that is, the double-robustness and orthogonality properties. Further, if the first-step algorithms converge fast enough, under regulatory conditions, nAIPW will be asymptotically normal. We also compare the performance of AIPW and nAIPW in terms of the bias and variance when small to moderate  Estimation of causal parameters such as the average treatment effect (ATE) in observational data requires confounder adjustment. The estimation and inference are carried out in two steps: In step 1, the treatment and outcome are predicted by a statistical models or machine learning (ML) algorithm, and in the second step the predictions are inserted into the causal effect estimator. If ML algorithms are employed in step 1, the non-linear relationships can potentially be taken into account. The relationship between the confounders and the treatment and outcome can be non-linear which make the application of machine learning (ML) algorithms, which are non-parametric models, appealing. Farrell et al. ,3,4,5. TFarrell et al.  argue thConditioning on IVs is harmful to the performance of the causal effect estimators such as ATE  but theThe positivity assumption  is fundaaugmented IPW-type methods as they involve modeling both treatment and outcome and can be The inverse probability weighting method dates at least back to Horvitz and Thompson  in the lWe propose and study a simple potential remedy to the empirical positivity violation issue by studying the normalization of the AIPW estimator , here rA general framework of estimators that includes nAIPW as a special case was proposed by . In theiTo the best of our knowledge, the performance of nAIPW has not been previously studied in the machine learning context, with the assumption that strong confounders and IVs exist in the data. We will prove that this estimator has the doubly robust  and the We argue that for causal parameter estimation, dNN with no regularization may lead to high variance for the causal estimator used in the second step. We compare AIPW and nAIPW through a simulation study where we allow for moderate to strong confounding and instrumental variable effects, that is, we allow for possible violation of the empirical positivity assumption. Further, a comparison between AIPW and nAIPW is made on the Canadian Community Health Survey (CCHS) dataset where the intervention/treatment is the food security vs. food insecurity and the outcome is individuals’ body mass index (BMI).Our contributions include presenting the proof for the orthogonality, doubly robust and rate doubly robust property of nAIPW. Further, it is proven that, under certain assumptions, nAIPW is asymptotically normal and we provide its consistent variance estimator. We analyze the estimation of ATE in the presence of not only confounders, but also IVs, y-predictors and noise variables. We demonstrate that in the presence of strong confounders and IVs, if complex neural networks without Organization of the article is as follows. In P, where P is the true observed data distribution, W is its empirical distribution and the expectation of the conditional distribution P. The symbol P is replaced by Let data Let the causal parameter of interest be the average treatment effect (ATE)controls .W are measured before the treatment), IID subjects and a linear causal effect.For identifiablity of the parameter, the following assumptions must hold true. The first assumption is the conditional independence, or unconfoundedness stating that, given the confounders, the potential outcomes are independent of the treatment assignments  estimator =j, These results assume no regularizations imposed on the NNs’ weights, and only the stochastic gradient descent (SGD) is used. Farrell et al. claim that the fact that SGD controls the complexity of the NN algorithm to some extent ,10 is suIn this section we will see that nAIPW  is doublthogonal  and is aQ and g in =tisfied a priori knowledge about the propensity scores  or we estimate the propensity scores with rate double robustness (RDR) property ((7](31)g and The theorem below states that the variance estimator of AIPW  can intuTheorem 2.The asymptotic variance of the nAIPW  iswhere The proof utilizing the estimating equation technique is straightforward and is left to ories of ,9, if thg and n.The above theorem states that the variance estimator of AIPW  can intuA Monte Carlo simulation study (with 100 iterations) was performed to compare AIPW and nAIPW estimators, where the dNN is used for the first-step prediction. There are a total of 2 case scenarios according to the size of the data. We fixed the sample sizes to be ed below . The strThe non-linearities are randomly selected from among the following functions:p), with The networks’ activation function is rectified linear unit (ReLU), with 3 hidden layers as large as the input size ..(35)BiasWe have noted that the results of the step 1 NN architecture without The Canadian Community Health Survey (CCHS) is a cross-sectional survey that collects data related to health status, health care utilization and health determinants for the Canadian population in multiple cycles. The 2021 CCHS covers the population 12 years of age and over living in the ten provinces and the three territorial capitals. Excluded from the survey’s coverage are: Persons living on reserves and other Aboriginal settlements in the provinces and some other sub-populations that altogether represent less than 3% of the Canadian population aged 12 and over. Examples of modules asked in most cycles are: General health, chronic conditions, smoking and alcohol use. For the 2021 cycle, thematic content on food security, home care, sedentary behavior and depression, among many others, was included. In addition to the health component of the survey are questions about respondent characteristics such as labor market activities, income and socio-demographics.In this article, we use the CCHS dataset to investigate the causal relationship of food insecurity and body mass index (BMI). Other gathered information in the CCHS is used which might contain potential confounders, y-predictors and instrumental variables. The data are from a survey and need special methods such as the resampling or bootstrap methods to estimate the standard errors. However, here, we use the data to illustrate the utilization of a dNN on the causal parameters in the case of empirical positivity violation. In order to reduce the amount of variability in the data, we have focused on the sub-population 18–65 years of age.Utilizing machine learning algorithms such as NNs in the first-step estimation process is comforting as the concerns with regard to the non-linear relationships between the confounders and the treatment and outcome are addressed. However, there is no free lunch, and using NNs has its own caveats including theoretical as well as numerical challenges. Farrell et al.  addresseWe note that the nAIPW estimator cannot perform better when the empirical positivity is violated as compared to when it is not. However, when the empirical positivity is violated, nAIPW can perform better than AIPW. If the empirical positivity is not violated, our results indicated that AIPW outperforms nAIPW.An alternative estimator might be trimming the propensity scores to avoid extreme values. However, the causal effect estimator will no longer be consistent and there is no determined method for where to trim. We hypothesize that Another reason why NNs without regularization fail in the causal estimation and inference is that the networks are not targeted, and are not directly designed for these tasks. NNs are complex algorithms with strong predictive powers. This does not accurately serve the purpose of causal parameter estimation, where the empirical positivity assumption can be violated if strong confounders and/or instrumental variables  exist inIn


Emergency department (ED) visits show a high volatility over time. Therefore, EDs are likely to be crowded at peak-volume moments. ED crowding is a widely reported problem with negative consequences for patients as well as staff. Previous studies on the predictive value of weather variables on ED visits show conflicting results. Also, no such studies were performed in the Netherlands. Therefore, we evaluated prediction models for the number of ED visits in our large the Netherlands teaching hospital based on calendar and weather variables as potential predictors.Data on all ED visits from June 2016 until December 31, 2019, were extracted. The 2016–2018 data were used as training set, the 2019 data as test set. Weather data were extracted from three publicly available datasets from the Royal Netherlands Meteorological Institute. Weather observations in proximity of the hospital were used to predict the weather in the hospital’s catchment area by applying the inverse distance weighting interpolation method. The predictability of daily ED visits was examined by creating linear prediction models using stepwise selection; the mean absolute percentage error (MAPE) was used as measurement of fit.The number of daily ED visits shows a positive time trend and a large impact of calendar events . The weather itself was a better predictor than weather volatility, but only showed a small effect; the calendar-only prediction model had very similar coefficients to the calendar+weather model for the days of the week, time trend, and special time periods (both MAPE’s were 8.7%).Because of this similar performance, and the inaccuracy caused by weather forecasts, we decided the calendar-only model would be most useful in our hospital; it can probably be transferred for use in EDs of the same size and in a similar region. However, the variability in ED visits is considerable. Therefore, one should always anticipate potential unforeseen spikes and dips in ED visits that are not shown by the model.The online version contains supplementary material available at 10.1186/s12245-021-00357-6. Large numbers of patients generally present at emergency departments (EDs). In the Netherlands, 2.3 million patients were seen on EDs in 2017 . HistoriED crowding is defined as a situation in which the demand for emergency services exceeds the ability of the department to provide quality care within acceptable time frames . ED crowBoth these systematic reviews included articles that identified insufficient staffing as a possible cause for ED crowding, and additional staffing as a possible solution , 8. To eThe predictive value of weather variables on ED visits is not yet clear as previous studies show conflicting results. Climatic differences between countries may be a possible explanation for this. Also, population’s adaptations to local climatic circumstances might be of influence , 15. FurData on all ED visits in the Jeroen Bosch Hospital, a large teaching hospital in the Netherlands, from June 2016 until December 31, 2019, were extracted and anonymized. The data set contained the birth year and gender of the visitor and the date and time of admission to the ED. The 2016–2018 data were used as training set, the 2019 data as test set.Descriptive analysis of the ED visit data showed a positive time trend Fig. , and infWeather data were extracted from three publicly available datasets from the Royal Netherlands Meteorological Institute (KNMI): daily readings from the automatic weather stations , hourly As shown in Fig. Two sets of weather-related predictors were created by applying the inverse distance weighting weather interpolation method, based on two alternative hypotheses. The first set contained the weather data predicted at the Jeroen Bosch Hospital as a means to describe the weather experienced by ED visitors. The second set contained the weather data predicted for two towns in the periphery of the Jeroen Bosch Hospital’s catchment area, Kaathoven and Drunen. The use of weather predictions at these locations has the potential to better describe the weather in the full catchment area than weather predictions at one central location .The predictability of daily ED visits was examined by creating linear prediction models. As many of the weather predictors were likely not to have a substantial effect on the number of ED visits, a variable selection method was used to refine and merge the available sets of calendar and weather-based predictors. This was done using the stepwise selection method which, starting with an empty model, adds or drops predictors one at a time in order to maximize improvement in some measurement of fit. For the measurement of fit the Akaike information criterion (AIC) was used, which estimates the out-of-sample error using only the sum of squared residuals of the model, the number of predictors in the model, and the number of observations in the training set. This variable selection method was applied on both the set of calendar variables plus the first set of weather-related predictors and on the set of calendar variables plus the second set of weather-related predictors in order to find suitable ED visit predictors. The results were used to evaluate the quality of predictors and refine/merge the sets of predictors accordingly. The final daily ED visits’ linear prediction model based on calendar plus weather-based predictors was created by reapplying stepwise selection on this refined set of predictors.The effect of predicted weather on the number of ED visits was evaluated by comparing the accuracy of test set predictions from the calendar and weather-based model with the accuracy of a linear prediction model without the weather variables from the final prediction model based on calendar plus weather-based predictors. The mean absolute percentage error (MAPE) was used as measurement of fit for the predictions, which allowed for interpretation as the average percentage the predictions are off compared to the true number of daily ED visits and for comparisons with ED visits models of other hospitals. The difference in MAPE of the models was used to verify whether and how much the weather affects the number of daily ED visits at the Jeroen Bosch Hospital.Automated stepwise variable selection on dataset A  is created by combining the best predictors found in datasets A and B using stepwise selection. This new set of predictors contains most calendar-based predictors from the previous sets, with the seasonal indicators replacing the monthly indicators which had a small effect on ED visits and were often omitted by stepwise selection. A small selection of important interactions seen in the results of the stepwise selections was included as well. For the description of the weather, 24 predictors were included, 12 weather variables describing 12 weather phenomena using weather predictions at the Jeroen Bosch Hospital and 12 weather variables describing the volatility of these 12 weather phenomena using the absolute difference in predicted weather between Kaathoven and Drunen. A more extensive description of the predictors in the new set can be found in additional file The final calendar- and weather-based daily ED visits linear prediction model is created by reapplying stepwise selection on dataset C Table  and showThe calendar-only dataset D that is used to assess the importance of weather for predicting ED visits consists of the calendar variables selected from dataset C in stepwise selection plus the seasonal indicators omitted from this model. The calendar-only prediction model Table  created Our study shows that adding weather variables did not substantially increase the performance of a linear model based on calendar variables in predicting the daily number of ED visits in a retrospective setting (MAPE 8.718% vs. 8.684%). Of course, the intended value of such a model is to predict future daily numbers of ED visits, implicating that weather forecasts instead of retrospective weather data would have to be used. The uncertainties inherent to weather forecasts would increase the error of the model that includes weather variables. Therefore, we conclude that a model based on calendar variables would be most suited for hospital EDs that are comparable to ours. Previous research work confirming that calendar variables have greater predictive value on the number of ED visits than weather variables can be found in the systematic review conducted by Wargon et al. and in a study of Marcilio et al. , 11.A constant, moderate number of predicted ED visits were found for regular Tuesdays, Wednesdays, and Thursdays with an increased number of predicted ED visits during regular Mondays and Fridays, and a decrease during regular Saturdays and Sundays. A previous study in a tertiary hospital in Brazil and a study in four academic hospitals in France also showed a decrease in the number of ED visits at weekends , 20, andOur study has several strengths and limitations. A strength of this study is the use of a linear model. More complex methods such as the  autoregressive integrated moving average ((S)ARIMA), which is a time series model, are sometimes favored; however, we used a linear model as these are easiest to understand for non-statisticians , 20. MarAs far as we know, this is the first model predicting ED visits using calendar and weather variables in the Netherlands. It has similar performance as prediction models described in the literature so far. In conclusion, our calendar based linear model is useful to predict the number of ED visits for EDs of the same size and in a similar region as the Jeroen Bosch Hospital. However, as shown in Fig. Additional file 1. List of calendar variables.Additional file 2. List of weather variables.Additional file 3. Details interpolation parameter estimation.Additional file 4. List of variables in the refined set of predictors.


Osteosarcomas of the jaws are very rare accounting for only 6 to 7% of all osteosarcomas and 1% of all head and neck malignancies.The clinicopathological characteristics as well as the radiological characteristics are not specific, which represents a diagnostic dilemma. Indeed, this lesion is often mistaken for benign lesions of the maxillary.We report a case of osteosarcoma of the jaws in a 45 year man who consulted in our service for a simple painless oral swelling evolving since a five months.The lesion was excised and histopathological report confirmed the diagnosis of osteosarcoma of the jaw.Osteosarcomas are a rarely progressive tumor in the oral cavity that can show up as a case of a benign process of dental origin. During our practice, we often meet similar cases which are treated by usual medication, while a whole malignant process is in the progress and can be life-threatening. The case we present illustrates this perfectly, and owing to a deep clinical investment, the malignant process was stopped at its start.The objective of presenting this case is to draw the attention of the doctors that any lesions even of benign appearance can hide a malignant process. Consider this point and integrate it into its diagnostic approach should be undertaken and should lead to a deepening of the clinical examination by comparing the clinical, radiological and histological data. •Never underestimate a lesion of usual appearence; a whole malignant process can be hidden.•A dippening of clinical and paraclinical investigations is an approch to adopt.•Early diagnosis can improve the management of the desease and save lives. The diagnosis is often wrong leading to treatment of the lesion in 2/3 of cases with tooth extraction and half with antibiotics Swellings are the most common presenting symptoms in cases with osteosarcoma. Pain, paresthesia, and ulcerations are less common The radiological examination is not specific; it can show a lytic, sclerotic or mixed image This article has been reported in line with the SCARE criteria 2A 45-year-old man in good general health condition, without any known health problem or any genetic pathology detected, consulted at the university center for dental treatment and care at the faculty of dental medicine at Rabat, for a maxillary swelling evolving for five months.During the interview, the patient reports the absence of pain or paraesthesia. Exobuccal examination doesn't report any abnormalities. No lymphadenopathy was palpable.The endobuccal inspection shows the presence of a round swelling located at the level of the mucosa of the right upper molar-premolar region . The sweRadiographic exam shows a radiolucency located on the middle third of the roots of the first right molar and the second right premolar .Fig. 2PrAn excisional biopsy was performed at the center for dental treatment  and an aThe patient was transferred to a specialized unit where an extension assessment was performed.There was no extension beyond the oral site and a wide local excision with tooth extraction was performed. No other treatments were performed.The patient benefited from a follow-up at the rate of three times the first year then one time each year. No malignant process or metastasis has been identified.3Osteosarcoma of the jaws accounts only for 6% to 7% of all osteosarcomas The patients are usually older than those who suffer long bone sarcomas, with a rare incidence of metastasis The mandible is marginally more commun site than the maxilla and tends to occur in the body and the ramus, whereas the canine and premolar are more likely site in the maxilla The etiological mechanisms are unknown The most common presenting symptoms are swelling, pain In view of these non-specific symptoms, the osteosarcoma is poorly diagnosed as a periapical lesion or odontogenic lesion Other differential diagnoses include fibrous dysplasia, ossifying fibroma, chondromyxoid fibroma Radiographic examination usually shows a lytic, sclerotic or mixed lesion with soft tissue extension in the majority of cases If the tumor invades the periosteum, many thin irregular spicules of new bone may develop outward and perpendicular to the surface of the lesion producing the so-called “sun ray appearance” Periodontal ligament space widening and lamina dura attenuation around a tumor are other features that may present in jaw osteosarcomas The importance of special investigations such as computerized tomography and magnetic resonance imaging lies in assessing the size of the lesion for staging, intramedullary and extramedullary involvement, tumor calcification and invasion into adjacent tissues particularly pterygopalatine fossa, infra temporal fossa and cranial cavity Histologically, the neoplastic cells are predominately spindle-shaped, with minimal atypia and relatively few mitoses A variable osteoid production is described with several subtypes. Among these, we find osteoblastic, chondroblastic, or fibroblastic subtypes The two therapeutic modalities used in the primary treatment of osteosarcoma include chemotherapy and radical surgery.Chemotherapy is indicated for head and neck osteosarcomas that are unresectable, with metastases, in unusual locations (base of skull) or with aggressive histopathology Radical surgery in the mandible consists of hemi-mandibulectomy. Maxillary lesions are often difficult to be treated as involvement of maxillary sinus, pterygopalatine fossa and orbital fossa often masks the tumor until extensive spread. Often, maxillectomy is inevitable. If cervical lymph nodes are involved, neck dissection would improve the survival Osteosarcoma is particularly resistant to radiotherapy, which should only be considered to prevent local recurrence owing to the possibility of tumor-positive margins after surgical resection Osteosarcoma of the jaws have better prognosis than conventional osteosarcomas 4Osteosarcoma of the jaws is a rare malignant lesion. However, dentists are most confronted with clinical and radiological signs.Hence, this article's main objective is to show the importance of early diagnosis.The latter is the key to a better prognosis avoiding the extension of the lesion and optimizing its management.No external funding.Not required.Written informed consent was obtained from the patient for publication of this case report and accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal on request.Dr Amminou Loubna: Drafting the work conception and design of the work.Dr Taleb Bouchra: Revising the work critically, Acquisition and analysis of work, Final approval to the version to be published.Amminou Loubna.Not commissioned, externally peer-reviewed.The authors declare no conflict of interest.