Over the last decade, education has been evolving to equip students with the fundamental skills required to cope with the challenges of sustainability and inclusivity, such as quality education, access to clean water, cultural heritage preservation and protection of marine life. Technology supports the learning process by providing useful tools that enrich the learning environment, encourage active participation, improve collaboration and prepare students for their future life. Educational Robotics is one of the most popular innovative methodologies that supports the development of many skills by assembling and programming robots in a meaningful way. In this paper, the authors aim at advancing their previous work in the field of Educational Robotics applied to the marine environment by proposing a novel bioinspired educational toolkit whose design and features support activities concerning sustainability, ocean literacy, as well as STEM subjects in kindergarten through to grade twelve education. Exploiting the established educational theories and methodologies underpinning Educational Robotics, the toolkit allows for marine-themed activities, as well promoting activities concerning STEM subjects. To explain the relevance of the toolkit, the authors present the robot design, the workshops that every teacher or student can explore as an Open Educational Resource (OERs), and the results of a case study. Interestingly, the latter shows that the use of the toolkit seems to have complemented the students’ initial keen interest in technology itself, with awareness about urgent issues related to the climate and the environment. Education has been evolving to adapt to the many requests of a fast-changing world. The aim of the educational changes is not for the sake of innovation itself, but to equip students with the fundamental competences they will need to cope with the overarching challenges of sustainability and inclusivity, including clean air and energy, quality education, access to clean water and protection of aquatic life .Technology can play a fundamental role in reaching a sustainable and inclusive future, and nowadays, many technological tools support the development and implementation of educational activities inside and outside the classroom. Educational Robotics (ER) seems to be one of the most popular trends in education since Papert’s pioneering work ,3.Many studies report the effectiveness of ER to teach robotics and coding or curricular subjects such as math, physics ,5 or genAlthough some ER activities about environmental education have already been implemented in K12 education , only a few of them focus on the marine environment.Usually, marine robotics activities are carried out in higher education programs, where students learn the knowledge and develop the skills they will need in their future work. However, the existing literature about ER applied to the marine environment highlights that it can be integrated also to primary ,16, middMarine Robotics can be useful to attract students to blue careers ,20,21 anMoreover, the toolkits used in existing non-formal activities about marine robotics and ER are Remotely Operated Vehicles (ROVs) or Autonomous Unmanned Vehicles (AUV) ,21,26,27The present paper aims at advancing the work of , proposiThe paper is organized in the following sections: The robot controller must be programmed and operated remotely, meaning without opening the sealed section where the onboard electronics is housed;In the same way, the batteries must be recharged without opening the sealed compartment housing the energy system;The controller buttons and display must be accessed from the outside;In case of failure, the robot servomotors must be replaced by untrained personnel;The robot must meet the high safety and environment protection requirements dictated by the Machine Directive, 2006/42/EC, and the guidelines provided by the Toys Safety Directive (TSD), 2009/48/EC.The bioinspired toolkit presented in this paper is shaped like a fish and is capable of propelling itself by a spinning the tail section hinged to rigid forebody which mimics ostraciiform swimmers . OstraciOn the basis of the stated requirements, although the robot propulsive performance would have severely improved through the adoption of a more complex design such as a multi-joint tail mechanism mimicking carangiform or thunniform swimming, the authors have chosen to focus on the simplest architecture and the resulting ostraciiform locomotion in order to craft a solid and inexpensive robot that can be exploited as a bioinspired toolkit for primary school workshops. As a matter of fact, the aim of the whole project is not to design the most efficient or a high-performance prototype, but to provide a robust, fully functional product which is easy to manufacture in large numbers in order to be distributed to as many public schools as possible.The robot thruster consists of an oscillating tail and a flat caudal fin attached to it. The assembly is driven by a waterproof servomotor embodied in the tail, which is manufactured as a thin hollowed shell. As stated before, and according to the nomenclature commonly adopted in the literature, the robot swims exploiting ostraciiform locomotion. Here, the tail performs harmonic oscillations, while the momentum transfer due to the relative motion between the caudal fin and the surrounding water generates the necessary thrust to balance the drag force. When the tail neutral position coincides with the robot vertical plane of symmetry, the average lateral force generated by the caudal fin in an oscillation cycle is null. On the contrary, when the tail neutral position forms a constant bias angle with the vertical plane, as shown in ρ is the water density, U is the cruising speed, fA is the robot frontal area, DC is the corresponding drag coefficient, whereas Sfin and TC are the fin surface and average thrust coefficient. In . Th. Th29]. The basic course  based on Blockly, an open-source software released by Google, the teachers learned how to create projects by means of a visual programming editor running on Windows and Android web browsers, which allows blocks of code to be dragged and dropped from toolboxes and palettes and arranged in the workspace. Additional blocks of code specific to the hardware devices supplied by M5Stack were also provided. Several tasks and exercises were assigned to the teachers in the practical session to practice with the most common features of coding, such as loops, input and output implementation, buttons, turning on LEDs, and so on.The second lesson of the three-module session was dedicated to sensors. Following an introduction about exteroceptive and proprioceptive sensors, the teachers were asked to solve quizzes about self- and environment awareness. The class also highlighted the analogies between robotic detection and human perceptions, providing several examples of commercial sensory devices such as cameras, probes and microphones which mimic the behavior of human vision, hearing, smell, taste and touch. In the following workshop, the teachers were asked to exploit the knowledge gained in the preceding lesson to implement a code which could measure the attitude of the M5StickC by means of its Inertial Measurement Unit (IMU). The assigned task also included the printing of an arrow, working as a bubble level, on the controller display and the computation of the vertical acceleration.The final lesson of the three-module session, as well as the basic curriculum, focused on actuators. In this class, the teachers were introduced to the analogy between the human musculoskeletal system and the electrical or pneumatic actuators commonly used in robotic systems. The teachers also became familiar with the fundamental concept of motor drives: just as muscles contracts when a bio-electrical signal is passed from the brain through the nervous system, in robots, motors spin when reference signals are sent from the robot controller and then converted in driving signals, such as Pulse-Width Modulation (PWM), by the motor drive. The theoretical class also focused on the purpose of actuators, such as manipulation and propulsion. In fact, motors drive robotic arms, tools, wheels and thrusters similarly to how muscles power arms, legs, hands and feet. Later, in the following workshop, teachers were asked to connect the M5StickC board with the servo drive produced by M5Stack, and then develop proper code to generate servo oscillations.The advanced course opened with the most important lesson of the FISH curriculum—ocean literacy, which is the understanding of our individual and collective impact on the ocean and its impact on our lives and wellbeing. The theoretical session focused on the importance of aquatic environment preservation and on the role of water for life sustainability on our planet, which is definitely one of the critical issues of modern society. The teachers were also introduced to the seven principles of ocean literacy, where they learned how weather and climate are influenced by the ocean, how life and ecosystems are supported by the aquatic environment, how the blue economy works and how human beings are obviously inextricably interconnected to the ocean. During this session, the teachers were taught about the importance of preserving cultural heritage, with a focus on how the ocean holds valuable treasures and mysteries that are concealed beneath the waves and buried in archaeological sites, waiting to be unearthed. Later, in the practical session, the teachers were introduced to the bioinspired toolkit and were familiarized with its safety features and maintenance operations, before starting practicing with its controller and tail actuator. These preliminary tasks were performed on a workbench and were preparatory for the upcoming “wet” operations.The second advanced class was the first of the physics-dedicated sessions. Particularly, the theoretical lesson focused on fluid statics and on the concept of static equilibrium in the aquatic environment. Here, the teachers were introduced to Archimedes’ principle and the conditions under which bodies rest in a stable equilibrium when immersed into a fluid. In the following workshop, the attendees of the course were trained to organize a simple experiment that was devised to allow primary students to practice with buoyancy and aptitude. When the bioinspired toolkit is immersed right out of the box, it floats with the head rising over the water level. Therefore, the assigned task consisted in attaching known weights to the hull until the robot floated leaving only the dorsal fin out of the water and the bubble level displayed on the controller screen indicated that the fish was leveled at a zero-degrees pitch aptitude or, equivalently, aligned to the horizontal plane.The second physics-dedicated lesson further investigated fluid mechanics by introducing the concept of dynamic equilibrium. Particularly, the teachers learned how thrust is required in order to balance or overcome the drag force exerted by the fluid. The Principle of Action and Reaction was also mentioned in this class. Regarding the practical activity, the teachers were asked to run the code developed in the basic course to drive the servo embodied in the fish tail and push the robot along a planned path, which was straight first and then followed a circle while navigating through a series of buoys floating inside a pool. This activity also allowed learners to experience the Principle of Action and Reaction discussed in the theoretical lesson, by observing the recoil affecting the fish head while swimming.The last of the physics-dedicated lessons was a workshop, where the teachers learned how to organize a simple speed race. The aim of the contest was to program the toolkit in order to follow a straight path and cross the finish line, which was indicated by a couple of buoys, in the shortest possible time. The main difficulty was to choose the fin rotation amplitude and oscillation frequency, which severely affect the cruising performance. The attendees of the course were welcome to practice by varying both parameters in the tail code, in order to figure out the best combination.Lessons about IoT concepts build on the lessons about communication that are delivered during the basic course. The first lesson quickly introduced the concept of a smart thing and how it can be connected to other smart things. Many examples were provided to highlight how smart things are already deployed in the world, and how they are extremely useful. Contextualizing the activity makes it meaningful and also aims at raising curiosity for the technological devices that support many aspects of modern human lives. Then, the lesson ended with a hands-on activity about the protocol for the communication among smart devices. The second lesson about IoT explained how to send a message between more than two smart things. A series of five hands-on activities were developed, each with increasing levels of difficulty that gradually guide the learner towards the goal of creating a sensor network. The real-world scenario for the last two activities was programming the behavior of a smart traffic light system at a crossroad and organizing a network of smart buoys that will send information about the surrounding water to the fish-shaped robot. Finally, the last lesson about IoT concerns the exploration of the basics of Control System Theory, in particular concepts of dynamical systems and open loop control and closed loop control. The last hands-on activity aimed at exploring the differences between a centralized alarm system and a distributed one.The concepts and activities reported in To test the validity of the tool and activities, in August 2021, authors carried out a pilot project at Istituto Comprensivo Giannuario Solari of Loreto , Before starting the activities  and after the activities ended  students answered a short questionnaire about student’s interest and self-efficacy  in robotAnswers to the questionnaire showed that most of the students (59%) participated in the summer camp activities because they liked robotics or because they expected to improve their ability with robotics (25%). Robotics activities were already perceived as fun and engaging before the camp started, and the same share of students agreed or strongly agreed to rate robotics activities as fun and engaging (88%) after the end of the experience, as shown in Students participating in the summer camp were interested in technology and robotics and they had previous experience with different kinds of media and devices. This was evident from the answers; for example, some free-text responses cited “improve videos on my YouTube channel”, “invent app and make videos”, and “use a 3D printer”. After taking part in the activities at the summer camp, students seemed to shift their attention from technology itself to what they can do with technology. Some of the sustainability issues that caught students’ attention were: “collect litter”, “observe plants and fishes underwater”, “remove plastics from the ocean”, and “save people during floods”. The fish robot seemed to engage students’ curiosity: in fact, when asked about the thing that they liked the most about the camp, they reported they liked working with the toolkit (51%), programming (35%) and also working with a different methodology if compared to the one they are used to at school (14%), as shown in The educational approach proposed in the paper aims at fostering STEM education, digital skills, and ocean literacy by exploiting smart pedagogies in a constructionist learning environment using a bioinspired robot. This paper introduces the marine environment thematic during ER activities thanks to the bioinspired toolkit, thus presenting students with vital ideas related to sustainability. Notably, the use of the bioinspired toolkit supported student interest, engagement and motivation in participating in the educational activities of the summer camp. Results from the case study showed enthusiasm was raised by the fish-like robot, even among students with a keen interest and previous experience with media and digital devices. While an excessive use of technology may cause students to concentrate more on the tool than the lesson content  or cause oversaturation of interest in the ER activity ,36,37, tThe fish-like robot helped achieve the target of sustainability. This can relate to the fact that the natural user interfaces are deemed as the most appropriate for engaging students in serious games . FurtherFuture activities envisage the use of the bioinspired tool in K12 curriculum to boost STEM learning, digital skills development, and ocean literacy. To this end, the authors founded the academic spin-off named ANcybernetics  in orderAbout 350 mL of resin are necessary to print the fish structure in a 32 h long process. Here, the skin-proofed resin is worth EUR 180 per liter, so the overall structure cost is EUR 62. The total cost of the mechatronic components—meaning the sealed servo and its driver, the robot controller, the batteries, the wireless charger and other minor electronic components—is about EUR 70 overall. Similarly, mechanical components and consumables—such as O-rings, screws, spacers, and the sealing glue—are together worth less than EUR 10. Hence, the manufacturing cost per robot unit is about EUR 142.The authors estimated that a skilled technician (EUR 60 per hour of work) can assemble three robots per hour, thus raising the manufacturing cost to EUR 162.Different scenarios were also analyzed for the fish structure manufacturing: for instance, a third-party 3D printing services company estimated about EUR 30 per robot structure. This offer could further decrease when its parts are manufactured by injection molding by a professional facility. However, the initial investment required to produce the molding equipment is worth thousands of euros, which in turn, means that hundreds or possibly thousands of robots need to be sold to raise a return on the investment.The high cost of the molding equipment is mainly due to the complex shape of the fish body, which requires a three parts-assembled mold. The process could be optimized by redesigning the main components of the robot for injection-molding manufacturing. In fact, Guizzo was designed using the principles of Design for Additive Manufacturing (DfAM) specifically for 3D-printing technology.Regarding the time and costs of training the teachers to operate the robot and carry on the activities presented in In summary, the total current cost of a toolkit and a training course for an unexperienced teacher is EUR 212, or EUR 180 if the robot structure is manufactured by a third-party 3D-printing company (shipping costs not included).On the other hand, the toolkit final cost is affected by several other factors such as the expenses normally associated to business activity—for example, rental of premises, power consumption, insurance and accountant fees, etc. Secondly, the consultancy cost that is necessary to comply with the Machine and Toy Directives was worth about EUR 4000. Other investments were necessary to equip the authors’ lab with a professional 3D printer, a soldering workstation, a finishing kit and other equipment, which amounted to a total cost of EUR 10,000.Through calculation of these factors, a rough assessment of the market cost for the toolkit plus the training package should be about EUR 450. This value was computed by considering a production rate equal to three toolkits per week, or 150 toolkits per year, which would coincide with the breakeven point for the ANcybernetics spin-off. With these costs, ANcybernetics already sold five courses, with 25 teachers each, in 2023.Further adjustments will be evaluated on a year-by-year basis depending on the market response.


Exploring the influence of social network users in the blockchain environment and identifying opinion leaders can help understand the information dissemination characteristics of blockchain social networks, direct the discovery of quality content, and avoid the spread of rumors. Members of blockchain-based social networks are given new responsibilities by token awards and consensus voting, which alters how users connect to the network and engage with one another. Based on blockchain theory and the relevant theories of opinion leaders in social networks, this article combines structural information and content contributions to identify opinion leaders. Firstly, user influence indicators are defined from the perspective of network structure and behavioral characteristics of user contributions. Then, ECWM is constructed, which combines the entropy weight method and the criteria importance through intercriteria correlation (CRITIC) weighting method to address the correlation and diversity among indicators. Furthermore, an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), called ECWM-TOPSIS, is proposed to identify opinion leaders in blockchain social networks. Moreover, to verify the effectiveness of the method, we conducted a comparative analysis of the proposed algorithm on the blockchain social platform Steemit by using two different methods (voting score and forwarding rate). The results show that ECWM-TOPSIS produces significantly higher performance than other methods for all selected top N opinion leaders. Steemit, Lit, HyperSpace, Sapien, SocialX, FORESTING and Minds . The blockchain social network allows users to achieve self-control of information, encourages users to generate high-quality content and spreads quality content by communicating with other users . TherefoOne of the common contexts of node importance is the identification of opinion leaders. By combining the local and global features of the network, we can determine which nodes have the most prominent positions. A rich body of literature has adopted a network-based approach to uncover these opinion leaders. , 2020. HConsensus voting and incentives promote users to contribute quality content and avoid the spread of rumors. As members constantly participate in contributions and community activities, new characteristics are generated, which lead to changes in members’ perceptions of the community and their influence . Therefo 1.In the blockchain social networks, users accumulate new characteristics through active contribution behaviors. Under the influence of self-interest or pro-social behaviors, these characteristics attract the attention of unfamiliar users, which can lead the topic direction by gaining more votes and expanding communication influence in topic discussions. Therefore, this article constructs a user influence indicator system based on structural information and content contribution dimensions to explore the cumulative effect of user influence. 2.Considering the correlation between indicators and the expectation of decision makers, we proposed the ECWM-TOPSIS algorithm which combines the Entropy and CRITIC weighting methods to identify opinion leaders, avoiding the adverse impact of the combination of single weight and TOPSIS on the evaluation results. 3.Experimental analysis is conducted by capturing data from Steemit, a mainstream blockchain social networking platform, on virtual currency opinion topics. In this study, we use voting scores and re-post rates to demonstrate the superiority of our proposed model over existing models.The rest of this article is organized as follows. The background of opinion leader identification is listed in ‘Related Work’. The proposed ECWM-TOPSIS algorithm is provided in ‘Theoretical Basis’. The data collection and processing, weight assignment comparison and baseline algorithms, are given in ‘The Proposed ECWM-TOPSIS Algorithm’. The results and analysis are presented in ‘Experimental Setup’. Finally, the conclusion of this work is in ‘Conclusions and Discussion’.N opinion leaders by simulating the bubble-net hunting behavior of humpback whales. LWC) based on information entropy, combining degree and clustering coefficient to identify opinion leader.In recent years, mounting studies have been conducted to identify opinion leaders for different network structures and user characteristics, such as centrality, reputation, and expertise. The selection of opinion leaders varies depending on the needs and environment. The social network in the blockchain environment is a network formed by the decentralized characteristics of the blockchain technology, and the characteristics of the social network still exist. For identifying and predicting social network influence nodes, social network analysis mainly relies on network structure . Yang ete.g., reputation) generated by blockchain technology for social network users can better understand the information transfer and sharing behavior among members, and effectively identify opinion leaders in blockchain social networks  is constructed in this article, where V= V1, V2..., Vn denotes the set of all nodes; E= e1, e2, …, el denotes the set of connected edges of nodes. In this article, we use X = (Xij) n×m to denote the adjacency matrix of G. It is obvious that xij ≠ xji. In the directed network, if there is a connection between nodes, then xij=1, otherwise xij=0.Online social networks are composed of a large number of users, each user can be considered as a node, and the edge is a channel for interaction between users. To simulate bidirectional propagation between users, a directed network The degree of a node, as a local attribute ranking index, reflects the direct influence of a node on other nodes in the network, which also means that nodes with larger degrees will have more connections and information channels. In order to eliminate the influence of network size on degree centrality, the node degree centrality index is shown in λ is the maximum eigenvalue of the adjacency matrix X; e = T is the eigenvector corresponding to the maximum eigenvalue of the adjacency matrix X. The importance of a node depends on the number of its neighboring nodes and on the importance of its neighboring nodes.The degree centrality does not consider the influence of neighboring nodes on the target node, nor does it consider the location of the target node in the whole network. Considering the importance of neighbor nodes can better reflect the influence of the target node, which can be measured by the eigenvector centrality. The eigenvector centrality index is shown in njk denotes the number of shortest paths between node j and node k, njk(Vi) denotes the number of shortest paths between node j and node k through node i. The larger betweenness centrality, the more it can control or influence the network relationship.Betweenness centrality describes how a node controls or influences the relationship between two nodes that are not directly connected to it, reflecting the interface of the node in the network, and can be used as an important indicator to control the information exchange in the network. The betweenness centrality can be measured by the number of times the shortest path between all node pairs passes through the target node. The betweenness centrality index is shown in dij represents the shortest distance from node i to node j. A higher value of the closeness centrality indicates that the node is more closely related to other nodes. The closeness centrality is more reflective of the global structure of the network than the degree centrality.Similarly, from a global perspective, closeness centrality measures the influence of nodes in the network. This metric is mainly used to reflect the proximity between a node and other nodes in the network, and is measured by the sum of the shortest distance from a node to all other nodes. The closeness centrality index is shown in Different from traditional social networks, blockchain’s incentive mechanism encourages parties without trusting relationships to communicate and collaborate, and rewards or punishes users for their contribution behavior, which improves the quality of discovered content . At the CP)Definition 1. Content Participation . The reputation of the user will be increased by the votes they get through the content participation. A higher reputation value will attract more users’ attention and enhance their influence. In User content engagement behaviors in blockchain social networks include posting and discovering quality content. By actively participating in the discussion of topics to attract more users’ attention so as to enhance the influence. RE)Definition 2. Reward Definition 3. Originality Presence Definition 4. Follower/following ratioDefinition 5. Time To Respond  and contribution characteristics indicators  calculated in the previous section are selected to form a comprehensive evaluation index of user influence. In identifying opinion leaders, the number of users in the blockchain social network is selected as n, and the number of indicators is m to form the evaluation matrix X = (Xij) n×m, the decision matrix is shown in Xij denotes the jth evaluation indicator of user i.Step2 Standardized Decision Matrix. In order to eliminate the influence of the scale on the evaluation results, the indicators need to be standardized. The standardized equation is shown in xij denote the normalized and original values of the jth indicator for the ith user, max xj and min xj denote the maximum and minimum values of the jth indicator, respectively.Step3 Weight assignment (ECWM). In this article, we combine EWM and CRITIC to determine the weights. CRITIC determines the weights by utilizing the contrast strength and conflict nature of the criteria. It takes m metrics and n alternatives  as inputs and normalizes the value of each metric to the interval  to compute the standard deviations. Based on the CRITIC weighting method, this study can construct the weighting formula as shown in jth indicator obtained by the CRITIC method, rjk is the correlation coefficient between indicator j and indicator k, and σj is the standard deviation of the measurement result of indicator j. The information entropy Ej is calculated shown in pij is the probability of the occurrence of a particular indicator value. It is defined as xij is the value of jth indicator of the user i. And Wj is the weight of jth indicator using ECWM, α and β are constants that represent the importance of Entropy and CRITIC method in calculating ECWM, and α + β = 1. It is generally assumed that the two methods have equal status, i.e., α = β = 0.5.Step4 Compute the weighted normalized matrix. In this step, the weighted normalized matrix Zij is calculated by multiplying the normalized data by the corresponding weights: Step5 Calculation of ECWM-TOPSIS based on Euclidean distance. Distance of positive ideal solution is shown in Step6 Ranking of alternatives. The user influence is ranked according to their performance scores, which is shown in N users are selected as opinion leaders in descending order.https://steemit.com/) is adopted for this study. Steemit blends blockchain technology and economic logic, and user-generated opinion data is permanently kept in the public blockchain. An economic system is designed to promote the content creation of information, which is a more typical online opinion platform under the blockchain environment. In this article, we select the virtual currency price with certain attention and influence as the topic. A web crawler is used to obtain data on Steemit, from which data in the time range from November 26, 2021 to June 16, 2022 are intercepted for analysis. First, the addresses of original opinion information on virtual currency price content are captured to form an initial address base. Secondly, based on the above addresses, field rules such as title author, post time, post content, bounty, number of retweets, retweeters, number of votes, voters, and number of posts were collected to further collect data. Finally, we obtain user reputation and other attribute information based on the author address database to form the original data samples, and use Excel, Spss and other data analysis software to clean and statistically analyze the collected data.The Steemit  and CRITIC methods and neutralizes their biases.Social networking in the blockchain environment eliminates the “centralization”. Content creators have direct ownership of their content and interact directly with followers, fans, buyers, and other parties, with only smart contracts between them. Each independent user is free to choose trustworthy users by consensus mechanism, and jointly maintain community security and create content assets. Therefore, a reasonable increase in the value of To verify the performance of our proposed algorithm, two comparison methods, internal and external, are used to verify the results. In this article, we integrate the network structure and user content contribution characteristics. Therefore, in the internal comparison, we conducted a comparative analysis of network metrics as well as user feature separately to verify that the proposed model is superior to a single perspective. In the second comparison method, the existing algorithm entropy weighting based on Vlsekriterijumska Optimizacija I Kompromisno Resenje (EW-VIKOR)  and SteeThe evaluation of opinion leader detection is actually a difficult task because there is no standard method to evaluate opinion leaders. The focus of blockchain social networks is to highlight quality content to expand its dissemination and attract the attention of more users to gain votes on the content to increase the influence. Voting scores , 2022 arIn our proposed method, the top 30 users are shown in Vscore, calculated as shown in uip represents the pth post by user i, i for the post. Users collect votes by posting content to gain support from more users. According to the features of blockchain social networks, content that receives more votes will be promoted to other users in priority. Therefore, influential users will always receive more votes, promote quality content and guide public opinion.According to the consensus mechanism, the blockchain records each transaction and publishes the content to each user by broadcasting. In the blockchain social network, users can vote on the content to choose the high value information to be delivered. Therefore, in this article, we measure the influence of users by analyzing the total number of votes they receive for content posted during the statistical time period, which we denote by i, i and rij represents the total number of reposts received by user i for all the posts. Reposting in blockchain social networks consumes certain resources, avoiding the abuse of reposting and the spread of rumors. Usually, rational users repost content posted by users with high influence to make quality content spread, so the influence of users can be assessed by analyzing the repost rate in blockchain social networks.In addition to voting scores, re-post rates can also assess the influence of users. The blockchain social network aims to highlight quality content, avoid rumors, and direct public opinion. The re-post rate reflects other users’ agreement with the user’s opinion, which expands the dissemination range to increase the user’s influence on the topic. Therefore, the influence of a user can be assessed by measuring the re-post rate, which is calculated as shown in In the Steemit, users are rewarded for voting on content they like. Voting on posts can be understood as an investment in shares, and once a post is published, it is rewarded based on the votes. For this reason, most users seek influencers to vote in the hope of receiving more rewards. We collected opinion information about virtual currency prices on the Steemit social platform to build the network. Based on the network, we built the network structure of users as well as the user contribution feature indicators, calculated the weights of the indicators, and ranked the users by our proposed method ECWM-TOPSIS. 1% (top 100) of users were selected as the candidate set of opinion leaders for the analysis of voting influence. In order to verify the validity of the model, the baseline model was constructed and tested under the same dataset. Users are ranked by the model to calculate the cumulative number of votes received by the top 100 users and compare it with our method. The voting influence is shown in  With the increasing number of candidate sets, the ECWM-TOPSIS algorithm identifies a much higher number of users than the EW-VIKOR and other methods. The total number of votes for the top 100 users identified by our algorithm is 163,768, which is 76% of the total cumulative votes for all users, while the total number of votes for the users identified by the EW-VIKOR method is 158,720. In the top 20 opinion leader candidate sets, ECWM-TOPSIS rapidly increases the number of cumulative votes, far exceeding the other methods. Compared to the model that only considers network structure, our method is able to identify users that receive more votes, proving that the natural cumulative influence formed by combining user contribution features can compensate for the deficiency of only considering network contagion. Similarly, for the model considering only user contribution features, although it shows a growing trend with the increase of the candidate set, the cumulative votes tend to level off after the set of identified users is 60, and it is much smaller than that of our proposed method. It is worth mentioning that the SP curve only rises slowly with the increase of the candidate set. Further analysis suggests that this may be due to the fact that users with high SP are more involved in the act of voting on other users and seldom involved in the act of posting, and thus only receive a small number of votes. In order to verify the robustness of the model, we extract some data from the dataset for model validation. The results show that our proposed method is still ahead of other methods, rapidly improving in the top 20 candidate sets and obtaining the highest number of cumulative votes in recognizing the top 100 users.In the above content we mentioned how to calculate the repost rates and analyze the spreading impact of different methods of identifying opinion leaders by comparing the repost rates. Similarly, we select the top 100 users as the candidate set of opinion leaders and analyze the total reposting rate accumulated by our proposed method in comparison with the total reposting rates accumulated by the users identified by the baseline method. In blockchain social networks, reposting consumes certain computational and storage resources, and users prefer to observe the topic’s direction under the premise of uncertainty. Users with high influence can attract more users to repost and promote the topic content. The spreading influence is shown in  With the expansion of the selected datasets, all methods show an upward trend, among which the ECWM-TOPSIS method consistently leads and accumulates 43 in recognizing the top 100 users, outperforming the other methods. In identifying the top 20 users, the EW-VIKOR method shows nearly the same growth as our method, while the other methods show high and low growth trends. Comparative analysis of the network structure method and the user feature method reveals that both of them show a gradual stabilization as the number of identified users increases, while our method continues to grow, proving once again that considering both network structure and user contribution characteristics can effectively identify opinion leaders. As the topic is discussed, opinion leaders express their views to guide the topic orientation, and other users will choose to re-post the content if they agree with the views, thus allowing more users to see the quality content and expanding the spreading influence. SP, as the user-owned equity in Steemit, highlights the quality information by supporting others’ content and represents the social influence of users. Observing the SP curve, we can see that it does not improve quickly in the initial stage, but instead, it improves rapidly in the later stages, and its re-post rate is 36.4 among the first 100 users, which is second only to our proposed algorithm. Similarly, we extract part of the data and repeat the above experimental process to compare and analyze the spreading influence of ECWM-TOPSIS with that of the baseline method to validate the robustness of the proposed method.This article proposes a blockchain social network user opinion leader identification and influence analysis model to deepen the application of blockchain theory in social network opinion leader. This article combines network structure information and user contribution characteristics to construct blockchain social network opinion leader influence indicators and identify opinion leaders based on ECWM-TOPSIS method. The validity of our proposed method is verified by analyzing the voting scores and repost rates, which can provide some reference and guidance for other blockchain social network platforms.Our contributions can be summarized as follows: firstly, the two aspects of user network structure and contribution behavior characteristics can achieve better identification of opinion leaders; secondly, although users with more equity (SP) have more voting rights on blockchain social networks, they cannot accurately analyze the influence of opinion leaders and need to contribute more quality content to enhance their influence. Third, blockchain social network opinion leaders are generated with a certain degree of randomness, and it is possible to become an opinion leader by creating enough quality content.α and β to see their impact on weight assignment. In addition, we only obtained data from the Steemit for comparative analysis of the models. In future work, we will explore different blockchain social platforms and build social networks to test the performance of our proposed method.However, there are still some shortcomings in this article. This article only establishes user influence evaluation indexes from network structure information and user contribution features, and it needs to capture more diverse information of blockchain social networks to improve the index system. A new ECWM method is proposed for this article, which uses EWM and CRITIC to assign the weights of indicators. Currently, the method gives the same importance to EWM and CRITIC to assign weights. As future work, we can explore different values of


The population aging, together with the shrinking caring potential of families, is a major challenge for social policy in the coming years. The aim of the study is to identify the factors that determine not only the use of long-term care (LTC) but also the selection of individual types of such care in Poland.Using unique data collected from inpatient LTC facilities in Poland and the Survey on Health, Ageing and Retirement in Europe (SHARE) database, we estimate logistic regressions explaining the choice of LTC solution.Our results suggest that social inequalities play a role in choosing the type of LTC. Better educated people choose private institutions, while people without support network use more often social residential homes. The impact of multimorbidity on choosing different types of inpatient facilities is limited, thus the number of ADL limitations remains a better indicator of long term care utilization.The study confirms that social inequalities influence decisions about the choice of LTC. However, multi-morbidity is a predictor of using LTC to a limited extent. The differences in LTC selection determinants between women and men are noticeable.The online version contains supplementary material available at 10.1186/s12877-023-04216-2. In the last 20 years, the percentage of people aged 65 and over increased in the EU-27 by 5.4 pp. reaching 24.6% in 2021The aging of the population increases the demand for LTC services. According OECD , LTC is The aim of our study is to identify factors influencing the use of LTC and the selection of specific forms of residential care in relation to informal care in Poland. According to our knowledge, this is the first study of this type, presenting a quantitative approach based on data from Poland, as well as the first study involving three different types of inpatient LTC facilities, especially still poorly researched private inpatient sector.In post-communist countries such as Poland, there is high supply of informal care and low supply of formal care , 9. The The criteria for admission to care facilities and the way in which they work are regulated by the relevant legal acts in Poland , 18. BotIn Poland, LTC remains underfunded compared to the countries of Western and Northern Europe, as the expenditures on LTC (as % of GDP) remain relatively low. In the coming years, with the progressive aging of the population, the pressure on their growth is expected to increase. Additionally, solutions used in Poland, based on universal and wealth-related systems , mean thIn our study, the following research hypotheses will be verified:Social inequalities play a role in long-term care decision-making.Multi-morbidity (number of chronic diseases) is not a good predictor of LTC use.There are different patterns of long-term care utilization between females and males.Andersen’s Behavioral Model of Health Services Use, although originally used to predict the use of healthcare services, is now also used extensively in research focusing on actual LTC use. The original version of the model from 1968 focused on the family as the unit of analysis  and listinter alia, demographic and social composition of the population, cultural norms, organizational and collective values, political factors; (2) the term enabling factors refers to the group of factors enabling the use of services, i.e. financial factors  and organizational factors . From the contextual perspective, enabling factors of a financial nature will therefore refer to e.g. income per capita, the relative price of goods and services, expenditure on health care, and in terms of organization to e.g. the type, structure, location, number and distribution of health facilities and personnel, education and information programs, or health policies; (3) the term need factors refers to health status, functional status and disease symptoms at the individual level, and to environmental needs or population health indicators at the contextual level.The explanation of the importance of the main factors  was extensively described by Andersen and Davidson , where: The determinants of LTC utilization based on the original version of the Andersen’s model or its extension was widely studied –35. ThesMany studies confirm the positive relationship between age and LTC demand , 36–38. Gender remains an important factor influencing the propensity for and use of LTC, but the results are inconsistent. Some findings show a higher probability of using LTC services among females than males , 30, mosThe relationship between the level of education and morbidity  and mortTaking into account the structure of households, it is indicated that the risk of using formal LTC increases when living alone , 55. LivOlder people with higher incomes less frequently use institutional LTC , 59–61 bThe place of residence is also relevant. People living in rural areas have a lower risk of being beneficiaries of institutional LTC than those living in urban areas , 67. It The morbidity and dependence that accompany the progressive aging processes are mentioned as the main determinants of the demand for formal LTC. The patterns of dependence and morbidity may, however, be different in particular countries, which is explained in the hypotheses existing in the literature: expansion of morbidity –71, compThe presence of an additional chronic disease increases the probability of utilizing any kind of LTC services  or instiIn the presented study, we combined two databases: data from LTC facilities collected by us and available data from SHARE. We decided to combine the data from both databases in order to be able to differentiate the choice of specific forms of care: residential  and informal. The SHARE data for Poland did not contain information on people using residential care, therefore, in order to achieve the purpose of the study, it was necessary to provide comparable information obtained directly from long-term care facilities. As no similar study was performed in Poland and there is no data available at the individual level on long-term care residents, we decided to collect unique data. At the stage of designing the research, we took care of the comparability of variables between the two databases. First, we used data collected by us in the years 2021–2022 on residents of inpatient LTC facilities . We sent out a questionnaire to the managers of institutions selected randomly from official registers kept by voivodeship offices and the Ministry of Health. Each type of facility is represented in all of the 16 voivodeships in Poland, and they vary in terms of the size of the place of their location. In the self-completion questionnaire, we asked for the data concerning selected socio-demographic information regarding health and independence, as well as family networks of all the residents. As a result, a unique database was created including 745 observations from the private rest homes, 2,258 observations from the residential social homes and 872 observations from the nursing homes. Another group of data was related to the people receiving informal care at home and those who do not receive any kind of care (no LTC). The data came from the SHARE, which is a biennial panel study conducted by using probability-based sampling on people aged 50 or older and their partners across European countries, including Poland , 85. TheDue to the necessity to make comparisons to SHARE, we decided to limit the sample to the age of 50+. From the SHARE database, regarding informal home care, we selected those who receive personal or domestic help (or both) at home, provided by members of the household or people outside the household. Regarding no LTC, we selected people who do not receive any kind of careWe divided our econometric analysis into 3 stages. In the first stage we used logistic regression to compare the factors differentiating the people receiving some kind of LTC  from those who do not receive any kind of care. In the second stage, we used the multinomial logit to compare the recipients of informal care with those using in-patient care. This time, a dependent variable on four levels was used: inpatient care in private rest homes, social residential homes and in nursing homes. Informal care at home was used as the reference category for the comparison. In the third stage, the previously used multinomial logit was applied again, but this time separately among females and males.Due to collinearity problem between the number of ADL limitations and particular ADL limitations, and also between the number of chronic diseases and particular chronic diseases, two versions of the model have been developed. In model 1, the number of ADL limitations and the number of chronic diseases were used, while in model 2 the type of ADL limitations and the type of chronic diseases were used.predisposing factors , enabling factors , need factors .In our models, we use the following three groups of factors considered at an individual level  involved in the organization of care. As this data was not available for residents of long-term care facilities, the level of education in our study remains a proxy for the economic situation.Differences between the two databases we used were noticeable in the case of 3 variables: frequency of visits by family members, ADL limitations, and chronic diseases. For the frequency of visits variable, we wanted to assess the degree of family involvement in care, so in the case of LTC facilities residents the proxy for this variable was the frequency of visits by family members, and in the case of informal care (SHARE database) the frequency of domestic and personal care received by family members.In our questionnaire, we asked about the 6 ADLs using the Katz Index  .We did not follow any specific reporting tool as the questionnaire we designed had to be simplified as much as possible to encourage LTC staff to respond. However, regarding SHARE dataset, information about questionnaires, variable definitions and codes can be found in the SHARE Wave 8 methodology book . All anaRegarding the predisposing factors, the statisticsGiven that, as mentioned earlier, the literature suggests that there is a close relationship between the level of education and health, and health inequalities caused by social factors are observed among older people in Poland , we deciThe results of our analysis show that differences in the percentage of the older adults who suffer from chronic diseases between care recipients and no LTC group are smaller than in case of ADL limitations between the same two groups. The statistics presented in Additional file Results of logistic regression regarding the first stage of our econometric analysis – the comparison between the older adults receiving any LTC with those who do not receive any kind of care – are presented in Table In terms of the factors belonging to the predisposing group, we see that age is a factor that positively correlates with using any form of LTC , which is consistent with other studies , 30, 31.As for the enabling factors, both having a living partner and a child negatively correlates with receiving LTC and this result is in line with a previous study , 57. MosIn terms of need factors, it is observed that both the increase in number of ADL limitations and the number of chronic diseases goes hand in hand with using any care, consistent with appropriately previous studies , 31. HowTable As for the enabling factors, support networks are a significant factor, which correlates negatively with using all three forms of inpatient care as compared to informal care. Having a living partner shows the strongest negative impact in the case of social residential homes, which suggests that people staying there most often experience loneliness comparing to the residents of other inpatient care. In the presented model, having children also negatively correlates with using residential care, but when it comes to choosing private nursing homes, this effect was the weakest or statistically insignificant. These results suggest that having more children correlates with using social residential homes and nursing homes, but does not affect the choice in the case of private institutions. The more frequent (at least once a week) are the visits by family members , the lower is the choice of using inpatient care rather than informal care . The invIn the case of the size of the place of residence, the results are inconclusive. Inhabitants of small towns up to 20,000 people are more likely to benefit from all forms of inpatient care compared to rural residents using informal care. Most likely, this is due to the differences in the possibilities of providing care between urban and rural residents, and perhaps the greater availability of residential care facilities in larger centers. In the case of medium-sized cities between 20 and 100 thousand, positive and statistically significant influence is observed only in the social welfare sector. The fact of living in a big city with over 100,000 inhabitants compared to people living in rural areas and receiving informal care, positively correlates with using both private and social residential homes, while it is statistically insignificant for nursing homes. Perhaps these results indicate the uneven distribution of residential LTC facilities depending on the size of the city, i.e. not all have equal access to the full offer of institutional care. Nevertheless, rural residents are less likely to become residents of inpatient LTC and use informal care most often , 67, 68.In the case of the group of factors classified as need factors, undoubtedly the level of dependence (measured by the number of ADL limitations) is the strongest positive predictor of using inpatient care  as compared to informal care , 35. ThiAmong the chronic diseases, only Alzheimer’s and dementia appear to positively correlate with using all three forms of inpatient care in a statistically significant way, as compared to informal care . Other mThe variable numbers of chronic diseases turn out to negatively correlate with the choice of each of the three analyzed types of inpatient care in relation to informal care. This means that Polish residents with a greater number of chronic diseases more often use care provided by family members or friends, as expected in the second hypothesis. In a way, this is a surprising result, as some literature  indicateAs gender may be a factor not only differentiating patterns of care but also the occurrence of chronic diseases and ADL limitations, additional models were conducted separately for females Table  and maleIn the group of models where informal care at home is the reference category, in terms of predisposing factors, the first significant difference between the sexes can be seen in terms of age – the threshold among females is 70 + as opposed to 90 + among males. When it comes to education, there are also differences observed. The fact of having secondary education significantly positively correlates with using private rest homes only among females. Males with the same level of education are more likely to stay in social residential homes. As for females, higher education positively correlates with using private care only. In the case of males, higher education increases using both private forms of care and nursing homes.For the enabling factors, having a living partner negatively correlates with utilization of all three forms of inpatient care only among females, while it is insignificant in case of nursing homes among males. Significant differences are also observed with regard to having a living child. This variable negatively correlates with receiving any type of residential care among females, while among males it is significant only in the case of social residential homes. The frequency of visits/help remains a variable that negatively correlates with receiving any kind of care. Hence, having a family, matters only if the family members are in close contact with the person who needs care. Living children are more likely to provide informal care for mothers than for fathers but it might be explained by the fact that females  most often play the role of informal caregivers , 91, henIn terms of the need factors, the direction of the impact of the variables, i.e.: number of ADL limitations (positive effect) and number of chronic diseases (negative effect) remains consistent among females and males. There are gender differences in the case of chronic diseases affecting the risk of using inpatient care compared to informal care. Heart diseases negatively correlate with using all three forms of inpatient care in the case of females compared to females receiving informal care . When it comes to males, heart diseases negatively correlate with utilization of private rest homes and nursing homes. Among females, problems with the respiratory system also negatively correlates with using private care, while among males this variable remains insignificant. As for diabetes, on average, females receiving informal care at home suffer from this condition more often than females in all three types of inpatient care . Among males, this variable is insignificant. In the case of depression among females, this variable remains insignificant . Among males, depression negatively correlates with using inpatient care in all three analyzed types of inpatient care. This means that, on average, depression occurs more often among males staying at home than in LTC facilities . Alzheimer’s disease goes hand in hand with using residential care among both sexes, but the effect is statistically significant among females only for private care and social residential homes, and among males only for social residential homes and nursing homes. Regarding Alzheimer’s disease, there is therefore a gender differentiation according to the type of LTC facility. Other mental illnesses are statistically significant and positively correlate with using social residential homes only among males, while among females this variable is insignificant. There are also differences between males and females when it comes to hearing problems. Among males, it is a factor positively correlated with using private care, and among females, a factor that is insignificant or negatively correlated with using social residential homes. It therefore seems that informal care is more often provided to females despite of hearing problems, and in the case of males, hearing loss is a factor that increases the risk of institutionalization (still private rather than public).In our study, based on the Andersen’s Behavioral Model of Health Services Use (1968)  we identWe are aware that the level of income of all family members involved in providing care correlates with the choice of the form of care, however due to unavailability of this variable, we used an education level as a proxy for economic situation of older adults. The study confirms the first hypothesis that social inequalities influence decisions about the choice of LTC. Better educated people more often choose private care than people with a lower social status. Among the latter, the phenomenon of multi-morbidity (more than two chronic diseases) is more common, so social inequalities translate into inequalities in health. Therefore, it is important to both invest in education and develop the healthcare sector earlier in life. Such actions on the part of the government should mitigate the existing inequalities in health among the older adults.However multi-morbidity is a predictor of using LTC to a limited extent. The influence of the number of chronic diseases depends on the variable used for comparisons. When we consider informal care vs. inpatient care the sign for multi-morbidity is negative, but when no LTC is used for the comparison with any kind of LTC, the sign is positive. This means that informal care beneficiaries suffer from more chronic diseases that residents of LTC facilities and no multi-morbidity itself, but particular diseases  should be taken into account when considering institutionalization, which confirms the second hypothesis. The number of ADL limitations is a much more relevant indicator, as it positively correlates with using LTC in each of the analyzed models.We confirm existence of different patterns of LTC utilization between females and males with respect to all three groups of factors. Differences are observed regarding correlation between having a living partner and a child and institutionalization. Also we confirm the third hypothesis that there are differences between females and males in diseases that predisposed them to use LTC. Thus gender differences should be taken into account when planning future LTC arrangements.Our results show that loneliness itself might be a strong predictor of social residential homes utilization. This observation is supported by two other results. Firstly, multi-morbidity is a factor with limited impact on shaping the demand for inpatient LTC. Secondly, for the older adults in social residential homes is noticed that number of ADL limitations is lower than for residents of other type of inpatient facilities. Thus, in the context of the public debate about the deinstitutionalization of the social LTC sector, our results suggest that in case of the older adults who stay in social residential homes because of their loneliness but without health reasons, there is a space to offer other type of LTC arrangements for example: housing estates for seniors. On the other hand, we also identified that Alzheimer’s disease, dementia or other mental health problems remain strong predictors of using social residential homes. For this group of the older adults it may be difficult or impossible to offer another form of care outside of institutional care. Therefore, it seems that the development of long-term psychiatric care and the promotion of behaviors that may delay the occurrence of Alzheimer’s and dementia from an early age are also the right direction to follow.Our study has some limitations. Combining databases from two sources was a challenge for several reasons. The period of data collection, which coincided with the Covid-19 pandemic and the restrictions, may have influenced the underestimation of the ‘frequency of visits’ variable, even though in our survey we asked LTC staff to specify the visit frequency ‘usually’. In addition, the statistics presented should not be generalized to the whole population due to the impossibility of weighting the data dictated by different target populations and different way of drawing of the samples. The importance of ensuring maximum possible comparability regarding diseases and ADLs, meant that we were forced to drop some diseases or aggregate them into more general categories. Thus, the list of chronic diseases used in our analysis does not exhaust all possible types of diseases that the older adults suffer from. Therefore, there is a risk that we were not able to identify diseases other than those described, which would significantly increase the probability of institutionalization.Also, it was not possible to include formal (paid) home care in this study, due to the lack of relevant data. Including this kind of care would allow us to extend the analysis, especially in the context of differences in care preferences depending on social status, as dependent people more often prefer to stay at their homes.We are aware that, apart from demand factors, the decision-making selection should also take the supply factors  into account, but due to the comparability with SHARE data and objective difficulties in estimating the costs of informal care –at this stage we decided not to include the supply factors in the analysis. Probably the biggest deficit of the presented analysis is the lack of information on the economic situation of households of LTC residents, which was not available.In addition, further analyzes should also use data from the households of dependent people, especially information on people directly involved in care , as the decision-making processes related to the choice of the form of care are often collective decisions of households. The data collection methodology forced a specific selection of variables used in the model, hence the use of other methods of data collection – interviews with residents  or asking questions about a hypothetical situation  would certainly offer a broader perspective on the factors determining the selection of specific forms of care.In this study, based on the Andersen’s Behavioral Model of Health Services Use, we examined the relationship between predisposing, enabling and need factors on the use of long-term care in Poland. Combining data from SHARE Wave 8 and data collected in the 2021/2022 LTC resident database, we made a comparison between older adults (aged 50+) receiving any LTC with those who do not use any kind of care. We also made a comparison between users of informal care and users of three different types  of inpatient LTC. The results of our study indicated that social inequalities influence LTC choice decisions. However, multimorbidity is a predictor of LTC use to a limited extent. There are also differences among men and women correlating with the use of specific forms of LTC, indicating gender-dictated variation in patterns of care. Limitations of ADLs, Alzheimer’s disease, dementia and other mental illnesses as factors that increase the risk of institutionalization in particular should be considered in projections of future LTC sector development as well as providing implications for health policy. Additional file 1: Table A1. Statistics for education level and place of living vs. chronic diseases and ADL limitations


In the context of China’s new stage of economic development, this study examines the role of higher education in China’s high quality economic development (HQED) strategy from a digital perspective. Using panel data of 30 Chinese provinces  collected from 2012–2020, comprehensive evaluations of the level of higher education and HQED are conducted through the entropy method, and a regression analysis is carried out with the fixed effect model. The results show that the level of higher education is positively associated with HQED and is able to achieve this effect through mechanisms that actively promote digital innovation and development. Further, the structure and quality of higher education plays a greater part in facilitating digital development than the scale and quantity. The heterogeneity analysis demonstrates that the impact of higher education on HQED is more significant in the eastern region of China than in the western region. An increase in the proportion of fiscal expenditure to GDP diminishes the impact of higher education on HQED, while an improvement in digital governance level enhances its influence. China Education Daily, China’s gross enrolment rate in higher education increased from 30% in 2012 to 57.8% in 2021—an increase of 27.8 percentage points—achieving a historic leap forward in building the world’s largest higher education system. Meanwhile, China’s economy has achieved rapid, but consistent, growth since it commenced its policy of economic reform and opening-up in the late 1970s. The popularization of higher education enhances its crucial role in facilitating economic growth [The impact of higher education on economic development has become a topic of increasing interest in both academic and policy circles . The varIn recent years, China is focused on shifting from prioritizing high-speed growth to a stage of high-quality development. This approach, put forward for the first time in 2017, is designed to better stimulate the endogenous dynamics of China’s economic development. Consequently, it becomes crucial to examine the impact of China’s higher education system on high-quality economic development (HQED) in light of the new environment and dynamics.Some researchers have conducted investigations into higher education in the context of high-quality personnel training as crucial support for socioeconomic development. Schultz’s  theory oNumerous models and methods have been applied to study the relationship between higher education and economic development. Granger methods, panel data models, and two-stage regression on a modified Cobb–Douglas production function are among the techniques used to test the causal relationship between higher education and economic growth –14. MacrThe literature does present divergent views, suggesting that, while education is important for economic development, it is not a sufficient condition. Limited higher education supply can promote economic growth by increasing overall savings, but when this growth-promoting effect becomes strong enough, it leads to a shortage of investable capital, thereby reducing economic growth . There aA number of studies have explored the mechanisms by which higher education impacts economic development. Education cannot only generate the accumulation of human capital, but can also promote technological progress, which is the key to stimulating economic growth . SimilarThis study seeks to examine the impact of higher education on HQED in China from the digital perspective, using the entropy evaluation method and fixed effects model with panel data collected from 2012–2020. As further robustness checks, two alternative methods, principal component analysis and standardization, are utilized to evaluate the level of higher education and HQED. Heterogeneity analysis was conducted based on the regional perspective, temporal dimension, fiscal expenditure level, and digital governance level. The impact of higher education on HQED was assessed from the relatively new perspective of digital development. This provides new insights into the mechanism of higher education’s influence on economic development, and will provide an important reference for the formulation and implementation of education policies to advance economic development.Based on previous experience gained and lessons learned both at home and abroad, alongside analysis of the current trend of global development, China has forged a new development philosophy of pursuing innovative, coordinated, green and open development, and having its fruits shared by everyone. This is intended to target problems that have been present in China’s development model since 2012. It is focused on shifting from a period of high-speed growth to a high quality development. Since this concept with Chinese characteristics was put forward for the first time in 2017, researchers in China have begun to turn to the study of high quality development, mainly discussing its cultural connotations , 29 and In the increasingly complex global economic landscape, human capital is one of the main pillars driving high-quality and sustainable economic growth . As the Hypothesis 1. Higher education has a positive impact on HQED.Higher education provides specialized training through the construction of digital-related disciplines, which, in turn, provide enterprises with digitally competent employees and contractors, who become a key support for the development of digital industries. In the course of governing and serving society, the dissemination of digitized knowledge and information through higher education becomes the basis for a vibrant digital economy. The transformative digital technological achievements of higher education enable new companies to develop and digital production to function soundly, implying that it is undoubtedly one of the key drivers of digital development. The digitization of education, exemplified by the use of shared digital learning space  and virtMeanwhile, the differences in the structure and quality of higher education, such as the composition of postgraduate and undergraduate levels, the proportion of teachers with senior titles, and the capacity for R&D, are key factors that influence the level of human capital available. These resources form the basis for technological transformation and upgrading, which can contribute to the rapid development of digital industries.Some studies have focused on the contribution of information and communications technologies (ICT) and digital development to economic growth and productivity , 39. FroHypothesis 2. Higher education stimulates the level of high-quality economic activity through digital development.Hypothesis 3. Both the “scale and quantity” and the “structure and quality” of higher education contribute to digital development.In this paper, educational scale and quantity along with structure and quality are considered to represent the level of higher education development with nine indexes selected . Based ohttp://www.stats.gov.cn/), and the Ministry of Education of the People’s Republic of China (http://www.moe.gov.cn/). The digital financial inclusion index of China is derived from the Institute of Digital Finance Peking University (https://idf.pku.edu.cn/). Some missing data, which are less than 1%, are supplemented by linear interpolation. Data entry and some calculations, such as the energy consumption per unit of output, the intensity of energy conservation and environmental protection , and the level of financial development , were executed in Excel 2010. Descriptive statistical analysis was conducted using SPSSAU. The Tibet, Hong Kong, Macao, and Taiwan regions were excluded from this analysis for statistical reasons.Statistical data from 2012 to 2020 required for this study were primarily obtained from the China Statistical Yearbook, the China Statistical Yearbook on Science and Technology, the Educational Statistics Yearbook of China, the China Energy Statistical Yearbook, China Statistical Yearbook on High Technology Industry, the statistical yearbooks of the provinces, the website of the National Bureau of Statistics of China , confirming that regression models were not affected by the presence of multicollinearity. The choice of a fixed effects specification was supported by the Hausman test.On this basis, an alternative method was used to measure the independent and dependent variables; the results remained robust. Columns 3 and 4 in Another robustness test was also performed using the standardized method. After calculating the level of higher education and HQED using the entropy evaluation method, the consequences were standardized using The regression results are reported in The above analysis has proved robustly that China’s higher education level significantly promotes HQED. I will now continue to discuss the mechanism.Higher education is enabling digital development. This channel is verified through Digitalization also drives HQED. As outlined in the theoretical framework section of this paper, previous studies have confirmed that digital-related industries such as new digital infrastructure, ICT, and AI can drive HQED –42, 44. What impact will different aspects of higher education have on digital development? According to the evaluation index system presented in this study, the contribution of higher education to digital development will be measured in terms of scale and quantity, as well as structure and quality. Column 1 in The economy is evolving, but not equally across different regions. According to information obtained from the National Bureau of Statistics, China’s economic zones are divided into the Eastern, Central, Western, and Northeastern regions to reflect different regional economic development dynamics. Therefore, regional heterogeneity can be better reflected by defining variables at the Four Regional levels. Higher education development and HQED were measured at the four regional levels to reexamine the relationship between the two. Using the western region as the reference group, the results, reported in Digital development as a means of driving changes in production methods has become an important theme of economic advancement in the new era. Policy documents related to digital development have served as the main source of guidance. The Digital China initiative, launched in December 2015, marked the transition of digital development from the initial stage of exploration to comprehensive construction. After 2016, policies such as the 13th Five-Year Plan for National Informatization and the 14th Five-Year Plan for the Development of Deep Integration of Informatization and Industrialization were proposed. I therefore use 2015 as the cut-off point for indirect analysis of the data for the two intervals 2012–2015 and 2016–2020, as shown in Restructuring fiscal spending is an important factor in economic growth . TherefoDigital governance enables the integration of policies and public services through e-government, fostering sustainable and inclusive economic growth and social development . The DigThis article examines the impact of Chinese higher education on HQED and its mechanisms from a digital perspective, using panel data from 30 Chinese provinces from 2012 to 2020. The main conclusions are as follows.First, the results of the benchmark regression show that the advancement of China’s higher education can significantly contribute to HQED, which is confirmed in the robustness tests. The regression results verify theoretical hypothesis 1, and is consistent with previous studies , 34, 47.Second, the mechanism test proves that higher education contributes to a high-quality economy at a national level through the promotion of digital development, and that the role of higher education is not only reflected in the expansion of scale and quantity, but also because of structural and quality improvements. This also validates theoretical hypotheses 2 and 3 of this study. I speculated that more higher education institutions, more students in general universities, and more educators might all contribute to a more advanced digital economy. China officially entered the stage of massification and generalization of higher education with a gross enrolment rate of over 15% in 2012 and 50% in 2019, respectively . The expThird, the contribution of higher education to quality economic development varies in different regions of China. The regional heterogeneity exhibited that higher education levels have a more positive influence on HQED in the east of China than in the west, which is consistent with existing research findings , 55. SinTaking it a step further, based on an analysis of fiscal expenditure level and digital governance, I found that an increase in the proportion of fiscal expenditure to GDP weakens the impact of higher education on HQED. This may reduce market orientation and competition mechanisms, ultimately affecting resource allocation efficiency. Therefore, policymakers must carefully consider structural adjustments to fiscal expenditure, which will help promote greater autonomy and innovation within higher education institutions, further supporting HQED. In contrast, an improvement in digital governance enhances the impact of higher education on HQED. Digital governance enables better digital administrative operations and data sharing, allowing governments to better meet market demands and promote effectiveness and fairness in resource allocation. Policymakers and stakeholders should prioritize investments in digital infrastructure and administrative capabilities to support the integration of higher education with digital government initiatives.These findings verify the positive impact of higher education on HQED, which enriches the existing research on the relationship between higher education and economic development. Moreover, the introduction of digital development factor lends a new perspective to the study. Using China’s new development concept to construct an evaluation index system is suitable for the characteristics of the economic development stage and expands the ideas for regional economic assessment.These conclusions have important practical implication for Chinese provinces to implement the coordinated development of higher education and the economy as a whole with the further advancement of digital development. It is conducive for local governments to understand the status of the relationship between higher education and economic development at the current stage, providing planning guidance and differentiated policy formulation according to the reality of the two systems. It is beneficial to leverage digital development to transform the advantages of higher education into a support guarantee for HQED through the cultivation of digital talents, increasing investment in educational innovation and effective expansion of the scale of education, and promoting a positive interaction pattern between higher education and HQED.This study demonstrates a strong relationship between higher education and HQED, which has been verified through a number of robustness checks. From the perspective of digital development, the findings provide useful insights and can be considered enlightening. This study provides some foundation evidence for future studies. However, limitations remain. First, this study did not cover a longer period due to limitations in the data available on digital evaluation. In the future, it is possible to examine over a longer period of time whether higher education and digital related policies have a superimposed and multiplier effect on the digital development and thus influence high quality economy, which will be an indirect examination of digital development as a mechanism from a policy angle. Second, the sample size of the provincial studies was small, so future studies should be conducted using a greater amount of municipal level data to draw general conclusions from. Moreover, it can also serve as an empirical reference for countries whose economies are transitioning from rapid growth.S1 File(XLSX)Click here for additional data file.


Adjustment to any illness is a ‘dyadic' process whereby patients and their partners mutually determine each other's perceptions, behaviours, and well-being. The present study explored the association between dyadic coping strategies and illness representations in newly diagnosed female cancer patients and their partners.The sample consisted of 92 female cancer patient-partner pairs from 3 oncology hospitals in Greece and Cyprus. The Actor Partner Interdependence Model was applied to test for dyadic regulation effects.The findings revealed that patients' evaluations of dyadic coping were related to their own illness representations and, in some cases, to partners' illness representations of control. However, partner evaluations of dyadic coping were not associated with either patients' or their own illness representations. Relationship satisfaction did not moderate the relationship between dyadic coping and illness representations.The study suggests that patients' perceptions of support provided by themselves and their partners play a significant role in shaping their illness representations. Future research could delve into the underlying reasons for the observed differences in the impact of dyadic coping on illness representations between patients and partners, considering factors such as gender roles and specific gender-related issues. For couples, psychological adjustment to cancer is well-documented and broadly studied. The great changes, imposed by the cancer experience on both individuals and their families, cause stress, and the psychological adaptation is challenging for the whole family  or treatment .In this study, the aim was to investigate the connection between positive DC strategies  and certain key illness representations among both patients and their partners in a dyadic regulation approach. Specifically, the focus was on examining how the DC strategies adopted by the couple were associated with the individuals' perceptions of personal control, treatment control, and timeline related to the illness. These components were chosen because they have been found to strongly impact health outcomes in cancer patients, as previously documented in studies by Hopman and Rijken  and Richself to the other member of the couple also reflects the broader sense of support available in the couple, we expected its relation to the illness representations to have the same direction as the one described above.Given that available coping and support resources are part of how a stressful condition is evaluated .A total of 126 patients were identified as eligible for participation. In all, 34 patients or their partners refused participation because they were not interested, felt unable to participate, or provided incomplete data. The final sample consisted of 92 couples. All patients were married or living with their male partners for at least 2 years. Patients' mean age was 49.37 years (SD = 10.52 years). Details regarding the demographic characteristics of the participants and the disease characteristics of the patients are presented in a = 0.68), treatment control , and timeline . A slightly re-worded version was used to assess spouses' representations regarding patients' illnesses . To answer the questionnaire, respondents used a 5-point Likert type scale ranging from 1 (strongly disagree) to 5 (strongly agree).Illness representations were assessed with the Revised Illness Perception Questionnaire  correlations were used to examine the relationship between patient and partner age and duration of the relationship for all variables.A MANOVA was performed to examine the potential impact of a series of sociodemographic and illness-related factors on the dependent variables. Specifically, the potential impact of the type of diagnosis (breast vs. other types of diagnosis), cancer stage, treatment type (single vs. combination of treatments), and patient and partner education level were examined. Pearson , was used to perform the APIM analyses. APIM_SEM automatically performs the appropriate statistical analyses , was used to perform the APIM moderation effects .The Actor-Partner Interdependence Model Kenny, ; APIM waFs  < 1.30, p > 0.05, η2s < 0.15. Patient age and relationship duration were not significantly related to DC, relationship satisfaction, or illness representations . With respect to partner age, only one statistically significant correlation was found with own treatment control . Also, patient and partner age were not related to (patient or partner) relationship satisfaction .The MANOVA revealed no statistically significant impact of the sociodemographic and illness-related variables  on the dependent variables, Wilks λs < 0.95, p < 0.05, respectively). Also, only a patient-actor effect was found with respect to own treatment control . No significant effects were found for the timeline.Regarding the APIM results, the dyadic effects of the support provided by self to the other member of the couple (DC-self) on illness representations are illustrated in p < 0.05) and positively to their own and also their partners' representation of personal control over illness . No other effects were detected.The dyadic effects of the perceived support provided by the other member of the couple on illness representations are presented in p < 0.05), as well as to partner corresponding representation . It was also related to its own representation of treatment control . There were no other statistically significant effects.In Bs < 0.22, p > 0.05, 95% confidence intervals = −0.47 to 0.49). Moreover, the comparison between the models that included possible interaction effects and those without interaction effects showed no statistically significant differences .Finally, there is no evidence that the relationships between DC and illness representations were moderated by patient or partner relationship satisfaction, as no statistically significant effects were revealed , treatment control , and the timeline of the illness . The focus was on understanding how the perceptions of DC within the couple were related to these representations in both patients and partners in a dyadic regulatory manner.Additionally, it aimed to examine the possible moderating role of relationship satisfaction in the relationship between DC and representations of illness for both the patient and partner.The findings provided partial support with regards to the hypothesis that a sense of more support received by the other member of the couple will be related to a more positive representation of illness for both parties. The patient evaluations of DC were related to more positive own illness representations, but this was not the case for partners. Partners' evaluations of DC were not related to either their own or patients' representations of illness.At the same time and to the extent that the (dyadic) support provided by self to the other member of the couple also reflects the broader sense of dyadic support available in the couple, our hypothesis that its relation to the illness representations will be related to more positive representations was confirmed in some cases. Patients' evaluations of DC were related to their partners higher levels of control over the illness. Moreover, patients' representations of higher levels of personal control—thus having a sense of being able to control their illness—were related to partners' sense of receiving support from the other/patient. As far as partners are concerned, their evaluations of DC were not related to patients' representations of illness or to their own representations.per se , as explained above, or specific gender issues. In our study, all patients were women and all partners were men, and no partner or actor effects between DC and representations were found for the partners/men. This could be explained by specific gender role stereotypes, where partners' coping resources may be influenced by other factors outside of the spousal dyad as well. Greece has undergone major changes in the past 35 years. While previously Greek culture was described as more traditional with stronger gender roles  may also be vague concepts, especially in the case of cancer patients. For example, the item “My illness will last a short time” may be interpreted in different ways by cancer patients. As Benyamini and Karademas  suggesteThe second hypothesis was not supported by the findings. Unexpectedly, the relationship satisfaction did not moderate the relationships between DC and representations of illness for either patients or partners. This may be due to the following: it is possible that some aspects of relationship satisfaction have a greater impact on representation than others. For example, satisfaction from the communication of emotions in a relationship may be a stronger predictor than satisfaction from engaging in more common coping behaviors, which, at the same time, are both considered good quality characteristics in a relationship. DC may be one of the composing factors of relationship satisfaction and may be incorporated into the concept of satisfaction. The idea that “dyadic relationship wellbeing could be covered under relationship satisfaction (RS)” has also been suggested by Chen et al. . Their fThere are limitations in our study concerning (a) the number of couples that participated was rather modest; (b) the fact that the data collection took place during the COVID-19 pandemic—when access to the hospital was restricted—may have impacted the recruitment process and the results; and (c) this is a cross-sectional study, and as such, it cannot provide any evidence about the direction of the relationships examined. A longitudinal approach is needed to provide us with more information on the process and the ways that DC may determine illness representations. Finally, our study focused only on certain illness representations and certain aspects of DC .Still, the findings of this study underline the importance of adopting a dyadic regulation perspective in relevant studies and also bear some considerable clinical implications.By recognizing the link between DC and illness representations of control and the illness timeline, healthcare professionals can focus on interventions that promote effective DC strategies. Encouraging partners to engage in supportive behaviors, active involvement in treatment-related decisions, and joint problem-solving can help foster a sense of control. This understanding can guide the development of couple-based interventions aimed at enhancing coping and improving psychosocial outcomes for both patients and partners in the context of cancer or other chronic illnesses.Overall, the present findings point out the significant relationship between DC and illness representations, and thus they underline the importance of DC in both the patient's and partner's experience of illness. It has been well documented that illness representations and the entire experience of illness do not take place in a vacuum but are rather linked to the patients' (and partners') lives (Leventhal et al., The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The study involving human participants was reviewed and approved by the Ethics Committee of the University of Crete, Greece and was conducted in accordance with the local legislation and institutional requirements. It was also approved by the Scientific Committee of the following hospitals: Elena Venizelou General & Maternity Hospital in Athens, Greece and Bank of Cyprus Oncology Centre, Nicosia, Cyprus. The patients/participants and their partners provided their written informed consent to participate in this study.ZG: data collection from Cyprus site, literature review, data interpretation, and writing original draft. CT: data analysis and data interpretation. EK and AP: conceptualization, investigation, methodology, and editing. AP: supervision and project administration. All authors contributed to manuscript revision and approved the submitted version.