An environment of physically linked, technologically networked things that can be found online is known as the “Internet of Things.” With the use of various devices connected to a network that allows data transfer between these devices, this includes the creation of intelligent communications and computational environments, such as intelligent homes, smart transportation systems, and intelligent FinTech. A variety of learning and optimization methods form the foundation of computational intelligence. Therefore, including new learning techniques such as opposition-based learning, optimization strategies, and reinforcement learning is the key growing trend for the next generation of IoT applications. In this study, a collaborative control system based on multiagent reinforcement learning with intelligent sensors for variable-guidance sections at various junctions is proposed. In the future generation of Internet of Things (IoT) applications, this study provides a multi-intersection variable steering lane-appropriate control approach that uses intelligent sensors to reduce traffic congestion at many junctions. Since the multi-intersection scene's complicated traffic flow cannot be accommodated by the conventional variable steering lane management approach. The priority experience replay algorithm is also included to improve the efficiency of the transition sequence's use in the experience replay pool and speed up the algorithm's convergence for effective quality of service in the upcoming IoT applications. The experimental investigation demonstrates that the multi-intersection variable steering lane with intelligent sensors is an appropriate control mechanism, successfully reducing queue length and delay time. The effectiveness of waiting times and other indicators is superior to that of other control methods, which efficiently coordinate the strategy switching of variable steerable lanes and enhance the traffic capacity of the road network under multiple intersections for effective quality of service in the upcoming IoT applications. With the continuous increase in the number of motor vehicles in my country, the contradiction between the supply and demand of road traffic is increasingly intensifying. Especially in the intersection scene, the traffic flow of each turn at the intersection presents an uneven distribution at different time periods, which easily leads to congestion and waste of lanes resources. In order to solve this problem, the variable steerable lane technology using intelligent sensors came into being, which uses the lane as a variable space resource and dynamically allocates it according to the needs of each turning traffic flow on the basis of communications between the wireless sensors, so as to improve the utilization rate of road space resources. An intelligent sensor is a function that can sense and detect information from a specific item, as well as learn, judge, and receive signals, and has a new type of sensor with management and communication features. The intelligent sensor is capable of autonomously calibrating, compensating, and collecting data. The intelligent sensor's capabilities determine its high precision and resolution, stability and dependability, and flexibility. It provides a great performance-to-price ratio when compared to standard sensors. Smart sensors come in three varieties: those that can judge; those that can learn; and those that can be creative. Intelligent velocity sensors, intelligent acceleration sensors, intelligent flow sensors, intelligent position sensors, intelligent attitude sensors, intelligent displacement sensors, and intelligent dimension sensors are a few examples of intelligent sensor types.Variable steerable lanes at a single intersection of the scene, the traditional control method can effectively alleviate the problem of unbalanced steering, but as the number of variable steering lanes increases, the traffic flow changes between multiple intersections become more complex, and the present ability of the traditional method is more difficult, and it is impossible to coordinately control multiple. Therefore, how to make the cooperation between the multi-intersection variable-guidance lanes more efficient becomes a new problem. Variable steering lane with many intersections is a technique for figuring out the length of a changeable guide lane for a signal control junction approach is disclosed in an invention in the field of road traffic sign marked lines. The process entails first obtaining traffic data information on the variable guide lane's steering in the desired direction, followed by translating the received traffic volume into an equal standard small vehicle traffic volume. Using a model in the theory of queuing, it is then possible to determine the number of vehicles queuing behind a stop line for the signal control intersection approach using a multipath queuing multichannel system and a combination of an approach lane queuing imbalance coefficient. Finally, the vehicle number is converted to a measurement of the span of the variable guide lane for the signal control intersection approach using the vehicle average queuing length in meters. The approach traffic efficiency may be increased by using the technique for estimating the length of the variable guide lane for the signal control intersection approach, which allows cars on the upper stream of the variable guide lane to enter the intersection approach smoothly.The term “variable-guided vehicle route” describes a track where the travel direction is not totally set for left-hand rotation, right turns, or continuing straight ahead, but instead incorporates various turning functions depending on the time of day. The overall traffic efficiency in the crossing can be greatly increased thanks to the way this track is laid out. Since the entrance driveway to the signalized crossing is not suitable for widening or having many canalizations, the wagon flow lacks uniformity significantly and is not suitable for the condition under which a signal control device can solve the problem. Apply in places like Hangzhou, Wuxi, Tangshan, and Yantai, China's equivalent of Shanghai. Variable lanes are now being installed at crossroads in several places in order to better use road resources and increase traffic capacity. The variable lane control method primarily employs manual observation of traffic conditions for switching, or switching is performed at a fixed-time based on statistics of historical data. The manual switching mode is inefficient, and the statistical technique does not accurately reflect current traffic conditions. However, the rapid development of artificial intelligence and vehicle-road cooperation technologies can supply more real-time and precise data for variable lane control. It is feasible to regulate the variable lane more efficiently by making proper use of these data.In this paper, we present a collaborative control system for variable-guidance sections at various intersections based on multiagent reinforcement learning and intelligent sensors. This study proposes a multi-intersection variable steering lane-appropriate control strategy that leverages intelligent sensors to minimize traffic congestion at multiple junctions in the next generation of Internet of Things (IoT) applications. The priority experience replay algorithm is also incorporated to increase the efficiency of the transition sequence's utilization in the experience replay pool and accelerate the algorithm's convergence for effective quality of service in forthcoming IoT applications.t,” receiving a reward for each observation, and then attempting to accumulate as many rewards as possible in order to make more observations.The traffic flow at the intersection changes dynamically in time and space. For example, in the morning and evening rush hours, the traffic flow of each turn at the intersection shows obvious regular changes, and there is a serious imbalance in the queue length of vehicles in different guide lanes. In order to improve the traffic capacity of the intersection and solve the problem of urban road congestion, some traffic researchers have carried out research on the dynamic control method of variable steering lane steering, mainly focusing on three aspects: traditional control method, intelligent control method, and reinforcement learning method. Supervised learning is a machine learning term that refers to the approach of constructing a function independently by learning from a number of related samples. This is the method of learning a broad notion from a small number of examples that are comparable to others. Contrarily, reinforcement learning is a branch of machine learning that builds on the idea of behavioral psychology and focuses on interacting with the environment directly. It is an important part of the field of artificial intelligence. Regression and classification are the two major jobs in supervised learning, whereas exploitation or exploration, Markov's decision processes, policy learning, deep learning, and value learning are the diverse tasks in reinforcement learning. Basic reinforcement is specified in the model Markov's decision process in reinforcement learning, whereas supervised learning examines the training data and generates a generalized formula. Each example in supervised learning will have a pair of input objects and an output with the desired values, whereas, in reinforcement learning, the agent interacts with the environment in discrete steps, making an observation for each time period “Lq=ρ2/1 − ρThe formula to measure the queuing length of the lanes can be explained as we can demonstrate for the M/M/1 queue that Lq is the mean number of customers in the queue, the likelihood that it is busy, and the percentage of time it is busy are all represented by the formula ρ = λ/(cµ), 1/E [Inter-arrival-Time], where E stands for the expectation operator, represents the mean rate of arrival, and σs2 variance of service time.We can demonstrate for the M/G/1 queue thatThe traditional variable lane control method research uses the method based on experience or historical data to set the control plan in advance and design the rules for the steering of the variable steerable lane at the intersection. Literature  proposedThe specific lane function and signal timing switching scheme are deployed, but the preplan must be repeatedly tested and the accuracy is not high. Literature , 6 carriThe abovementioned method of presetting the steering rules for variable steering lanes through experience or summarizing historical data rules can adapt to the needs of regular traffic state changes to a certain extent, but it is difficult to dynamically adapt to road traffic conditions and sudden changes in supply and demand abnormal traffic flow.Research on intelligent control methods for variable steering lanes makes intelligent decisions based on various traffic flow data collected in real-time, and improves the adaptability to real-time traffic flow changes at intersections. Some works use the collected real-time traffic flow data on roads, such as the space of each turning lane, occupancy rate , trafficThe above-related research work has two limitations: (1) it is mainly applied to intelligent control decision-making of variable-steering lane steering at a single intersection and (2) the prediction-based intelligent algorithm is mainly based on historical and real-time data, and cannot quickly update rules to adapt to the dynamics of traffic flow variety.Q-value as the weighted sum of local Q-values, by minimizing the weighted sum of individual Q-values and the global Q-value to ensure that a single agent can take into account the learning process of other agents and realize automatic control of large-scale traffic signals. Literature [In recent years, reinforcement learning technology has developed rapidly. It has low requirements for prior knowledge of the environment and can achieve good learning optimization performance in complex nonlinear systems. Therefore, it is suitable for complex and changeable multi-intersection and variable-guided lane intelligent control scenarios. In the multi-intersection collaborative control problem, traffic signal optimization research has widely used the reinforcement learning method. Literature  combinedterature  proposedQtot denotes the total value function and Qa denotes the value function for each agent. The weights of each particular value function Qa should be positive, according to an obvious explanation. If the weights of the individual value function Qa are negative, the agent will be discouraged from cooperating since the greater Qa, the lower the joint value Qtot. For consistency between the centralized and decentralized rules, QMIX comprises agent networks that represent each Qa and a mixing network that brings them together into Qtot rather than just adding them up like in VDN. By requiring the mixed network to have positive weights, it also imposes constraints. Since QMIX's factored representation grows well with the number of agents, it is able to describe complicated centralized action-value functions and makes it simple to extract decentralized policies using individual argmax operations in linear time.This research proposes a multi-intersection variable-direction lane cooperative control algorithm using intelligent sensors based on multiagent reinforcement learning. The method mainly includes multiagent reinforcement learning model merged with intelligent sensors, a global reward decomposition algorithm, and a priority experience playback algorithm. The multiagent reinforcement learning model proposed is constructed based on the value function decomposition algorithm of the QMIX algorithm . The QMIQ-learning. The development of autonomous agents is frequently done via reinforcement learning. In the RL framework, the agent is permitted to behave in the environment and is rewarded numerically at each step rather than being explicitly programmed. The goal of RL algorithms is to discover a strategy that maximizes the overall predicted reward (or some related criterion). Therefore, the reward function implies that optimal behavior is described. In order to assess actions in terms of trade-offs among the kinds, the technique decomposes rewards into sums of semantically relevant reward categories. To concisely describe why one action is preferable over another in terms of the kinds, we particularly propose the idea of minimal adequate explanations.The global reward decomposition algorithm improves the global reward distribution method in the value function decomposition algorithm and imposes constraints between the global value function and the value function of a single agent. In some complex scenarios, the global optimal joint action may require the intelligent sensor to make some behaviors that sacrifice individual interests. Decomposition techniques change a difficult problem into a simpler one. Only binary constraints, whose scopes form a directed acyclic graph, are present in the new problem. Each set of variables from the original problem is represented by a variable in the new problem. These sets encompass the set of the initial variables even if they are not necessarily disjoint. In relation to each set of variables, the translation uncovers all partial solutions. The interplay between local solutions is reflected in the translation problem. A decomposition approach creates a binary acyclic issue by definition; these problems may be solved in a time that is polynomial in their size. In response to this problem, this study decomposes the global reward into two parts, one part is the basic reward, and the specific distribution to different agents is realized through the QMIX hybrid network; the other part is the performance reward, according to the agent. The state hierarchically is assigned to each agent which is the IoT so that a single agent can maximize the global reward while taking into account its own reward, and realize the secondary distribution of the global reward. In RL, the agent receives a reward that is often a sum of many reward components, each designed to encode some aspect of the desired agent behavior. From this composite reward, it learns a single composite value function. Using value decomposition, an agent learns a component value function for each reward component. To perform policy optimization, the composite value function is recovered by taking a weighted sum of the component value functions. While prior work has proposed value decomposition methods for discrete-action In the priority experience replay algorithm, in view of the uneven quality of randomly sampled experience, resulting in low training efficiency and slow algorithm convergence speed, the joint value function in the value function decomposition algorithm is used to calculate the error, and combined with the number of extractions to calculate priority of samples to speed up algorithm convergence.τ is the global state, α is the global action. In the multi-intersection variable steering lane scenario, the involved elements such as state space, action space, and reward function are defined. In order to make the input state more realistic and richer, the queue length of the lanes in each direction is adopted as the average waiting time of vehicles, and the ratio of average delay time as indicators. In addition, in order to accurately describe the position distribution of vehicles, the variable steering lane area is discretized and encoded to obtain the vehicle mapping matrix, as shown in The multiagent sensor reinforcement learning model based on value function decomposition is shown in T is the number of signal cycles, LL, WL, DL are the average queue length, average waiting time, and average delay time of the lane group in the left-turn direction, respectively, Ls, WS, DS is the average queue length, average waiting time, and average delay time of the lane group in the straight direction, and M is the vehicle position mapping matrix.In the multi-intersection scenario, the state space expression is defined as follows:In the variable steering lane scenario, the straight-left variable steering lane is mainly studied and applied, and the right turn direction is not considered, so the action space is left turn or straight.(1)L of vehicles in all lanesThe average queue length (2)D of vehicles in all lanesThe average delay time ratio Di is as follows:The expression of the single lane delay time ratio vlane is the average speed of vehicles on lane i, and vmax is the vehicle speed maximum speed limit for road i:where (3)W of vehicles in all lanes. When a vehicle starts to stop and wait, that is, when the speed is less than 0.1 m/s, the parking waiting time of this vehicle starts to accumulate;The average waiting time (4)N is the average number of vehicles on all lanes that left the current lane after the previous action;(5)V is the average speed of vehicles in all lanes leaving the current lane after the previous action. The expression of the average speed is given as follows:vj is the average speed of each vehicle.where The global reward function is defined as the weighted sum of the following metrics.Assign corresponding weights to the above-mentioned different traffic indicators, and finally calculate the global reward:k1, k2, k3, k4, k5 are the weight parameters, and the effect of the final traffic condition optimization is set by analyzing the global reward results in 3200 experiments.In the formula, R is decomposed into two parts, the basic reward Rb and the performance reward Rp based on the proportion, as shown in The global reward The global reward decomposition function is given as follows:The traditional hybrid network method is used to distribute the basic rewards to each agent which are the intelligent sensors. The performance reward is used to motivate the agents that contribute more in the process of regional cooperative control. The expression of the performance reward obtained by each agent at the current time is given as follows:Real-world situations like strategic conflict resolution, coordination between autonomous vehicles, and agent cooperation in defensive escort squads all include cooperative multiagent challenges. Such issues may be modeled as dual-interest situations, in which each agent simultaneously seeks to maximize its individual payout  and the team's performance as a whole . Two different forms of modern, state-of-the-art MARL algorithms exist. While algorithms like MADDPG and M3DDPG concentrate on optimizing local rewards without any explicit idea of coordination, algorithms like COMA and QMIX strive to maximize the global reward for the success of the group. We first define multiagent cooperation as a joint optimization on reward assignment and demonstrate that each agent has an approximately optimum strategy that decomposes into two parts: one that just depends on the agent's own state and the other that is connected to the states of adjacent agents. CollaQ decomposes each agent's Q-function into a self-term and an interacting term, using a multiagent reward attribution (MARA) loss to regularize training. CollaQ is tested on multiple StarCraft maps and surpasses existing state-of-the-art approaches  by increasing the win rate by 40% while using the same amount of samples.Rpi is the performance reward obtained by the ith agent, L′ is the ratio of the average queuing length of the lane group in a certain direction of the agent to the overall length of the lane, LS′ is the overall length of the lane, and LL′ is the ratio of the average queuing length of the straight lane group to the overall length of the lane, the ratio of the average queuing length of the left-turn lane group, LT the threshold of the determination level of the lane, Vout the maximum traffic flow that can be driven out during the green light of the lane, and the capacity of the lane. Vmax is the maximum traffic flow [In the formula, fic flow .Rp, and the performance reward corresponding to each level is different [LL′ left turn and LS′ straight lane group queue length ratio and are LT less than the threshold when the traffic flow of the road section is determined to be small, LL′ and Ls′ the performance reward is allocated by the ratio of the average and the larger, more performance rewards should be allocated [All agents are graded when the performance reward is allocated ifferent , 23. As llocated .Q network, the loss is computed using the predicted Q value, target Q value, and observed reward from the data sample. In single-agent reinforcement learning, in order to solve the problem of uneven quality of training samples extracted during the training process, a priority experience playback algorithm [Ln must be calculated:Deep learning uses target networks to increase the training's stability. The main training network and the target network are the two networks that the DQN method trains. The difference between the two networks, squared, is the loss the algorithm trains on (often replaced with Huber loss nowadays). The primary training network periodically replaces the target network's weights as training progresses. The target network predicts the optimum Q value from all actions that may be done from the next state in each data sample. This is the desired Q value. To train the lgorithm  was propS, A are the joint state and joint action of multiagent, γ is the attenuation coefficient, S′, A′ is the joint state and joint action of multiagent at the next moment. The larger the value, Ln the higher the corresponding experience sequence will be priority.In the formula, Ln as the only indicator to measure the importance of samples may cause some samples to be drawn less frequently due to their small size. Therefore, this study combines the target network loss and the number of times to be drawn Nsam as an indicator to measure the importance of samples. At the same time, considering different empirical the loss value of the target network has a large difference fTop(Ln), and it is converted into a dimensionless sorting amount, which is the position of the loss in the increasing sorting. The expression of the final priority PRi is given as follows:Using fBot(Nsami) is the position of the number of extractions in descending order; ε ∈  is the offset of the probability, which is used to correct the situation that the priority is too small to cause the probability of sample selection to be too low.In the formula, Qi during centralized training to factorize the combined value function Qtot. Decentralized policies may be easily determined during execution by greedily picking individual actions from the local value function Qi. Because Qi is learned by maximizing the overall temporal-difference error on a single global reward signal, an implicit multiagent credit assignment is accomplished.In order to verify the effectiveness of the cooperative control algorithm that employs intelligent sensors to alleviate the traffic congestion at multiple intersections methods for efficient quality of service in the next generation IoT applications using reinforcement learning. In the multi-intersection variable steerable lane scenario, the cooperative control BASE algorithm is combined with fixed-time control (FT) and traditional adaptive control algorithm for multi-intersection (MTAC), single-agent reinforcement learning adaptive algorithm (DQN), multiagent reinforcement learning adaptive algorithm (QMIX) and other methods, and analyze the performance of each algorithm in the data set, including algorithm-level reward value, traffic average queue length, average delay-to-time ratio, average waiting time, and average travel time metrics at the level. In cooperative multiagent systems, agents work together to complete tasks in exchange for a group reward as opposed to individual benefits. Credit assignment techniques are often used to differentiate the contributions of various agents in the absence of individual reward signals in order to promote successful cooperation. As credit assignment has recently been widely implemented using the value decomposition paradigm, QMIX has emerged as the leading technology. Robot swarms, autonomous vehicles, sensor networks, and cooperative multiagent reinforcement learning are just a few areas where this technique has found widespread use. In these activities, each agent must learn a decentralized policy through a shared team reward signal because individual incentives are not available. In order to achieve successful collaboration, agents must allocate credit in a discriminatory manner. Credit assignment techniques using cooperative multiagent reinforcement learning have made significant strides in recent years. Value-based approaches have among them demonstrated cutting-edge performance on difficult challenges. Value factorization, which is based on the centralized training with decentralized execution (CTDE) paradigm, has recently gained a lot of popularity. It specifically integrates separate value functions The experimental equipment configuration of this research is as follows: the CPU is AMD 2.10 GHz, the memory is 16 GB, and the operating system is Windows 10 (64 bit). Simulation experiments are carried out based on the microscopic traffic simulation platform SUMO v1.7.0. Interface to interact with the simulation environment, obtain the traffic status in real-time and adaptively adjust the control strategy of the variable steering lane. As shown in The experimental data set is collected from different traffic capture data of streets, roads, etc. such as upstream and downstream road section codes, capture time, lane number, and license plate number.In this experiment, traffic flow data were collected at intersections in the urban area. The types of vehicles mainly included cars, station wagons, buses, and large passenger cars. The actual collected vehicle types and numbers were input into the simulation system, which were 24,17,592 cars, 156,008 station wagons, 91,089 buses, and 25,113 large passenger cars, accounting for 89.88%, 5.80%, 3.39%, and 0.93% of the total flow, respectively. In the calculation process of the simulation system, in order to standardize the vehicle position information at the intersection is converted into a discretized encoded vehicle position matrix, which is used as the quantitative input of the reinforcement learning model. In addition, the actual input vehicle type and quantity are converted into standard cars based on the conversion standard. The equivalent conversion coefficients of the models corresponding to standard cars are 1.2 for station wagons, 2.0 for buses, and large passenger cars. As is well known, traffic noise conversion and calculation used to be done using the prediction model in “Specifications for the Environmental Impact Assessment of Highways,” whereas we now know that its levels or grades, as well as its equivalent conversion, can be calculated in a variety of ways using the equivalent conversion coefficient. The findings of the speed survey and their analysis allow us to conclude that the speed computation for all types of vehicles must follow the Gaussian distribution in free traffic flow, allowing us to suggest a speed discretization approach. The approach described above assists in converting various vehicles with varying speeds into car comparable numbers, while overall traffic volume may be converted into that of passenger cars by evaluating themselves at the same noise levels.ε is 0.05, and the size of the memory bank is 1000. The number of samples for each update is 32, and the model update step is 5 signal cycles. In order to improve the stability of the algorithm, the target network is updated with a delay. The weight replacement step of the target network is set to 30 signal cycles and RMS prop (root mean square prop) algorithm is the update algorithm.In order to ensure the fairness of the comparison algorithm, the network structure and hyperparameter settings of the reinforcement learning algorithm are the same. The value of the discount factor is 0.95, the value of the learning rate is 0.001, the value of the greedy strategy λ = 0.4 in the BASE algorithm. Queuing length is used as an indicator to measure the efficiency of road traffic diversion under unbalanced traffic flow, and 32 combinations of weight values are set. In the process of training and testing, the accumulated vehicle queue length is calculated, 100 experiments are carried out for each scheme, and the average queue length is obtained by taking the average of the results. The smaller the average queue length the better the traffic diversion effect a total of 3,200 experiments were conducted using the numerous sensors. The results determine the weight of each influencing factor in the global reward function set in this experiment k1, k2, k3, k4, k5 are set to −1.0, −0.5, −0.5, 0.5, 1.0, respectively.In this study, the weight of global reward decomposition is set to Performance the test set is data of 6 periods, namely morning peak , evening peak , and flat peak periods 5, 6). The performance of each control method is shown in Figures , 6. The As shown in It can still maintain the leading performance, which further proves the effectiveness of the improved algorithm. As shown in 6The best performance of the algorithm in this study verifies the improvement of the multiagent collaborative algorithm for the multi-intersection variable-guidance lane scene: global reward decomposition, the proposed algorithm in this study can learn the policy better than the QMIX algorithm performance of the training process by comparing the BASE algorithm with the QMIX algorithm, the performance indicators of the training process are tested. The comparison of the average accumulated reward value index is shown in Figure – and The changes in traffic indicators during the training process are shown in Computational intelligence is built on several learning and optimization techniques. Therefore, an emerging trend of major significance in the future generation of IoT applications is the integration of new learning methods, such as opposition-based learning, optimization approaches, and reinforcement learning. This research proposes a multiagent reinforcement learning-based collaborative control method employing intelligent sensors for variable-guidance lanes at multiple intersections. This method improves the performance in congested scenarios through a global reward decomposition algorithm and improves learning efficiency through a priority experience replay algorithm. Cooperative control of variable-guidance lanes at multiple intersections: compared with other control methods, this method has better effects in reducing the average queue length, average delay time, average travel time, etc., while convergent faster, and therefore, enhancing the quality of service in the next generation of IoT applications.The follow-up work includes combining the algorithm with traffic signal control and performing joint optimization in the two dimensions of time and space to further improve the traffic capacity of multi-intersection scenarios.


Highly filled plastics may offer a suitable solution within the production process for bipolar plates. However, the compounding of conductive additives and the homogeneous mixing of the plastic melt, as well as the accurate prediction of the material behavior, pose a major challenge for polymer engineers. To support the engineering design process of compounding by twin-screw extruders, this present study offers a method to evaluate the achievable mixing quality based on numerical flow simulations. For this purpose, graphite compounds with a filling content of up to 87 wt.-% were successfully produced and characterized rheologically. Based on a particle tracking method, improved element configurations were found for twin-screw compounding. Furthermore, a method to characterize the wall slip ratios of the compounded material system with different filler content is presented, since highly filled material systems often tend to wall slip during processing, which could have a very large influence on accurate prediction. Numerical simulations of the high capillary rheometer were conducted to predict the pressure loss in the capillary. The simulation results show a good agreement and were experimentally validated. In contrast to the expectation, higher filler grades showed only a lower wall slip than compounds with a low graphite content. Despite occurring wall slip effects, the developed flow simulation for the design of slit dies can provide a good prediction for both low and high filling ratios of the graphite compounds. The fuel cell is an important part of the ongoing development of the automotive industry and energy sector. It has become a central subject of interdisciplinary research. Similar to a battery, a fuel cell consists of two electrodes, an anode and a cathode, which are called bipolar plates . MetalliComposite materials based on a thermoplastic polymer matrix and a very high graphite filling content (>80 wt.-%) are gaining increasing importance, especially in mobile fuel cells . These cHowever, the processing of such highly filled composite materials still poses an enormous challenge. Processing by injection compression molding  or, moreIn polymer processing applications, many flow processes are based on the principle of wall adhesion, or this is assumed to be the case. Thus, the flow velocity of the molten polymer assumes the value zero at the wall of the flow channel. This assumption constitutes an important boundary condition for the computer-aided engineering design of polymer processing tools.However, there are materials, such as highly filled composites, where the effect of wall slip is noticeable. In general, wall slip describes the phenomenon in which the melt flow loses its adhesion to the wall, so that shear flow and block flow occur simultaneously and overlap. If additives are added to the polymers to change their properties, e.g., as in the case of bipolar plates to increase electrical conductivity, the assumption of wall adhesion may lose its validity. This rheological phenomenon is difficult to analyze and makes the computer-aided engineering of polymer processing tools rather complicated, since the choice of boundary conditions significantly affects the obtained results.Some important studies in recent years regarding the wall slip of polymer melts have been conducted. Even though the assumption of wall adhesion is valid for most unfilled plastics, there are some exceptions such as PVC ,7,8,9, PIndependently from the material, two different models have been established. On the one hand, the Coulomb wall slip model is widely used, which assumes a Coulomb friction behavior between the wall and the polymer melt . Here, tIn general, the wall slip ratios and velocities that occur during processing are difficult to quantify. Often, the relevant properties are investigated as a function of the critical wall shear stress. According to the Coulomb wall slip model, wall slip should occur above the critical wall shear stress. However, the literature on rubber compounds shows partly contradictory results. For example, Schramm  and EggeMost commonly, the quantification of wall slip is determined by rheometry. For this purpose, rotational rheometers, as well as high-pressure capillary rheometers in combination with round capillaries or slit dies, are used. The evaluation of wall slip velocity according to Mooney  or MooneIn Equation (1), function  describi. Geiger  finally If Even though the wall slip behavior for some material systems such as rubber and ceramics has already been extensively investigated, the influence of highly filled polymers with mass fractions above 80 wt.-%, such as graphite, remains unknown or insufficiently investigated. Although the flow behavior of such highly filled systems was investigated in , the walHowever, before the material can be rheologically characterized and processed to a sheet, a homogeneous compound, consisting of a polymer and a specific graphite filling content must be produced in the first step. The equipment which fulfills this requirement is the co-rotating twin-screw extruder. Possessing the typical modular screw design, including several conveying elements, kneading disks, and mixing elements, the extruder constantly assures the high final quality of the product. The aim of the powder mixing process is to give the best homogenization of all components. Nevertheless, difficulties can occur during the process, especially for new materials such as highly filled compounds. In this case, numerical simulation of the process can be very helpful to save costs and time. The development of new numerical models and techniques are essential and will strongly raise efficiency. The fluid mechanical characterization of the mixing capability of different mixing elements is one fundamental issue for efficiently designing the process and selecting an appropriate mixing element for new complex materials and higher product qualities. This can be achieved through the analysis of the fluid flow in the twin-screw extruder.In general, the dispersive and the distributive mixing mechanism can be distinguished. The first one deals with the reduction in the minor component (fluid droplets or solid agglomeration) in the matrix or the major component, and the second one with their spatial distribution. The focus here is on the spatial distribution of the minor phase, namely the distributive mixing.The literature shows that many methods have been used to characterize and evaluate different mixing processes, such as the extended mapping method , particlSimulating solid mixing processes, and particularly powder mixing, as well as evaluating the degree of mixing, is challenging due to its discretized characteristics. However, the particle tracking method was applied for the Lagrangian particle mixing concept , and alsOne of the objectives of this study is to investigate the rheological behavior of highly filled graphite compounds as well as wall slip behavior based on the theory of Geiger . The novFurthermore, a combined numerical method for the evaluation of the distributive mixing process in co-rotating twin-screw extruders for highly filled polymers is proposed. First, mixing elements and kneading blocks were characterized in respect to their mixing efficiency via the particle tracking method and the statistical evaluation of the scale of segregation. Secondly, the obtained results for two element combinations were confirmed in a complex multiphase calculation, simulating the real mixing process between premixed polymer and powder in the mixing zone of the twin-screw extruders. However, the focus was based on the individual element characterization of the distributive mixing efficiency, the multiphase calculation, and the developed numerical model were mainly used for the confirmation of obtained results achieved by the particle tracking method. 3, large amounts of air are taken into the process, which must be reduced as much as possible. Air intakes reduce the conveying capacity, the throughput, the mixing quality of the twin-screw extruder, and finally the compound quality. Therefore, it is imperative that the air included in the fillers is minimized as far as possible before they are added to the twin-screw extruder. Bipolar plates made of highly filled polymer compounds require a very high amount of electrically conductive fillers such as graphene, carbon black, carbon nanotubes, and so on. The incorporation of such high amounts of filler with widely varying bulk densities, especially very low bulk densities, is a major processing challenge. Therefore, special equipment is needed to feed the fillers into the extruder without reaching feeding limits. Furthermore, the material behavior of highly filled compounds can hardly be investigated by standard measurements. The segregation of different filler types with different bulk densities must be avoided during compounding. Therefore, no premixed filler combinations should be used, but each filler must be fed individually and gravimetrically into the process. At bulk densities below 0.3 g/cmFor this purpose, the fillers are fed to the twin-screw extruder via a side feeder that has a vacuum degassing by the so-called feed enhancement technology (FET). This ensures that there are more fillers, but less air is added to the process. Without this degassing process at the side feeders, the targeted fill levels could not be achieved.Furthermore, adding all components at the twin-screw extruder inlet is not practical, because, on the one hand, feed limits are reached and, on the other hand, the polymer cannot be completely molten under these conditions. Thus, the amount of filler added must be split to different points along the process section.For this investigation, a twin-screw extruder type ZSK26Mc18 from Coperion GmbH, Stuttgart, Germany, was used to produce the compounds. The process parameters for compounding are listed in The screw has a feeding zone, a melting zone, an incorporation zone, a homogenization zone, a degassing zone, and a pressure build-up zone, as shown in the schematic diagram in The polymer is added via the main feeder in the first barrel. Part of the filler is added via a side feeder with vacuum degassing before the melting zone. The remaining filler is fed to the extruder after the melting zone, also via a side feeder with vacuum degassing. To avoid segregation of the filler, no premixes are used. The different filler types and portions are fed to the side feeders by separate gravimetric feeders. After the second feeding point, all components are dispersed and homogenized in the twin-screw extruder to produce a homogenous compound. Between the two side feeders, excess air is removed from the process by atmospheric degassing. Another vacuum degassing system is also installed upstream of the discharge unit. The discharge of the highly filled compounds takes place via a hot cutting device onto a vibrating chute. The cooling of the pellets takes place in the ambient air but can also be actively accelerated by an air stream or a water-cooled vibration chute. The pellet size is defined for a given throughput by means of the blade speed and number of blades of the granulator.With increasing filler content, the fines content in the granules typically increases. Through various equipment and process optimizations, the fines content could be reduced to a minimum. Due to the good dispersive and distributive incorporation of all filler types into the polymer matrix, the granules are very form-stable even at very high-filler contents of up to 90 wt.-%.The particle tracking method is based on tracking of massless particles, initially located in the fluid domain. These particles simply follow the fluid flow and do not interact with each other nor with the fluid. The statistical evaluation of the distribution of these particles at the end of the considered element is its mixing characteristic. This method is used for general characterization of the distributive mixing efficiency considering one phase flow, using the final mixing viscosity. The simulation model comprises one or a combination of more elements. Only the fluid region is studied, as pictured in In the present study different elements combination were characterized, based on their mixing efficiency. For this purpose, the fluid domain was divided in two (or more for a combination of elements) cross-sectional planes, one at the beginning and one at the end of the considered elements. Both cross-sectional planes were divided into 1150 small bins. A total of 2562 massless particles were homogeneously and equidistantly distributed in the initial cross-sectional plane, one half on the top, the other half on the bottom as shown in A color or index property is assigned for both streams . The size of the bins and the number of the injected particles are determined in a preliminary internal study.The particle position and flow characteristics over the time are used for the statistical evaluation of the mixing achieved by counting the number of particles over the bins over time. The concentration of each bin is calculated as follows:m is number of bins, Subsequently the standard deviation is built over the entire grid, as well as the variance, followed by the scale of segregation, or mixing index:To calculate the scale of segregation over time, a special post-processing routine was developed. The commercial software ANSYS FLUENT™ is used for the numerical particle tracking. The software predicts the trajectory of a discrete phase particle (or droplet or bubble) by integrating the force balance on the particle, which is written in a Lagrange reference frame. The use of massless particles, in this case, eliminates this force and the discrete massless particle follows the flow and temperature of the continuous phase. The trajectory equations are solved by stepwise integration over discrete time steps. Integration of time yields the velocity of the particle at each point along the trajectory, with the trajectory itself predicted by .(8)dx→dThe used numerical scheme to solve the equation of motion of the particle is the trapezoidal. The massless particles are injected continuously over the time. The chosen time step for the particles is different from the time step used for the continuous phase. The particle time step size assures an equidistant position during the injection also in the fluid flow direction. 5 Pas, A continuous fully filled fluid flow calculation over multiple rotations is performed. A 3D isothermal numerical simulation is carried out. The highly filled polymer (87 wt.-%) is assumed to obey the Carreau viscosity model shown, as below, where The viscosity data are achieved by rheological characterization of the compounds by Institut für Kunststofftechnik at the University of Stuttgart, as described in The mixture model is used for the calculation of the real mixing process due to its simplicity versus the full Eulerian model, and because of the possibility for the modeling of the granular phase for liquid–solid flows. The simulation procedure of the mixing process is described in In previous work, a three-phase model for simulation and calculating the mixing process in semi filled solid-liquid flow was developed and verified experimentally. This model is used, as follows:3 Pas, Only the mixing zone is considered. The second portion (higher percentage) filler, defined as the granular phase, is added to the premixed filled polymer, which is described as a continuous phase using the Carreau viscosity model. The values for A, B, and C for a filling degree of 41 wt.-% are, respectively: The rheological characterization of the compounds was carried out with a plate–plate rotational rheometer (PPR), Discovery HR-2 from TA Instruments, New Castle, DE, USA, and also with a high-pressure capillary rheometer (HCR), Rheograph RG 50 from Göttfert Werkstoff-Prüfmaschinen GmbH, Buchen, Germany.Furthermore, the melt density of all compounds is determined by the quotient of melt mass-flow rate (MFR) and the melt volume-flow rate (MVR).The different melt flow indices were measured by means of the melt flow testing machine MP-D from Göttfert Werkstoff-Prüfmaschinen GmbH, Buchen, Germany.Initially, round capillaries were used to characterize the material by HCR, but this did not lead to reproducible results, and the necessary Bagley correction  made it A loading of the feed channel by hand, which has a diameter of 20 mm, did not provide reproducible results for rheological characterization despite uniform compaction of the material with a plunger. This is most likely due to a strong inhomogeneity throughout the melting process within the feed channel. This effect is usually not observed with unfilled thermoplastics. For this reason, a feed extruder was used to fill the feed channel with a homogeneous melt.In this study, the viscosity curves were determined for a total of nine different compounds, with filler fractions containing 30, 50, 65, 70, 75, 80, 83, and 87 wt.-% graphite at 250 °C, including the raw polymer (0 wt.-%). In order to reduce the effort, the compounds with filler fractions up to 50 wt.-% were analyzed on a plate–plate rotational rheometer by oscillating shear stress. The diameter of the plate was 20 mm and the height of the shear gap was 1 mm.−1. Higher shear rates are not expected during the extrusion of bipolar plates with a dimension of 150 mm × 2 mm, which can be confirmed via a simple analytical estimation. All measurements were performed three times to ensure a high reproducibility. The obtained values were subsequently arithmetically averaged.The compounds with filler fractions above 50 wt.-% were analyzed on the high-pressure capillary rheometer with slit dies. In addition, the temperature effect on the viscosity was investigated for the compound with a filler fraction of 87 wt.-% for two further temperatures at 210 and 230 °C. Furthermore, two different compound recipes for the filler fraction of 87 wt.-% were investigated, which will be denoted as #1 and #2 in the following. The analyzed shear rate range is defined between 1 and 250 sIn accordance to the performed experiments, a 2D simulation model of the die and feed channel was developed  in OpenFThe melt flow is assumed to be isothermal, incompressible, and stationary. The flow behavior is derived from the viscosity measurements carried out at the HCR. Depending on the filling ratio, a Carreau model (acc. to Equation (9)) as well as a power law (Equation (11)) were used. In Equation (11), The geometry preparation as well as the computational mesh were created by means of ANSYS ICEM CFD™. The structured mesh consisted of approximately 284,000 hexahedral elements. The simulations were performed with OpenFOAM (version 5.x) and subsequently analyzed by ParaView.In the following, the results for the compounded graphite mixtures to produce bipolar plates are presented. First, the mixing quality of the compounding process within the twin-screw extruder was evaluated on the basis of numerical simulations. Subsequently, the produced compounds were characterized rheologically by means of PPR and HCR measurements and the determined material behavior was reproduced with suitable models. Finally, the material model was applied in a flow simulation of a slit die to reproduce the HCR measurement and thus determine the existing deviation between virtual prediction and reality.In the initial screw configuration for highly filled polymer compounding, a set of two 45° staggered wide kneading blocks were used in the mixing zone in the extruder. The target was to find a new configuration of elements, ensuring better distributive mixing between the powder and the premixed polymer. Referring to this, the mixing capability of different elements was analyzed based on the particle tracking method. These characteristics were subsequently confirmed in a multiphase semi-filled calculation.It is well known that kneading blocks, and especially special mixing elements, are responsible for good distributive mixing in the extruder. Therefore, the mixing characteristics of those elements were investigated numerically. To quantify the distributive mixing performance, the particle tracking method and the scale of segregation over time were applied, as described in As a result, the evolution of the mixing over the number of revolutions was achieved, which is the same as the evolution of the mixing over time. In the following, the results for all studied combinations of elements are presented and compared.Four kneading block combinations were studied. Their geometry is shown in The direct comparison of the kneading blocks leads to the following conclusion: the best mixing characteristic offers the three-element combination of conveying (45°) narrow blocks with a value of 0.75, as well as both configurations of neutral (90°) kneading blocks; however, this includes only two of them, also reaching a value of 0.75. These configurations are an improvement on the reference one, having a value of 0.8. Again, it is important to note that the mixing value goes from 0.5 for the best mixing to 1 for the fully segregated state.Both backflow elements ZME and TME and their configurations are shown in The calculated mixing coefficients are summarized in the The analysis of the mixing behavior for different kneading blocks and the comparison of the calculated mixing coefficients revealed some potential improved configurations, which could be used in the mixing zone during extrusion, namely two narrow neutral kneading elements or three narrow conveying kneading elements, or even two wide neutral kneading elements. They all offer a mixing improvement when compared to the reference. On the other hand, both special mixing elements TME and ZME exhibit the best mixing efficiency between the studied elements. Mixing and processing high-filled polymers is challenging not only for choosing the best mixing element or element combinations, but also to find out those producing the lowest dissipated energy that is additionally generated by the high amount of filler. This is the energy due to the inner friction of the product during the process and depends highly on its viscosity and the square of the shear rate. It is calculated as the integral value of the product of the viscosity and square of shear rate.In Two screw configurations, namely two elements of narrow 90° blocks and three elements narrow 45° kneading blocks, were chosen from the particle tracking-based mixing analysis and simulated in a multiphase calculation, as described in As already predicted by the particle tracking method and the evaluation of the scale of segregation, both configurations yield to similar distributive mixing, one of the best between the studied elements. This behavior was confirmed by the multiphase simulation. Both configurations reached the stationary state and thus the mixing density at about four revolutions, as shown in Moreover, the slower rise of the mixing density progress for the three-element combination was in correlation with the mixing behavior achieved by the scale of segregation, namely the third element is the deciding element, which affirmed the achievement of the highest mixing characteristic. On the other hand, this is also due to the better conveying efficiency of this combination when compared to the neutral element.This can be seen in the velocity profile distribution for the mixture for both elements shown in These two configurations were also tested in the trials and showed a similar level of quality as trials prescribed numerically. The quality evaluation consists of measuring the typical physical properties for high-filled products after compounding.Moreover, it should be noticed that not only is the mixing efficiency to be considered, but the dissipated energy especially needs to be considered while operating with highly filled polymer. The advantageous use of conveying kneading blocks regarding their lower dissipated energy (about 20%) is recommended in this case, particularly in view of the same mixing efficiency. The high dissipated energy predicted numerically is also a reason not to use the special mixing elements for highly filled compounds, although they offer the best mixing quality (similar to TME).Different element configurations were studied, and their mixing characteristics were evaluated using the particle tracking method. Compared to the reference configuration, some improved combinations were found. Moreover, two configurations were evaluated in a multiphase semi-filled calculation and in the experimental set-up, which confirmed the already obtained results.In the following, the results of the rheological characterization of the produced compounds are presented.3 for the unfilled base polymer to a value of 1.75 g/cm3 for the highly filled compound, with a mass fraction of 87 wt.-% graphite. The density of pure graphite tends to be around 2.1 g/cm3. Compared to the unfilled raw material, the melt density doubles already at a filling ratio of approximately 75 wt.-%. This already suggests a processing challenge.The following The resulting apparent shear viscosities for all analyzed compounds, as well as the raw polymer, are shown in The influence of the temperature on the viscosity was also investigated. The results can be found in Based on the theory used to characterize wall slip effects, HCR measurements with two geometrically similar slit dies were compared. The use of the Geiger–Mooney  approachIn In contrast, the divergence of the curves appears to increase with lower filler content. However, unlike the expectation that the wall slip ratio increases with a higher graphite content, this result indicates that the wall slip ratio of the analyzed graphite compounds is higher the lower the filler content. For the observed material behavior, a conclusive explanation based on these results is not yet possible. Therefore, further research must be conducted for this purpose.This result in particular simplifies the design of different flow geometries for processing of the highly filled compounds into customized end products. According to the results obtained, only a very small amount of wall slip effects occur for the highly filled compounds, which may be neglected within the design process. In contrast, the effects for the low filler contents are quite large and, accordingly, we expect them to have a significant impact on the reliable prediction of the flow.The purpose of the following numerical investigation is to calculate the pressure loss along the slit die based on different flow approaches to determine the resulting deviation from the experimental values. Hereby, the influence of the chosen flow approach on the predicted pressure can be tested. −1) for this compound, the presence of wall slip effects could hardly be excluded completely. However, a plausible explanation for the abnormally high deviation for this specific simulation run has not yet been found. Nevertheless, a numerical problem could not yet be found.Even though the previous results have shown a high wall slip effect for the lower-filled compounds, the predicted deviation of pressure loss is quite small for all analyzed shear rates. However, the deviation in the range of high shear rates for the first recipe of the highly filled compound with 87% graphite by mass is rather conspicuous. Since the results determined by HCR measurement only provided information on the wall slip effects up to the suspicious shear rate (200 sIn this study, graphite compounds for the future production of bipolar plates for fuel cells were successfully produced with a mass fraction up to 87% graphite. As expected, the melt density of the compounded materials shows a significant increase with increasing graphite content. Furthermore, the rheological investigations by means of the plate–plate rheometer and the high-pressure capillary rheometer show a significant increase in the viscosity with increasing graphite content. The curves measured with the HCR, however, show an almost linear shear thinning behavior in the entire shear rate range for all analyzed compounds compared to the PPR. The investigated temperature dependence of the viscosity does not show any remarkable peculiarities and corresponds perfectly to the expectation. In contrast, the wall slip behavior characterized according to the theory of Geiger shows a behavior deviating from the expectation. As the graphite content increases, the effect of the wall slip decreases. Only for compounds with a low graphite content is a distinct wall slip behavior observed. The highly filled compounds, on the other hand, only show slight wall slip effects. A conclusive explanation for this behavior cannot be found either in the investigations carried out here nor in existing studies in the literature.Despite occurring wall slip effects, the developed flow simulation for the design of slit dies can provide a good prediction for both low and high filling ratios of the graphite compounds.Different mixing element configurations were studied, and their mixing characteristics were evaluated using the particle tracking method. Compared to the reference configuration, some improved combinations were found out. Moreover, two configurations were evaluated in a multiphase semi-filled calculation, which confirmed the already obtained results performed by the particle tracking method. Based on the adopted numerical techniques, a combination of elements was chosen, offering the best mixing efficiency and the lowest dissipated energy for use in the experimental set-up. An optimized screw configuration will be used in the future and should finally confirm the results from the numerical simulation.


Research on proteins and their interactions with other proteins yields many new findings that help explain how diseases emerge. However, manual curation of scientific literature delays new discoveries in the field. Artificial intelligence and deep learning techniques have played a significant part in information extraction from textual forms. In this study, we used text mining and artificial intelligence techniques to address the issue of extracting protein–protein interaction networks from the vast amount of scientific research literature. We have created an automated system consisting of three models using deep learning and natural language processing methods. The accuracy of our first model, which employs recurrent neural networks using sentiment analysis, was 95%. Additionally, the accuracy of our second model, which employs the named entity recognition technique in NLP, was effective and achieved an accuracy of 98%. In comparison to the protein interaction network, we discovered by manual curation of more than 30 articles on Autism Spectrum Disorder, that the automated system testing on 6027 abstracts was successful in developing the network of interactions and provided an improved view. Discovering these networks will greatly help physicians and scientists understand how these molecules interact for physiological, pharmacological, and pathological insight.Text mining methods are being developed to assimilate the volume of biomedical textual materials that are continually expanding. Understanding protein–protein interaction (PPI) deficits would assist in explaining the genesis of diseases. In this study, we designed an automated system to extract PPIs from the biomedical literature that uses a deep learning sentence classification model, a pretrained word embedding, and a BiLSTM recurrent neural network with additional layers, a conditional random field (CRF) named entity recognition (NER) model, and shortest-dependency path (SDP) model using the SpaCy library in Python. The automated system ensures that it targets sentences that contain PPIs and not just these proteins mentioned in the framework of disease discovery or other context. Our first model achieved 13% greater precision on the Aimed/BioInfr benchmark corpus than the previous state-of-the-art BiLSTM neural network models. The NER model presented in this study achieved 98% precision on the Aimed/BioInfr corpus over previous models. In order to facilitate the production of an accurate representation of the PPI network, the processes were developed to systematically map the protein interactions in the texts. Overall, evaluating our system through the use of 6027 abstracts pertaining to seven proteins associated with Autism Spectrum Disorder completed the manually curated PPI network for these proteins. When it comes to complicated diseases, these networks would assist in understanding how PPI deficits contribute to disease development while also emphasizing the influence of interactions on protein function and biological processes. Intracellular and extracellular proteins are the core building blocks of many intracellular signaling pathways . ProteinWith the emergence of Natural Language Processing (NLP) methods, PPI extraction from biomedical abstracts becomes feasible. The most challenging aspect of the job involves interpreting biological and biomedical language in order to obtain a meaningful explanation of all living things’ complicated nature. Another challenge lies in the need to search through a large number of articles to find an explanation for the cause of the disease, particularly in the case of complex diseases. PPI extraction can be interpreted in several different techniques, and many machine learning and deep learning techniques have been implemented. Kernel-based machine learning methods have attained good performance, but they require extensive feature engineering, including lexical and syntactic features ,10,11,12In the beginning of developing named entity recognition (NER) models, generative models such as Hidden Markov Models (HMMs) were used. Generative models such as HMM have strict rules of learning that rely on the joint distribution of the data, which results in dependent features . The conhttp://corpora.informatik.hu-berlin.de, accessed on 15 March 2019) [This study proposed a comprehensive method for generating graph figures of PPI networks from only the biomedical literature obtained from the PubMed database, combining the three approaches mentioned above. This method is comprised of three main phases including the development of DL models and the application of patterns to extract information from the text and transfer it to a knowledge graph. In the first step, PPI sentences that only contain the two proteins’ names and their relationship words are extracted from the biomedical literature using a DL with a recurrent neural network (RNN) methodology that competes with the current state-of-the-art methods in extracting PPI from the biomedical literature. The method takes advantage of the AIMed and BioInfer corpora (both available at ch 2019) , which ach 2019) . The secch 2019) , the thiDespite the methods that have been developed previously, either using Kernel-based machine learning or deep learning approaches, most of them require identifying the lexical and syntactic features of words, characters in the words, and sentences and manipulating these features to improve the performance of machine learning or deep learning models. For example, in article , the resThe methods presented in the current study overcame these problems and simplified the approach by using BioWordVic, the newly developed 2019 word-embedding vector of 4 billion tokens , and thrThe remaining sections are grouped as follows: The The development of the PPI network, which includes extracting the relationship between the proteins from biomedical literature and transferring it to a knowledge graph, was accomplished using machine learning and deep learning methods. Three phases were involved in the process of establishing the PPI network, including developing a sentence classification model, named entity recognition model using a conditional random field (CRF) method, and employing patterns to extract information from the text and transfer it to a knowledge graph .The model was trained with AImed and Bioinfer corpus data. The two datasets were integrated and prepped for processing via the Python NLTK library . CombineThe model was created in order to distinguish between sentences containing protein relationships and sentences containing no protein links in biomedical abstracts.Word embedding is a representation learning technique comprising aligning words with similar meanings and convergingly representing them in a low-dimensional vector space. In the dataset, each word is represented as a vector of positive real values. Specifically, the publicly available pretrained word embedding BioWordVic  and GloVLong short-term nemory (LSTM) artificial recurrent neural network (RNN) is useful in reducing the vanishing gradient mistakes and capturing the semantic information in long sentences because it is fast and efficient . Each LSi is the input gate, f is the forget gate, o is the output gate, c is the cell states, tx is the word embedding vector, th−1 is the hidden state, the W’s are the weight matrices, The b’s are the bias vectors, σ is the sigmoid function, and tanh is the hyperbolic tangent function.Each LSTM cell’s mathematical representation and the equations governing its three gates are as follows:BiLSTM is well suited for use in sentiment analysis and text classification models. The LSTM cells in the forward and backward layers of the recurrent neural network form the structure of the BiLSTM recurrent neural network. To obtain optimal performance, it is important to train on both the input sequence and its reverse duplicate. As a result, the sequence categorization classification is swiftly and thoroughly acquired.tx is the word embedding vector. ty is the joining outputs from the forward and backward hidden layers. The output layer values are processed as follows:W is weights matrices, b is the bias term, σ is sigmoid function, and th is the hidden state. A dense layer was added at the end to ensure that all of the output neurons in the BiLSTM neural network were fully connected. Because of the usage of two classes, positive and negative sentences, the output prediction of the model with a Softmax activation function is performed using the dense layer. This layer predicts a multinomial probability distribution. In this case, the prediction probability range is 0 to 1. A prediction of less than 0.5 is regarded as a negative prediction, whereas a prediction of equal to or greater than 0.5 is seen as a positive prediction.As demonstrated in , xt is tThe model has been developed to tag the names of proteins after they have been extracted from positive sentences that describe the relationships between proteins.After text pre-processing for the two datasets (Aimed/Bioinfer) and using the letters (P) and (O) to label the protein names and other words, respectively, the dataset is used to train the model using the sklearn-CRFSuite library in Python (version 3.8) . ConditiFollowing the selection of sentences containing relationships between proteins using the sentence classification model and the tagging of proteins names using the NER-CRF model, the shortest dependency path model in spaCy was then employed to extract the shortest path between the names of the proteins in the selected positive sentences. Interaction sentences in PPI are composed of nouns and verbs. The verbs are almost always the focal point of all sentences. Dependency parsing illustrates the sentences as trees and recognizes and labels the center of the sentences as the ROOT of the tree, which is usually reflected in the verbs. The dependency labels for the remaining words are assigned by the spaCy shortest dependency path model based on the syntactic structure of the sentence .It is necessary to transform each sentence’s syntactic structure into a graph representation, which is performed using the networkx module in Python , in ordeThe approach developed in this paper to extract PPI networks from biomedical publications involves text dataset preparation, sentence classification model generation, named entity recognition model creation, and relation extraction. In Two datasets were prepared to train the NLP models. For the sentence’s classification model, AImed and Bioinfer corpora were used and processed. The processing includes joining all of the sentences from the two corpora, multiword tokenization, and changing the protein names in sentences, as well as labelling positive sentences with 1 and negative sentences with 0. The number of positive (sentences contain relationships between the proteins) and negative (sentences do not contain relationships between the proteins) in the AIMed/BioInfer PPI dataset are 3067 total sentences, 1267 total positive sentences, and 1800 total negative sentences.For the named entity recognition model, AImed and Bioinfer corpora were also used and processed, but we only consider the positive sentences that exhibit a relationship between proteins. Processing the sentences included multiword tokenization, position-tagging, and labelling the words in the sentences. Label P was considered for the proteins’ words and O for other words. So, the datasets were transformed to a dataset with words labeled as P or O. It is estimated that there were 31,472 words with an O label and 4078 words with a P label.Sentence classification is one of the natural language processing (NLP) tasks that labels sentences according to predefined classes. In the context of this study, the primary sentence classes are positive and negative, with positive sentences indicating the presence of two protein names and a relation between them. We followed the architecture of the previous state-of-the-art methods devised in the field of PPI extraction, which consists of a neural network layer followed by a pretrained word embedding layer. The following describes the initialization of the two layers:We utilized the BioWordVic and GloVe models of vector representations for words while modelling the sentence classification model. BioWordVic was trained on 28 million PubMed articles and 2 million MIMIC III Clinical notes. GloVe was trained using news stories and the Wikipedia database. Because various earlier state-of-the-art models have been created utilizing these two pretrained word embedding models, we aimed to compare and contrast the differences and precisions of the models developed in this paper using those pretrained word embedding techniques.−4. In the second two models, we stacked three hidden BiLSTM layers after the word embedding layer that has a weight matrix of the two pretrained word embedding models. Also, we dropped the number of nodes in all BiLSTM layers to 32. There is no predetermined number of nodes used for the BiLSTM layer. The number of nodes needs to be nearly compatible with the number of the input sequence of 120. In the model that has one BiLSTM layer, 100 nodes were used, and in the model that has three hidden BiLSTM layers, 32 nodes were used. The total number of nodes in the three hidden layers is 96 (32 × 3); that is, the input sequence passes 96 nodes forward and 96 nodes backward. Although our network would be considered small, when many dropout rates were tested, the best rate was 0.5 for the dropout layer and 0.2 for the recurrent dropout layer. The activation function is SoftMax because the model has two labels: 0 for negative sentences with no PPI and 1 for positive sentences with PPI. Adam optimization is a method for stochastic gradient descent that utilizes adaptive methods to estimate first-order and second-order variables. Rates from 0.01 to 0.0001 were tested with the finding that the lower the rate of learning, the higher the accuracy of the model prediction. The following are the parameters that govern the sentence classification models are shown in (We constructed four sentence classification models using Keras v 2.13.1 with TensorFlow v 2.13 (Chollet and others). In the first two models, input sentences from both datasets (AIMed and BioInfer) are fed into the model, and the words in the input sentences are given their real number representation through the word embedding layer, which contains the weight matrix of the above-mentioned pretrained word embedding vectors, before being transferred to the neural nodes. We set the number of BiLSTM nodes to 100, the dropout rate to 0.5, and the learning rate for the Adam optimizer to 1 × 10shown in .The F1 score, which is often used to measure the performance of classification models, was utilized to evaluate the models. The F1 score is determined by the precision and recall scores that describe the model’s performance. The final two scores depend on the model’s true positive, true negative, false positive, and false negative predictions. The following equations define the precision, recall, and F1 score:We utilized the classification report matric in the Python sklearn library. The data was divided into 60% training data and 40% testing data. The global average F-score was calculated in order to evaluate the overall performance of the models regardless of the class.In this stage, the Python sklearn-CRFSuite module was utilized. Initially, the training data consisted of a list of each word, its position tag, and its label (P or O). This data list would be utilized to train the NER model. Certain conditions are implemented while training the model, such as defining the surrounding word features. Finally, a labeler that recognizes protein names in sentences will be created. When evaluating the model’s efficacy, the weighted F1 score was employed due to the presence of label imbalance in the dataset, as there were only 4078 words with portion label (P) and 31,472 other words with label (O) . The metThe shortest dependency path model provided and modelled by the spaCy library in Python was used to extract the shortest path between the proteins in the PPI sentences after using the first two proposed models to first extract the sentences that have PPI and then extract and label the protein names in the PPI sentences. This model’s unique contribution is the way it assigns the dependency labels for words according to the norms of sentence formation. We found that words with a ROOT dependency label locate and define the relationship between the two protein names in the sentences. The dependency label range of the first protein is usually , and the dependency label range of the second protein is usually . The patterns we chose to locate and define the relationship words are as follows: , , , , and we used matcher library in Python to locate the relationship words from the sentences, with their dependency labels matching the arrangement we created in the patterns.The aim of this section is to elucidate how the learning performance of the models developed in this study was determined. Also, the sequence of using these models in the order of searching for positive sentences containing the relation between proteins, followed by searching for the names of the proteins and the relation words, was successful when compared to a manual method of reviewing abstracts related to seven proteins.Lately, BiLSTM neural networks have been shown to be effective in PPI tasks. With the differences in text pre-processing , the hyperparameter settings, and the models used as pretrained word embedding, we are comparing our four created models with state-of-the-art previously created models that are nearly similar to the structure of our models. The following table shows the performance of the four models compared to other models. Because the previous models were trained on Aimed and BioInfer separately, we took the average F1 scores for both models .Taking advantage of the pretrained word technique and the availability of its models, the BioWordVic model performed better than the GloVe model between our models (3 and 4), (5 and 6). This means that the RNN had the ability to automatically collect contextual information from the BioWordVic model more than the GloVe model in terms of PPI tasks. If we compare our models 3 and 4 to 1 and 2, our models nearly achieved similar accuracy to the previously created models in the case of 1 and 2, with differences in the word embedding vectors that the models were trained on. Stacking the three hidden layers of BiLSTM in our models 5 and 6 further improves the models’ performance and increases their accuracy.For named entity recognition (NER) models and capturing biological molecules  from texts and biomedical abstracts, CRF models proved to be effective in the field. Our NER-CRF model trained on 35,550 AIMed/BioInfer corpus words achieved an F1 score of 0.98. Previous NER attempts developed tools using the CRF method. ABNER is a biomedical NER tool used to tag biomedical entities in texts using CRF. They trained the model on an NLPBA corpus (modified version of the GENIA corpus) that has 22,402 training and testing sentences and a BioCreative corpus that has 10,000 training and testing sentences. The F1 scores of the tagging models are 0.705 on the NLPBA corpus and 0.699 on the BioCreative corpus . ProNER Four patients with Autism Spectrum Disorder (ASD), as mentioned in study , with a INTS6, its paralog, has been well studied in previous studies; the latter protein was discovered to be a critical element affecting restrictive dorsal cell growth through the Wnt pathway [After an exhaustive manual annotation of the protein databases and literature curations, a streamline of the interactions between these proteins and the other molecules in the Wnt and mTOR pathways was discovered, and convergence was found between these two signaling pathways according to the evidence extracted from previous studies . The man pathway . SimilarFLNA mutations alter important cellular processes and have been linked to several disorders, including ASD [IDS is a sulfatase lysosomal enzyme important in protein metabolism and specifically in degrading large carbohydrate molecules, glycosaminoglycans, from its substrate dermatan sulfate and heparan sulfate [IDS [From the summary of the interactions between the proteins , it can ding ASD ,57. The ding ASD ,59,60. I sulfate . Heparan sulfate . SUMF1 iate [IDS . Mutatioate [IDS .Using automated text mining and pattern recognition techniques, a PPI network diagram was generated . The PubHere, our results demonstrated that the system developed for PPI extraction successfully extracted the manually curated relationships between seven proteins and discovered additional relationships. The majority of PPI extraction research focuses on improving the accuracy of their models rather than validating their models to demonstrate actual results of PPI network creation and compete with existing tools that generate PPI networks. Testing our designed system was the most crucial phase in proving our theory regarding the formation of PPI networks. We have already manually evaluated 25 articles for seven proteins identified as the cause of autism in four patients. To evaluate our methodology, we extracted all abstracts pertinent to the seven proteins, totaling 6027, from the scientific literature. After applying our system to the abstracts, we generated an expandable image of all the proteins that interact with the seven proteins. What was intriguing was the inclusion of the manually curated PPI network  in the eIn this study, we proposed developing and combining effective mining and generation techniques for PPI networks. In addition, we organized these methods in a manner that helps facilitate the development of PPI networks. When we first created the sentence classification model, we were aware that the application of deep learning has significantly supported PPI initiatives in recent years. The effect of deep learning models is determined by their architecture. In general, the arrangement of the word embedding layer and the neural network layer was the most commonly used arrangement. The word embedding techniques can influence the classifier’s performance. To accelerate feature learning, the semantic and syntactic features of incoming text are embedded in their distribution representations. In our project, using the BioWordVic, the most recently developed word vector model for biomedical texts and which has never been used in any previous study, improved the deep learning model’s feature learning more than using the CloVe word embedding model, which is trained on news and Wikipedia articles. Then, our sentence classification model demonstrated that stacking three hidden BiLSTM layers as opposed to utilizing a single BiLSTM layer improved the efficacy of extracting sentences with PPI. Moreover, our model showed an improvement of 21% and 13% in precision compared to the previous studies , respectAfter successfully extracting the sentence with PPI, we needed a model that automatically searched for protein names in these sentences. NER was the most suitable strategy for this objective. The CRF method performed remarkably well in identifying protein names in the biomedical abstracts, as it was the most dependable and effective method we evaluated. Our identifiers for protein words in the AImed/BioInfr corpus sentences have been simplified. We labeled non-protein words with the letter O and protein words with the letter P. Previous methods included P1 and P2 labels for the names of the two proteins because they relied on NER models to find PPI sentences ,38. In oOnly our method, which consists of two NLP models and a pattern recognition technique, shown in However, the field of NLP is swiftly evolving. For the upkeep of our method, the NLP model must be revised continuously to remain current in relation to developments in the field of NLP. As mentioned earlier, BiLSTM RNN was chosen over CNN because the developed models trained on textual datasets and RNN have a sequential architecture more suitable for a sequence of words. GRU is another form of RNN. Because of their shared architecture and similar performance characteristics, LSTM and GRU are often compared as two variants of the same model. LSTM has three gates in its cells: the input gate, the output gate, and the forget gate. The GRU architecture incorporates two different gates within its cells, namely the update gate and the reset gate. One notable characteristic of these gates is their ability to retain previous data without eliminating them, even if they become irrelevant to the prediction. In LSTM, the forget gate is used to selectively keep or discard specific components of the preceding cell state. The GRU update and reset gate is better in keeping the data and would be more beneficial in generative AI models . In our In this study, an automated method for defining and constructing a protein–protein interaction (PPI) network was described. This paper summarizes the use of artificial intelligence (AI) approaches in text mining, which provides the opportunity to learn more about the other factors that contribute to the development of a disease. This strategy has the potential to reduce the amount of time spent manually searching for reliable information and to streamline the development of a comprehensive picture of the events leading to the onset of disease phenotypes. Although we focused our research on genetic variants associated with ASD, we have found that this strategy may be applicable to other types of disorders and diseases. As part of our future research, we are discussing the creation of a web tool that is accessible to the scientific community, with the expectation that additional discoveries will be made in the future. In addition, a generative AI method should be implemented so that our model can retrain itself without human intervention in the process of updating the system.