Output Circuit Research Articles

Convergent Comodulation Reduces Interindividual Variability of Circuit Output.

Ionic current levels of identified neurons vary substantially across individual animals. Yet, under similar conditions, neural circuit output can be remarkably similar, as evidenced in many motor systems. All neural circuits are influenced by multiple neuromodulators, which provide flexibility to their output. These neuromodulators often overlap in their actions by modulating the same channel type or synapse, yet have neuron-specific actions resulting from distinct receptor expression. Because of this different receptor expression pattern, in the presence of multiple convergent neuromodulators, a common downstream target would be activated more uniformly in circuit neurons across individuals. We therefore propose that a baseline tonic (non-saturating) level of comodulation by convergent neuromodulators can reduce interindividual variability of circuit output. We tested this hypothesis in the pyloric circuit of the crab, Cancer borealis Multiple excitatory neuropeptides converge to activate the same voltage-gated current in this circuit, but different subsets of pyloric neurons have receptors for each peptide. We quantified the interindividual variability of the unmodulated pyloric circuit output by measuring the activity phases, cycle frequency, and intraburst spike number and frequency. We then examined the variability in the presence of different combinations and concentrations of three neuropeptides. We found that at mid-level concentration (30 nM) but not at near-threshold (1 nM) or saturating (1 µM) concentrations, comodulation by multiple neuropeptides reduced the circuit output variability. Notably, the interindividual variability of response properties of an isolated neuron was not reduced by comodulation, suggesting that the reduction of output variability may emerge as a network effect.

Design of High-Reliability Low-Dropout Regulator Combined with Silicon Controlled Rectifier-Based Electrostatic Discharge Protection Circuit Using Dynamic Dual Buffer

Overshoot and undershoot caused by the current load impact the accuracy of the required output voltage and circuit performance. The transient response issue in existing low-dropout (LDO) regulators is a dynamic specification that must be addressed at the design stage. This transient response is influenced by system parameters such as stability and gain. The LDO regulator suggested in this study is designed to minimize the change in output voltage by considerably enhancing the gain using a dynamic dual buffer structure. A dynamic dual buffer is utilized to effectively control undershoot and overshoot. Under the conditions that the input voltage range is from 3.3 to 4.5 V, the maximum load current is 300 mA, the output voltage is 3 V, and the output of the proposed LDO regulator with the dynamic dual buffer structure has undershoot and overshoot voltages of 41 mV and 31 mV, respectively. That is, the output voltage of the proposed LDO regulator effectively provided and discharged an additional current suited for the undershoot/overshoot conditions to enhance the transient response characteristics. Furthermore, the electrostatic discharge (ESD) robustness characteristics of the proposed LDO regulator improved because of the silicon-controlled rectifier underlying the ESD protection device embedded in the output node and power line.

Overlapping Central Clock Network Circuitry Regulates Circadian Feeding and Activity Rhythms in Drosophila.

The circadian system coordinates multiple behavioral outputs to ensure proper temporal organization. Timing information underlying circadian regulation of behavior depends on a molecular circadian clock that operates within clock neurons in the brain. In Drosophila and other organisms, clock neurons can be divided into several molecularly and functionally discrete subpopulations that form an interconnected central clock network. It is unknown how circadian signals are coherently generated by the clock network and transmitted across output circuits that connect clock cells to downstream neurons that regulate behavior. Here, we have exhaustively investigated the contribution of clock neuron subsets to the control of two prominent behavioral outputs in Drosophila: locomotor activity and feeding. We have used cell-specific manipulations to eliminate molecular clock function or induce electrical silencing either broadly throughout the clock network or in specific subpopulations. We find that clock cell manipulations produce similar changes in locomotor activity and feeding, suggesting that overlapping central clock circuitry regulates these distinct behavioral outputs. Interestingly, the magnitude and nature of the effects depend on the clock subset targeted. Lateral clock neuron manipulations profoundly degrade the rhythmicity of feeding and activity. In contrast, dorsal clock neuron manipulations only subtly affect rhythmicity but produce pronounced changes in the distribution of activity and feeding across the day. These experiments expand our knowledge of clock regulation of activity rhythms and offer the first extensive characterization of central clock control of feeding rhythms. Despite similar effects of central clock cell disruptions on activity and feeding, we find that manipulations that prevent functional signaling in an identified output circuit preferentially degrade locomotor activity rhythms, leaving feeding rhythms relatively intact. This demonstrates that activity and feeding are indeed dissociable behaviors, and furthermore suggests that differential circadian control of these behaviors diverges in output circuits downstream of the clock network.

A circuit protection method based on replacing minterms with combinatorial circuits

This paper introduces an integrated circuit camouflage strategy based on the minterms. The modified circuit can be well protected against SAT attacks. The method consists of two main steps: finding minterms and perturbing the circuit. This approach applies to multi-output circuits. This method allows for quick and accurate identification of minterms and the generation of circuits with significantly enhanced output disturbances. Experiments conducted on the OpenSPARC microprocessor using the ISCAS'85 and ISCAS'89 benchmark circuits demonstrate the effectiveness of our method. The time to find a minterm is only about 0.03s. The average correctness of the search is more than 90 %. The combinatorial circuit is resistant to SAT attack after replacement of the minterm. Moreover, the perturbation of circuit outputs significantly surpasses that of the original CamoPerturb structure, with one of the most notable improvements being an enhancement of approximately 330 times.

Tuning the Response of Synthetic Mechanogenetic Gene Circuits Using Mutations in TRPV4.

Conventional gene therapy approaches for drug delivery generally rely on constitutive expression of the transgene and thus lack precise control over the timing and magnitude of delivery. Synthetic gene circuits with promoters that are responsive to user-defined stimuli can provide a molecular switch that can be utilized by cells to control drug production. Our laboratory has previously developed a mechanogenetic gene circuit that can deliver biological drugs, such as interleukin-1 receptor antagonist (IL-1Ra), on-demand through the activation of Transient receptor potential family, vanilloid 4 (TRPV4), a mechanosensory ion channel that has been shown to be activated transiently in response to physical stimuli such as physiological mechanical loading or hypo-osmotic stimuli. The goal of this study was to use mutations in TRPV4 to further tune the response of this mechanogenetic gene circuit. Human iPSC-derived chondrocytes harboring targeted gain-of-function mutations of TRPV4 were chondrogenically differentiated. Both mutants-V620I and T89I-showed greater total IL-1Ra production compared with wild type following TRPV4 agonist treatment, as well as mechanical or osmotic loading, but with altered temporal dynamics. Gene circuit output was dependent on the degree of TRPV4 activation secondary to GSK101 concentration or strain magnitude during loading. V620I constructs secreted more IL-1Ra compared with T89I across all experimental conditions, indicating that two mutations that cause similar functional changes to TRPV4 can result in distinct circuit activation profiles that differ from wild-type cells. In summary, we successfully demonstrate proof-of-concept that point mutations in TRPV4 that alter channel function can be used to tune the therapeutic output of mechanogenetic gene circuits.

Synergy between noisy quantum computers and scalable classical deep learning for quantum error mitigation

We investigate the potential of combining the computational power of noisy quantum computers and of classical scalable convolutional neural networks (CNNs). The goal is to accurately predict exact expectation values of parameterized quantum circuits representing the Trotter-decomposed dynamics of quantum Ising models. By incorporating (simulated) noisy expectation values alongside circuit structure information, our CNNs effectively capture the underlying relationships between circuit architecture and output behaviour, enabling, via transfer learning, also predictions for circuits with more qubits than those included in the training set. Notably, thanks to the quantum information, our CNNs succeed even when supervised learning based only on classical descriptors fails. Furthermore, they outperform a popular error mitigation scheme, namely, zero-noise extrapolation, demonstrating that the synergy between quantum and classical computational tools leads to higher accuracy compared with quantum-only or classical-only approaches. By tuning the noise strength, we explore the crossover from a computationally powerful classical CNN assisted by quantum noisy data, towards rather precise quantum computations, further error-mitigated via classical deep learning.

Assessment on thermophysical properties of nano enhanced heat transfer fluid with hexagonal boron nitride nanoparticles for thermal management of photovoltaic thermal (PVT) system

One of the most promising sources of energy to meet demand and reduce pollution from fossil fuels is solar energy. To maximize energy conversion, solar technology efficiency, whether it comes from thermal systems, photovoltaic panels, or a hybrid known as photovoltaic-thermal (PVT) systems is critical. This work looks into the formulation and thermophysical of hBN-water nanofluids, with an emphasis on how they might be used as coolants in PVT systems to improve electrical performance. After a meticulous preparation process, the nanofluids exhibited exceptional stability, confirmed through visual inspection and zeta potential evaluation. Zeta potential analysis revealed consistent values across different temperatures and volume concentrations. Density decreased with temperature, while viscosity increased with volume concentration but decreased with temperature. Thermal conductivity showed a consistent increase with volume concentration and temperature. Through optimization, the 0.5 vol% concentration was identified as optimal for the PVT system. Compared to no coolant and water-based coolant scenarios, hBN-water nanofluids effectively regulated cell temperatures between 40.25°C and 46.34°C, demonstrating superior thermal conductivity and heat transfer properties. Moreover, the nanofluid coolant enhanced the PVT system's electrical performance. Open circuit voltage remained consistent (19.67 V to 20.81 V), short circuit current and output power improved with higher irradiance levels, and electrical efficiency, thermal efficiency and overall efficiency reached 5.73–5.88 %, 54.15–62.73 % and 59.88–68.62 % respectively These findings underscore the potential of hBN-water nanofluids in enhancing thermal management and electrical performance in solar energy systems. By minimizing thermal losses and maximizing electrical output, nanofluid coolants offer promising avenues for optimizing the efficiency of renewable energy technologies.

SAFE LOCK SECURITY PROTOTYPE USING FINGERPRINT AND PIN

In general, security safe box is currently still using key combination that has some drawbacks. One of the weaknesses is the risk of lossing of the keys and forgetting the lock codes that impact at users of not being able to operate the safe box. Therefore, the researcher is designing a tool that is more practical and safe that can replace the key of security safe box. In the research, the researcher uses the method of fingerprint and a PIN by designing a circuit input tool using the output fingerprint sensor and keypad. For the series of processor,the researcher applies the Arduino microcontroller Uno R3 while for series output the researcher applies three output circuit such as LCD, Buzzer, and Selenoids. Security tool safe box can only be operated by entering PIN and fingerprint which have been registered. Hence, the users do not need to save an object to operate the safe box. Based on the results of testing, this tool is able to open the safe box by inputting 5 registered ID interchangeably as much as 25 times. The Solenoids then are able to operate when they are given voltage > 9, 5V 350 mA to 12V 800 mA.

An Algorithm for Reversible Logic Circuit Synthesis Based on Tensor Decomposition

An algorithm for reversible logic synthesis is proposed. The task is, for a given n -bit substitution map, to find a sequence of reversible logic gates that implements the map. The gate library adopted in this work consists of multiple-controlled Toffoli gates with m control bits, where m ∈ {0, …, n − 1}. Controlled gates with large m (> 2) are then further decomposed into smaller gates ( m ≤ 2). A primary goal in designing the algorithm is to reduce the number of Toffoli gates which is known to be universal. The main idea is to view an n -bit substitution map as a rank-2 n tensor, and to transform it such that the resulting map can be written as a tensor product of a rank-(2 n − 2) tensor and the 2 × 2 identity matrix. It can then be seen that the transformed map acts nontrivially on n − 1 bits only, meaning that the map to be synthesized becomes ( n − 1)-bit substitution. This size reduction process is iteratively applied until it reaches a tensor product of only 2 × 2 matrices. The time complexity of the algorithm is exponential in n as most previously known heuristic algorithms for reversible logic synthesis are, but it terminates within reasonable time for not too large n which may find practical uses. As stated earlier, our primary target is to reduce the number of Toffoli gates in the output circuit. Benchmark results show that the algorithm works well for hard benchmark functions, but it does not seem advantageous when the function is structured. As an application, the algorithm is applied to find reversible circuits for cryptographic substitution boxes, which are often required in quantum cryptanalysis.

Analysis of Energy During Spark Discharge in Short Circuit of Capacitive Circuit

ABSTRACT In the explosive environments, the electrical sparks with short circuit of capacitor output will ignite the surrounding explosive medium, and the relationship between effective ignition energy, capacitance energy conversion rate, capacitor capacitance and capacitor terminal voltage is unclear. Based on the three stages of breakdown, discharge and attenuation of the discharge characteristic curve of capacitive circuit, this paper deduces the expressions of spark resistance and discharge current including voltage, capacitance and polar distance parameters. Based on the programmable micro-nano gap discharge experimental platform, experiments under different voltages and different capacitances were carried out. It is found that the theoretical voltage-current curve and spark energy conversion rate are in good agreement with the experimental fitting curve and spark energy conversion rate. And with the increase of capacitance or voltage, the peak current and discharge time increase, which leads to the increase of energy. The conversion rate of spark energy decreases with the increase of capacitance or voltage, and the conversion rate changes between 20% and 30%. The change of spark energy conversion rate is also related to spark resistance, and with the decrease of spark resistance, the spark energy conversion rate also decreases. This study can further understand the energy variation in the discharge gap and is of great significance for the research of intrinsically safe circuits.

Design and Performance Analysis of Photovoltaic Power Generation Light Emitting Diode Device

This research focuses on an independent photovoltaic power generation system with supercapacitor energy storage as the study subject. The model is simplified, and the photovoltaic power generation light emitting diode (LED) device is designed based on the given parameters. This system selects a Boost-type step-up converter for the supercapacitor charging circuit, capable of achieving Maximum Power Point Tracking (MPPT). The supercapacitor discharge circuit employs an LM2596 series regulator to power the rated load LED (12 V, 1 W), providing good linear regulation capability. In designing other hardware components of the device, the characteristics of a photoresistor are utilized to implement an automatic load switch circuit. An efficient single-chip integrated circuit LM2575 series is used as a regulator to convert the voltage across the supercapacitor terminals to +15 V and +5 V. Considering the need to collect the output voltage of the photovoltaic cell array and the voltage across the supercapacitor terminals, a resistor divider method is used to sample these two voltages. The sampling circuit includes a resistor divider circuit and a linear optocoupler isolation circuit. An HNC-25LTS series Hall current sensor is used for current sampling to measure AC, DC, and pulse signals under electrical isolation conditions. The supercapacitor, solar photovoltaic panel, control unit, and the main circuit for supercapacitor charging and discharging have been assembled in the experiment. Connecting the charging and discharging circuit with the photovoltaic panel and LED, the system provides power to the LED during the night. Under varying light intensity and temperature conditions, the photovoltaic output voltage waveform, PWM waveform, and Boost circuit output voltage waveform remain stable when reaching the MPPT point. The output power with added MPPT control at different times is compared. The results indicate that the designed system has effectively achieved the functionality of MPPT for solar energy.

Rapid ultrasensitive and specific BNP biosensor with LED readout.

Biosensing for diagnostics has risen rapidly in popularity over the past decades. With the discovery of new nanomaterials and morphologies, sensitivity is being constantly improved enough for reliable detection of trace biomarkers in human samples, like serum or sweat. This precision has enabled detailed research on the efficacy of biosensors. However, current biosensors suffer from reduced speed of operation. To make better use of this sensitivity, the development of a conductometric biosensor with in-situ use of an Laser Emitting Device (LED) display can provide rapid determination of sample results, steadily pushing biosensors toward more clinical, point-of-care (POC) applications. In this research, a simple LED was used for facile optical determination and visual output of an ultrasensitive bio-signal amplification circuit was made to interface with a B-type Natriuretic Peptide (BNP) biosensor. Tuning circuit gain enables an elegant method for adjustable separation of concentrations into 3 discrete categories: sub-threshold, analog, and saturation regions. These regions corresponded to 0 < [C] < 500pg/mL (25, 100, 250pg/mL, LED off), 500 < [C] < 1000pg/mL (LED varying intensity), and 1000pg/mL < [C] (LED full intensity). System efficacy was tested using human blood serum samples from University of Pittsburgh Medical Center patients, which were able to be accurately detected and sorted for rapid low cost and power. determination without need for complex digital elements. Additional specificity testing suggests insignificant impact of non-target biomarkers.

Neuronal Subtypes and Connectivity of the Adult Mouse Paralaminar Amygdala.

The paralaminar nucleus of the amygdala (PL) comprises neurons that exhibit delayed maturation. PL neurons are born during gestation but mature during adolescent ages, differentiating into excitatory neurons. These late-maturing PL neurons contribute to the increase in size and cell number of the amygdala between birth and adulthood. However, the function of the PL upon maturation is unknown, as the region has only recently begun to be characterized in detail. In this study, we investigated key defining features of the adult mouse PL; the intrinsic morpho-electric properties of its neurons, and its input and output circuit connectivity. We identify two subtypes of excitatory neurons in the PL based on unsupervised clustering of electrophysiological properties. These subtypes are defined by differential action potential firing properties and dendritic architecture, suggesting divergent functional roles. We further uncover major axonal inputs to the adult PL from the main olfactory network and basolateral amygdala. We also find that axonal outputs from the PL project reciprocally to these inputs and to diverse targets including the amygdala, frontal cortex, hippocampus, hypothalamus, and brainstem. Thus, the adult mouse PL is centrally placed to play a major role in the integration of olfactory sensory information, to coordinate affective and autonomic behavioral responses to salient odor stimuli.

Green energy generation via water splitting by non-photocatalytic process based on Gd doped magnesium ferrite hydroelectric cells

This study presents compelling empirical evidence that highlights the notable effect of increased defect concentration on the splitting of water molecules in MgGdxFe2-xO4 hydroelectric cells (HECs), which serve as a promising avenue for environmentally friendly electricity generation. The MgGdxFe2-xO4 (x = 0.00, 0.02, 0.06 and 0.10) series for eco-friendly electricity generation has been successfully synthesized using a facile sol-gel technique. These materials exhibit the characteristic spinel cubic structure with the Fd-3 m space group confirmed using Rietveld analysis of X-ray diffraction plots. A maximum strain of 7.49×10-3 was observed in the MgGd0.10Fe1.90O4, indicating the formation of a probable defective structure. Moreover, the X-ray density values decrease with increase in Gd concentration, suggesting an increase in porosity of the samples. Langmuir hysteresis curve follows a typical type IV pattern further consolidating the mesoscopic nature of the materials. Furthermore, the conclusive identification of defects, particularly oxygen vacancies, through Photoluminescence and X-ray Photoelectron Spectroscopic (XPS) studies, holds an utmost importance in facilitating the water dissociation process at room temperature by ferrites. Impedance spectroscopy has been employed to scrutinize the contribution of small polaron hopping-assisted ionic conduction to the observed improved current density. The maximum short circuit output current of 6.8, 10.1, and 13.4 mA was achieved for MgGd0.02Fe1.98O4 (GMF-1), MgGd0.06Fe1.94O4 (GMF-2) and MgGd0.10Fe1.90O4 (GMF-3) HECs, respectively. The corresponding open cell voltage values were found to be 0.85 V, 0.92 V and 0.98 V. Off-load peak power output was 5.78 mW, 9.29 mW, and 13.13 mW, respectively. The estimated Gibbs free energy values were -164.02, -177.53, and −189.11 kJ/mol. MgGdxFe2-xO4-based hydroelectric cells possess the advantages of being cost-effective and yielding eco-friendly by-products without reliance on photocatalytic activity, acid/alkali solutions, or electrolytes. The incorporation of Gadolinium (Gd) dopant not only enhances the hydroelectric current but also contributes to sustained current generation over extended periods. The extended operational lifespan of the cell positions it as an excellent candidate for practical applications

Iron and neuromelanin imaging in basal ganglia circuitry in Parkinson's disease with freezing of gait

ObjectiveThis study aimed to examine the structural alterations of the deep gray matter (DGM) in the basal ganglia circuitry of Parkinson's disease (PD) patients with freezing of gait (FOG) using quantitative susceptibility mapping (QSM) and neuromelanin-sensitive magnetic resonance imaging (NM-MRI). MethodsTwenty-five (25) PD patients with FOG (PD-FOG), 22 PD patients without FOG (PD-nFOG), and 30 age- and sex-matched healthy controls (HCs) underwent 3-dimensional multi-echo gradient recalled echo and NM-MRI scanning. The mean volume and susceptibility of the DGM on QSM data and the relative contrast (NMRC-SNpc) and volume (NMvolume-SNpc) of the substantia nigra pars compacta on NM-MRI were analyzed among groups. A multiple linear regression analysis was performed to explore the associations of FOG severity with MRI measurements and disease stage. ResultsThe PD-FOG group showed higher susceptibility in the bilateral caudal substantia nigra (SN) compared to the HC group. Both the PD-FOG and PD-nFOG groups showed lower volumes than the HC group in the bilateral caudate and putamen as determined from the QSM data. The NMvolume-SNpc on NM-MRI in the PD-FOG group was significantly lower than in the HC and PD-nFOG groups. Both the PD-FOG and PD-nFOG groups showed significantly decreased NMRC-SNpc. ConclusionsThe PD-FOG patients showed abnormal neostriatum atrophy, increases in iron deposition in the SN, and lower NMvolume-SNpc. The structural alterations of the DGM in the basal ganglia circuits could lead to the abnormal output of the basal ganglia circuit to trigger the FOG in PD patients.

Homeostatic regulation of circuit activity through temperature-sensing rather than activity-sensing

Electrical signaling between nerve cells coordinates essential functions of the body. However, neurons face their own regulatory challenges, as they need to produce relatively constant activity patterns as the environment changes. Like homeostatic control that occurs within other organ systems, neural circuits are subject to homeostatic regulation as well, whereby deviations from a set point level of activity elicit corrective responses to maintain circuit output. To achieve this end, neural systems are thought to sense variables that relate to neuronal activity, such as firing rate, ion concentrations, and neuromodulators, to detect when an imbalance in activity occurs. While recording rhythmic motor activity associated with breathing in the American bullfrog, we made the surprising observation that acute cooling to 10°C silenced network activity, but after several minutes, bursting resumed and then overshot baseline frequency upon rewarming. Burst frequency drifted back to the initial value over tens of minutes, consistent with the fast regulation of an activity set point (n=9). Time controls with no temperature changes were stable throughout this period (n=7). Exposing the network to manipulations inducing similar perturbations that silence activity over the same timescale– hypoxia (n=6), glycine (n=6), and tetrodotoxin (n=4)– did not engage feedback regulation. These results suggested that temperature sensing, rather than activity sensing, controls network output. Cation channels activated by cooling in amphibians (TRPM8, n=4; TRPV3, n=4) and L-type Ca2+ channels (n=4) did not contribute to these responses. Experimental activation of the Na+/K+ pump with monensin, which raises intracellular Na+, reduced the compensation response both in the cold (p=0.01, unpaired t-test) and in the overshoot phase following warming, suggesting that cold-dependent inhibition of the Na+/K+ pump enhances network excitability. Finally, NMDA-glutamate receptors played a role in constraining the amount of compensation, as block of NMDARs with D-APV led to compensation that greatly exceeded those observed in the control experiments (n=5, p&lt;0.0001, unpaired t-test), remaining elevated above baseline upon rewarming in all experiments. The cellular mechanisms by which raising intracellular Na+ with monensin and NMDAR signaling influence compensation responses are ongoing. Overall, these results uncover a temperature-sensitive control system that regulates activity in neural circuits. Our results introduce that some neural systems may replace typical activity sensors with regulatory systems that are instead tuned to environmental cues to defend activity set points. R01NS114514, R15NS112920-01A1. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Learning quantum many-body systems from a few copies

Estimating physical properties of quantum states from measurements is one of the most fundamental tasks in quantum science. In this work, we identify conditions on states under which it is possible to infer the expectation values of all quasi-local observables of a state from a number of copies that scales polylogarithmically with the system&amp;apos;s size and polynomially on the locality of the target observables. We show that this constitutes a provable exponential improvement in the number of copies over state-of-the-art tomography protocols. We achieve our results by combining the maximum entropy method with tools from the emerging fields of classical shadows and quantum optimal transport. The latter allows us to fine-tune the error made in estimating the expectation value of an observable in terms of how local it is and how well we approximate the expectation value of a fixed set of few-body observables. We conjecture that our condition holds for all states exhibiting some form of decay of correlations and establish it for several subsets thereof. These include widely studied classes of states such as one-dimensional thermal and high-temperature Gibbs states of local commuting Hamiltonians on arbitrary hypergraphs or outputs of shallow circuits. Moreover, we show improvements of the maximum entropy method beyond the sample complexity that are of independent interest. These include identifying regimes in which it is possible to perform the postprocessing efficiently as well as novel bounds on the condition number of covariance matrices of many-body states.

AC-Modulated XPS Enables to Externally Control the Electrical Field Distributions on Metal Electrode/Ionic Liquid Devices.

X-Ray Photoelectron Spectroscopy (XPS) has been utilized to extract local electrical potential profiles by recording core-level binding energy shifts upon application of the AC [square-wave (SQW)] bias with different frequencies. An electrochemical system consisting of a coplanar capacitor with a polyethylene membrane (PEM) coated with the Ionic Liquid (IL) N,N-diethyl-N-methyl-N-(2-methoxyethyl) ammonium bis(trifluoromethanesulfonyl)imide (DEME-TFSI) as the electrolyte is investigated. Analyses are carried out in operando, such that XPS measurements are recorded simultaneously with current measurements. ILs have complex charging/discharging processes, in addition to the formation of Electrical Double Layers (EDL) at the interfaces, and certain properties of these processes can be captured using AC modulation within appropriate time windows of observation. Herein, we select two frequencies, namely, 10 kHz and 0.1 Hz, to separate effects of the fast polarization and slow migratory motions, respectively. Moreover, the local potential developments after adding two equivalent series resistors at three different physical positions of the device have been carefully evaluated from the binding energy shifts in the F 1s peak representing the anion of the IL. This circuit modification allows us to quantify the AC currents passing through the device, as well as the system's impedance, in addition to revealing the potential variations due the IR drops. The complex AC-modulated local XPS data recorded can also be faithfully reproduced using the unmodulated F 1s spectrum and by convoluting it with electrical circuit output provided by the LT-Spice software. The outcome of these efforts is a more realistic equivalent circuit model, which can be related to chemical/physical makeup of the electrochemical system. An important finding of this methodology emerges as the possibility to induce additional local electrical field developments within the device, the directions of which can be reversed controllably.

Toffoli-depth reduction method preserving in-place quantum circuits and its application to SHA3-256

When implementing a quantum circuit for a desired reversible function, an attempt is made to design an accurate quantum circuit. Then, the quantum circuit is optimized based on a specific cost function, such as design cost, width (the number of qubits), depth, etc. In particular, if an in-place subcircuit itself can be optimized while maintaining its in-place property, it will be a very useful way to increase efficiency without changing the initial architecture of the entire quantum circuit. Furthermore, since its (clean) work qubits can easily be utilized in subsequent subroutines of the quantum circuit, it has an additional important advantage in terms of width. In this paper, for the first time to the best of our knowledge, we present a global Toffoli-depth reduction methodology for an in-place version reversible circuit in the case that the given input circuit is optimized with Toffoli-count. We mainly introduce a process to optimize the χ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\chi $$\\end{document} internal function block in SHA3-256 to explain our Toffoli-depth reduction approach preserving the in-place property, and hence, well-balanced five χ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\chi $$\\end{document} quantum circuits are induced in terms of its width and T-depth. And then, we apply these five χ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\chi $$\\end{document} circuits to design the entire SHA3-256 cryptosystem. One of the proposed SHA3-256 quantum circuits has a width of 1600 and a T-depth of 264, and this shows a result of 50% and 38.9% reduction compared to the previous circuit in terms of width and T-depth, respectively. Other versions of our SHA3-256 circuits just required 10–33 qubits per one T-depth compared to the previous results which require over 1800 qubits per T-depth, and so this means that our SHA3-256 circuits are well-balanced. Finally, we constructed Grover’s algorithm circuit using each version that realized SHA3-256. When output circuits for the presented method were used, quantum volume values of Grover’s algorithm circuits became 33 and 50%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} of the value when the input circuit was used.

ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems

In the proposed paper, an artificial neural network (ANN) algorithm is applied to predict the electronic circuit outputs of voltage signals in Industry 4.0/5.0 scenarios. This approach is suitable to predict possible uncorrected behavior of control circuits affected by unknown noises, and to reproduce a testbed method simulating the noise effect influencing the amplification of an input sinusoidal voltage signal, which is a basic and fundamental signal for controlled manufacturing systems. The performed simulations take into account different noise signals changing their time-domain trend and frequency behavior to prove the possibility of predicting voltage outputs when complex signals are considered at the control circuit input, including additive disturbs and noises. The results highlight that it is possible to construct a good ANN training model by processing only the registered voltage output signals without considering the noise profile (which is typically unknown). The proposed model behaves as an electronic black box for Industry 5.0 manufacturing processes automating circuit and machine tuning procedures. By analyzing state-of-the-art ANNs, the study offers an innovative ANN-based versatile solution that is able to process various noise profiles without requiring prior knowledge of the noise characteristics.