Information Transmission Delay Research Articles

Decision-making optimization for post-disaster restoration of multimodal transport networks in terms of resilience

IntroductionPublic health emergencies can have a ripple effect on the resilience of multimodal transport network, which will lead to problems such as route disruptions or blockages, route selection change and information transmission delay spreading to the whole network, further hindering the transportation planning and operational efficiency of the network. MethodsThis study constructs a multimodal transport route optimization model under uncertainty with the objective of the sum of transportation cost, transshipment cost, penalty cost and carbon emission cost. To enhance the computational efficiency of the model, a novel invasive weed optimization with memory and encoding value clustering capabilities is proposed. In addition, by fusing the Q-learning algorithm in reinforcement learning with the novel invasive weed algorithm, the action-value function table obtained from the training facilitates the selection of optimal routes. Based on empirical data, explore the sensitivity analysis of node disruptions, time windows, and fuzzy demand on route decision-making under public health emergencies. ResultsThe transport network is affected by public health emergencies, which makes the optimal route deviate from the expected goal, resulting in an increase in the total cost. The proportion of total cost is determined by the position of nodes in the network, with critical nodes suffering more losses than ordinary nodes. Reasonable setting of time windows and fuzzy demand intervals is an effective way to improve the resilience and transportation efficiency of multimodal transport network. ConclusionsThis study provides more applicable decision-making references for enterprises to prevent the risk of supply chain disruptions caused by public health emergencies.

A Mathematical Model for Inducing Delays in Transmissions of Information Between Spinors Within the Heart, Hemoglobin Molecules and Cells by Mobile Waves

Biological systems like the heart, hemoglobin and cells are built from entangled spinors. Entangled spinors exchange information through two ways: (i) Spinor waves with the velocity of infinity; and (ii) Photons with the limited velocity of the speed of light. If any spin in a pair reverses, then the other spin changes sogn immediately. However, initial exchanged photons do not understand these changes, and retain the memory of the previous stage. Thus, when they reach the spinors, the spinors repel them. With time, the new emitted photons from the spinors obtain the correct information, and absorb other spinors in a pair. This repelling and absorbing causes the oscillation of heart cells and the motions of blood cells including hemoglobin. These molecules transmit information and oxygen and pass them on to cells. A mobile wave could break the entanglement between the spinors of the hemoglobin, and cause a delay in information and oxygen transmission from the heart to some cells, thereby creating non-normal cells like tumor ones. The reason for this is that without information, cells have to act independently of other cells. Also, without oxygen, cells have to use other mechanisms for respiration like the ones which were used by tumor cells in the Warburg proposal. This does not depend on the wavelength because the wavelength only determines the probability of existence of photons at each point and waves with any wavelength (even bigger than cell size) are formed from many photons with smaller size than cells. However, after some time, after establishing electromagnetic towers, the spinor structure of the body could resist these wave-fronts, and the probability for the emergence of tumors decreases. Without this resistance and according to the Warburg proposal, generated tumor cells produce different numbers of spinors which help them to diagnose the motion of T-cells, making them ready to respond. To avoid this event, we may use again some mobile waves which produce some noise around tumor cells.

Frequency chimera state induced by time delays in FitzHugh-Nagumo neural networks

The delay of information transmission is an inherent factor of the nervous systems, which has great influences on their collective dynamic behaviors. In this paper, we constructed a ring neural network using FitzHugh-Nagumo (FHN) neuron model as the network node and memristive synapses as the connection mode. Our primary focus was on investigating the effects of time delay and coupling strength on the firing frequency of neurons. Simulation results revealed that the frequency chimera state could be induced in the neural network with appropriate time delay, which is a new type of chimera state characterized by firing frequency rather than traditional membrane potential. By adjusting the time delay properly, the neural network can also display multi-cluster frequency chimera states that coexisted with various incoherent regions and coherent regions. Meanwhile, we exhibit that initial value and coupling strength could have great influences on the effects of time delay on inducing frequency chimera state of the nervous systems.

A neural basis for learning sequential memory in brain loop structures.

Behaviors often involve a sequence of events, and learning and reproducing it is essential for sequential memory. Brain loop structures refer to loop-shaped inter-regional connection structures in the brain such as cortico-basal ganglia-thalamic and cortico-cerebellar loops. They are thought to play a crucial role in supporting sequential memory, but it is unclear what properties of the loop structure are important and why. In this study, we investigated conditions necessary for the learning of sequential memory in brain loop structures via computational modeling. We assumed that sequential memory emerges due to delayed information transmission in loop structures and presented a basic neural activity model and validated our theoretical considerations with spiking neural network simulations. Based on this model, we described the factors for the learning of sequential memory: first, the information transmission delay should decrease as the size of the loop structure increases; and second, the likelihood of the learning of sequential memory increases as the size of the loop structure increases and soon saturates. Combining these factors, we showed that moderate-sized brain loop structures are advantageous for the learning of sequential memory due to the physiological restrictions of information transmission delay. Our results will help us better understand the relationship between sequential memory and brain loop structures.

CycLight: Learning traffic signal cooperation with a cycle-level strategy

This study introduces CycLight, a novel cycle-level deep reinforcement learning (RL) approach for network-level adaptive traffic signal control (NATSC) systems. Traditional RL-based step-by-step traffic signal controllers, while precise and responsive, demand high computational resources for frequent data exchanges and can increase traffic accident risks due to disordered phase switching. Different from the step-by-step control strategy aforementioned, the proposed CycLight adopts a cycle-level strategy, optimizing cycle length and splits simultaneously using Parameterized Deep Q-Networks (PDQN) algorithm. This cycle-level approach effectively reduces the computational burden associated with frequent data communication, meanwhile enhancing the practicality and safety of real-world applications. Moreover, a decentralized framework is formulated for multi-agent cooperation, while attention mechanism is integrated to accurately assess the impact of the surroundings on the current intersection. CycLight is tested in a large synthetic traffic grid using the microscopic traffic simulation tool, Simulation of Urban MObility (SUMO). Experimental results not only demonstrate the superiority of CycLight over other state-of-the-art approaches but also showcase its robustness against information transmission delays.

Joint non-fragile automatic generation control and multi-event driven mechanism co-design for wind integrated power system under denial of services attacks

Denial of services (DoS) attacks exist in wind integrated power system. DoS attacks can cause network-induced delay and packages loss in information transmission. Meanwhile, considering the parameter perturbation of controller and system model uncertainty in wind integrated power system, these may cause the system dynamic performances degradation or even instability. Based on the above considerations, the joint non-fragile automatic generation robust control of wind integrated power system under DoS attacks is studied in this paper. In order to ensure the expected system performance and more effectively utilize the limited network communication resources under DoS attacks, a novel dynamic multi-event driven mechanism based joint non-fragile H∞ automatic generation control method is proposed. By constructing a suitable Lyapunov-Krasovskii functional and utilizing the Shur complement lemma to handle nonlinear matrix inequality, the sufficient conditions are derived to guarantee the asymptotic stability of wind integrated power system under DoS attacks. Furthermore, the performance of the proposed non-fragile regulator is demonstrated through a four-area wind integrated power system to show the feasibility and applicability. The analysis result indicates that the proposed scheme provides stronger robustness, higher wind energy utilization efficiency and more efficient communication mechanism.

Detecting stochastic multiresonance in neural networks via statistical complexity measure

This paper employs statistical complexity measure (SCM) to investigate the occurrence of stochastic multiresonance (SMR) induced by noise and time delay in small-world neural networks coupled with FitzHugh-Nagumo (FHN) neurons. Our findings reveal that SCM exhibits four local maxima at four optimal noise levels, providing evidence for the occurrence of quadruple stochastic resonances. When time delay τ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au$$\\end{document} is taken into account in the information transmission, under moderate noise levels, SCM shows several local maxima when τ=nTe\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au = nT_{e}$$\\end{document} with n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n$$\\end{document} being a positive integer and Te\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$T_{e}$$\\end{document} being the period of subthreshold signal. This indicates the appearance of delay-induced SMR at the multiples of the period of subthreshold signal. Intriguingly, at low noise levels, a strong coherence between time delay and neuronal firing dynamics emerges at τ=nTe-2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au = nT_{e} - 2$$\\end{document}, as confirmed by a series of SCM maxima at these time delays. Furthermore, the study demonstrates that by adjusting the degrees and sizes of small-world networks, as well as the coupling strength, it is possible to optimize the strength of delay-induced SMR, thus maximizing the detection capability of subthreshold signal. The research results may provide us with an effective approach for understanding the role of time delay in signal detection and information transmission.

Reliability Analysis of Cyber–Physical Energy Hubs: A Monte Carlo Approach

In this paper, the reliability of cyber-physical energy hubs (CPEHs) is evaluated based on the Monte Carlo simulation (MCS) method. Previous works in the related literature are generally focused on the physical layer of energy hubs (EHs). However, the security of EHs heavily relies on the reliable and safe performance of the cyber system. Therefore, this study considers the failure of cyber-physical components, data packet errors, and information transmission delay to improve the reliability assessment accuracy. The physical layer of the proposed CPEH is divided into two sections, namely heat and power sections, and reliability indices are calculated for both sections. Various scenarios are suggested to precisely analyze the effects of the cyber layer consideration on the CPEH reliability results. Sensitivity analysis on the failure rate of the components is also conducted to understand better the performance of the CPEH in situations with a higher risk of heat components failure. The notable impacts of the cyber layer disturbances on the CPEH reliability evaluation are demonstrated through the simulation results.

Effect of internal and external chaotic stimuli on synchronization of piezoelectric auditory neurons in coupled time-delay systems.

Hearing impairment is considered to be related to the damage of hair cells or synaptic terminals, which will cause varying degrees of hearing loss. Numerous studies have shown that cochlear implants can balance this damage. The human ear receives external acoustic signals mostly under complex conditions, and its biophysical mechanisms have important significance for reference in the design of cochlear implants. However, the relevant biophysical mechanisms have not yet been fully determined. Using the characteristics of special acoustoelectric conversion in piezoelectric ceramics, this paper integrates them into the traditional FitzHugh-Nagumo neuron circuit and proposes a comprehensive model with coupled auditory neurons. The model comprehensively considers the effects of synaptic coupling between neurons, information transmission delay, external noise stimulation, and internal chaotic current stimulation on the synchronization of membrane potential signals of two auditory neurons. The experimental results show that coupling strength, delay size, noise intensity, and chaotic current intensity all have a certain regulatory effect on synchronization stability. In particular, when auditory neurons are in a chaotic state, their impact on synchronization stability is sensitive. Numerical results provide a reference for exploring the biophysical mechanisms of auditory neurons. At the same time, we are committed to providing assistance in using sensors to monitor signals and repair hearing impairments.

A unified power flow controller-based robust damping controller considering time delay in electrical power systems

Nowadays, different power systems are connected to each other due to technical and economic reasons and form a complex developed network. Such networks measure and send system data to decision-making centers for control and protection. This information transmission is accompanied by a delay and causes the performance of the system’s damping controllers to be affected. To improve power system dynamic stability, the supplementary controller in flexible alternating current transmission system (FACTS) devices is designed to account for information transmission delays. In these methods, to increase the controllability and observability of the inter-area mode, the remote signals extracted from the wide area measurement system (WAMS) are used as the input of the modulator. WAMS systems’ time delay reduces power system stability and even causes instability, so it’s crucial to find the maximum delay margin that ensures stability. Therefore, it is necessary to carefully study the role of FACTS tools in stabilizing the power system and damping interregional fluctuations, considering the delay. This research shows that supplementary controller design can dampen frequency and load angle fluctuations in multi-zone power systems despite information transmission delays. The method works in power system analysis toolbox (PSAT) simulation and MATLAB programming.

CSR-SVM: Compositional semantic representation for intelligent identification of engineering change documents based on SVM

Timely and accurate identification of engineering change documents plays an important role in the document management system of the Architecture, Engineering, and Construction (AEC) industry, which facilitates decision making. The current way of manual review and analysis may cause a delay or omission in information transmission, which may severely affect the project schedule and cost. This paper adopts text classification to explore the intelligent identification of unstructured engineering change documents. However, the paucity of available corpus and the limited number of engineering change texts are challenges in practice. Additionally, the semantic information of texts cannot be elegantly exploited. Especially there are unregistered words that interfere with semantics in engineering change texts. To tackle these problems, we propose a compositional semantic representation (CSR) and develop an SVM-based method named CSR-SVM. We introduce a language model to produce word embeddings; here, a domain dictionary is established for unregistered words. The embeddings are then exploited in CSR so that CSR can incorporate both key and global semantic representations. The former is obtained based on dependency parsing and word embeddings, and the latter is obtained according to all the word representations of a text. The CSR provides sufficient semantic representation for SVM in an efficient way. The advantages of CSR-SVM have been validated by experiments on a real-world dataset.

Performance evaluation of political teaching MOOC system based on task scheduling and AI system

In the network control system, due to the limitation of network bandwidth and control system conditions, the information transmission of sampling and control must be completed in a certain time, otherwise it will cause information transmission delay, thus reducing the control performance of the network system. This paper builds a MOOC political teaching platform based on task scheduling and artificial intelligence system, and the use of MOOC ideological and political education model can improve students' participation and practicability. Under the MOOC model, students should be able to learn by participating in online video courses, skillfully applying theoretical knowledge of ideological and political education, and then by participating in physical classroom discussions. The simulation results show that the system can realize real-time data transmission, and the information transmission of sampling and control must be completed in a certain time, otherwise it will cause information transmission delay, thus reducing the control performance of the network system. Due to the limitation of network bandwidth, if multiple control systems work together, there will be transmission delays and other situations, which will directly affect the control performance of the system and cause system instability.

Analysis of data transmission network construction technologies with high requirements for information security, reliability, and latency

The scientific article is dedicated to the analysis of contemporary approaches and technologies in the construction of data transmission networks aimed at ensuring information security, reliability, and minimal latency. The necessity of comprehensively considering factors influencing network efficiency with a focus on reliability and security is substantiated. Network analysis enables the creation of an infrastructure that meets the demands of the modern information environment, comprehensively weighing the advantages and disadvantages of each network technology during construction. For the mathematical evaluation of data transmission network reliability, a Markov model is proposed, which is based on Markov processes theory. Developed using the Python programming language and relevant libraries, the model allows for a quantitative assessment of data transmission network reliability, considering various failure factors, state transitions, and recovery. The model facilitates forecasting network states and planning preventive organizational and technical measures to ensure reliability and security, in light of the constantly increasing demands for service quality and confidential information protection. An analysis of transmission delay methods has revealed a correlation between the transmission speed and volume of information with various factors, such as bandwidth, network load, connection type and speed, and data processing by network nodes. The research underscores the need to choose specific methods based on the network’s characteristics and its intended tasks. Based on the analysis results, to construct efficient networks that meet information security, reliability, and transmission delay requirements, there is a recognized need for comprehensive development of evaluation systems and the creation of a unified evaluation model.

Distributed optimization for intelligent IoT under unstable communication conditions

The design of distributed optimization algorithms for intelligent Internet of Thing (IIoT) environments has attracted extensive attention. However, the dynamic network environment, the delay of information transmission between devices and the risk of privacy disclosure pose great challenges to algorithm designs. To address this problem, we propose a distributed gradient descent algorithm, which can converge under very mild assumptions that there will be an unbounded (stochastic) delay from sender to receiver. To the best of our knowledge, our proposed algorithm is the first work to take unbounded stochastic communication delays into consideration, in addition to time-varying networks and privacy disclosure. Rigorous analysis shows that our algorithm can converge under such mild assumptions. Extensive experiments exhibit that the proposed algorithm performs well in complex IIoT environments.

Метод оцінки зв’язності вузлів бездротових епізодичних мереж за умови застосування безпілотних літальних апаратів

The paper considers a method for assessing the connectivity of nodes of wireless episodic networks (WEN) under the condition of using unmanned aerial vehicles (UAVs). It is proposed to perform an evaluation of the connectivity of a pair of nodes according to a method, which is based on: an evaluation of geometric connectivity, which is limited to the maximum range of radio visibility at the physical level and the vulnerability interval of a given multiple access protocol at the channel level; assessment of information connectivity, which takes into account the presence of not only a physical connection of a given reliability, but also the presence of a free channel resource, a given amount of transmission delay at a given traffic limit value; assessment of the duration of connectivity taking into account the mobility of network nodes. On the basis of analytical mathematical models, the duration of connectivity of mobile subscribers (nodes) (MS) of the WEN consisting of MS and UAVs in direct radio visibility conditions and taking into account relaying was investigated. The connectivity of a pair of nodes is determined by the characteristics of different levels of OSI information interaction, such as radio range, channel bandwidth, information transmission delay, etc. It is shown that the duration of connectivity is directly proportional to the size of the coverage zone and inversely proportional to the speed of movement of nodes. The nature (scenario) of node mobility also affects the duration of connectivity. The simulation of the movement of nodes was carried out under 4 scenarios: «march», «skirmish», «random wandering in the field» and «random wandering in the city». The largest values of the duration of connectivity correspond to the third scenario, and the smallest to the second (with a fixed radius of the coverage area and the speed of movement of nodes). Thus, the average connection duration of the UAV-pedestrian connection in the event of a «difference» will be of the order of 36 minutes, and the UAV-car connection of the order of 5 minutes. The obtained results can be used in the UAV network topology control method.

Effect of topology on delay-induced multiple resonances in locally driven systems

Information transmission delays are inherent in neuronal systems and significantly influence their dynamic properties. Recent studies have shown that delay-induced multiple resonances occur when delays in synaptic transmission are taken into account. We reveal the influence of topology on the delay-induced multiple resonances phenomenon in locally driven systems in this paper. It is expressed as the maximum value of the Fourier coefficient, which occurs at an integer multiple of T/N, where T is the external signal period or the neuronal intrinsic oscillation period, N is determined by the topology of the network. We briefly discuss the obtained results with the underlying reasons in terms of the network’s topology. The emergence of delay-induced multiple resonances depends on the interaction of the delay enhancement effect and the delay inhibition effect. In the case of weak coupling, the signal response performance is enhanced over a wider range of delay windows, which is dominated by delay enhancement effects. These results may provide a novel perspective on weak signal response and transmission in delay-coupled neural networks.

Variation in Newborn Screening Follow-up Processes for Sickle Cell Disease

Introduction Newborn screening (NBS) for sickle cell disease (SCD) has significantly improved childhood survival. However, gaps remain in implementation of follow-up after NBS as demonstrated by poor implementation of penicillin prophylaxis[1] and stroke risk screening.[2] It is unknown if initial engagement between healthcare providers and parents of affected children impact follow-up with SCD providers. In addition, states may have varying ways of informing families of diagnosis and ensuring follow-up care. This qualitative study examined the NBS process for SCD and subsequent access and referral to specialty care. Information was collected from a large, geographically diverse sample of healthcare providers as part of the Dissemination and Implementation of Stroke Prevention Looking at the Care Environment (DISPLACE) study. DISPLACE is an NHLBI-funded study designed to evaluate and improve stroke prevention practices in children with sickle cell anemia.[2] Methods Semi-structured interviews were conducted with 25 healthcare providers of pediatric patients with SCD. Interviews were conducted in person or by phone by a PhD-prepared nurse and a PhD-prepared psychologist with extensive research and clinical experience with SCD, including qualitative data collection and analysis. Questions were designed to determine barriers and facilitators to accessing appropriate stroke prevention practices in SCD, but responses revealed information more broadly about the NBS process and subsequent access to specialty care. Results Provider participants were a mean 50 years of age, 56% female, and 80% White. Most were physicians (76%), with a subset of advanced practice providers (8%) or other healthcare professionals (12%). Most were Hematologists/Oncologists or sickle-cell specialists (84%), and the remainder were general pediatric providers (16%). Providers practiced in 16 states across the U.S. Process NBS processes varied substantially across states. Most often, NBS results are sent to the pediatrician of record; in a few states, results are also sent to a SCD specialty clinic, a NBS coordinator, and/or the family. Despite results often being sent to the pediatrician, in several states, responsibility for ensuring children are seen for follow-up of NBS results lies on the SCD clinic. Provider participants in 6 states described a coordinator who provides services, including tracking children with positive screens, facilitating referrals, collecting outcomes data, and providing education to families. Providers in 3 states (18.8%) described an auditing process by which data on confirmatory (SCD) diagnosis, appointment attendance, and prophylactic penicillin initiation are collected to ensure appropriate follow-up care. Processes by which confirmatory diagnoses are reported back to the health department for tracking purposes (without quality assessment) were described in a few states. Challenges Providers in 9 states did not report challenges to NBS processes. In the remaining 7 states, key challenges included family and systems barriers. Family barriers (5 states) included insurance, transportation, and scheduling (2 states), difficulties contacting families, and "no shows" or appointment delays (5 states). Systems challenges included "clunky" system that uses outdated communication methods and is inundated with referrals (1 state), and lack of communication between centers in proximity regarding which will follow the child (1 state). Additional systems barriers (4 states) included inaccurate follow-up information (newborn sees different pediatrician than recorded at birth), poor communication (between sickle cell clinic and health department), or delays in information transmission (delay in confirmatory diagnosis and referral from pediatrician). Conclusion Results of interviews with healthcare providers in the DISPLACE study suggest significant variation in initial reporting of NBS results to families, establishing care with the SCD clinic, and variability in follow-up evaluation and monitoring, with few centers reporting an established quality assessment plan. Despite the overall success of the NBS program for SCD, important changes are needed including standardized NBS practices, protocols for notifying families of diagnosis, and NBS follow-up programs that perform quality assurance and assure adherence to SCD guidelines.

Telematics Collaborative Resource Allocation Algorithm Based on Cloud Sidecar

This paper provides an in-depth study and analysis of a distributed allocation algorithm for collaborative resources for cloud-edge-vehicle-based Telematics. The approach starts from the emerging application of urban environmental monitoring based on vehicular networking, with an integrated design of data sensing detection and transmission, and collaborative monitoring of vehicle swarm intelligence based on urban air quality collection to avoid redundancy of information and communication overload. A hybrid routing method with minimal delay for reliable data transmission is proposed. The power adjustment algorithm divides the channel into 3 states. When the CBR is less than 0.5, the channel is in an idle state, and when the CBR is greater than 0.5 and less than 0.8, the channel is in an active state. The algorithm designs redundancy strategies based on coding mechanisms to improve the reliability of data transmission, combines coding mechanisms with routing design, incorporates routing switching ideas, and performs probability-based routing decisions to minimize the delay. In straight-line road sections, a fuzzy logic prediction-based vehicle adaptive connectivity clustering routing algorithm is proposed to reduce the communication overhead during vehicle collaboration and ensure high network connectivity; at intersections, a probability-based minimum delay routing decision algorithm is proposed to reduce the information transmission delay. Experiments show that the proposed method effectively improves the efficiency of data-aware collection and transmission, and increases the reliability of transmission. With the explosive growth of video services, the problem of intelligent transmission of DASH-based video streams has become another research hotspot in mobile edge networks. Based on the edge container cloud architecture of vehicular networking, the resource constraints of microservices when deployed in the edge cloud platform were analyzed, and a multi-objective optimization model for microservice resource scheduling was established with the comprehensive performance objectives of shortest microservice invocation distance, highest resource utilization of physical machine clusters, and ensuring load balancing as much as possible.

Transition dynamics and optogenetic control of epileptiform activity in a modified mean filed model of human cortex

Physiological experiments have shown that inhibitory interneurons can induce and maintain epileptiform activities, and different interneuron subtypes may be responsible for different types of seizures. Here we aim to link these electrophysiological experimental phenomena with theoretical dynamic mechanisms based on an improved Liley mean field model. Fascinatingly, the number of synapses between inhibitory neural populations can induce a rich state transition. Typically, the system will experience normal rhythm discharges, multispike discharges, spike wave discharges (SWD), generalized periodic discharges (GPD), the beta band oscillation, and eventually return to the normal state. Interestingly, the transition process can also be reversed. Meanwhile, disinhibition circuits can cause more epileptiform activities after taking into account the delay of synaptic information transmission. Furthermore, we are committed to designing different control strategies for epileptiform activities. As we expected, both deep brain stimulation and optogenetic technology can destroy or even eliminate pathological waves. It is need to emphasis that the cell type specificity of optogenetic regulation allows it to precisely target inhibitory neural populations, which is agree with experiment. Particularly, three different optogenetic regulatory strategies targeting inhibitory neural populations are modeled and proposed, of which the intermittent is designed to save energy. These modeling results reproduce the experimental phenomena and more significantly, help understand the mechanism of epilepsy to guide the clinical practice.

Delay Factors Modelling for Real-Time Traffic Information Systems

The paper is presenting an overview of the factors that lead to critical delays which may occur in multi-modal realtime traffic and travel information systems (MRTTI) employing cellular data network communications. The analysis is taking into account MRTTI systems that use mobile devices to detect position and speed of a vehicle and receive traffic information furthermore used to adjust a previously defined route from origin to destination points. Information transmission delays are also investigated for route guidance systems used in critical applications, such as emergency vehicles management. The identification of the delay factors and some models regarding the delays are presented along with measurements on a real system.