The widespread use of the Internet of Things makes it relevant to use public IP networks for simultaneous access by both users and wireless sensors. To achieve this, a significant reduction in the subscriber devices’ energy consumption is required. This paper analyzes the application features of the collective dynamic routing method both with and without the use of a robust method for estimating the channel data rate for sensors’ communication in wireless public networks. Based on the analysis, a novel modification of the collective dynamic routing method has been developed that reduces the sensors’ energy consumption while keeping a high data rate and short delivery time for users. An analysis of the network load, the total data transfer rate over the network, and the parameters affecting the sensors’ energy consumption was carried out for a segment of a seamless IEEE 802.11ax network. The simulation demonstrated a high efficiency of using a novel modification of the collective dynamic routing method for access to users and wireless sensors. With the development of the Internet of Things, the requirements for communication networks that provide data transmission from sensors have increased dramatically. A particular challenging situation occurs with the use of wireless sensors, which have strict restrictions on energy consumption.To date, dedicated networks based on specially developed technologies such as LoRaWAN , SigFox The use of existing public wireless networks for simultaneous servicing of both users and sensors is economically more profitable. However, the very different requirements for the data transmission volume, data rate, delays, and power consumption significantly complicate the implementation of such networks.In modern wireless networks, high data transfer rates are provided using high-transmitting devices’ power, and small delays are due to constant listening to the medium, which requires to keep the subscriber’s device receiver constantly switched on. Both these factors lead to significant subscribers’ devices’ power consumption. It reduces their lifespan without regular recharging and complicates the use of public wireless networks for transmitting data from sensors.Thus, it is necessary to significantly reduce the power consumption of subscribers’ devices to enable effective use of existing public wireless networks for transmitting data from sensors. The main directions in solving this problem include a reduction of the energy required for data transmission and decreasing the operating time of the subscribers’ devices’ receivers. The first task can be solved by dynamic planning of the wireless communication networks’ energy parameters , and theTwo main routing methods are used to manage data flows in modern public wireless networks: static and dynamic. However, both routing methods do not consider the intra-system interference that occurs during data transmission.All valid routes are formed before the start of data transmission, and not during the first packet transmission.The optimal routes set is determined based on the criterion of minimizing the delivery time before the start of data transmission in order to reduce the intra-system interference flow.The packet sequence can be delivered to the same node via different routes.To reduce the intra-system interference in public wireless networks in the case of data transmission only by users, the authors proposed a new routing method—the collective dynamic routing (CDR) method, protected by a patent . Its keyHowever, the computational complexity of determining the optimal routes set makes it impossible to use the CDR method  in publiCurrently, specially built networks are used to ensure data transmission from wireless sensors. These networks can effectively transmit data from sensors and have low power consumption.LoRaWAN is a low-power wireless network technology with a long-range coverage area. This technology was developed by LoRa Alliance to meet the requirements of long-range communication of wireless sensor networks with low power-consuming, low data rate communication networks. LoRaWAN cancels the need of repeaters between the nodes, thus reducing the network cost, improving the longevity of their batteries, and increasing network capacity. Altogether, this makes them suitable for wireless sensor networks (WSN) that require low energy consumption and a low data transmission rate ,9.SigFox was developed for the largest global IoT network to connect large numbers of objects broadcasting data. Sigfox provides a software-based communications solution, where computing is controlled in the Cloud, instead of the devices. It reduces energy consumption and connected nodes’ cost .For IoT applications, Bluetooth wireless technology can be used to exchange data at short distances with minimum power consumption . The useSince dedicated networks based on specially developed technologies are designed to transfer small data volumes at low rates, it is difficult to implement the simultaneous traffic transmission from sensors and users.An alternative option is to transfer sensor data through the existing architecture of public wireless networks of Wi-Fi , LTE 4]4], and 5The first approach that allows these networks to provide wireless access to sensors is the implementation of special radio interface functions.LTE networks support the machine-type devices’ communication (MTC), which include sensors . To implIn LTE-M , unlike Additionally, for LTE technology networks, a modification of the NB–LTE radio interface  was propThe use of special radio interface functions allows public networks to provide wireless access to sensors, but significantly limits their bandwidth.Another approach that allows public wireless networks to provide access to wireless sensors is the use of special routing methods.One of the simplest routing methods in public networks is static routing . HoweverTo exclude these disadvantages, the dynamic routing method is used. It is implemented using a large number of protocols. Most dynamic routing protocols implement distributed routing, in which each router builds routes only for devices connected to it. For example, in LTE, the channel resources’ distribution is carried out by scheduler algorithms, considering SINR at each base station, without accounting for the channel resources’ distribution throughout the network.Most of the dynamic routing protocols used in public networks that provide access to sensors can reduce the devices’ power consumption by jointly considering routing and network topology. One of the approaches is the self-organization of sensors in clusters. In , the senIn Wi-Fi networks, a new Neighbor Awareness Network (NAN) standard  has beenHowever, when self-organizing sensors into clusters, it is necessary to control the network topology, which requires the service traffic transfer. It reduces the network throughput. For solving this issue, the authors of  proposedIn the existing clustering protocols, the near base station undergoes a large number of receiving, aggregating, and transmitting operations in comparison to a faraway station. This imbalance of load on the base station and lack of a structured multi-level clustering framework leads to a reduction of lifetime of WSNs. In , a HCR ,  sensors .Considering DL) and UL) accumulated during the interval The network’s task is to transfer the data vector The CDR method contains two stages: the analysis stage and the routing stage. At the analysis stage, the valid routes set At this stage, the valid routes set All data transmitting channels can be split into dependent and independent according to their mutual influence. In the dependent channels, data transmission leads to the intra-system interference, affecting other channels. Data transmission over independent channels does not lead to intra-system interference.When data transmission in downlink and uplink channels is going in separated periods  or different frequency bands (LTE FDD), then channels can be considered as independent. It allows to split the valid routes set The route channels from To account for the intra-system interference flow that occurs during data transmission over dependent channels, end-to-end routes are used. Each end-to-end route connects users and sensors with BR and includes all channels involved in data transmission.A one-dimensional route is an end-to-end route connecting one user or sensor with BR. The route construction is performed according to the network topology described by the network’s graph  and Depth-First Search (DFS).According to , the conGraph traversal occurs by sequentially processing all nodes in the unvisited nodes queue. In the beginning, the first node is selected from the queue and placed in the visited nodes list. Next, all neighbor nodes for the selected one are found. If the found nodes are not included in the visited nodes list and are not users or sensors, then they are appended to the unvisited nodes queue and temporary routes between BR and these nodes are formed. If the found nodes are users or sensors, then they are not appended to the unvisited nodes queue. In this case, two one-dimensional routes are formed based on the available temporal routes: one route is appended to the subset The main BFS method’s disadvantage is the need to form temporary routes, which increases the computational complexity of the formed one-dimensional routes.To initialize the graph traversal using the DFS method, it is necessary to create a stack containing the start node (BR) .The search for each node from the stack is performed sequentially for the next node connected to the selected one. If no such node is found, the selected node is removed from the stack. Otherwise, if the found node is not a user or a sensor, then it is appended to the stack. If the found node is a user or a sensor, two one-dimensional routes are formed based on the information available in the stack: one route is appended to the subset In this case, using the DFS method allows to avoid construction of the temporary routes and reduce the forming time of one-dimensional routes by 33%.Since the CDR method considers the influence of channels on each other, after finding all one-dimensional routes, it is necessary to form multi-dimensional routes consisting of one-dimensional routes that do not have common channels. In graph theory, such one-dimensional routes are called edge-disjoint . This isFinding multi-dimensional routes of maximum dimension could be performed using the Bron–Kerbosch algorithm . Multi-dEach subset, Each valid route, With centralized access to the data transmission medium, the transmission time of each data block is known. This allows to consider the data transmission process over the network synchronous with the frame duration, In this case, for each valid route from the subset The task of the routing stage is to determine the optimal routes set In each frame with duration To transmit the current volumes of delivered data, The data delivery time Based on the current volumes of delivered data, rding to , the optFrom the mathematics point of view, this issue belongs to the problems of integer linear programming (ILP). The ILP problem solution is carried out using precise methods, for example, the method of branches and boundaries, Gomori’s algorithm, etc. . These mOne of the ways to reduce the computational complexity solving the ILP problem is to remove the restriction on the integers by rounding the resulting values up. In this case, the simplex method  can be uAnother option for reducing computational complexity solving ILP problems is the use of various metaheuristic approaches that differ in the generating solutions rule. This approach’s implementation assumes two options: generating possible solutions and choosing the best one among them , or sequSince data are transmitted for each interval in both directions (downlink and uplink), optimal sets of routes The optimal routes set RU). In the CDR method, each RU is considered as a separate channel. In this case, the principle of constructing multi-dimensional routes used in the CDR method leads to a sharp increase in the set’s dimensionality.Modern wireless networks , 5G) use OFDMA technology, where data transmission is carried out using resource blocks (units)  to estimate the channel data rate, considering the intra-system interference influence along multi-dimensional routes in the subset In the uplink, intra-system interference is created only by signals from different users (sensors). Therefore, it is necessary to know the RUs except the RU used for data transmission by the considered device. Additionally, all devices connected to other access points use all available RUs.To reduce the multi-dimensional routes’ quantity in this subset, the worst-case scenario is considered. In this case, intra-system interference is created by only one user or sensor connected for each access point. This device provides the maximum interference level for the analyzed access point. At the same time, such devices connected to the analyzed access point use all RURUs from RURUs, respectively.In this case, the choice of a multi-dimensional route on the remaining access points does not affect the operation of this access point’s devices. Then, a wireless network segment, shown in Since the robust method considers the worst case, the channel data rates in one-dimensional routes do not change depending on these routes’ links into multi-dimensional routes.In , a methoIn this case, it is sufficient to form one-dimensional routes’ subsets Therefore, using the robust method for estimating the channel data rate, the task of the routing stage is not to determine the optimal routes set, but the recurrent formation of multi-dimensional routes.Consider the process of multi-dimensional routes forming in downlink. First, the user In this case, there is no need to transfer the optimal routes set When using the robust method, users, sensors, and BR also accumulate the received data on the interval Since the optimal routes set Transmitting the data volumes The constant frame duration In the case of data transmission from sensors, the CDR method with a robust method for estimating the channel data rate has the following disadvantages:Increase the sensor polling period to Split the current volumes of delivered data Split the data accumulation interval Construct a one-dimensional routes vector In this regard, it is necessary to develop a novel modification of the CDR method. It should include the following changes:In the novel modification of the CDR method, users and sensors are serviced at interval Since the changes do not concern data transmission between BR and users, only the case of data transmission with sensors will be considered.In the beginning of the first period The evaluation of the novel modification of the CDR method’s effectiveness was provided by modeling a seamless IEEE 802.11ax network segment.In the novel modification of the CDR method, only the routing stage is changed. Therefore, the simulation was carried out with the constant and known traffic and communication channels’ parameters. Rotes reconfiguration in dynamic routing protocols is only used in the case of changing the topology or traffic parameters. In this scenario, the routes do not vary, and any dynamic routing protocol becomes a static one.The Wi-Fi network segment consists of 8 Eltex WEP-3ax access points working on 1, 6, and 11 frequency channels in the 2.4 GHz frequency range, with a 20 MHz band. The network is designed to serve 20 users and 100 stationary sensors. The location of access points, users, and sensors is shown in The locations of access points are indicated by red triangles, and users and sensors by green and yellow dots, respectively.The simulation was provided using the “OFDM Planning” software package. SINR’s for users and sensors were calculated on the basis of the floor plan, according to the International Telecommunication Union recommendations ITU-R R.1238-10  and ITU-Each device connects to an access point that provides the highest channel data rate among available access points. In accordance with the obtained network topology, one-dimensional routes’ subsets In this case, considering the absence of service traffic in static routing, this type of routing will always have better characteristics than any dynamic protocol. Therefore, the effectiveness comparison is provided only for the novel modification of the CDR method and static routing.The connection of users and sensors to access points is indicated in In the simulation, TCP, HTTP, and FTP protocol packets are generated using the 4IPP model . The trato model , and theTraffic transmission over a seamless IEEE 802.11ax network segment is simulated as follows.Initially, the “OFDM Planning” software package generates packets in accordance with the specified models, indicating the arrival time and the length of each packet.Further, the generated packets are transmitted over the network using both novel modification of the CDR method and static routing.When using the novel modification of the CDR method, the entire simulation time is divided into rithm in  and descWhen using static routing, the users’ and sensors’ data are transmitted in Initially, the first packet not delivered to (from) the user (sensor) is found. Next, a one-dimensional route is selected from the valid routes set Based on the simulation results, the load on the network When simulating a novel modification of the CDR method, the sensors were separated into 10–100 groups depending on the information accumulation vector interval The load on the network The ratios Since in the novel modification of the CDR method, users and sensors are serviced in the interval According to An increase in the information accumulation interval The main indicators of the novel modification of the CDR method’s effectiveness for sensors are the total data transfer rate over the network The use of the novel modification of the CDR method allows to increase the data transfer rate over the network as a whole for sensors by more than 50 times according to the graph in The closest case was considered in . Howeverure 5 in ). For thFrom the graphs shown in The average of the wireless sensor current consumption Consider a wireless sensor built around an ESP32 microcontroller with a Wi-Fi module that has of 0 dBm . The senThus, the novel modification of the CDR method for IEEE 802.11ax allows reducing the sensor’s active operation time and the wireless sensor’s power consumption. At the same time, this method provides a high transfer rate and a short data delivery time for users.This paper described the development of a novel modification of the collective dynamic routing method that allows public wireless networks to simultaneously serve both users and sensors for sensors’ communication in wireless public networks. For the chosen seamless IEEE 802.11ax network segment, the effectiveness of using the novel modification of the collective dynamic routing method was demonstrated.The novel modification of the collective dynamic routing method improved the data rate for sensors by more than 50 times for the considered network. Additionally, the operation time for the sensor’s receiver became less than 1% of the polling period with the same quality of service for the users. This reduced the wireless sensor’s average current consumption radio modules based on the ESP32 microcontroller by 18 times.


Contraction Hierarchies routing algorithm with the default routing step and the Krauss car-following model with the default configuration parameters.Driver behavior models are an important part of road traffic simulation modeling. They encompass characteristics such as mood, fatigue, and response to distracting conditions. The relationships between external factors and the way drivers perform tasks can also be represented in models. This article proposes a methodology for establishing parameters of driver behavior models. The methodology is based on road traffic data and determines the car-following model and routing algorithm and their parameters that best describe driving habits. Sequential and parallel implementation of the methodology through the urban mobility simulator SUMO and Python are proposed. Four car-following models and three routing algorithms and their parameters are investigated. The results of the performed simulations prove the applicability of the methodology. Based on more than 7000 simulations performed, it is concluded that in future experiments of the traffic in Plovdiv it is appropriate to use a  Simulation is the main approach in presenting systems for which it is difficult and expensive to perform real experiments. The degree of correspondence between the real system and the simulation describes the validity of the model. The acceptability of the degree of correspondence depends on the purpose of the further experiments .Road traffic is one of the most intensive domains of simulation modeling due to the great interest in transport and the many pressing issues in this field. Road traffic simulation models are usually agent-based. This makes them suitable for dynamic enrichment, integration of complex behaviors, and modeling intelligent human behavior. The drawback is that the complexity of the model complicates maintenance ,2.Road traffic simulation models include many components: the road network, traffic load data, the choice of simulation tool (traffic simulator), the model of interaction between agents, and parameter settings. Model evaluation and calibration is often complex and goal-specific. The use of real input data increases the correspondence to the real world but complicates the validation process ,4.Fitting the input data to the simulator (traffic demand estimation) is one of the important tasks to solve. Traffic demand estimation converts input traffic information (lifestyle statistics and detector data) into single-trip routes using routing algorithms ,6.Another important component of the road traffic simulation model is the driver behavior model. This takes into account characteristics such as mood, fatigue, and responses to distracting conditions ,8. The rIn articles ,12,13, tArticles ,10 invesThe authors of ,15,16 exArticles ,11,17 deThe current article proposes a methodology for choosing simulation model components that best reflect driver habits. The methodology is not tied to a specific simulation tool.In The proposed methodology aims to select the routing algorithm and car-following model and their settings that best reflect driving habits.A car-following model is a function of position, speed, and acceleration. The most commonly used are micro models, as, with their many adjacent parameters, a higher degree of correspondence to the real system can be achieved. Examples of popular microscopic car-following models are Gipps, Krauss, and Widermann ,5.Routing algorithms are the main part of traffic demand estimation. They formalize networks as weighted graphs and compute routes. Examples of commonly used routing algorithms are Prim, Kruskal, and Dijkstra ,19. RoutThe proposed methodology selects a routing algorithm and a car-following model and their parameters that most closely correspond to driver habits by choosing the combination with the smallest deviation between the input and simulated values.The simulation setup encompasses the road map, traffic light cyclograms, and traffic loads for road lines. Each vector of the Cartesian product of the possible values of the car-following model and routing algorithm and their parameters are input into the simulator. The discrepancy between the simulated data and the real load data shows how well the respective configuration matches driver behavior. For example, the configuration  is an input vector for a simulation. This means that the simulation uses a Krauss car-following model with a minimum distance when stopped of −1 m, a minimum distance when moving of −2 m, and a Dijkstra routing algorithm with a route recalculation step of −10 s. After simulation execution, the information about the number of cars that passed through each lane is compared with the input data. The discrepancy between these two quantities in percent is calculated for each line. The average value of the discrepancies for the certain simulation is a metric for the validity of the configuration. the configuration with the lowest discrepancy is considered to best match driving habits.The part of the road network in the city of Plovdiv between “Ruski” Boulevard (to the east), “Osvobozhdenie” Boulevard (to the west), “Gladstone” Street and “Princess Maria Luisa” Boulevard (to the north), and “Hristo Botev” Boulevard and Saint Petersburg Boulevard (to the south) is integrated in SUMO (Simulator for Urban Mobility) .SUMO is an open-source multimodal traffic simulator created by the German Aerospace Center . SUMO ofThe data for the traffic loads in the experiment are obtained through virtual detectors built on the basis of road cameras. They are extracted from the database of the Municipality of Plovdiv in the form of Excel reports. Each report contains information about the number of vehicles that entered the line of a single intersection per hour. The available reports cover the periods from 15 January to 27 January 2021. Reports for the period 19 February–19 March 2021 for three of the junctions are added to other reports, and an additional set of simulations is performed with the extended data set.As illustrated in , a repeaThe actual duration of the traffic light cycles is used based on the municipality’s documentation.For the purposes of the investigation, three routing algorithms and four car-following models and their configuration parameters are selected . All thrSeveral Python scripts are implemented to prepare the simulation inputs, run the simulations, and process the output data.The ControllerReports.py script converts the municipality reports into two CSV  input files—working day (wCFG.csv) and holiday (hCFG.csv)—by calculating average values per hour for 24 h.CallFlowrouter.py calculates set of routes and traffic flows from given detectors in the network. The script uses the SUMO tool flowrouter by setting different measurement intervals .TrafficDemand.py performs dynamic user assignment and runs the SUMO simulation. For dynamic user assignment, the SUMO tool duarouter is used. At this stage, the routing algorithm and car-following model and all their parameters are set. The result is a Dua.xml file that contains all the traffic information for the SUMO simulation. The output of each simulation and the configuration files are saved in a separate folder named after the configuration input.CalculateDiscr.py compares the number of vehicles that passed each road line from the simulation to the input data in the corresponding CSV input file and calculates the average discrepancy for each simulation.The proposed methodology requires evaluation of many different simulation configurations: the Cartesian product of the possible values of the car-following model and routing algorithm and their parameters. All configurations have no data dependencies . Some authors  show thaTraficSim implements the control logic for executing a single simulation. It generates the routes and configuration files, executes the simulation, and saves all outputs. This application with all of its dependencies is packaged inside Docker image. To automate and parallelize the execution of simulations with different input configurations, we used an Apache Airflow WMS instance running on a Kubernetes cluster inside a private cloud environment. Airflow is an open-source platform that can programmatically build, schedule, and monitor workflows. They are defined in a Python-based descriptor called a DAG (directed acyclic graph). Inside a DAG, the parameters, execution order, and level of parallelism of each group of simulations are configured as separate tasks. Airflow then uses the Kubernetes API to schedule a pod with the Docker image and configuration for each task in the DAG. Kubernetes scheduling is resource-aware and actively manages workloads. For each scheduled simulation, we set a “Resource request” based on the average of a couple of test runs, and “Resource limits” that restrict the maximum that can be used by a single simulation. All configuration files and the results of the simulation are saved to a cloud object storage bucket.A total of 7539 simulations were run; among them, 7103 were with simulation step of 1, and 36 were with smaller simulation steps. The big disadvantage of the more-precise simulation steps is that it requires much more computational resources and/or time .Krauss (450) and Wagner (450) car-following models over the working day data set are simulated. The Wiedemann and Modified Krauss car-following models are performed for, respectively, 828 and 825 simulations over the working day data set. Additional sets of simulations with different simulation steps and extended input data are performed in order to investigate the factors that influence discrepancy  and Wagner (450) car-following models over the holidays data set are simulated. For the Wiedemann and Modified Krauss car-following models, we performed, respectively, 828 and 825 simulations over the holiday data set. Additional sets of simulations with different simulation steps, extended input data, and fixed parameters are performed in order to investigate the factors that influence discrepancy . When extending the data with reports for the period 19–21 February 2021, the discrepancy increases.On working days, driver behavior is best described by the The probable reason for the worse performance of the routing algorithm with the extended data set is that the driver behavior pattern changes over time, and mixing data from different periods may lead to data contamination.Dijkstra routing algorithm and the Modified Krauss, Wagner, and Wiederman car-following models show the best performance. The minimum discrepancy in the holiday data set is observed in the extended data set with the Contraction Hierarchies routing algorithm and the Krauss and Wagner car-following models.In the original holiday data set (15–27 January 2021), the Contraction Hierarchies routing algorithm under heavy loads.In the original holiday data set, there are only a few records. When adding the data for 19–21 February 2021 to the holiday set, discrepancy decreases significantly. The probable reason for this result is that in larger data sets, some of the measurement errors affect the averages less. The different performance of the routing algorithm on the holiday data set confirms the effectiveness of the The possible reason for the different results with the original and expanded data set is that driver behavior varies at different periods and depends on many preconditions. Another possible reason for the different results when expanding the data set is random measurement errors.Contraction Hierarchies routing algorithm is suitable for heavy loads, while Dijkstra is suitable for lower loads or when data are missing. When using the default simulation step, the routing step and additional model parameters do not affect the final result. The results regarding the car-following model are the most contradictory. In the extended and shortened data sets, different models show minimal discrepancy. For working days, the Krauss and Modified Krauss models show the best results, while for holidays, Krauss and Wagner are the best.It can be concluded that the The described methodology suggests fitting the simulation parameters to real traffic data regarding the number of vehicles that pass on an hourly basis in order to reach a more realistic simulation model. This idea can be developed using more detailed data about traffic in the city: for example, the types of vehicles and their sizes, the purpose of the trip, the presence of public events, etc.The described methodology offers to find the most suitable car-following model and routing algorithms and their parameters. It is possible to extend the methodology by adding more components to be tuned: for example, line-change model and its parameters.Another option for extending the methodology is to look for simulation model components and parameters on an hourly basis instead of working days and holidays.The proposed implementation via SUMO and Python is a good starting point for future research. The methodology realization with the same input data and different traffic simulators would help to evaluate the universality of the proposed approach. Repeating the experiments with the current implementation and data from other cities or different periods would help the completeness of the research.Contraction Hierarchies routing algorithm with the default routing step and the Krauss car-following model with the default configuration parameters.The performed experiments may be a good starting point for setting up the SUMO road traffic simulator for future traffic studies in the city of Plovdiv. Based on the preformed experiments, it can be concluded that in future work it is appropriate to use the Examining the selected road network with different sets of input data and a simulation step less than 0.001 and the exhaustion of all possible 484,200 configurations would be indicative of the components of the simulation model that best describe the behavior of drivers in the city of Plovdiv. This would require a massive amount of time or computing power. One of the possible future directions of work is finding heuristics and developing a feedback-based workflow.


Roadway systems and their assets are the backbone of the transport sector and are vital for social and economic prosperity. Hence, it is important to design and develop transportation networks that can withstand natural hazards such as earthquakes. In recent decades, research concerning disaster risk management for roadway systems has received a lot of attention, particularly via the use of seismic vulnerability assessment methods. The majority of those models focus on a single criterion e.g., physical degradation of road assets, traffic disturbance, and/or functionality loss of the network, rather than considering how different criteria interact, such as association between asset damage, functionality, and network traffic. The main purpose of this study is to provide an integrated methodology for evaluating the seismic vulnerability of road networks to inform decision-making for risk mitigation. The proposed framework correlates the Intrinsic Seismic Vulnerability Index (ISVI) scores with the variation of accessibility rates to critical service centers. The methodology is demonstrated through an application to a part of a road network for specific seismic scenarios. The ISVI quantifies the impact caused by a parameter's physical performance on the road behaviour using Non-Linear Dynamic Analysis (NLDA) technique, which can reduce or limit the role of the studies that are based on expert opinion decisions. The validated results shows that the embankment height is considered the most effective parameter in the physical assessment approach, followed by the number of lanes, while the soil type and pavement strength are the least effective parameters with a better effectiveness for soil type compared to pavement strength. Additionally, the integration between the physical assessment approach and the analysed accessibility rates is clearly showing compatibility between the vulnerability and accessibility approach, demonstrating more precise assessment tool by considering the correlation between the vulnerability rates and the reduced accessibility levels. The proposed approach can assist infrastructure owners and operators to reduce risk and boost emergency accessibility.• Conducting ISVI for roadway and its assets based on physical damage approach.• Assessing road networks accessibility rates by introducing an accessibility index (AI) using different geographical aspects.• Formulating an integrated model between physical damage and traffic accessibility through building transport performance maps.  Specifications tableRecent earthquakes, that had a significant impact on human life and economic development, have raised worries about the robustness of road networks and their components exposed to seismic hazards. As a result, various approaches for evaluating the vulnerability and mitigating seismic risk for these systems have been developed. Vulnerability assessments at the asset level consider the degree of damage to a specific asset (e.g. a bridge) by using fragility functions and/or vulnerability indexes, whereas studies at the network level take into consideration the functionality of the network by studying traffic-related aspects such as the accessibility or link importance Step 1, the roadway network is constructed with respect to the main classified assets, by taking into account different factors, for instance: node-to-node connectivity, roadway length, and the role of the assets in the network. The Intrinsic Seismic Vulnerability Index (ISVI) is then evaluated based on critical vulnerability parameters, namely the embankment height, pavement strength, ground condition, and road width. The vulnerability is then categorized into three main classes based on the range of the estimated ISVI scores. In particular, the road segment is classified as safe, moderately resistant, and low resistant. This is followed by weighting the investigated parameter based on the developed Non-Linear Dynamic Analysis (NLDA) model. The latter is employed to develop the fragility functions that are utilized to evaluate these simulated parameters. This step helps in prioritizing the investigated parameters, where the extracted results from the NLDA are used to weight the parameters by using Analytical Hierarchy Process (AHP). Subsequently, the ISVI scores are calculated from these weighted parameters based on their effectiveness, where the most effective parameter is defined based on the difference of damage probability between the two transmission stages at collapse state as described in Step 1.In Step 2, the obtained ISVI values for the main assessed roadways are used to develop the traffic disruption approach, by determining the Accessibility Index (AI) of road networks. This step is targeting to the identification of the main corridors that can be used in case of emergency for accessing critical service centers after earthquake incidents.In Step 3, forms an integrated approach between physical (Step 1) and traffic-based (Step 2) approaches, where the transportation performance for the area under investigation is mapped, by combining the ISVI and AI outcomes. This step highlights the reduced network accessibility and the accessibility of the critical service centers and provides an integrated tool for obtaining big data analytics that facilitate efficient natural disaster security measures and solutions, and hence reduction of losses.Finally, The present paper introduces a methodology that includes a modified Intrinsic Seismic Vulnerability Index (ISVI) for the road network and its components, which is then integrated with the road network functionality to evaluate accessibility rates. This framework focus on the earthquake vulnerability assessment of roadway and its assets based on the minimum identified scale of a specific city. Identifying this scale is considered a crucial step because the assessment procedure is considered complex and needs to be precise, where the challenge lies in identifying the minimum possible scale of expression of the city and its elements to assess the vulnerability of roadway system. This scale can be categorized into two main levels, the macro-level, which considers the whole city development plan, and the micro-level, which focuses on the road network itself. The developed framework is built on the basis of the micro-level, where three main steps are introduced in the proposed method as illustrated in Step 1: Intrinsic Seismic Vulnerability Index (ISVI) for roadways and their assetsIn The Intrinsic Seismic Vulnerability Index (ISVI) is established through building an analytical evaluation approach that considers four important parameters for roads and their investigated parameters . The parameters are evaluated based on their relevant criticality as indicated in prior research functions are commonly used to assess the physical vulnerability of roadway assets for a given Intensity Measure (IM). A cumulative damage distribution function is represented by fragility curves, which correlate the seismic IM and the probability of exceeding different damage states of the infrastructure asset Probabilistic damage distribution The degradation of the main parameters of the roadway can be quantified by defining the probability of damage at collapse stage for a specific seismic intensity. To improve the overall reliability of roadways and their assets, the resistance design concept is primarily evaluated for two main transmission stages. As demonstrated in After ranking the investigated parameters with respect to their effectiveness, the Analytical Hierarchy Process (AHP) is used to give a specific weight Where Subsequently, the calculated ISVI scores are used to categorize the investigated roadways under earthquake effect at seismic intensity of IX, where these roadways are considered as low damage (ISVI = 0-0.4), moderate damage (ISVI = 0.4-0.7), or high damage (ISVI = 0.7-1). The map is created at a critical seismic intensity (IX), because the ISVI values at the two different seismic intensities show minor differences. Moreover, the IX intensity provides greater critical damage ranges . The map in Road networks are specified based on various factors, such as the roadway length, connections between roadways, circuit, manoeuvring path for vehicles, and the criticality of these elements in a specific network. For instance, Kilanitis and Sextos The seismic and geotechnical qualities of the studied region, as well as other physical and geometric aspects, influence the roadway vulnerability. The main investigated parameters used in this framework were selected based on previous research that described and prioritised their criticality, and how these parameters affect the roadway system during earthquakes. For instance, Maruyama et al. As a result of the insufficient data and expert opinion used to build the seismic vulnerability index, empirical methodologies associated by uncertainty. On the contrary, analytical methods can give more accurate and reliable vulnerability evaluations Different scenarios are considered in developing the assessment model for each asset. For instance, when assessing the probability of damage based on the embankment height, different height values h=4 m, or h=6m or h=9m are considered, while the other parameters  are fixed at a specific value. The worst-case scenarios that have the most critical impact are finally included in the model. The same process is repeated to assess all the main parameters on the roadway system.To sum up, traffic and earthquakes loadings are two kinds of loads with different natures and should be combined in seismic assessment of single parameter. The destructive power and duration of earthquakes are greater than that of vehicles on roadways. Therefore, it is proper to consider earthquake as the condition for load combination. Since the methodology in this paper is based on the condition of earthquake, the whole load combination is divided into those with time history analysis and traffic loadings. The developed analytical approach that is used in this methodology is built by using the four determined parameters solely by referring to their classification in the vulnerability classes that is described in Section 2.2. All of this is employed through the Incremental Dynamic Analysis (IDA) This is followed by defining the Intensity Measure (IM) by using the spectral acceleration as a fundamental period Sa . The spectral acceleration (Sa) for the vibration period (T1) is related to the critical deformed shape in the road structural element of the 1st mode shape with a 5% damping ratio and is considered the most appropriate measure in relevant research Where,For several seismic hazard scenarios that are generated from the microzonalization research for the Penang case study, the vulnerability assessment of the roadways and their assets is carried out. According to Sa values, the probability of damage is determined using appropriate fragility functions for Penang roadway systems. Mainly, when evaluating and prioritising the impact of the parameters on the stabilisation of the roadway system, it is important to consider the concept of resistance design for roadway systems. At the collapse stage, the roadway system's destabilisation can be predicted. Two different transmission stages should be examined in order to better understand how this system can be stabilised using the resistance design perception. By examining the % difference/improvement in two different stages based on the damage states and primarily the collapse state at particular collapse intensity measures, it is possible to quantify each parameter's influence on its structural performance and vulnerability behaviour patterns from the fragility functions. The first stage is from the low to moderate state, and the second stage is from the moderate to high state. For the roadway system, interpretation of the Earthquake Resistance Design (ERD) principle can be implemented with respect to the developed Incremental Dynamic Analysis (IDA) and percentage of improvement. The main identified parameters are weighted based on the results, where the most effective parameter is concluded from the probability of damage difference (percentage of improvement) between the two transmission stages at collapse state. The calculation of the ISVI scores is determined using Step 2: traffic disruption assessment approachThe vulnerability classification is determined using The Accessibility Index (AI) is used to analyse the road network's accessibility disruption. The AI scores show the accessibility rates between different intersecting nodes in the road network, with higher scores reflecting a better accessibility rate. The relative accessibility that investigates the ratio of interconnectivity between different intersecting nodes, should be assessed with respect to distance, travel costs, and travel time in order to determine the AI. The distance is considered the most essential factor in calculating the AI in this method by which, when compared to travel time requirement, it is measured as a constant component with less variations ,15.The road network and critical service center topologies are created, as well as the critical infrastructure elements and geographical aspects. The critical service centers are classified and located on the map  as shown in The goal of this stage is to identify the undamaged corridors, which are the main elements by which, traffic can rely on in the case of an emergency event (emergency corridors). The roads are categorized as safe, moderately resistant, or low resistant depending on the results of the physical damage approach (damaged roads) described in step 1. If there is an available link connecting the intersecting nodes of the road network, the network is characterized as completely linked, and the nodes are regarded equally accessible. On the other hand, when a road network isn't completely connected, it is divided into separate connected sections. The intersecting nodes in these connections may become inaccessible (isolated region) if links are destroyed or obstructed, and the network graph may split up into several elements (road network nodes and links). To locate the isolated regions of the network, a connectivity analysis is performed through the undamaged components (low vulnerable components).Orthogonal cost: (3)Diagonal cost: (4)The accessibility assessment is carried out both before and after the earthquake. Distance, cost of travel, and trip time are all terms that may be used to calculate accessibility. The cost-distance analysis is used to generate the accessibility index in this study. The latter is evaluated using ArcGIS to convert a raster grid from a road network vector feature, as seen in Step 3: Integrated approachThe result of the difference in the two cumulative costs is used to determine reduced accessibility (degree of isolation). Because of the short distance between the source and the cells, the cost distance of the linkages is valued by zero (undamaged). Damaged links, on the other hand, result in higher accessibility ratings, which are scaled depending on accessibility values ranging from 0.00 to 0.06 The generated damage maps based on Intrinsic Seismic Vulnerability Index (ISVI) and the Accessibility Index (AI) approach have shown that it is important to reflect the correlation between the physical damage and traffic disruption, because the generated integrated damage maps are showing the main accessible roads. This is determined by the evaluated physical damage that is affecting roads during earthquakes, aiming at identifying the critical service centers that are expected to have higher accessibility, and hence, should be used considered in the emergency planning. This correlation can be achieved by developing the built emergency accessibility GIS maps that are described in the following section.This process includes the creation and visualization of a transportation performance map using the ArcGIS platform, with the objective of determining the primary corridors that will be operative following earthquakes. As explained in step 2, the retrieved data is displayed as ArcGIS maps to show the accessibility for the investigated areas. The integrated maps show Finally, based on the created integrated approach between traffic functionality and physical damage assessment for the road network for all the investigated roadways, a link between the ISVI and AI is drawn. The visualized results will help stakeholders and decision-makers to figure out which parts of the city are expected to have lower accessibility scores to emergency service centers during earthquakes, allowing for additional preventative steps and re-maintenance operations in the near future. These steps are anticipated to make the network more accessible, resulting in more resilient cities.A preliminary study is developed in Penang area, Malaysia to validate the present methodology, based on the research by Adafer and Bensaibi For the Penang case study, a vulnerability assessment of the roadway and its assets is carried out for several seismic hazard scenarios established from the micro-localization research ,16. The The most significant parameter, embankment height, appears to have by far the highest percentage of improvement, followed by the number of lanes, while the soil type and pavement strength are the least influential criteria, with the soil type having a tiny benefit. ISVI is primarily concerned with the individual qualities of roadway infrastructures, but it is unaffected by external influences. The number of elements utilized depends on the type of vulnerability being investigated and the major context of this ISVI, where weighting factors are typically used to analyse the relevance of different aspects to road vulnerability. Due to the obvious wide range of outcomes, it is critical to provide this methodology that is considered a more accurate ISVI assessment method.The proposed methodology works on assessing the seismic vulnerability of roadways and their assets. A modified Intrinsic Seismic Vulnerability Index (ISVI) approach is conducted based on previous studies that tackled this issue, where the ISVI scores are considered the baseline of the vulnerability assessment of roadway systems and to extract the accessibility rates. These ISVI scores are integrated with the Accessibility Index (AI) approach that is conducted on the basis of geographical aspects. The integration between the physical and traffic approach is making this methodology more significant, since most of the past studies are focusing on single criterion  without considering the integration between these different assessment factors and approaches. In real-world scenarios, the integrated model may aid in the discovery of correlations between all assets of road networks, as well as the development of a disaster management tool to prevent fatalities and economic losses during disasters. Future research should focus on developing well-informed disaster management maps, which include the most effective placements for vital service centers and focus on the interdependencies between various infrastructures, such as electricity, water, transit, airports, and fuel, as well as the pace of interaction between transportation networks and other systems. Finally, the proposed framework focus on road embankments and pavements; however, its application can be extended to other assets such as bridges, tunnels, and retaining walls in future studies.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Extracting quantitative information of neuronal signals by non-invasive imaging is an outstanding challenge for understanding brain function and pathology. However, state-of-the-art techniques offer low sensitivity to deep electrical sources. Stimulus induced rotary saturation is a recently proposed magnetic resonance imaging sequence that detects oscillatory magnetic fields using a spin-lock preparation. Phantom experiments and simulations proved its efficiency and sensitivity, but the susceptibility of the method to field inhomogeneities is still not well understood. In this study, we simulated and analyzed the dynamic of three spin-lock preparations and their response to field inhomogeneities in the presence of a resonant oscillating field. We show that the composite spin-lock preparation is more robust against field variations within the double resonance effect. In addition, we tested the capability of the chosen composite spin-lock preparation to recover information about the spectral components of a composite signal. This study sets the bases to move one step further towards the clinical application of MR-based neuronal current imaging. Unfortunately, the sensitivity of these methods is limited for in-depth activity sources4. On the other hand, conventional functional magnetic resonance (fMRI) detects 5. This technique presents good spatial resolution, but it measures neuronal activity indirectly, and the hemodynamic response function limits its temporal resolution6.Neuroscience seeks to understand brain activity and its pathologies but still struggles to extract electrical neuronal information in a non-invasive way. Non-invasive detection methods such as electroencephalogram7. The SIRS contrast is based on the interaction between the magnetization locked by a spin-lock (SL) pulse and an oscillating magnetic field induced by neuronal activity. Several studies proved the capabilities of the method with simulations and phantom experiments11. Even more, the dependence of the contrast with the change of the excitation angle has been proposed as a new method for direct functional connectivity measurements12.Stimulus Induced Rotational Saturation (SIRS) is an MRI-based sequence with the potential to directly detect neuronal activity with sufficient spatial resolution13. However, researchers in the field of 17. Furthermore, some researchers within the SIRS community tested and used composite pulses18. But, to the best of our knowledge, no comprehensive study of the influence of the double resonance effect in combination with field inhomogeneities has been presented. Magnetization dynamics during a spin-lock pulse changes radically in the presence of an oscillating resonant field. Here, we present a simulation model based on rotation matrices that allow us to study the new dynamics and capture the effect of any oscillating field over time. We studied three well known SL preparations, the basic or standard SL pulse (BASL), the rotary echo SL pulse (RESL), and the composite rotary echo SL pulse (CRESL). Using the simulator and phantom experiments, we define the optimal strategy by examining the response of the preparations to field imperfections considering the new dynamics.The susceptibility of the SL preparations to 21. In addition, certain pathologies manifest with neuronal activity at specific frequencies. For example, high-frequency oscillations (HFOs) are considered markers of the seizure onset zone in epilepsy23. This case is noteworthy because non-invasively outlining the seizure onset zone can have a direct impact on patient care quality. Therefore, we consider that the next step to take towards the clinical application of the SIRS sequence is a more realistic signal analysis. The use of a repetitive acquisition of successive measurements with and without SL preparation has previously been proposed24. With this method, the BOLD effect and image artifacts are minimized by dividing the two signals. In the second part of this work, we tested the ability of this acquisition method to reconstruct the frequency distribution of multi-frequency signals.Finally, to validate the chosen preparation in more realistic conditions, we studied the filter properties of the preparation in the presence of composite signals. Physiological signals fluctuate in time and are composed of multiple frequenciesThe purpose of this study is to advance research towards the clinical application of the SIRS technique. First, we define the optimal SL preparation and metric to detect oscillating fields in the presence of field inhomogeneities and second, we analyzed the response of the chosen preparation to composite signals.25. However, modulated oscillating fields are more representative of those found in physiological activity. In general, it is not possible to analytically describe these fields, and numerical simulations are the only option. Among the numerical simulation methods, solving differential equations9 and stepwise constant rotations as presented in this work, give similar results. Based on previous bibliography in the 17, where composite and refocusing SL pulses were developed, we decided to adopt the rotation matrix method presented in those works and added the influence of an arbitrary oscillating target field. Figure r by 17. This results inThe dynamics of the magnetization with the BASL preparation in the presence of simple sinusoidal oscillating fields has been analytically describedmentclass2pt{minimmentclass2pt{minimmentclass2pt{minimmentclass2pt{minimWe can now consider the dynamic under the influence of an arbitrary target field mentclass2pt{minimFinally, the magnetization in the presence of relaxation is modified as mentclass2pt{minimmentclass2pt{minim,and Eq.  changes mentclass2pt{minimWe validated the rotation matrices simulator using the comparison with both analytical and numerical models  using the method presented in “9b using We performed measurements in a 3T whole-body MR scanner , using a Single-Shot-EPI readout with fixed parameters: Figure Rigol DG1022Z) through the circuit shown in Fig. The coil connects to the function generator  as a metric. Second, the ppd signal can separate and estimate the amplitude of frequency components within individually acquired composite signals. However, there are two strong limitations of this method. The first one, common to the three SL preparations, is that the susceptibility of the SL amplitude to We investigated the magnetization dynamics of three Spin-lock preparations in the presence of a resonant oscillating field and characterized the influence of field inhomogeneities. By simulations and phantom experiments, we found that all three spin-lock preparations exhibit an oscillating contrast as a function of time. Due to the oscillating behavior, we used the standard deviation of the signal as a metric to quantify the influence of the target field. We showed the std metric within the CRESL preparation is the most robust against field inhomogeneities even in the presence of the double resonance effect.By acquiring composite signals with the chosen CRESL preparation, we found that is possible to distinguish the frequency components of individual signals composed of multiple frequencies. These results indicate that the CRESL-prepared SIRS sequence has the potential to be used for the detection and localization of oscillating fields with frequencies associated with specific pathologies. In the future, we aim to assess the detection limit of the presented methods under realistic physiological and pathological conditions.Supplementary Information.


Green credit policy (GCP), as one of the key financial instruments to achieve ’carbon peaking’ and ‘carbon neutrality’ targets, provides capital support for the green development of enterprises. This paper explores the impact mechanism of GCP on the sustainability performance of heavily polluting enterprises (HPEs) from the perspectives of technological innovation level (TIL) and credit resource allocation (CRA), using panel data for Chinese A-share listed manufacturing companies from 2010 to 2015 to construct a propensity score matching and differences-in-differences (PSM-DID) model. We find that GCP has a causal effect on corporate sustainability performance (CSP). Although GCP significantly improves CSP, there is no long-term effect. Heterogeneity analysis shows that the relationship between GCP and CSP is only significant in non-state-owned enterprises and in eastern and low-market-concentration enterprises. Mechanism tests indicate that GCP stimulates HPEs to invest more in technological innovation and thereby improves CSP through the innovation compensation effect; the credit constraint and information transfer effects caused by GCP reduce the credit resources available to HPEs but have a significant forced effect on CSP. This paper enriches the study of the economic consequences of GCP and provides implications for stakeholders to improve the green financial system and achieve green transformation of HPEs. Green and low-carbon production and lifestyle are the common and beautiful pursuits of all mankind. How to balance the relationship between economic development and environmental protection has become an urgent and realistic issue for countries around the world. With the rapid industrialization and urbanization of China, environmental pollution has become a non-negligible problem in the process of high-speed economic development . EcologiAt present, research on green credit policy mainly focuses on implementation risks and implementation effects. Zhao and Chen  analyzedAt the micro level, green credit policy strengthens the supervisory function of financial institutions towards users of funds and enhances their own environmental and financial performance, which significantly promotes their high-quality sustainable development . HoweverAbove all, existing studies describe the financing constraint punishment effect and innovation compensation effect of GCP. Research on the economic consequences of GCP remains controversial due to differences in analytical methods and perspectives. In addition, sustainability performance is a composite measure of corporate financial and environmental performance, which reflects whether a company is making the best use of relevant resources for value creation and environmental protection. There is still a shortage of studies from this perspective. Considering that HPEs are the primary source of environmental pollution, the GCP aims to support green economy development by limiting the flow of funds to polluting areas. Using the ‘Green Credit Guidelines’ as a quasi-natural experiment to analyze the impact of GCP on corporate sustainability performance (CSP) is the best way to objectively reflect the effects of GCP. Therefore, the purpose of this paper is to explore the impact mechanism of GCP on the sustainability performance of HPEs through panel data of Chinese A-share listed companies from 2010–2015. The main contributions of this paper are as follows: (1) The propensity score matching (PSM) method is introduced to filter the covariates, which indicates that the standardized mean difference (SMD) values of all covariates are less than 0.2. This method significantly reduces the sample selection bias and improves the reliability of the results. (2) Verifying not only that the ‘Green Credit Guidelines’ policy significantly contributes to the improvement of the technological innovation level (TIL), but also that there is a significant positive relationship between GCP and CSP, these findings enrich the applicability of the Porter effect in the Chinese manufacturing industry. (3) This paper shows that there is a causal effect between the ‘Green Credit Guidelines’ policy and the sustainability performance of HPEs, but that their positive relationship does not have a long-term effect, and the policy effect gradually diminishes as time increases. (4) The reliability of the baseline regressions is verified using four methods: replacing the dependent variable, counterfactual test, shortening the time window and introducing other policies. The innovations of this paper are that: (1) We do not study the impact of GCP on financial performance or environmental performance, but rather, by using the entropy method to construct a composite index of CSP from financial and environmental social responsibility dimensions, we verify that GCP can significantly improve CSP, thereby enriching the theoretical study of the relationship between GCP and CSP. (2) Control variables are selected from the firm and regional aspects to make the regression results more convincing. (3) The heterogeneity of the relationship between GCP and CSP from the internal characteristics of enterprises and the external environment is verified; the impact of GCP on CSP is more significant among non-state-owned enterprises (SOEs), eastern firms, firms with low market concentration and high green innovation firms. (4) The impact mechanism of GCP on the sustainability performance of HPEs from the perspectives of TIL and CRA is explored; GCP can significantly improve the sustainability performance of HPEs through credit financing and technological innovation. This study enriches the theory of green credit and provides insights for sustainable corporate development and government green financial system improvement.GCP can increase the level of external financing constraints faced by enterprises, which is essentially the impact of environmental regulation on the behavior of HPEs. GCP combines administrative penalties to both limit the financing capacity of HPEs and increase their environmental costs to stimulate enterprises to undertake environmental governance and green transformation . GCP hasCSP is a composite indicator that evaluates corporate financial, environmental and social responsibility performance. Jiang et al.  empiricaTo summarize the above studies, we believe that GCP has the characteristics of the command–control type of environmental regulation, forcing HPEs to carry out green upgrading of their industrial structure, and thereby improving environmental performance . It alsoHypothesis 1 (H1).The GCP significantly enhances CSP.From the supply-side perspective of financial resources, GCP forces HPEs to develop green projects through CRA, thereby achieving the green upgrading of industrial structure. However, based on the demand-side perspective, the sensitivity of GCP may vary among enterprises with different characteristics, leading to heterogeneity in economic consequences.China’s special political system determines the dominance of SOEs in the national economy. For a long time, under a highly centralized financial system dominated by large state-owned banks, there has been the phenomenon of a ‘political-pecking order’ in credit resource allocation in China . ExternaHypothesis 2a (H2a).The GCP has a more significant impact on the sustainability performance of non-SOEs than that of SOEs.Regional coordinative development policy implemented by the central government reduces the economic gap among regions to a certain extent but does not fundamentally change the local economic development model and the political environment. The green credit policy has a positive impact on the upgrading of China’s industrial structure and significantly contributes to green and advanced industrial structure . TechnolHypothesis 2b (H2b).The GCP contributes more significantly to the sustainability performance of eastern enterprises than that of non-eastern enterprises.Industry characteristics determine that there is significant variation in the intensity of competition among enterprises. GCP as an environmental regulation can significantly reduce the credit financing capacity of HPEs, but oligopolistic enterprises that are at high market concentration have an absolute advantage in the market . As a reHypothesis 2c (H2c).The positive impact of GCP on the sustainability performance of enterprises with low market concentration is more significant than that of enterprises with high market concentration.Green credit is an environmental economic policy in which financial institutions consider corporate environmental performance to be a requirement for credit approval. From the perspective of the compliance cost effect, the implementation of GCP can increase the production cost and pollution control cost of enterprises, which has a crowding-out effect on R&D investment activities and reduces the productivity of enterprises. Li et al.  propose We believe that innovation compensation effects can offset compliance costs to generate net benefits, thereby improving production management efficiency and corporate competitiveness. Following the implementation of GCP, green innovation can improve the financial, environmental and social performance of companies, while ‘greenwashing’ can harm the performance of companies . Based oHypothesis 3 (H3).GCP can significantly increase TIL, thereby positively affecting CSP.Loans distributed by Chinese financial institutions accounted for 63.6% of social financing in 2021, and the main source of debt financing for China’s real economy is bank borrowing. Commercial banks strictly regulate the audit system for green credit; in addition to considering the financial indicators of the lending enterprises, they also pay attention to corporate environmental performance. The implementation of GCP can increase financing difficulties for companies in the short term, reduce external credit resources and generate credit constraint effects. Zhang et al.  find thaHypothesis 4 (H4).The GCP has a positive impact on CSP by adjusting the CRA of HPEs.We develop a conceptual model in this paper based on the above hypotheses, as shown in The definition of heavily polluting enterprises is based on the ‘Guidelines on Environmental Information Disclosure for Listed Companies’ published by the State Ministry of Environmental Protection in 2010, and we collate the heavily polluting industries using the industry classifications of the ‘China Securities Regulatory Commission (2012 edition)’. This paper takes Chinese A-share listed manufacturing enterprises from 2010–2015 as the sample and refers to studies by Yang and Niu  and Liu We refer to the study by Alexopoulos et al. , which dWe use the implementation of the ‘Green Credit Guidelines’ as a quasi-natural experiment, and After the GCP was issued, commercial banks would use corporate environmental information as the basis for granting credit. Therefore, the GCP constrains corporate finance through the financing punishment effect. According to the strong version of Porter’s hypothesis, technological innovation is an important approach to achieve green transformation. Moreover, technological innovation activities are characterized by high risk and long terms. Only companies with sufficient, long-term available capital can satisfy the requirements of technological innovation activities. Therefore, we further explore the internal transmission effect between GCP and CSP by using total long- and short-term credit financing capacity, long-term credit financing capacity and technological innovation investment level as mediating variables.We select company size, financial leverage, growth, nature of ownership, percentage of independent directors and financial background of directors as corporate control variables. Moreover, as the green credit policy has different effects in different regions, we use the level of market development and the level of economic development as regional control variables. The specific variables involved in this paper and their descriptive statistics are presented in In this paper, we propose an integrated model to explore the impact mechanism of GCP on CSP, as shown in Step 1. The PSM-DID model: Financial performance and environmental performance are the most effective indicators of CSP. We use the entropy method to calculate a composite index of sustainability performance and compare panel data about enterprises with other financial information. The sample enterprises are classified according to the environmental attributes of the manufacturing industry, and HPEs are used as the treatment group and non-HPEs are used as the control group. Since classifying enterprises based on environmental attributes is prone to selection bias, we use PSM to screen valid covariates to make the conclusions of this paper more accurate. The impact of GCP on CSP is analyzed by the PSM-DID model and tested for parallel trends and dynamic effects.Step 2. Robustness test: The omission of variables may lead to unreliable conclusions from the baseline regression. We use four methods, namely, replacing the dependent variable, conducting a counterfactual test, reducing the time window and introducing other policies, to verify the reliability of the baseline regression results.Step 3. Extended research: To further explore the impact mechanism of GCP on CSP, we conduct empirical analyses from the perspectives of heterogeneity and mediating effects. Differences in the internal nature and external environment of enterprises may lead to heterogeneity in the relationship between GCP and CSP. Therefore, we test them in terms of four dimensions: nature of ownership, market concentration, green innovation level and regional characteristics. How does GCP affect CSP? This question is the focus of our research. Commercial banks are the main implementers of GCP, and Chinese enterprises’ sources of debt financing are mostly bank borrowing. The strong version of Porter’s hypothesis suggests that environmental regulations stimulate companies to invest more in technological innovation to gain a first-mover advantage. Therefore, we test whether there are mediating effects in the relationship between GCP and CSP from the perspective of CRA and TIL. Finally, we conclude the paper with meaningful recommendations for companies, government departments and other stakeholders.Size, Lev, Growth, State, Idr, Fbd, Gdp and Mai are selected as two groups of corporate characteristic variables. In the matching process, a Logit regression model is constructed with the policy target as the dependent variable, and the regression model is formulated as follows:To improve the comparability between the treatment and control groups, we use PSM method to reduce sample selection bias. Since the treatment and control groups include companies from different industries, there can be some differences in the characteristics of the companies in the two groups, and these differences may affect the estimation results. In addition, the trends in the two groups may be different in the DID estimate before the implementation of GCP. Therefore, the preconditions for using the propensity score matching method are that the common support test and the balance test are satisfied to ensure that the matching effects of the samples meet the requirements. We carry out matching using the psmpy package developed by Kline and Luo . In thist-test and quantitative indicators to evaluate the effect of the balance test. The results of the t-test are shown in p-values of all covariates are greater than 10%. This result indicates that after matching there is no significant difference between the two groups for any covariates. In the following, we use quantitative indicators to verify the findings of the t-test.This paper uses This paper uses the SMD as a quantitative indicator to evaluate the quality of matching. Cohen  set a thTo assess whether there is a causal effect between the implementation of GCP and CSP, we estimate the average effect of treatment on the treated (ATT) by ordinary least squares (OLS) using a causal inference package. The results are shown in To effectively estimate the net effect of the policy while addressing the sample selection bias, this paper constructs a PSM-DID model to explore the impact mechanism of GCP on CSP. After matching, valid observations are obtained for 186 companies, including 610 observations for the treatment group and 154 observations for the control group. The baseline model for PSM-DID in this paper is set as follows:State and Fbd have a significant positive impact on CSP.Based on Equation (2), we use stepwise regression to estimate the effect of GCP on CSP with and without the introduction of control variables. In Currently, there are two methods to test for parallel trends: (1) comparing the time trends of the means of the dependent variables in the treatment and control groups, and if the trends are the same, then there is a parallel trend in the two groups; and (2) adding the interaction term The test results are shown in evements . Enterprevements ,56.The baseline regression shows that the implementation of GCP significantly enhances CSP. To verify the reliability of this finding, this paper uses four methods, including replacing the dependent variable, counterfactual, shortening the time window and introducing other policies for robustness testing. The results of the tests are shown in The counterfactual test means that GCP is chosen to be implemented at a dummy time, and since GCP is not truly implemented at that time, CSP is not affected by GCP. If the net effect coefficient is significant, then the baseline regression result is not valid; if the coefficient is not significant, then the counterfactual test is passed. The paper uses 2010 as the time of GCP implementation for the counterfactual test. Model (2) shows that the coefficient of The CSP may be affected by other policies within the same time period after the implementation of the Green Credit Guidelines policy. For this reason, we sort out the relevant policies and regulations. The most representative ones are the ‘Green development’ and ‘Innovation development’ in the 13th Five-Year Plan issued in 2015, the ‘Opinions on innovation-driven development strategy’, and the new ‘Environmental protection law’ . We use If the coefficient of the interaction term Do differences in corporate internal characteristics and the external environment create heterogeneity in the relationship between GCP and CSP? To answer this question, we select the nature of property rights and green innovation level for the internal characteristics, and the regional characteristics and market concentration for the external environment, as the grouping basis. The results of grouping by the nature of property rights show that the relationship between GCP and CSP is only significantly positively at the 1% level in non-SOEs, and not in SOEs. This finding fully supports H2a. The main reason for this is that SOEs are in a special position as the mainstay of the national economy, with their funding and control coming from the government. The strong financial support from the government enables SOEs to face lower financing constraints. Stable R&D and investment budgets weaken the impact of environmental pollution on debt costs. Furthermore, the natural political connections of SOEs can amplify their green image; therefore, their sensitivity to GCP is lower. However, for non-SOEs, bank loans are their main source of financing. Commercial banks use environmental information as a criterion for granting loans. In the short term, this will have a significant negative impact on the financing ability of non-SOEs, but the forcing effect will encourage them to actively carry out technological innovation activities and establish a green image, thereby achieving green upgrading of the industrial structure.The results of the regional grouping show that the relationship between GCP and CSP is only significantly positive at the 5% level in eastern enterprises, and is not significant in non-eastern enterprises. This finding fully supports H2b. The ecological civilization construction and sustainable development model not only stimulates development potential in the East, but also local governments are paying more attention to environmental issues, requiring companies to disclose social responsibility information and report substantive corporate social responsibility (CSR) activities . These eThe paper uses the Herfindahl–Hirschman Index (HHI) as a measure of industry concentration. The higher the market concentration is, the lower the competitive pressure within the industry. We refer to the US Department of Justice’s market structure classification criteria and classify industries with HHI <1000 as competitive, which means they are in a low level of market concentration group, and industries with HHI ≥1000 as oligopolistic, which means they are in a high level of market concentration group. The results of the market concentration grouping show that the relationship between GCP and CSP is only significantly positive at the 5% level for companies with low market concentration, and is not significant for companies with high market concentration. This finding fully supports H2c. The implementation of GCP stimulates companies with low market concentration to invest more in technological innovation to produce low-carbon, green products. The increased market share and improved external financing environment contribute to CSP. We use the number of green patents to measure the green innovation level of enterprises, and those with green patents during the year are classified as the high green innovation level group, while those without green patents during the year are classified as the low green innovation level group. The regression results for the grouping of the green innovation level show that the relationship between GCP and CSP is only significantly positive at the 5% level in high green innovation level companies, but is not significant in low green innovation level companies. The finding is consistent with the Porter effect theory, which verifies that the positive impact of GCP on CSP is due to the innovation compensation effect more than to the cost compliance effect. Enterprises should increase their investment in technological innovation and introduce green processes and technologies to improve their green innovation output.Baseline regressions indicate that the implementation of GCP significantly contributes to CSP. However, the impact mechanism needs to be further explored. As shown by the theoretical analysis in H3 above, GCP may have compensation effects through technological innovation, thereby improving CSP. To test whether TIL has a mediating effect on the relationship between GCP and CSP, this paper continues to construct the following models based on Equation (2):As shown by the theoretical analysis in H4 above, GCP may have a forcing effect through CRA and thus enhance CSP. To test whether the overall level of credit financing mediates the relationship between GCP and CSP, this paper continues to construct the following models based on Equation (2):To test whether the level of long-term credit financing has a mediating effect on the relationship between GCP and CSP, this paper proceeds to construct the following models based on Equation (2).,To enhance the accuracy of our findings, we adopt the bootstrap method to test the mediation effects of CRA and TIL. The algorithm follows the process procedure developed by Igartua and Hayes . If the Model (3) and model (4) test whether the overall level of credit financing has a mediating effect on the relationship between GCP and CSP. Model (3) shows that GCP does not have a significant relationship with the overall level of credit financing, model (4) shows that the overall level of credit financing also does not have a significant effect on CSP. The bootstrap sampling test shows that the 95% confidence interval of the coefficient on the interaction term To summarize, the results of this paper are as follows: When using the DID model to study the economic consequences of policy, the problem of sample selection bias may easily arise. PSM is the best way to address this issue. Therefore, this paper explores the impact mechanism of GCP on CSP by constructing a PSM-DID model. The SMD values for most covariates decreased significantly after matching, and none of their absolute values exceeded 0.2, indicating that the matching effect is effective. The result of the causal effect test shows that the coefficient of the treatment group is significant at the 1% level  performance of enterprises can be used as a comprehensive indicator to measure CSP, and we will also further study the impact mechanism of different environmental regulation tools on ESG.


Mapping of short metagenomic (or metatranscriptomic) read data to reference isolate or single-cell genomes or metagenome-assembled genomes (MAGs) to assess microbial population relative abundance and/or structure represents an essential task of many studies across environmental and clinical settings. The filtering for the quality of the read match and assessment of read mapping results are frequently performed without visual aids or with the assistance of visualizations produced through ad-hoc, in-house approaches. Here, we introduce RecruitPlotEasy, a fully automated, user-friendly pipeline for these purposes that integrates statistical approaches to quantify intra-population sequence and gene-content diversity and identify co-occurring relative populations in the sample. Hence, RecruitPlotEasy should also greatly facilitate population genetics studies.https://github.com/KGerhardt/RecruitPlotEasy.RecruitPlotEasy is implemented in Python and R languages and is freely available open source software under the Artistic License 2.0 from  Metagenomics studies of natural microbial populations have recently revealed that bacteria and archaea predominantly form sequence-discrete populations with intra-population genomic sequence relatedness typically ranging from ∼95 to 100% genome-aggregate average nucleotide identity (or ANI) depending on the population considered . In contrast, ANI values between distinct populations are typically lower than 90% [Read recruitment plots are one of the most in-depth analyses to reveal and study sequence-discrete populations. In these plots, the reads of a metagenome are mapped against a genomic reference sequence that is representative of the population to be studied . The mapping patterns that are revealed are informative about how well the metagenomic population matches the reference genome, gene content differences if any, the level of intra-population sequence diversity, and regions of sequence-discontinuity  Figure . Thus, rwithin population diversity), while reads showing lower than 95% identity are considered to represent distinct, co-occurring populations (a.k.a. outside population). Using the RecruitPlotEasy tool requires no previous bioinformatics or coding skills.Recently, we have developed bioinformatic scripts that can be applied to the read mapping output of a read recruitment plot to provide information based on read mapping that is not available by previous tools such as what is the average coverage of the genome by reads (a proxy for relative metagenome abundance), whether or not co-occurring populations exist in the dataset (sample) , and whiRecruitPlotEasy is operated entirely through a graphical user interface (GUI) which manages the selection of inputs, the manipulation of data, and the creation of plots through simple buttons and drop-down menus. Further, all menus and options are annotated with tooltips and reports that help the user easily navigate the workflow of the Recruitment Plot without prior experience using the tool. RecruitPlotEasy is written in a pair of scripts, one in R and one in Python 3. This two-script design takes advantage of multiple visualization libraries and the GUI of the Shiny library available in R, while operating with modest computational resources enabled by Python.To use the tool, the user needs only to download and install R, Rstudio, and then run a single command from the R terminal. This command installs any missing R dependencies, installs Miniconda if it is absent , retrieves the R and Python functions, and launches the GUI for the user. While installation only occurs on the first use of RecruitPlotEasy, this same command is used in subsequent sessions to activate the GUI again. RecruitPlotEasy requires a user to supply one (or more) genome file(s) in FASTA format and at least one set of reads mapped to that genome file in either tabular BLAST or SAM/BAM formats. Users may optionally supply gene functional annotation in GFF for gene-level analysis.The graphical interface of RecruitPlotEasy opens in the user’s default web browser. This interface is organized into 4 tabs: Database Creation, Database Management, Recruitment Plot, and Interactive Plot . The tabThe underlying data shown in a recruitment plot is a 2-dimensional matrix (or table) of counts. For a given genome, columns of this matrix correspond to successive regions of the genome and rows correspond to windows of percent nucleotide identity values. The width and height of each cell of the matrix are defined by the user, with defaults of 1000 base pairs for width and 0.5% identity for height. If viewing genes, percent identity windows are determined in the same way, but genome regions instead correspond exactly to the starts and ends of the gene sequences, with intergenic regions forming additional columns, as needed, to fill in the rest of the matrix. The cells of the matrix effectively form a 2-dimensional histogram of bins into which reads may fall.To fill the matrix, reads aligning to the genome are assigned to their appropriate percent identity window and genome position bin. A user may choose to define percent identity to the reference as either the number of matches divided by the alignment length  or matches divided by the entire length of the read, including unaligned sections . After the percent identity row is determined, the read will increase the base pair (bp) count of the bin it falls into by its length. Should a read span two or more bins, each bin will receive its respective share of the read’s length according to exactly where the read mapped to the genome and the boundaries of the bins. The count for each bin represents the sum of all bases of all reads that map within the corresponding percent identity and region of the genome. Once every read has been processed, the filled matrix is passed to the plotting component of RecruitPlotEasy.Reads may also be filtered based on minimum alignment length, minimum percent of the read aligning to the reference (not available for tabular BLAST), and by selecting only those reads which map best to the currently viewed genome . Reads are not processed in any way prior to their selection from the database, meaning that any plot and any statistics or data export based on a plot are created only from the reads which pass the filtering parameters selected by the user (or the defaults when user makes no choices). A record of the exact query used to select reads from the current RecruitPlotEasy database is exported alongside any saved plot or data export in order to ensure that the reads used to generate a plot can be recovered at later dates.Staphylococcus aureus reference genome. We consider the subplot in the bottom left the main panel, which shows how metagenomic reads of sufficient nucleotide identity and alignment (user defined) thresholds map against the reference genome. Because read recruitment plots commonly recruit tens of thousands to millions of reads from a metagenome it is computationally intractable to directly plot each read individually. Instead, we plot the sum of all bases from the reads that fall within a specific region of the main panel defined as a grid with cells consisting of base pair width  and percent sequence identity of the read alignment height  (Methods section above).The recruitment plots from RecPlotEasy show four views of a single dataset; the main panel of a recruitment plot directly displays the underlying counts matrix, while the other three panels display useful summaries derived from the data in the main panel. S. aureus population in the sample, represented by a high density of high identity (>98% identity) reads mapping evenly across the reference genome along with a less abundant, closely related and co-occurring S. aureus-like population represented by the second lower peak in the number of mapped bases in the 94–95% identity range. The region between these two peaks shows the sequence discontinuity between the two sequence-discrete populations, one representing the reference genome and the other representing the sum of the remaining genomes.The panel on the bottom right shows the number of nucleotide bases  at each unit of nucleotide identity (y-axis) in the main panel. Note that the specific case in Similarly, the upper left panel shows the coverage  of the genome at each unit of genomic position (x-axis) in the main panel. The darker blue color represents reads within the target population (default >95% nucleotide identity to the reference sequence) while the lighter blue represents lower identity reads considered outside the population (nucleotide threshold can be adjusted by the user). Note in S. aureus example in in-situ abundances. For further details on the panels, see also Finally, the top-right panel shows the histogram of coverage depth values over regions of the genome, which should reveal a tight distribution around the mean in cases where the reference genome represents a single population and a not-chimeric genome, like in the RecruitPlotEasy provides the user with the option to export their plots as high-quality figures and save the plotting data used to create them. Once the user has decided that they are satisfied with their plot as it appears in the GUI, they may provide a name and save the plotting data used to generate each recruitment plot sub-plot three in tab-delimited files and save a PDF of the plot image, laid out in a 16:9 aspect ratio to match the majority of modern screens. While RecruitPlotEasy uses the PDF format to save its plots, the graphics within each PDF are ultimately saved as an SVG that is both infinitely scalable without any loss in the figure’s resolution (i.e. it can be zoomed in on as much as desired without losing any clarity) and can be easily imported into common figure editing software that support SVG manipulation such as Adobe Illustrator for further editing, labelling, or other manipulations desired by a user.The interactive plots created by RecruitPlotEasy may also be exported, but the files produced are quite different from the PDFs that are generated by the normal plotting approach. The R Plotly library is used to generate interactive recruitment plots, and these interactive plots are saved as HTML files that contain an independent copy of the data used to create the plot and the graphical results. These can be subsequently opened by most internet browsers, and do not require RecruitPlotEasy, R at all, or any other external software to be shared and viewed.To better support integration in workflows, RecruitPlotEasy also includes a built-in read filtering function. When a database is built, RecrutPlotEasy makes a note of the location of each input read file and indexes reads by their name and the genome each mapped to. When viewing plots, reads at or above a user-defined percent identity cutoff may be added to a cart. By returning to the database management tab of the GUI, reads in the cart can be exported to filtered outputs in the same format as the inputs  that will contain only those reads matching the selection criteria currently chosen by the user. Depending on the users’ chosen options, this can  include filtering reads to only their best matches, thus removing instances of reads mapping to multiple locations, filtering poorly aligned reads, selecting only reads mapping to specific genomes, and selecting only reads sufficiently similar to the reference sequence. This function is intended to be used in tandem with the plot saving component of RecruitPlotEasy: a user may inspect their read mapping results, tailor their filtering settings to match the properties of each genome as needed, and export reads alongside one or multiple recruitment plots that aid in justifying the choice of filtering parameters.In summary, we have presented an advanced read recruitment plot tool that can provide a thorough and quantitative view of the natural population in a metagenome and its diversity, which represents highly useful information for several downstream analyses. Accordingly, we expect that RecruitPlotEasy will greatly facilitate microbiome research across the clinical and environmental fields and advance the toolbox for the analysis and summarization of large-scale genomic/metagenomic data and the communication of those results.