Initial Route Research Articles

A Heuristic Algorithm Based on Travel Demand for Transit Network Design

This study proposes a simultaneous optimization model that considers flow assignment and vehicle capacity for the problem of transit network design to determine the route structure and frequencies simultaneously. The problem is focused on reducing the total travel time and the number of transfers. A heuristic algorithm is developed to solve this problem. In the proposed algorithm, the initial routes are generated according to a changing demand matrix, which can reflect the real-time demand with transfers and ensure that the direction of route generation maximizes the percentage of direct service. A regulating method for a sequence of stops is used during route generation to guarantee the shortest trip time for a formed route. Vehicles are allocated to each route according to the flow share. The concept of vehicle difference is introduced to evaluate the distinction between actual allocated vehicles and required vehicles for each route. The optimization process of frequencies based on vehicle difference can ensure that the solution meets the constraints. Two scale networks are used to illustrate the performances of the proposed method. Results show that route structure and frequencies can be optimized simultaneously through the proposed method. Different scenarios are created to test the algorithm properties via various parameter values. The test result indicates that the upper bound is a key parameter to balance the proportion of direct service and average in-vehicle travel time (AIVTT), and the increased number of planning routes can improve the proportion of direct service.

Behavioral patterns of people who use synthetic psychostimulants: Results of a qualitative study in St. Petersburg, Russia.

Russia, like many other places, is currently experiencing a proliferation of new psychoactive substances, many of which are psychostimulants. In St. Petersburg, these appear to be mostly congeners of cathinone. We sought to obtain qualitative data to better understand the impact on the health and behaviors of people who use drugs (PWUD) by recruiting individuals who reported recent use of psychostimulants. In-depth qualitative data on current drug use and its effects were collected through thirty interviews (n=30) and two focus groups (n=10: five male, five female). The interviews and focus groups also provided data on the social contexts of drug use including sexual behaviors and associated medical issues. Secondary data about online drug purchases were obtained from a source that accesses and analyzes darknet purchases. Qualitative data were initially coded using a priori codes developed on the basis of the interview guide, and then data were coded again inductively based on emergent findings from the data. Thematic analysis was carried out using OpenCode 4.0 qualitative data analysis software. Thematic analysis of the interviews and focus groups identified distinct differences in behavior patterns between older, more experienced PWUD and a "new generation" of PWUD. Routes of initiation of drug use and sexual behaviors associated with drug use differed, but both groups reported high levels of unsafe injection and sexual behaviors. In interpreting the texts and purchasing data, we have attempted to anticipate how the drug use patterns can influence HIV transmission. The emergence of a new class of psychostimulant drugs presents new threats to the health of drug users and new opportunities to intervene to reduce those risks. The information obtained may assist HIV/AIDS prevention specialists and drug user support groups in their efforts to decrease unsafe drug use and sexual behaviors.

Mild movement sequence repetition in five primate species and evidence for a taxonomic divide in cognitive mechanisms

When animals forage, they face complex multi-destination routing problems. Traplining behaviour—the repeated use of the same route—can be used to study how spatial memory might evolve to cope with complex routing problems in ecologically distinct taxa. We analyzed experimental data from multi-destination foraging arrays for five species, two cercopithecine monkeys (vervets, Chlorocebus pygerythrus, and Japanese macaques, Macaca fuscata) and three strepsirrhines (fat-tailed dwarf lemurs, Cheirogaleus medius, grey mouse lemurs, Microcebus murinus, and aye-ayes, Daubentonia madagascariensis). These species all developed relatively efficient route formations within the arrays but appeared to rely on variable cognitive mechanisms. We found a strong reliance on heuristics in cercopithecoid species, with initial routes that began near optimal and did not improve with experience. In strepsirrhines, we found greater support for reinforcement learning of location-based decisions, such that routes improved with experience. Further, we found evidence of repeated sequences of site visitation in all species, supporting previous suggestions that primates form traplines. However, the recursive use of routes was weak, differing from the strategies seen in well-known traplining animals. Differences between strepsirrhine and cercopithecine strategies may be the result of either ecological or phylogenetic trends, and we discuss future possibilities for disentangling the two.

How do infants enter and move through the care and family justice systems in Wales? Protocol for a population-based data linkage study.

ObjectivesWe aim to determine the proportion of infants entering care in Wales via the two primary legal routes (section 76 of the Social Services and Wellbeing (Wales) Act 2014, and section 31 of the Children Act 1989), and associations between mode of entry and infant characteristics and outcomes. ApproachThis is a longitudinal cohort study using routinely collected data held in the Secure Anonymised Information Linkage (SAIL) Databank. We will link the Looked After Children dataset with family justice (Cafcass Cymru) data on section 31 proceedings, to explore pathways through the care and family justice systems for infants aged <1 year entering care in Wales between 2012 and 2019. We will follow up each child for two years from the date of their entry into care to track legal outcomes (final legal orders) and placement outcomes (placement type, number of placements, and re-entry into the care system). ResultsDescriptive statistics will include frequencies and proportions of infants who initially enter the care system via voluntary arrangements (section 76) and care proceedings (section 31), by age, year, local authority, and category of need. We will describe the proportion and characteristics of those with voluntary arrangements who later become the subject of care proceedings, and the distribution of final legal orders and placement types by initial route of entry to care. We will use funnel plots to investigate variation between local authorities. We will use linear regression to test for statistically significant differences in the proportions of infants entering care via the two different routes over time, and chi-square tests to investigate associations between mode of entry and infant characteristics and outcomes. ConclusionThere is limited information on the care journeys of children in Wales at the individual level. This study will help us to understand the patterns of use of voluntary arrangements for infants over time, the proportion subsequently involved in section 31 applications and the impact on outcomes for children.

An Internet-Oriented Multilayer Network Model Characterization and Robustness Analysis Method.

The Internet creates multidimensional and complex relationships in terms of the composition, application and mapping of social users. Most of the previous related research has focused on the single-layer topology of physical device networks but ignored the study of service access relationships and the social structure of users on the Internet. Here, we propose a composite framework to understand how the interaction between the physical devices network, business application network, and user role network affects the robustness of the entire Internet. In this paper, a multilayer network consisting of a physical device layer, business application layer and user role layer is constructed by collecting experimental network data. We characterize the disturbance process of the entire multilayer network when a physical entity device fails by designing nodal disturbance to investigate the interactions that exist between the different network layers. Meanwhile, we analyze the characteristics of the Internet-oriented multilayer network structure and propose a heuristic multilayer network topology generation algorithm based on the initial routing topology and networking pattern, which simulates the evolution process of multilayer network topology. To further analyze the robustness of this multilayer network model, we combined a total of six target node ranking indicators including random strategy, degree centrality, betweenness centrality, closeness centrality, clustering coefficient and network constraint coefficient, performed node deletion simulations in the experimental network, and analyzed the impact of component types and interactions on the robustness of the overall multilayer network based on the maximum component change in the network. These results provide new insights into the operational processes of the Internet from a multi-domain data fusion perspective, reflecting that the coupling relationships that exist between the different interaction layers are closely linked to the robustness of multilayer networks.

Performance-Driven X-Architecture Routing Algorithm for Artificial Intelligence Chip Design in Smart Manufacturing

The new 7-nm Artificial Intelligence (AI) chip is an important milestone recently announced by the IBM research team, with a very important optimization goal of performance. This chip technology can be extended to various business scenarios in the Internet of Things. As the basic model for very large scale integration routing, the Steiner minimal tree can be used in various practical problems, such as wirelength optimization and timing closure. Further considering the X-architecture and the routing resources within obstacles, an effective performance-driven X-architecture routing algorithm for AI chip design in smart manufacturing is proposed to improve the delay performance of the chip. First, a special particle swarm optimization algorithm is presented to solve the discrete length-restricted X-architecture Steiner minimum tree problem in combination with genetic operations, and a particle encoding scheme is presented to encode each particle into an initial routing tree. Second, two lookup tables based on pins and obstacles are established to provide a fast information query for the whole algorithm flow. Third, a strategy of candidate point selection is designed to make the particles satisfy the constraints. Finally, a refinement strategy is implemented to further improve the quality of the final routing tree. Compared with other state-of-the-art algorithms, the proposed algorithm achieves a better total wirelength, which is an important index of performance, thus better satisfying the demand for delay performance of AI chip design in smart manufacturing.

SARS-CoV-2 Infection (COVID-19) and Rhinologic Manifestation: Narrative Review.

Patients with severe pneumonia of unknown etiology presented in December 2019 in Wuhan, China. A novel coronavirus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), was isolated from the respiratory tracts of these patients. The World Health Organization (WHO) defined respiratory diseases due to SARS-CoV-2 infection as coronavirus disease 2019 (COVID-19). Many researchers have reported that the nasal cavity is an important initial route for SARS-CoV-2 infection and that the spike protein of this virus binds to angiotensin-converting enzyme 2 (ACE2) on epithelial cell surfaces. Therefore, COVID-19 is thought to significantly affect nasal symptoms and various rhinological diseases. In this review, we summarize the association between COVID-19 and various rhinological diseases, such as olfactory dysfunction, rhinosinusitis, and allergic rhinitis.

Solving large-scale dynamic vehicle routing problems with stochastic requests

Dynamic vehicle routing problems (DVRPs) arise in several applications such as technician routing, meal delivery, and parcel shipping. We consider the DVRP with stochastic customer requests (DVRPSR), in which vehicles must be routed dynamically with the goal of maximizing the number of served requests. We model the DVRPSR as a multi-stage optimization problem, where the first-stage decision defines route plans for serving scheduled requests. Our main contributions are knapsack-based linear models to approximate accurately the expected reward-to-go, measured as the number of accepted requests, at any state of the stochastic system. These approximations are based on representing each vehicle as a knapsack with a capacity given by the remaining service time available along the vehicle’s route. We combine these approximations with optimal acceptance and assignment decision rules and derive efficient and high-performing online scheduling policies. We further leverage good predictions of the expected reward-to-go to design initial route plans that facilitate serving dynamic requests. Computational experiments on very large instances based on a real street network demonstrate the effectiveness of the proposed methods in prescribing high-quality offline route plans and online scheduling decisions.

Reinforcement learning based dynamic distributed routing scheme for mega LEO satellite networks

Recently, mega Low Earth Orbit (LEO) Satellite Network (LSN) systems have gained more and more attention due to low latency, broadband communications and global coverage for ground users. One of the primary challenges for LSN systems with inter-satellite links is the routing strategy calculation and maintenance, due to LSN constellation scale and dynamic network topology feature. In order to seek an efficient routing strategy, a Q-learning-based dynamic distributed Routing scheme for LSNs (QRLSN) is proposed in this paper. To achieve low end-to-end delay and low network traffic overhead load in LSNs, QRLSN adopts a multi-objective optimization method to find the optimal next hop for forwarding data packets. Experimental results demonstrate that the proposed scheme can effectively discover the initial routing strategy and provide long-term Quality of Service (QoS) optimization during the routing maintenance process. In addition, comparison results demonstrate that QRLSN is superior to the virtual-topology-based shortest path routing algorithm.

Vascular accesses for hemodialysis in Ceará-Brazil

Backgroung: the ideal access for hemodialysis is represented by arteriovenous fistulas both as initial access and in use and are determinants of health care parameters for chronic renal patients. Objective: to evaluate vascular access for hemodialysis. Methods: this is a cross-sectional study, with a sample of 2513 individuals on hemodialysis in Ceará. Data were collected on age, sex, time of treatment, underlying disease, initial access and access in use. The data were divided into two groups, the interior of the state and the Metropolitan Region of Fortaleza, in the State of Ceará, and compared using the Student’s Test (p<0.05). Results: the most common age group was between 19 and 64 years (73%). Men prevailed in both regions, 713 (63%) in the countryside and 792 (57%) in the metropolitan area. The most common cause of Chronic Kidney Disease was Hypertension 306 (27%), followed by Disease of undetermined cause 295 (26%) in the countryside; in the metropolitan region, Diabetes Mellitus 356 (26%) was the main cause, followed by Hypertension and Disease of undetermined cause, each with 344 (25%), p=0.001. In the countryside, 9% started treatment for fistula while in the metropolitan area 12%, p=0.011. 79% of patients in the countryside used fistulas and 81% in the metropolitan area, p=0.156. Conclusion: arteriovenous fistulas are more frequent as initial accesses in the metropolitan region than in the interior, but there is an equivalence of fistulas in use in both regions. Catheter is the main initial access route. The evaluation of vascular accesses in Ceará showed that hemodialysis services are able to maintain good parameters of care for patients with chronic kidney disease.

Neighbor Coverage and Bandwidth Aware Multiple Disjoint Path Discovery in Wireless Mesh Networks

With the ease of appending new nodes without re-installing the whole network, the Internet of Things (IoT) builds several smart applications on Wireless Mesh Network (WMN). One of the important aspects of integrating WMN and smart IoT applications is to provide an energy-efficient and reliable routing protocol. Seeking the communication route that delivers the high-quality stream quickly over WMN is an important issue, but the maximum utilization of a single high-quality path leads to poor throughput and large communication delay, including route discovery and data forwarding delay. The broadcasting mechanism creates redundant transmissions of control packets into the network and reinitializes the blind route discovery process due to link disconnections leading to network resource constraints and high delay during the route discovery process. Moreover, the congestion in the communication route incurs data transmission latency. This paper proposes the Multiple Disjoint Path Determination (MDPD) mechanism based on-demand routing in WMN to formulate the path discovery and data transmission latency. Reducing the neighbor list into the uncommon neighbor set reduces the unnecessary latency in route discovery, and deriving high capacity multiple disjoint communication routes reduce the communication delay in the proposed work. The proposed work employs the queue dynamics in queuing delay, which mainly provides adaptability to the dynamics in network capacity and efficient diversity paths to the gateway node to infer the available bandwidth and optimize the network traffic. To fully utilize the advantage of heterogeneous routers, it disables the flooding of control packets across the stable mesh routers, excluding the initial route discovery process, because it enables the available route storage system in each mesh router. Hence, the proposed work efficiently supports wireless broadband internet access with reduced delay and control overhead. The simulation results demonstrate the fast detection of the multiple disjoint routes and data traffic optimization over the discovered disjoint routes in the proposed MDPD mechanism over WMN.

Continuous Network Update With Consistency Guaranteed in Software-Defined Networks

Network update enables Software-Defined Networks (SDNs) to optimize the data plane performance. The single update focuses on processing one update event at a time, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , updating a set of flows from their initial routes to target routes, but it fails to handle continuously arriving update events in time incurred by high-frequency network changes. On the contrary, the continuous update proposed in “Update Algebra” can handle multiple update events concurrently and respond to the network condition changes at all times. However, “Update Algebra” only guarantees the blackhole-free and loop-free update. The congestion-free property cannot be respected. In this paper, we propose Coeus to achieve the continuous update while maintaining consistency, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , ensuring the blackhole-free, loop-free, and congestion-free properties simultaneously. Firstly, we establish the continuous update model based on the update operations in update events. With the update model, we dynamically reconstruct the operation dependency graph (ODG) to capture the relationship between update operations and link utilization variations. Then, we develop a composition algorithm to eliminate redundant operations in update events. To further speed up the update procedure, we present a partition algorithm to split the operation nodes of the ODG into a series of suboperation nodes that can be executed independently. The partition algorithm is proven to be optimal. Finally, extensive evaluations show that Coeus can improve the update speed by at least 179% and reduce redundant operations by at least 52% compared with state-of-the-art approaches when the arrival rate of update events equals three times per second.

Effect of Early Nutritional Support on Clinical Outcomes of Critically Ill Patients with Sepsis and Septic Shock: A Single-Center Retrospective Study.

The initial nutritional delivery policy for patients with sepsis admitted to the intensive care unit (ICU) has not been fully elucidated. We aimed to determine whether an initial adequate nutrition supply and route of nutrition delivery during the first week of sepsis onset improve clinical outcomes of critically ill patients with sepsis. We reviewed adult patients with sepsis and septic shock in the ICU in a single tertiary teaching hospital between 31 November 2013 and 20 May 2017. Poisson log-linear and Cox regressions were performed to assess the relationships between clinical outcomes and sex, modified nutrition risk in the critically ill score, sequential organ failure assessment score, route of nutrition delivery, acute physiology and chronic health evaluation score, and daily energy and protein delivery during the first week of sepsis onset. In total, 834 patients were included. Patients who had a higher protein intake during the first week of sepsis onset had a lower in-hospital mortality (adjusted hazard ratio (HR), 0.55; 95% confidence interval (CI), 0.39–0.78; p = 0.001). A higher energy intake was associated with a lower 30-day mortality (adjusted HR, 0.94; 95% CI, 0.90–0.98; p = 0.003). The route of nutrition delivery was not associated with 1-year mortality in the group which was underfed; however, in patients who met > 70% of their nutritional requirement, enteral feeding (EN) with supplemental parenteral nutrition (PN) was superior to only EN (p = 0.016) or PN (p = 0.042). In patients with sepsis and septic shock, a high daily average protein intake may lower in-hospital mortality, and a high energy intake may lower the 30-day mortality, especially in those with a high modified nutrition risk in the critically ill scores. In patients who receive adequate energy, EN with supplemental PN may be better than only EN or PN, but not in underfed patients.

A Coordinated Vehicle-Drone Arc Routing Approach Based on Improved Adaptive Large Neighborhood Search.

Through urban traffic patrols, problems such as traffic congestion and accidents can be found and dealt with in time to maintain the stability of the urban traffic system. The most common way to patrol is using ground vehicles, which may be inflexible and inefficient. The vehicle–drone coordination maximizes utilizing the flexibility of drones and addresses their limited battery capacity issue. This paper studied a vehicle–drone arc routing problem (VD-ARP), consisting of one vehicle and multiple drones. Considering the coordination mode and constraints of the vehicle–drone system, a mathematical model of VD-ARP that minimized the total patrol time was constructed. To solve this problem, an improved, adaptive, large neighborhood search algorithm (IALNS) was proposed. First, the initial route planning scheme was generated by the heuristic rule of “Drone-First, Vehicle-Then”. Then, several problem-based neighborhood search strategies were embedded into the improved, adaptive, large neighborhood search framework to improve the quality of the solution. The superiority of IALNS is verified by numerical experiments on instances with different scales. Several critical factors were tested to determine the effects of coordinated traffic patrol; an example based on a real road network verifies the feasibility and applicability of the algorithm.

Optimization of New Energy Public Transportation Network Based on Ant Colony Algorithm and Low-Carbon Concept

In order to solve the optimization of new energy bus line network, a new energy bus line network based on ant colony algorithm and low-carbon concept is proposed. Firstly, the model of public transport network is established combined with OD matrix, and the improved ant colony algorithm is used to iteratively optimize the initial suboptimal route set, optimize the objective function, and finally obtain the optimal public transport route set. Secondly, the improved ant colony algorithm based on simulated annealing solves the design of public transport network, and the optimization scheme greatly reduces the number of passenger transfers and the total travel time of passengers. Finally, simulated annealing algorithm, basic ant colony algorithm, and simulated annealing improved ant colony algorithm are used to optimize the objective function. It is proved that the improved ant colony algorithm of simulated annealing can find the optimal solution of the three algorithms when solving the problem of public transport network design, which is better than the solution of basic ant colony and simulated annealing algorithm. The solution efficiency is 10.6 times that of simulated annealing algorithm and 3.5 times that of basic ant colony algorithm.

Development of Bus Routes Reorganization Support Software Using the Naïve Bayes Classification Method

Reorganizing city bus routes is generally accomplished by designing bus supply methods to meet passenger demand. The bus supply method involves establishing bus routes and planning their schedules. The actual bus route reorganization decisions are not determined simply by balancing passenger demand and bus supply, but are based on other complex interests, such as bus routes that must exist for welfare but where profits are low. Machine learned prediction models could be helpful when considering such factors in the decision-making process. Here, the Naïve Bayes algorithm was applied to develop the classifier model because of its applicability, even with a limited amount of training data. As the input characteristics for the Naïve Bayes algorithm, data for each individual bus route were featured and cleansed with the actual route improvement decisions. A number of classification models were created by changing training sets and then compared in terms of classification performance such as accuracy, precision, and recall. Modeling and tests were conducted to show how Naïve Bayes classifiers learned in the form of supervised learning can help the route reorganization work. Results from a local governments’ actual route reorganization study were used to train and test the proposed machine learning classification model. As the main contribution of this study, a prediction model was developed to support shortening decision-making for each route, using machine learning algorithms and actual route reorganization research case data. Results verified that such an automatic classifier, or initial route decision proposal software, can provide intuitive support in actual route reorganization research.

Multi-Strategy Layer Assignment Algorithm Considering Bus Timing Matching

Layer assignment stage affects the timing matching of the bus by controlling the level of bus net in the global routing stage. Therefore, a multi-strategy layer assignment algorithm considering bus timing matching is proposed. First, a network prioritization strategy based on wired network attributes is adopted to acquire a routing sequence that satisfies not only the wire lengths but also the bus deviation according to the different attribute characteristics of the network. Second, using the initial routing strategy based on the greedy strategy, every shortest route in the network being configured is selected from each routing process, which keeps networks inside similar buses to be of equal lengths whenever possible. Finally, a local adjustment strategy formulated based on the bus wire length is employed to rip and reroute the completed initial routing, so as to lengthen parts of the network to minimize the length discrepancy between the bus longest wired network. The algorithms run in a Linux environment with a 2.60 GHz CPU and 64 GB of RAM. The algorithm validation experiments use the test data from the ISPD competition, and the experimental results show the bus deviation generated by this algorithm is reduced by 23.5%, and it is possible to significantly optimize the bus timing matching effect.

Dynamic Rolling Horizon Scheduling of Waterborne AGVs for Inter Terminal Transportation: Mathematical Modeling and Heuristic Solution

The demand for transport between terminals within port areas, known as inter terminal transportation (ITT), is increasing. This paper proposes a dynamic rolling horizon scheduling strategy for ITT using a fleet of waterborne Autonomous Guided Vessels (waterborne AGVs). The strategy is dynamic in that it can handle dynamically arriving ITT requests. Every certain period of time, transport schedules are updated according to the current vessel states, dynamic waterway transport network, and ITT requests over a future time horizon. Specifically, the dynamic scheduling problem is mathematically modeled in a rolling horizon fashion considering time windows of ITT requests, capacity limits of waterborne AGVs and load/unload service times at terminals. Considering the computational complexity for possible large scale ITT scenarios, we further propose an efficient solution approach based on improved insertion, tabu search and restart heuristics. Initial routes are first constructed by inserting new ITT requests into the previously computed routes in the rolling horizon framework. Tabu search with two types of neighborhoods are then designed to improve the initial routes. Moreover, a select-remove-insert restart procedure is activated to diversify the search space whenever necessary. A waterborne ITT network in the port of Rotterdam is considered. Comprehensive simulations based on realistic ITT dataset are run to demonstrate the effectiveness of the proposed dynamic scheduling strategy. This work could be readily used to build towards a fully autonomous waterborne ITT system. Insights that support long term strategical decisions, such as the fleet size, could also be gained from the simulations.

GLBR: A novel global load balancing routing scheme based on intelligent computing in partially disconnected wireless sensor networks

Load balancing is of great significance to extend the longevity of wireless sensor networks, due to the inherent imbalanced energy overhead in such networks. However, existing solutions cannot balance the load distribution in partially disconnected wireless sensor networks. For example, if a network is partitioned into several segments with different area sizes, some areas have much more traffic load than other areas. In this article, we propose a load-balanced routing scheme, which aims to balance energy consumption within each segment and among different segments. First, we adopt unequal transmission distances to build initial routing for intrasegment load balancing. Second, we adopt the genetic algorithm to build extra routing between different segments for intersegment load balancing. The unique character of our work is twofold. On one hand, we investigate partitioned wireless sensor networks where there are several isolated segments. On the other hand, we pursue load balancing from a global perspective rather than from a local one. Some simulations verify the effectiveness and the advantages of our scheme in terms of extra deployment cost, system longevity, and load balancing degree.

A ReaxFF-based molecular dynamics study of the pyrolysis mechanism of hexamethyldisiloxane

Hexamethyldisiloxane (MM) is one of the commonly used working fluids of organic Rankine cycle at medium and high temperatures, but there is a risk of decomposition of organic working fluids in high temperature environment. In this study, the pyrolysis mechanism of MM is analyzed by using ReaxFF force field and density functional theory (DFT). During the simulation process, three initial reaction routes were observed. By calculating the reaction energy barriers, it is found that the fracture of C-Si bond is the main initial reaction of pyrolysis. In the MM pyrolysis process, small molecule gases such as CH4, C2H6 and H2, linear siloxane represented by C8H24O2Si3（MDM）are the main thermal decomposition products. Free radical attacking reaction and free radical binding reaction are the main formation mechanism of methane. The intermediate condensation reaction is the formation mechanism of MDM. Most of the chain reactions are exothermic reactions, which can provide the energy for the system and further promote the decomposition of MM.