Search Results (2,183)

Future Artemis-class missions to Mars will expose astronauts to prolonged space radiation (SR), sleep disruption, and operational demands requiring greater autonomy, placing decision making and executive function at heightened risk. Both SR and sleep fragmentation (SF) independently impair cognition, yet their combined effects remain poorly understood. Using the Associative Recognition Memory and Interference (ARMIT) task, we assessed cognitive performance in male rats exposed to 10 cGy of Galactic Cosmic Ray simulation (GCRsim), SF, or both. Under well-rested conditions, GCRsim-exposed rats exhibited overt deficits in the C.1.2 stage, performing at chance when reinforcement contingencies shifted, consistent with impaired cognitive flexibility. In contrast, high-performing GCRsim-exposed rats that initially performed comparably to Sham s revealed latent deficits following a single night of SF. Specifically, the SF-induced loss of C.1.3 performance was accompanied by perseverative errors (persistently selecting outdated cues despite negative feedback), reflecting impaired attentional control and decision updating. Sham s maintained stable performance after SF. These findings support a two-hit vulnerability model in which SR primes corticostriatal and frontoparietal networks for collapse under subsequent sleep disruption. Operationally, this suggests that astronauts may display either persistent or stress-induced deficits, with both modes threatening mission success. Identifying mechanisms of such vulnerabilities is essential for countermeasure development. Full article

Neuronal membrane capacitance (Cm) has traditionally been viewed as a static biophysical property determined solely by the geometric and dielectric characteristics of the lipid bilayer. Recent discoveries have fundamentally challenged this perspective, revealing that Cm exhibits robust circadian oscillations that profoundly influence neural computation and behavior. These rhythmic fluctuations in membrane capacitance are orchestrated by intrinsic cellular clocks through coordinated regulation of molecular processes including transcriptional control of membrane proteins, lipid metabolism, ion channel trafficking, and glial-mediated extracellular matrix remodeling. The dynamic modulation of Cm directly impacts the membrane time constant (τm = RmCm), thereby altering synaptic integration windows, action potential dynamics, and network synchronization across the 24 h cycle. At the computational level, circadian Cm oscillations enable neurons to shift between temporal summation and coincidence detection modes, optimizing information processing according to behavioral demands throughout the day–night cycle. These biophysical rhythms influence critical aspects of cognition including memory consolidation, attention, working memory, and sensory processing. Disruptions in normal Cm rhythmicity are increasingly implicated in neuropsychiatric and neurodegenerative disorders, including depression, schizophrenia, Alzheimer’s disease, and epilepsy, where altered membrane dynamics compromise neural circuit stability and information transfer. The integration of circadian biophysics with chronomedicine offers promising therapeutic avenues, including chronotherapeutic strategies that target membrane properties, personalized interventions based on individual chronotypes, and environmental modifications that restore healthy biophysical rhythms. This review synthesizes evidence from molecular chronobiology, cellular electrophysiology, and systems neuroscience to establish circadian Cm regulation as a fundamental mechanism linking molecular timekeeping to neural computation and behavior. Full article

Adhesively bonded joints between galvanized steel and carbon fiber-reinforced polymers (CFRPs) are critical in modern lightweight structures, but their performance is often limited by failure at the zinc–adhesive interface. This study presents a parametric analysis to investigate the influence of key geometric parameters on interfacial cracking in a single-lap joint (SLJ) configuration, employing a simplified analytical methodology based on Interface Fracture Mechanics (IFM). The model combines the Goland–Reissner approach for estimating crack-tip loads with highly simplified, constant shape functions to calculate the energy release rate ($G_{int}$) and phase angle ($\psi$). To provide a practical reference, experimental data from shear tests on S350 GD galvanized steel bonded to CFRP were used to estimate the range of interfacial fracture toughness for this material system. The parametric results demonstrate that, for a constant load, increasing the overlap length reduces the crack driving force ($G_{int}$), while increasing the adhesive thickness raises it. Crucially, the model indicates that a thicker adhesive layer shifts the fracture mode from shear- to opening-dominated, a trend consistent with the established mechanics of SLJs, where increased joint rotation amplifies peel stresses. The study concludes that while the use of constant shape functions limits the model’s quantitative accuracy, this simplified analytical framework effectively captures the qualitative influence of key geometric parameters on the joint’s fracture behavior. It serves as a valuable and resource-efficient tool for preliminary design explorations and for interpreting experimentally observed failure trends in galvanized steel–CFRP joints. Full article

This study presents the Raman spectral characteristics and selected electrical parameter measurements of WS₂ films deposited by magnetron sputtering on sapphire and subsequently sulfurized. The analysis of the Raman spectra focuses on the positions and shifts of the E¹_2g and A_1g vibrational modes. The effects of different sputtering times on WS₂ films and the corresponding activation energy values were also investigated. From both physical and experimental perspectives, the Raman spectral features of WS₂ films were found to depend on the laser excitation wavelengths (532 nm and 632.8 nm) as well as on possible crystallographic defects and variations in the excitation point position. These defects have a significant influence on both the Raman spectra and the activation energies of the studied samples. The calculated activation energies (~ 0.15–0.19 eV) of the conduction charge carriers correlate with shallow defect-related energy levels indicated by the Raman characteristics. Full article

The proliferation of algorithmically generated malicious URLs necessitates a shift from syntactic detection to semantic analysis. This paper introduces PhishGraph, a disk-aware Approximate Nearest Neighbor (ANN) search system designed to perform billion-scale semantic similarity searches on URL embeddings for threat intelligence applications. Traditional in-memory ANN indexes are prohibitively expensive at this scale, while existing disk-based solutions fail to address the unique challenges of the cybersecurity domain: the high velocity of streaming data, the complexity of hybrid queries involving rich metadata, and the highly skewed, adversarial nature of query workloads. PhishGraph addresses these challenges through a synergistic architecture built upon the foundational principles of DiskANN. Its core is a Vamana proximity graph optimized for SSD residency, but it extends this with three key innovations: a Hybrid Fusion Distance metric that natively integrates structured attributes into the graph’s topology for efficient constrained search; a dual-mode update mechanism that combines high-throughput batch consolidation with low-latency in-place updates for streaming data; and an adaptive maintenance policy that monitors query patterns and dynamically reconfigures graph hotspots to mitigate performance degradation from skewed workloads. Our comprehensive experimental evaluation on a billion-point dataset demonstrates that PhishGraph’s adaptive, hybrid design significantly outperforms strong baselines, offering a robust, scalable, and efficient solution for modern threat intelligence. Full article

Rapid and reliable detection of pesticide residues in vegetables is essential for food safety and sustainable agriculture. This work presents a four-port closed-loop ring resonator (CLRR) sensor for quantitative detection of carbamate residues in leafy vegetables. Operating through the S₃₁ transmission path, the sensor enhances electric-field coupling and sensing resolution in the high-field region. Four resonance modes were identified at 1.05, 2.10, 3.12, and 4.11 GHz, with the third mode (3.12 GHz) showing the most stable and linear response. Vegetable extracts of Chinese kale and Choy sum were prepared with carbamate concentrations of 0–8% (w/v). Increasing concentration caused a red-shift in resonance frequency corresponding to a reduction in dielectric constant. Regression analysis revealed a strong linear correlation between frequency shift and concentration (R² = 0.9855–0.9988). The CLRR achieved average normalized sensitivities of 6.39% and 6.54% per unit dielectric variation, outperforming most planar and metamaterial sensors. Fabricated on a single-layer FR-4 substrate, the sensor combines high sensitivity, low cost, and excellent repeatability, offering a practical, label-free, non-destructive tool for on-site monitoring of pesticide contamination in leafy vegetables. Full article

This study examines how the Donggyeong Daejeon (東經大全), the principal scripture of early Donghak, receives and theologically reconfigures the conceptual lexicon of Confucian classics through text mining-based analysis. Drawing on the classical Chinese texts of the Four Books and the Donggyeong Daejeon, and employing computational techniques such as keyword frequency, keyword-in-context (KWIC), and co-occurrence mapping, the study identifies structural parallels and semantic shifts across the two corpora. These patterns are then interpreted hermeneutically to assess how early Donghak appropriates, repurposes, and theologically transforms inherited Confucian categories. Findings suggest that while the Donggyeong Daejeon retains key Confucian terms, it situates them within a distinct theological framework. The Confucian triad of human being, the Way, and Heaven (人–道–天), for example, is recast in Donghak as “Heaven’s heart is the human-heart” (天心卽人心), a theological affirmation of the human as the locus of Heaven’s immanence. Similarly, the Confucian virtue of sincerity (誠) is reinterpreted through the lens of faith (信), transforming it from a metaphysical ideal into a performative mode of spiritual judgment. Most notably, the Confucian dualism of li (理) and qi (氣) is overcome through the theology of “ultimate energy” (至氣), a divine substance that animates and unifies all beings. By combining quantitative text analysis with interpretive discussion, this study presents Donghak not as a rhetorical appropriation of Confucian discourse, but as a conceptual innovation rooted in the resemanticization of its inherited language. This methodology offers a new model for tracking doctrinal transformation in East Asian religious texts and contributes to broader discussions on intertextual borrowing, and the semantic evolution of classical traditions. Full article

The ring-assisted Mach–Zehnder interferometer (RA-MZI) has high sensitivity and fast optical response time, and it has been used as a label-free nanophotonic biosensor. Most RA-MZI-based biosensors, however, require chemical modification of the ring surface to immobilize biomolecules that can interact with target molecules for sensing. Here, we report a novel microfluidic-integrated RA-MZI (μFRA-MZI) where a microfluidic channel was fabricated right above the photonic ring resonator. μFRA-MZI allows for direct sample delivery to the RA-MZI without chemical modification of the ring surface and measures shifts in the resonance wavelength induced by the presence of target molecules, enabling label-free detection. In order to optimize the sensitivity of μFRA-MZI, seven devices were fabricated with varied design parameters, including the gap distance between the ring and the bus waveguide (G_ring), the length of the multi-mode interferometer (L_MMI), and the length of the directional coupler (L_DC). Photonic characterization showed that the device with G_ring = 1.2 μm, L_MMI = 15.5 μm, and L_DC = 13.5 μm exhibited the highest extinction ratio (ER) compared to the other six devices, consistent with the simulation-optimized design. Testing with NaCl solutions of varying concentrations yielded a bulk sensitivity of 11.48 nm/refractive index unit (RIU) and an ER of 0.41. With the potential to further improve the device’s sensitivity and the ability to detect samples directly in flow without chemical modifications of the ring resonator, μFRA-MZI will provide a robust and effective approach for label-free biosensing. Full article

The electrification of mobility sectors, including automotive, aerospace, and robotics, has accelerated the need for compact and high-efficiency power converters in electric motor drives. Wide-bandgap (WBG) semiconductor–based inverters offer significant advantages over conventional silicon IGBT inverters by enabling higher switching speeds, elevated switching frequencies, and improved power conversion efficiency. However, the adoption of high-frequency switching introduces several challenges, particularly increased motor neutral point voltage stress, originating from inverter common-mode (CM) voltage. The increased neutral point voltage directly elevates motor bearing voltage, the primary driver of motor bearing currents, among which electrostatic discharge machining (EDM) bearing current is the primary cause of bearing degradation in low-power motors. This paper experimentally investigates the root causes of the EDM phenomenon and identifies the key factors influencing its occurrence and severity in WBG-based drive systems. The conventional CM choke designs effectively attenuate motor CM currents and EMI; however, they are ineffective in suppressing EDM bearing currents. In this paper, an alternative CM choke design methodology is proposed to eliminate EDM bearing currents by optimizing the choke inductance to shift the motor CM antiresonance frequency below the inverter switching frequency, thereby ensuring that nearly all source CM voltage is absorbed by the choke. This design approach effectively minimizes the voltage appearing at the motor neutral point and across the bearings, thereby suppressing EDM bearing current spikes without affecting motor DM performance. The choke parameters are mathematically derived for optimal performance and validated through experimental testing on a 2.2 kW three-phase star-connected induction motor powered by a wide-bandgap two-level voltage-source inverter. Full article

(1) Background: Ultra-short-term photovoltaic (PV) power prediction is crucial for optimizing grid scheduling and enhancing energy utilization efficiency. Existing prediction methods face challenges of missing data, noise interference, and insufficient accuracy. (2) Methods: This study proposes a single-step hybrid neural network model integrating Temporal Convolutional Network (TCN), Temporal Shift Transformer (TST), and Bidirectional Gated Recurrent Unit (BiGRU) to achieve high-precision 15-minute-ahead PV power prediction, with a design aligned with symmetry principles. Data preprocessing uses Variational Mode Decomposition (VMD) and random forest interpolation to suppress noise and repair missing values. A symmetric parallel dual-branch feature extraction module is built: TCN-TST extracts local dynamics and long-term dependencies, while BiGRU captures global features. This symmetric structure matches the intra-day periodic symmetry of PV power (e.g., symmetric irradiance patterns around noon) and avoids bias from single-branch models. Tensor concatenation and an adaptive attention mechanism realize feature fusion and dynamic weighted output. (3) Results: Experiments on real data from a Xinjiang PV power station, with hyperparameter optimization (BiGRU units, activation function, TCN kernels, TST parameters), show that the model outperforms comparative models in MAE and R²—e.g., the MAE is 26.53% and 18.41% lower than that of TCN and Transforme. (4) Conclusions: The proposed method achieves a balance between accuracy and computational efficiency. It provides references for PV station operation, system scheduling, and grid stability. Full article

Novel nanocomposites based on carbon black or multi-walled carbon nanotubes functionalized with carboxylic groups and Neutral red electropolymerized from reline were obtained in a one-step protocol and used for DNA biosensor development. The synthesis was carried out in potentiodynamic mode in a deep eutectic solvent reline consisting of a mixture of choline chloride and urea. The nanocomposite based on carbon black and poly(Neutral red) was applied for a voltammetric DNA biosensor developed to discriminate DNA damage. The sensor developed allowed the native, thermally denatured, and chemically oxidized DNA discrimination with either current changes or peak potential shifts. The nature of the DNA used had affected the sensor’s analytical response value. The DNA biosensor suggested was tested for the assessment of antioxidant capacity in such beverages as tea, coffee, white wine, and fruit-based drink purchased from local market. Simple, fast, and inexpensive approach of sensor modifying layer assembly would be demanded in control of food products and beverages quality, as well as for medical purposes. Full article

Characterized by its low melting temperature of 138 °C, the eutectic Sn-58Bi solder expands the melting temperature range of interconnect joints in electronic packaging, making it widely used in multi-level packaging processes. However, its reliability at higher current densities poses a challenge. This paper employs a hybrid process combining laser soldering and hot-air reflow to fabricate Cu/Sn-58Bi/Cu solder joints in ball grid array (BGA) structures. Through mechanical testing under current loading, the effects of increasing current density (0 A/cm², 0.85 × 10³ A/cm², 1.70 × 10³ A/cm², 2.55 × 10³ A/cm², 3.40 × 10³ A/cm², 4.25 × 10³ A/cm²) were studied systematically. Results indicate that the shear strength decreases markedly with increasing current density, exhibiting a reduction of approximately 5.63% to 95.75%. This degradation is initiated by the overall temperature increase and material softening due to Joule heating. It is further exacerbated by the loss of the non-thermal electron wind’s strengthening contribution, which weakens as the dominant thermal impact escalates with current density. Fracture mode transitions from ductile failure within the solder matrix to a ductile-brittle mixture at the solder/IMC interface, with the transition initiating at 3.40 × 10³ A/cm². Finite element simulations reveal that current crowding in Sn-rich regions and at the solder/IMC interface induces localized Joule heating and thermomechanical strain, which jointly drive the degradation in shear strength and the shift in fracture path. Full article

This paper presents a Doppler lidar system based on a mode-locked semiconductor laser (ML-SL) source. The ML-SL consists of two sections: a Fabry–Pérot (F-P) cavity and a saturable absorber (SA) region. The system utilizes multiple phase-correlated modes of the optical frequency comb to acquire multiple Doppler shift signals; through cross-referencing of these signals, the robustness of the velocimetry system is enhanced. Experimental validation of precise velocity measurements for moving objects was conducted within the speed range of 0.005 m/s to 0.5 m/s. For target speeds of 0.563 m/s and 0.00563 m/s, the maximum and minimum absolute errors were 0.00064 m/s and 0.00003 m/s, respectively, with relative errors consistently below 1%. Comparative experiments demonstrated that utilizing multiple comb teeth reduces the maximum absolute error from 0.001286 m/s (observed when using a single tooth) to 0.000833 m/s. Furthermore, the velocity resolution of the system was analyzed: a frequency resolution of 30 Hz corresponds to a velocity resolution of 0.1117 m/s, while improving the frequency resolution to 1 Hz yields a velocity resolution of 0.0037 m/s. Full article

Single-phase ground faults (SPGFs) in isolated neutral medium-voltage networks are difficult to detect, especially under high transient resistance. This paper proposes a centralized ground fault protection unit (CGFPU) that combines zero-sequence current (ZSC) magnitude and phase-angle analysis to enhance selectivity. Simulation results show that as transient resistance increases from 1 Ohm to 10 kOhm, fault currents decrease significantly, yet the CGFPU reliably identifies the faulty feeder by exploiting the characteristic 180° phase shift of ZSC phasors. The method remains selective with angular deviations up to ±20° and distinguishes between feeder and busbar faults. Compared with conventional amplitude- or model-based techniques, the proposed approach achieves faster detection, lower computational complexity, and robustness against unbalanced and charging currents. Furthermore, the CGFPU operates adaptively in alarm or trip mode depending on fault severity, thus preserving continuity for high-resistance faults and ensuring rapid isolation of bolted faults. These contributions establish a practical, scalable, and future-ready solution for SPGF protection in medium-voltage isolated neutral networks. Full article

The increased penetration of power electronics interfaced resources in modern power systems is unlocking new opportunities and challenges. New concepts like multiport converters can further enhance the efficiency and power density of power electronics-based solutions. The triple active bridge is an isolated multiport converter with soft switching and high voltage gain that can integrate different sources, storage, and loads, or act as a building block for modular systems. However, the triple active bridge suffers from power flow cross-coupling, which affects its dynamic performance if it is not removed or mitigated. Unlike the extensive literature on two-port power converters, studies on modeling and control comparison for multiport converters are still lacking. Therefore, this paper presents and compares different modeling and decoupling control approaches applied to the triple active bridge converter, highlighting their benefits and limitations. The converter operation and modulation are introduced, and modeling and control strategies based on the single phase shift power flow control are detailed. The switching model, generalized full-order average model, and the reduced-order model derivations are presented thoroughly, and a comparison reveals that first harmonic approximations can be detrimental when modeling the triple active bridge. Furthermore, the model accuracy is highly sensitive to the operating point, showing that the generalized average model better represents some dynamics than the lossless reduced-order model. Furthermore, three decoupling control strategies are derived aiming to mitigate cross-coupling effects to ensure decoupled power flow and improve system stability. To assess their performance, the TAB converter is subjected to power and voltage disturbances and parameter uncertainty. A comprehensive comparison reveals that linear PI controllers with an inverse decoupling matrix can effectively control the TAB but exhibit large settling time and voltage deviations due to persistent cross-coupling. Furthermore, the decoupling matrix is highly sensitive to inaccuracies in the converter’s model parameters. In contrast, linear active disturbance rejection control and sliding mode control based on a linear extended state observer achieve rapid stabilization, demonstrating strong decoupling capability under disturbances. Furthermore, both control strategies demonstrate robust performance under parameter uncertainty. Full article