A laser scanning confocal microscope (LSCM) is an effective scientific instrument for studying sub-micron structures, and it has been widely used in the field of biological detection. However, the illumination depth of LSCMs is limited due to the optical aberrations introduced by living biological tissue, which acts as an optical medium with a non-uniform refractive index, resulting in a significant dispersion of the focus of LSCM illumination light and, hence, a loss in the resolution of the image. In this study, to minimize the effect of optical aberrations, an image-based adaptive optics technology using an optimized stochastic parallel gradient descent (SPGD) algorithm with an adaptive coefficient is applied to the optical path of an LSCM system. The effectiveness of the proposed aberration correction approach is experimentally evaluated in the LSCM system. The results illustrate that the proposed adaptive optics system with an adaptive coefficient SPGD algorithm can effectively reduce the interference caused by aberrations during depth imaging. Nondestructive techniques such as the use of a laser scanning confocal microscope (LSCM) ,2,3 provIf the aberration can be measured, a wavefront modulator can be employed to introduce compensatory distortion and minimize the net aberration before image formation. Commonly, these systems require reference light sources to measure aberrations, and the light emitted by such a “guide star” accumulates aberrations while traversing the sample and instrument before reaching a wavefront sensor. Fluorescent beads, structures labeled with fluorescent proteins in tissues, and fluorescent protein centrosomes are usually used as reference beacons to measure the aberrations directly and to improve the signal and contrast of images ,5,6. HowInstead of measuring phase aberrations directly, image-based aberration correction methods without directly using the wavefront sensor carry out aberration correction from a set of image measurements that are obtained when intensity or phase perturbations are introduced into the system . WavefroThe stochastic parallel gradient descent algorithm is considered in this optics system due to its characteristics such as easy implementation and strong robustness against phase fluctuations. In 1997, Vorontsov, M.A. et al. ,17 of thA simplified diagram of a laser scanning confocal microscope with the image-based adaptive optics system is shown in In the adaptive optics system, a performance metric, which is supposed to reach the expected extreme value, is used in the algorithm to make the judgment for the completion of the optimization. The performance metric is normally selected to meet the following requirements: it should be obtained easily and be related to the aberration; additionally, the metric of the image collected in the confocal microscope imaging system needs good robustness to background interference.The grayscale variance value , the shaSPGD is a blind optimization heuristic search algorithm, which is based on the principle of the gradient descent algorithm. The iterative calculation formula of the gradient descent can be written as the following equation:itten as (4)∂J∂ujThe optimized formula of the SPGD algorithm can be written asEquation (7) is adopted to ensure that the function converges, where With the adaptive coefficient, the algorithm can first approach the optimal solution with a larger gain coefficient when seeking the extreme value of the objective function, and then it can adjust to a smaller one to improve the accuracy. The flow of the adaptive coefficient SPGD algorithm for LSCM is shown in The experimental devices are shown in The fluorescence of the sample excited by the excitation light returns along the original optical path and transmits through the dichroic mirror. The filter  behind the dichroic mirror can filter out the residual excitation laser light reflected by the sample. The fluorescent light focuses on the pinhole  through the pinhole lens and is ultimately detected by the photomultiplier tube detector  through an optical fiber.The test object is a stained leech specimen. Firstly, the fixed coefficient SPGD algorithm is adopted to control the deformable mirror for the unknown aberration elimination. Then, the proposed adaptive coefficient SPGD algorithm is further evaluated, and the experimental results are compared with the algorithm with a fixed coefficient. The experimental parameters in both algorithms are selected as shown in The microscope displacement platform is located at −0.0415 mm in the Z-axis direction to obtain a clearer image of the leech with the Olympus 40× objective lens, as shown in When the fixed coefficient SPGD algorithm is used to control the deformable mirror to correct unknown aberrations, a relatively clear image with a strong fluorescence signal is obtained, as shown in Then, the control signal of the deformable mirror is reset, and the adaptive coefficient SPGD algorithm is further tested to obtain the resulting image shown in The grayscale value curves at the yellow lines in The proper settings of the initial values of the parameters in Equations (7) and (8) could affect the final convergence performance of the SPGD algorithm, which can normally be optimized based on enough experiment testing data or experience. In the future, the optimal selection approach of the initial parameters will be further studied, e.g., combining the AI algorithm to achieve generalized rules for different applications.In this paper, an image-based AO is applied for aberration correction in a laser scanning confocal microscope system. The SPGD algorithm with an adaptive coefficient is used to control the DM to compensate for the unknown wavefront aberration caused by the non-uniform refractive index of samples. Unlike the traditional SPGD algorithm, the gain coefficient of the optimized SPGD algorithm updates according to the phase of the iteration. Evaluated through a series of imaging experiments, the proposed SPGD algorithm can not only reduce the number of iterations but can also improve the accuracy, and it performs well in LSCM depth imaging.


In this article, a new efficient and robust approach—the high-resolution microwave imaging system—for early breast cancer diagnosis is presented. The core concept of the proposed approach is to employ a combination of a newly proposed delay-and-sum (DAS) algorithm and the specific absorption rate (SAR) parameter to provide high image quality of breast tumors, along with fast image processing. The new algorithm enhances the tumor response by altering the parameter referring to the distance between the antenna and the tumor in the conventional DAS matrices. This adjustment entails a much clearer reconstructed image with short processing time. To achieve these aims, a high directional Vivaldi antenna is applied around a simulated hemispherical breast model with an embedded tumor. The detection of the tumor is carried out by calculating the maximum value of SAR inside the breast model. Consequently, the antenna position is relocated near the tumor region and is moved to nine positions in a trajectory path, leading to a shorter propagation distance in the image-creation process. At each position, the breast model is illuminated with short pulses of low power waves, and the back-scattered signals are recorded to produce a two-dimensional image of the scanned breast. Several simulations of testing scenarios for reconstruction imaging are investigated. These simulations involve different tumor sizes and materials. The influence of the number of antennas on the reconstructed images is also examined. Compared with the results from the conventional DAS, the proposed technique significantly improves the quality of the reconstructed images, and it detects and localizes the cancer inside the breast with high quality in a fast computing time, employing fewer antennas. The early detection of breast cancer is vital for the disease’s treatment. It has been proven to be cost-effective at reducing morbidity and mortality. X-ray mammography is currently the gold-standard method for breast cancer detection. However, it has the disadvantages of harmful radiation, uncomfortable compression of the breast during examination, and relatively high false-negative rates ,2. RecenThe approach of confocal microwave imaging (CMI) was first proposed by Hagness et al. ,4 and FeTo construct radar images, various image reconstruction algorithms have been proposed by multiple research groups in order to improve the tumor response ,16,17,18One of the most promising DI algorithms considered in microwave imaging is delay-and-sum (DAS). It involves transmitting an ultra-wideband (UWB) pulse into the breast under study, from various antenna locations encircling the breast. Nevertheless, it is a blind and non-adaptive beam-former that results in low image resolution with high levels of side lobe . This inIn this article, we propose a new reconstruction algorithm approach for high-accuracy UWB microwave confocal imaging system that uses a specific absorption rate (SAR) to infer the locations of the most energy-absorbing tissue such as tumors. The new algorithm involves improving the DAS confocal algorithm by adjusting the distance parameter between antenna and tumor in the synthetic focusing matrices of DAS algorithm. This alteration aims to detect tumors inside the female breast with high accuracy and to provide high image quality. In addition, it will reduce the size of matrices and hence reduce the computational time of image processing. In this work, we have worked in a simulation environment under a CST MICROWAVE Studio Simulator to prove the efficiency of the proposed simulated imaging system. We adopted this technique, which proved to be effective in theory and was performed by many authors in the literature ,21,22,23In order to detect tumor location, a high-directional Vivaldi antenna is applied to a simulated cancerous breast model. At the beginning, the antenna is located at a random position around the breast at a distance of 10 mm to calculate the maximum value of SAR. As reported in ,23,27, tThe detection of breast tumors based on (SAR) parameter.The development of a modified delay-and-sum (DAS) algorithm to enhance tumor response.Combine the modified algorithm and (SAR) parameter to provide high quality image of breast tumor with fast processing.Speed up the computing time by employing fewer antennas.The main objectives of the presented work are:This paper is organized as follows: Several breast phantom models have been reported in the literature ,29,30. IZ-axis for suitable penetration of microwave signal inside the tissue layer, while the tumor coordinates in (mm) are determined at . The origin of the coordinate system lies at the center of the hemisphere breast model. The microwave propagation in the breast is described by the finite-difference time domain (FDTD) method.In near-field applications such as the UWB microwave imaging system for breast cancer detection, employing an antenna with UWB, high directivity, and moderate gain characteristics is very advantageous. In fact, using a high directional antenna entails the antenna’s power more effectively on the region of interest. The directivity feature effectively increases scattered energy bounced from the tumorous region of the breast tissue. Moreover, the antenna is required to be small, low-cost, and to support a very short pulse transmission with negligible distortion . We therE is the internal electric field (V/m), and 3). According to [The target detection in an image is highly sensitive to the positioning of antennas, which guarantees better focusing of the image . In thisrding to , the difd and much less degree scan S(t)=S(t)=(3)Sx location, the time domain data of the tumor-free breast model and cancerous breast model were obtained by simulation using the CST Microwave Studio software.Using a developed MATLAB algorithm, the tumor signature is determined by subtracting the time domain signals received from a tumor-free from those where a tumor exists. However, this tumor response still contains additional unwanted signals such as antenna reverberation. Therefore, to remove clutter, the tumor response is first averaged, and the averaging signal is then subtracted from the tumor response extracted on each antenna location. The signals obtained after clutter removal, called the processed signal, are synthetically focused at a specific point in the breast.After localizing the tumor position using SAR, the Vivaldi antenna is positioned close to the area where the tumor exists, and it is moved to nine different locations arranged in three rows/three columns. The angular separation between each antenna location is 10X and column Y in a grid of 100 mm × 100 mm. The grid is represented as a matrix of 3 × 3. Each B and n = 9 antenna positions, Let us represent the tumor-free breast model signals and the cancerous breast model signals by t for nine antenna positions and is defined asThe first step in image reconstruction is the calibration process. This involves suppressing the response of the n. This process is carried out by adding the n. The resultant signals are known as the processed signals In order to construct an accurate image, we need to improve the tumor response by suppressing the clutter resulting from the antenna reflection and environment. First, t can be expanded asFor the overall antenna positions, the processed signals The processed signals are synthetically focused at a specific point in the breast for image formation. For that, the interested area to be imaged is meshed into a pixel grid as illustrated in Next, the time required to travel the round trip distance between the focal point h is the height of the antenna from the skin along the Z-axis, representing Z-coordinates; X and Y represent the antenna positions. Since the radar approach employed in this study is based on the monostatic radar, the round trip distance is multiplied by 2.The distance(s) of each antenna position c is the speed of light in cm/s, For the antenna position r is the total number of antenna positions in a row, c is the total number of antenna positions in a column, and After calculating the time values for each antenna position For image construction, a new MATLAB program is written to image the tumor using the algorithm discussed in previous section besides the program written in  for compIt is clear that the skin reactions are the same for different tumor sizes, and the tumor information appears within the time intervals 2 ns to 3 ns, which are zoomed in each figure. The efficacy of advancing the microwave breast cancer detection technique depends on the microwave dielectric properties of normal and malignant breast tissues. These dielectric properties determine the interactions of the tissue with the electromagnetic fields. Knowledge of these properties is therefore considered as crucial. In this work, we tested the performance of the new approach by examining several different dielectric properties of tumor models, which were considered in . Table 3x = 0. It is noted that the effectiveness of the new approach signifies the tumor responses for all the permittivities. In all figures, the maximum scattered energy occurs at the tumor location. This indicates that all tested tumors are clearly detected. Further, the size and location of the tumors are well represented in images.In a multi-static mode, the total number of antennas required for imaging affects the quality of the reconstructed image. In fact, enhanced image quality is achieved when employing a larger number of antennas. However, the use of multiple antennas increases the cost of RF modules, while a low number of antennas fail to obtain the correct targets detection due to smaller scattered data-sets.In this article, we investigate the influence of number of antennas on the reconstructed images. On the first simulation, nine antennas are used to transmit and receive signals, while on the second only four antennas are used. This paper presented a new method for high-quality image reconstruction based on a specific absorption rate aimed at early breast cancer detection. First, to detect the tumor, the maximum value of SAR was calculated using a high directional Vivaldi antenna. Consequently, we relocated the antenna position near the tumor surface, making the separation distance between them smaller. This action reduced the propagation distance of the RF signals in the image reconstruction operation. The antenna was then moved to nine different positions to transmit short pulses of low power waves into the breast model. The reflected signals were collected to be processed in MATLAB for image reconstruction. Finally, we improved the conventional DAS algorithm by optimizing the distance parameter in the synthetic focusing matrices. The proposed technique was tested and analyzed using several testing scenarios for reconstruction imaging, and different sizes and permittivities of tumors were investigated to prove its effectiveness. The reconstructed images with fewer antennas were also verified. The resulting images were clear enough to indicate the size and the position of the tumor. In addition, when increasing the number of antennas, the image quality remained accurate. These results showed that the proposed method is low-cost; more precise; and faster, 61% faster than the existing method. Although the proposed system yielded excellent results, it has some limitations in detecting multiple tumors. Further work will involve searching for new techniques to be employed in the proposed system in order to detect more than one tumor with a 3-D imaging technique, recovering more accurate dielectric property distribution.


Countries become global leaders by controlling international and domestic transactions connecting geographically dispersed production stages. We model global trade as a multi-layer network and study its power structure by investigating the tendency of eigenvector centrality to concentrate on a small fraction of countries, a phenomenon called localization transition. We show that the market underwent a significant drop in power concentration precisely in 2007 just before the global financial crisis. That year marked an inflection point at which new winners and losers emerged and a remarkable reversal of leading role took place between the two major economies, the US and China. We uncover the hierarchical structure of global trade and the contribution of individual industries to variations in countries’ economic dominance. We also examine the crucial role that domestic trade played in leading China to overtake the US as the world’s dominant trading nation. There is an important lesson that countries can draw on how to turn early signals of upcoming downturns into opportunities for growth. Our study shows that, despite the hardships they inflict, shocks to the economy can also be seen as strategic windows countries can seize to become leading nations and leapfrog other economies in a changing geopolitical landscape. In this network, strategic access to scarce resources plays a critical role in shaping international relationships between countries and across industries, and in enabling countries to secure and maintain economic prominence globally and over time2. The study of how products and services flow within countries and from exporters to importers is therefore crucial to better understand how countries can secure prominent roles in the global value chains.One of the defining features of modern production systems is the organization of value chains into distinct stages that are geographically spread out across the entire globe and to which countries contribute in complex and non-linear ways. Spatially disaggregated production systems result in a worldwide trade network in which companies of multiple countries from different production sectors exchange intermediary products along a multi-stage, non-linear, and geographically boundless production trajectory ending with products and services directed at the final demand13. Using simplex networks to represent countries as nodes and transactions as directed links from exporters to importers, it has been suggested that the worldwide trade network exhibits a community structure15, a heavy-tailed degree distribution6, and small-world properties13. By extending the simplex representation to bipartite networks, in which nodes in one class can only connect to nodes in another class16, researchers have focused on early signals of economic downturns17, the competitiveness of countries18, and the complexity of products11.Recent years have witnessed a growing number of network studies concerned with the worldwide trade system19, a number of scholars have highlighted the limitations of simplex network representations and projected networks, and in particular have emphasized how these network paradigms can poorly capture the dynamics of complex systems21. In contexts where the system has multiple layers interconnected with each other—such as, for example, the case of transportation systems where fluxes of individuals traveling by bus, train, and airplane can be seen as belonging to different layers of the same interconnected network—the multi-layer network has been shown to provide a more adequate representation23. This is also the case of the international trade network, in which the structure unfolds within and across industries, and the countries are involved in multiple stages of production along the global value chains24. In the multi-layer representation, industries refer to the layers of the network, the countries are the nodes that populate every layer, and connections can be drawn from one country to another in the same industry (within-layer connections) or between different industries (cross-layer connections)25.In spite of these recent advances28. Applications of multi-layer networks to trade include the analysis of layer-specific local constraints on international trade26, the study of the emergence and unfolding of cascading failures27, the study of countries’ influence based on betweenness centrality measures28, the analysis of the nested structural organization of worldwide trade25, and the assessment of the impact of technological innovations on industrial products7, to name only a few. However, despite the increasing popularity of network approaches to global trade, relatively little attention has been paid to the formalization of countries’ economic dominance from a multi-layer perspective. Traditionally, scholars and policy-makers interested in comparative assessments of countries’ global roles and competitive advantage have relied on macro-economic measures of market power based on countries’ overall share of world trade29. These traditional aggregate measures, however, suffer from shortcomings, mainly because aggregate trade flows, on which dominance is predicated, cannot account for the increasingly widespread internationalization of production processes31.Over recent years, researchers have adopted a multi-layer perspective to better capture the structure and dynamics of international trade33. That is, economic dominance is a multi-layer property that should quantify the salience of a country for the entire production network, and thus explicitly depend on the centrality of the other countries located at other production stages and with which the focal country is connected.Countries’ economic dominance of global trade is intrinsically rooted in the international fragmentation of production and the resulting structural intricacies that characterize the global value chains. Production processes are typically disaggregated into various stages stretching across multiple countries so as to exploit the comparative advantages of locations. For example, production might involve intermediary stages and assembly spots located in countries that hold only a negligible share of the market of the final product. Countries may receive inputs from multiple suppliers and, in turn, contribute to the production process at multiple stages located in various other countries. Thus, the internationalization of production makes it difficult to assess countries’ role in global trade simply by using traditional aggregate values such as gross imports or exports. By contrast, a country’s economic dominance of global trade should be a function of the share of value the country brings to each production stage of the underlying global value chains and of the share of benefits obtained from the exchange of intermediate and final productsHere, we take a step in this direction, and investigate the dynamics of dominance in the worldwide multi-layer trade network from 2000 to 2014. First, we take a micro perspective and investigate the dynamics of the economic dominance of individual buyers and sellers in global trade. We show that the ranking of buyers and sellers varied over time in a non-trivial way, resulting in an abrupt and remarkable reversal of leading roles precisely before the 2008 financial crisis. Using hierarchical clustering analysis, we uncover common patterns of variation in economic dominance, and use these patterns to partition countries into meaningful groups characterized by similar power dynamics. We then shift focus from individual countries and take a macro perspective. First, we identify a localization effect in the system using the inverse participation ratio, and examine whether variations in localization can be associated with the occurrence of exogenous shocks. Second, we shed light on the contributions of individual industries to countries’ global dominance, and uncover the localization effect within each industry by computing the corresponding inverse participation ratio. Finally, we explore the role that domestic trade played in amplifying the system’s power concentration and facilitating variations in the geopolitical landscape before the 2008 crisis.34 34 Fig. . The WIO see Fig ”.Figure 36. The idea underpinning this measure is that a node is important to the extent that it is connected to other important nodes, and thus belongs to a chain in which importance is transmitted from one node to another along various connections35. Research has shown that under commonly occurring conditions  the eigenvector centrality undergoes a localization transition as a result of which most of the weight of the centrality will concentrate on a small number of nodes in the network, while the vast majority of the remaining nodes will be assigned only a vanishing fraction39. This phenomenon, typically referred to as “localization effect”, has been extensively studied in the case of simplex complex networks40, and more recently it has been investigated in multi-layer networks39. Traditionally the literature has regarded the localization effect as a drawback of the eigenvector centrality, which in turn has motivated the proposal of alternative centrality measures better able to assess the relative importance of peripheral nodes that would otherwise remain indistinguishable. Here, we take a different perspective: we shift focus from the ranking of peripheral nodes to the emergence of power structures in which a minority of nodes take on a leading role. In particular, we explore how the localization process in the multi-layer trade network can unmask important structural changes in global trade associated with the emergence of a few dominant players. We also assess how these changes can result in transformations of power hierarchies in the global value chains, and ultimately affect the relative economic dominance of the leading global suppliers and destination markets.In graph theory, a common measure for assessing the importance of nodes in a network is the eigenvector centralityFirst, we define the economic dominance of a country from a network-based perspective. More generally, a country can be seen as a global leader when it exerts control over the whole range of transactions occurring along the international global value chains. This implies that a country’s global dominance should be a function of the dominance of all its trading partners at the local levels of the upstream, midstream, and downstream stages of production within and across all industries. Moreover, countries can be global leaders as both buyers and sellers. On the one hand, a country is a leading global buyer to the extent that it represents a major destination market of intermediary and finished products sold by countries that, in turn, are key destination markets of products originating from other key destination markets, and so forth. On the other, a country is a leading global supplier to the extent that it controls the sales of intermediary and finished products to countries that, in turn, are key suppliers of products to other key suppliers, and so forth.i from layer j in any layer 41. Thus, we can compute the centrality of buying nodes in a given layer by calculating the leading eigentensor associated with the most positive eigenvalue38. To also account for sellers, we can use the left leading eigentensor when calculating the centrality . Thus, the node centrality can be obtained by solving the following equation41:i in each layer 38, namelyp value We measure the economic dominance of buyers and sellers using a suitable adaptation of eigenvector centrality to the multi-layer network. The centrality of nodes can be measured through the tensorial formulation of the multi-layer network by calculating the spectral properties of the graph. Specifically, the corresponding formalization of the multi-layer network in the tensorial notation is rank-4 tensor entclass1pt{minimaentclass1pt{minimaWe now focus on the dynamics of dominance in the worldwide trade multi-layer network. We computed the eigenvector centrality for each year in the period from 2000 to 2014, for both buyers , and centrality is homogeneously distributed across the nodes, whereas an IPR close to one reflects a network where the centrality is very localized in very few nodes 47.To further investigate the dynamics of eigenvector centrality, we computed the inverse participation ratio (IPR) of buyers and sellers. From network theoryUsing Eq. , we compTo assess the statistical significance of the high values of IPR , we used a random-edge assignment procedure to construct an ensemble of synthetic multi-layer networks, with 1000 realizations, and with the same topological features as the empirical networks. To produce the null model, we randomly redirected the in- or out-going edges by preserving their weights, thus also preserving the in- or out-degree distributions and the in- or out-strength distributions in the different layers, and then computed the eigenvector centrality see “”. So con39. To this end, we computed the contribution of individual layers to the IPR using the following measure:k in layer We investigate the contribution of individual industries to the localization effect observed globally in the systemWe now illustrate how countries experienced variations in economic dominance within individual industries and how individual industries contributed to the rise and fall of countries. To this end, here we focus on the two industries that experienced the largest variation in the power concentration of purchases and supplies  and domestic trade . To this end, we first disaggregated our supra-matrix c and, as usual, by distinguishing between the eigenvector centralities of buyers and sellers. Figure c. Results clearly indicate an abrupt transition to the localized regime at c, at which the largest variation in IPR occurs, peaks precisely just before the financial crisis, both for buyers  tend to avoid trading with one another and concentrate transactions with other partners in different power-based blocs.Recently there have been a number of attempts in the economic literature to cluster countries into meaningful blocs with distinctive preferential trade patterns within specific industries17, and more generally on the topological antecedents and consequences of shocks in economic and financial systems50. We did not assess any causal relation between structural changes and exogenous events, but simply the association between the timing of a financial shock and the emergence of new winners and losers in the market. All the analysis suggests is that changes in localization can be seen as a topological signal of an upcoming global crisis and at the same time as an opportunity for countries to develop effective strategies for safeguarding and strengthening their market positions. China serves as a case in point. The onset of the financial crisis in 2008 spurred an abrupt reversal of roles leading China to cement its status as the world’s dominant trading nation underpinned by an uninterrupted surge in domestic trade. While the role of countries in global trade has traditionally been gauged mainly based on exports and more generally international trade, this finding may suggest a change in perspective. First, an assessment of countries’ market dominance needs to take into account not only the role of exports and imports but also of domestic trade taking place within the global value chains. Second, the role that domestic trade played in the geopolitical landscape in 2007 has the potential to assist governments, world leaders, and policy-makers on how to help countries to strengthen their competitive advantage during critical periods marked by extraordinary and globally unfolding events, such as financial crises or pandemics.Our study contributes to the emerging literature on the structural early signals of economic downturns53. The recent unfolding of geopolitical disruptions, the COVID-19 pandemic, and various climate-related events have highlighted how vulnerable global value chains can be to exogenous shocks, and how disturbances originating within certain industries or geographic regions may quickly spill over to other parts of the economy55. As the world starts to reshape supply-chain strategies, the costs of intermediate inputs and factors of production are no longer the only primary determinant of how suppliers are selected. Beyond the standard cost optimization problem, a natural path for countries and industries aiming to secure stability and flexibility of input supplies is to redirect their resources by focusing on domestic alternatives and reducing dependence on offshore connections.More generally, our study contributes to the ongoing debate on how, during more turbulent times, the design of supply chains should take advantage of new opportunities for increasing flexibility and sustainability, and for finding value-creating paths that combine openness and securityRecent events have clearly illustrated how a country’s excessive reliance on input suppliers that are not geographically close or even politically aligned can heighten the risk of localized disruptions that can, in turn, propagate through the world economy along the various linkages and layers of the global value chains. For example, the ongoing armed conflict in Europe’s breadbasket has triggered a rise in wheat prices and a supply shock that is ripping through the world economy. In addition, many European countries are expected to face gas shortages and a squeeze on nickel used in batteries. Just as occurred after the 2008 financial crisis, it looks increasingly likely that, as the conflict continues, new winners and losers will emerge amid an increasing degree of volatility. Similarly, the conflict will not affect all industries equally. While pricey inputs can severely affect industries further up the value chain , there are other industries that may benefit from the turmoil, including armsmakers and commodities firms.56. According to the OECD57, maritime shipping accounts for around 80% of global trade by volume. Because marine transportation relies primarily on fossil fuels, it is reasonable to expect that policies and tariffs could serve as an incentive for countries to transform their current geographically dispersed production network into a more modular and localized one.Other examples of how exogenous shocks can exacerbate strains on global supply chains that may prove too fragile to depend on include changes in environmental policies and the COVID-19 pandemic. First, environmental policies that adjust the cost of logistics to promote technological decarbonization and reduce global warming may trigger localized micro disturbances potentially reverberating throughout the global value chains58 forcing carmakers to implement a shutdown of the production of certain models. In response to that, many countries are now investing in onshore alternatives to avoid further shortages and contain disruptions.Second, a comparison between the 2008 financial crisis and the current economic downturn induced by the COVID-19 pandemic is inevitable. Like the previous global systemic crisis, COVID-19 is an exogenous shock to the economy, likely to induce structural changes and a repositioning of countries in the global value chains. Indeed, the COVID-19 pandemic has shown that the suppliers’ ability to attend to demand can be at risk if a country or industry cannot cope with the pace of production at the other stages in the global value chains. For instance, the recent “chip shortage” has directly affected the automotive industryThese events are an indication of just how pervasive the knock-on effects of exogenous shocks can be on countries and industries grappling with supply chains. They are also an indication of how countries and industries can realize the full potential of global production networks through a proper redesign of supply chains that transforms supply shocks into opportunities for growth. While there are certainly many ways in which this can be achieved, our discussion of the 2008 financial crisis and of more recent threats to globalization has suggested that a more geographically localized trade network, organized into economic blocs that are politically aligned, may be a response to deal with a global crisis or idiosyncratic shocks to individual countries and industries. All this raises an intriguing possibility: despite the hardships and economic losses countries suffer in the short term, it is possible that shocks to the economy turn out to be strategic inflection points. They can be the start of a sharp decline or the opportunity to rise to new heights. In an increasingly perilous global economy, the 2008 crisis can certainly offer useful insights to policy-makers and governments on how to redesign effective geo-economic strategies to chart countries’ way forward and redefine their global roles. The new emphasis on production reshoring and relocation of supply chains, the current climate crisis, the accelerating technological revolution, and the fast-changing geopolitical landscape will likely set the scene for a new balance of power between countries, and the emergence of new winners and losers in a reshaped world order.Our study draws on data from the WIOD (Release 2016) covering 28 EU countries and 15 other major countries in the world within the period from 2000 to 2014. For every year, a World Input-Output Table (WIOT) is provided in current prices, expressed in millions of US dollars (USD). Each table represents economic transactions among the 56 economic activities (industries) in each country. The core of the database is a set of harmonized supply and use tables, as well as data on the international trade of goods and services. These two sets of data have been integrated into sets of inter-country WIOT. The full lists of countries and industries are provided in Supplementary Information  and is otherwise randomized according to three reshuffling procedures. In the first model (Model I), for each buyer (seller), the weighted stub of each incoming (outgoing) edge is connected with the unweighted stub of an outgoing (incoming) edge chosen uniformly at random across the entire network. In this way, buyers (sellers) preserve their in-strength (out-strength) but are randomly assigned different suppliers (destination markets). In the second model (Model II), weights are reshuffled globally across the network, which thus randomizes nodes’ in- and out-strength. Finally, in the third model (Model III), weights are reshuffled locally across each buyer’s (seller’s) incoming (outgoing) edges. In this way, each node’s in-strength (out-strength) is preserved while its out-strength (in-strength) is randomized. For each model, 1, 000 network realizations were produced and compared to the original network.Given a multi-layer network Supplementary Information.


This paper is based on a project of developing small and medium-sized enterprises in South Khorasan, Iran. A combination of multiple criteria decision making and robust multi-objective optimization is used for prioritizing industrial clusters and optimally assigning governmental funds. First, criteria for evaluating clusters are weighted using the best-worst method and clusters are prioritized using the VIKOR method. Second, governmental funding is assigned to the highest priority cluster using robust multi-objective mathematical programming. An innovative method is applied to identify the solution of the Pareto-front with the highest efficiency. In a case study from South Khorasan, computational results show that international market share attraction is the most important criterion and the age of the cluster is the least important criterion in the development of the industrial clusters. The cluster defined for barberry and jujube fruits is determined to be the first-ranked cluster. Most of the total available budget for the development of this cluster is assigned to action plans for marketing and trade development and for investment and financial planning. The proposed methodology thus successfully aided the decision makers to plan their work regarding funding of industrial clusters. We believe that the methodology can be applied as a general tool to help managers of industrial development to better assign governmental funding to develop industrial clusters.The online version contains supplementary material available at 10.1007/s12351-023-00761-1. In the last decade, managers in various sectors of agriculture, economy, and industry have considered the development of small and medium-sized enterprises (SMEs) as a key strategy to increase economic growth and to improve the business environment  model for evaluating industrial clusters based on criteria and sub-criteria extracted from upstream documents of ISIPO as well as national experts’ opinions. In this phase, global weights of criteria are determined using the best-worst method (BWM)Rezaei  and thenThe remaining part of the paper is as follows. Section The development of economic activities formed in specific geographical locations based on the natural potentials in the same region, is considered one of the most effective strategies to reach a sustainable economy .Innovation and scalability criterion The rapid advancement of technology and the unprecedented growth in international communications during globalization causes international competition to be directly related to innovation and scalability competition  extending a hybrid approach based on MCDM methods and multi-objective optimization to evaluate industrial clusters, (2) assigning government budget to each action plan optimally and (3) considering a comprehensive set of criteria and sub-criteria to evaluate industrial clusters.The proposed framework consists of two phases. In the first phase, industrial clusters are evaluated using a MCDM model based on a set of criteria approved by SME development experts. In the second phase, the assignment of governmental funding to ICDPs is studied through a three-objective mathematical programming model under uncertainty with the goal of improving the implementation of development plans. Figure In this phase, the weights of criteria and the prioritization of industrial clusters are determined. A two-step procedure is applied to identify criteria and sub-criteria. In the first step, a wide range of criteria are extracted from upstream documents of ISIPO as well as from national experts’ opinions and the research literature. In the second step, as shown in Fig. Then, pairwise comparisons of BWM are performed to determine weights for each criterion and sub-criterion according to questionnaires completed by the experts. Optimal weights are obtained through a mathematical model with a low number of calculations Rezaei . Since ti is the index of an option, j is the index of a criterion, t is the index of an expert, m is the number of options, and n is the number of criteria. The robustness of the choices made by VIKOR is then evaluated using a method used by Mir et al.  providing enterprises with technical and human support, (2) reducing or eliminating asset taxes for enterprises (3) reducing or eliminating sales taxes for enterprises, and (4) providing cash payments. The type of government support is also determined by the proposed optimization model, which is presented next.i:action plan, from the set j:government incentive type, from the set Eij:i with incentive jeffect on cluster economic growth from implementing action plan Sij:i with incentive jeffect on cluster employment growth from implementing action plan Rij:i with incentive jrisk level from implementing action plan Pij:i can be implemented with incentive j, and 0 otherwiseequals 1 if action plan Li:ilower bound of budget assignment to action plan Ui:iupper bound of budget assignment to action plan B:total available budgeti using incentive type j: amount of financial support assigned to plan Since parameters  and Sim  proposedDefine  Soyster . Thus, tquations . We illux is decision variable vector, S is the feasible space. In the augmented epsilon-constraint method, model .To choose a solution from the Pareto front, first the mathematical model is solved for each objective function and the optimal value of the objective functions is calculated separately. Then the Euclidean distance of each Pareto-optimal solution to the ideal solution is calculated. The solution with the shortest distance to the ideal solution is selected for implementation. The steps of this method are discussed as follows.Step 1: Solve the mathematical model for each objective function separately and store the optimal values in Step 2: Solve the mathematical model using the augmented epsilon-constraint method and store objective function values for the solutions in the set Step 3: Calculate the Euclidean distance from solutions in the set Equation  and prodStep 4: Select the Pareto-optimal solution with the lowest MID value as the final solution.p is the number of objective functions, and jth objective function for the ith solution in the Pareto front. After doing the related calculations, values of the first The equation for calculating MID isTable To investigate the sensitivity of the proposed model to uncertain parameters, different combinations of the robustness parameters are considered and the solution from the Pareto front with the best MID value for each combination is determined. Numerical comparisons are then provided to illustrate managerial perspectives. For this purpose, Table The value of In general, when increasing For According to the results obtained in Sect. Agro-food industries generally have not developed in spite of their great potential in the province; there is only one active concentrate barberry juice plant and its capacity is less than 5% of the barberry production. Moreover, barberry can be used in pharmaceutical products, but the necessary infrastructure for this requires joint ventures between the government and the private sector. Based on the results of the previous section, the action plans for marketing and trade development and for investment and financial planning receive the largest share of the total budget assigned to the barberry and jujube cluster. These two action plans can directly affect the improvement of export conditions and the development of agro-food industries. Therefore, the proposed model can be used as a suitable tool to assign financial resources.From a technological point of view, all the steps of planting and harvesting are currently conducted using large amounts of pesticides, due to farmers’ lacking knowledge in how to produce without synthetic chemicals. Therefore, the development of an educational infrastructure, including cooperation agreements with knowledge-based centers to produce modern equipment for harvesting and storing barberries, as well as cooperation with educational and scientific institutions to inform farmers of new practical methods, can be important solutions.From the perspective of developing group cooperation and creating production and sales networks, there is no systematic structure and all market participants operate individually and on a small scale. Thus, there can be some people who act as brokers, buying barberry from farmers at a low price and bringing the same product to the market at a higher price without any added value. Therefore, the development of cooperation networks between farmers, exporters and processing companies can increase the share of producers in the final price of the product in the market.Finally, a weakness of management in various sectors of production, pricing, and sales in internal and external markets can be identified. Although the Agriculture Jihad Organization and the Chamber of Commerce, Industries, Mines, and Agriculture are responsible for managing the trends in barberry and jujube businesses, they perform poorly. For instance, the outbreak of the Corona virus in 2020 prevented many producers from selling their products, and barberry prices fell by 50%, severely affecting the economic situation of farmers who did not receive any government support. Therefore, creating flexible sales channels and resilient production and sales chains can play an important role in controlling critical situations.Based on the results of the proposed mathematical model, implementation of action plans including cluster management system development (P3) with 13%, information technology development (P6), infrastructure development (P8) and raw materials planning (P9), each with 12% of the total cluster development budgets, can be used as appropriate solutions. The fact that cash payments receive a 24% share of the total budget shows that cash loans cannot be the main solution to the problems; however, a large part of government support programs are currently in the form of cash loans. The high share of financial resources to reduce asset tax and sales tax of enterprises confirms the existence of strict laws in the tax institutions as a major obstacle to the development of barberry and jujube cluster. If some laws are amended by the government, it can be a great help to solve the existing problems. In fact, in many sectors there is no need to use financial resources directly, and the government can help SMEs development simply by amending the laws.In this paper, the prioritization of industrial clusters and the assignment of governmental funding are investigated as a decision-making problem in two phases. In the first phase, a set of criteria and sub-criteria related to development of SMEs and industrial clusters are studied to prioritize different industrial clusters in the South Khorasan province of Iran. For this purpose, BWM is applied to determine weights of the criteria. Based on the obtained results, international market share attraction is the most important criterion, while the age of the cluster is the least important. Then, industrial clusters are prioritized by the VIKOR method. After performing numerical calculations, the barberry and jujube cluster is selected as the best option. In the second phase, a multi-objective mathematical model is designed to assign financial resources to various action plans for the development of industrial clusters. After solving the proposed model using CPLEX, 71 Pareto-optimal solutions are obtained, each of which can be used as the final solution. To select the top Pareto-front solution based on decision makers’ views, an innovative approach based on the mean ideal distance is presented to select one of the solutions for the final implementation.Supplementary file 1 (docx 93 KB)Supplementary file 2 (rar 67 KB)Below is the link to the electronic supplementary material.


The proposed system enables quantitative and refined assessment of wrist motor function in stroke patients and has the feasibility of helping rehabilitation physicians in evaluating patients’ motor function clinically.Motor function evaluation is a significant part of post-stroke rehabilitation protocols, and the evaluation of wrist motor function helps provide patients with individualized rehabilitation training programs. However, traditional assessment is coarsely graded, lacks quantitative analysis, and relies heavily on clinical experience. In order to objectively quantify wrist motor dysfunction in stroke patients, a novel quantitative evaluation system based on force feedback and machine learning algorithm was proposed. Sensors embedded in the force-feedback robot record the kinematic and movement data of the subject, and the rehabilitation doctor used an evaluation scale to score the wrist function of the subject. The quantitative evaluation models of wrist motion function based on random forest (RF), support vector machine regression (SVR), k-nearest neighbor (KNN), and back propagation neural network (BPNN) were established, respectively. To verify the effectiveness of the proposed quantitative evaluation system, 25 stroke patients and 10 healthy volunteers were recruited in this study. Experimental results show that the evaluation accuracy of the four models is all above 88%. The accuracy of BPNN model is 94.26%, and the Pearson correlation coefficient between model prediction and clinician scores is 0.964, indicating that the BPNN model can accurately evaluate the wrist motor function for stroke patients. In addition, there was a significant correlation between the prediction score of the quantitative assessment system and the physician scale score ( A stroke is a neurological defect caused by acute focal injury of the central nervous system caused by vascular causes, including cerebral infarction, cerebral hemorrhage, etc. It is one of the leading causes of disability and death worldwide . GloballCurrently, rehabilitation physicians’ one-to-one scale evaluation method is often used to evaluate upper limb and wrist function in clinical practice, with shortcomings such as being time consuming, strong subjectivity, and coarse grading . With a The evaluation of motor function is a significant part of the rehabilitation of stroke patients. Researchers and therapists have developed functional evaluation scales such as the Brunnstrom scale and Fugl-Meyer scale ,15. As sAlthough many researchers have conducted studies on the quantitative evaluation of motor function in stroke patients, the results vary due to the different motor parameters selected in the process of evaluation, which poses a greater challenge to motor function quantitative evaluation studies. The intelligent rehabilitation robot equipment used by some researchers is more complex and expensive, making it difficult to achieve portability and to extend it to clinical applications as well as remote rehabilitation and evaluation. In addition, there are few studies on intelligent rehabilitation training and quantitative evaluation of the wrist at the end of the upper limb, which has significant implications for improving the quality of life and daily living standard of stroke patients.Regarding the problems raised above, a quantitative wrist motor function evaluation system based on force feedback and machine learning algorithms was proposed in this paper. The low-cost haptic force feedback robot Novint Falcon was selected for wrist function evaluation, and a virtual reality interactive system was designed based on force feedback and visual feedback. The subjects manipulate the control handle to complete the trajectory tracking task and record the data, such as the trajectory coordinates of the handle and the time of executing the task. By conducting data processing and analysis, indicators that can characterize wrist motion, such as the number of peak velocity points, average acceleration, and track coincidence, were selected. In addition, the article proposed a quantitative evaluation model based on machine learning algorithms to achieve a quantitative evaluation of the wrist motor function of stroke patients. The haptic force feedback motion mode and virtual reality system proposed in this paper enable stroke patients to have greater willingness and interest in participating in the evaluation of wrist motor function. The use of robot-assisted rehabilitation technology and machine learning algorithms can simplify and intellectualize motor function evaluations, which is more conducive for rehabilitation clinicians to provide accurate and targeted rehabilitation training and guidance for patients.The system block diagram of the quantitative wrist motor function evaluation system proposed in the article is shown in In the data acquisition phase, stroke patients manipulate Novint Falcon’s end effector to complete the task of wrist motor function evaluation. The commercial haptic Novint Falcon device provides the patient with force feedback of various magnitudes, and the PC-based control software provides patients with visual feedback of real-time trajectories. As the patient performs the task, Novint Falcon’s built-in sensors record trajectory coordinate changes of the end-effector as well as the magnitude of the force feedback and the time spent performing the task in real-time; it then transmits the collected data to the PC-based control software. At the same time, the wrist motor function of stroke patients is scored and recorded by an experienced rehabilitation physician using a clinical assessment scale.In the machine learning algorithm models training phase, all machine learning algorithms displayed in the figure were used to establish evaluation models and to conduct model training. The data collected by the sensors were first subjected to data preprocessing; the features related to the wrist motion function were extracted from the motor data to obtain a feature dataset. We selected filtering and normalization for data processing and compared the effects of using the two methods separately and using the two methods combined for data processing. The evaluation model obtained higher accuracy and performance using only normalization. Therefore, we selected normalization for the method of data processing. Four machine learning algorithms, including random forest regression, support vector machine regression, K-nearest neighbor regression, and Back Propagation neural network, were selected to build the evaluation and prediction model for wrist motor function. The score of rehabilitation physicians was used as the label for models training, and the motion feature dataset obtained by processing was used as the model’s input for training the regression prediction models. The models with high regression prediction performance were trained by adjusting the parameters of each model.In the quantitative assessment phase, the participating subjects completed the evaluation task, and their wrist motion data were collected by sensors and transmitted to the computer, and then the model prediction score of the patient’s wrist motion function was obtained by using the trained model after data pre-processing, which enables the quantitative evaluation of the wrist motion function of stroke patients.3. The Novint Falcon communicates with the control computer using a USB interface that uses a 1 kHz frequency for data sampling [The commercial haptic force feedback robot Novint Falcon  was selected for the wrist motor function evaluation trial, as shown in sampling .The Novint Falcon is equipped with a detachable standard spherical handle, but it is not suitable for stroke patients with wrist motor dysfunction, a cylindrical handle for stroke patients with easy grip was designed using a 3D printing method ,22. FiguThe experimental device in the quantitative evaluation system consists of the haptic force feedback robot and the upper computer control system. In this paper, LabVIEW  was used to develop the upper computer control system of Novint Falcon. As shown in To test and verify the quantitative wrist motor function evaluation system proposed in this paper, a clinical experimental protocol was designed. The subjects were recruited to participate in the wrist motor function evaluation experiment, and the real scene of the subjects who participated in the experiment is shown in The clinical evaluation scale in the experiment consists of the wrist function part of the Fugl-Meyer scale and the wrist function part of the Nottingham Sensory Assessment Scale, which is commonly used in clinical practice according to the experienced rehabilitation physicians ,24. The Barak et al.  demonstrAt the beginning of the experiment, the subjects were asked to hold the Novint Falcon’s end modification handle, follow the graphical trajectory displayed on the screen in a clockwise motion, and make the end-effector’s motion trajectory coincide with the graphical trajectory as much as possible. During the experiment, the Novint Falcon’s built-in sensors recorded the coordinate change of the end-effector handle in the X-Y plane, as well as the completion time of each trajectory-following task, and transmitted the data to the upper computer control program, which displayed the given graphical curve in red and the real-time trajectory of the subject’s manipulation of the handle in blue, as shown in To comprehensively evaluate wrist motion function, a force feedback-based trajectory following task was designed in this study. The experiment includes three force feedback modes: (1) no force feedback 0 N; (2) the smaller force feedback 1.5 N; (3) the larger force feedback 5 N. The curves’ shape used for the trajectory following included square, triangle, and circle. The subjects were required to perform three tasks with force feedback at 0 N, 1.5 N, and 5 N, respectively. The subjects need to perform the trajectory following-tasks of the three task graphical curves under three force feedback modes, respectively . The wrist movements mainly involved in the trajectory following task were wrist flexion and extension, wrist dorsiflexion, and wrist circular motion. The experiment was conducted in Suzhou Xiangcheng People’s Hospital. This study was approved by the Ethics Committee of Suzhou Xiangcheng People’s Hospital ), and each subject signed a written informed consent prior to inclusion in the study. Twenty-five patients  with impaired wrist motor function after stroke were recruited from Suzhou Xiangcheng People’s Hospital, while ten healthy volunteers  were recruited to participate in the experiment as a control group. The inclusion criteria of patient selection are as follows: (1) patients between the ages of 18 and 80 years; (2) patients with a first ischemic or hemorrhagic stroke; (3) patients with the upper limb Brunnstrom stage II or above; (4) patients without major post-stroke complications and without severe cognitive deficits; (5) patients who are able to participate rehabilitation treatment and sign informed consent. The exclusion criteria of patient selection are as follows: (1) patients with recurrent stroke; (2) patients with aphasia, cognitive impairment, and psychiatric symptoms, etc.; (3) patients with hemiplegia or upper limb impairment that prevented normal participation in the experimental procedure. The inclusion criteria for volunteers in the control group are healthy, with no history of neurological or musculoskeletal-related diseases. Considering the differences between the motor behaviors of the dominant and non-dominant hand, ten control volunteers recruited to participate in the experiment were asked to use the dominant hand and the non-dominant hand, respectively. The experienced rehabilitation physicians were invited to evaluate the subjects’ wrist motor function using the clinical evaluation scale. The scale score of the subject scored by the clinical doctor and the patient’s demographic information is shown in Research on the motor performance of stroke patients has identified several indicators to quantify the patient’s related motor function, including smoothness and coordination of movement. Cappa et al.  selectedhttps://github.com/DKJ00/Quantitative-evaluation-of-wrist-motor-function, accessed on 24 March 2022). The symbols and definitions of the specific feature parameters are shown in Referring to the above-mentioned related studies on the characterization of motor performance and the professional advice of rehabilitation physicians, this paper selected seven motor feature parameters, such as the number of peak velocity points in the evaluation of wrist motor function for stroke patients. The scripts for data processing were developed using MATLAB , which has been uploaded to Github  is easy to interpret and can rapidly learn; these abilities made it popular in the tele-rehabilitation domain and especially in multi-class activity recognition problems . The priSupport Vector Machine Regression (SVR) is mainly used for clinical assessments where participants are given a clinical score ,31. SVR  problem . The regK-Nearest Neighbor (KNN) is based on distance metrics and is widely used in real-time applications as it is free from the underlying assumptions about the distribution of the dataset. The different values of ‘K’ can contribute to different results for the same problem, which makes it an additional hyperparameter for finding the highest performing model, especially in activity recognition . KNN regBack Propagation Neural Network (BPNN) is a set of Machine Learning algorithms inspired by the brain’s neurons. Multilayer perceptron (MLP) was the most common method of the interconnection of layers of nodes. It does not require feature engineering and, thus, necessitates less domain expertise. It can achieve a good result for activity recognition and predictability regression . The BPNZhang M et al.  proposedAccuracy is defined as the percentage of the number of samples with absolute error less than 3 of the total number of samples in the test set, and the formula is as follows.The mean absolute error is calculated as follows.The mean square error is calculated as follows.The determination coefficient is calculated as follows:To verify the effectiveness of the quantitative evaluation system proposed in this paper, we used correlation analysis to analyze the correlation between motor features and physician scale score and the correlation between model prediction score and the physician scale score separately. Pearson’s correlation analysis was performed between the motor features extracted from kinematic data and the scale scores of the rehabilitation physicians. As shown in The motor feature data sets of 35 subjects were used as the input of the quantitative evaluation model, and the data set was randomly divided into a training set and a test set . The evaluation model based on the machine learning algorithm was trained and fitted by the training set, the trained evaluation model was tested and inspected by the test set, and each evaluation index and accuracy of the machine learning algorithm model are calculated separately. The model prediction score was obtained by processing and analyzing the motor data in the test set by the trained machine learning algorithm evaluation models. As shown in As shown in The reason BPNN has a higher accuracy over the others can be attributed to the fact that the BPNN model used multilayer perception as the method for the interconnection of layers of nodes; it has input layers and hidden layers and output layers to establish data processing model . Seven mCorrelation analysis was performed between the prediction score obtained from the evaluation model and the physician scale score using Pearson’s correlation analysis and Spearman’s correlation analysis, which validated the effectiveness and performance of the quantitative evaluation model and the wrist motor function evaluation system proposed in this paper. Specifically, Pearson’s correlation test and Spearman’s correlation test were used to analyze the correlation between the scale score of rehabilitation physicians and the model prediction score. Statistical analysis was performed using SPSS 22 .Pearson’s correlation analysis between the prediction score of the models and the scale score of the doctor is shown in p-values of the four evaluation models are all less than 0.05. There is a significant correlation between the prediction score of four evaluation models and the scale score of rehabilitation physicians.As shown in In this paper, a quantitative evaluation system for evaluating wrist motor function in stroke patients was proposed based on the commercial haptic force feedback robot Novint Falcon. Patients completed the experimental task of wrist evaluation by manipulating the force feedback robot, during which they received force feedback from Novint Falcon and visual feedback from the real-time trajectory of the upper computer control software. The kinematic data of the subjects were collected by using the built-in sensors of the haptic device Novint Falcon, as well as the magnitude of the force feedback and the time required to complete the task under different conditions were recorded. The number of peak speed points, average speed, average acceleration, average trajectory deviation, trajectory coincidence, and other motor features was extracted from the kinematic data and used as the input data set for the quantitative evaluation model. In the article, four supervised machine learning algorithms, namely random forest regression, support vector machine regression, K-nearest neighbor, and BP neural network, were selected for building a quantitative evaluation model of wrist motor function. The differences in evaluation performance between the models were compared by calculating the indicators of the models.Pearson’s correlation between the motor feature parameters and the scale score indicated that the feature parameters characterizing the patient’s wrist motor function obtained by feature extraction have a strong correlation with clinical scale scores. The experimental results show that the accuracy of random forest, support vector machine, K-nearest neighbor, and BP neural network is 90.98%, 88.50%, 89.34%, and 94.26%, respectively, in the prediction score of wrist motor function. It can be seen that the BP neural network has the highest accuracy rate. In the index of mean square error, the BP neural network model has the smallest value of 3.6974, while the support vector machine has the largest mean square error of 5.9344. In the index of mean absolute error, the support vector machine has the largest mean absolute error of 1.4918, and random forest has the smallest of 1.073, while the mean absolute error of BP neural network is only larger than the random forest at 1.1393. In addition, in the index of determination coefficient, the maximum value is the BP neural network of 0.9284, and the minimum value is the support vector machine of 0.8820. The BP neural network algorithm has better evaluation performances for the evaluation of wrist motor function in stroke patients in this study, while the prediction performance of random forest and K nearest neighbor model is similar, and the support vector machine performed slightly worse in terms of evaluation performance. The BP neural network is a multilayer feed-forward neural network with error back propagation [Given the consideration of related research, Otten et al.  recruitep-values of all four models are less than 0.05, indicating that the prediction score of the four machine learning algorithm models selected in the article is all significantly correlated with the doctor’s scale score. Part of the reason for the high correlation can be attributed to the selection of the doctor’s scale score as the training label during model training. According to clinical rehabilitation physicians, the error of four models in this paper is clinically acceptable, which indicates that the size of the sample in this paper is enough for model establishment. On the other hand, the small size of samples could limit the performance of the evaluation model, and more clinical trials are needed to improve the performance of the evaluation model.The correlation analysis showed that in the correlation test between the model prediction score and the doctor’s scale score, Pearson’s correlation coefficient between the BP neural network model score and the scale score was 0.964, which was greater than the other three models selected in the article. The To address the problems of traditional assessment, which is coarsely graded, lacks quantitative analysis, and is time consuming, we proposed a quantitative wrist motor function evaluation system for stroke patients in the article. The clinical feasibility and effectiveness of the system were verified experimentally. Our study has significance in the progress of rehabilitation, which helps reduce clinicians’ workload, grasp the rehabilitation status of stroke patients, and develop individualized rehabilitation programs for them.The limitation of the article is the small sample size of patients recruited during the experiment, and more clinical experiments are needed to validate the performance of the evaluation model as well as to further improve the prediction evaluation accuracy of the model. On the other hand, more sensors can be used to verify the influence of the compensatory effects of the upper limbs, including the shoulder, during the patient’s performance of the task. Relevant studies have shown that EMG signals have a good correlation with the motor function of the upper limbs of stroke patients ,35. The In this paper, a quantitative evaluation system of wrist motor function in stroke patients based on force feedback and machine learning algorithm is proposed. The main contribution of this work is to establish a quantitative evaluation model of wrist motor function based on a haptic force feedback robot and four machine learning algorithms, which can enable refinement and the quantitative evaluation of wrist motor function in stroke patients. Twenty-five stroke patients and ten healthy volunteers were recruited to participate in the experiment, and rehabilitation doctors were invited to evaluate the subjects’ performance on the clinical scale. The experimental results show that BPNN has the best performance among the four evaluation models, with an accuracy of 94.26%. In addition, there was a significant correlation between the model’s predicted score and the physician’s scale score. The quantitative evaluation system proposed in this article can effectively help clinicians to evaluate the wrist motor function of stroke patients and formulate personalized rehabilitation programs for them. In the future, this study will further conduct more clinical trials to improve the performance of the evaluation model and explore experimental protocols that combine rehabilitation training with clinical evaluation and develop remote home rehabilitation applications for stroke patients.


The increasing role of virtual environments in society, especially in the context of the pandemic and evolving metaverse technologies, requires a closer study of the physiological state of humans using virtual reality (VR) for entertainment, work, or learning. Despite the fact that many physiological reactions to the content presented in various modalities under VR conditions have already been described, often these studies do not reflect the full range of changes in the physiological reactions that occur to a person during their immersion in the virtual world. This study was designed to find and compare the most sensitive physiological indicators that change when viewing an emotionally intense video fragment in standard format on screen and in virtual reality conditions (in a VR helmet). The research methodology involved randomly presenting a group of subjects with visual content—a short video clip—first on screen (2D) and then in a virtual reality helmet (3D). A special feature of this study is the use of multimodal physiological state assessment throughout the content presentation, in conjunction with psychological testing of the study participants before and after the start of the study. It has been discovered that the most informative physiological indicators reflecting the subjects' condition under virtual reality conditions were changes in theta rhythm amplitude, skin conductance, standard deviation of normal RR-intervals (SDRR), and changes in photoplethysmogram (PPG). The study results suggest that in the process of immersion in a virtual environment, the participants develop a complex functional state, different from the state when watching on screen, which is characterised by the restructuring of autonomic regulation and activation of emotion structures of the brain. Currently, more than two hundred engineering companies around the world are making and researching virtual reality (VR) equipment. VR's entertainment potential is beyond question . HoweverIn addition, VR systems are used in learning processes to increase student productivity and motivation . VirtualHowever, despite the subjective reports showing that most subjects report a stronger engagement when perceiving information or experiencing emotions in the virtual environment, objective indicators of the autonomic nervous system and brain activity in VR are more ambiguous.The literature describes the results of a large number of works indicating how virtual environment influences the change in psychophysiological parameters and the emotional state of a person, but the data presented is largely contradictory. Thus, in some studies, only the subjective assessments of the subjects were taken into account; in these cases, participants described viewing emotional video clips in VR as more exciting than viewing content on a usual screen –18. OtheIn most cases, objectification of emotional reactions is performed through the indicators of the cardiovascular system, skin conductivity, muscle tension, respiratory parameters, and bioelectrical activity of the brain.It has been shown in some studies that unpleasant scenes cause a relatively much slower heart rate than pleasant ones when viewing usual pictures, suggesting the influence of the emotion type on the heart rate , and thaIn their studies, Nardelli et al.  and ValeHeart rate variability indicators were used as a predictor of emotion. The following indicators changed significantly: mean RR-interval duration, standard deviation of RR-interval duration, RMSSD, TINN, LF%, LFnu, HF%, HFnu, LF/HF, and nonlinear HRV analysis indicators—SD1, SD2, SD12, and S (Poincaré plot). The precision of emotion detection was 84.72% for valence and 84.26% for power of emotion.When analysing heart rate variability indicators, it was detected that intense stimulation under virtual reality conditions leads to an increase in HF power and a decrease in LF/HF ratio .Skin conductance  is one of the most sensitive indicators of unconscious processes. Skin conductivity indicator is often used in research devoted to emotion because it varies significantly with the level of activation evoked by emotional stimuli .When studying the reactions to images assessed on a two-dimensional arousal-valence scale, it has been shown that autonomic indicators, including skin conductivity, are related to subjective evaluations of the emotionality of visual stimuli . Early wBreathing, which is an autonomic function, is also closely related to emotion. A study by  showed tIn most studies, where one of the study objects was the electroencephalogram (EEG), it has been demonstrated that the total EEG power decreased under conditions of psychoemotional stress , 14. HowHowever, according to some data, no significant changes in EEG alpha rhythm power were obtained , 36. In Thus, a review of a number of articles comparing changes in physiological indicators when presenting the stimuli in the usual mode on screen and in virtual reality showed that currently there is no unequivocal idea of the direction of changes in both autonomic functions and encephalogram parameters.In addition to the data, indicating multidirectional changes in physiological indicators during the presentation of emotional stimuli in the VR environment, the analysis of the literature showed that there are certain difficulties in comparing the results of similar studies. This is due to the fact that the study groups use stimuli of different modality (static images or video fragments), the time of content presentation can vary from several seconds to an hour, and the equipment is drastically different. In addition, the effect of the equipment itself on the test subject's condition is often not taken into account. For example, studies assessing EEG power when using a VR helmet note a significant increase in the power of high-frequency rhythms , which iThus, despite a growing number of studies on the immediate reactions and objective signs of human engagement when presented with various kinds of stimuli in virtual reality, there is still much contradictory data on the dynamics of physiological processes under different conditions of presentation of emotionally saturated content.This study was aimed at finding and comparing the most sensitive physiological indicators that change when viewing an emotionally intense video fragment in usual mode on screen and in virtual reality conditions (in a VR helmet).The procedure followed the general criterion of the local ethics committee, based on the Helsinki Declaration principles, and was approved by the Bioethics Committee of the P.K. Anokhin Research Institute of Normal Physiology, Moscow.Eighteen young men aged 19-23 years participated in the study (mean age 20.53 ± 1.23 years and median age 21 years). All participants were somatically healthy, denied bad habits, and had no neurological problems at the time of the study. All participants were informed in advance about the goals and objectives of the research project, as well as about the absence of health risks during the study, and signed a voluntary informed consent to participate in the study.In order to avoid the occurrence of the virtual reality disease, we chose a short fragment for the presentation.Nonetheless, we asked our participants to self-report whether they felt any negative symptoms or not while watching the video in the VR environment. None of them noted any significant signs of fatigue or discomfort.Eleven subjects (61%) had visual acuity problems—myopia (-0.5 to -5.5)—and one subject had astigmatism, which had been corrected at the time of study participation. There were no problems with stereo vision or colour perception in the subjects.The comparison groups in our study consisted of the same participants who viewed the same content randomly on screen and in a virtual reality helmet. Participants were first introduced to the content of the video segment through screenshots to avoid the novelty effect.The study included several stages. Before the main stage of the study, all participants were asked to fill out the following psychological tests: the Spielberger-Hanin State-Trait Anxiety Inventory (STAI)  and the In the main phase of the study, subjects were presented with a video clip that contained emotionally relevant content.We used a 360 video fragment adapted for virtual reality environment. The video clip was emotionally significant (a short horror movie fragment) and was obtained from open Internet sources. The participants were in a sitting position in front of the monitor throughout the study. VR helmet was worn by participants during the stage of 3D-mode content demonstration.The video clip was shown on screen and in a virtual reality helmet in a randomised order . The duration of the video segment was 130 seconds .It is known that the sign of the experienced emotion has a greater influence on physiological indicators than the way the stimulus is presented , and neg″ monitor at a distance of no more than 1 m  i7 10750Н, NVIDIA GeForce RTX 2070. The video file was demonstrated to the participant on a 15than 1 m .Between watching the video in different modes, a 180-second rest was provided. While watching the clip, the subjects sat in a chair without having to move around the room or turn their head.The following physiological indicators were recorded for 180 seconds before the main stage of the study, as well as throughout the main stage of the study: EEG (monopolar from Cz lead), photoplethysmogram (PPG) from left thumb, skin conductivity, electromyogram (EMG) of forehead muscles, respiratory frequency, and amplitude. Physiological indicators were recorded using a polygraph by Thought Technology (Canada). The duration of the entire study for one participant was 13 min.After the main phase of the study, participants completed the Self-Assessment Manikin  Figure . ParticiDuring the video, the action within the story was not evenly distributed. The first and last 30 seconds of the clip did not contain any events and were the background to assess the impact of the main fragment. Further, according to the clip plot, we identified the three most emotional episodes to which the participants showed the most pronounced reaction; the last part of the clip also did not contain any emotional moments.Data analysis was performed using appropriate polygraph software (BioGraph Infiniti) and standard statistical processing software packages.Due to large individual differences in physiological parameters, it was decided to use normalised values for analysis.Physiological indicators during viewing were assessed relative to the previous background state of the participants. Relative changes in the indicators were calculated compared to the resting state with eyes open (control) using the following formula:An index value equal to one meant that there were no changes relative to the rest state.Thanks to the normalised representation of the data, we were able to compare indicators with different units of measurement.The nonparametric Mann–Whitney criterion was used for statistical analysis of the values of differences in physiological indicators when a video fragment was shown on screen and in a virtual reality helmet.According to the results of psychological testing, the participants of the study had an average or low level of alexithymia and an average or low level of anxiety .The presented video fragment contained emotionally intense content—a fragment of a horror movie. The results of the self-assessment of the emotions evoked by this fragment are presented in The analysis of subjective assessment showed that the presented video fragment provoked quite a strong emotional reaction in the majority of the participants  and the evaluation of the sign of the emotion experienced was divided; despite the expected negative characteristic of the video fragment, many evaluated the feeling they experienced during viewing as positive .When analysing the indicators of cardiovascular system state , we calcAs you can see in The heart rate increases (compared to the previous baseline state) while using virtual reality and does not change or even decreases while watching a video clip on screen. Decrease in photoplethysmogram (PPG) when viewed in a VR helmet is more pronounced than when the clip is presented on screen.Both abdominal and thoracic breathing and respiratory rate were recorded in the study participants When watching a video fragment in the VR helmet, the amplitude of thoracic breathing decreased and respiratory rate increased (compared to the previous baseline state). The participants, who viewed the video on screen (2D), did not have any changes in breathing parameters compared to the baseline state.The equipment we used allowed us to record only one electroencephalogram channel. In our study, the electrode was placed on the Cz area, which helped to reduce artefacts from the virtual reality helmet attachment and obtain a signal containing all major rhythmic components: theta (4-7 Hz), alpha 1 (8-9.5 Hz), alpha 2 (10-13 Hz), and beta oscillations (14-35 Hz) of EEG .In addition to changes in cardiovascular system values, respiration and EMG, skin conductivity, and electromyography demonstrated significant differences in values when watching a video fragment in a VR helmet, which increased compared to the baseline state .Thus, when demonstrating a video fragment in the virtual reality mode, we identified the most informative physiological indicators, which are those that change the most relative to the baseline state Figures .We consider such “informative” physiological indicators as changes of theta rhythm amplitude, skin conductance, SDRR, and changes of photoplethysmogram amplitude (PPG). The values of these indicators change relative to the baseline state more than any other and show statistically significant differences when the video fragment is shown on screen and in the VR helmet.In the present study, we used randomised content presentation: half of the participants watched the fragment first on screen and then in the VR helmet, while the other half of the participants watched it in reverse order. In addition, participants were introduced to the content of the video beforehand , which allowed us to compare reactions to the nature of the presentation in the same subjects and avoid novelty effects for both the presentation of content on screen and in the VR helmet.As in the majority of studies investigating emotional reactions when using VR, the present study found that the content presented in VR caused greater emotional excitement in the subjective evaluation of the participants when compared to viewing it on screen.The data obtained is generally consistent with similar studies . Thus, aNevertheless, subjective feelings should not be the only indicators of human involvement and immersion in the offered content, as a number of works note that visual stimuli presented to participants in VR may not cause more emotional excitement in comparison with content demonstration in 2D mode. In this regard, the present study used multimodal registration of objective physiological indicators during video viewing both on screen and in the VR helmet.In assessing cardiovascular performance, the present study demonstrated an increase in heart rate spectral analysis, such as HF%, when presented with a video clip in a VR helmet. Accordingly, we observed a decrease in the LF/HF ratio , indicating the predominance of parasympathetic influences on heart rhythm regulation under the indicated conditions. Similar results have been demonstrated by many , 42. In Changes in EEG parameters in the participants of the present study were associated with changes in the spectral power of all EEG rhythms, which increased when watching the video in the VR helmet compared to watching it on screen. The most noticeable increase was specific to fluctuations in the beta and theta ranges. Similar data is presented by a number of authors , 14, 17.Regarding the growth of high-frequency activity (starting from the beta range), we should note its high correlation with the tension of the muscles of the face, head, and neck . Given tAs for theta activity, which in our study also increased when a stimulus was presented in the VR helmet, it is known to play a key role in the operation of attention systems , spatialIn the present study, we found that the skin conductance increased when the video fragment was presented to the subjects on screen and to an even greater extent when it was presented in the VR helmet, which indicates a more pronounced activation of the sympathoadrenal system under the described conditions. Despite the fact that skin electrical activity is traditionally considered to be a reliable indicator of the sympathetic nervous system's response to affective stimuli, some works have shown that skin conductivity under virtual reality conditions does not differ significantly from the baseline values . Some stThus, we can assume that the aggregate results of the assessment of changes in physiological indicators and psychological tests when presented with video content in virtual reality suggest that the selected stimulus caused interest rather than fear or surprise in the participants of this study, and it is more pronounced when presented in the VR helmet compared to the screen.Results of multimodal physiological assessment when watching video on screen and in a virtual reality helmet suggest greater involvement of participants in the VR environment.Despite the chosen content (a fragment of a horror movie), it is more likely that participants felt interest rather than fear or consternation.The most informative physiological indicators reflecting the subjects' condition under virtual reality conditions were the following: theta rhythm power, skin conductivity, standard deviation of RR-intervals (SDRR), and blood volume pulse amplitude.Based on the obtained results, we can suggest that in the process of immersion in a virtual environment, the participants develop a complex functional state different from the state when watching on screen, which is characterised by the restructuring of autonomic regulation and activation of emotional structures of the brain.The results presented in this study have to be interpreted in the light of some limitations. We used male participants only because it is known that females during different stages of menstrual cycle demonstrate significant changes in the physiological parameters that we used to assess the condition of the participants: heart rate variability  and EEG