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A cell's fate involves transitions among its various states, each defined by a distinct gene expression profile governed by the topology of gene regulatory networks, which are affected by 3D genome organization. Here, we develop thermodynamic models to determine the fate of a malignant cell as governed by the tumor suppressor p53 signaling network, taking into account long-range chromatin interactions in the mean-field approximation. The tumor suppressor p53 responds to stress by selectively triggering one of the potential transcription programs that influence many layers of cell signaling. These range from p53 phosphorylation to modulation of its DNA binding affinity, phase separation phenomena, and internal connectivity among cell fate genes. We use the minimum free energy of the system as a fundamental property of biological networks that influences the connection between the gene network topology and the state of the cell. We constructed models based on network topology and equilibrium thermodynamics. Our modeling shows that the binding of phosphorylated p53 to promoters of target genes can have properties of a first order phase transition. We apply our model to cancer cell lines ranging from breast cancer (MCF-7), colon cancer (HCT116), and leukemia (K562), with each one characterized by a specific network topology that determines the cell fate. Our results clarify the biological relevance of these mechanisms and suggest that they represent flexible network designs for switching between developmental decisions. [ABSTRACT FROM AUTHOR]

The incorporation of molecular switches into polymer networks has been a powerful approach for the development of functional polymer materials that display macroscopic actuation and function enabled directly by molecular changes. However, such materials sometimes require harsh conditions to perform their functions, and the design of new molecular photoswitches that can function under physiological conditions is highly needed. Here, we report the design and synthesis of a spiropyridine-based photoswitchable hydrogel that exhibits light-driven actuation at physiological pH. Owing to its high pKa, spiropyridine maintains its ring-open protonated form at neutral pH, and the resulting hydrogel remains in a swollen state. Upon irradiation with visible light, the ring closure of spiropyridine leads to a decrease in the charge and a reduction in the volume of the hydrogel. The contracted gel could spontaneously recover to its expanding state in the dark, and this process is highly dynamic and reversible when the light is switched on and off. Furthermore, the hydrogel shows switchable fluorescence in response to visible light. Bending deformation is observed in the hydrogel thin films upon irradiation from one side. Importantly, the independence of this spiropyridine hydrogel from the acidic environment makes it biotolerant and shows excellent biocompatibility. This biocompatible spiropyridine hydrogel might have important biorelated applications in the future. [ABSTRACT FROM AUTHOR]

In the video compression industry, video compression tailored to machine vision tasks has recently emerged as a critical area of focus. Given the unique characteristics of machine vision, the current practice of directly employing conventional codecs reveals inefficiency, which requires compressing unnecessary regions. In this paper, we propose a framework that more aptly encodes video regions distinguished by machine vision to enhance coding efficiency. For that, the proposed framework consists of deep learning-based adaptive switch networks that guide the efficient coding tool for video encoding. Through the experiments, it is demonstrated that the proposed framework has superiority over the latest standardization project, video coding for machine benchmark, which achieves a Bjontegaard delta (BD)-rate gain of 5.91% on average and reaches up to a 19.51% BD-rate gain. [ABSTRACT FROM AUTHOR]

In this paper, the minimal observability of switching Boolean networks (SBNs) is investigated. Firstly, applying the semi‐tensor product (STP) method of matrices, a parallel extension system is constructed, based on which a necessary and sufficient condition to detect the observability of the SBNs is given. Secondly, when an SBN is unobservable, the specific steps to obtain the required measurements to make the system observable are given using the set reachable method; however, the measurements given in this part are not necessarily the fewest. Then, a criterion for determining the minimum number of measurements is further proposed through a constructed indicator matrix. Lastly, the effectiveness of the new results is verified by an example. [ABSTRACT FROM AUTHOR]

Biological systems are robust and redundant. The redundancy can manifest as alternative metabolic pathways. Synthetic double lethals are pairs of reactions that, when deleted simultaneously, abrogate cell growth. However, removing one reaction allows the rerouting of metabolites through alternative pathways. Little is known about these hidden linkages between pathways. Understanding them in the context of pathogens is useful for therapeutic innovations. We propose a constraint-based optimisation approach to identify inter-dependencies between metabolic pathways. It minimises rerouting between two reaction deletions, corresponding to a synthetic lethal pair, and outputs the set of reactions vital for metabolic rewiring, known as the synthetic lethal cluster. We depict the results for different pathogens and show that the reactions span across metabolic modules, illustrating the complexity of metabolism. Finally, we demonstrate how the two classes of synthetic lethals play a role in metabolic networks and influence the different properties of a synthetic lethal cluster. [ABSTRACT FROM AUTHOR]

In response to the challenge of existing wireless communication anti-jamming methods in effectively handling unknown jamming, this paper proposes a multi-parameter control anti-jamming algorithm for wireless communication systems based on the Linear–Quadratic Regulator (LQR). First, the proposed algorithm models the wireless communication system as a linear switched system based on the modulation and coding scheme. Subsequently, a feedback controller design method based on the LQR is introduced. By utilizing the multiple Lyapunov function method combined with linear matrix inequalities, sufficient criteria for the asymptotic stability of the system under unknown jamming conditions are provided. Finally, theoretical analysis and simulation results indicate that the proposed algorithm can rapidly adjust modulation and coding schemes as well as transmission power in complex jamming environments, thereby maintaining bit error rate (BER) stability and enhancing the reliability of the communication system. [ABSTRACT FROM AUTHOR]

This paper is dedicated to studying the mean square exponential stability of switched stochastic time‐varying delay neural networks with asynchronous switching aperiodic intermittent control. By utilizing the Halanay‐type inequality and designing appropriate asynchronous switching aperiodic intermittent control, several novel stability criteria of switched stochastic delay neural networks with asynchronous aperiodic intermittent control are presented under the Lyapunov function method and Lyapunov functional method. Among them, asynchronous switching aperiodic intermittent controllers are more practical and flexible compared to state feedback controllers. Finally, we provide two examples to verify the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This research paper primarily focuses on the synchronization of a specific class of multi‐weighted coupled neural networks (MWCNNs) with switching topology, employing two adaptive control methods. In complex environments and under the influence of communication disturbances, the topology structures of coupled neural networks with multiple weights inevitably undergo time‐varying changes. To address this, we propose a novel type of MWCNNs with switching topologies. To ensure synchronization within this network, we develop sufficient conditions based on Lyapunov functional and inequality techniques. These conditions guarantee the achievement of synchronization. Moreover, we address the synchronization problem by employing node‐based and edge‐based adaptive controllers. Finally, we provide a numerical example to demonstrate the effectiveness of the obtained results. This example serves as empirical evidence showcasing the successful application of the proposed synchronization approach in practical scenarios. [ABSTRACT FROM AUTHOR]

This study examines the problem of minimizing the amount and distribution of time delays or latencies experienced by data as they travel from one point to another within a software-defined network (SDN). For this purpose, a model is proposed that seeks to represent the minimization of the distances between network switches in proportion to the total nodes in a network. The highlights of this study are the proposal of two mixed-integer quadratic models from a fractional initial version. The first is obtained by transforming (from the original fractional model) the objective function into equivalent constraints. The second one is obtained by splitting each term of the fraction with an additional variable. The two developed models have a relationship between switches and controllers with quadratic terms. For this reason, an algorithm is proposed that can solve these problems in a shorter CPU time than the proposed models. In the development of this research work, we used real benchmarks and randomly generated networks, which were to be solved by all the proposed models. In addition, a few additional random networks that are larger in size were considered to better evaluate the performance of the proposed algorithm. All these instances are evaluated for different density scenarios. More precisely, we impose a constraint on the number of controllers for each network. All tests were performed using our models and the computational power of the Gurobi solver to find the optimal solutions for most of the instances. To the best of our knowledge, this work represents a novel mathematical representation of the latency density management problem in an SDN to measure the efficiency of the network. A detailed analysis of the test results shows that the effectiveness of the proposed models is closely related to the size of the studied networks. Furthermore, it can be noticed that the performance of the second model compared to the first one presents better behavior in terms of CPU times, the optimal solutions obtained, and the reduced Mipgaps obtained using the solver. These findings provide a deep understanding of how the models operate and how the optimization dynamics contribute to improving the efficiency and performance of SDNs. [ABSTRACT FROM AUTHOR]

Random variations in microelectronic circuit structures represent the source of entropy for physical unclonable functions (PUFs). In this paper, we investigate delay variations that occur through the routing network and switch matrices of a field-programmable gate array (FPGA). The delay variations are isolated from other components of the programmable logic, e.g., look-up tables (LUTs), flip-flops (FFs), etc., using a feature of Xilinx FPGAs called dynamic partial reconfiguration (DPR). A set of partial designs is created to fix the placement of a time-to-digital converter (TDC) and supporting infrastructure to enable the path delays through the target interconnect and switch matrices to be extracted by subtracting out common-mode delay components. Delay variations are analyzed in the different levels of routing resources available within FPGAs, i.e., local routing and across-chip routing. Data are collected from a set of Xilinx Zynq 7010 devices, and a statistical analysis of within-die variations in delay through a set of the randomly-generated and hand-crafted interconnects is presented. [ABSTRACT FROM AUTHOR]

A finite-time consensus protocol is presented in this paper for a network of connected heterogeneous agents with fixed and switching topology. In this study, two cases directed networks with fixed topology and directed networks with switching topology are examined. Convergence analysis for both the cases are presented. An independent tracking controller, which assures that an agent follows an ideal trajectory designed by sliding mode based consensus control, is devised. Agents stability are independent of the distributed consensus algorithm. Under the proposed control methodology, the measurement vectors of heterogeneous agents converge to the agreement value in a finite amount of time. The comparison results with the existing works show the proposed algorithm's improved performance in terms of convergence time in presence of disturbances. [ABSTRACT FROM AUTHOR]

Based on the adaptive control structure of neural networks, this paper proposes a novel output voltage control strategy for DC converters. The strategy regulates the inductor current to maintain a constant voltage by adjusting the duty cycle of four-switch buck—boost (FSBB) converters. A nonlinear average model for the FSBB converter, derived from its energy consumption, is introduced, and its effectiveness is demonstrated through simulations. The simulations confirm that the FSBB converter enables zero-voltage switching (ZVS) of the four switches across the entire operating voltage range. The comparative simulation results show that the proposed control strategy achieves faster voltage regulation while ensuring ZVS, leading to improved converter performance across the full power range. [ABSTRACT FROM AUTHOR]

This paper proposes an extended DC‐DC converter with high voltage conversion ratio and soft‐switching ability. The proposed converter has active switched‐inductors, switched‐capacitors included in the conventional high gain converter and operates in continuous conduction mode (CCM). Simple auxiliary resonant elements are added on the primary leg of the converter to provide design freedom for soft‐switching operation. The significant merits of the proposed converter are lesser voltage and current stresses, high voltage gain with reduced component count and better efficiency. Additional feature of this converter is soft‐switching operation under different load conditions and duty ratios without considerably increasing stresses. The zero voltage switching (ZVS) turn‐on operation is obtained for all switching devices. Therefore, switching power losses are minimized greatly. This paper presents the description, principles of operation and steady state analysis in comparison with the existing high gain converters. The theoretical analysis is verified with a 350 W prototype operated at input is 20 V and output is 220 V. The overall efficiency achieved is 96.7%. The obtained results confirm ZVZCS operation at full load. [ABSTRACT FROM AUTHOR]

This paper introduces an advanced observer-based control strategy designed for fractional multi-agent systems operating in hostile environments. We take into account the dynamic nature of the agents with nonlinear delayed dynamics and consider external disturbances affecting the system. The manuscript presents an improved observation-based control approach tailored for fractional-order multi-agent systems functioning in challenging conditions. We also establish various applicable conditions governing the creation of observers and disturbance compensation controllers using the fractional Razmikhin technique, signed graph theory, and matrix transformation. Furthermore, our investigation includes observation-based control on switching networks by employing a typical Lyapunov function approach. Finally, the effectiveness of the proposed strategy is demonstrated through the analysis of two simulation examples. [ABSTRACT FROM AUTHOR]

In this article, we study the leader‐following formation problem for high‐order nonlinear multi‐agent systems with an uncertain leader system over switching networks. We first divide the uncertain leader system into two types and then design adaptive distributed observers for them respectively. Then, based on our designed observers, we synthesize two adaptive distributed control laws respectively to solve our problem, forcing the errors between the states of the followers and that of the leader to the prescribed time‐varying desired formation vectors. Finally, our main result is illustrated by two examples. [ABSTRACT FROM AUTHOR]

Network reconfiguration is a very economical technique that can improve electrical system performance. The development of semiconductor electrical equipment technology to meet human needs and work, known as nonlinear loads, has had a negative impact in the form of the spread of harmonic distortion which can accelerate the aging process and even damage equipment. In this paper, the effect of the optimization results of network reconfiguration techniques on the Sungguminasa 165-bus Executive Unit Service or Unit Layanan Pelanggan (ULP) electrical system is contaminated with harmonic distortion due to the use of nonlinear loads. This technique was optimized using the particle swarm optimization (PSO) method with a multi objective function in the form of minimizing %THDv and total losses with several limitations. Simulation results from the optimization process of several study cases are shown by activating the five tie switches from the network reconfiguration process on the Sungguminasa 165-bus ULP system which is able to improve power quality by reducing the average %THDv by 3.89% and total losses by 8.19%. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic event‐triggered fixed‐time control scheme for the phase synchronization of Kuramoto networks with switching topology and stochastic coupling noise. To deal with the problems of switching topology and stochastic coupling noise, a multilayered control structure with a specially designed noise reduction layer is developed. The effectiveness is proved based on stochastic differential theory. To save communication resources and reduce control frequency, a dynamic event‐triggered mechanism is proposed. By introducing a dynamic variable into the triggering condition, the triggered instants are further reduced compared to the traditional static event‐triggered method. The synchronizing criteria and parameter conditions are established with stochastic Lyapunov stability theory. The analysis of Zeno behavior is also given. Finally, numerical simulation experiments are conducted to demonstrate the effectiveness of the proposed dynamic event‐triggered control scheme. [ABSTRACT FROM AUTHOR]

Vehicle ad hoc networks (VANETs) have garnered considerable attention for their potential to enhance road safety and facilitate advanced driver assistance systems. A fundamental aspect of VANET is the formation of stable clusters and cluster heads (CH) for improved network performance. Due to the dynamic nature of VANET and the different mobility of the vehicles, maintaining CH stability is significant. To address this issue, this study presents the network as connecting hypergraphs. The proposed approach uses an improved tensor-trace maximization-based spectral clustering algorithm (iTTM) and eigen heuristics to generate an optimal. The suggested clustering approach is followed by the CH selection based upon the four vehicle attributes: modularized link lifetime, connectivity level, relative speed, and consensus trust score. The multi-decision CRITIC approach will decide the CH in each cluster using those four attributes. These metrics have improved throughput and reduced packet delay to improve network performance. Simulation results, conducted in Delhi's Connaught Place using SUMO, demonstrate the proposed method's superiority in CH stability, throughput, and packet delay compared to existing algorithms. The state-of-the-art comparison is done on the criteria of CH stability, which comes out to be 84% in the proposed case and 82% in the previous work. The evaluation is done with recently published research on the CH rate of change and to validate the lower switching frequency in the network. The network is evaluated with different average speeds of the vehicles, and superior performance is noted in the proposed approach with the increase in lesser switching frequency with higher average speed than state-of-the-art. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for rapidly balancing lithium-ion batteries using a single DC–DC converter, enabling direct energy transfer between high- and low-voltage cells. Utilizing relays for cell pair selection ensures cost-effectiveness in the switch network. The control system integrates a battery-monitoring IC and an MCU to oversee cell voltage and ensure battery protection. A prototype circuit with twelve lithium-ion batteries demonstrates the method's efficacy, achieving a remarkable balancing time of 48 min during charging with a maximum efficiency of 89.85%. Comparative analysis with other methods underscores the superior performance of the proposed balancing circuit in terms of balancing time and implementation cost. Furthermore, this paper delves into hardware aspects of battery management systems (BMSs) for electric vehicles and stationary applications. It offers an overview of prevailing concepts in state-of-the-art systems, aiding readers in assessing considerations essential for BMS design in various applications. The discussion includes examples of battery packs sourced from commercially available electric vehicles. Subsequently, the manuscript addresses implementation aspects concerning the measurement of critical physical variables such as voltage, current, and temperature, alongside balancing strategies. [ABSTRACT FROM AUTHOR]

In the literature, three-stage switching networks have been considered for nodes in elastic optical networks, where switches with spectrum conversion capability are placed in the first and third stages (wavelength–space–wavelength—WSW) or only in the second stage (space–wavelength–space—SWS). This paper proposes three-stage switching networks where the switches with spectrum conversion functions are located only in the first stage (wavelength–space–space—WSS) or only in the third stage (space–space–wavelength—SSW). For these networks, the strict-sense non-blocking conditions are derived and proved, and the number of elements required for their construction is assessed. It turns out that the proposed networks can be constructed with 50% fewer tunable spectrum converters than in the WSW networks, and this reduction is even greater in the case of the SWS networks. [ABSTRACT FROM AUTHOR]

Sensor networks generate large volumes of data that require robust processing, storage, and analysis capabilities provided by data centers. Software-defined networking, as a new network technique for designing and building networks, provides new concepts and solutions for the current data center system construction. However, the software-defined networking (SDN) framework based on the OpenFlow protocol cannot support new protocols or actively add unknown protocols, since they cause the control plane protocol to become more bloated. It is difficult to solve the problem of network scalability because components are being continuously redesigned. Therefore, an optimized source routing technology is proposed in combination with the protocol-oblivious forwarding technology. Furthermore, a low-cost and extensible network switch is designed to realize the independent data forwarding mechanism that effectively reduces the amount of network control signaling. The experimental results show that the scheme can significantly reduce the scale of flow table items and improve the hardware forwarding rates as well as the flexibility of forwarding rules. Moreover, the design scheme has a good application prospect. [ABSTRACT FROM AUTHOR]

The microscopically structural switching of supramolecular networks endows programmable gel materials with dynamic hiding properties and functionalities. However, the reconstruction of supramolecular network will destroy the programming information imprinted on the original network structure, making it difficult to restore to the original state. Here, a novel intelligent programmable photonic gel composed of cellulose nanocrystals (CNC) and acrylamide is constructed. The mechanical properties and optical signals of gel can be switched reversibly on demand by reasonably designing the winding and compression between molecular structures. Under this conversion mechanism, the rigid "skeleton" constructed from the CNC chiral structure perfectly acts as the coding substrate. Importantly, even after multiple dynamic switching of the supramolecular network, the encoded information can be displayed completely and accurately on the CNC chiral structure in the stretched state. In addition, the 5D controllable conversion of stiffness, transparency, stretchability, color, and shape greatly improves the security and confidentiality of the encoded information inside the gel. This is a successful example of the application of CNC chiral structure in switchable supramolecular network materials. It is believed that the flexible variability and advanced camouflage give the intelligent gel the potential to be used in a wider range of practical scenarios. [ABSTRACT FROM AUTHOR]

Introduction: Gene regulatory networks (GRNs) are characterized by their dynamism, meaning that the regulatory interactions which constitute these networks evolve with time. Identifying when changes in the GRN architecture occur can inform our understanding of fundamental biological processes, such as disease manifestation, development, and evolution. However, it is usually not possible to know a priori when a change in the network architecture will occur. Furthermore, an architectural shift may alter the underlying noise characteristics, such as the process noise covariance. Methods: We develop a fully Bayesian hierarchical model to address the following: a) sudden changes in the network architecture; b) unknown process noise covariance which may change along with the network structure; and c) unknown measurement noise covariance. We exploit the use of conjugate priors to develop an analytically tractable inference scheme using Bayesian sequential Monte Carlo (SMC) with a local Gibbs sampler. Results: Our Bayesian learning algorithm effectively estimates time-varying gene expression levels and architectural model indicators under varying noise conditions. It accurately captures sudden changes in network architecture and accounts for time-evolving process and measurement noise characteristics. Our algorithm performs well even under high noise conditions. By incorporating conjugate priors, we achieve analytical tractability, enabling robust inference despite the inherent complexities of the system. Furthermore, our method outperforms the standard particle filter in all test scenarios. Discussion: The results underscore our method's efficacy in capturing architectural changes in GRNs. Its ability to adapt to a range of time-evolving noise conditions emphasizes its practical relevance for real-world biological data, where noise presents a significant challenge. Overall, our method provides a powerful tool for studying the dynamics of GRNs and has the potential to advance our understanding of fundamental biological processes. [ABSTRACT FROM AUTHOR]

Analyzing visual scenes and computing ensemble statistics, known as perceptual averaging, is crucial for the stable sensory experience of a cognitive agent. Despite the apparent simplicity of applying filters to scenes, the challenge arises from our brain's seamless transition between summarization and individuation across various reference frames (retinotopic, spatiotopic, and hemispheric). In this study, we explore the capability of a neural network to dynamically switch between individuation and summarization. Our chosen computational model, a fully connected on-center off-surround recurrent neural network previously employed for enumeration/individuation, demonstrates the potential to extract both summary statistics and achieve high individuation accuracy. Notably, our results show that the individuation accuracy can reach close to perfection within a presentation duration of 100 ms, but not so for summarization. We have also shown a spatially varying excitation version of the network that can explain quite a few interesting spatio-temporal patterns of perception. These findings not only highlight the feasibility of such a neural network but also provide insights into the temporal dynamics of ensemble perception. [ABSTRACT FROM AUTHOR]

This article presents a fault‐tolerant platooning control algorithm for a class of nonlinear homogeneous multi‐vehicle systems under unreliable network communication environment. The considered types of actuator failures in individual vehicle are loss‐in‐effectiveness, and bias faults. Suppose the communication network among vehicles is here described as a switching graph subject to intermittent communications. Unlike the existing results dealing with vehicle platoon under continuous communications, this article develops a hierarchical fault‐tolerant platooning control approach, under which, the multiple vehicle coordination platooning objective is ensured even in the cases of discontinuous communication faults and actuator failures. First, each following vehicle can estimate the desire reference information via cooperative adaptive virtual estimator even in intermittent communications. Second, the local fault‐tolerant controller with adaptive parameter update mechanism can guarantee the practical position and velocity of following vehicles asymptotically track the desire estimations. Finally, a simulation example of a team of autonomous vehicles is given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a new simultaneous framework of distribution network operation is proposed based on the scheduling of photovoltaic/wind/battery multi-microgrids with network reconfiguration considering self-healing. The objective function is considered to minimize the energy losses, and multi-microgrids cost and improve the reliability indices including minimization of energy not-supplied (ENS) and minimizing system average interruption duration (SAIDI) and system average interruption frequency (SAIFI) indices. The optimization variables are defined as the situation of the distribution network switches to find the network's optimal configuration with the installation location and size of renewable resources and battery energy storage during 24 hours. An improved beluga whale optimization (IBWO) based on a nonlinearly diminishing inertia weight (NDIW) approach is used to find the optimal variable set of the problem. The recommended methodology is implemented on 33-bus and real 59-bus distribution networks. The results demonstrated that by obtaining an optimal state of the network switches in the event of a fault, as well as the optimal scheduling of the two microgrids, the energy losses have decreased and the reliability indices have improved. The outcomes of the proposed methodology based on the hybrid multi-microgrid allocation and network reconfiguration are stated that the losses, ENS, SAIDI, and SAIFI are reduced by 66.39%, 54.00%, 50.24%, and 33.61%, respectively, for 33-bus network and are declined by 65.29%, 57.44%, 48.63%, and 62.33%, respectively, for the real 59-bus Ahvaz network in comparison with the base network. The obtained results illustrated that in the condition of the network line outage, by simultaneously implementing the reconfiguration with the change of network switches and the optimal allocation and scheduling of HMGs in the network, self-healing is provided to prevent a significant weakening of the network performance so that the resilience is improved in addition to minimizing the energy losses and the cost of HMG energy injection compared to the base network. The findings revealed that the objectives including losses, ENS, SAIFI, and SAIDI are increased by 78.29%, 33.00%, 83.33%, and 35.11%, respectively, due to outage of line 22 of the 33-bus network and these objectives are increased by 52.32%, 10.30%, 32.29%, and 79.66%, respectively, due to outage of line 12 of the 59-bus network compared with not considering the line outage. Moreover, the superior capability of the recommended NDIW-based IBWO has been confirmed in comparison with the well-known particle swarm optimizer (PSO) and ant lion optimizer (ALO) in solving the problem to achieve better objective value. [ABSTRACT FROM AUTHOR]

Circadian rhythms are generated by complex interactions among genes and proteins. Self-sustained ∼24 h oscillations require negative feedback loops and sufficiently strong nonlinearities that are the product of molecular and network switches. Here, we review common mechanisms to obtain switch-like behavior, including cooperativity, antagonistic enzymes, multisite phosphorylation, positive feedback, and sequestration. We discuss how network switches play a crucial role as essential components in cellular circadian clocks, serving as integral parts of transcription-translation feedback loops that form the basis of circadian rhythm generation. The design principles of network switches and circadian clocks are illustrated by representative mathematical models that include bistable systems and negative feedback loops combined with Hill functions. This work underscores the importance of negative feedback loops and network switches as essential design principles for biological oscillations, emphasizing how an understanding of theoretical concepts can provide insights into the mechanisms generating biological rhythms. [ABSTRACT FROM AUTHOR]

This article addresses the hostile-based bipartite containment control of nonlinear fractional multi-agent systems (FMASs) with input delays. Several fundamental algebraic criteria have been offered by the use of signed graph theory. To make the controller design more realistic, we assumed that the controller was under some disturbance. For the analysis of bipartite containment control, we used a fixed and switching signed network. The commonly used Lyapunov function approach and the Razumikhin technique were used. The use of these techniques can conquer the challenge brought on by switching, temporal delays, and fractional mathematics. To better elucidate the theoretical results, two examples are provided. [ABSTRACT FROM AUTHOR]

This study describes an array antenna that allows for reconfiguration of the three significant antenna properties, that is, radiation pattern, half‐power beamwidth (HPBW), and single and bi‐directional beam. A reconfigurable reflecting surface (RRS) has been designed and incorporated with the array antenna on the top and bottom. The RRS works to reflect or pass the incident electromagnetic wave. It enables us to reconfigure from single to multibeam radiation in the ±z direction. It also provides two levels of HPBW, that is, 49.6° and 95°. Eventually, the same three sets of antennae have been stacked and rotated with a 60° angle to achieve the radiation pattern reconfiguration in the entire azimuth plane. It conforms to reconfigure all three antenna properties in six directions at an equal interval of 60° angle. The achieved maximum gain in a narrow beam is 9.56 dBi. The proposed reconfigurable antenna array can be used in modern wireless communication applications. [ABSTRACT FROM AUTHOR]

Data traffic is rapidly growing due to e-commerce, digital communication, and the digital world. Optical networks must provide a better solution to enhance transmission and improve service with cost efficiency. Considerable investigations of the 64QAM (Quadrature Amplitude Modulation) model have been conducted on wireless and optical communication to improve transmission efficiency. The next generation 400Gb/s systems are now widely recognized to be commercially accessible. At the same time, optical communication networks need a superior method to provide the best Quality of Transmission with improved data transmission efficiency. This paper aims to improve the Quality of Transmission in Optical Communication Networks (OCN). Therefore, a simple and fast reconfigurable network model is essentially required. However, there is a problem primarily related to configuring the network parameters to improve the Quality of Transmission (QoT). Research has shown that deep learning algorithms can improve QoT. Recurrent Neural Networks and Long-Short-Term-Memory (RNN-LSTM) algorithms are employed to leverage the factors of QoT in OCN. The RNN model analyzes the physical network parameters, and LSTM analyzes the dynamic data parameters and data from OCN. The Gaussian processes and path computation elements are utilized to obtain a mean absolute Signal Noise Ratio error of merely 0.1dB as a better result by the Gaussian process. The proposed model can provide high flexibility in changing the network topologies, planning, and period with less complexity by including a dataset evolution, adjusting the parameters, and examining output. A real-time dataset is analyzed using RNN-LSTM, implemented in Python, and experimented with. The throughput, bandwidth, and modulation path following light paths are calculated in the experiment to estimate the QoT. The experimental results showed that the proposed RNN-LSTM model provides better QoT. From the comparison, the proposed model provides a better throughput of 1 to 2 Gbps and Under Provisioning Ratio of 0.5 Hz over the existing models. [ABSTRACT FROM AUTHOR]

The distributed adaptive fuzzy coordination control (DAFCC) method for the leader-following for non-affine nonlinear (NAN) multi-agent systems (MASs) with actuator failures (including partial failures and actuator bias failures) under the unreliable switching communication environments is proposed in this article. First, based on the neighborhood state information, a group of distributed reference observer (DRO) is constructed so that all followers can easily estimate the leader's information. Second, the implicit function theorem and the mean value theorem are utilized to convert the non-affine term into the corresponding affine term in order to solve the non-affine term in the NAN system. Third, to achieve global synchronization, we design an adaptive fuzzy coordination fault-tolerant controller with local estimation. Besides, the DAFCC method presented in this paper solves the cooperative control problem when switching undirected networks with intermittent communications. Finally, simulation is used to validate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The problem of the resilient filtering for a class of discrete-time complex networks over switching topology is investigated. Taking into account the limitation of channel bandwidth, a refined adaptive event-triggered scheme is derived, whose threshold is determined by the change rate of measurement. The large change rate of measurement results in a smaller threshold, which means that more data packets will be transmitted to guarantee the performance of filtering, and the smaller one leads to a bigger threshold to save the network energy. Under the adaptive event-triggered scheme, considering the switching topology and uncertain inner coupling, a resilient filtering with a variable filtering gain is proposed. Additionally, the minimal upper bound of the covariance of estimation error is developed and the sufficient conditions are also given to obtain the exponentially bounded in mean square of the estimation error system. Finally, a simulation is presented to certify the effectiveness of the derived resilient filtering. • A refined adaptive event-triggered scheme, for the first time, is proposed in complex networks. Unlike the threshold is fixed of existing event-triggered scheme, the proposed threshold is adaptively adjusted relied on the change rate of measurement. • Considering the complex networks containing some stochastic parameters, a resilient filtering with variation of gain matrix is propose, which is rarely researched for complex networks with random topology. • By using the stochastic analysis, the sufficient conditions are given to guarantee the stochastic stability of the estimation error system. [ABSTRACT FROM AUTHOR]

This study employed an event-triggered control (ETC) strategy to investigate the problems of fixed-time stabilization (FTS) and preassigned-time stabilization (PTS) for state-dependent switching neural networks (SDSNNs) that involved mixed time delays. To enhance the network's generalization capability and accelerate convergence stabilization, a more intricate weight-switching mechanism was introduced, then to mitigate transmission energy consumption, this paper proposed a tailored event-triggering rule that triggered the ETC solely at predetermined time points. This rule ensured the stability of the system while effectively reducing energy consumption. Using the Lyapunov stability theory and various inequality techniques, this paper presented new results for FTS and PTS of SDSNNs. The validity of these findings was supported by conducting data simulations in two illustrative examples. [ABSTRACT FROM AUTHOR]

Given the escalating need for greater bandwidth, there is a growing interest in implementing mechanisms that ensure reliable and optimal service levels for specific traffic classes, especially during periods of heavy network traffic. One such mechanism is the prioritization mechanism, where certain portions of resources are exclusively allocated to predefined services. In the context of contemporary flexible WDM networks that employ advanced data transmission techniques, efforts have been made to create a simulation program capable of assessing the traffic characteristics of nodes within flexible WDM networks. This article presents a simulator of flexible WDM nodes with an implemented mechanism for prioritizing call classes. The simulator allows us to determine the loss probability for individual traffic classes in a switching network with point-to-point selection. In the simulator, the Clos structure was adopted as the structure of the flexible WDM network node due to its popularity in many studies and applications. [ABSTRACT FROM AUTHOR]

The biophysical properties of neurons not only affect how information is processed within cells, they can also impact the dynamical states of the network. Specifically, the cellular dynamics of action-potential generation have shown relevance for setting the (de)synchronisation state of the network. The dynamics of tonically spiking neurons typically fall into one of three qualitatively distinct types that arise from distinct mathematical bifurcations of voltage dynamics at the onset of spiking. Accordingly, changes in ion channel composition or even external factors, like temperature, have been demonstrated to switch network behaviour via changes in the spike onset bifurcation and hence its associated dynamical type. A thus far less addressed modulator of neuronal dynamics is cellular morphology. Based on simplified and anatomically realistic mathematical neuron models, we show here that the extent of dendritic arborisation has an influence on the neuronal dynamical spiking type and therefore on the (de)synchronisation state of the network. Specifically, larger dendritic trees prime neuronal dynamics for in-phase-synchronised or splayed-out activity in weakly coupled networks, in contrast to cells with otherwise identical properties yet smaller dendrites. Our biophysical insights hold for generic multicompartmental classes of spiking neuron models (from ball-and-stick-type to anatomically reconstructed models) and establish a connection between neuronal morphology and the susceptibility of neural tissue to synchronisation in health and disease. Author summary: Cellular morphology varies widely across different cell types and brain areas. In this study, we provide a mechanistic link between neuronal morphology and the dynamics of electrical activity arising at the network level. Based on mathematical modelling, we demonstrate that modifications of the size of dendritic arbours alone suffice to switch the behaviour of otherwise identical networks from synchronised to asynchronous activity. Specifically, neurons with larger dendritic trees tend to produce more stable phase relations of spiking across neurons. Given the generality of the approach, we provide a morphology-based hypothesis that explains the differential sensitivity of tissue to epilepsy in different brain areas and assigns relevance to cellular morphology in healthy network computation. [ABSTRACT FROM AUTHOR]

This paper studies the exponential stabilization problem for a class of discretetime linear switched systems arising from establishing the output-based distributed observer and the output-based adaptive distributed observer for discrete-time linear leader systems over jointly connected switching networks. The existing results on distributed observers and adaptive distributed observers are state-based in the sense that they need to make use of the full state of the leader system, which is quite restrictive since, in many applications, only the output of the leader system is available. As an application, the output-based distributed observer is used to solve a leader-following consensus problem for discrete-time linear multiagent systems by distributed output feedback control. [ABSTRACT FROM AUTHOR]

This article discusses the development and application of an event-triggered distributed observer for a leader-following consensus problem in multiple rigid body systems. The authors present a design framework called the distributed observer based approach, which involves designing control laws and a distributed observer to estimate the state of the leader. The main result of the article is the existence of upper bounds for certain variables in the system. The article also discusses the convergence of the system and presents a lemma and theorem to prove the convergence. Additionally, the article applies the event-triggered distributed observer to the leader-following adaptive consensus problem of multiple uncertain rigid body systems and provides a numerical example to illustrate the result. The paper concludes by acknowledging support received and suggesting future directions for improvement. [Extracted from the article]

This paper addresses a distributed time-varying optimization problem with inequality constraints based on multi-agent systems over switching communication graphs. To reduce the influence of time-varying inequality constraints, an exact penalty method and smoothing technique are employed. Then, a Hessian-based distributed control protocol is presented to seek the time-varying optimal solution of the distributed time-varying optimization problem by virtue of only local information and interaction. It is shown that all agents not only achieve finite-time consensus but also track the time-varying global optimal target eventually. Compared with the existing distributed optimization protocols, the proposed control protocol is suitable for more general distributed time-varying optimization problems and enjoys high-efficiency convergence. Finally, numerical examples and experiment on moving target tracking of Unmanned Aircraft Vehicle (UAV) are performed to illustrate the effectiveness of the proposed control protocol. [ABSTRACT FROM AUTHOR]

In this paper, the event‐triggered distributed consensus tracking control problem for a class of multi‐agent systems (MASs) is studied, where denial‐of‐service (DoS) attacks and Bouc‐Wen hysteresis inputs are considered in the communication channel and the actuator, respectively. First, when communication networks are subject to malicious DoS attacks, the connection weights of the communication topology graphs are affected. To address this problem, this paper establishes topological models of the communication network using a switching topology method, and gives connection recovery strategies for the communication network. Second, in order to save communication resources, a distributed control protocol based on an event‐triggered mechanism and locally deployed estimator is designed for DoS signals occurring in the communication network of states and control inputs. In addition, the effect of unknown Bouc–Wen hysteresis input is eliminated using the Nussbaum‐type function. Then, based on Lyapunov stability theory, the boundedness of all signals in the closed‐loop system is verified, and the objective of consensus control is achieved. Finally, the effectiveness of the proposed control scheme is demonstrated by numerical and practical simulation examples. [ABSTRACT FROM AUTHOR]

Summary: In this article, we study the mean square synchronization problems for complex networks with a target of nonzero inputs under stochastic switching topology that is driven by an ergodic continuous‐time Markov process. First, we design an adaptive continuous controller based on relative full states that is consist of a synchronization term and an uncertainty elimination term, where the latter term can completely eliminate the effects of target's nonzero inputs and is continuous by adding an exponential decay function with positive initial values into the denominator. By developing a stochastic Lyapunov function, we show that zero error synchronization in the mean square sense can be achieved if the union of switching graphs contains a directed spanning tree with the target being the root. Second, we design an adaptive continuous controller based on relative outputs. Finally, we give two simulation examples to validate the theoretical results. [ABSTRACT FROM AUTHOR]

It is widely believed that memory storage depends on activity-dependent synaptic modifications. Classical studies of learning and memory in neural networks describe synaptic efficacy either as continuous or discrete. However, recent results suggest an intermediate scenario in which synaptic efficacy can be described by a continuous variable, but whose distribution is peaked around a small set of discrete values. Motivated by these results, we explored a model in which each synapse is described by a continuous variable that evolves in a potential with multiple minima. External inputs to the network can switch synapses from one potential well to another. Our analytical and numerical results show that this model can interpolate between models with discrete synapses which correspond to the deep potential limit, and models in which synapses evolve in a single quadratic potential. We find that the storage capacity of the network with double well synapses exhibits a power law dependence on the network size, rather than the logarithmic dependence observed in models with single well synapses. In addition, synapses with deeper potential wells lead to more robust information storage in the presence of noise. When memories are sparsely encoded, the scaling of the capacity with network size is similar to previously studied network models in the sparse coding limit. Author summary: A long-lasting question in neuroscience is whether synaptic efficacies should be described as continuous variable or discrete variables. Recent experiments indicate that it is a combination of both: synaptic efficacy changes continuously, but its distribution peaks at several discrete values. In this study, we introduce a synapse model described by a double well potential, and investigate the memory properties of networks of neurons connected with such synapses. Our results show in networks with a bimodal weight distribution, the storage capacity depends on network size as a power law. In addition, we demonstrate that networks with such synapses store information more robustly in the presence of noise, compared to networks with synapses with a single well potential. [ABSTRACT FROM AUTHOR]

Neural network accelerator based on photonic‐integrated circuits is emerging as a promising technology for fast, power‐efficient, and parallel computing. Under such technology, optics is mainly used for linear transformations, e.g., through an array of cascaded switching networks. Nonlinear activation is implemented either electronically requiring extra optical–electrical conversion or via nonlinear optical materials that often suffer from high loss, large power consumption, and difficulty in integration. Herein, an optical neural chip with only one multimode waveguide, fabricated using low‐cost linear optical materials, plus seven heater electrodes to control the multimode interference, is proposed. The nonlinear networks are intrinsically integrated in the electrical‐to‐optical signal conversion through the waveguide. The linear computation, in the electronic domain, is included in the mandatory step to convert the input matrix to the intermediate current values on the seven electrodes. Though extremely simple, the proposed system can classify nonlinear datasets and images by optical readout with high accuracy and without calibration. Prospects for future development are given at the end. In this work, an alternative route is offered to exploiting the classic multimode interference for advanced optical computing applications. [ABSTRACT FROM AUTHOR]

The signal systems under the traditional architecture have a high degree of independence and cannot achieve information fusion among multiple systems. In order to realize information sharing with the urban rail production network and enable big data mining, the signal ATS subsystem can be included in the urban rail cloud platform. On the basis of ensuring the independence and safety of the core equipment of the signal systems, the ATS subsystem center-level servers and workstations are included in the cloud platform. This paper calculates the computing resource requirements of the ATS subsystem with a reuse ratio of 1.0 as an example. The connection between the cloud part and the non-cloud part of ATS is realized through the core switching network of the cloud platform. The application server and interface server are configured with blade servers, and the database server is configured with a rack server. To meet the requirements of ATS subsystem redundancy and undisturbed switching, the cloud platform provides two sets of server resources with the same configuration for the ATS subsystem. Considering that the urban rail signal systems need to meet the requirements of Level 3 of Information Security Level Protection, the cloud platform should also be built according to Level 3. [ABSTRACT FROM AUTHOR]

As the usage of information and communication technology (ICT) solution have been exploded in the last decades, the research community exhibits a deficiency in terms of methodologies for accurately quantifying the energy consumption of individual ICT components. This paper presents a novel methodology aimed at addressing the existing research gap by offering a comprehensive analysis of energy consumption ranges for ICT components, measured in Watt. The methodology being proposed involves the classification of ICT components into distinct classes, namely sensors, gateways, servers-platforms, and databases, while these classes are further subdivided into sub-classes. The calculation of energy consumption places emphasis on particular components within each sub-class, encompassing CPUs, RAM, GPUs, hard drives, network switches, cooling systems, and communication units. Compared with existing green ICT standards where mainly focused on strategy-related recommendations, the methodology utilises manufacturers' specifications for standard operational procedures in order to compute energy consumption. The objective of the methodology is to extend the current research and provide countable energy consumption attributes of various component types, that are used in the ICT architecture, supporting the decision-making process of architects regarding environmentally sustainable and high-performance device selection. This study concludes with the development of a tree diagram for calculating the energy consumption of an ICT system. [ABSTRACT FROM AUTHOR]

The Blumlein pulse forming network (PFN) has widely been used in pulsed power technology to generate square waves with short pulse widths. In this paper, we developed a repetitive frequency square wave generator based on Blumlein PFN and pseudospark switch (PSS). A Blumlein PFN with unequal capacitances has been proposed, and the PFN parameters have been optimized for better output waveforms. A single-gap PSS with a withstand voltage of 40 kV and a repetitive frequency of 100 Hz has been designed to switch the Blumlein PFN. The experiment results show that the square wave generator can output pulses with a voltage of 26 kV, a rise time of 25 ns, and a pulse width of 90 ns on a matched load of 11 Ω. It has operated steadily for 10 h with a repetitive frequency of 100 Hz, and the jitter remains at around 1 ns after 1.05 × 106 shots. [ABSTRACT FROM AUTHOR]