Quantitative modeling is increasingly utilized in the drug discovery and development process, from the initial stages of target selection, through clinical studies. The modeling can provide guidance on three major questions–is this the right target, what are the right compound properties, and what is the right dose for moving the best possible candidate forward. In this manuscript, we present a site-of-action modeling framework which we apply to monoclonal antibodies against soluble targets. We give a comprehensive overview of how we construct the model and how we parametrize it and include several examples of how to apply this framework for answering the questions postulated above. The utilities and limitations of this approach are discussed. Modeling and simulation tools have become an essential part of the drug development process . ApplyinEarly stage biologics drug discovery programs concern themselves with three main questions: (1) is the proposed target biologically relevant and is hitting the target feasible; (2) what are the drug characteristics that would allow for biomarker modulation or efficacy; (3) what is the efficacious dose in humans? These three questions can be summarized as three components to each project: right target, right compound, right dose. Considering the high number of potential new targets, a flexible mechanistic modeling framework is needed that can be used to perform sensitivity analysis on a discrete number of parameters. This approach would quickly pinpoint gaps in knowledge that can be tested experimentally and make timely recommendations for each of the three components of project development. Therefore, for this purpose, one needs a model that is on the spectrum of complexity somewhere between the traditional PKPD and the multiscale systems biology models. We propose a site-of-action (SoA) model for assisting in the discovery and development of biologics.The site-of-action model extends a two compartment PKPD model by including the mechanistic interactions of the drug and its target , the relevant properties of the target, as well as a separate compartment that models the tissues in which the disease progresses  . Such a A previous iteration of the model has been described by The model is an extension of a drug-target mechanistic binding two-compartment model that accounts for the relevant disease tissue, which is referred to as site of action (SoA). Free plasma drug  PK typically shows biphasic behavior and such data can be modeled using two-compartmental models, resulting in the estimation of 4 PK parameters  . A questnd V2) . Antibodnd V2) , therefond V2)  (which ind V2) . The ratnd V2) . TherefoThe following method can account for any ratio deemed appropriate for the particular project.The calculations for the drug distribution constants presented here have two simultaneous aims: to retain mAb plasma PK and maintain the average concentration ratio , synthesis rate, concentrations (both in plasma and the SoA) and distribution rate (between the plasma and the SoA).There are many published methods or approaches for measuring target levels both in serum and tissue , includiTarget expression levels vary widely depending on their biological function, disease state, tissue localization, and many other factors. For example, the growth and differentiation factor 8 (GDF-8), is present in circulation at very high expression levels of approximately 7 ng/ml in adult humans owing to its function in regulating muscle mass . While tin-vivo stable isotope labelling and proteomics have been established to measure physiologically relevant turnover  are not feasible or are estimated from extrapolation. Also, in some cases, measurements are not feasible due to low concentrations of the target protein. Other methodologies have also been published in the literature,  is defined as:It is rare that one has information about the ratio between target synthesis in plasma versus the SoA. This ratio is generally assumed taking into account what is known about the biology. For the remainder of this section and for the purpose of equations and calculations, the fraction of total synthesis in the SoA is captured by the parameter While drug distribution constants can be calculated from the PK and At an exploratory stage, we use a parameter value for patients . While tpatients , or by be target .Since these approaches have not be largely validated and adopted, one is advised to employ sensitivity analysis regarding target distribution parameters.If This section provides four examples of application of the SoA modeling structure to soluble targets. The examples are divided into the three main categories for successful use of translational modeling and simulation: right target, right compound, and right dose.In the pre-clinical space, especially in early project stages, it is appropriate to conduct feasibility analysis. At this stage, a successful assessment is both one that progresses a target as a part of the portfolio and eventually into the clinic, as well as one that shows that a target is infeasible from a clinical utility standpoint. Such analyses are performed to determine whether sufficient levels of target coverage can be achieved via neutralization with a monoclonal antibody and should not be confused with determining whether the target is “right” from a disease standpoint. Often these analyses are done with just in-vitro functional assays, whose utility is limited - they capture a narrow aspect of the biology and may be done in the pre-portfolio stage. Once the project is part of the portfolio, we recommend a more thorough analysis with a SoA model since more resources for modeling and biomeasures/biomarkers are available. In many cases, the required levels of target coverage for efficacy are unknown so a threshold is set, depending on the disease, competitive landscape, and other factors, often at >90% or >99% target neutralization. If the required coverage for pharmacology cannot be achieved at a commercially viable dose, project termination is recommended.The first example in this section is an example of the latter-targeting osteopontin for rheumatoid arthritis (RA). Osteopontin is a secreted protein from a plethora of cells, that has been implicated in a variety of biological functions, from inflammation and fibrosis, to tumorigenesis and metastasis . The goaBinding Parameters), while Another feasibility example is IL-33. IL-33 is an alarmin, member of the IL-1 cytokine family, released by cells at the barrier surfaces  after disruption in the barrier function by pathogens, tissue injury, and cell death, and has been associated with atopic dermatitis and asthma . Asthma This early-stage feasibility analysis omits several potentially important aspects of the biology of IL-33, e.g., quick inactivation due to oxidation and proteolytic activities, and synthesis in cellular nucleus and release under inflammatory conditions . The forOnce feasibility has been established, the team delves deeper into assessing the molecular properties of the antibody necessary to neutralize the target. Most of the pharmacokinetic properties would depend on molecular assessment and there are currently few models that connect antibody molecular assessment and pharmacokinetics . PredomiChemokine (C-C motif) ligand 20 (CCL20) is a chemoattractant for lymphocytes and dendritic cells in a variety of mucosal tissues . GSK3050While the general conclusion may still hold, several assumptions affect the results of the model. Some, like the synthesis of CCL20 in the skin, are reasonable given that the disease of interest is atopic dermatitis. The SoA:plasma ratio was assumed similar to the one found in IL-33 see . That raretrospective analysis of the clinical compound IMA-026, an antagonist monoclonal antibody against IL-13 , guide drug properties for sufficient target engagement (right compound) and inform the necessary doses for achieving the degree of target engagement required for efficacy in the clinic (right dose). While the case studies described were categorized into examples of each of these three questions, these questions are inter-linked and in practice the modelling approach addresses all three if used at an appropriate stage of the drug discovery process. Ideally at project inception, a model should be generated to explore feasibility, affinity, and PK requirements for a desired dose level. Such an approach allows drug companies to focus on programs with the highest chance of success and limit the “wasted” resources on those which are more likely to fail. Hence, our proposal is to utilize this model in the early stages of drug discovery and, if possible, validate with clinical data.The main feature of the SoA modeling approach is its flexibility. Depending on the target, a modeler can include any number of SoA compartments, ranging from zero to including every main tissue in the human body. Along with the system of equations, we suggest two key biomeasures: target concentrations and turnover, which represent essential parameters in early stage pre-clinical work. This is evident in the examples we presented - osteopontin could not be covered at any reasonable dose or affinity because of its high abundance and turnover; IL-33 needed high affinity for high neutralization but was deemed feasible due to relatively low expression and slow turnover; CCL20 also needed high affinity and a high dose to neutralize due to fast turnover. Different targets have different associated biology and will require different strategies to overcome difficulties in neutralizing them. Hence, while for the purpose of this discussion we are focused on the modeling structure, a capable biomarkers/biomeasures group is essential for the translational research effort.Within the SoA model framework one can implement a variety of biological complexities: downstream or upstream biomarkers, different cell types, ligand-receptor binding, etc. The SoA model facilitates their implementations but one must weigh the complexity of the model versus the questions it tries to answer. The SoA modeling approach is not appropriate for a full disease model, for that goal a more complex QSP implementation would be appropriate. The SoA model is also not a physiologically based pharmacokinetics (PBPK) model - if the distribution of the drug in the whole body is important for the project, a PBPK model would likely be the appropriate tool.The SoA model, as presented here, is fit for mAb modeling with its representation of mechanistic target binding and unbinding. The model can be adjusted and has been utilized to incorporate different molecular modalities - pegylated Fab fragments, bispecific antibodies, etc.The examples in the manuscript are focused on soluble targets, which simplified some aspects of the presentation. Membrane-bound targets often present different challenges from modeling standpoint  and relevant biomarker and biomeasures assays . These aspects can be described in a separate manuscript but there are excellent discussions on the topic, among which There are a number of shortcomings to the SoA modeling platform. The peripheral compartment may be underutilized - the model as presented here does not include target expression and turnover in the peripheral compartment or drug:target complex distribution in and out of the peripheral compartment. The method of fixing the ratio of drug concentration in plasma vs. the SoA assumes similar distribution to other previously measured antibodies. The rate of distribution of the drug into the SoA may have an effect on the target neutralization, and here it is calculated. Some of the mathematical methods may lead to non-physiological rates in order to preserve the measured concentrations at steady state. For example, the use of target distribution fixes the rate of distribution from plasma to the SoA and calculates the rate of distribution from the SoA into the plasma when assuming that the target is only synthesized in the SoA. This can lead to non-physiological differences in the plasma:SoA back and forth distribution rates. The very assumptions of target synthesis and distribution can alter the estimate of target suppression. However, despite these potential caveats of the base SoA model described in this manuscript, it is possible to adapt the framework to capture the relevant biological mechanisms as appropriate so that the sought physiological modulation can be described more accurately. Therefore, all the assumptions and calculations presented in this manuscript are just the most current iterations of ideas and are subject to scrutiny in the face of new facts and better representations.As with all models, this framework requires validation. Early decisions can be made with sparse data and limited measurements but in order to improve confidence in the modeling results, ideally, measurements of key dynamics behaviors predicted by the model  in relevant species with the candidate molecule or a suitable surrogate are needed for model validation. Furthermore, a retrospective validation using clinical data  should be performed when data is available in order to bridge the gap between theoretical and practical model projections. Some aspects of these validations include clinically-relevant disease-dependent level of target neutralization, distribution of the mAb into various types of SoA, or evaluation of the pre-clinical affinity biomeasures or functional assays and their translatability to the clinical setting. Not every project needs a site-of-action or a quantitative systems pharmacology model for successful translation from discovery to development. For the ones where understanding of the underlying pharmacology is limited, a simple exposure-response approach may be sufficient.Ultimately, the SoA platform model is a useful framework that has allowed us to inform the progression of many successful mAb programs. In particular, we have used the model to determine the doability of targets, drug requirements for “best in class” mAbs and dosing regimens to achieve required levels of target coverage to demonstrate efficacy. This modeling approach is fully integrated in the drug discovery process with the ability to make decisions believed to be high.


In the present work, heat transfer and fluid flow and their effects on entropy generation in a realistic catalytic converter of a Lada Niva 21214 vehicle are studied using large eddy simulation. At first, the pressure drop over the catalytic converter is measured for dry air at constant temperature ( Modern automobiles powered by conventional internal combustion engines (ICEs) are subjugated to two major challenges: firstly, to comply with the increasingly stringent emission legislation, and secondly, to have the lowest possible carbon footprint by operating in a manner of best fuel efficiency. These challenges are being addressed either by replacing the existing power-train in battery electric vehicles (BEVs) or by operating ICEVs with so-called carbon-neutral fuels such as: bio-fuels or low carbon fuels such as hydrogen, natural gas, liquid petroleum gas, methanol, and synthetic fuels such as E-fuels, etc. Moreover, the complete replacement of traditional power-trains (ICEVs) by battery-operated engines (BEVs) is not foreseeable in the near future, rather, multiple power-trains will coexist , with moA crucial part of an EGAS is the catalytic converter, in which the harmful species in the exhaust gas are converted or reduced into less-toxic pollutants. In general, a modern catalytic converter consists of ceramic or metal monoliths, which has a honeycomb structure with channel densities of 64–124 cells/cmous form ,8, underous form ,10, analous form  or in deWith regard to the numerical modeling and design optimization of EGAS, computational fluid dynamics (CFD)-based analysis is becoming a popular tool, since it can provide a comprehensive and macroscopic understanding of the coupled thermal and fluid flow dynamics inside the monolith. Additionally, it has the potential to reduce the overall designing cost and product life cycle. However, development of CFD-based numerical models of such a complex system itself is a challenging task, which requires careful consideration of numerical models at the individual component level. Focusing on the monolith of the catalytic converter, many studies have been carried out using the volume-averaging Navier–Stokes (VANS) methodology in the context of the Reynolds-averaged Navier–Stokes (RANS) model, in which the monolith is treated as a continuous porous medium, while the flow is resolved in a time-averaging manner ,13,14. TAs already pointed out in previous paragraphs, the catalytic converter features a complex and coupled thermofluid flow phenomena, making the design and process optimization a challenging task. In this aspect, by following the second law of thermodynamics, the entropy-generation-based irreversibility analysis of such a system can be a useful tool for both detailed understanding of the underlying physical phenomena and in identifying the key parameters for efficient operation of the EGAS. Such analysis is essentially based on the fact that a reduction in the thermodynamic efficiency of a thermal device is essentially associated with increase in the entropy production of this system, also known as entropy generation ,18. TherThe lack of numerical investigation on the heat and turbulent flow in the catalytic converter using LES, especially to resolve the heat and fluid flow in all channels in the monolith, together with the absence of a study based on the entropy generation for such configuration, provides motivation to the present study. Thereby, the objective of this work is to propose the methodology for the prediction of heat transfer and fluid flow effects on entropy generation in a realistic automobile monolithic catalytic converter using LES. Notice that chemical reaction is not considered in this work and left for the future research. In the present work, the thermofluid dynamics in a realistic three-way catalytic converter of a Lada Niva 21214 with/without (conjugate) heat transfer is studied using LES methodology. To validate the applied numerical method, experimental measurements were also carried out, in which the pressure drop between the inflow and outflow of the monolith is measured for different volumetric flow rates. The present work is organized as follows. The applied numerical methods is outlined in h the sensible enthalpy, P the mechanical pressure, In the present numerical study, the solution domain consists of two parts, namely a fluid and a solid domain. In accordance with the procedure described in our previous works ,34, the  as see ,36):(1)∂:(1)∂Ma<0posed by  is appliFor the solid part, the classical heat equation is valid, and reads:n is the direction normal to the solid surface.Here, In this work, the working fluids, namely the air and the exhaust gas, are treated as homogeneous single component Navier–Fourier gases. Thereby, the thermodynamic and transport properties are calculated by means of seven-coefficient NASA polynomials , and traIn this study, the coupled turbulent heat and fluid flow within a realistic three-way catalytic converter of a Lada Niva 21214 are investigated. The pressure drop of the catalyst is determined by experiment under nonreacting isothermal conditions and for different flow rates. These measurements are used to derive a Darcy–Forchheimer relation for the catalyst and to validate the numerical simulation. Then, the validated numerical approach is used to analyze nonreacting heat and fluid flow of exhaust gas within the catalytic converter under realistic operating conditions. The experimental and numerical setups applied in the present work are described in the following.An illustration of the experimental test rig of the catalytic converter is depicted in ter (see ).In the numerical study, the honeycomb structure of the monolith with 12,468 small ducts is fully resolved by means of the numerical grid. Regarding the boundary conditions, the no-slip velocity boundary condition is applied to the interface between fluid body and solid body, while coupled thermal boundary conditions are used for the temperature (see Equation ). Additiσ-model , while tIn order to analyze the grid dependency of the numerical results, three numerical grids with different spatial resolutions are employed see , denotedIn this section, the obtained numerical results for nonreacting turbulent heat and fluid flow characteristics of an exhaust-after-treatment system of a Lada Niva 21214 are discussed in detail. First, in the case of isothermal flow conditions −ΔPΔlAs it can be seen in Besides the total pressure drop of the catalytic converter, it is also of interest to compare predicted skin friction at the duct walls in the monolith, which is directly connected to entropy production related to viscous dissipation. In the case of fully developed laminar duct flow, the friction coefficient can be calculated analytically as:ape (see ). This lIn summary, deviations of LES predictions with the finest grid resolution are relatively small (∼10%) considering such a complex test case of a realistic catalytic converter. This allows us to conclude that the present numerical approach is suitable to predict the key operation parameters of the catalytic converter. It can therefore be used for further analyses of the heat and fluid flow properties and entropy production inside the catalytic converter.Along with other factors, the efficiency of a catalytic converter is largely determined by how uniformly the flow and thermal fields are distributed upstream to the monolith. This allows the effective utilization of available surface area in the monolith for the intended purpose of particulate filtration, oxidation, reduction, etc. In this context, D is the diameter of the inlet pipe and r is the radial coordinate that points to the center . This observation is analyzed quantitatively in It can be seen in The temperature distribution inside the catalytic converter is shown in The exhaust gas temperature is relatively low upstream the monolith due to intense heat transfer caused by recirculation see also , S1. In Based on the observations in the previous section, it appears that heat and fluid flow phenomena inside the catalytic converter are extremely complex and are essentially a coupled heat and fluid flow process together with turbulence transition inside the monolith. To obtain further insight about such complex and interacting processes, entropy generation-based analysis is carried out next.In turbulent fluid flow with heat transfer, the entropy production rates can be separated into two parts, namely entropy production rate by viscous dissipation rding to ,36, in tconstant , CS the fficient , and Δ tThe calculated time-averaged entropy production rate related to viscous dissipation As it is visible in file see  on the eNext, n S5 see , the entTo provide a more global perspective of entropy generation in the catalytic converter, the integral values of From The Darcy-Forchheimer equation is well suited to describe the pressure drop in this specific catalytic converter. Correlation equations based on the experimental data are provided.Important characteristic flow features are identified in the catalytic converter, namely the impinging flow with stagnation, recirculation, flow separation, and laminarization within the fine ducts of the monolith.The rms velocity decreases rapidly in the monolith, attributing to the flow laminarization process in the narrow monolith channels. This physical process is influenced by the heat transfer dynamics through temperature-dependent thermophysical properties as simulations with and without heat transfer testify.The entropy production by viscous dissipation (The entropy production rate due to heat transport In the present work, the pressure drop over a realistic catalytic converter of a Lada Niva 21214 vehicle was measured for dry air at


With the development of image recovery models, especially those based on adversarial and perceptual losses, the detailed texture portions of images are being recovered more naturally. However, these restored images are similar but not identical in detail texture to their reference images. With traditional image quality assessment methods, results with better subjective perceived quality often score lower in objective scoring. Assessment methods suffer from subjective and objective inconsistencies. This paper proposes a regional differential information entropy (RDIE) method for image quality assessment to address this problem. This approach allows better assessment of similar but not identical textural details and achieves good agreement with perceived quality. Neural networks are used to reshape the process of calculating information entropy, improving the speed and efficiency of the operation. Experiments conducted with this study’s image quality assessment dataset and the PIPAL dataset show that the proposed RDIE method yields a high degree of agreement with people’s average opinion scores compared with other image quality assessment metrics, proving that RDIE can better quantify the perceived quality of images. Image restoration is a long-standing and active area of research in digital image processing, including image denoising, deblurring, and super-resolution. Image restoration plays an important role in image understanding, representation, and processing. The goal of image restoration is to recover a clean potential image from a degraded image. However, while image restoration technology is achieving increasingly high-quality results, objective image quality assessment (IQA) metrics for restored images are not well aligned with subjective assessment metrics, which limits the development of the technology. Therefore, the design of objective IQA metrics to maintain consistency between subjective and objective assessments of restored images has become an important issue in image restoration.Image recovery is an ill-posed inverse problem, and the infinite number of possibilities between the degraded image and the corresponding reference image determines the uncertainty of the problem. Traditional mean squared error loss methods for image restoration tend to generate the average of multiple potentially clean images. These methods yield high scores for mainstream objective assessment metrics such as the peak signal-to-noise ratio (PSNR), the mean squared error (MSE) between the original and degraded images, and the structural similarity index metric (SSIM) proposed by Wang et al. . HoweverHowever, such objective assessment metrics are designed to compare the degree of the pixel difference between the recovered image and the original image or the level of similarity between the two and do not correspond well to the perceived quality of the image. Over the past few years, an increasing number of scholars have adopted subjective assessment methods to properly evaluate the perceptual quality of restored images, using mean opinion scores (MOS) and differential mean opinion scores (DMOS) as the metrics for assessing restored images. The former judges the quality of an image by normalizing the observer’s score, while the latter judges the quality of an image by normalizing the difference between the distortion-free and distorted images by the observer. However, it is time-consuming and sometimes impractical to obtain large-scale and valid subjective assessment results. Therefore, there is an urgent need for an effective IQA method.An effective image quality assessment metric requires feature information to be extracted from the image and extracted features must be capable of expressing characteristics of certain image aspects. This enables such information to be used to measure both current and changing image quality in some manner. Entropy measures image information, therefore, it can be used to study image quality. Image information entropy , an IQA In comparison with traditional IQA methods, the regional differential information entropy (RDIE) method proposed in this paper yields objective assessment results that better agree with subjective assessments.Image information entropy is viewed and described from a new perspective, that is, as a neural network application, which demonstrates the possibility of simulating traditional methods using convolution with specific weights and particular activation functions.The traditional information entropy calculation method is serial, whereas the RDIE method proposed in this paper has a high degree of parallelism and great improvement in computing speed.However, the global information entropy of an image is solely a global statistical image characteristic, calculated using the probability of each grey-level occurrence in the image. Nevertheless, this does not reflect spatial image information. Hence, the regional information entropy was used to evaluate image quality. Building on traditional image information entropy, this paper proposes a combination of the concept of regional information entropy and a reshaping of the calculation process for image information entropy using neural networks to overcome the shortcomings of traditional image information entropy and more intuitively describe the degree of detail of the recovered image. This approach can better quantify the perceptual quality of an image than metrics such as PSNR, SSIM, MSE, and MS-SSIM . GradienAn ideal IQA method should be fast and reliable. IQA methods can be classified as subjective or objective depending on whether or not there is human involvement. Subjective quality assessment evaluates the quality of an image based on people’s subjective perceptions, and since people may have different assessments of the same image, it is common practice to take the average of multiple people’s assessments of distorted images as the assessment result. Objective IQA requires a mathematical model to calculate quantitative assessment results. Excellent IQA requires consistency between objective and subjective quality assessment scores. Objective quality assessment can be classified as full-reference image quality assessment (FR-IQA) or no-reference image quality assessment (NR-IQA), depending on the presence or absence of a reference image. The approach described in this paper is a full-reference objective IQA method.The NR-IQA method involves quantifying the perceived quality of an image without a reference image, using only the image’s own information for quality assessment. This method can be used in a wide range of scenarios because it is not limited by the reference image. Early NR-IQA methods were geared more toward specific types of distortion tasks. Ye et al.  obtainedFR-IQA uses the full image information to quantify image quality by assessing the degree of similarity between the image and the corresponding reference image. The early and most representative methods were MSE and PSNR, which calculate the difference between image and reference image pixels, but this approach does not take into account the human visual system’s ability to perceive distortion differences, and thus inconsistent subjective and objective assessment results occur.Compared with PSNR and MSE, the SSIM metric better reflects the quality of the restored image. SSIM assumes that human visual perception is adaptive in extracting structural information from the scene; therefore the luminance, contrast, and structural information between the distorted image and the reference image are separately measured, and the similarity is calculated, with higher scores being better. On this basis, Chen et al.  combinedSheikh et al. ,17 propoRecently, IQA methods based on deep neural networks have become popular, and these metrics are used as loss functions in image restoration problems, resulting in better image restoration results ,23,24. DTraditional global information entropy calculates the proportion of each pixel to all pixels in an image to represent the amount of information contained in the grayscale distribution aggregated features of the image. This captures the global image information, however, it fails to distinguish the spatial distribution of information; therefore, images with the same global entropy may significantly differ. Compared with global information entropy, regional information entropy divides the image into several regions and separately calculates the information entropy, so that each region has a different structural content and can more clearly express the structural content of the image. Therefore, this paper uses regional information entropy to solve this problem.Our method can be viewed as a series of transformations, transforming the image into a unique feature space and calculating their root mean squared error as their perception of similarity. The IQA method proposed in this paper is shown in RDIE can be calculated as follows:Since the traditional use of sliding windows to calculate regional information entropy is very time-consuming, this paper uses neural networks to optimize the RDIE so that multiple windows can be used to process images in parallel to calculate image information entropy. As shown in the RDIE calculation process in With the existing parallel computing platform, the computational efficiency of this paper is greatly improved compared with the traditional method. A comparison between the traditional method and the method proposed in this paper is shown in h and w, and the strides The main factors affecting the RIE are the quantization level Different window sizes affect the degree of detail in the image structure, and as shown in Increasing the strides will reduce the impact of the image due to movement but will also increase the computational effort of the method.Detailed experiments were conducted to explore the effects of different quantization levels, window sizes, and strides on RDIE, as described later in this paper.We studied the image super-resolution and denoising sub-problems of the image restoration problem using different data to produce IQA datasets. For the super-resolution sub-problem, we used images from the DIV2k  dataset.In this paper, bicubic interpolation , EDSR 2, WDSR 2, SAN 2929, SRGANTo evaluate the adaptability of RDIE to a wider range of image restoration tasks, this study tested the method proposed in this paper on the PIPAL  dataset.PSNR-driven image restoration class algorithms are typically based on deep learning and produce outputs with sharper edges and better PSNR than traditional methods. The results of GAN-based image restoration class methods are more complex and challenging for IQA; they often contain similar but not identical texture details to the reference image and are difficult to effectively assess with IQA methods similar to PSNR.This paper assesses the strengths and weaknesses of the IQA method by calculating the Spearman rank correlation coefficient (SRCC) between the IQA method and the subjective scores. This metric provides a good assessment of the monotonic correlation between IQA methods and people’s perceived image quality, with the larger the absolute value of SRCC, the stronger the correlation.The main factors affecting RDIE are the sliding window size, quantization level, and stride. In this study, extensive experiments were done with PIPAL to find the optimal parameters, using a grid search method with window sizes from 2 to 16 and quantization levels from 2 to 80. In this paper, we define As shown on the left side of As shown on the right side of In theory, increasing the strides can reduce the effect of pixel misalignment on the results. In this paper, quantitative experiments were conducted on step sizes, as shown in The results of the different restoration models are presented to the user, who rates the images from one to five stars based on perceived quality. Manual assessment results were collected to quantitatively evaluate the perceptual quality of images generated by different methods. The IQA dataset produced in this study contains a total of 30 images; 11 image recovery methods, i.e., six SR methods and five denoising methods; and 1000 human judgments. The results of the SR are shown in Bicubic as a traditional interpolation algorithm is undoubtedly the least effective, EDSR and WDSR are very similar, and SAN is much better, especially for the shape edges of the characters’. SPGAN and SPSR yielded a more realistic natural texture, and because SPSR introduces additional edge loss, the edges have a higher definition and therefore higher perceived quality.The denoising results are shown in This paper uses the RDIE to compare with 15 other IQA methods. The other methods’ SRCC from benchmarks  are showThis experiment explores the sensitivity of the regional information entropy to artifacts. Two common artifacts, motion and ringing artifacts, are synthesized in the test image species, and the generated results are evaluated and compared using RDIE and PSNR methods, respectively. The results are shown in This paper presents a full-reference evaluation index of image recovery quality based on the regional differential information entropy. Currently, the reference-free IQA method has wider prospects in practical application systems, thus entropy-based reference-free IQA research is considered extremely valuable. It is clear based on the research conducted herein that the local entropy of the image is extremely closely related to the human subjective perception of the image, and the size of the entropy value is highly sensitive to different types and degrees of image degradation. For example, the introduction of higher-frequency noise will produce a larger local entropy value, while the addition of blurring will also contribute to a decrease in the local entropy value owing to the loss of detail. Using this property, we can implement entropy-based image quality evaluation without a reference model using traditional machine or deep learning. The mapping model of visual features to regional information entropy feature map quality was learned using traditional machine learning methods such as support vector regression, or the regional information entropy map features are learned using deep learning to build an image quality evaluation model.We propose a regional information entropy-based IQA method that is a reconstruction of the regional information entropy calculation process using a neural network approach. We validated the perceived ability of the RDIE method to restore images experimentally and determined the optimal RDIE parameter values for the image restoration task by ablating the window size, quantization level, and strides. We tested the proposed method using a dataset developed in this study and the PIPAL dataset. The results show that the proposed RDIE method is more responsive to subjective perceptions of images than other IQA measures, such as PSNR, SSIM, and MS-SSIM, and achieves better subjective and objective agreement.


Small Medium Enterprises (SMEs) are vital to the global economy and all societies. However, they face a complex and challenging environment, as in most sectors they are lagging behind in their digital transformation. Banks, retaining a variety of data of their SME customers to perform their main activities, could offer a solution by leveraging all available data to provide a Business Financial Management (BFM) toolkit to their customers, providing value added services on top of their core business. In this direction, this paper revolves around the development of a smart, highly personalized hybrid transaction categorization model, interconnected with a cash flow prediction model based on Recurrent Neural Networks (RNNs). As the classification of transactions is of great significance, this research is extended towards explainable AI, where LIME and SHAP frameworks are utilized to interpret and illustrate the ML classification results. Our approach shows promising results on a real-world banking use case and acts as the foundation for the development of further BFM banking microservices, such as transaction fraud detection and budget monitoring. Small Medium Enterprises (SMEs) are the backbone of the global economy as, according to Organisation for Economic Cooperation and Development (OECD) , SMEs acRadical technology advancements may have an impact on nearly every area of a SME’s operation, resulting in data-driven strategic decision-making procedures. An SME’s digital transformation could provide a fresh perspective on business and financial management, resulting in a competitive advantage through increased productivity and quality control, the introduction of new marketing techniques, and the ability to identify new markets and anticipate business opportunities.However, the digital transformation of an SME hinders many risks and challenges, having to do both with accessing the business’s data, as well as analyzing them. According to the 2019 OECD SME Outlook , with soIt is also noted that some commercial data analytics applications are starting to be available horizontally for SMEs through cloud computing services offered by large corporations, allowing SMEs to access tailored A.I. services even when they lack the internal resources to develop them . Other cBanks, retain data of SME customers as required for their main services. .This could offer a business financial management toolkit to their customers by leveraging all available data , providing value added services on top of their core business. In that direction, banks can harness all available data to provide accurate business insights and analytical services to retail or business customers resulting, as noted by Winig , to incrHowever, developing personalized segmented services is not a straightforward task for banks, as it poses a variety of business and technical challenges. From a technical perspective, a key challenge that arises during the development of data analytics services and the utilization of supervised ML techniques for the financial sector, is the lack of labelled data and the frequent appearance of non-numeric data. Considering the volume of financial data generated or utilized by banks, hand labelling the data becomes a cumbersome task and developing personalized services could prove both resource demanding and time consuming. Moreover, most of today’s Financial Management tools for Small Medium Enterprises (SMEs) are geared towards analysing only past transactions, utilizing mainly statistical methods, making such tools inadequate in today’s world. Today, SMEs and their customers alike, demand just-in-time processing, transparency and personalized services to assist SME owners not only in understanding better their SME business/financial health but also to be able to model their options and to decide on the next best action to take. Most prevalent techniques for financial data extraction include rule-based approaches and supervised machine learning methods aiming at text extraction. When rule-based approaches are followed, keeping all the rules up to date and regularly updating them due to new customers, new scenarios or changing consumer behaviors, in many cases is a real headache for practitioners.Timely transactions categorization based on efficient classification of each transaction according to the underlying product/service. Providing a sound approach for how multidimensional tabular data along with unstructured text data (i.e. transaction description) can be classified using AI based methods and represented in the time domain as time-series enabling the exploit of the forecasting method.Systematically forecast inflows and outflows based on historical data applied for each general transaction category, by applying and comparing different probabilistic Deep Learning. This is extended towards the utility of surrogate data as a dataset enrichment approach.While the scientific contributions that support and extend the proposed solution can be briefly summarised in terms of added value, as the proposed approach enables: Continuous optimisation of the model (and as a result of its outcomes as well) based on the utilization of feedback loop technique that facilitates the nature of ‘self-learning’,Enhanced model outcomes due the usage of surrogate data that enrich the dataset and contribute to better generalization of the proposed model.In this context, the proposed approach enables the development of personalized the development of personalized value adding services for the SME customers of banks. To this direction, the paper proposes a transaction categorization model that classifies transactions according to the underlying utility of the transaction, and an approach to forecast inflows and outflows based on historical data applied for each general transaction category. The classification of all SME transactions is essential to label all historical data and enable most of the functionality of modern BFM toolkits. Based on the classified data, further personalized analytical tools may be developed which can add value to SMEs by offering a holistic approach to revenue and expense analysis. Regarding the transaction categorization model, a hybrid approach is proposed, by applying a rule-based step approach based on a set of elements including transaction, account and customer data aggregation, following an implementation of a tree-based ML algorithm for the remaining uncategorized transactions. Based on the above, the Transaction Categorization model’s ML algorithm is built on a Catboost model [This paper addresses the emerging challenge of SMEs financial management by introducing a data-driven approach to facilitate timely decision making for SMEs. In this context, the operational objective is twofold: st model  and revoTo the best of the authors knowledge, there is no open-source model or data available for the specific case of SMEs transactions categorization. Given that lack of prominent research, the utilized dataset , that is provided by a commercial bank and consisted by real-life data was labeled based on various rules, incorporating various internal categorical values present in the dataset. Consequently, the resulting dataset, consisting of 20 ‘Master Categories’ tailor-made for SMES, was highly imbalanced as in most multiclass classification problems where some classes have significantly smaller instances compared to other classes. Many conventional machine learning algorithms suffer from this uneven distribution of data instances among classes and make it less effective in predicting instances of minority class. Although these types of problems belong to an active research area, in our case the Catboost  model waThe latter task, Cash Flow Prediction refers to an accurate probabilistic time-series model which can predict cashflows of SMEs per giver category. In this case, deep learning techniques were utilized to provide information regarding the future cash flow of SMEs. The proposed framework, as part of a holistic BFM tookit integrates a continuous learning approach that considers the SMEs transactions represented as timeseries able to capture rare market events with very short training time by utilizing probabilistic forecasting based on auto-regressive recurrent neural networks. The techniques mainly focus on time-series probabilistic forecasting, utilizing a Recurrent Neural Network model To this end, a DeepAR model is leveraged within our research. A DeepAR Estimator implements a Recurrent Neural Network (RNN) based model, close to the one described in . More spThe remainder of the paper is organized as follows: "With regard to transaction categorization in the banking industry, there are plenty of commercial applications based on ML classification approaches, focusing mainly at personal transaction categorization solutions. A related research comes from , where sRecently, transaction Categorization research revolves around text identification and word embeddings. Recent approaches try to extract latent information from the unstructured text of the description. One representative research is  where a Furthermore, various more researches based on more traditional supervised models can be found in the pertinent literature such as , 16. TheRegarding the time-series forecasting task, there is another related approach of applying probabilistic forecasting referring to potential food recalls . Of courNumerous studies regarding the effectiveness of AI techniques in the financial and specifically the banking sector denote that the automation and digitization of banking services presents large economic advantages. For example, in , the autTimely transaction categorization based on efficient classification of each transaction according to the underlying expense category. Providing a sound approach for how structured unlabelled tabular data can be classified using a Hybrid Model and represented in the time domain as time-series enabling the exploit of the forecasting method.Systematically forecasting expenses based on historical data applied for each general transaction category. To the end, the utility of surrogate data as a dataset enrichment approach was texted and exploited.The main contribution of this paper is a complete framework leveraging a ML and DL approaches for SMEs financial management. The framework consists of two major components, a classification mechanism relying on a Tree based boosting technique, classifying the transactions according to the implicated product/merchant and a time-series forecasting method consuming the time-series defined from the classified transactions, predicting future cashflows. The main difference of the proposed work compared to the existing ones, is the introduction of a self learning hybrid (i.e. rule-based and ML) approach to map the problem as a time-series case. More specifically, the approach includes: (i) the development of a hybrid transaction categorization model, that offers continuous optimisation and adaptability, by the feedback loop. (ii) Given that for ‘small’ sized time-series datasets, finding an ML/DL forecasting model that offers qualitative forecasts is a major challenge in machine learning as stated in [The main difference of the proposed work compared to the aforementioned ones is that both the transaction categorization model, as well as the cash flow prediction one, revolve around the specific needs of SMEs and their specific business needs. Moreover, the two developed approaches are interconnected with changes in the categorization process being reflected in the cash flow prediction model. Specifically, this paper addresses the emerging challenge of automated Business Financial Management for SMEs by introducing a data-driven approach to facilitate timely decision making. In this context, the operational objective/contribution is twofold: tated in , the preThe main research challenges addressed in this paper enable the classification of SME transactions and the generation of accurate cash flow predictions per category. These two research topics are interconnected as the forecasting model is based on the results of the prior categorization/classification. However, these tasks pose different challenges and methodological approaches, both in terms of data aggregation and preprocessing, as in terms of utilizing and extending prevalent ML/DL techniques. In order to address the various challenges and research achievements, it is key to present the data used for this research, which were obtained by the Bank of Cyprus in order to realize a real-world case scenario.To the best of the authors knowledge, there is no open-source dataset including SMEs transactions. In the frame of our research, the utilized dataset had been provided by Bank of Cyprus as a real-world use case, composed by tokenized transaction attributes of the bank’s clients (i.e. SMEs). The provided data was represented in a tabular format and were available within the INFINITECH H2020 European Project (GA 856632). These data include transactions, customers and accounts data of over a thousand SMEs for the years 2017 to 2020, exceeding 3,5 million data entries and 3GB in terms of storage. As many variables were included in the datasets obtained by the bank, the main categories and variables/features utilized in our approach are presented in the Table All SME data were anonymized prior to acquisition, except from the various internal codes kept by the bank. Although there are many internal tokens, not all transactional data is categorized without an initial reference to its base class.The primary data source for both the transaction classification and cash flow forecasting models was the available transaction data, including over 40 variables/characteristics represented by key information such as dates, amounts, credit indicators/debits, descriptions, etc.. More sophisticated information such as contactless payment indicators, merchant category codes and standby order indicatorsThe SME Accounts dataset contains symbolic information about a customer’s individual accounts, available balances and corresponding NACE Rev2 codes, which are statistical classifications of economic activity used in the European Community  also useCustomer data contains the various accounts belonging to each individual SME, as well as the type of the respective account.The metadata of each transaction includes the timestamp of each transaction, in this approach another dataset of 9494 time series was created, representing the daily number of Amount spent for each account per category for our given time period. Apart from these time series representing the daily expenses, we also generate some covariates  for individual time series.The use of surrogate data is introduced in this paper to increase the added-value and impact of the utilized deep learning models, since they benefit from the increased diversity and volume of the training data. It is expected that the usage of analogous data will help the generalization of some, if not all deep learning models in the case of time series forecasting.The most commonly used techniques for generating surrogate data for statistical analysis of nonlinear processes include random shuffling of the original time series, Fourier-transformed surrogates, Amplitude Adjusted Fourier-Transformed (AAFT surrogates), and Iterated AAFT surrogates (IAAFT) . In our Through this proposed approach, with each iteration the change to the distribution that occurs when the Fourier amplitudes are adjusted will be smaller than in the previous iteration, and thus the alteration of the power spectrum when the rescaling is performed will also be smaller than in the previous iteration. In fact, Schreiber  showed tA decision tree Machine Learning algorithm, able to represent how different input variables lead to predicted target categories, and a probabilistic forecasting Deep Learning model, offering probabilistic forecasts for future inflows and outflows of SMEs per category have been chosen as the most suitable ones based on the pertinent literature, since the problem to be addressed is consisted of a combination of classification (i.e. transaction categorization) and time-series prediction (i.e. cash-flow prediction) tasks. In more detail, the transaction categorization task can be translated as a task of classifying multi class highly imbalanced tabular data, where the Catboost classifier utilized seems to thrive compared to other approaches, as argued in . At the In more detail, the DeepAR Estimator implements an RNN-based model, close to the one described in . More spA more detailed view of the proposed approach provided in Fig. What is more, the DeepAR model learns seasonal behavior patterns from given covariates which strengthens its forecasting capabilities ,with inner processed illustrated in Fig In addition, we have included Algorithm 1 to better illustrate the transaction categorization process.Use the dataset as is, with its daily frequency resulting in a 90-time-step window of prediction. This is not recommended as most of the time-series produced are sparse leaving no obvious pattern to be learnt from.Resample the data in weeks, resulting in a 16-timestep window of prediction. While some patterns begin to emerge, the data in some cases are still very sparse and the cumulative error from the 16-step prediction is theoretically relatively big.Resample the data in months periods, providing stationary time-series with visible patterns and a lower theoretical accumulated error of prediction, since the window has been reduced to 3-timesteps.The data were re-sampled in weekly time frame by calculating the aggregate amount of inflows and outflows respectively per account as depicted in Fig The stationarity of the data was checked by using augmented Dickey-Fuller test . This prThe Catboost Classifier  is a graOur goal is to utilize the above mentioned ML/DL models to enable explanatory insights by leveraging available SMEs financial transactions in order to track and optimize their financial behavior. The conceptual architecture of the proposed mechanism is depicted in Fig. 4A description of the evaluation strategy regarding the approaches introduced in this paper is initially provided, followed by the results of both the transaction categorization and the cash flow prediction models. A more detailed view of this is provided in Fig. As mentioned above, the absence of labelled data is the main challenge when developing a transaction categorization model. In these scenarios, two prevalent approaches arise. The first one is based on unsupervised ML models to create clusters with no prior knowledge of the expected outcomes. However, as labels are fixed and non-negotiable in the finance sector, this approach may lead to a less robust model in terms of confidence level, with some clusters not being able to reflect a distinct category with real-world value to the SME. The second one (here lies our approach as well) is to first label a representative subset based on expert knowledge, creating a rule-based model, which can then be integrated with a supervised machine learning model, with a high degree of updated automation enabling the transaction reclassification. To this end each part of the Hybrid Model  has to address specific challenges. Specifically A step approach was followed for the rule-based model, incorporating various internal codes of the Bank, some of them being interpreted only by the specific banking employees (i.e. Transaction Type Code) and others being used universally in the business world (i.e. Merchant Category Code and NACE codes) . In moreBefore assigning the variables above to given categories, it was important to capture all transactions between the same accounts of the same SME, since those would be classified as ’Transfers between own accounts’. The exact flowchart of the hybrid transaction is illustrated in Fig. As far as the Catboost Classification model is concerned, a key challenge in developing an effective hybrid classification model is to reduce the bias by the rule-based model, allowing the user to re-categorize a given transaction category. This process of updating the labels is out of the scope of this paper, however, the way of updating our knowledge and adapting the model to re-categorizations is fundamental. To this end, a Catboost model was trained in a way of adopting to changes made by the SMEs. Given the different steps utilized of the rule-based model and towards high efficiency and categorization accuracy, a hybrid-model was preferred instead of a fully-AI one, as a result of the confidence provided by the categorization based on the first two steps of the rule-based model.In further detail, because the first two phases of the rule-based model (transfer across accounts and Transaction Type Code mapping) yield fixed root categories, the goal of this model is to learn and duplicate the final three steps. The number of the remaining labels (i.e. Master Categories) that can be produced in these steps are 16, so the evaluation on multi-class tasks in terms of various metrics was applied. In order to have a better notion of the result and the predictive power in each label, a confusion matrix is presented in Table The main outcome is that the model learns the rules which are based on the NACE code and MerchanCode and correctly categorize the results with 98% accuracy. It is worth mentioning that an “Uncategorized Expense” category is included as the transactions falling to this category shall be then categorized by the SME and incorporated to the model.Although statistics is well established and researched, with its varied hypothesis testing and systematic examination of variable relevance, the same cannot be said for the explainability of various approaches in machine and deep learning. Stronger forms of interpretability could offer several advantages, from trust in model predictions and error analysis to model refinement. This applies better in sectors where heavy-cost decision making is involved such as medicine, or in cases where proactive measures should be taken.Machine learning model built-in feature importance;Post-hoc feature importance utilizing models such as LIME  and SHAPBoth the LIME and SHAP methodologies were used as a qualitative evaluation of the findings in our transaction classification instance. Fig. Given the nature of the banking services, the interpretation and comprehension of the results is more significant than the results themselves in order to conclude to both technical and business insights. In general, explainable methods can be categorized in two general classes: Finally, as far as the Catboost evaluation is concerned, apart from the feature importance based on SHAP analysis, it is of high importance to qualitatively check some of the outcomes based on the Local Interpretable Model-agnostic Explanation (LIME), a recent technique capable of explaining the predictions by using local approximations (sample-based) of models. In Fig. The aim of this second part of our research is to accurately predict the cashflows per category produced by the hybrid transaction categorization model per SME. The way that the task and the objective were modeled highlighted the need for data transformation in time-series representation. To this end, we resampled and aggregated the amount of the transactions per account and date.The “cold start” problem, which in this specific case refers to the time-series that have small number of transactions. The ‘cold start’ refers to the difficulty for obtaining accurate predictions for a new customer. As it could be further examined in future research, in order to give a notion, we can argue that in such cases where a new customer account is added in the pool of accounts, the prediction will be based on similar accounts based on the characteristic of the underlying SME .The stationarity–seasonality trade-off, where it is assumed that in order to have predictable time-series they have to be stationary . Within the context of this paper, it is argued that this challenge it has been tackled through the leverage of the DeepAR model. As described, the specific implementation of DeepAR utilized addresses these types of problems.The presence of noisy data and/or many observed outliers. The solution to this challenge came from the resample approach used. The scope of the resampling was mainly to reduce the effect of noise in the dataset,while also adding at the proposed model and procedures additional efficiency in terms of time and resources. The time frame of ‘months’ was selected as it corresponds to the data velocity for the given case—i.e. the strategy of the SMEs can be projected in a 4-month horizon.The short length of the dataset hampers the applicability of ML/DL techniques. The final challenge is caused as a side-effect from the fact that the data was re-sampled in weekly time frame getting the aggregation of the Amount of the transactions made for a specific category. This process concludes to the total training data consisting of a matrix When machine learning is applied, cross-validation methods hide a pitfall in time series forecasting approaches, as they may introduce an overlap between train and test data, known as data leak. Thus, the optimum approach is to simulate models in a “walk-forward” sequence, periodically re-training the model to incorporate specific chunks of data available at that point in time. This procedure is depicted in Fig. Various long-established challenges regarding time-series forecasting arose that should be tackled in the context of our research. These challenges can be summarized as follows: To develop an efficient Transaction Categorization model, various state-of-the-art models were applied and evaluated, with the comparison illustrated at Table Another interesting results seem to be the difference in performance of DQA compared to Tree based models. The worst classification result was obtained with the QDA algorithm with 0.01% accuracy.Quadratic Discriminant Analysis is a classifier with a quadratic decision boundary, generated by fitting class conditional densities to the data and using Bayes’ rule. Quadratic discriminant analysis (QDA) models the likelihood of each class as a Gaussian distribution, then uses the posterior distributions to estimate the class for a given test point. The method is sensitive to the knowledge of priors. Despite being a popular choice for classification, QDA does not perform very well when the class-conditional probability densities are very different from the normal distribution. This is also noticed on other tasks such as NLP tasks .Table 6AIn Table The presented results highlight that the proposed methodology produces outcomes that can hardly accomplish the required approach based on the Coverage. In a probabilistic way, that is translated that for a given confidence level of e.g. 80%, the model is underperforming given the average values of the categories.Furthermore, in Table In Fig. The iterative prediction only uses one model to forecast all the horizons needed. The objective is to analyze a short sequence of data while trying to predict the rest of the data sequence until a predefined time step is reached. The main drawback of this approach is the accumulative nature of the error. The implementation of a DeepAR model in our approach falls in this latter case, yielding higher error values when the prediction horizon widens.Dynamic budget monitoring, where the DeepAR model can be extended to predefined set budgets per category.Fraud detection and transaction guard, where new transactions beyond a given confidence level set per category could indicate malicious actions and fraudulent attempts.Sector analysis and benchmarking, by utilizing open-source data based on the available SME NACE code and comparative analytics, based on mutual business sector SME customers of the bank.Extended prescriptive analytics services, focusing on the provision of business insights and added value in the SMEs functional areas or improved VAT reconciliation.From the bank’s perspective, the proposed services will allow the bank to get a deeper understanding of their SMEs customers, enabling the provision of tailor-made personalized financial products, while also attracting new business customers, seeking to utilize the bank’s extended business financial management services as a cost-effective solution.This paper sets the foundation for the development and provision of new banking services, tailor-made for SMEs, on top of the bank’s core activities. This is achieved by introducing a hybrid transaction classification model, which is in turn used for the development of an accurate Cash Flow prediction model based on DeepAR probabilistic forecasting model. Moreover, the classification task is extended towards the explainable AI aspect of the transaction categorization which assist interpreting the proposed model results while increasing transparency and trustworthiness. Having developed an accurate hybrid classification model, based on 20 Master Categories tailor-made for SMEs, we pave the way for the development of further banking microservices based on the results of the classification. Combined with the developed Cash Flow prediction model, those services could include:


Background: Three-dimensional (3D) printing is an emerging tool in the creation of anatomical models for surgical training. Its use in endoscopic sinus surgery (ESS) has been limited because of the difficulty in replicating the anatomical details.Aim: To describe the development of a patient-specific 3D printed multi-material simulator for use in ESS, and to validate it as a training tool among a group of residents and experts in ear-nose-throat (ENT) surgery.Methods: Advanced material jetting 3D printing technology was used to produce both soft tissues and bony structures of the simulator to increase anatomical realism and tactile feedback of the model. A total of 3 ENT residents and 9 ENT specialists were recruited to perform both non-destructive tasks and ESS steps on the model. The anatomical fidelity and the usefulness of the simulator in ESS training were evaluated through specific questionnaires.Results: The tasks were accomplished by 100% of participants and the survey showed overall high scores both for anatomy fidelity and usefulness in training. Dacryocystorhinostomy, medial antrostomy, and turbinectomy were rated as accurately replicable on the simulator by 75% of participants. Positive scores were obtained also for ethmoidectomy and DRAF procedures, while the replication of sphenoidotomy received neutral ratings by half of the participants.Conclusion: This study demonstrates that a 3D printed multi-material model of the sino-nasal anatomy can be generated with a high level of anatomical accuracy and haptic response. This technology has the potential to be useful in surgical training as an alternative or complementary tool to cadaveric dissection. Endoscopic sinus surgery (ESS) represents the current gold standard technique for several pathologies, ranging from chronic rhinosinusitis to benign and malignant neoplasms . The parThese peculiarities account for the importance of specific training for this type of surgery, aimed to both comprehensively understand sino-nasal anatomy and develop the required surgical skills in a risk-free environment, before operating on a patient .The restrictions of traditional training based on cadaveric dissections have led to the investigation of alternative training methods, including the use of synthetic anatomical models created with 3-dimensional (3D) printing. The emerging advanced additive manufacturing technologies have lately expanded the possibilities of representing 3D patient-specific anatomies, which are extremely useful for both surgical planning, training  and mediThe aim of this study was to develop a patient-specific simulator for ESS using the 3D printing multi-material jetting technology, which reproduces both soft tissues and bony structures, thus increasing the anatomical realism and tactile feedback of the model. Moreover, a preliminary validation of this simulator as a training tool for ESS among a group of ear-nose-throat (ENT) residents and experts, is described.The design and fabrication of the simulator consisted of three steps: A) Image segmentation and 3D modeling of the anatomical components; B) Design of the simulator box and the external face mold; C) 3D printing of the simulator components.The simulator was then assembled and tested by the surgeons in the operation room.TM , which is a certified software for medical 3D modeling. The anatomical structures of interest, i.e., the midface bony structures, the turbinates, and nasal septum were segmented in collaboration with a radiologist with expertise in head and neck anatomy. The segmented mask for the turbinates was slightly reduced, in accordance with the surgical team, to simulate the effect of decongestants used before the ESS. 3D meshes were then generated from all the segmented masks and saved in STL format; For converting the segmentation mask to 3D mesh, we used the standard settings provided by D2P software. In detail, these settings include selection of: “Maintain original morphology” method  and selection of “Simplify and smooth” (to decimate and apply extra smoothing to the mesh surface).The preoperative computed tomography (CT) scan of a patient with no evidence of sino-nasal pathologies was acquired. The Digital Imaging and Communications in Medicine (DICOM) data were imported into D2PThe meshes were imported and processed into 3-Matic Medical software : all the structures were cut coronally into two sections to ease the cleaning of the internal parts after the printing process. According to the surgeons’ requests, the medial periorbital surface and the dura mater of the anterior skull base were selected as relevant anatomical landmarks for surgery, and they were enlarged in order to be more visible in the simulator. Moreover, the nasolacrimal duct and the lacrimal sac were reproduced, filling the segmented bony duct and the lacrimal sac fossa , 2. FinaAs final step, the Fix Wizard tool of 3-Matic software was applied to the meshes to make sure they were printable.A box including the reconstructed patient-specific paranasal sinuses model was created using the open-source software FreeCAD. The box was designed as a hollow rectangle combined with a larger base in order to secure it to the operating table during the simulation.TM, then converted into a mesh and exported as STL file to be further processed in MeshMixer software .The portion of the facial skin was segmented in D2PUsing the negative of the facial skin model, a rectangular mold was designed to allow the subsequent silicone casting .Lastly, the box was provided with a closing lid that was designed in FreeCAD. The patient-specific portion of skin was combined with the lid to let its borders assume the shape of the patient-specific face.The patient-specific model of the paranasal sinuses was printed using the J720 Dental 3D printer . This printer is based on material jetting technology, a process that resembles inkjet paper printing since the material is dropped through small diameter nozzles. In this case, the printing material is a photopolymeric resin subsequently hardened by a UV lamp.via a mixing of rigid resins (VeroMagenta) and a rubber-like material from the Polyjet photopolymer family (Agilus30) with a resulting Shore-A 50. Also, the membranes replicating the orbital content and the dura mater as well as the middle, the superior turbinates, and the inferior turbinates were printed combining Agilus30 and rigid resin VeroMagenta with Shore-A 50 and Shore-A 30 respectively. The lacrimal sac was printed by mixing VeroYellow and Agilus30 with a Shore-A 35 , which was previously used for reproducing bones in other 3D printed phantoms of thorax, pelvis and prosthetic fingers . Consideore-A 35 . ParticuRegarding the other components of the simulator, the box and the lid were both printed using a stereolithography (SLA) 3D printer  with White resin.The components of the patient-specific facial skin mold were also printed using Form 3 SLA printer but with a Clear resin so that, during the silicone injection, both the level of silicone and the presence of air bubbles could be seen.The patient-specific facial skin was realized by a silicone casting through the mold: the selected silicone was Dragon Skin FX-Pro  which is a bi-component silicone frequently used in literature to reproduce human skin and surgical simulation . The silAfter the cleaning of the two parts of the model, the inferior turbinates were filled up with Dragon Skin FX-Pro, to make them more compressible. The two parts were then glued together shaping the entire model. To replicate the nasal mucosa, some layers of silicone were added over the nasal septum. Lastly, the skin portion was placed on the lid and the box was closed .As an additional feature, we implemented an Augmented Reality (AR) view to be integrated into the simulator. This allows a “hybrid” training, i.e. the fusion of digital and physical tools, that may improve the potentialities of the simulator.The anatomical virtual models and some cutting planes planned by the surgeon on the reconstructed anatomy  were impTo track the physical simulator, we designed and printed a tracker with a well-contrasted texture, which was then anchored to the external box of the simulator .The tracking algorithm and the registration between the digital models and the real scene were implemented using the Image Target function of Vuforia engine: the engine detects the image texture of the tracker by comparing extracted natural features from the camera image against the known image target . Once the image target is detected, Vuforia engine will track the image target on the tracker and augment the scenario with digital content anchored to the target itself.The AR application was built as a UWP  app deployed on Microsoft HoloLens 2 smart glasses. The user just needs to look at the tracker on the simulator while wearing the AR glasses, until the image target matching occurs. After that, tracking of the real object and virtual content overlay can begin.The AR application generates holographic overlays on the physical model, by rendering the underlying anatomical structures , not visible from the outside of the simulator, together with the cutting planes which may help the trainee in understanding the deep anatomical structures while navigating the physical model with instruments see .Interactable user interface toggles (check boxes) were added to turn off and on the rendering of each virtual anatomical structure and cutting planes. In particular, cutting planes following progressive lines perpendicular to the nasal bones were created to convey the anatomical configuration that the surgeon has to bear in mind during the endoscopic approaches to the frontal sinus . The possibility to virtually remove the middle turbinate allows for realistic visualization of the intraoperative field across the steps to performing DRAF procedures.Voice commands to show/hide the virtual structures were also implemented in order to provide a completely hands-free AR application. see .The process to assess the fidelity and utility of the simulator as a training tool for ESS consisted of two phases which are described below.First, the anatomical realism of the nasal cavity and the reproducibility of the instruments handling inside it were evaluated.1 Identification of normal anatomy landmarks on one nasal cavity ;2 Removal of a foreign body  from the choana;3 Medialization of the middle turbinate and insertion of a cottonoid into the middle meatus;4 Incision of the simulated nasal septum mucosa with a curved scalpel and its detachment from the underlying simulated cartilage with a Cottle dissector-elevator.This phase involved 12 participants with different expertise levels: 3 ENT residents from the third and fourth year of residency, 3 young ENT specialists (less than 3 years of practice), and 6 ENT surgeons experienced in ESS (more than 10 years of practice). Each participant was asked to perform the following non-destructive tasks on the simulator using the standard instruments , in a tiThe completion of each task (yes/no) and the time needed to complete it were annotated by an external observer not involved in this phase (N.E.). A 5-point Likert scale questionnaire evaluating the anatomical, haptic feedback and utility of the simulator for training was filled in by each participant immediately after performing the non-destructive tasks .This phase was aimed at assessing the feasibility of different ESS procedures on the simulator and its potential as a training tool for ESS. Only four expert surgeons participated in this second phase. They were asked to simulate all the basic procedures of ESS, namely uncinectomy, maxillary antrostomy, anterior and posterior ethmoidectomy, sphenoidotomy  and DRAFAfter completing these tasks, the experts were required to fill out the task-specific questionnaire , using at-test for unpaired data was used to compare mean times for each pair of participant groups  for each task.All measured times needed to complete the tasks were reported as mean values and standard deviation (SD). p-value of <0.05 was considered statistically significant.All statistical tests were performed using SPSS software, version 26.0 , and a All the 12 participants completed the face and anatomical content validation of the model and answered the survey. The non-destructive tasks were accomplished by 100% of participants . The surn = 11) of the participants agreed or strongly agreed about the high anatomical fidelity of the simulator and the tactile feedback of both the turbinates and bony structures. On the other hand, 25% (n = 3) of the participants were neutral about the tactile feedback of the nostrils and nose tip and the use of instruments and endoscope inside the cavities. All the participants gave positive feedback (100% agreed or strongly agreed) regarding the role of the simulator in learning anatomy and improving accuracy and precision in endoscopic settings. Moreover, the utility of the simulator in developing hand-eye coordination received the highest scores (100% strongly agreed) from all responders.A total of 92% , the training has been traditionally performed either on ovine models or cadaveric heads, which both lack from pathology and specific anatomical variants, and could be dissected just once. The development of synthetic models for ESS training has encountered several difficulties in relation to the replication of the complex anatomy and of the variable haptic feedback from different parts of the nose and paranasal sinuses.The first low-fidelity models date back to 10 years ago, and despite having been demonstrated to improve basic endoscopic skills, the lack of anatomical and haptic fidelity hampered the possibility to simulate ESS procedures . More reThe unmet needs of 3D printed simulators for ESS encouraged our group to develop a 3D printed multi-material and multi-color patient-specific simulator for ESS, using the Polyjet printing technology. The potentiality and the precision of this recent technology allowed us to create a model with bony and soft structures simultaneously, reaching a high level of anatomical fidelity and an overall satisfactory realistic haptic feedback, as suggested by our results. Indeed, the technology has many benefits, including excellent resolution (up to 0.016 mm), smooth surfaces, and the ability to print multi-material and multi-color parts, thanks to its multiple print heads. Furthermore, it is possible to dynamically mix materials and create “digital materials” with new characteristics. Indeed, it is possible to print rubber-like materials targeting a specific Shore-A hardness, as well as to mix up rigid and rubber-like materials, creating new materials with hybrid properties.However, we decided not to use the rubber-like Agilus Stratasys material, which has a minimum achievable Shore-A 30, to reproduce the facial skin, since preliminary tests with surgeons confirmed that the obtained 3D printed part is still too rigid if compared to the real facial skin. Moreover, using Agilus material at the minimum Shore-A 30, the parts can be printed only in white, so it is not possible to achieve a realistic color for the skin. Many papers in the literature report the use of the Dragon Skin FX-Pro, especially for the replication of the human skin using specific pigments to have different realistic skin coloration .Regarding the turbinates, we decided to fill them with silicone because the filling with Agilus Shore-A 30 made them too rigid. Conversely, the silicone filling allows the turbinates to be more compressible even if they still maintain a greater elasticity if compared to the real ones.Moreover, the silicone has realistic feedback to cut, and we decided to use it also for simulating the nasal mucosa; also in this case, using an additional layer of Agilus Shore-A 30 on the nasal septum is not a good solution to achieve a realistic replication of the mucosa.In our simulator, we were able to replicate the mucosa on the nasal septum so that the simulation of mucosal incision and mucoperichondrial flap elevation could be successfully performed on the 3D printed model by all participants. To the best of our knowledge, this study is the first to consider the feasibility of endoscopic lacrimal surgery on a 3D printed model. The nasolacrimal duct and sac were realized with a different material (VeroYellow and Agilus30 with Shore-A 35), obtaining a color quite similar to the surrounding bone, as it is in real anatomy. The consistency of the sac resulted quite fragile so that in one case it was accidentally drilled before identifying it.in vivo and on cadaver).Moreover, in our model some high-risk anatomical landmarks such as the lamina papyracea and the dura mater of the anterior skull base were replicated with layers of material that, despite differences in color from the real structures, resulted not easily identifiable during endoscopic dissection, as in a real-life situation during ESS , and about 20 h for the design and printing of the external box and face mold.A strategy to reduce these times is to take advantage of the modular design of the simulator, in which the internal part relating to the patient-specific paranasal sinuses anatomy can be changed and customized from time to time, while the external part (box and face mold) can be reused.Despite all our efforts in the cleaning of the paranasal sinuses model, some residual printing support material remained inside some ethmoidal cells, reducing the anatomical reliability and the performance of selected ESS steps. Future studies will include improvements in the design of the model to entirely clean it and reproduce the variable degree of pneumatization of the paranasal sinuses.The performance of ESS procedures was evaluated by a subjective Likert scale applied to not-validated questionnaires. However, for the purposes of this preliminary study, these methods could be considered acceptable in identifying positive feedback from the users. This limitation is shared with several other validation studies on the same topic .Our results are promising but the evaluation was performed on a restricted number of participants.A future study will be planned to compare the ESS training based on our simulator and the one based on cadaver dissection, involving a higher number of trainees.The AR feature we introduced can certainly be improved, e.g. by implementing the possibility to manipulate the virtual objects in addition to making them visible or not. Although we have received enthusiastic feedback from surgeons who have also experimented with the AR feature, its educational and training benefits must be more systematically evaluated.This study demonstrated that a detailed multi-material 3D printed model of sinus anatomy can be generated with a high level of anatomical accuracy and an overall satisfactory haptic response. This technology has the potential to be used in surgical training of ESS procedures, as an alternative or complementary training solution to cadaveric dissection. It could also potentially play a significant role in preoperative planning, since it may provide an individualized preoperative design on which the surgeon can rehearse before surgery, to shorten operation duration and ensure safe and effective surgery in patients.