Search Results (335)

Cycle-to-cycle variability of switching parameters inherent to memristive devices introduces significant problems in the design of neuromorphic systems and non-volatile memory. This study investigates the dynamics of a second-order memristive system incorporating capacitive effects that model parasitic charge within individual memristors, addressing both the technical need for accurate analysis of complex regimes and the demand for exploratory environments. Simulations were performed using CUDAynamics, an interactive software suite developed by the authors, which utilizes parallel computing, primarily via NVIDIA Compute Unified Device Architecture (CUDA). It integrates multiple analysis tools for dynamical systems, including bifurcation diagrams, the largest Lyapunov exponent and periodicity mapping, and interactive navigation in multidimensional parameter spaces. The memristive system was discretized applying multiple integration methods with a fixed time step and various waveforms of the input signal. Analysis tools revealed well-defined regions of chaotic dynamics in the memristor resistance parameter space as functions of input signal properties. Sinusoidal and triangular waveforms produced topologically similar distributions of dynamical regimes, whereas the square waveform, mimicking digital inputs, generated distinct dynamical patterns while still preserving chaotic trajectories under specific conditions. Interactive visualization capabilities of CUDAynamics effectively demonstrate attractor evolution and hysteresis deformation, providing immediate visual feedback that significantly enhances conceptual comprehension of nonlinear feedback mechanisms. Beyond its practical implications for the design of analog and digital memristive devices, CUDAynamics offers a scalable, open-source toolkit to aid researchers and engineers in exploring complex dynamical phenomena. Full article

An operationally simple procedure for the synthesis of 2-alkyl-4-quinolone N-oxides, relying on controlled platinum-catalyzed partial hydrogenation of 2-nitrobenzoyl enamines, has been developed. The Pseudomonas aeruginosa metabolite 2-heptyl-4-quinolone-N-oxide (HQNO) and four analogous products have been prepared in good yield and high purity by this method. All products showed ampholytic properties, with a tendency to form isolable organic-soluble hydrochlorides by switching from the N-hydroxy-4-quinolone to 4-hydroxyquinoline-N-oxide tautomeric form upon partitioning between dichloromethane and 1M aqueous HCl. In basic medium, on the other hand, water-soluble salts of the N-hydroxy-4-quinolone tautomers were formed. NMR measurements indicate pH-dependent equilibrium with fast exchange between the 4-quinolone and the protonated 4-quinolinol tautomer. Full article

Background/Objectives: Inflammation is a critical contributor to the development of diabetic retinopathy (DR). In the early stages of DR, the compromised permeability of the blood–retina barrier facilitates the infiltration of macrophages and the activation of microglia. These specific retinal immune cells can adopt morphologies M1 or M2, linked to pro- or anti-inflammatory responses, respectively. This dual response represents a new therapeutic target against DR progression. This study aimed to investigate the modulation of the response M1/M2 and the molecular mechanism of two algal diterpenoids, rugukadiol A (RK) and ruguloptone A (RL), in the early inflammatory events associated with DR. Methods: LPS-stimulated microglial (Bv.2) and macrophage (RAW264.7) cells and an ex vivo physiological model of DR were used to analyze the effects of RK and RL on M1 and M2 inflammatory markers. Results: Compounds RK and RL, besides decreasing the expression of the M1 pro-inflammatory factors iNOS, Il6 mRNA, and NLRP3 in LPS-stimulated Bv.2 cells, caused enhancements in Arg-1 mRNA and Il10 mRNA expression consistent with the induction of an M2 anti-inflammatory response. RK promoted p38α-MAPK phosphorylation, suggesting a non-classical activation of p38α related to the induction of anti-inflammatory responses. Consistently, treatment of retinal explants of BB rats in the early stages of DR with RL decreased M1 pro-inflammatory mediators and induced M2 anti-inflammatory markers, with a reduction in gliosis and a phenotype switch from activated to resting microglia. Conclusions: This study provides the first evidence of algal diterpenoids attenuating pro-inflammatory mediators and promoting the resolution of inflammation in a diabetic retinopathy context, thus opening the way to further explore this class of marine natural products and analogs for early DR management. Full article

Memristors, as programmable resistive switching devices, offer two fundamental computational modalities for artificial intelligence: static conductance for parallel data processing and dynamic switching for temporal, logical, and stochastic operations. This Review systematically distinguishes these two modalities and evaluates their respective hardware implementations. In terms of our review scope, we first examine how static conductance modality is exploited in analog matrix computing, which encompasses matrix–vector multiplication and matrix equation solving, and discuss how these primitives enable efficient neural network inference and training. Second, we survey dynamic switching modality and its algorithmic applications, including stateful logic for digital in-memory acceleration, attractor networks for associative memory, reservoir computing and spatiotemporal signal processing using transient device dynamics, biologically inspired spike-timing-dependent plasticity, and stochastic computation. In addition, we discuss key challenges such as device variability, stochastic switching, interconnect parasitics, peripheral circuit overhead, and endurance limitations. We also highlight opportunities for future development, emphasizing algorithm–hardware co-design to leverage application-specific error tolerance and mitigate device non-idealities. Finally, we outline promising research directions aimed at realizing robust, scalable, and energy-efficient memristor-based AI systems. Full article

Background/Objectives: Insulin glargine U300 (IGlarU300) is a second-generation, long-acting insulin analog designed to provide a more stable pharmacokinetic profile compared to insulin glargine U100. However, long-term real-world data reflecting its long-term impact on glycemic control and hypoglycemia across diverse populations remain limited. This study evaluated the 24-month clinical outcomes of transitioning to IGlarU300 in a real-world setting. Methods: This retrospective, single-center, observational study enrolled patients with type 1 (T1DM) or type 2 diabetes mellitus (T2DM) who transitioned to IGlarU300 between 2017 and 2021. HbA1c levels, body weight, insulin doses, and hypoglycemia rates were evaluated at baseline and up to 24 months. Results: A total of 242 patients (T1DM: n = 68, T2DM: n = 174) were analyzed. HbA1c levels significantly declined at all follow-up points compared to baseline (mean change at 12 months: −0.85% [95% CI: −1.24 to −0.47%]; p < 0.001]). No significant change in total insulin dose was observed over the one-year follow-up; however, improved glycemic control led to a significant reduction in oral antidiabetic medication use, reflecting successful treatment simplification and a decrease in polypharmacy burden (mean change: -0.50 [95% CI: −0.70 to −0.30]; p < 0.001). Notably, both severe and mild hypoglycemia episodes showed significant reductions (p = 0.010 and p = 0.019, respectively). Switching to IGlarU300 was associated with sustained improvements in glycemic control and a reduction in hypoglycemia rates. Conclusions: These findings suggest that IGlarU300 may be an effective clinical option for optimizing metabolic outcomes, though further controlled studies are warranted to confirm these observational results. Full article

Accurate measurement of spool displacement is essential for achieving high-performance closed-loop control and condition monitoring in hydraulic systems. However, conventional inductive displacement transducers typically rely on sinusoidal excitation and complex analog signal conditioning circuits, resulting in higher hardware cost and limited system integration. To address these issues, this paper proposes a software-based demodulation method for a differential inductive displacement transducer under symmetric complementary square-wave excitation. First, the structure and operating principle of the transducer are analyzed, and an electromagnetic model describing the nonlinear relationship between coil inductance and the position of the inductive core is established, along with its electrical characteristics. Then, a simplified signal acquisition circuit is designed to enable digital extraction of inductance variations using a microprocessor. Compared with conventional approaches, the proposed scheme significantly reduces hardware complexity and cost while being more suitable for embedded system integration. A simulation model is developed to analyze the inductance variation and to validate the proposed hardware circuit. In addition, a test platform is built to conduct static calibration and dynamic response experiments. The experimental results show that the proposed method achieves a linearity of 2.36% and a sensitivity of 155.6 mV/mm and exhibits strong robustness against switching noise. Finally, application tests in a hydraulic valve system demonstrate that the proposed transducer and demodulation method enable accurate and stable spool position measurement, providing a low-cost and easily integrated solution for embedded hydraulic control systems. Full article

The long-term success of dental implants is significantly influenced by primary stability, which is commonly assessed through insertion torque (IT) and removal torque (RT) measurements in vitro. While polyurethane (PU) blocks are accepted by the American Society for Testing and Materials (ASTM) as the standard bone analog material for biomechanical testing, the use of polyethylene (PE) as a bone model material for dental implant research remains limited and not well established. This operator-blinded, in vitro study compared the IT and RT values of dental implants placed in PE and PU blocks of identical density (60 pounds per cubic foot [pcf]; 0.96 g/cm³). A total of 60 tapered dental implants (4.2 × 12 mm, RBM surface, platform switching) were placed into PE (n = 30) and PU (n = 30) blocks by a calibrated operator blinded to the material type. Implant sockets were prepared by an independent surgeon following the manufacturer’s drilling protocol. IT and RT values were recorded using a physiodispenser with torque measurement capability (5–80 N·cm). Statistical analysis was performed using Student’s t-test (α = 0.05), with Mann–Whitney U tests reported as a sensitivity analysis for non-normally distributed variables. No statistically significant difference was observed in IT between PE and PU groups (58.50 ± 8.42 vs. 58.17 ± 9.60 N·cm; p = 0.887; Cohen’s d = 0.04; 95% CI of mean difference: −4.33 to 5.00 N·cm). However, RT was significantly higher in the PU group compared to the PE group (71.17 ± 7.15 vs. 64.33 ± 9.17 N·cm; p = 0.002; Cohen’s d = 0.83; 95% CI: −11.08 to −2.58 N·cm; Mann–Whitney U sensitivity analysis p = 0.004). Under the specific high-density (60 pcf) conditions tested, the absence of a statistically significant IT difference does not constitute formal evidence of equivalence or non-inferiority, and the significantly higher RT in PU indicates that PE and PU are not interchangeable bone analogs. Further studies across a range of densities, implant macrogeometries, and using formal equivalence testing are required before PE can be considered for in vitro dental implant stability research. Full article

Memristors, as transformative electronic devices designed to transcend the von Neumann architecture, enable the physical unification of information storage and computation, thereby offering a foundational hardware pathway toward energy-efficient, brain-inspired computing. Their intrinsic analog resistive switching, non-volatility, and history-dependent learning capabilities allow them to natively implement in-memory computing and emulate synaptic plasticity, addressing the critical bottlenecks of energy and speed in conventional systems. Notably, the evolution from electrically controlled memristors to optoelectronic memristors marks a paradigm shift from pure computing to integrated sensing-processing, opening new dimensions for high-speed, parallel, and adaptive signal processing. In recent years, significant progress has been made in the development of memristor-based neuromorphic vision and tactile systems, on-chip signal processors, and dynamic trajectory trackers, demonstrating their potential in edge intelligence, adaptive robotics, and real-time perceptual tasks. This review systematically summarizes the latest advances in memristor technology, providing a comprehensive analysis of their operating mechanisms, material and structural innovations, and cutting-edge applications in neuromorphic perception and computing. Furthermore, it discusses the key challenges and future directions for the development and integration of memristor-based systems in the post-von Neumann era. Full article

Time-interleaved analog-to-digital converters (TI-ADC) are sensitive to inter-phase timing skew, which degrades effective resolution unless mitigated by careful phase alignment or calibration. This paper presents a low-speed proof-of-concept four-phase TI-ADC based on dual-slope pulse-width modulation, incorporating an adjustable ON-TIME delay mechanism at the analog front end. The proposed approach enables controlled shifting of the effective sampling instant at the comparator/D-flip-flop interface without altering waveform amplitude or functional linearity. A four-phase up/down binary counter implemented using a Gray-code-based phase multiplier provides evenly spaced phases with reduced switching activity. Measurements from a breadboard prototype operating at approximately 1.5 MHz demonstrate that the adjustable ON-TIME delay can align adjacent phases and constrain observed inter-phase timing skew to the order of approximately 30 ns within the measurement resolution. The results indicate that analog front-end phase pre-alignment can complement or relax subsequent digital background calibration in time-interleaved ADC systems. Full article

Fire safety plays a vital role in protecting lives, property, and the environment, and it keeps communities and organizations running safely. Many existing fire pump control systems fall short in educational and small-to-medium industrial settings: they often control only one pump at a time, rely heavily on manual monitoring, and come with high costs that limit accessibility. To address these gaps, we developed an affordable, hands-on educational kit that brings real-world fire safety systems into the classroom using modern automation technology. The system is built around a Delta DVP12SA211R PLC chosen for its built-in real-time clock, integrated RS-232/RS-485 ports for reliable communication, and expanded with DVP16SP11R digital I/O and DVP04AD-S2 analog input modules to interface with simulated sensors mimicking smoke detection and water pressure. Students interact with the system through a Delta DOP-110IS HMI, which features Ethernet connectivity for remote observation, electrical isolation for safe operation, and a 200 ms screen update rate to ensure responsive, realistic feedback. The kit enables learners to explore critical emergency scenarios, including automatic switching between jockey and main pumps, low-pressure alerts, and system failover, transforming theoretical concepts into tangible skills. In user evaluations, 57.1% of students with no prior experience reported that the simulations closely mirrored real-world systems, while 80% of those with a fire safety background found the kit reinforced their existing knowledge; notably, 57.1% of instructors rated it as highly effective for teaching core fire safety principles across diverse learner profiles. By integrating industrial-grade hardware with scenario-based learning, this tool not only deepens understanding of fire protection systems but also better prepares future engineers for the practical demands of fire safety and industrial automation careers. Full article

Frequency-selective attenuation is widely needed in integrated analog front-ends, yet conventional on-chip RC low-pass filters occupy unfeasibly large silicon areas for low-frequency cutoffs and inherently introduce cumulative phase lag. Motivated by the nonlinear, frequency-dependent state evolution of memristive devices, this work experimentally demonstrates a highly compact, capacitor-free memristor–resistor network that functions as a variable-cutoff, zero-phase-lag resistive attenuator. An Au/HfO₂/Au memristor (15 µm × 15 µm) is connected in series with a load resistor and characterized over a wide frequency range. By leveraging the finite time constant of internal ionic drift, the attenuation bandwidth is strictly programmable via the device’s initial resistance. Cutoff frequencies of approximately 10 Hz, 1 kHz, and 10 kHz are achieved for initial resistances of 400 k$\Omega \pm$30 k$\Omega$, 300 k$\Omega \pm$30 k$\Omega$, and 200 k$\Omega \pm$30 k$\Omega$, respectively. Remarkably, the nonlinear state-switching mechanism enables a steep post-cutoff attenuation rate approaching −60 dB/dec—equivalent to a cascaded third-order RC network—using only a single nanoscale device. Rather than functioning as a strictly linear time-invariant (LTI) filter, the proposed circuit operates as a state-adaptive edge-processor. Its inherent amplitude-dependent dynamics and total absence of reactive poles make it exceptionally suited for highly specialized, area-constrained applications, including zero-phase closed-loop noise suppression, frequency-to-amplitude conversion, and amplitude-aware event-driven sensory preprocessing. Full article

This paper presents a secure 2.4 GHz frequency shift keying (FSK) transmitter front-end with minimal overhead on the data stream using analog obfuscation techniques applied to the modulated waveform. An off-chip true random number generator (TRNG) unit is used to generate the required key for the encryption. Moving away from traditional FSK schemes, which benefit from constant local oscillator (LO) frequency within the channel, the proposed secure FSK scheme shifts the LO frequency in very small steps using an innovative capacitor-bank structure with a calibrated digitally controlled oscillator (DCO). The proposed capacitor bank uses a combination of parallel switches and series capacitors to minimize the impact of the layout parasitics on the minimum capacitor in the bank, thereby reliably creating sub-fF unit capacitors. When combined with the proposed capacitor bank, the cross-coupled CMOS LC voltage-controlled oscillator (VCO) forms a digitally controlled oscillator (DCO). The post-layout simulation results of the DCO reveal that the proposed scheme can achieve a resolution of <20 kHz for the LO frequency shifting while maintaining the phase-noise performance. The reported phase shift allows an equivalent entropy > 6 bits in the implemented analog-inspired secure transmitter front-end. Full article

The deployment of Discrete Variable Quantum Key Distribution (DV-QKD) in high-traffic, short-reach environments, such as intra-data center networks, is currently constrained by the saturation of single-photon detectors. While CMOS Single-Photon Avalanche Diodes (SPADs) offer a cost-effective solution, their Secure Key Rate (SKR) is limited by detector dead time. To the best of the authors’ knowledge, this work is the first to derive a generalized detection rate model for SiPMs that addresses the dead-time bottlenecks of gigahertz-rate quantum cryptography. While methods for managing deadtime via active optical switching have been proposed, our model quantifies the benefits of passive spatial multiplexing inherent in standard SiPM arrays. Furthermore, contrasting with models designed to optimize energy resolution or characterize nonlinear charge response to light pulses, our work focuses on maximizing the detection count rate. We derive exact detection rate models for both analog (paralyzable) and digital (non-paralyzable) SiPM architectures, incorporating correlated noise sources such as optical crosstalk and afterpulsing. Simulation results indicate that SiPMs can increase detection rates by over an order of magnitude compared to single SPADs. Full article

For next-generation biomedical and biochemical sensor nodes, the analog front-end demands a direct interface with current-output sensors, extreme miniaturization, and nanowatt power consumption to enable energy autonomy. This work directly addresses these needs by presenting a comparative analysis of four minimalist, first-order, current-mode $\Delta \Sigma$ modulator ($\Delta \Sigma$M) architectures. Optimized for ultra-low-voltage operation (supply $\le 0.5$ V), the investigated topologies—including resistive, switched-capacitor, and current-reference-based cores—exploit passive integration and charge-domain feedback, eliminating the need for power-hungry active blocks. Detailed circuit-level simulations confirm that, with ad hoc techniques, it is possible to achieve stable first-order noise shaping in the deep near-threshold region, delivering up to 10-bit resolution while consuming less than 10 nW at a 0.5 V supply voltage achieving a signal bandwidth in the sub-10 hertz range. This study validates that robust $\Delta \Sigma$ conversion is feasible under extreme area and power constraints by leveraging architectural simplicity. The clear performance–complexity trade-offs outlined make these current-mode architectures ideal candidates for monolithic integration within miniaturized, energy-autonomous sensing systems. Full article

Timing skew is a critical bottleneck in high-speed Time-Interleaved (TI) Analog-to-Digital Converters (ADCs) that severely degrades dynamic range. This paper presents a mathematically rigorous, data-driven synchronization framework for calibrating effective sampling timing in TI-ADCs based on the Kuramoto oscillator model. Conventional clock-alignment methods often fail to capture signal-path mismatches, such as sampling switch aperture delay, while correlation-based techniques suffer from signal-dependent “blind-spot” regions. Overcoming this fundamental limitation without analog complexity is achieved via a fully digital feedback loop where each sub-ADC channel is modeled as a coupled oscillator following discrete-time Kuramoto dynamics. Unlike traditional approaches that rely on auxiliary analog phase detectors, the proposed scheme utilizes the ADC outputs to estimate and correct the effective sampling instants directly. A Lyapunov-based stability analysis proves that global phase synchronization is guaranteed when the adaptive coupling strength exceeds a critical value $K_c$. Theoretical results show that the system ensures exponential convergence of phase alignment, driving the total inter-channel timing error toward zero without relying on input-signal statistics. Behavioral MATLAB R2025a simulations of a 12-bit, 4-channel, 10 GS/s TI ADC confirm the analytical predictions. The proposed Kuramoto-based calibration achieves a residual skew reduction of over 99% and an SFDR improvement of 55.12 dB compared to correlation-based methods, even at blind-spot input frequencies, while adaptively reducing digital control power through dynamic coupling adjustment. The study demonstrates that data-driven, synchronization-based calibration provides an input-independent, energy-efficient, and mathematically verifiable solution for system-level timing correction in TI ADCs. Full article