Small Circuit Research Articles

The Brain's Best Kept Secret Is Its Degenerate Structure.

Degeneracy is defined as multiple sets of solutions that can produce very similar system performance. Degeneracy is seen across phylogenetic scales, in all kinds of organisms. In neuroscience, degeneracy can be seen in the constellation of biophysical properties that produce a neuron's characteristic intrinsic properties and/or the constellation of mechanisms that determine circuit outputs or behavior. Here, we present examples of degeneracy at multiple levels of organization, from single-cell behavior, small circuits, large circuits, and, in cognition, drawing conclusions from work ranging from bacteria to human cognition. Degeneracy allows the individual-to-individual variability within a population that creates potential for evolution.

The fly connectome reveals a path to the effectome

A goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome1–3 specifies the synaptic paths by which neurons can affect each other, but not how strongly they do affect each other in vivo. To overcome this limitation, we introduce a combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the ‘effectome’. Specifically, we propose an estimator for a linear dynamical model of the fly brain that uses stochastic optogenetic perturbation data to estimate causal effects and the connectome as a prior to greatly improve estimation efficiency. We validate our estimator in connectome-based linear simulations and show that it recovers a linear approximation to the nonlinear dynamics of more biophysically realistic simulations. We then analyse the connectome to propose circuits that dominate the dynamics of the fly nervous system. We discover that the dominant circuits involve only relatively small populations of neurons—thus, neuron-level imaging, stimulation and identification are feasible. This approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, we provide evidence that fly whole-brain dynamics are generated by a large collection of small circuits that operate largely independently of each other. This implies that a causal model of a brain can be feasibly obtained in the fly.

Fully integrated microneedle biosensor array for wearable multiplexed fitness biomarkers monitoring

Fitness monitoring has become increasingly important in modern lifestyles; the current fitness monitoring always relies on physical sensors, making it challenging to detect pertinent issues at a deeper level when exercising. Here, we report a fully integrated wearable microneedle sensor that simultaneously measures fitness related biomarkers (e.g., glucose, lactate, and alcohol) during physical exercise. Such a sensor integrates a biocompatible 3D-printed microneedle array that can comfortably access skin interstitial fluid and a small circuit for signal processing and calibration, and wireless communication. The microneedle array features good biocompatibility and highly sensitive biochemical sensors that can detect even the slightest variations within the biomarkers of this fluid. On-body experimental results indicate that such a sensor can monitor fitness-related biomarkers across multiple subjects and support multi-day monitoring, with results showing a good correlation with commercial devices. The data was transmitted to a smartphone via Bluetooth and uploaded to cloud platforms for further health assessment. This study has the potential to boost intelligent wearable devices in sports health.

Resistance distance and sharp bounds of two-mode electrical networks

Electrical networks are ubiquitous in our daily lives, ranging from small integrated circuits to large-scale power systems. These networks can be easily represented as graphs, where edges represent connections and vertices represent electric nodes. The concept of resistance distance originates from electrical networks, with this term used because of its physical interpretation, where every edge in a graph G is assumed to have a unit resistor. The applications of resistance distance extend to various fields such as electrical engineering, physics, and computer science. It is particularly useful in investigating the flow of electrical current in a network and determining the shortest path between two vertices. In this work, we have investigated seven different resistance distance-based indices of bipartite networks and derived general formulae for them; the sharp bounds with respect to these resistance distance indices are also identified. Additionally, we introduced a novel resistance distance topological index, the Multiplicative Eccentric Resistance Harary Index, and derived general formula for it. The sharp bounds with respect to this newly introduced index are also identified for bipartite networks.

Unlocking the potential of a chemically modified blue phosphorene as anode materials for high-performance sodium-ion batteries

Blue phosphorene (Blu-Pn) has recently been identified as a new two-dimensional (2D) form of black phosphorus via the epitaxial growth method and has shown the ability to host a substantial amount of Na while serving as an anode in sodium-ion batteries (NIBs). However, its structural instability is an issue that hinders its further exploitability in NIBs. In this paper, we propose to chemically modify the Blu-Pn network by substituting six phosphorus atoms with a carbon source in the form of a C6 ring, constructing a new and robust CP2 nanosheet. Based on the state-of-the-art non-local van der Waals corrected density functional theory and ab initio molecular dynamic (AIMD) simulations, we systematically explore the feasibility of the designed CP2 as a potential anode material for NIBs. Adding a C6 ring in the Blu-Pn lattice reduces its energy gap from 2 eV to 1.3 eV, which disappears after Na adsorption, indicating a conductive character. Attributed to the synergistic effect, the adsorption energy of Na on CP2 nanosheet (−2.22 eV) is greatly decreased in comparison to the pristine carbon source (e.g., graphene with −0.78 eV) and pristine Blu-Pn (−1.85 eV). The Na diffusivity on CP2 at 300 K is predicted to be 4.90 × 10−11 m2/s by AIMD. The specific capacity is up to 604.1 mAh/g, which exceeds that of the commercialized graphite anode (372 mA h/g). Ultimately, the designed electrode provides a small open circuit potential of 0.66 V that falls within a desirable voltage range. This work paves the way for exploring Blu-Pn 2D anode materials for NIBs technology and provides valuable insights into surface chemical modification towards the long-term stability of Blu-Pn.

Advanced Fire & Gas Detection System

Abstract: This paper presents a cost-effective, user-friendly, and dependable fire and gas detection system leveraging the capabilities of an Arduino microcontroller. The system employs an MQ2 gas sensor module for gas detection and a flame sensor module for fire detection. Upon the detection of either gas or fire, the system initiates an alarm, activates LED indicators, and communicates the situation through a 16x2 LCD display. Furthermore, the system can be configured to issue SMS alerts to specific recipients. Designed for versatile applications, it finds utility in diverse settings, including residential, commercial, and educational environments. It exhibits proficiency in detecting an extensive array of gasses, encompassing methane, propane, and carbon monoxide, while also displaying sensitivity to various flame types, including those generated by wood, paper, and plastic. Installation and upkeep are straightforward, with the Arduino microcontroller and sensor modules easily adaptable to a small breadboard or circuit board. The strategically positioned LCD display and alarm ensure swift notifications to occupants in case of potential hazards. Notably, this system is budget-friendly, with the Arduino microcontroller and sensor modules available at reasonable prices from numerous online and retail suppliers. Rigorous testing has validated its effectiveness in gas and fire detection, establishing it as an invaluable asset for safeguarding lives and property against fire and gas-related risks

A compact self-powered inductor-less piezoelectric energy harvesting circuit using gyrator

In traditional low-frequency energy harvesting circuits, a large matched inductor with a large size is unavoidable. To reduce the size of the circuit, this paper proposes a compact self-powered inductor-less high-efficiency piezoelectric energy harvesting circuit using a low-power-consumption gyrator. A self-powered floating gyrator inductor is used in place of an inductor in the proposed circuit, and the required phasor response is acquired by using its voltage–current (V–I) relationship. The proposed circuit offers easy adjustability and performance benefits in small integrated circuits packages. The proposed circuit can be cost-effective and provide reduced area advantages in autonomous self-powered Internet-of-Things and wireless sensor nodes applications. Regarding harvested energy, the proposed circuit with a storage capacitor of 0.24 F can obtain 320% improved performance than standard energy harvesting along with the lowest power consumption of 0.25 µW in self-powered operation. The proposed technique can also be applied to similar piezoelectric energy harvesting strategies with large inductors.

Design Wallace Tree Multiplier Using Reversible Gates

Abstract: Multipliers are essential components in digital circuit design. They have wide spread applications in digital signal processing and communication systems. Circuit designers in VLSI design seek compact, small scale circuits with low power consumption and minimal delay. The Wallace tree multiplier is an advanced version of tree based multipliers. Numerous algorithms have been developed to create the fastest multipliers, and the Wallace tree multiplier is one such example. It utilizes reversible compressors, full adders, and half adders. The results demonstrate that the Wallace tree multiplier using reversible gates outperforms traditional multipliers in terms of power dissipation and delay, with values of 0.027W and 29.327nS re­spectively.

New exponentially weighted inequality and its application to finite-time stability of descriptor time-delay systems

The paper considers the finite-time stability of descriptor time-delay systems. Firstly, we establish a set of exponentially weighted orthogonal polynomials, and based on this, we obtain a new exponentially weighted inequality(EWI). Secondly, an augmented Lyapunov-like functional (LLF) is established driven by the presented EWI. Finally, by applying the EWI and the LLF, some sufficient conditions for ensuring the finite-time stability of the system are obtained. A small partial element equivalent circuit model and two numerical examples are used to verify the effectiveness of the method.

An Integrated All‐Optical Ising Machine with Unlimited Spin Array Size and Coupling

Artificial spin systems are used to solve combinatorial optimization problems by mapping them to the ground state search of the Ising model. Ising machines of various designs, in fields as diverse as photonics and electronics, have been proposed and demonstrated in recent years. One important mathematical operation required in photonic Ising machines for nearly all Hamiltonian minimization algorithms is repeated matrix–vector multiplication with the vector size limited by the physical size of the optical system. Herein, an integrated photonic Ising machine based on matrix partitioning and phase encoding, which effectively allows the use of physically small optical circuits to handle arbitrarily large spin vectors, is proposed. All the complicated calculations are carried out optically, rather than electronically, in this approach. How the required surface area of a hypothetical chip‐based photonic Ising machine can be resized according to convenience, with the trade‐off being the solution time, and that this trade‐off can be done in a way that does not impact the algorithm's efficacy, is shown. Through simulation, the system is predicted to have a high success rate, high noise tolerance, and high error tolerance.

Small RNA-modulated anaerobic respiration allows bacteria to survive under antibiotic stress conditions.

Despite extensive knowledge of antibiotic-targeted bacterial cell death, deeper understanding of antibiotic tolerance mechanisms is necessary to combat multi-drug resistance in the global healthcare settings. Regulatory RNAs in bacteria control important cellular processes such as cell division, cellular respiration, metabolism, and virulence. Here, we investigated how exposing Escherichia coli to the moderately effective first-generation antibiotic cephalothin alters transcriptional and post-transcriptional dynamics. Bacteria switched from active aerobic respiration to anaerobic adaptation via an FnrS and Tp2 small RNA-mediated post-transcriptional regulatory circuit. From the early hours of antibiotic exposure, FnrS was involved in regulating reactive oxygen species levels, and delayed oxygen consumption in bacteria. We demonstrated that bacteria strive to maintain cellular homeostasis via sRNA-mediated sudden respiratory changes upon sublethal antibiotic exposure.

Research on ultrawideband photodetector module based on parasitic parameter regulation.

A four-channel ultrawideband photodetector (PD) module with a size of 26.1 mm ×33.2 mm × 8.5 mm has been demonstrated in our laboratory. We propose a method to improve the bandwidth of the PD module based on compensating parasitic parameters by dual resistance regulation on the P and N terminals of the PD chip. A small signal equivalent circuit model with package matching network is established for the PD module, and the effectiveness of the proposed method and the accuracy of the model are verified by experiments. A four-channel photodetector module with a -3 dB bandwidth of up to 67 GHz is fabricated by using photodetector chips with -3 dB bandwidths of 46 GHz, and the responsivity is up to 0.50A/W.

Distributed quantum neural networks via partitioned features encoding

Quantum neural networks are expected to be a promising application in near-term quantum computing, but face challenges such as vanishing gradients during optimization and limited expressibility by a limited number of qubits and shallow circuits. To mitigate these challenges, an approach using distributed quantum neural networks has been proposed to make a prediction by approximating outputs of a large circuit using multiple small circuits. However, the approximation of a large circuit requires an exponential number of small circuit evaluations. Here, we instead propose to distribute partitioned features over multiple small quantum neural networks and use the ensemble of their expectation values to generate predictions. To verify our distributed approach, we demonstrate ten class classification of the Semeion and MNIST handwritten digit datasets. The results of the Semeion dataset imply that while our distributed approach may outperform a single quantum neural network in classification performance, excessive partitioning reduces performance. Nevertheless, for the MNIST dataset, we succeeded in ten class classification with exceeding 96% accuracy. Our proposed method not only achieved highly accurate predictions for a large dataset but also reduced the hardware requirements for each quantum neural network compared to a large single quantum neural network. Our results highlight distributed quantum neural networks as a promising direction for practical quantum machine learning algorithms compatible with near-term quantum devices. We hope that our approach is useful for exploring quantum machine learning applications.

A Consideration on Amplification Function in BJT Evers-Moll Model and PTT (II) ---- H Parameters in the Small Signal Amplifier Circuit----

A Consideration on Amplification Function in BJT Evers-Moll Model and PTT (II) ---- H Parameters in the Small Signal Amplifier Circuit----

Comprehensive Noise Modeling of Piezoelectric Charge Accelerometer with Signal Conditioning Circuit.

This paper reports on noise modeling of a piezoelectric charge accelerometer with a signal conditioning circuit. The charge output is converted into voltage and amplified using a JFET operational amplifier that has high input resistance and low noise. The noise sources in the whole system include electrical and mechanical thermal noises of the accelerometer, thermal noises of resistors, and voltage and current noises of the operational amplifier. Noise gain of each source is derived from small signal circuit analysis. It is found that the feedback resistor of the operational amplifier is a major source of noise in low frequencies, whereas electrical thermal noise of the accelerometer dominates the rest of spectrum. This method can be used to pair a highly sensitive sensor with a single JFET operational amplifier instead of a multi-stage signal conditioning circuit.

DNA Logic Gates for Small Molecule Activation Circuits in Cells.

DNA-based devices such as DNA logic gates self-assemble into supramolecular structures, as dictated by the sequences of the constituent oligonucleotides and their predictable Watson-Crick base pairing interactions. The programmable nature of DNA-based devices permits the design and implementation of DNA circuits that interact in a dynamic and sequential manner capable of spatially arranging disparate DNA species. Here, we report the application of an activatable fluorescence reporter based on a proximity-driven inverse electron demand Diels-Alder (IEDDA) reaction and its robust integration with DNA strand displacement circuits. In response to specific DNA input patterns, sequential strand displacement reactions are initiated and culminate in the hybridization of two modified DNA strands carrying probes capable of undergoing an IEDDA reaction between a vinyl-ether-caged fluorophore and its reactive partner tetrazine, leading to the activation of fluorescence. This approach provides a major advantage for DNA computing in mammalian cells since circuit degradation does not induce fluorescence, in contrast to traditional fluorophore-quencher designs. We demonstrate the robustness and sensitivity of the reporter by testing its ability to serve as a readout for DNA logic circuits of varying complexity inside cells.

Response fitting in low-cost radiation sensors

To obtain low-cost solar radiation sensors with energy applications, small commercial photovoltaic cells have been adapted. With the purpose to evaluate the resulting thermal drifts, a temperature sensor has been connected to a small electronic circuit. The calibration curve provided by the cells supplier is not useful in this case because it is made for a constant 20ºC temperature. Besides, the additional circuit also affects the results. This work presents not only the design and implementation of the additional elements, together with the data acquisition system, but also the results of the calibration work, the fitting of new incident solar radiation functions regarding the temperature of the cell and the intensity of the resulting current, besides the validation of the results for four of these cells.

SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks.

Investigations in the field of spiking neural networks (SNNs) encompass diverse, yet overlapping, scientific disciplines. Examples range from purely neuroscientific investigations, researches on computational aspects of neuroscience, or applicative-oriented studies aiming to improve SNNs performance or to develop artificial hardware counterparts. However, the simulation of SNNs is a complex task that can not be adequately addressed with a single platform applicable to all scenarios. The optimization of a simulation environment to meet specific metrics often entails compromises in other aspects. This computational challenge has led to an apparent dichotomy of approaches, with model-driven algorithms dedicated to the detailed simulation of biological networks, and data-driven algorithms designed for efficient processing of large input datasets. Nevertheless, material scientists, device physicists, and neuromorphic engineers who develop new technologies for spiking neuromorphic hardware solutions would find benefit in a simulation environment that borrows aspects from both approaches, thus facilitating modeling, analysis, and training of prospective SNN systems. This manuscript explores the numerical challenges deriving from the simulation of spiking neural networks, and introduces SHIP, Spiking (neural network) Hardware In PyTorch, a numerical tool that supports the investigation and/or validation of materials, devices, small circuit blocks within SNN architectures. SHIP facilitates the algorithmic definition of the models for the components of a network, the monitoring of states and output of the modeled systems, and the training of the synaptic weights of the network, by way of user-defined unsupervised learning rules or supervised training techniques derived from conventional machine learning. SHIP offers a valuable tool for researchers and developers in the field of hardware-based spiking neural networks, enabling efficient simulation and validation of novel technologies.

Evaluation of efficacy, outcomes and safety of infant haemodialysis and ultrafiltration in clinical use: I-KID a stepped wedge cluster RCT

Abstract Background Critically unwell babies in intensive care units may develop acute renal failure. Options for renal replacement therapy are limited by their small size and available technology. Objectives To determine the clinical efficacy, outcomes and safety profile of the NIDUS® (a novel infant haemodialysis device) for babies under 8 kg, compared with current renal replacement therapy. Design A clinical investigation using a non-blinded cluster stepped wedge design with paediatric intensive care units randomised to sequences. Setting Paediatric intensive care units in six UK hospitals. Participants Children under 8 kg who required renal replacement therapy for fluid overload or biochemical disturbance. Interventions Continuous renal replacement therapy was provided by the usual methods: peritoneal dialysis and continuous haemofiltration (during control periods) and by the NIDUS (during intervention periods), a novel device designed for babies with a smaller circuit and filter and volumetric control of ultrafiltration. Main outcome measures Primary outcome was precision of ultrafiltration compared with prescription; secondary outcomes included biochemical clearances, accuracy of reported ultrafiltration and mortality. Data sources Bedside study data collected by weighing bags of fluid entering and leaving the device were entered into the study database along with case descriptors. Some secondary outcome data was collected via the Paediatric Intensive Care Audit Network. Results Ninety-seven participants were recruited by study closure, 62 to control and 35 to intervention. The primary outcome was obtained from 62 control but only 21 intervention patients, largely because of technical difficulties using NIDUS. The analysis comparing the available primary outcomes showed that ultrafiltration with NIDUS was closer to that prescribed than with control: standard deviations controls 18.75, intervention 2.95 (ml/hour), adjusted ratio 0.13, 95% confidence interval (0.03 to 0.71); p = 0.018. The mean clearances for creatinine, urea and phosphate were lower on peritoneal dialysis than NIDUS, which were in turn lower than continuous veno-venous haemofiltration. The variability in the clearances was in the same order. Of the 62 control patients, 10 died (2/62 on peritoneal dialysis; 7/13 on continuous haemofiltration) before discharge from paediatric intensive care unit (16%), compared with 12 out of 35 (34%) in the NIDUS group: p = 0.04, 95% confidence interval for difference (0 to 36%). Harms No important adverse events occurred and the NIDUS has an acceptable safety profile compared with other renal replacement therapies in this critically ill population with multi-organ failure. Mortality was lowest for Peritoneal Dialysis, highest for continuous haemofiltration, with the NIDUS in-between. Only one serious adverse device event which was reported to the Medicines and Healthcare products Regulatory Agency. Conclusions NIDUS works effectively, delivering appropriate blood clearances and accurate, controllable fluid removal (ultrafiltration), indicating that it has an important place alongside other dialysis modalities for infant renal replacement therapy. Future work Findings from this study indicate some modifications are required to NIDUS to improve usability. Further studies on use of the NIDUS device in other populations of babies for example those with chronic renal failure, and long-term outcomes are required. Trial registration This trial is registered as ISRCTN 13787486. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation Programme (NIHR award ref: 14/23/26) and is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 1. See the NIHR Funding and Awards website for further award information.

Complex rhythm and synchronization of half-center oscillators under electromagnetic induction

<p>Half-center oscillators are typical small circuits that are crucial for understanding CPG. The complex rhythms of CPG are closely related to certain diseases, such as epilepsy. This paper considered the influence of electromagnetic induction on the discharge mode of the half-center oscillators. First, we analyzed the response of individual firing neuron rhythms to electromagnetic induction when the slow-variable parameters vary. We also discussed the changes in the dynamic bifurcation structure when the intensity of electromagnetic induction varies. Furthermore, we determined the effects of mutually inhibitory and self-inhibitory synaptic parameters on the firing rhythm of the half-center oscillators. The different responses induced by electromagnetic induction interventions, showed that mutually inhibitory synapses modulate the firing rhythm weakly and self-inhibition synapses have a significant impact on firing rhythm. Finally, with the change of synaptic parameter values, the combined effects of autapse and mutually inhibitory synapses on the discharge rhythm of half-center oscillators were analyzed in symmetric and asymmetric autapse modes. It was found that the synchronous state of the half-center oscillators had a more robust electromagnetic induction response than the asynchronous state.</p>