Dynamic Tuning Research Articles

Synaptic-like plasticity in 2D nanofluidic memristor from competitive bicationic transport.

Synaptic plasticity, the dynamic tuning of signal transmission strength between neurons, serves as a fundamental basis for memory and learning in biological organisms. This adaptive nature of synapses is considered one of the key features contributing to the superior energy efficiency of the brain. Here, we use molecular dynamics simulations to demonstrate synaptic-like plasticity in a subnanoporous two-dimensional membrane. We show that a train of voltage spikes dynamically modifies the membrane's ionic permeability in a process involving competitive bicationic transport. This process is shown to be repeatable after a given resting period. Because of a combination of subnanometer pore size and the atomic thinness of the membrane, this system exhibits energy dissipation of 0.1 to 100 aJ per voltage spike, which is several orders of magnitude lower than 0.1 to 10 fJ per spike in the human synapse. We reveal the underlying physical mechanisms at molecular detail and investigate the local energetics underlying this apparent synaptic-like behavior.

A 2DEG‐Based GaN‐on‐Si Terahertz Modulator with Multi‐Mode Switchable Control

AbstractAs terahertz (THz) technology has been widely considered as a key candidate for future sixth‐generation wireless communication networks, THz modulators show profound significance in wireless communication, data storage, and imaging. In special, dynamic tuning of THz waves through 2D electron gas (2DEG) incorporated with a hybrid design of metasurface has attracted keen interest due to the combined merits of deliberate structural design, rapid switching speed and the process compatibility. Current meta‐modulator enables very high modulation depth but encounter limited bandwidth. In this paper, by taking into account the co‐functional effects of temperature and voltage‐dependent dynamic control on transmission amplitude, a 2DEG‐based GaN‐on‐Si modulator with two switchable operational states (or four modes) of active wave control is proposed. Under cryogenic temperature conditions, the proposed device exhibits prominent 2D plasmons characteristics with switchable transitions between the gated mode and ungated mode for active control. Under room temperature conditions, the proposed device exhibits non‐resonance broadband spectra characteristics with tunable transitions between the linearity mode and depletion mode for transmission control. The scheme provides an option for the development of the actively tunable THz meta‐modulator and paves a way for the robust multifunctionality of electrically controllable THz switching, and biosensors.

Exploiting reprogrammable nonlinear structural springs for enhancing the manoeuvrability of a vibro-impact capsule robot

Vibro-impact capsule robots, propelled by rhythmic collisions of an internal mass triggered by an external magnetic field, are emerging as promising tools for minimally invasive surgery. This innovative actuation mechanism allows for delicate interaction with tissues, making them ideal candidates for navigating confined surgical spaces. However, their limited manoeuvrability and controllability remain significant hurdles, restricting their ability to navigate complex anatomies and perform precise interventions, ultimately hindering their broader clinical applications. This paper investigates the integration of reprogrammable structural springs in capsule robots, demonstrating how dynamic tuning can tailor the interaction between the inner mass and the capsule, thereby unlocking enhanced manoeuvrability and precise control of the capsule robot. A mathematical model describing the dynamic response of a vibro-impact capsule robot integrated with von Mises trusses (VMT), which are used to tailor the interaction between the inner mass and the capsule, is developed and verified using finite element modelling. Using the verified mathematical model, we explore how the transition between mono-stability and bi-stability of VMTs affects the capsule robot’s propelling performance. Our findings demonstrate that this state switch enables four distinct propulsion modes of the capsule robot. This work paves the way for a new paradigm in small-scale robot design by incorporating reprogrammable nonlinear structures. These structures empower the robots with unprecedented manoeuvrability and controllability within a compact, deployable form factor.

Revealing Enhanced Optical Modulation and Coloration Efficiency in Nanogranular WO3 Thin Films Through Precursor Concentration Modifications

Electrochromic (EC) materials allow for dynamic tuning of optical properties via an applied electric field, presenting great potential in energy-efficient technologies, such as smart windows for effective light and temperature regulation. The precise control of precursor concentration has proven to be a powerful approach in tailoring the physicochemical properties of semiconducting metal oxides. In this study, we employed a one-step electrodeposition technique to fabricate tungsten oxide (WO3) thin films, systematically exploring how varying precursor concentrations influence the material’s characteristics. X-ray diffraction analysis revealed significant changes in diffraction patterns, reflecting subtle structural modifications due to concentration variations. Additionally, scanning electron microscopy revealed significant changes in the microstructure, showing a progression from small nanogranules to larger agglomerations within the film matrix. The W-25 mM thin film delivered exceptional EC performance, efficiently accommodating lithium ions while showcasing superior EC properties. The optimized electrode, denoted as W-25 mM, showcased exceptional EC metrics, featuring the highest optical modulation at 82.66%, outstanding reversibility at 99%, and a notably high coloring efficiency of 83.01 cm2/C. These findings emphasize the importance of precursor concentration optimization in enhancing the EC properties of WO3 thin films, contributing to the advancement of high-performance, energy-efficient materials.

Tunable mid-infrared absorber based on Dirac metamaterials

Abstract In this paper, a tunable metamaterial absorber based on Dirac semimetal is proposed, which consists of three different structures, from top to bottom, namely a double semicircular Dirac semimetal resonator, a silicon dioxide (SiO2) substrate, and a continuous vanadium dioxide (VO2) reflector layer. When the Fermi energy level of the Dirac semimetal is 10\ meV, the absorber absorbs more than 90% in the 39.06-84.76\ THz range. Firstly, taking advantage of the tunability of the conductivity of the Dirac semimetal, the dynamic tuning of the absorption frequency can be achieved by changing the Fermi energy level of the Dirac semimetal without the need to optimise the geometry and remanufacture the structure. Secondly, the structure has been improved by the addition of the phase change material VO2, resulting in a much higher absorption performance of the absorber. Since vanadium dioxide is a temperature-sensitive metal oxide with an insulating phase below the phase transition temperature (about 68℃) and a metallic phase above the phase transition temperature, this paper also analyses the effect of vanadium dioxide on the absorptive performance at different temperatures, with the aim of further improving the absorber performance.

Dynamic tunable triple-band terahertz perfect absorber based on graphene metamaterial

This work developed a graphene-based perfect absorber with dynamic tuning capabilities. This absorber consists of a metal substrate, a layer of graphene square rings, a layer of graphene disks, and two layers of SiO2 dielectric sandwiched between them. Simulation results demonstrate absorption peaks at 1.23 THz, 4.2 THz, and 6.38 THz, with respective absorption rates of 99.9 %, 99.8 %, and 98.3 %. By analyzing the distributions of the electric field and surface current at the central frequencies of the three absorption peaks, the excitation mechanism of each absorption peak was discussed. It was found that by adjusting graphene's chemical potential, the absorption spectrum can be actively controlled. Furthermore, the absorber is insensitive to polarization and can sustain the stability of the absorption spectrum within an incident angle range of 0∼35°. This work has the potential to advance the research of multi-band terahertz absorbers, dynamically tunable devices, and other graphene-based photonic devices.

Detection of carbon monoxide using a polarization-multiplexed erbium dual-comb fiber laser

Abstract We present a simple method to develop a compact, reliable, and robust free-running erbium single-cavity dual-comb (DC) laser via polarization multiplexing. The key features of our design include dynamic tuning in the difference in repetition rates of the laser, long-term stability, and the use of off-the-shelf components. Polarization multiplexing exploits the fast and slow axes of the fiber, while modelocking is achieved through a nonlinear amplifying loop mirror scheme using readily available components. The laser operates at a repetition rate of around 74.74 MHz with a tuning capability in the difference in repetition rates from 500 Hz to 200 kHz. This tunability makes the system more flexible for DC spectroscopy experiments. Consequently, using this laser, we demonstrated a proof-of-principle DC spectroscopy of carbon monoxide, operating without any active stabilization.

Enhanced four-band absorption in a tunable graphene subwavelength grating structure for photodetection

This paper investigates a sub-wavelength multilayer dielectric grating structure based on a simple metasurface, achieving dynamically switchable four-band absorption enhancement. This enhancement spans the visible to near-infrared (NIR) range, with absorption exceeding 90%, representing nearly a 40-fold increase over the intrinsic absorption of a single graphene layer. The contributions of guided-mode resonance (GMR), optical Tamm state (OTS), and photonic crystal defect cavity resonance to the absorption spectra are analyzed separately, offering a method for independent multi-channel signal extraction. Additionally, we explore the impact of structural parameters on the absorption response and examine the broad-spectrum multiband detection mechanism. This mechanism, explained through the integration of resonant cavity perturbation theory and the dynamic tuning of absorption efficiency via graphene's Fermi energy levels, results in an independently tunable optical system for four key operating bands. These properties make the structure suitable for applications such as multiband biomedical photodetectors or components in optoelectronic devices with multiple operating bands.

Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study

The increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, heterogeneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments.

An improved takagi - sugeno variable step size perturb and observe MPPT algorithm based lead acid battery charge controller for photovoltaic system

The main objective of photovoltaic system controller is to reduce the price of the kWh compared to traditional fuel-based electricity. Thus, the improvement of simple and low – cost controllers is a major challenge. Perturb and Observe (P&O) is the commonly used algorithm in order to ensure the power maximization of the photovoltaic system, due to its simplicity and effectiveness. However, on the main drawbacks of that algorithm is the fixed step size of perturbation, due to working environmental changes, which require dynamic tuning of the controller parameter. In order to overcome the cited disadvantage, a new Takagi - Sugeno variable step P&O is proposed. Based on the real-time measurements of the solar radiation and temperature, the proposed fuzzy system schedules the appropriate step size based on human expertise. A zero order Takagi – Sugeno with singleton output is proposed. The fuzzification were carried out using triangular and trapezoidal membership functions. The centroid defuzzification method based 25 If -Then rules are adopted. In order to verify the effectiveness of the improved P&O, three scenarios were used. The obtained results show the effectiveness of the proposed controller under shaded conditions and various temperatures.

Enhanced permeability prediction in porous media using particle swarm optimization with multi-source integration

Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues. However, the complexity of porous media often limits the effectiveness of individual prediction methods. This study introduces a novel Particle Swarm Optimization-based Permeability Integrated Prediction model (PSO-PIP), which incorporates a particle swarm optimization algorithm enhanced with dynamic clustering and adaptive parameter tuning (KGPSO). The model integrates multi-source data from the Lattice Boltzmann Method (LBM), Pore Network Modeling (PNM), and Finite Difference Method (FDM). By assigning optimal weight coefficients to the outputs of these methods, the model minimizes deviations from actual values and enhances permeability prediction performance. Initially, the computational performances of the LBM, PNM, and FDM are comparatively analyzed on datasets consisting of sphere packings and real rock samples. It is observed that these methods exhibit computational biases in certain permeability ranges. The PSO-PIP model is proposed to combine the strengths of each computational approach and mitigate their limitations. The PSO-PIP model consistently produces predictions that are highly congruent with actual permeability values across all prediction intervals, significantly enhancing prediction accuracy. The outcomes of this study provide a new tool and perspective for the comprehensive, rapid, and accurate prediction of permeability in porous media.

All-Optical Control of Vortex Lasing in Silicon-Organic Lattices.

This paper reports a silicon-organic hybrid lattice that can lase with vortex emission and allow all-optical control. We combine an array of amorphous silicon nanodisks with gain from dye molecules in organic solvents to generate vortex lasing from bound states in the continuum under pulsed optical pumping. Irradiating the device with an additional continuous wave green laser beam can cause optical heating in silicon and lead to negative change in the refractive index of the organic solvents; meanwhile, the green laser beam can provide additional gain. Dynamic tuning of the lasing wavelength is achieved by varying the intensity of the controlling beam. Furthermore, the vortex beam lasing can be switched to single-lobed beam lasing by moving the controlling spot to break the in-plane symmetry within the pumping spot. Our findings could shed new light on active silicon topological devices.

Stacking integrated learning-based inverse design of four-nanopore high-Q all-dielectric metasurface

In this paper, we establish a four-nanophole hollowed out all-dielectric metasurface structure as the object of reverse design, which can realize many types of high Q-value Fano resonance effects through dynamic tuning of a single structure, with Q value up to 4401. We used the time domain finite difference method for data collection, and then select appropriate models according to the characteristics of the data from the perspective of the data itself. We select in small sample prediction excellent support vector regression (SVR), gradient promotion decision tree (GBDT), prediction generalization performance and stability of high random forest (RF) as a base learners, with good nonlinear fitting ability BP neural network (BPNN) yuan learners, establish a fusion model based on Stacking integrated learning strategy. Based on this fusion model, the multi-parameter all-dielectric metasurface structure is reverse designed instead. The results show that the proposed method has high prediction accuracy with an absolute average error of only 0.0043, and excellent average accuracy of 82.5%, 77.9% and 14% compared with the combined model GBDT-SVM-BP, GBDT-RF-BP, and RF-SVM-BP. This study provides a new perspective on the reverse design of all-dielectric metasurface structures.

Dynamic near-field and far-field radiation manipulation using a reprogrammable guided-wave-excited metasurface

The dynamic and integrated control of near- and far-field electromagnetic waves is essential for advancing emerging intelligent information technology. Metasurfaces, distinguished by their low-profile design, cost-effectiveness, and ease of fabrication, have successfully revolutionized various electromagnetic functions. However, current research on the dynamic integrated manipulation of near-field and far-field electromagnetic waves using a single metasurface remains relatively constrained, due to the complexity of element-level control, restricted dynamic tuning range, and tuning speed. Herein, we propose an element-level controlled, versatile, compact, and broadband platform allowing for the real-time electronic reconstruction of desired near/far-field electromagnetic wavefronts. This concept is achieved by precisely regulating the 1-bit amplitude coding pattern across a guided-wave-excited metasurface aperture loaded with PIN diodes, following our binary-amplitude holographic theory and modified Gerchberg–Saxton (G–S) algorithm. Consistent findings across calculations, simulations, and experiments highlight the metasurface’s robust performance in 2D beam scanning, frequency scanning, dynamic focusing lens, dynamic holography display, and 3D multiplexing holography, even under 1-bit control. This simplified and innovative metasurface architecture holds the promise of substantially propelling forthcoming investigations and applications of highly integrated, multifunctional, and intelligent platforms.

A terahertz broadband and narrowband switchable absorber based on joint modulation of vanadium dioxide and graphene surfaces

Abstract In this paper, a terahertz broadband and narrowband switchable absorber is proposed. The absorption performance tuning for both broadband and narrowband functions is realized based on the joint modulation of vanadium dioxide (VO2) and graphene surfaces. Concretely, while VO2 is in the metallic state, the absorber achieves broadband absorption function. The overall bandwidth of over 90% absorption is 4.04 THz corresponding to a relative bandwidth of 84%. Through regulating the conductivity of VO2, dynamic tuning of the absorption amplitude is obtained and the modulation depth is 96%. By manipulating the graphene Femi energy and VO2 conductivity simultaneously, dynamic tuning of the absorption bandwidth is realized. In particular, the spectral center frequency of broadband absorption remains stable without drifting during the tuning process. While VO2 is in the insulating state, the absorber achieves narrowband absorption function. Calculated results show that two separate perfect absorption peaks are formed, and the absorption amplitudes are 99.6% and 99.2% respectively. Through regulating the Fermi energy of graphene surface, the dynamic tuning of narrowband absorption frequency is realized. Compared with the ones reported in recent years, our absorber has the advantage on function realization, absorption characteristics and performance tuning.

Increasing the energy efficiency of an electric vehicle powered by hydrogen fuel cells

The article considers fuel cell electric vehicle energy efficiency improvement by means of traction battery remanent state of charge modern control algorithms application which based on fuzzy logic. Fuel cell vehicle traction system used as experimental setup for the study includes brushless dc motor, lithium-ion batteries pack and electronic differential approach implementation. The results of simulation modelling in MATLAB-Simulink software comparing conventional PID-controller based and fuzzy logic based controller for vehicle speed control systems are presented. Fuzzy logic controller provides an electric vehicle dynamic behavior tuning for different driving modes automatically transferring the control system to economy mode when it possible. However, the control system is able to provide fast dynamic behavior when it is required. The proposed control algorithm is tested by means of developed experimental setup. Experimental results validate conducted simulation modelling results and the proposed control algorithm. Application of the proposed fuzzy logic control algorithm to control the electric vehicle dynamic behavior allows energy efficiency improvement and driving range increase.

Dynamics of directional motor tuning in the primate premotor and primary motor cortices during sensorimotor learning

Sensorimotor learning requires reorganization of neuronal activity in the premotor cortex (PM) and primary motor cortex (M1). To reveal PM- and M1-specific reorganization in a primate, we conducted calcium imaging in common marmosets while they learned a two-target reaching (pull/push) task after mastering a one-target reaching (pull) task. Throughout learning of the two-target reaching task, the dorsorostral PM (PMdr) showed peak activity earlier than the dorsocaudal PM (PMdc) and M1. During learning, the reaction time in pull trials increased and correlated strongly with the peak timing of PMdr activity. PMdr showed decreasing representation of newly introduced (push) movement, whereas PMdc and M1 maintained high representation of pull and push movements. Many task-related neurons in PMdc and M1 exhibited a strong preference to either movement direction. PMdc neurons dynamically switched their preferred direction depending on their performance in push trials in the early learning stage, whereas M1 neurons stably retained their preferred direction and high similarity of preferred direction between neighbors. These results suggest that in primate sensorimotor learning, dynamic directional motor tuning in PMdc converts the sensorimotor association formed in PMdr to the stable and specific motor representation of M1.

Non‐Hermitian Skin Effect in Non‐Hermitian Optical Systems

AbstractThe development of non‐Hermitian (NH) physics in nonconservative systems has led to various interesting wave phenomena, including the emergence of the non‐Hermitian skin effect (NHSE). NHSE is a topological effect caused by the nontrivial winding number of the energy spectrum, which results in many bulk eigenmodes localized on low‐dimensional boundaries under open boundary conditions. Although this phenomenon is striking, the flexible and dynamic tuning of NHSE has not been reviewed in optics systems. As an ideal NH platform, an optical system can introduce artificially constructed gains and losses and flexibly design optical structures to control the transmission and regulation of light. This exploration based on NH optical systems deepens the understanding of NHSE and facilitates technological breakthroughs in high‐energy directional localized optics applications. In this review, the focus is on the latest progress and applications of NHSE in NH optical systems, including the basic theory and local property modulation methods. Finally, possible future research directions for NHSE based on NH optical systems are discussed.

Protein Charge Neutralization Is the Proximate Driver Dynamically Tuning Reflectin Assembly.

Reflectin is a cationic, block copolymeric protein that mediates the dynamic fine-tuning of color and brightness of light reflected from nanostructured Bragg reflectors in iridocyte skin cells of squids. In vivo, the neuronally activated phosphorylation of reflectin triggers its assembly, driving osmotic dehydration of the membrane-bounded Bragg lamellae containing the protein to simultaneously shrink the lamellar thickness and spacing while increasing their refractive index contrast, thus tuning the wavelength and increasing the brightness of reflectance. In vitro, we show that the reduction in repulsive net charge of the purified, recombinant reflectin-either (for the first time) by generalized anionic screening with salt or by pH titration-drives a finely tuned, precisely calibrated increase in the size of the resulting multimeric assemblies. The calculated effects of phosphorylation in vivo are consistent with these effects observed in vitro. The precise proportionality between the assembly size and charge neutralization is enabled by the demonstrated rapid dynamic arrest of multimer growth by a continual, equilibrium tuning of the balance between the protein's Coulombic repulsion and short-range interactive forces. The resulting stability of reflectin assemblies with time ensures a reciprocally precise control of the particle number concentration, encoding a precise calibration between the extent of neuronal signaling, osmotic pressure, and the resulting optical changes. The charge regulation of reflectin assembly precisely fine-tunes a colligative property-based nanostructured biological machine. A physical mechanism is proposed.

Liquid microlens compound structure for enhanced sub-micron resolution imaging and accelerated reconfigurable deformation manipulation

The optical observation of sub-micron structures encounters significant challenges due to the inadequate spatial shape matching of optical devices and the limitations imposed by diffraction. These factors result in suboptimal imaging resolution and the introduction of defocus distortion. One approach to mitigating these limitations involves utilizing a liquid microlens (LML) assisted microscope, which offers real-time and localized control capabilities. However, the dynamic tuning of LML is subject to volatility and instability, adversely affecting sub-micron resolution imaging. To address this issue, a contour-following coating inspired by the natural cornea is applied to the droplet's surface, resulting in the formation of liquid microlens compound structures (LMLCS). Firstly, photoresist microholes are fabricated on the optical disc. Then, liquid self-assembly technology is used to fill glycerol in the microholes. At last, a conformal spreading method of UV-curable adhesive precursor film is developed to ensure the combination of the microlens bilayer structure. Through the application of optical information transmission theory, finite difference time domain (FDTD) method, and the principle of Abbe microscopic imaging, the imaging magnification process and sub-micron resolution characteristic of the LMLCS are revealed. Remarkably, the obtained focal width at half maximum (FWHM) of 1.54 μm is smaller than the theoretical value, enabling reconfigurable sub-micron resolution observation and 1.63 times imaging magnification of 226 nm grating lines under the optical microscope. Compared with unencapsulated LML, this compound structure exhibits better sub-micron resolution capability, greater imaging magnification, and faster adaptive dynamic response characteristics. Consequently, the LMLCS introduces exciting prospects for exploring optical sub-micron measurement and sensing devices with exceptional performance.