In this paper the extended method of lines (E-MoL) is proposed for the analysis of multilayer graphene-loaded three dimensional structures in cylindrical coordinates. Accordingly, the impedance and admittance matrices are defined as the ratios of the electric and magnetic fields at each plane of the stack. The impedance and admittance parameters are transformed from the input to the output of the structure through layers and interfaces, from which, the scattering parameters are extracted. It is assumed that there is an anisotropic graphene layer at the interface of two successive layers. The impedance and admittance transformations at the interfaces are extracted in the cylindrical coordinates. Then the impedance and admittance values at all planes of the stack and consequently, the scattering parameters of the whole structure are derived. To validate the presented method, two validation benchmarks are provided at the microwave frequency band. A circular waveguide and a coaxial cable loaded with graphene plates are analyzed and the results are compared with those of CST simulation software which show good accordance. It is observed that the E-MoL, as a semi-analytical semi-numerical method, is much more time-efficient than the CST software numerical procedure. This 2D material is a promising candidate in electronics and electromagnetics and has attracted the researchers’ attention all around the world in the recent years3. Nowadays, graphene sheets with dimensions up to 30 cm are fabricated, and this has led to the use of graphene in the new microwave and millimeter wave applications4. Graphene sheets have remarkable electric properties like controlling their surface conductivity through applying electromagnetic fields or DC voltages. This feature is used to design tunable electronic or electromagnetic devices6. New nano-sized electronic and electromagnetic components and devices such as antennas, flexible electronic devices, touch screens and high-speed transistors are examples of the states of the art in the graphene world. The unique properties of graphene are due to its gapless electronic band structure10.Graphene is a one-atom thick allotrope of carbon arranged in a hexagonal lattice with extraordinary properties11 per unit length equivalent circuits are proposed for the accurate characterization of propagation in different graphene-loaded waveguides. For that purpose, analytical expressions for the graphene conductivity as well as lumped circuit components are calculated through which the propagation of surface waves along spatial graphene waveguides is illustrated. The finite difference time domain (FDTD) method has the advantage of solving transient broadband problems in one simulation. In12, the FDTD method is exploited to investigate the electromagnetic interaction with graphene by treating it as a surface boundary condition and sampling the computational space. Recently, in13 the magnetized graphene is characterized by an anisotropic surface boundary condition using FDTD. Physically, the aim of the proposed method in13 is to incorporate magnetically-biased graphene formulation into electromagnetic wave scattering problems macroscopically by modeling the graphene with a conducting sheet. Li et al.14 introduce the discontinuous Galerkin time-domain (DGTD) method to characterize the transient response of the graphene from microwaves to terahertz frequencies. Having considered an infinite graphene sheet with its finite surface conductivity14, deals with resistive boundary conditions for the graphene. Moreover, the method of moments (MoM) is used for the diffraction analysis of graphene nanoribbons in15. The authors also try to model non-local effects of graphene by the MoM analysis. In16 graphene nanoribbons are analyzed using space-domain MoM with the restriction that the method is valid only for low wavenumbers. Lastly, the theory of characteristic modes is used to characterize the input impedance and efficiency of a plasmonic graphene-based antenna17. In19, based on integral equations governing the surface current density of magnetically biased graphene, an analytical approach is proposed to analyze an array of graphene ribbons.Various methods have been used to analyze multilayer three-dimensional graphene-loaded structures. In22. This makes MoL especially appropriate to deal with general multilayered structures loaded with complicated boundy conditions of arbitrary shapes, where the wave propagation in the direction perpendicular to the layers stacking is treated analytically. In directions transverse to the layers the structure is discretized and is sloved numerically. MoL can be used to analyze 2D and 3D structures that require 1D and 2D discretizations, respectively23. The MoL enjoys the advantages of both analytical and numerical methods while avoiding the shortcomings of each method when modeling multilayered structures. In25, we have proposed MoL for the analysis of 2D structures including graphene plates in Cartesian coordinates. To verify the efficiency of the method, several 2D graphene-loaded structures such as microstrips, striplines and parallel-plate waveguides are presented and analyzed. In26, we extend the MoL for the analysis of graphene-loaded 3D structures in Cartesian coordinates. Besides, different graphene-based periodic structures again in Cartesian coordinates, are studied in27 using MoL.The approaches mentioned above have advantages and disadvantages. Pure analytical methods are very time-effective but are able to solve only certain simple structures. On the other hand, fully numerical methods are capable of analyzing complex structures, but generally they are very time consuming. The method of lines (MoL) is a simple, fast, and accurate approach that solves an electromagnetic problem analytically in one coordinate direction and numerically in other coordinate directions29.In this paper the MoL is extended in cylindrical coordinates for the analysis of multilayered structures with an arbitrary number of layers loaded with graphene plates of arbitrary shapes. Here the graphene plates are treated as layers represented by surface boundary conditions. To do so, the impedance and admittance transformation formulas through a graphene plate are extracted. Having obtained the transformations, one can compute at every plane of the structure the impedance and admittance values, and subsequently the scattering parameters are derived. For the purpose of validation, the MoL results are compared with those of CST full wave simulation software which are in good accordance. In the first example, we assume a circular waveguide loaded with graphene sheets and perform a parametric analysis in terms of the graphene chemical potential for different numbers of plates. The second example deals with a coaxial cable loaded with annular graphene sheets. The proposed method is general and can be used to analyze generic 3D multilayered stackings in cylindrical coordinates with applications in absorbers and radiating structures. Note that the MoL is a pure classical theory which is applied to Maxwell’s equations and thus it is not able to model quantum mechanical phenomena such as Klein tunneling which occurs in graphene sheetsThe rest of the paper is organized as follows: in “In this section, the generalized transmission line (GTL) equations are first given in cylindrical coordinates to analyze structures that require two-dimensional discretization. Then, the impedance and admittance transformation formulas in a homogenous layer are given. The presence of graphene at the interface of two adjacent layers will result in a new set of boundary conditions through which the impedance and admittance transformation formulas at the interface are extracted. As a result, the impedance and admittance values are derived in each layer of the multilayer structure. Having obtained all impedances and admittances, the electromagnetic fields all over the structure and subsequently the scattering parameters of the whole structure are achievable.In what follows, we assume that the constitutive parameters are defined by:z direction propagation. The first and second equations of Ampere's law and Faraday's induction law result in30:z-direction. Using the third equations of the Ampere’s Law and Faraday induction’s Law, the longitudinal field components 30:First, we present the GTL equations suitable for Then, we combine the two first-order differential equation systems in Eqs.  and 6)  6) in orThese equations are called as wave equations. To solve these equations using MoL, the fields and their derivatives in transverse directions should be discretized.30:30:In this section, we present the discretization of the fields components in the cross section as well as the solution of the GTL equations in the discretized form. The discretization scheme is shown in Fig. and use the following abbreviations:As a result, Eqs. –8) are  are 8) aIn Eqs.  and 16)16), \docNow we are going to solve Eq. . For thiIntroducing into Eq. , one obtSince The general solution of Eq.  can be gd in Eq. , the chad, are obtained as30:Having known the electric and magnetic fields in each plane according to Eq. , the relOn the other hand, the electric and magnetic fields at each plane are related to each other through impedance/admittance matrices as follows:Consequently, the impedance and admittance transformation formulas between planes A and B are as follows:B. Besides, the two sides of this interface are distinguished by + and − indices.To analyze the whole structure, it is necessary to be able to pass through the graphene plates at the interface of two adjacent layers by transforming the impedances and admittances through the graphene interface. As shown in Fig. or equivalently As shown in Fig. This equation must be transformed to the cylindrical coordinates system, which will be performed as below. The transverse electric field components must be continuous at the interface. Hence,The transverse magnetic field components in cylindrical coordinates at The boundary conditions for the magnetic field components at plane Then, transformation of d by Eq.  into cylThen, the s in Eq.  are sepaDefining the electric and magnetic field components in cylindrical coordinates asrite Eq.  in the vIn this step, we need to discretize the field components and graphene parameters in their appropriate places as shown in Fig. As mentioned earlier, the field components, for example n of Eq.  can be eAccording to Eq. , we rewrSubstituting ^ in Eq.  results Multiplying Eq.  by \docug to Eq.  the relaFinally, the admittance matrix at Thus, transformation of admittance at graphene interface will be done using Eq. . Note thStarting from the output of the multilayer structure and using the formulas provided for impedance and admittance transformation through layers and interfaces, the input impedance and admittance can be obtained. Usually the load impedance is considered as the output wave impedance of the output waveguide which is normalized to be the identity matrix, i.e. Using the admittance transformation formulas, the input admittance Consequently, for the scattering parameter If the modes are ordered properly, the first element in the In order to calculate the transmission coefficient, the field coupled to the output port should be divided into the injected field at the input port. To do this, the fields should be calculated at the whole structure and they must be transformed in a numerically stable manner. The general solution of the wave equation in the Cartesian coordinates can be written as Eq. . The indThe forward and backward parts are connected by the characteristic impedance Then, the forward and backward parts can be obtained using the total fieldsUsing Eqs.  and 51)51), the Using the vectors The total field at the plane B is computed asUsing the definition of the impedance at any arbitrary plane, the above equations are written asThen, supposing that the graphene plate is placed between the layers, the transmission coefficient is obtained. First, we assume that a graphene plate is located between the two layers of the structure, as shown in Fig. The values of the admittances from Eq. . Assuminsing Eq. , the totThe electric field at the interface of the plane A is continuous and hence From Eq. , the valTherefore, we haveThen, the transmission coefficient matrix is calculated asThe first element of the matrix As shown in Fig. Therefore, the forward and backward fields at the plane Consequently, using Eq. , the totConsidering the continuity of the electric field components at the plane Since the electric field at the plane rom Eqs. –63) we  we \docuBy substitution of Eqs.  and 52)52) in 665\documeAs a result, from Eq. , the traHence, in general, for an Note that the scattering matrices To validate the proposed method, two multilayer graphene-loaded structures are examined in the cylindrical coordinates system: a circular waveguide and a coaxial cable. Both structures are analyzed for different values of graphene chemical potential and varying numbers of graphene plates. The results of the proposed method and CST simulations are compared to each other.14. In this model the relaxation time 7.In this section, as an example of a graphene-loaded multilayer structure, graphene plates are placed inside a circular waveguide. The cross-section of the waveguide is shown in Fig. 11, for the graphene chemical potentials Figure Figure Table In this section the graphene plates are placed inside the coaxial cable and the structure is analyzed in terms of the chemical potential and the number of graphene plates. The cross-section of the coaxial cable is shown in Fig. Figure Considering the reflection and transmission coefficient plots, increasing the chemical potential increases the graphene conductivity and thus reduces its transparency which results in an increase/decrease in the reflection/transmission coefficient, respectively. In other words, the reflected and transmitted powers of the waveguide can be tuned by an electric voltage controlling the graphene plate. This finds applications in tunable microwave waveguide attenuators. Also, this can be utilized in phased array antennas to control the feeding amplitude of each element of the antenna array. Note that no resonant behavior is observed in the reflection and transmission coefficients because the resonance frequency of a graphene plate is far beyond the microwave frequencies. This nearly flat response offers wideband microwave devices.In this paper, an extended method of lines (E-MoL) is presented for the analysis of graphene-loaded multilayer structures in the cylindrical coordinates. To do so, first, the impedance and admittance transformations imposed by graphene plates are obtained in the cylindrical coordinates system. Having the impedance and admittance transformations, the impedance and admittance matrices can be calculated in each plane of the structure. Then from the input impedance of the structure the reflection coefficient is derived. Finally by transferring the fields from the input to the output of the multilayered stack, the transmission coefficient is computed. To validate the proposed method, a circular waveguide and a coaxial cable loaded with graphene plates are analyzed and the results are compared with the CST full wave simulations. The proposed theory has the potential to model multilayered absorbers and radiating structures accurately and efficiently which is a topic of the future research. The MoL can be generalized to model complicated boundary conditions other than graphene in planar and quasi-planar waveguide structures in integrated microwave and optical circuits and also in circular and conformal antennas. The graphene-loaded optical fibers are attractive optical devices in which the dispersive properties of graphene emerges and can be characterized using the E-MoL preseted in this paper.


Metro systems extended rapidly in China, especially in the last decade, developing over a half-century. This work explores the dynamical evolution of the structural efficiency of metro systems interpreted as complex networks for 14 large cities in mainland China. Based on the empirical observations, we find that the global efficiencies scale with the number of stations and counter-intuitively decreases as the metro networks expand, which shows a long-tail characteristic. The evolution of metro networks is, in essence, the improvement of the relative ratio of average nodal efficiency in the core compared to global efficiency. These relationships are in good agreement with the temporal structure of metro networks. Besides, we find that the metro stations with the higher efficiencies are those surrounding the urban center, and most of them dwell within the core and gradually expand the branches in space. Our findings suggest that the evolution properties of metro systems influenced by numerous geographical, historical, and social activities suggest that underlying, universal mechanisms are at play during their evolution in the spatial–temporal dimension. Compared with other transportation modes, metro systems offer an effective solution for addressing the urban issues of being safer, punctual, quicker, cleaner, land-saving, and high capacity. The dependence on metro systems keeps growing in cities worldwide as societies become ever more urbanized, which leads to a rapid increase in metro scale. This massive growth is credited to development in a few countries, especially China2.Metro systems are integral parts of transit systems in large cities, acting as a critical solution in supporting commuter traffic demand within the metropolis area3. As one type of transportation mode, the evolution of metro networks reflects the evolution of the population and activity densities. Indeed top-down planning controlled by a central authority plays a vital role in the construction of metro networks which are always intentionally structured in a core-periphery shape. This would be true if these metro networks were planned from the beginning to their current shape, but this is not the case for most networks. The metro network results from a superimposition of multi-type actions such as urban planning, the reorganization and regeneration of economic activities, and the growth of residential populations4. Although central intervention is severe limitations to the possibility of modeling the dynamic metro network, top-down planning, as one of the influencing factors, could be thought of as an external perturbation5 when modeling a city and its expansion as a self-organized phenomenon. Understanding how metro networks evolve under different geographical, historical, and social-economic circumstances and what are the dominant mechanisms in a long time. If any, the universal features of such a large-scale self-organized process, are more critical than ever as policymakers, professionals, and the scientific communities are actively looking for new paradigms in urban planning and land management7.As continually adding new lines and stations, the metro system, developed to improve mobility and reduce congestion in urban areas, has become an increasingly large and complex network with the city's development8 adapted graph theory to describe the metro network characteristics of the state, from, and structure. They concluded that the network topology plays a crucial role in attracting travelers to public transit. Despite their geographical and economic differences, Roth et al.9 found that metro networks worldwide share a common generic feature . However, the analysis was restricted to configurational metrics and ignored other attributes such the travel time and distances. Cats10 conducted longitudinal research on the topological evolution by investigating the Stockholm rail network from 1950 to 2025, which exhibited smooth long-term technological and spatial trends. Even though the growth of transportation networks is complicated and multidimensional, and the duration is usually measured in decades, it may still be tractable and predictable with a further understanding of the underlying mechanisms11. However, literature related to the evolution of metro networks has rarely been studied. Leng et al.12 proposed a new growth model composed of expanding and intensifying modes to measure the development of the transfer network of the Beijing metro. Motivated by the shape of metro networks, Aldous et al.13 calculated the optimal structure of metro networks as a function of the total length. They found that in the medium-length regime, resources go preferentially to radial branches as the total length increases, and this is a sharp transition when a loop appears.The detection and characterization of these universal features require us to understand the evolution of these spatial structures. The metro network embedded in space forms a graph where stations are nodes and links represent rail connections. The evolution of the metro network is a critical aspect of the time-dependent spatial topology, which has been studied in recent years for the availability of longitudinal infrastructure data. Derrible et al.accessibility in transport geography) measures how people interact with other people, and places were first used in transportation planning since Hansen proposed14. Transport operators and planners have increasingly adopted efficiency as a desirable planning goal and a key performance metric15. Efficiency has always been evaluated in single-mode, such as car and public transport17. A recent study proposed a new measurement approach, called Urban Accessibility Relative Index, for understanding the spatiotemporal patterns of efficiency in urban areas. Results demonstrated that the metro has a higher impact on public transit efficiency than bus18. The evolution of the metro network is essential to the progress of changing the opportunities of locations by metro systems, along with the developing spatial pattern, although this type of infrastructure network also lacks a high redundancy because of the high cost of adding new lines or stations. We now understand pretty well how to characterize the structure of their temporal evolution19, while transportation networks are the underlying critical infrastructures for improving the movement of people and reducing congestion in urban areas21. Unfortunately, the efficiencies changing with the structures and their evolution characteristics are generally ignored in previous studies, although the calculation and comparison of static efficiencies among metro networks appeared in several studies24.The metro network plays a vital role in improving commuting efficiency and promoting economic development. The efficiency . An intuitive idea would be that the number of branches . For each metro network, a specific page per metro line includes various information such as the station names, the first date of operation for each station, the distance between neighbor stations, and the total length of each line. This information allows for a complete reconstruction of the metro lines and corresponding operating stations at any time.The first step toward analyzing the evolution of metro systems is to collect data on their structures. We gathered and analyzed data related to the metro networks from 1969 to 2019. The primary topological data were obtained from Wikipedia (https://map.baidu.com/) to double-check the structure. It is worth mentioning that the metro maps in 2019 cannot process all the stations for each year of their existence. For example, a station that closed before 2019 and remained closed  will be handled separately. Eventually, the topological structure of metro networks is modeled in L-space as an undirected and weighted graph For a given year 9 found that the topological structures of large metro networks in the world gradually developed into the shape with a denser core  and branches reaching out to the suburbs or areas further from the core.Metro network in the early stage always begins with linear, circular, or star form, then becomes increasingly large and complex. Although it is challenging to classify the form of the metro network into a single topology category, it is evident that its form has changed from a simple to a more complicated form. In 2012, Roth et al.k-core decomposition25, where k-core subgraph considered the metro network's core, which seems to be located in the center of the spatial topology in Fig. 9.Metro networks investigated here display a core-branches structure through spatial representations plotted by the relative location of stations in Fig. mentclass2pt{minim30. However, this definition of barycenter has several restrictions on the application of the metro network: First, the metro network is always considered a planar spatial graph formed with an anisotropic shape for the most extensive networks . While several metro networks may not have this characteristic due to geographical constraints such as the sea in the south of Shenzhen, this may cause the movement of barycenter with the evolution of the network and generate deviations for measuring the spatial organization of the network9. Besides the developing metro networks , the barycenter is not surrounded by the core nodes, as shown in Fig. The geographical barycenter is always considered as the center defined as the average location of all the stations. The geographical barycenter (marked in black nodes in Fig. The network efficiency is an indicator of exchange information based on the shortest path calculated as path distances or the opportunity costs at a station to other stations in a metro network. The average efficiency of node 32.The global efficiency 38 rescaled the value of global efficiency in The value of For all 36.For metro networks, the physical distances are well-defined by the underlying geometry and computed as corresponding great-circle distance about spatial coordinates of stations


Kazakhstan strives to obtain Universal Health Coverage (UHC) by using health technology assessment (HTA) for determining their health benefit package. This paper reports on employing evidence-informed deliberative processes (EDPs), a practical and stepwise approach to enhance legitimate health benefit package design in Kazakhstan. Methods: The Ministry of Health of Kazakhstan approved the operationalization and application of EDPs during March 2019 and December 2020. We used a combination of desk research, conducting HTA, online surveys as well as a face-to-face workshop in Nur-Sultan, Kazakhstan, and two online workshops to prioritize 25 selected health technologies. During the latter, we tested two alternative approaches to prioritization: quantitative multicriteria decision analysis (MCDA) and the use of decision rules. Results: For each of the HTA reports, evidence summaries were developed according to the decision criteria . When appraising the evidence, the advisory committee preferred using quantitative MCDA, and only when this would result in any controversy could decision rules be applied. Conclusions: Despite several challenges, including a partial disruption because of the COVID-19 pandemic, implementation of the process will likely play a key role in determining an evidence-informed and transparent health benefit package. The Republic of Kazakhstan (Kazakhstan) is a land-locked country in central Asia and has transitioned from lower- to upper-middle-income status in 2006. The country is subdivided into 17 administrative divisions. Life expectancy over the past ten years increased to 76.4 years for females, and 67.5 for males (as of 2017). This is lower than the average in OECD countries (over 80 years) [Like many other countries in the world, Kazakhstan considers health technology assessment (HTA) as an important policy instrument on the path towards achieving universal health coverage (UHC) . HTA is In May 2019, the Minister of Health signed an act that provided the possibility of conducting HTA to inform health benefit packages. From January 2020, the aim was for the population to benefit from the State Guaranteed Benefit Package (SGBP) funded from the government budget. The services under the SGBP were mainly emergency care and outpatient care. Additional services, including inpatient care, would be provided through the Mandatory Social Health Insurance (MSHI) system which was introduced in 2017. Only employers, individual entrepreneurs, private lawyers, court executors and other persons who receive income based on civil contracts were eligible for the MSHI package. Kazakhstan’s MSHI system was aimed at increasing the competitiveness of the healthcare sector and improving the quality of health services.This paper reports on the process used to determine both the SGBP and the MSHI benefits packages in the period March 2019–December 2020, employing evidence-informed deliberative processes (EDPs).The EDP framework see  is a praThe practical guidance on EDPs that was developed by Radboudumc, and of which WO is the lead author, provides recommendations on how these elements can be implemented in each step of the decision-making process of benefits package design. The guide takes the current decision-making context in a country as the starting point, offering practical support depending on the country’s level of HTA development.We subsequently report on the EDP implementation and each of the Steps, A through D, excluding communication and appeal (Step E) and monitoring and evaluation (Step F). For the latter two steps, we provided future guidance to the MoH. The paper concludes with a discussion of these results from a broader perspective.desk research to conduct a situational analysis . This included existing and future relevant regulations, data for monitoring the health-related 2020 Strategic Development Goals in Kazakhstan, WHO Global Health Data and OECD data;questionnaire to two senior HTA representatives of the RCHD, including the acting head of the HTA department, to gain a more in-depth understanding of certain aspects related to the current conduct of HTA in Kazakhstan;n = 6), Social Health Insurance Fund (n = 1), and RCHD (n = 6) ;online survey on EDPs among 13 representatives of the Ministry of Health (online surveys among 15 members of the advisory committee that was formed for this project (step A) to elicit their views regarding coverage decision criteria (step B), selecting health technologies for assessment (step C) and for determining the weights of decision criteria to be used in the quantitative MCDA (step D3);a face-to-face workshop in Nursultan, Kazakhstan on 3 September 2019 with the advisory committee to define decision criteria (step B);work visit to Kazakhstan in the week of 17 February 2020 to engage with the RCHD who are supporting the development of the HTA reports (step D1-D2);conducting 25 HTA reports according to international standards, and weekly video-conference sessions with the assessment team to discuss progress and issues during February–August 2020 (step D1-D2);two online workshops (23 September and 6 October 2020) with the advisory committee to prioritize 25 selected health technologies, using quantitative multicriteria decision analysis or decision rules (step D3).The operationalization and application of EDPs for health benefit package design for Kazakhstan was funded by the Ministry of Healthcare of the Republic of Kazakhstan and executed by an international project team  of Radboud university medical center, located in the Netherlands. The project was initiated during a two-day meeting between the project team and representatives of the Ministry of Health, including the acting vice-minister, on 16–18 April 2019 in Nursultan, Kazakhstan. For the operationalization of each step of the EDPs, we used a combination of:We will elaborate on the methods when reporting on the EDP implementation step by step below.A survey was conducted among 13 representatives of the MoH, Social Health Insurance Fund, and the RCHD. The survey was developed based on the EDP framework, consisting of elements that reflect each part of the framework and the contextual factors for HTA development and use. Respondents were asked about the presence of each element , and whether this needed guidance (yes/no). Furthermore, we asked if any element was missing and for potential examples of best practices relating to each step of the framework. The survey has been used previously to collect information on the use of EDPs by members of the International Network of Agencies for Health Technology Assessment (INAHTA)  and expeThe survey showed that an advisory committee, the Joint Commission on Healthcare Quality (JHCQ), is present in Kazakhstan. This commission is a permanent advisory body of the MoH and is established to develop recommendations for improving clinical protocols, standards for medical education, drug supply, and a system for controlling the quality and accessibility of health services. However, 76% of the respondents felt that guidance was needed for installing an advisory committee. It was mentioned that there are committees with overlapping functions and responsibilities and that the members of the JHCQ and their subcommittees need training in HTA. It was felt that guidance was also needed (92%) in the roles and responsibilities of such a committee, the stakeholders involved in the process, as well as the formal approach followed by the committee.Several options for installing a committee were proposed to the MoH based on international examples as presented in the EDP guide. The MoH decided on the composition of an advisory committee consisting of 15 permanent members reflecting broad stakeholder groups, with the responsibility of developing recommendations on HTA and benefit package design. The advisory committee comprised the vice-minister of the MoH, who acted as chair, two senior representatives of relevant MoH departments, two senior representatives of the Social Health Insurance Fund, two HTA experts and a scientist with ethics expertise of the RCHD, two senior representatives of public associations, two patient representatives, a clinician (oncologist), a nurse, and an expert in disease management.A survey to develop a consensus on the importance and definition of criteria for the prioritization of health technologies was developed based on a review of Kazakhstan strategic documents and international best practices. It was distributed electronically to the 15 advisory committee members of which 11 responded. The non-respondents included the two representatives of the MoH, the two representatives of the public associations and one patient representative. The survey was sent prior to the face-to-face workshop (held on 3 September 2019), which was attended by 10 committee members representing all stakeholder groups, and one member delegated the task to a colleague (oncologist). During the workshop it became clear that committee members had a very high level of agreement regarding the decision criteria for health benefit package design. The following criteria were selected by the advisory committee and approved by the MoH: social priorities, financial burden for households , severity of disease, effectiveness, level of evidence of effectiveness, safety, cost-effectiveness, level of evidence of cost-effectiveness, and budget impact. The description of the criteria was based on international standards, including the HTA glossary .One of the tasks of the advisory committee was to compile a list of 25 health technologies that will be subject to full HTA. During the workshop held in Nursultan on 3 September 2019 the advisory committee agreed to use the burden of disease and budget impact criteria for selecting technologies. A local study team member worked with the MoH and subordinate organizations to undertake the following steps in selecting 25 health technologies.Include all technologies from 2016 to 2018  that were recommended by the JHCQ as well as technologies that were prioritized by RCHD but that have not yet have been assessed by the RCHD for 2019 (22 technologies).n = 43; 67%).The entire list of 111 (only new) technologies was classified by the disease type according to the top diseases across burden of disease (in terms of disability-adjusted life years) and mortality in Kazakhstan: ischemic heart disease, stroke, neonatal disorders, respiratory diseases, and cancer. We focused on the technologies that were targeting these diseases, which led to 64 eligible candidates for selection. It appeared that the majority of the 64 health technologies are related to the field of oncology  budget impact of 54 out of the 83 health technologies were estimated .new health technologies including available information on costs was sent to the advisory committee members for selection. They were asked to make an initial selection on the basis of the highest  budget impact. Members were then asked to choose technologies that target different disease groups. In addition, they were instructed that when considering an existing health technology (n = 19), they were advised to select those with potential low or no evidence regarding effectiveness, or those that may potentially be excluded from the benefit package, and / or have a potential large budget impact. All advisory committee members responded and the level of agreement between them ranged from 0% to 79%. We felt it was acceptable to select those health technologies with a level of agreement of at least 50%. This was the case for 22 health technologies. We complemented the list with two health technologies that had 43% level of agreement and one existing health technology. See The list of 83 TuberculosisDisease caused by human immunodeficiency virus (HIV)Chronic viral hepatitis and cirrhosisMalignant neoplasmsDiabetesMental and behavioral disordersChildren’s cerebral palsyAcute myocardial infarction (first six months)RheumatismSystemic lesions of connective tissueDegenerative diseases of the nervous systemDemyelinating diseases of the central nervous systemOrphan diseasesIn determining which health technologies could be considered for reimbursement via the SGBP or MSHI, we proposed that if the health technology targeted a social priority condition listed in the Order of the MoH (dated 17 October 2019 No. ҚP ДCM-13) then it would be considered for inclusion in the SGBP. If not, the health technology could be considered for inclusion in the MSHI. This was agreed by the MoH. The social priority diseases are:Before undertaking the assessments, we reviewed the quality of seven existing (‘old’) HTA reports, conducted by the RCHD, covering a sample of priority disease areas and types of technologies  using the INAHTA checklist on HTA reporting ,9. The qThe objective was to develop an HTA product defined by INAHTA as a rapid review , as thisA dedicated assessment team, including three local HTA experts, collected available evidence on all decision criteria, excluding financial risk protection, for all 25 technologies. Literature reviews were conducted to assess the safety, effectiveness and cost-effectiveness. A local team worked with the MoH and subordinate organizations to obtain information on intervention and comparator costs to determine incremental costs. We estimated the incremental cost as information on the cost-effectiveness of health technologies was frequently unavailable. The estimations of the budget impact were presented in actual figures, while for the incremental costs we used a classification scale .An evidence summary sheet was developed which allowed for the classification of the decision criteria according to the measurement levels  as well as a short description or an extract from the HTA report justifying the classification.We were not able to retrieve any information for financial risk protection hence this was excluded from the (quantitative) MCDA. Rather it was only referred to in the deliberation where the advisory committee had to provide a  qualitative indication on the question: “How much will a patient need to pay out of pocket when this intervention will not be (completely) covered?”The MoH agreed to conduct appraisal of the 25 health technologies to be undertaken using two methods: 1) quantitative MCDA and 2) decision rules. This was requested by the advisory committee to demonstrate the differences between the two methods, of which the committee would adopt a single method into their processes going forward.quantitative MCDA method and those eligible for reimbursement under the MSHI (12 technologies) were evaluated using decision rules. Health technologies were eligible for reimbursement under the SGBP if they were classified as a social priority and listed in the Order of the MoH , if not, they were then eligible under the MSHI.It was agreed that health technologies eligible for reimbursement under the SGBP (13 technologies) were evaluated using the We defined each criterion and used measurement levels for each criterion using international agreed standards and key references ,17,18,19Quantitative MCDA uses a value measurement model to interpret the performance matrix, followed by deliberation. To determine the value of a technology for use in the quantitative MCDA, an online survey was distributed to the advisory committee members requesting them to score the following criteria: effectiveness , safety, severity of disease. We instructed them to allocate a total of 100 points between these four criteria by requesting the following: “Please rate the importance of the following decision criteria by distributing 100 points between them to reflect their importance.” We calculated the criteria weights by taking the average of the points provided by the committee members.Cost-related criteria such as cost-effectiveness and budget impact are also important criteria, however, these should not be part of the quantitative MCDA as it is unrealistic to assume that committee members can adequately attach weights to them as they are likely not aware of health budget constraints and alternative ways of using resources . TherefoThe survey yielded a high response rate (12 out of 15 advisory committee members) for which we were able to assign weight to criteria given  for the To undertake quantitative MCDA, we performed the following steps.Allocate a performance score based on its classification, to the criteria: severity of disease, effectiveness, level of evidence of effectiveness and safety.Multiply scores with criteria weights.Calculate the total MCDA score by adding up the sums for each health technologyRank health technologies based on the total score.Include additional economic information  for use in the deliberation step.Undertake a deliberation with the advisory committee to arrive at a consensus on ranking.Two online meetings were organized with the advisory committee to discuss the ranking of selected health technologies. The committee indicated their understanding of the quantitative MCDA and did not provide any adjustments to the rankings.To undertake the analysis, we performed the following steps.Knock out of health technologies.The advisory committee members agreed during its meeting in September 2019 to use effectiveness as a first knock-out criterion before considering other criteria-in effect, this is the use of a ‘decision rule’. This also applies to safety as it would be unethical to provide unsafe treatments to patients. However, in our analysis, we did not have any health technologies under evaluation that were ‘not clinically effective’ or ‘not-safe’. Therefore, there were no health technologies that were excluded from this step.2.Prioritization according to potential cost-effectiveness and severity of disease.Generally, cost-effectiveness thresholds are set for the local context. Cost-effectiveness analysis then needs to take place to estimate quality-adjusted life years (QALYs) or disability-adjusted life years (DALYs) to determine if it is highly, moderately, or not cost-effective. In our project, the evaluation of cost-effectiveness was based on literature reviews. Given that the majority  of health technologies did not have any local estimates on cost-effectiveness, we were only able to assume the potential cost-effectiveness based on international estimates. The level of evidence also plays an important factor when interpreting the cost-effectiveness data.In this step, health technologies were ranked from highest performing according to the combined ranking of their performance in potential cost-effectiveness and severity of disease. The highest-ranked health technologies were those which were rated as highly cost-effective with moderately severe disease (as no interventions had a high severity of disease). The lowest-ranked interventions were those that were not cost-effective (or no-information) and a ‘not-severe’ disease see .3.Include additional information on other relevant decision criteria.Once the health technologies were ranked according to priority categories (combination of cost-effectiveness and severity of disease), we included further information on economic  and clinical information . The classification details are similarly given in 4.Undertake a deliberation to arrive at a consensus on ranking.As described earlier, two meetings were organized with the advisory committee. The committee expressed that they understood the ranking using decision rules and did not provide any adjustments to the rankings. However, the advisory committee preferred using quantitative MCDA, and only when this would result in any controversy could decision rules be applied.Benefit package design is without a doubt intrinsically complex, and there are increasing demands to ensure that health benefit package design is fair and legitimate. By using EDPs, an explicit process is developed ensuring that the format and content are consistent with expectations of the stakeholders involved. Having an explicit process enables more effective collaboration and sharing of information to overcome challenges of variance in the extent and scope of analysis and differences in reporting the results.Standards for presenting information largely remove authors’ judgements on what, and how, evidence should be reported. The INAHTA checklist does not help in all areas particularly concerning the assessment of content quality, however, it does assist in the examination of what content is presented. In assessing the quality of assessments and reporting, we found an improvement when making use of the INAHTA checklist. HTAs in Kazakhstan have been undertaken on a wide range of topics by a variety of individuals. We expect a growing body of evidence to be generated from HTA for decision making, however, there could be a concern in the variation in the quality of HTA reports and approaches taken in their preparation. By instituting a process where researchers make use of the INAHTA checklist, we expect higher quality and more transparent HTA reports to be produced. Overall, this should contribute to better decisions being made by policymakers.advisory committees (Step A), a survey indicated that guidance was needed for installing members of the advisory committee and we were successfully able to establish a committee using the principles outlined in the EDPs. A survey also indicated that the members of the advisory committee needed training in HTA. We believe that not all members may have the capacity to fully grasp the presented evidence as there were limited opportunities to train members although we did provide instructions and explanations in all steps, such as criteria explanation sheets, evidence briefs and had explicit rounds for clarification questions during appraisal. The success of this is uncertain as we were unable to measure the impact of these.There were several practical challenges to the implementation of each of the steps of the EDP framework. Firstly, on the Another area of concern are the changes in the leadership within the MoH. At the time of formation, the vice-minister of the MoH was appointed chair of the committee however, there have since been changes in the leadership which makes it challenging for training and results in the loss of institutional memory.decision criteria (Step B), burden of disease was initially proposed as a generic decision criterion. However, after discussion with the broader study team, we advised not to use this criterion since using burden of disease would systematically prioritize more common diseases over more rare diseases. Furthermore, burden of disease is being used as a selection criterion for prioritizing HTA. Instead, we recommended the use of severity of disease as a decision criterion. Using severity of disease would inform advisory committee members on the related severity of diseases that health technologies address at the individual level, without systematically giving common diseases more priority over rare diseases. A survey among workshop participants showed that committee members had a very high level of agreement concerning both the use of proposed criteria and definitions.Second, on the assessment of interventions (Step D2), the team faced several challenges in compiling evidence. Due to both capacity and time constraints, only a review of high-level evidence and restricted literature searches were conducted. While this is in line with INAHTA rapid review characteristics, there were challenges in the contextualization of evidence and this was not always feasible given the lack of data in the country or region as a whole.Third, regarding the There were also challenges in writing up ‘standardized’ assessment reports as there were numerous researchers, each with different levels of capacity (both in time and skills). While the template indicated the type of information needed, it did not provide information on the level or breadth of information to be included. At times there was too much, or to little information included. Researchers were able to successfully compile completed reports but it was noted that more local evidence would be an improvement to the process. However, we feel that the methods used to assess the evidence were acceptable and would still contribute to improved decision making.In addition, there were challenges in assessing information regarding some decision criteria. The assessment of financial burden for households and financial risk protection, both measures of equity, was not possible, so they were excluded. This highlights that certain criterion recognized as being important, such as ‘equity’, have then been excluded due to their complexity.appraisal of interventions (Step D3), several aspects may have compromised the decision-making processes by the advisory committee. It is unclear if all committee members had sufficient background knowledge to fully understand and interpret the presented evidence for all criteria. We did provide descriptions of the evidence, but it is not clear whether this was sufficient, we know from experience that interpretation of evidence can be cognitively challenging.Fourth, in the The health technologies were rank ordered by the assessment team using MCDA methods, however, there were few comments raised by committee members expressing their own judgement on the ordering. The advisory committee preferred using quantitative MCDA as they felt this would be a better fit for determining the health benefit package for Kazakhstan, i.e., it could be used for all health technologies, whether it is a social priority or not. Specifically, they mentioned that quantitative MCDA is more acceptable as it clearly shows a ranking based on the available evidence. Reducing the cognitive load of processing several criteria simultaneously has often been mentioned as an advantage of quantitative MCDA . HoweverFifth, the overall decision-making process was heavily disrupted by the COVID-19 pandemic. Committee meetings that helped establish the process were held on-site until February 2020, but this transitioned fully online afterwards. This has likely compromised stakeholder participation and the quality of the decision-making process.The implementation of the stepwise approach faced several challenges including a partial disruption because of the COVID-19 pandemic, but implementation of the process will likely play a key role in determining an evidence-informed and transparent health benefit package. An important contribution of this paper is explaining how policymakers can improve decision making with limited capacity and resources. Limited capacity can be overcome by training a small team to undertake rapid assessments of existing evidence thereby increasing the number of technologies that are reviewed. The limited resources not only relate to human and financial resources but also the limited amount of country specific data. While there may be limited studies conducted within Kazakhstan, there is a plethora of data that exists internationally which can be used for decision making.


Mental fatigue is a common phenomenon in our daily lives. Long-term fatigue can lead to a decline in a person’s operational functions and seriously affect work efficiency. In this paper, a method that recognizes the degree of mental fatigue based on relative band power and fuzzy entropy of Electroencephalogram (EEG) is proposed. The N-back experiment was used to induce mental fatigue in subjects, and the corresponding EEG signals were recorded during the experiment. A preprocessing method based on complementary ensemble empirical modal decomposition (CEEMD) and independent component analysis (ICA) was designed to remove noise from the raw EEG signal. The relative band power feature, which has been used extensively in fatigue recognition studies, was extracted from the EEG signals. Meanwhile, fuzzy entropy, a feature commonly used in attention recognition, was also extracted for fatigue recognition, based on previous findings that an increase in fatigue is accompanied by a decrease in attention. The two features were fed into an extreme gradient boosting (XGBoost) classifier to distinguish three different degrees of fatigue, which resulted in an average accuracy of 92.39% based on data from eight subjects. The promising results indicate the effectiveness of the proposed method in mental fatigue degree identification. The definition of mental fatigue, first proposed by Grandjean in 1979, considers fatigue to be a physical state, a transitional state between wakefulness and sleep . With thElectromyography (EMG) and electrocardiography (ECG) are two commonly used measures for fatigue detection. Wu et al. studied the effect of musical rhythm on the subjective and objective fatigue of runners at different exercise intensities using EMG . ButkeviPrevious research has reported some promising results in the field of fatigue recognition based on EEG signals. Zhang et al. calculated the power of the EEG signals in four frequency bands as features to distinguish between fatigue and wakefulness states. An accuracy of 81.07% was achieved in a multi-task learning framework . Trejo ePreprocessing is an essential part of EEG signal processing that plays an important role in removing noise and improving the signal-to-noise ratio (SNR) of the signal. It is generally believed that the noise in EEG signals exists at frequencies above 30 Hz, so using a low-pass filter to remove signals higher than 30 Hz is the traditional method of noise removal, but the processing of signals by this method is relatively rough. In order to further improve the signal quality and lay a good foundation for following processing, a signal denoising method based on complementary ensemble empirical modal decomposition (CEEMD) and independent component analysis (ICA) is designed in this paper, with CEEMD responsible for decomposing the signal into several frequency bands and ICA responsible for separating out the noise components in the expectation of achieving more detailed denoising. CEEMD decomposes the signal into several intrinsic mode functions (IMFs) and a residual signal without modal aliasing and auxiliary noise residuals ,10. ICA The feature extraction of EEG signals is also a key factor affecting the effectiveness of the model. The fatigue degree can eventually be accurately recognized only when the selected features contain valid information that helps to classify. EEG features associated with band power  have been widely used in the study of fatigue degree recognition. Hendrawan et al. extracted power percentage features for each EEG segment and used the LDA algorithm to recognize fatigue with an accuracy of 92.82% . Zeng etA number of studies have shown a correlation between fatigue and attention. Van der Linden et al. suggested that mental fatigue can also be manifested as a decrease in attention . AzarnooIn this work, eight subjects were invited to participate in an N-back experiment to induce fatigue, while the corresponding EEG signals were recorded through the process. A denoising method based on CEEMD and ICA was designed to remove noise from the raw EEG signals. Relative band power and fuzzy entropy were extracted as features of the EEG signal. Based on these features, the extreme gradient boosting (XGBoost) algorithm was employed to classify three different degrees of mental fatigue.The paper is organized as follows. The N-back experiment, which is one of the most common experimental paradigms in brain load elicitation experiments, was first proposed by Kirchner in 1958 to study the short-term memory capacity of people of different ages for rapidly changing information [At the beginning of the experiment, a cross symbol appears in the center of the screen to remind subjects to focus. During the experiment, a capital letter will appear randomly on the screen, and the subject needs to determine whether the current letter is consistent with the letter presented N items ago, and if it is consistent, press the “1” key, otherwise press the “0” key. The 1-back experiment requires the subject to determine whether the current letter is the same as the previous letter, while the 2-back experiment requires a comparison with the letter two items ago. The process of the 2-back experiment is shown in Studies have demonstrated that 30 min of continuous 1-back or 2-back tasks can effectively induce fatigue . In ordeThe experiment was divided into three stages: Stage 1, where the EEG signals were recorded in the subject’s calm state for 5 min; Stage 2: where the subject completed a 30 min 1-back task and recorded the EEG signals in the last 5 min of the task; Stage 3, where the subject completed a 30 min 2-back task and recorded the EEG signals in the last 5 min. The EEG signals acquired in the experiment contained 19 channels, the electrodes were arranged according to the international 10–20 system, the right and left earlobes were selected as reference electrodes, and the sampling rate was 512 Hz.After each stage of the experiment, subjects were guided to fill out a subjective fatigue scale, as shown in Preprocessing has two main tasks: segmentation of the acquired raw EEG signal for further processing, and removal of noise from the raw EEG.The EEG signals were first segmented as shown in Each segment of the EEG signal then needed to be denoised. In this paper, a method based on CEEMD and ICA was designed for EEG denoising. It is first desired to decompose the EEG signal into several frequency bands and then analyze which of them contains more noise. The noise-containing band should be denoised and then the signal of each frequency band is reconstructed to obtain the denoised EEG signal. According to the frequency range, EEG is divided into five frequency bands: delta, theta, alpha, beta, and gamma, but this way of division is rather rough and is not suitable for a more detailed analysis of where the noise lies in the frequency range. Wavelet analysis can decompose the signals into multiple frequency bands in a more detailed way, but its effect is affected by the wavelet function used, so it needs to analyze which wavelet function to use to obtain the best results. CEEMD, on the other hand, performs an adaptive decomposition of the signal into a number of IMFs, each representing a frequency component, without any other analysis. Therefore, this paper used CEEMD to decompose the EEG signal. The value of sample entropy is positively correlated with the noise power . That isThe raw EEG signal was decomposed into several IMFs using CEEMD, and the sample entropy of each IMF was calculated. In this work, the relative band power and the fuzzy entropy of EEG signals were extracted as features for fatigue recognition. Relative band power has been widely used in fatigue recognition studies. We assumed here that this feature would play an important role in recognizing fatigue degrees. Considering that an increase in fatigue level is often accompanied by a decrease in attention level, this paper introduced fuzzy entropy, which is a feature commonly used for attention level recognition, to help improve the accuracy of fatigue level recognition. The details of these features are described as follows. 1 and f2 represent the minimum and maximum frequencies of the EEG signal, respectively, and Relative band power (RBP) is the ratio of the power of the EEG signal in a particular frequency band to the total power. The PSD of the EEG signal was first estimated using the Welch method, and then the power percentage of the different frequency bands was calculated according to (1).cy bands . In partFuzzy entropy is used to measure the complexity and irregularity of a time series. It is calculated as follows . Let theThe fuzzy entropy function u.The raw EEG signal was divided into 4 s segments after pre-processing, and each segment contained 19 channels. The above five features were calculated for each channel, so a total of 95 feature values were obtained for each segment.i, the final output H(xi) is expressed as (8).k(xi) denotes the output of sample xi on the i when building the k-th base estimator is shown in (9).XGBoost is currently one of the most advanced algorithms in the field of ensemble learning. It follows the general modelling process of the Boosting algorithm, i.e., building the base estimators in turn, computing a loss function based on the output of the previous base estimators, and adaptively influencing the construction of the next base estimators. The final output of the ensemble algorithm is influenced by all of the base estimators. Specifically, suppose the XGBoost algorithm has a total of Unlike other Boosting algorithms, XGBoost adaptively influences the creation of weak evaluators by fitting pseudo-residuals. Specifically, before each new base estimator is built, the first-order derivative When building a new base estimator, the model needs to fit the pseudo-residuals When performing the classification task, XGBoost feeds the ensemble output into the Softmax function and obtains the classification probability. The final classification decision is made by choosing the class with the highest probability. In this work, we employed XGBoost to identify fatigue degrees due to its fast modeling speed and high resistance to over-fitting. In this paper, the relative band power of the In this study, the relevant EEG signals were acquired and processed according to the method described in First, subjects were invited to perform the N-back task to induce mental fatigue while EEG signals were collected (as described in Then, the signal is segmented and denoised using the method described in Then, the relative band power of the four frequency bands of EEG and the fuzzy entropy are calculated. As described in Finally, the 357 × 95 feature matrix of each subject was used to train the subject-specific XGBoost classifier and the model was tested using the 87 × 95 feature matrix.Accuracy is a common evaluation metric for classification problems and is defined as the percentage of correctly classified samples in the test set. Once the prediction results are obtained, one of the three degrees of fatigue is considered as a positive case and the others as negative cases. Then, three confusion matrices are drawn based on the classification results. Precision and recall are calculated separately on each confusion matrix, denoted as For each subject, the XGBoost classifier was trained on the training data following a five-fold cross-validation procedure, and the hyperparameters of the model were adjusted with its average classification accuracy as the optimization target. After training, the model was tested and the classification accuracy, macro-precision, macro-recall, and macro-f1-score of the classifier on the test data were calculated. Finally, the model classified the fatigue degree of eight subjects with an average accuracy of 92.39%, macro-precision of 92.67%, macro-recall of 92.39% and macro-f1-score of 92.31%. The specific evaluation metrics are shown in In this paper, an EEG preprocessing method based on CEEMD and ICA was proposed. To further illustrate the effectiveness of the method, three other methods were used to preprocess the data in this paper separately. Both features, relative band power and sample entropy, were also calculated, and the XGBoost classifier was also used to compare their classification performance. The three preprocessing methods were low-pass filtering, wavelet threshold denoising, and CEEMD, where low-pass filtering used a low-pass filter with a passband of less than 30 Hz, wavelet threshold denoising used a 4-layer decomposition of the db4 wavelet with a soft threshold function and a fixed threshold, and CEEMD decomposed the signal into several IMFs and reconstructed it after discarding the first six IMFs. This paper proposed a fatigue recognition method based on EEG signals that includes four parts: data acquisition, preprocessing, feature extraction and classification. Experimental results demonstrated that our method is able to recognize three different degrees of mental fatigue with high accuracy. However, there were certain limitations of the current study that need to be improved in further studies.First, the method may suffer from feature redundancy. Relative band power of four frequency bands and fuzzy entropy were selected as EEG features. Therefore, five feature values needed to be calculated for each segment of EEG. Since there were 19 channels per segment of EEG, there were a total of 95 feature values per sample. This led to a computationally intensive algorithm, which is not conducive to practical applications, especially for real-time detection of fatigue degree. The use of feature selection algorithms to select the features that contribute most to the classification helps to reduce the redundancy of features, and the design of channel optimization algorithms to find the most important channels can also further reduce the computational effort. Second, how the features of the EEG signal affect the classification results remains to be further studied. In this paper, a variety of features were extracted, and the classification results showed that these features together were effective in distinguishing different fatigue levels. However, it is unclear whether there is a significant difference between the features at three fatigue levels. For example, the same method obtained an accuracy of 100.00% on subject S01, but only 83.91% on S03. Is this because one or more features did not differ significantly between the three levels of fatigue for S03? More in-depth analysis is needed to understand the effect of each feature on the classification results.Admittedly, the method proposed in this paper is still not representative of the state-of-the-art. Designing a more rational experimental paradigm to more effectively induce mental fatigue in subjects, optimizing the preprocessing process to further improve signal quality, screening and using features that are valid across all subjects, and using a classification model with greater learning ability are all areas for improvement in this study. The resolution of these issues will contribute further to the practical application of fatigue recognition technology.Analyzing mental fatigue EEG signals can identify different levels of fatigue, thereby helping to reduce the negative effects of fatigue. In this paper, we proposed a method to recognize three degrees of fatigue based on EEG signal analysis. Subjects were invited to participate in the N-back experiment, and the subjective scale demonstrated that the experiment was successful in inducing different degrees of fatigue. Raw EEG signals were preprocessed by CEEMD and ICA to effectively remove noise and retain as much useful signal as possible. The relative band power commonly used in fatigue recognition studies and the fuzzy entropy commonly used in attention recognition studies were selected as the features of EEG signals, and XGBoost classifier was used to classify the different degrees of mental fatigue. Experimental results show that the proposed method can recognize three different degrees of fatigue with high accuracy.


Automation of sub-tasks during robotic surgery is challenging due to the high variability of the surgical scenes intra- and inter-patients. For example, the pick and place task can be executed different times during the same operation and for distinct purposes. Hence, designing automation solutions that can generalise a skill over different contexts becomes hard. All the experiments are conducted using the Pneumatic Attachable Flexible (PAF) rail, a novel surgical tool designed for robotic-assisted intraoperative organ manipulation.rlman. In rlman, contextual RL agents are trained to solve different aspects of the pick and place task using the PAF rail system. rlman is implemented to support both low- and high-dimensional state information to solve surgical sub-tasks in a simulation environment.We build upon previous open-source surgical Reinforcement Learning (RL) training environment to develop a new RL framework for manipulation skills, rlman to train state of the art RL agents to solve four different surgical sub-tasks involving manipulation skills using the PAF rail. We compare the results with state-of-the-art benchmarks found in the literature. We evaluate the ability of the agent to be able to generalise over different aspects of the targeted surgical environment.We use rlman framework can support the training of different RL algorithms for solving surgical sub-task, analysing the importance of context information for generalisation capabilities. We are aiming to deploy the trained policy on the real da Vinci using the dVRK and show that the generalisation of the trained policy can be transferred to the real world.We have shown that the The online version contains supplementary material available at 10.1007/s11548-022-02630-2. The rlman framework can support diverse surgical operation scenarios. To demonstrate its capabilities, we present a case study focused on the use of the PAF rail system for intraoperative organ repositioning [https://github.com/Cladett/rlman. The paper’s main contributions can be summarised as follows:rlman, a training framework for manipulation skills.We build upon the previous dVRL work  to develWe conduct extensive experiments on RL algorithms comparing four different manipulation skills with state-of-the-art benchmarks .We implement contextual RL agents which are able to generalise over context meta data.We evaluate the ability of the trained agent to generalise over the pick and place task environment conditions.The adoption of Robotic Minimally Invasive Surgery (RMIS) has been growing worldwide thanks to the introduction of robotic platforms like the da Vinci Surgical System . Besides its extensive clinical use, the da Vinci System has been widely investigated by researchers thanks to the open-source da Vinci Research Kit (dVRK)  developeitioning . Code isDuring RAMIS, surgeons frequently reposition tissues and organs with the help of the tool shafts. The PAF rail, shown in Fig. Gym and the dVRK. Other works focused on environments with better dynamics interactions between the da Vinci tools and soft tissues [Reinforcement Learning (RL) is a machine learning training method  that has tissues , 13. The tissues , 14 wherS represents the state space for which a given state A the action space for which P the dynamics probabilities R the reward space for H represents the horizon [a from state s with goal g and the parameters w. In Q-learning problems, the solution relies on the minimisation of the Bellman [Reinforcement Learning is a paradigm used to solve problems that can be formalised as an agent interacting with an environment. Formally, this type of problems is known as Markov Decision Process (MDP). An MDP is defined by the following tuple:  horizon . In goal Bellman  error:1\Manipulation tasks can be defined as a sequence of different steps, executed one after the other applying different skills. For example, the pick and place task can be divided into different surgemes each of which can be associated with different skills. Therefore, autonomous agents need to master multiple skills in a sample-efficient manner to adapt effectively to real world scenarios. Multi-Task Reinforcement Learning (MTRL) represents a promising approach to train effective real-world agents . As prevC is the context space, S is the state space and A is the action space. M is a function that maps a context A CMDP is defined by a tuple S, context can be applied. However, In a multi-task setting, where a family of MDPs is defined as each MDP with a shared state space rlman trains different parts of the intraoperative organ manipulation task , where g represents the designed goal for the task, the reward will be 0 when the task is successful and k is the normalisation factor.Reward shaping is a crucial step when designing a RL problem . In rlmaWe evaluate every skills environment with model-free RL algorithms, including the on-policy method Proximal Policy Optimisation (PPO) , the offrlman framework. These skills were selected based on the need of dexterity and high precision in the surgical context of intraoperative organ manipulation applied to the PAF rail test case. All the experiments were run using as robotic tool the Large Needle Driver (LND). For the experiments, the manipulation workspace was set to cm and the success function set to Reach Rail this environment was mainly used as validation for testing the feasibility of using low-dimensional state information (RR - ReachRail) and high-dimensional ones (RT - ReachTarget). In both cases, the goal is to reach a circular target whose position is randomised, without considering jaw angles. For the RR case, the tooltip position has to reach the target over the rail within a tolerance and to be perpendicular to the table’s surface. For RT, the relative position between the endoscope and the robotic arm is selected based on [based on , to maxiPick Rail in this environment (PR) we introduce the jaw angle. The robotic tool has to approach the rail and actually open and close the jaw to ensure a correct grasp. The PAF rail is characterised by eight different grasping sites, which are randomised during training.Retrieve Rail this skill aims to train the capability of properly locating the rail over the target organ. Fundamental task to assess the clinical accuracy, the base of the rail needs to be parallel to the kidney surface. The RetrieveRailkidney (RRk) is characterised by a kidney phantom representing the target. Further details on the phantom choice are provided in the supplementary material.The goal of this task is to retrieve the rail, whose starting configuration is fixed, and place it over the kidney target position with the correct orientation to guarantee a successful suction.Pick and place Rail in this environment  the goal is to sequentially pick the rail and drive it towards the target position. Grasping site, rail position and target are all randomised inside the training volume.Due to the extreme challenge of learning manipulation skills that need to be transferable to a surgical environment characterised by small size objects and high-precision movements, we decided to adopt low-dimensional state representation when involving the rail, and test the high-dimensional one as proof of concept using a simplified circular target.We divided the intraoperative organ manipulation task into different part that we associated with a different skill and trained them in the We selected the PPR environment to run further experiments to assess the capability of the agent to generalise over environment variables and unseen portions of the training volumes.rlman library” as: target. We ran six different experiments, indicated by the E#, randomising, for each experiment, different sets of environment variables. In addition, each of these experiments have been run twice in order to test them using the two different volumes rlman library”.As highlighted in Table rlman-skills evaluation”, the anatomy was approximated with a cuboid shape to reduce overhead. As described in the methods section, we enrich the state space using the meta data characterising these obstacles. Specifically, we input the 3D Cartesian position of the centroid of each cube and their size (represented by their volumes). The size is randomised at every episode with the following criteria: given We augmented the PPR environment with three additional obstacles ideally representing anatomical regions we want the RL agent to avoid when executing the pick and place task. A picture of the environment is reported in the supplementary material. We wanted to test if the agent could execute the task successfully regardless of the condition of the context of the experiments. Therefore, obstacles have been added to the environment to simulate anatomical regions. Whenever a collision is detected between the robot, the PAF rail and any of the obstacles, the environment resets and the episode restarts. This environment behaviour will discourage the agent to move close to the obstacles, learning behaviour that can translate safely for future clinical practice. As explained in section “rlman are compared to SurRoL benchmarks. For the comparison, we selected only the SurRoL environments that were presenting the same state-space definition adopted in rlman. Therefore, even if the task had slightly different definition, e.g. using a different object, the characterization of the problem remains the same. We replicate the results on our machine only for tasks that are strictly related to the skills analysed in rlman. Each experiment was run four times, randomly changing the seed for every execution. The supplementary material provides a more comprehensive picture showing also the training graphs.It becomes very challenging for a RL agent to solve multi-step tasks, such as pick and place, without any previous knowledge of the task . Thereforlman library”.The results from the pick and place generalisation experiments are shown in Fig. rlman, a learning framework for manipulation skills compatible with the dVRK. Four learning-based surgical manipulation skills were developed and validated using the PAF rail system as a test case. Extensive experiments were carried out in simulation to compare skills trained in rlman with similar ones trained in the benchmark environment. Despite the tasks present the same formulation, differences encountered in the results might be due to different environment’s formulation and variables. We also reported results, Fig. rlman involve the implementation of different skills allowing the agent to transfer-learn across them and extending the high-dimensional states to multiple tasks. In this scenario, the agent would be able to select which task to execute based on the type of state information that will receive as input.We presented Supplementary file 1 (pdf 7952 KB)Below is the link to the electronic supplementary material.


The reconstruction of missing information in epidemic spreading on contact networks can be essential in the prevention and containment strategies. The identification and warning of infectious but asymptomatic individuals , the well-known patient-zero problem, or the inference of the infectivity values in structured populations are examples of significant epidemic inference problems. As the number of possible epidemic cascades grows exponentially with the number of individuals involved and only an almost negligible subset of them is compatible with the observations , epidemic inference in contact networks poses incredible computational challenges. We present a new generative neural networks framework that learns to generate the most probable infection cascades compatible with observations. The proposed method achieves better  or comparable results with existing methods in all problems considered both in synthetic and real contact networks. Given its generality, clear Bayesian and variational nature, the presented framework paves the way to solve fundamental inference epidemic problems with high precision in small and medium-sized real case scenarios such as the spread of infections in workplaces and hospitals. The time-forward simulations of most epidemic models, even those incorporating detailed demographic and mobility data, can be efficiently performed using Monte-Carlo based sampling techniques or, at least at the meta-population level, exploiting approximation methods, such as stochastic differential equations and moment closure schemes5. These computational methods have been largely applied to large-scale epidemic forecasting9 and containment12. Their effectiveness crucially depends on the capacity to exploit the available information on the past behavior of the epidemic outbreak. At the meta-population level, such information, represented by temporal series of aggregate quantities  can be rather easily included within traditional Bayesian computational frameworks based on Monte Carlo sampling techniques15.Discrete-state stochastic compartmental models have been traditionally used to model infectious diseases17, which could make it possible to access/use a large amount of (possibly inaccurate) individual-based observational data, traditionally available only for case studies in rather small and controlled environments20. The availability of individual-based observational data unveils a crucial limitation of traditional Monte-Carlo based inferential techniques. While the number of possible epidemic realizations generated by a specific epidemic model on a given contact network scales exponentially with the systems size and the duration of the process, those compatible with individual-based observations are just an exponentially small fraction of them21. It follows that inferential methods based on the direct sampling of epidemic realizations on individual-based contact networks rapidly become inefficient as the size of the outbreak increases21. As an example, let us consider several simulated epidemic realizations  in a real graphs of temporal contacts between patients and staff members of a hospital22  during the epidemic process. The plots in Fig. 24. In the Bayesian inference framework, the objective is to approximately compute the posterior probability of the system, assuming the epidemic model as a prior and the individual-based observations as the evidence. BP-based algorithms make it possible to obtain estimates of the local marginals of the posterior distribution and, as shown in26, this approach outperforms competing methods on sparse contact networks on a variety of inference problems. In particular, the integration of such algorithms in the framework of digital contact tracing for COVID-19 was recently shown to provide a better assessment of the individual risk and improve the mitigation impact of non-pharmaceutical interventions strategies27.A step forward in this field is represented by the introduction of efficient algorithms for Bayesian inference based on Belief Propagation (BP)28, audio29, text31 and protein sequences32 generation tasks and, more generally, as a probability density estimator35. Autoregressive neural networks have recently been used to approximate the joint probability distributions of many (discrete) variables in statistical physics models36, and applied in different physical contexts40. In this work, we show how to use a deep autoregressive neural network architecture to efficiently sample from a posterior distribution composed of a prior, given by the epidemic propagation model , and from an evidence given by (time-scattered) observations of the state of a subset of individuals. Neural networks have already been applied to epidemic forecasting43 but rarely to epidemic inference and reconstruction problems. Two recent preliminary works apply neural networks to epidemic inference problems: in44 the patient zero problem is tackled using graph neural networks, while a similar technique is applied to epidemic risk assessment in45. The presented approach allows to address successfully a large class of epidemic inference problems, ranging from the patient-zero problem and individual risk assessment to the inference of the parameters of the propagation model under a unique neural network framework. We believe this to be a strong point in favour of this technique. In all such problems, the proposed autoregressive neural network architecture provides results that are at least as good as all other methods considered for comparison and outperforms them in most cases. The implementation of the algorithm and the instructions to reproduce the results are available at46.BP-based algorithms may experience non-convergence issues, for instance in dense and very structured contact networks, a phenomenon that calls for the search of alternative inference methods which could overcome such a limitation while maintaining comparable performances on sparse networks. Here we propose to use generative neural networks, specifically autoregressive neural networks (ANN), to learn the posterior probability of an epidemic process and efficiently sample from it. In practice, the autoregressive neural network can generate realizations of the epidemic process according to the stochastic dynamical rules of the prior model but compatible with the evidence. Deep autoregressive neural networks are used to generate samples according to a probability distribution learned from data, for instance for imagesi being at time step t in the Susceptible (S), Infected (I) or Recovered (R) state. The infection of a susceptible individual due to a contact with an infected individual occurs with rate i at time t. Defining the individual epidemic-state trajectory as T isThe dynamics of epidemic spreading in a contact network is commonly described by means of individual-based stochastic models in which individuals can be in a finite set of possible states, usually called epidemic compartments. For the sake of concreteness, consider the discrete-time SIR model, in which Suppose that some information about the individual states at different times is available . We will assume that this information comes in the form of a set of independent observed variables i at least one time during the interval . The quantity i. On the other hand, if the present time is T, the marginal probability i. The exact computation of probability marginals requires a sum over all admissible realizations of the process, which becomes unfeasible for more than a few dozens of individuals because their number typically grows exponentially with the number of individuals. In the next subsection, we describe a method to approximately compute the marginals of the posterior distribution in Eq.  Kullback-Liebler (KL) divergenceGiven a realization 47, see Fig. The computational bottleneck of these calculation in the Eq.  and theientclass1pt{minima48 to reduce the number of parameters. Due to the possible high variability in the dependence of 40, instead of adopting a sharing parameters scheme we reduce the number of parameters by limiting the dependency of the conditional probability to a subset of i in the graph induced by the contact network, i.e., the one in which there is an edge between two individuals if they had at least one contact during the epidemic process. The permutation order of the variables generally influences the approximation. For acyclic graphs, it is possible to define an order by which the aforementioned second-order neighbors’ approximation is exact: the variables are ordered according to a spanning tree computed starting from a random node chosen as a root  to sample epidemic realizations from a given posterior distribution, we reconsider the example in Fig. 21 is based on Monte Carlo methods in which samples are weighted according to the overlap between the observations and the generated epidemic cascade , while the second one was collected in a hospital 22. In both cases, the dataset used is the temporal list of contacts, respectively between 95 and 330 individuals, for a period of two weeks. Since the real duration i, j at time t is computed as rrg) with proximity), in which i and j are in contact is i and j and l is a parameter  the patient-zero problem, in which the unique source of a partially observed epidemic outbreak has to be identified, (ii) the risk assessment problem, in which the epidemic risk of each individual has to be estimated from partial information during the evolution of the epidemic process, and (iii) the inference of the epidemic parameters. Results are compared with those obtained using already existing methods in the field of epidemic inference. We also evaluate how the efficiency of the ANN algorithm depends on the size of the epidemic outbreak, measuring the number of generated epidemic samples necessary to obtain nearly optimal results. The comparison between different inference techniques, the Autoregressive Neural Network (ANN), a Belief Propagation based approach (SIB) , together with the Soft Margin estimator (SM), is carried out on both random graphs and real-world contact networks. The Soft Margin (SM) estimatorT, the patient-zero problem consists in identifying the (possibly unique) source of the epidemics. In a Bayesian framework, this problem can be tackled by computing for each individual the marginal probability of being infected at time rrg, proximity, work, hospital), we considered 100 different realizations of the epidemic model with only one patient zero. The three algorithms were used to rank infected and recovered individuals in decreasing order according to the estimated probability of being infected at time zero for each epidemic realization. Figure Given the exact knowledge of the final state of the epidemics at time ion [Eq. " with alT is fully observed. With this choice, we ensure that the probability marginals computed by the SIB algorithm are exact; hence they can be taken as a reference to compare the performances of the other algorithms. The ANN algorithm with a second-order neighbors approximation is exact when the interaction graph is acyclic , the larger is the number of epidemic cascades that are compatible with the observation. For instance, in an epidemic realization of duration T time steps in which T, the number of epidemic configurations compatible with the observations scales as i is infected at the initial time according to each method. The results on the scaling properties of the ANN and SM as a function of the epidemic size on a tree contact network with degree and depth both equal to 6 (tree) are shown in Fig. In the case of complete observation of the final state, the larger the epidemic size  where half of the infected individuals are observed with certainty at the final time T. The results of the risk-assessment analysis obtained by means of the ANN algorithm are compared with those provided by the SIB algorithm and two other methods recently proposed in27. The Simple-Mean-Field (SMF) algorithm is based on a mean-field description of the epidemic process in which information about the observed individuals is heuristically included. The Contact Tracing (CT) algorithm computes the individual risks according to the number of contacts with observed infected individuals in the last The risk assessment problem consists in finding the individuals who have the highest probability of being infected at a specific time given a partial observation T is represented by the area under the Receiving Operating Characteristic (ROC) curve. This quantity, averaged over 100 instances of the epidemic realizations, is reported, for the methods considered above, in Table rrg, proximity and work). All algorithms perform similarly on random graphs, whereas ANN and SIB outperform the other two methods in the case of the work contact network.A measure of the ability to correctly identify the unobserved infected individuals at the final time 51. A comparison between the performances of the two algorithms in learning the infectiousness parameter governing the spreading process on different contact networks  is displayed in Fig. T. The ANN algorithm largely outperforms SIB in rrg and proximity graphs, obtaining comparable results for the tree and work instances. We also test the performance of parameters inference in a more challenging scenario where the population is split into two classes, with two different rates of infections hospital contact network. Then we infer the parameters with two different epidemic models: in the first one, the population is correctly divided ; in the second, we split the population randomly (null model). The goal is to verify whether the true model has a larger likelihood than the null model, that is it can better explain the observations. In the central plot of Fig. true model can infer the correct values of the infections rate of the two sub-populations. As expected, the two values of null model are similar to each other but different from the correct ones. From the rightmost plot of Fig. true model is much larger of the one of the null model, indicating the former better explains the observations. This example shows how the ANN approach can therefore be used to select the epidemic model that best explains the observations based on the estimate of their log-likelihood.The parameters ion see "". Other This work shows how significant individual-based epidemiological inference problems defined on contact networks can be successfully addressed using autoregressive neural networks. In problems such as patient zero detection and epidemic risk assessment, the proposed method exploits the generative power of autoregressive neural networks to learn to generate epidemic realizations that are sampled according to the epidemic model and, simultaneously, are compatible with the observations. When the model parameters are unknown, it can also infer them during the learning process. The approach is flexible enough to be easily applied to other epidemic inference problems and with different propagation models. The proposed architectures for the autoregressive networks significantly reduce the number of necessary parameters with respect to vanilla implementation. Moreover, convergence properties are improved by means of a regularization method that exploits the introduction of a fictitious temperature and an associated annealing process.According to the results obtained on three different problems  on both synthetic and real contact networks, the proposed method equals the currently best methods in the literature on epidemic inference, outperforming them in several cases. In particular, the ANN approach is computationally less demanding than standard Monte Carlo methods, as shown in Fig. Although showing suitable scaling properties with the system’s size, our framework reasonably needs improved architectural schemes and learning processes to be applied in epidemic inference problems regarding more than few thousand individuals. Work is in progress in this direction, possibly guided by symmetries and regularity of the prior epidemic models.For all these reasons, the method seems very promising for epidemic inference problems defined in small communities such as hospitals, workplaces, schools, and cruises, where contact data could be available. In such contexts, it could detect the source of an outbreak, measure the risk of individuals being infected to improve contact tracing, or estimate the channels of contagion and the infectivity of classes of people, thanks to the possibility of inferring the propagation parameters.Supplementary Information.