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The field of advanced metamaterial design has witnessed the great progress of artificial intelligence (AI) for science. Typically, the widely used deep learning is a purely data‐driven approach. However, it is often assumed as a black box performing interpolation among training data in a fuzzy way, which suffers from poor generalization outside the training domain of critical physical parameters. Inspired by physics‐guided deep learning, a circuit‐theory‐informed neural network (CTINN) in order to strengthen the generalization of metamaterial design is proposed. A series of plasmonic stack metamaterials (PSMs) are taken as learning paradigms of CTINN. Compared to conventional deep learning, the scheme decreases one‐order lower test loss of spectral prediction beyond the structure training span, and it possesses an extraordinary function to predict spectra of the extrapolated wavelength range. Due to the physics‐informed mechanism, the CTINN only adopts 10% training samples of the pure data‐driven counterpart, while it reduces >50% test loss. Moreover, the CTINN design is used to guide the PSM experiments and demonstrate its great potential for metamaterial development. This work introduces the theory of equivalent circuits into the neural network, empowers physics supervision to AI, and accomplishes the smart and powerful design of PSMs. [ABSTRACT FROM AUTHOR]

Harnessing quantum correlations can enable sensing beyond classical precision limits, with the realization of such sensors poised for transformative impacts across science and engineering. Real devices, however, face the accumulated impacts of noise and architecture constraints, making the design and success of practical quantum sensors challenging. Numerical and theoretical frameworks to optimize and analyze sensing protocols in their entirety are thus crucial for translating quantum advantage into widespread practice. Here, we present an end-to-end variational framework for quantum sensing protocols, where parameterized quantum circuits and neural networks form trainable, adaptive models for quantum sensor dynamics and estimation, respectively. The framework is general and can be adapted towards arbitrary qubit architectures, as we demonstrate with experimentally-relevant ansätze for trapped-ion and photonic systems, and enables to directly quantify the impacts that noise and finite data sampling. End-to-end variational approaches can thus underpin powerful design and analysis tools for practical quantum sensing advantage. [ABSTRACT FROM AUTHOR]

ABSTRACT The generated mechanism of negative emotion involves the prefrontal cortex, hippocampus, and amygdala in the brain. Based on the biological mechanism, this paper proposes a memristive neural network circuit of negative emotion inhibition with self‐repair and memory. The proposed memristive neural network circuit consists of five modules: memory (hippocampus) module, inhibition (prefrontal cortex) module, damage detection module, repair module, and output (amygdala) module. The memory module does not respond to a small negative signal, but a large negative signal will cause the memory module to form memories. If the large negative signal repeats, the memory module will output a memory signal to the inhibition module. The inhibition module receives the negative signal and the memory signal and then generates an inhibition signal. When the negative signal is small, the inhibition module outputs normally. When the large negative signal is applied for the first time, the inhibition module becomes damaged and the output is abnormal. As the memristor in the inhibition module has exceeded its damaged threshold, the damage detection module will generate a damaged signal to the repair module. The repair module will restore the memristance of the memristor in the inhibition module after receiving the damaged signal. If the large negative signal is applied again, the memory signal from the memory module will help the inhibition module to output normally. The output module receives the negative signal and the inhibition signal and then generates the negative emotion signal appropriately. The PSPICE simulation results show that the hippocampus generates memories after learning and transmits them to the prefrontal cortex, and then the prefrontal cortex inhibits the amygdala to generate the negative emotion appropriately. The proposed memristive neural network circuit can be applied to the robots, which can flexibly alter the intensity of the negative emotion expressed by the robots. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper discusses an analytical framework to model one‐ and two‐ports in a way which avoids prioritizing either the voltage or the current as model variables. This approach allows for a truly general symbolic treatment of such circuits, avoiding the need to perform repetitive analyses in different working scenarios, and also displays a number of benefits from a theoretical point of view. The key ingredient is a homogeneous version of the Thévenin–Norton theorem for one‐ports which possibly include controlled sources and coupling effects. This paves the way for the introduction of a comprehensive form for the transfer functions and the driving point immittances of two‐ports, accounting for all possible impedance/admittance descriptions of the devices connected at the ports and also of the internal devices. Our framework allows, for example, for a universal definition of reciprocity but also for a straightforward derivation of Middlebrook's formulae in the context of the extra element theorem. Several examples illustrate the results. [ABSTRACT FROM AUTHOR]

Summary: Memristors exhibit potential applications in neuromorphic computing, because of their nanoscale and low power. Non‐volatile passive memristors usually behave as electronic synapses, while volatile locally active memristors can be used to construct artificial neurons. In this paper, we apply an Nb2O5 locally active memristor with the parasitic capacitance as a LIF neuron and analyze the possibility of generating spiking oscillations by the neuron through small signal equivalent circuits and the Hopf bifurcation method. By combining Nb2O5 memristive neurons with the voltage‐controlled non‐volatile memristive synapses, we construct an unsupervised learning network and classify 5 × 3 letter images and 5 × 5 number images. In particular, before building the hardware circuit, we predict the training time, recognition time, and recognition accuracy of the pattern recognition network through theoretical analysis, which guides the actual circuit experiment. Specifically, the training time of the network is related to the synaptic memristor resistance change rate, the recognition time of the network is related to the oscillation period of the Nb2O5 memristor, and whether the network can work properly is related to the parameters of Nb2O5 memristor and NMOS. The LTspice simulation results manifest that the proposed circuit can recognize different patterns and can be applied to the neural morphological system of pattern recognition. [ABSTRACT FROM AUTHOR]

Purpose Methods Results Conclusion The performance of detectors is key for a PET scanner to achieve high spatial resolution and high sensitivity. This work aims to develop flood histogram generating algorithms to reduce the edge effect and improve the crystal identification of a PET detector consisting of two optically coupled pixelated scintillator detectors.The PET detector consists of two optically coupled detectors, each consisting of a 23×23 LYSO crystal array with a crystal size of 1.0×1.0×20 mm3 read out by an 8×8 SiPM array with a pixel size of 3.0×3.0 mm2. The SiPM array is read out with a resistor network circuit to obtain four position encoding energy signals. A novel center of gravity (COG) positioning algorithm using six signals from the two detectors was proposed and compared to the traditional COG algorithms using either four or eight signals from the detectors. The raised‐to‐the‐power (RTP) method was applied to the three COG algorithms for the PET detector. Different powers of the RTP from 1.0 to 2.5 were evaluated.The proposed COG algorithm significantly improves the crystal identification at the junction of the two detectors as compared to the COG algorithm using four signals of each detector, and improves the crystal identification at the center of the two detectors as compared to the COG algorithm using eight signals from both detectors. The RTP method significantly improves the overall flood histogram qualities of the two COG algorithms using either eight or six signals from the two detectors, and the two COG algorithm provide similar flood histogram quality when a power of 1.5 is used.The novel positioning algorithms reduce the edge effect and improve the flood histogram quality for a PET detector consisting of two optically coupled detectors, each consisting of a pixelated scintillator crystal array and a SiPM array with highly multiplexed four signal readout. The positioning algorithms can be used in a PET scanner to improve the spatial resolution and sensitivity. [ABSTRACT FROM AUTHOR]

Designing the donor–acceptor polymers‐based modifiers with good charge mobility and abundant surface functional groups to bind on perovskite material is highly demanded to boost interfacial charge extraction and transport while yet realized. Here, two [WS4Cu4Br]+ and [WS4Cu5Br2]+ cluster units are bridged by triphenylamine ligands to yield an unprecedented a heteronuclear W/Cu/S clusters‐based donor–acceptor polymer. Due to the effect of Rydberg orbital components in different clusters mainly contributed by Cu ions, Cu‐rich units have negative potential, whereas Cu‐deficient groups display positive potential. This facilitates the formation of a circuit network with ligands acting as "wires" to realize efficient charge transport. Mobility tests reveal that the hole mobility of polymer film is 3.81 × 10−5 cm2 V−1 s−1. Such a polymer can efficiently extract and transport the holes from perovskite film, and thus improving the cell performance and stability. This work opens the opportunities for designing donor–acceptor polymers based on heteronuclear W/Cu/S clusters. [ABSTRACT FROM AUTHOR]

Physical and functional constraints on biological networks lead to complex topological patterns across multiple scales in their organization. A particular type of higher-order network feature that has received considerable interest is network motifs, defined as statistically regular subgraphs. These may implement fundamental logical and computational circuits and are referred to as "building blocks of complex networks". Their well-defined structures and small sizes also enable the testing of their functions in synthetic and natural biological experiments. Here, we develop a framework for motif mining based on lossless network compression using subgraph contractions. This provides an alternative definition of motif significance which allows us to compare different motifs and select the collectively most significant set of motifs as well as other prominent network features in terms of their combined compression of the network. Our approach inherently accounts for multiple testing and correlations between subgraphs and does not rely on a priori specification of an appropriate null model. It thus overcomes common problems in hypothesis testing-based motif analysis and guarantees robust statistical inference. We validate our methodology on numerical data and then apply it on synaptic-resolution biological neural networks, as a medium for comparative connectomics, by evaluating their respective compressibility and characterize their inferred circuit motifs. Author summary: Networks provide a useful abstraction to study complex systems by focusing on the interplay of the units composing a system rather than on their individual function. Network theory has proven particularly powerful for unraveling how the structure of connections in biological networks influence the way they may process and relay information in a variety of systems ranging from the microscopic scale of biochemical processes in cells to the macroscopic scales of social and ecological networks. Of particular interest are small stereotyped circuits in such networks, termed motifs, which may correspond to building blocks implementing fundamental operations, e.g., logic gates or filters. We here present a new tool that finds sets of motifs in networks based on an information-theoretic measure of how much they allow to compress the network. This approach allows us to evaluate the collective significance of sets of motifs, as opposed to only individual motifs. We apply our methodology to compare the neural wiring diagrams, termed "connectomes", of the tadpole larva Ciona intestinalis, the ragworm Platynereis dumerelii, and the nematode Caenorhabditis elegans and the fruitfly Drosophila melanogaster at different developmental stages. [ABSTRACT FROM AUTHOR]

Self‐assembled supramolecular DNA tetrahedra composed of programmed sequence‐engineered complementary base‐paired strands represent elusive nanostructures having key contributions to the development and diverse applications of DNA nanotechnology. By appropriate engineering of the strands, DNA tetrahedra of tuneable sizes and chemical functionalities were designed. Programmed functionalities for diverse applications were integrated into tetrahedra structures including sequence‐specific recognition strands (aptamers), catalytic DNAzymes, nanoparticles, proteins, or fluorophore. The article presents a comprehensive review addressing methods to assemble and characterize the DNA tetrahedra nanostructures, and diverse applications of DNA tetrahedra framework are discussed. Topics being addressed include the application of structurally functionalized DNA tetrahedra nanostructure for the assembly of diverse optical or electrochemical sensing platforms and functionalized intracellular sensing and imaging modules. In addition, the triggered reconfiguration of DNA tetrahedra nanostructures and dynamic networks and circuits emulating biological transformations are introduced. Moreover, the functionalization of DNA tetrahedra frameworks with nanoparticles provides building units for the assembly of optical devices and for the programmed crystallization of nanoparticle superlattices. Finally, diverse applications of DNA tetrahedra in the field of nanomedicine are addressed. These include the DNA tetrahedra‐assisted permeation of nanocarriers into cells for imaging, controlled drug release, active chemodynamic/photodynamic treatment of target tissues, and regenerative medicine. [ABSTRACT FROM AUTHOR]

This paper introduces a novel node centrality index based on edge dynamics in a complex network: adjacent edge index. Through the analysis of network dynamics and topology, we provide a neat index to qualify node importance, which is calculated by the eigenvector components corresponding to the Fiedler value of the network. Notably, it identifies nodes whose adjacent edge sets play a significant role in network synchronization. The index indicates that not only a node's degree but also its location should be considered. To demonstrate the effectiveness of the proposed index, consider a double-star network and a Jazz Musicians network in which chaotic Chua's circuits as nodes are coupled. By factoring in the dynamic information exchange between nodes and their neighbors, this index offers valuable insights for optimizing strategies in engineering areas such as efficient information dissemination. [ABSTRACT FROM AUTHOR]

Bound states in the continuum (BICs), referring to spatially localized bound states with energies falling within the range of extended modes, have been extensively investigated in single-particle systems, leading to diverse applications in photonics, acoustics, and other classical-wave systems. Recently, there has been theoretical interest in exploring many-body BICs in interacting quantum systems, which necessitate the careful design of impurity potentials or spatial profiles of interaction. Here, we propose a type of many-body BICs localized at boundaries, which can be purely induced by the uniform onsite interaction without requiring any specific design of impurity potential or nonlocal interaction. We numerically show that three or more interacting bosons can concentrate on the boundary of a homogeneous one-dimensional lattice, which is absent at single- and two-particle counterparts. Moreover, the eigenenergy of multi-boson bound states can embed within the continuous energy spectra of extended scattering states, thereby giving rise to interaction-induced boundary many-body BICs. Furthermore, by mapping Fock states of three and four bosons to nonlinear circuit networks, we experimentally simulate boundary many-body BICs. Our findings enrich the comprehension of correlated BICs beyond the single-particle level, and have the potential to inspire future investigations on exploring many-body BICs. Bound states in the continuum (BICs) have been extensively investigated in single-particle systems, leading to diverse applications in photonics, acoustics, and other classical-wave systems. Here, the authors propose a type of interaction-induced many-body BICs at boundaries and experimentally simulate these boundary many-body BICs using nonlinear circuit networks. [ABSTRACT FROM AUTHOR]

This paper proposes a novel memory state-feedback control (MSFC) strategy for multiagent systems (MASs) operating on time scales. The communication topology is modeled by a collection of directed graphs with switching edges. We analyze the exponential stability of the error dynamics within a leader-follower synchronization framework, crucial for MAS control. Employing linear matrix inequalities (LMIs) and Lyapunov-Krasovskii functionals (LKFs), we establish sufficient conditions to guarantee global exponential stability for the considered systems. Notably, the synchronization conditions for MASs on different time scales differ from those on discrete or continuous time scales alone. This approach allows for a unified framework encompassing both discrete and continuous-time global synchronization problems, Finally, a simulation study utilizing a neural network (NN)-based circuit model demonstrates the effectiveness of the proposed control design. [ABSTRACT FROM AUTHOR]

The main complexity classes of the Parameterized Intractability Theory are based on weighted Boolean circuit satisfiability problems and organized into a hierarchy so-called W-hierarchy. The W-hierarchy enables fine-grained complexity analyses of parameterized problems that are unlikely to belong to the FPT class. In this paper, we introduce the Th-hierarchy, a natural generalization of the W-hierarchy defined by unweighted threshold circuit satisfiability problems. Investigating the relationship between Th-hierarchy and W-hierarchy, we discuss the complexity of transforming Threshold circuits into Boolean circuits, and observe that sorting networks are powerful tools to handle such transformations. First, we show that these hierarchies collapse at the last level (W[P] = Th[P]). After that, we present a time complexity analysis of an AKS sorting network construction, which supports some of our results. Finally, we prove that Th[ t ] ⊆ W[SAT] for every t ∈ ℕ. As a by-product, our studies suggest that it is relevant to consider a new class based on logarithmic depth circuits in the W-hierarchy. [ABSTRACT FROM AUTHOR]

The Steiner tree problem in graphs is widely studied because of its usefulness in network design and circuit layout. In this context, given a set of vertices S (| S | ≥ 2 ,) a tree that connects all vertices in S is called an S-Steiner tree. This helps to measure how well a network G can connect any set of S vertices together. In an S-Steiner tree, if each vertex in S has only one connection, it is called a pendant S-Steiner tree. Two pendant S-Steiner trees, T and T ′ , are internally disjoint if E (T) ∩ E (T ′) = ∅ and V (T) ∩ V (T ′) = S. The local pendant tree-connectivity, denoted as τ G (S) , represents the maximum number of internally disjoint pendant S-Steiner trees in graph G. For an integer k with 2 ≤ k ≤ n , where n is the number of vertices, the pendant k-tree-connectivity, denoted as τ k (G) , is defined as τ k (G) = m i n { τ G (S) : S ⊆ V (G) , | S | = k }. This paper focuses on studying the pendant 3-tree-connectivity of augmented cubes, which are modified versions of hypercubes designed to enhance connectivity and reduce diameter. This research demonstrates that the pendant 3-tree-connectivity of augmented cubes, denoted as τ 3 (A Q n) is 2 n - 3 . This result matches the upper bound of τ 3 (G) provided by Hager, specifically for the augmented cube graph A Q n . [ABSTRACT FROM AUTHOR]

In this study, a method for measuring the active reflection coefficient of an active phased array antenna is proposed. Owing to the narrow array spacing in the Ka-band, the measurement must be conducted within a measurement system that includes cables. The time-domain transform and gating of a network analyzer were used to exclude cable characteristics. The circuit network of the measurement system was analyzed mathematically and measured to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Irreversibility, in which a transient perturbation leaves a system in a new state, is an emergent property in systems of interacting entities. This property has well-established implications in statistical physics but remains underexplored in biological networks, especially for bacteria and other prokaryotes whose regulation of gene expression occurs predominantly at the transcriptional level. Focusing on the reconstructed regulatory network of Escherichia coli, we examine network responses to transient single-gene perturbations. We predict irreversibility in numerous cases and find that the incidence of irreversibility increases with the proximity of the perturbed gene to positive circuits in the network. Comparison with experimental data suggests a connection between the predicted irreversibility to transient perturbations and the evolutionary response to permanent perturbations. [ABSTRACT FROM AUTHOR]

Frequency response analysis (FRA) is the widely used technique for diagnosis of power transformer winding. Previous works in the field of FRA have steer to the standardization of its measurements procedure. To date, there is no reliable standard code for the interpretation of FRA results. In this context, this paper proposes a new parametric method to synthesis the High frequency electrical ladder network circuit (HF-ELNC) of the transformer winding. Initially, a nondestructive process is applied to extract three main winding parameters. Mainly, the shunt-series capacitances and the total equivalent inductance basing on the frequency response data from the winding terminal. To this end, the proposed continuous-time state–space model is converted into a gain-numerator-denominator form. After that, the derived matrix equations are iteratively estimated by population-based algorithm using enhanced logistic choatic marine predator algorithm (ELCMPA), from where, three objective functions are considered in the model of identification including the capacitances, the inductances and the resistances. As well, classical methods such as finite elements or analytical formulas require design winding specifications. In this study, instead of winding geometry knowledge, the proposed method certifies a unique, physically and mutually coupled HF-ELNC. The accuracy of the proposed method is validated through case study of a real transformer winding. [ABSTRACT FROM AUTHOR]

Abstract—This article describes the step-by-step design and implementation of a GaAs power amplifier (PA) that operates at low voltage and low power, while providing broadband coverage. The design methodology used to ensure high performance within the desired frequency range is explained in detail, and the matching network and bias circuit are implemented using microstrip technology. A PA prototype is then fabricated to demonstrate the performance of the proposed design, using the Agilent Technologies (hp)® ATF13786 field effect transistor. The evaluation of the PA's performance involves the application of both small-signal and large-signal measurements. Notably, large-signal assessments reveal a power gain of 10 dB, a saturated output power of 15.8 dBm, power-added efficiency (PAE) of 21.8%, and drain efficiency (DE) of 26.3% at a frequency of 3.5 GHz. This design, characterized by its simplicity, compactness, and versatility, exhibits applicability across a diverse array of contexts. [ABSTRACT FROM AUTHOR]

A mixed-signal binarized neural network (BNN) processor for the MNIST image classification is demonstrated based on analogue circuit networks. BNN algorithm for training neural networks with binary weights and activations reduces power consumption and memory size. This algorithm is a design to perform core operations of multi-layer perceptron (MLP) to reduce complexity and power consumption using analogue circuits. The mixed-signal BNN processor employs a current mirror neuron to perform multiply-and-accumulate (MAC) operations and sign activation functions. The near-threshold current mirror neuron that computes the key operations of the BNN algorithm is used to achieve low power consumption. The design occupies 0.065 mm2 in 28-nm CMOS with 560B of on-chip SRAM. The 28-nm CMOS test-chip achieves energy efficiency of 24.1 TOPS/W and 94% accuracy on the MNIST image classification. This design with binary weights and activations exhibits only 5% degradation in accuracy compared with the models with floating-point precision. [ABSTRACT FROM AUTHOR]

[Display omitted] Exceptional bound (EB) states represent a unique new class of robust bound states protected by the defectiveness of non-Hermitian exceptional points. Conceptually distinct from the more well-known topological states and non-Hermitian skin states, they were recently discovered as a novel source of negative entanglement entropy in the quantum entanglement context. Yet, EB states have been physically elusive, being originally interpreted as negative probability eigenstates of the propagator of non-Hermitian Fermi gases. In this work, we show that EB states are in fact far more ubiquitous, also arising robustly in broad classes of systems whether classical or quantum. This hinges crucially on a newly-discovered spectral flow that rigorously justifies the EB nature of small candidate lattice systems. As a highlight, we present their first experimental realization through an electrical circuit, where they manifest as prominent stable resonant voltage profiles. Our work brings a hitherto elusive but fundamentally distinctive quantum phenomenon into the realm of classical metamaterials, and provides a novel pathway for the engineering of robust modes in otherwise sensitive systems.. [ABSTRACT FROM AUTHOR]

Circuit implementations of neuronal networks so far have been focusing on synaptic weight changes as network growth principles. Besides these weight changes, however, it is also useful to incorporate additional network growth principles such as guided axon growth and pruning. These allow for dynamical signal delays and a higher degree of self-organization, and can thus lead to novel circuit design principles. In this work we develop an ideal, bio-inspired electrical circuit mimicking growth and pruning controlled by guidance cues. The circuit is based on memristively coupled neuronal oscillators. As coupling element, we use memsensors consisting of a general sensor, two gradient sensors, and two memristors. The oscillators and memsensors are arranged in a grid structure, where oscillators and memsensors realize nodes and edges, respectively. This allows for arbitrary 2D growth scenarios with axon growth controlled by guidance cues. Simulation results show that the circuit successfully mimics a biological example in which two neurons initially grow towards two target neurons, where undesired connections are pruned later on. [ABSTRACT FROM AUTHOR]

Renewable energy‐based distributed generation presents new challenges for power inverters, requiring greater flexibility, reliability, cost‐effectiveness, and efficiency. Impedance source network‐based inverters (INIs) are an attractive option for addressing some of these issues since they provide a single‐stage power conversion structure with buck‐boost capability, allowing them to manage shoot‐through conduction on the inverter arms. However, some INI topologies based on magnetic coupling, such as those of the Y‐source impedance network family, have problems related to overvoltages at the power switches because of the presence of unwanted leakage inductances in the coupled‐inductor windings. The insertion of the regenerative and passive LCD snubber in the impedance network circuit is a solution to mitigate this problem, but there is currently a lack of a clear and concise methodology to design its components, which hinders a wider adoption of this solution in different types of INI. This paper presents a comprehensive and analytical design for determining LCD snubber components. This study was carried out with the quasi‐Y‐source inverter, but the same methodology can be extended to other types of INI based on the Y‐source network. [ABSTRACT FROM AUTHOR]

Heat conduction in the porous media (PM) with gas-liquid-solid three-phase has been numerically investigated. The validation of the adopted numerical method is performed by comparing the effective thermal conductivity (ETC) obtained from the simulation results with the experimental data. The results show that the ETC of the PM increases with increasing solid volume fraction (ϕs) and liquid saturation degree (Sr). Sr has a weaker effect on the ETC compared to ϕs. Similar variation laws of the ETC with Sr and ϕs are found for systems with different solid and liquid thermal conductivity. In this work, the PM is simplified as a circuit network. The modeling for the ETC is performed by defining a weighted harmonic parameter (η) to connect the serial and parallel models. η is closed as a function of Sr, ϕs and the thermal conductivity of the solid and liquid phases. The predictability of the model is evaluated. The results demonstrate that the model can provide accurate estimations for the ETC of the three-phase PM. [ABSTRACT FROM AUTHOR]

Owing to the high impedance transformation ratio, the Doherty power amplifier (DPA) with a large output power back-off generally has bandwidth limitations. This study proposes an asymmetric DPA with an impedance distribution and control circuit (IDCC) at the postmatching network to improve the bandwidth. The IDCC, based on a resonance circuit and a series transmission line, distributes and controls the load impedance according to the frequency so that the bandwidth of the DPA can be extended. To verify the proposed IDCC, an asymmetric DPA was designed using GaN HEMT with a power capacity of 6 W and 10 W for the carrier and peaking amplifiers, respectively. The implemented DPA was evaluated for the broad frequency band between 3.3 GHz and 3.8 GHz using a 5G new radio (NR) signal with a bandwidth of 100 MHz and a peak-to-average power ratio of 7.8 dB. A drain efficiency between 43.2% and 50.7% and an adjacent channel leakage power ratio between –23.4 dBc and –27.3 dBc were achieved at an average power level that ranged between 33.5 dBm and 34.3 dBm. [ABSTRACT FROM AUTHOR]

The development of cell-type-specific dendritic arbors is integral to the proper functioning of neurons within their circuit networks. In this study, we examine the regulatory relationship between the cytosolic chaperonin CCT, key insulin pathway genes, and an E3 ubiquitin ligase (Cullin1) in dendritic development. CCT loss of function (LOF) results in dendritic hypotrophy in Drosophila Class IV (CIV) multi-dendritic larval sensory neurons, and CCT has recently been shown to fold components of the TOR (Target of Rapamycin) complex 1 (TORC1) in vitro. Through targeted genetic manipulations, we confirm that an LOF of CCT and the TORC1 pathway reduces dendritic complexity, while overexpression of key TORC1 pathway genes increases the dendritic complexity in CIV neurons. Furthermore, both CCT and TORC1 LOF significantly reduce microtubule (MT) stability. CCT has been previously implicated in regulating proteinopathic aggregation, thus, we examine CIV dendritic development in disease conditions as well. The expression of mutant Huntingtin leads to dendritic hypotrophy in a repeat-length-dependent manner, which can be rescued by Cullin1 LOF. Together, our data suggest that Cullin1 and CCT influence dendritic arborization through the regulation of TORC1 in both health and disease. [ABSTRACT FROM AUTHOR]

Human mobility in an urban area is complicated; the origins, destinations, and transportation modes of each person differ. The quantitative description of urban human mobility has recently attracted the attention of researchers, and it highly related to urban science problems. Herein, combined with physics inspiration, we introduce a revised electric circuit model (RECM) in which moving people are regarded as charged particles and analogical concepts of electromagnetism such as human conductivity and human potential enable us to capture the characteristics of urban human mobility. We introduce the unit system, ensure the uniqueness of the calculation result, and reduce the computation cost of the algorithm to 1/10,000 compared with the original ECM, making the model more universal and easier to use. We compared features including human conductivity and potential between different major cities in Japan to show our improvement of the universality and the application range of the model. Furthermore, based on inspiration of physics, we propose a route generation model (RGM) to simulate a human flow pattern that automatically determines suitable routes between a given origin and destination as a source and sink, respectively. These discoveries are expected to lead to new approaches to the solution of urban science problems. [ABSTRACT FROM AUTHOR]

In this article, I propose a cartographic approach to literature that privileges the study of personal itineraries through the city. More specifically, my proposal consists of tracing these itineraries and analyzing how they subvert the dominant narratives of the city. My hypothesis is that the representations that emerge from such an analysis allow for a reconceptualization of the city that dislocates and actualizes the hegemonic imaginaries of urban settlements. To demonstrate the insights that can flow from this approach, in this article I analyze the itineraries of Salvador Novo through Mexico City as depicted in La estatua de sal. In doing so, I trace the complex network of queer circuits in post-revolutionary Mexico and present a more holistic understanding of the city that corrects the dominant representations of the capital as hyper-masculine and heterosexual. [ABSTRACT FROM AUTHOR]

In this paper, a radio frequency energy harvesting system with a wide dynamic range rectifier is presented. This rectifier has two feedback and feedforward structures. These paths keep the rectifier's power conversion efficiency (PCE) high at different input powers and thus create a high dynamic range (DR). This rectifier also has better sensitivity. Advances in the rectifier contribute to more satisfactory results for the final system. The circuit is simulated with 180 nm TSMC CMOS technology at a frequency of 900 MHz. Also, a π-type input impedance matching network circuit is used. This circuit is matched at Pin = − 19.5 dBm and f = 900 MHz. In addition, an off-chip balun is utilized to convert the received single-ended signal to the differential one. The achieved PCE and DR are 86.03% and 9.76 dB, respectively, with a sensitivity of − 19.32 at 1 V output voltage. Furthermore, the overall circuit results indicate a PCE of 76.13%, a DR of 6.3 dB, and a sensitivity of − 18.75 dBm. [ABSTRACT FROM AUTHOR]

This paper proposes contributions to the efficient wave digital (WD) modeling of large oscillator networks which are emerging as energy‐efficient alternatives to traditional computers. The WD concept enables in‐operando parameter tuning, real‐time testing, and the associated algorithms are highly parallelizable. We present a general electrical model of N‐shaped nonlinearities that are commonly found in nonlinear oscillators. Our model offers the flexibility to design the current–voltage characteristic based on specific requirements. We show how this model can be used to derive efficient and explicit WD algorithms for nonlinear oscillators. Furthermore, we propose the use of lossless transmission lines between the oscillators and the coupling network to obtain an ideal circuit for an oscillator network that can function as an Ising machine and be efficiently and exactly evaluated in the WD domain. The proposed algorithms are compared against the classical method involving iterative techniques, and their capabilities are evaluated through the emulation of a single FitzHugh‐Nagumo oscillator as well as an Ising machine involving transmission lines. In the latter case, we show that, for large networks, the proposed methods decrease the runtime by up to 75% compared to using iterative techniques. [ABSTRACT FROM AUTHOR]

The online calibration method for current transformers is an important research direction in the field of smart grids. This article constructs a transformer error prediction model based on spatiotemporal integration. This model draws inspiration from the structure of forgetting gates in gated loop units and combines it with a graph convolutional network (GCN) that is good at capturing the spatial relationships within the graph attention network to construct an adaptive GCN. The spatial module formed by this adaptive GCN is used to model the spatial relationships in the circuit network, and the attention mechanism and gated time convolutional network are combined to form a time module to learn the temporal relationships in the circuit network. The layer that combines the time and space modules is used, which consists of a gating mechanism for spatiotemporal fusion, and a transformer error prediction model based on a spatiotemporal correlation analysis is constructed. Finally, it is verified on a real power grid operation dataset, and compared with the existing prediction methods to analyze its performance. [ABSTRACT FROM AUTHOR]

The reliability of circuit systems is primarily affected by cascading failures due to their complex structural and functional coupling. Causes of cascading failure during circuit operation include the continuous degradation process of components and external random shocks. Circuit systems can exhibit asymmetric structural changes and functional loss during cascading failure propagation due to the coupling of degradation and shock and their uncertainty effects. To tackle this issue, this paper abstracts the circuit into an impedance network and constructs a component failure behavior model that considers the correlation between degradation and shock. The interactions between soft and hard failure processes among different components are discussed. Two types of cascading failure propagation processes are described: slow propagation associated with continuous degradation and damage shock, and fast propagation due to fatal shock. Based on this, a cascading failure simulation algorithm is developed. This article presents a case study to demonstrate the proposed models and to analyze the reliability of a typical circuit system. [ABSTRACT FROM AUTHOR]

This article investigates the stabilization issue of highly non-linear hybrid stochastic delayed networks (HSDNs) via periodic self-triggered control under impulse (PS-TCI). Firstly, the existence of a unique global solution for highly non-linear HSDNs under PS-TCI is studied. Then, a stabilization criterion for highly non-linear HSDNs is established, by combining a graph-theoretic approach with a novel Lyapunov-based analysis, based on a 'genuine' Lyapunov function defined by introducing an auxiliary timer. Therein, the less conservative polynomial growth condition and local Lipschitz condition for the drift and diffusion coefficients are used than the linear growth condition and global Lipschitz condition. Meanwhile, the design idea of PS-TCI is based on the evolution of an upper bound of the mathematical expectation for Lyapunov function (not directly Lyapunov function or system state), which implies that the triggered instant of PS-TCI is not a random variable. Finally the theoretical results are employed to study the stability of a class of FitzHugh–Nagumo circuits networks and the central pattern generators networks of a hexapod robot, and correlative numerical simulations are provided for demonstration. • The stabilization of highly non-linear HSDNs is first investigated via PS-TCI. • Some existing stabilization conditions for PS-TCI in [30,34] are weakened. • By introducing an auxiliary timer, a novel Lyapunov-based analysis is proposed. • The theoretical results could be applied to FNCNs and CPGNs successfully. [ABSTRACT FROM AUTHOR]

Paroxysmal kinesigenic dyskinesia (PKD) is associated with a disturbance of neural circuit and network activities, while its neurophysiological characteristics have not been fully elucidated. This study utilized the high‐density electroencephalogram (hd‐EEG) signals to detect abnormal brain activity of PKD and provide a neural biomarker for its clinical diagnosis and PKD progression monitoring. The resting hd‐EEGs are recorded from two independent datasets and then source‐localized for measuring the oscillatory activities and function connectivity (FC) patterns of cortical and subcortical regions. The abnormal elevation of theta oscillation in wildly brain regions represents the most remarkable physiological feature for PKD and these changes returned to healthy control level in remission patients. Another remarkable feature of PKD is the decreased high‐gamma FCs in non‐remission patients. Subtype analyses report that increased theta oscillations may be related to the emotional factors of PKD, while the decreased high‐gamma FCs are related to the motor symptoms. Finally, the authors established connectome‐based predictive modelling and successfully identified the remission state in PKD patients in dataset 1 and dataset 2. The findings establish a clinically relevant electroencephalography profile of PKD and indicate that hd‐EEG can provide robust neural biomarkers to evaluate the prognosis of PKD. [ABSTRACT FROM AUTHOR]

Epsilon‐and‐mu‐near‐zero (EMNZ) metamaterials have garnered significant attention in near‐zero‐parameter metamaterial research for their exceptional ability to attain concurrent near‐zero permittivity and permeability. Nowadays achieving EMNZ properties through the use of metacomposites remains a novel endeavor. Presented here is an innovative approach of near‐zero‐parameter metacomposites, illustrating excellent and tunable EMNZ properties in the radio frequency regime. The self‐organization approach is applied to construct the conductive 3D network and the circuits, serving as the underlying framework for achieving EMNZ properties. Near‐zero permeability is effectively maintained while permittivity reaches epsilon‐near‐zero frequency regime. Efficient manipulation of electromagnetic parameters is initially realized via adjusting component content in metacomposites. Significantly, an excellent EMNZ property is observed as carbon content reaches 15 wt.% at 915 MHz. Through both numerical simulations and experimental testing, the PGC metacomposites have exhibited tunneling effects and directional emission characteristics, confirming their EMNZ properties. Besides, the Lego‐like adjustment facilitates the achievement of EMNZ property and advances the EMNZ frequency point to 700 MHz, expanding the EMNZ range. Furthermore, thanks to the remarkable excitation effect of photo‐induced adjustment, the metacomposite with low‐carbon content also achieves extraordinary EMNZ properties. This research offers promising self‐organized EMNZ metacomposites and lays the foundation for future endeavors in precisely adjusting near‐zero parameters. [ABSTRACT FROM AUTHOR]

Nicotine addiction circuits involve integrating specific brain regions that alter to frequent smoking. Detection of these circuits via fMRI contributes to understanding addiction-related mechanisms. Identification of the functional circuits and networks altered by nicotine is essential to improve the treatment of nicotine addiction. However, analyzing fMRI data and detecting functional addiction circuits still have challenges. In this work, we developed a generative AI-enabled framework, rat addiction-related circuits detection platform (RADP), to detect nicotine-related circuits. It has an end-to-end pipeline: functional imaging data acquisition from neurobiological experiments, computational modeling for brain networks, and a novel generative model including spatiotemporal transformer auto-encoder (STA) and dynamic circuits analysis. The proposed spatiotemporal representation contrasting trains the encoder of STA to contrastively capture representations between the addictive and the control groups. Experimental results indicate that the framework can efficiently detect the verified addiction circuits and discover the unknown but significant circuits. Moreover, RADP can be served as a general tool which can be extended to other brain circuits. [ABSTRACT FROM AUTHOR]

Film festivals are a key component in the global film industry in terms of trendsetting, publicity, trade, and collaboration. We present an unprecedented analysis of the international film festival circuit, which has so far remained relatively understudied quantitatively, partly due to the limited availability of suitable data sets. We use large-scale data from the Cinando platform of the Cannes Film Market, widely used by industry professionals. We explicitly model festival events as a global network connected by shared films and quantify festivals as aggregates of the metadata of their showcased films. Importantly, we argue against using simple count distributions for discrete labels such as language or production country, as such categories are typically not equidistant. Rather, we propose embedding them in continuous latent vector spaces. We demonstrate how these "festival embeddings" provide insight into changes in programmed content over time, predict festival connections, and can be used to measure diversity in film festival programming across various cultural, social, and geographical variables—which all constitute an aspect of public value creation by film festivals. Our results provide a novel mapping of the film festival circuit between 2009–2021 (616 festivals, 31,989 unique films), highlighting festival types that occupy specific niches, diverse series, and those that evolve over time. We also discuss how these quantitative findings fit into media studies and research on public value creation by cultural industries. With festivals occupying a central position in the film industry, investigations into the data they generate hold opportunities for researchers to better understand industry dynamics and cultural impact, and for organizers, policymakers, and industry actors to make more informed, data-driven decisions. We hope our proposed methodological approach to festival data paves way for more comprehensive film festival studies and large-scale quantitative cultural event analytics in general. [ABSTRACT FROM AUTHOR]

In this letter, a novel design of electrically tunable substrate integrated waveguide (SIW) equalizer, based on PIN diodes, is proposed. Compared to previous SIW equalizers, this one introduces PIN diodes that exhibit varying impedance under different direct current (DC) voltages. The equalizing values of the SIW equalizer are directly linked to the DC voltage applied to the PIN diode within a specific voltage range. This feature allows it to be considered as an electrically tunable SIW equalizer. The experimental results demonstrate that the equalizing values are consistently monotonic and adjustable within the range of 1.59–7.56 dB in Ku‐band. This adjustment is attained through a DC voltage step of −0.01 V, applied within the range of 1.23–1.11 V. Additionally, the microwave equalizer is a planar structure that can be conveniently implemented on traditional printed circuit board, resulting in lower costs, easier realization, and integration. This SIW equalizer can be applied in amplitude compensation circuits and networks to compensate flatness dynamically. [ABSTRACT FROM AUTHOR]

The objective of this study is to investigate the synchronization criteria under the sampled-data control method for multi-agent systems (MASs) with state quantization and time-varying delay. Currently, a looped Lyapunov–Krasovskii Functional (LKF) has been developed, which integrates information from the sampling interval to ensure that the leader system synchronizes with the follower system, resulting in a specific condition in the form of Linear Matrix Inequalities (LMIs). The LMIs can be easily solved using the LMI Control toolbox in Matlab. Finally, the proposed approach's feasibility and effectiveness are demonstrated through numerical simulations and comparative results. [ABSTRACT FROM AUTHOR]

The temporal activity of many physical and biological systems, from complex networks to neural circuits, exhibits fluctuations simultaneously varying over a large range of timescales. Long-tailed distributions of intrinsic timescales have been observed across neurons simultaneously recorded within the same cortical circuit. The mechanisms leading to this striking temporal heterogeneity are yet unknown. Here, we show that neural circuits, endowed with heterogeneous neural assemblies of different sizes, naturally generate multiple timescales of activity spanning several orders of magnitude. We develop an analytical theory using rate networks, supported by simulations of spiking networks with cell-type specific connectivity, to explain how neural timescales depend on assembly size and show that our model can naturally explain the long-tailed timescale distribution observed in the awake primate cortex. When driving recurrent networks of heterogeneous neural assemblies by a time-dependent broadband input, we found that large and small assemblies preferentially entrain slow and fast spectral components of the input, respectively. Our results suggest that heterogeneous assemblies can provide a biologically plausible mechanism for neural circuits to demix complex temporal input signals by transforming temporal into spatial neural codes via frequency-selective neural assemblies. [ABSTRACT FROM AUTHOR]

Common overcurrent criteria used to detect phase-to-phase short circuits in medium-voltage (MV) networks in some cases do not bring the expected results. In particular, this applies to instantaneous overcurrent protections in lines with variable ranges and lines leading to local power plants (LPP), which generate a short-circuit current slightly greater than the rated current. When using overcurrent protections, there are some problems with ensuring the appropriate sensitivity and selectivity. This article proposes a protection based on impedance measurement against the effects of phase-to-phase short circuits in MV lines. The protection can be used at switchgears and also at protection points (PP) located deep in the network. The under-impedance criterion has a range independent of the value of the short-circuit current, and it can be alternative or complement to classic overcurrent protection. The introduction of these criteria allows the protection range to be independent of the type of short circuit and the short-circuit power on the busbar, and to determine this range more precisely. The under-impedance protection is particularly useful in conditions of a growing number of energy sources deep inside the networks and its automation. The main idea was to develop a uncomplicated characteristic that would ensure the possibility of setting the under-impedance protection by people who are not specialists in the field of distance protections. The characteristics have been optimally developed both in terms of the area of detected impedances and easy implementation, operation, and configuration. [ABSTRACT FROM AUTHOR]

This paper studies the dissipative filtering of uncertain singular time-varying delay Markovian jump systems (MJSs) with nonlinear perturbations and generally uncertain transition rates (GUTRs). We focus on the design of a mixed filter, which can include mode-dependent and mode-independent filters in a unified and simple structure. First, a new Lyapunov–Krasovskii functional is proposed through the delay-decomposition approach, and improved Wirtinger-based integral inequalities are used to obtain a sufficient condition to ensure the stochastic admissibility of the considered uncertain nonlinear singular time-varying delay MJSs with dissipative performance. Based on these ingredients, the explicit expression of the desired mixed filter is obtained. Finally, some numerical examples and the filter design of a two-loop circuit network reflect the less conservatism and effectiveness of the method in this article. [ABSTRACT FROM AUTHOR]

A novel implementation of an analogue low-power, second-order, low-pass filter with tunable quality factor (Q) is presented and discussed. The filter feature is a relatively simple, buffer-based, circuit network consisting of eleven transistors operating in a subthreshold region. Q tuning is accomplished by injecting direct current into a network node, which changes the output resistance of the transistors and, as a result, modifies the filter network's loss, and thus its Q. Q tuning is independent of a filter cut-off frequency (ω0). The filter, with a nominal ω0 of 1 kHz, was fabricated using a 0.18 µm CMOS technology, and features a Q range of 2–11, power consumption of up to 52 nW, and a 59 dB dynamic range when using a 0.5 V supply. The ω0 can be tuned from 0.5 to 2.5 kHz using a traditional method by changing the transistor transconductances, but this process partially affects the quality factor. [ABSTRACT FROM AUTHOR]

This research presents the development of an optimization model to estimate parameters for reactive power compensators in medium voltage networks using reactors. The proposed mathematical modeling belongs to the family of nonlinear programming models. The proposed mathematical model considers multiple measures regarding applied voltage in terminals of the reactor as well as data regarding active and reactive power behavior and input current. The objective function considered corresponded to the minimization of the mean square error between the measured and calculated variables. To solve the proposed optimization model is employed the General Algebraic Modeling System (GAMS) software. Numerical results in two reactors with nominal compensation capabilities of about 2 Mvar and 6.75 Mvar, operated with 13, and 25 kV, demonstrate the effectiveness of the proposed optimization model to characterize the electrical circuit of these compensation devices. Different nonlinear programming algorithms available in GAMS were employed in the solution of the proposed optimization model with objective functions lower than 1 x 10-10, which confirms that the measured and calculated variables have the same numerical behavior, which allows concluding that the characterized circuit reflects the expected electrical behavior of the reactors under different voltage input. [ABSTRACT FROM AUTHOR]

This paper introduces the case of marriage migration into the debate on the intra-Asian circuit of capital flow, human mobilities and urban and regional development. While intra-Asian marriage migration lies at the intersection of governmental policy, increased capital flow and human mobilities, it has been absent from the discussion on capital flows within the global East. Employing ethnographic research methods, we explain how Vietnamese marriage migrants in South Korea and their Korean husbands restyled their remittances to invest in land and businesses in hometowns, the Mekong Delta area and big cities through transnational marriage networks. Explanations for what caused marriage migrants' position to evolve include a discussion of macro-politics of political and economic reforms in South Korea and Vietnam and the micro-politics of transnational networks. By demonstrating the roles of transnational marriage networks, this research contributes to the debates on migration-developmentmobility circuit. [ABSTRACT FROM AUTHOR]

Modern telecommunications cannot function without optical communication networks, which enable high-speed data transmission across large distances. However, several variables, such as peak-to-average power ratio (PAPR) distortion, might impact the signal's quality. In optical communication networks, PAPR deformation is a significant problem that may contribute to signal deterioration and deformation, resulting in errors in the delivered data. To lower the PAPR of the message in DC-biased optical communication networks, this research attempts to design an efficient and effective optimization methodology for smart cities. A robust tree-seed optimization (RTSO) algorithm is suggested explicitly in this study as a brand-new optimization method to deal with this issue. According to the convergence assessment, the RTSO technique converges more quickly than other optimization techniques. In conclusion, the suggested RTSO algorithm offers a practical and efficient solution to the PAPR issue in DC-biased optical communications networks. The method can enhance optical communication efficiency and lessen PAPR's detrimental effects on signal quality which can be used in smart cities.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 1 Given name: [Md Altab] Last name [Hossin], Author 2 Given name: [Jamal Ahmed] Last name [Alenizi]. Also, kindly confirm the details in the metadata are correct.Thank you. Yes, It is right.Author details: Kindly check and confirm whether the corresponding affiliation is correctly identified.Yes, It is right [ABSTRACT FROM AUTHOR]

This article addresses the exponential bipartite synchronization (EBS) of multilayer signed networks with time-varying coupling (MSNs) under aperiodic intermittent-based adaptive dynamic event-triggered control (AAIDETC). Firstly, to increase the elasticity, a novel AAIDETC strategy is presented, whose superiority is that the control gains and the triggering parameters can vary with the evolution of the considered networks. Meanwhile, concerning the aperiodic intermittent control, a new definition of average control ratio (ACR) is put forward, which is more rigorous compared with the relevant results. Then, by the method of ACR, graph theory and Lyapunov approach, the simpler synchronization criterion is gained, which avoids the topology structure of MSNs. Moreover, the EBS issues of Chua's circuits and neural networks established on MSNs are studied, which are two practical applications of our theoretical results. Finally, corresponding numerical simulations are presented to verify the availability of the obtained results. • A new MSN model with time-varying coupling is proposed. • A novel AAIDETC is developed. • An improved definition of average control ratio is presented. [ABSTRACT FROM AUTHOR]

Classical combinational logic circuits (CCLCs) are widely used in various fields. Corresponding to the CCLCs, here schemes are given for some quantum combinational logic circuits (QCLCs) based on the quantum NAND tree. Three typical circuits, adder, comparator, and seven‐segment display decoder, are discussed in detail as examples. All the designs of the schemes are based on the quantum random walk theory. Furthermore, these QCLCs are mapped onto the classical circuit networks and design new types of CCLCs, and take advantage of the fact that there is a good correspondence between the voltage in the circuit satisfying Kirchhoff's law and the system wave function satisfying the Schrodinger equation. These CCLCs that are designed have exponential speedup functions compared with conventional ones, which have been demonstrated experimentally. Because classical circuit networks possess good scalability and stability, the realization of QCLCs on classical circuits is expected to have potential applications for information processing in the era of big data. [ABSTRACT FROM AUTHOR]

In accordance with the IEEE standard, the zero-sequence impedance test of transformers necessitates an open or short circuit test on the high-voltage side winding. Power source excitation is applied to the high-voltage test winding using a high-capacity test power supply. To circumvent the need for a large-capacity, high-voltage test power supply in field tests, this paper proposes a method for measuring zero-sequence impedance in autotransformers based on low-voltage excitation and disconnection testing. By applying a three-phase power supply to the lowest voltage side of the autotransformer and creating a disconnection condition, an unbalanced test scenario is established. Subsequently, the composite sequence network equivalent circuit for one-phase and two-phase disconnections between the high-voltage side and the low-voltage side of the transformer is formulated. Calculation formulas for the zero-sequence impedance of the autotransformer under various conditions are derived. The accuracy of the zero-sequence impedance is verified using MATLAB/Simulink simulation software 2018b, evaluating double-winding and three-winding autotransformers in breaking tests under different connection modes. The error is found to be less than 3%. The results of this study affirm the high accuracy of the proposed method. [ABSTRACT FROM AUTHOR]