Search Results (5,796)

Generative artificial intelligence (AI) models including GPT, Gemini, and DeepSeek are reshaping embodied agents, temporal prediction, and autonomous driving, demanding a ten-fold annual growth in training FLOPS that Moore’s law can no longer sustain. Consequently, scale-out GPU clusters require >400 Gb/s lane-rate optical interconnects within AI data-centers (AIDCs). Single-photodiode direct detection offers density, latency, and energy advantages, but DAC bandwidth remains limited to around 70 GHz. We present an optical triple-band multiplexing scheme that replaces high-frequency radio frequency (RF) mixers and local oscillators (LOs) with photonic components. A Mach–Zehnder modulator (MZM) generates 80-GBd PS-PAM-20 signal while an in-phase/quadrature (IQ) modulator driven by a wavelength-offset laser creates two independent 35-GBd PS-64-QAM bands. The proposed optical multiplexing method breaks conjugate symmetry and enhances dispersion tolerance of the direct detection system. After 200 m SSMF transmission and single 70-GHz photodiode (PD) detection, digital signal-signal beating interference (SSBI)/cross-beating compensation enables the recovery of net 543.9 Gb/s signal (line rate of 686.6 Gb/s) using only 45-GHz DACs. The optical multiplexing architecture provides a path to beyond-400 Gb/s lanes and demonstrates a scalable, energy-efficient solution for next-generation AI clusters. Full article

To enhance the monitoring and conservation efforts for China’s Class II endangered species, specifically the wild giant salamander and its ecosystems, this study addresses the urgent need to counteract the rapid decline of its wild population caused by habitat loss and insufficient surveillance. We present an innovative localization system based on passive Ultra-High-Frequency Radio Frequency Identification (UHF RFID) technology, employing a Double-Transform (D-Tr) methodology that integrates an enhanced 3D LANDMARC algorithm with GAIN generative adversarial networks. This system effectively reconstructs missing Received Signal Strength Indicator (RSSI) data due to environmental barriers by applying a log-distance path loss model. The D-Tr framework simultaneously generates RSSI sequences alongside their first-order differential characteristics, allowing for a comprehensive analysis of spatiotemporal signal relationships. Field tests conducted in the Hubei Xianfeng Zhongjian River Giant Salamander National Nature Reserve reveal that the positioning error consistently remains within 10 cm, with average accuracy improvements of 20.075%, 15.331%, and 12.925% along the X, Y, and Z axes, respectively, compared to traditional time-series models such as long short-term memory (LSTM) and gated recurrent unit (GRU). This system, designed to investigate the behavioral patterns and movement paths of farmed giant salamanders, achieves centimeter-level tracking of their cave-dwelling activities. It provides essential technical support for quantitatively assessing their daily activity patterns, habitat choices, and population trends, thereby promoting a shift from passive oversight to proactive monitoring in the conservation of endangered species. Full article

The growing demand for miniature mechanical components has increased the importance of ultra-precision micro machine tools and real-time monitoring. This study examines acoustic emission (AE) sensing for the intelligent control of an ultra-precision micro lathe. AE signals were measured while brass and aluminum alloys were turned with cermet and diamond tools at different spindle speeds and cutting depths. Finite element simulations were performed to clarify the AE generation mechanisms. The AE waveform amplitude changed stepwise corresponding to tool–workpiece contact, elastoplastic deformation, and chip formation, enabling precise contact detection at the 0.1 μm level. The AE amplitude increased with increasing spindle speed and increasing depth of cut except during abnormal conditions (e.g., workpiece adhesion). Frequency analysis revealed a dominant peak near 0.2 MHz during normal cutting, as well as high-frequency (>1 MHz) components linked to built-up edge formation. Simulations confirmed that these AE features reflect variations in the strain rate in the shear zone and on the rake face. They also confirmed that cutting force spectra under high friction reproduce the experimentally observed high-frequency peaks. These findings demonstrate the feasibility of using AE sensing to identify the cutting state and support the development of self-optimizing micro machine tools. Full article

This study presents the design of a high-efficiency pulse width modulation (PWM) power amplifier for marine biological sound reproduction. Due to the capacitive nature of underwater transducers and step-up transformers, output LC filter design is constrained, making it difficult to achieve a flat frequency response and low total harmonic distortion (THD). To address this, the electrical characteristics of these components were measured and modeled to construct equivalent circuits for the PSPICE simulator. Based on these models, an optimized LC filter was designed, and its performance was validated through simulation and experiments. The cause of THD occurring in specific frequency bands was analyzed, and two types of notch filters were applied to improve THD and switching signal attenuation. The proposed methodology offers a practical approach to improving PWM amplifier performance in underwater acoustic systems, supporting the development of compact, efficient, and reliable SONAR transmitters. Full article

Visible Light Communication (VLC) systems commonly employ optical orthogonal frequency division multiplexing (O-OFDM) to achieve high data rates, benefiting from its robustness against multipath effects and intersymbol interference (ISI). However, a key limitation of asymmetrically clipped direct current biased optical–OFDM (ACO-OFDM) systems lies in their inherently high peak-to-average power ratio (PAPR), which significantly affects signal quality and system performance. This paper proposes a joint chaotic encryption and modified μ-non-linear logarithmic companding (μ-MLCT) scheme for ACO-OFDM–based VLC systems to simultaneously enhance security and reduce PAPR. First, image data is encrypted at the upper layer using a hybrid chaotic system (HCS) combined with Arnold’s cat map (ACM), mapped to quadrature amplitude modulation (QAM) symbols and further encrypted through chaos-based symbol scrambling to strengthen security. A μ-MLCT transformation is then applied to mitigate PAPR and enhance both peak signal-to-noise ratio (PSNR) and bit-error-ratio (BER) performance. A mathematical model of the proposed secured ACO-OFDM system is developed, and the corresponding BER expression is derived and validated through simulation. Simulation results and security analyses confirm the effectiveness of the proposed solution, showing gains of approximately 13 dB improvement in PSNR, 2 dB in BER performance, and a PAPR reduction of about 9.2 dB. The secured μ-MLCT-ACO-OFDM not only enhances transmission security but also effectively reduces PAPR without degrading PSNR and BER. As a result, it offers a robust and efficient solution for secure image transmission with low PAPR, making it well-suitable for emerging wireless networks such as cognitive and 5G/6G systems. Full article

In this study, we propose the Frequency-domain Feature Fusion Module (F3M) to address the challenges of underwater object detection, where optical degradation—particularly high-frequency attenuation and low-frequency color distortion—significantly compromises performance. We critically re-evaluate the need for strict invertibility in detection-oriented frequency modeling. Traditional wavelet-based methods incur high computational redundancy to maintain signal reconstruction, whereas F3M introduces a lightweight “Separate–Project–Fuse” paradigm. This mechanism decouples low-frequency illumination artifacts from high-frequency structural cues via spatial approximation, enabling the recovery of fine-scale details like coral textures and debris boundaries without the overhead of channel expansion. We validate F3M’s versatility by integrating it into both Convolutional Neural Networks (YOLO) and Transformer-based detectors (RT-DETR). Evaluations on the SCoralDet dataset show consistent improvements: F3M enhances the lightweight YOLO11n by 3.5% mAP₅₀ and increases RT-DETR-n’s localization accuracy (mAP_50–95) from 0.514 to 0.532. Additionally, cross-domain validation on the deep-sea TrashCan-Instance dataset shows F3M achieving comparable accuracy to the larger YOLOv8n while requiring 13% fewer parameters and 20% fewer GFLOPs. This study confirms that frequency-domain modulation provides an efficient and widely applicable enhancement for real-time underwater perception. Full article

Aggressive behavior is a complex and multifactorial trait influenced by several genes and shaped by societal and cultural constraints. To trace adaptation signals and identify potential new genes related to aggressive behavior, we explored variations in nine genes previously linked to aggressive behavior, as well as their 74 interacting genes retrieved from the STRING database. We identified 15 SNPs under positive selection in four genes (SEC24B, NCOA2, CTNNA1, and ALDH3A2), with selection consistently confirmed by both iHS and xp-EHH analyses. Among these, 15 SNPs showed high pairwise F_ST values and pronounced allele frequency differences between populations, suggesting their potential role in the local adaptation of the studied populations. The functional importance of these SNPs was confirmed by ten acting as eQTLs and five located in transcription factor binding sequences. The observed selection signatures may reflect adaptation in diverse biological processes, including protein trafficking and signal transduction, cell proliferation and differentiation, endocrine regulation, and lipid and aldehyde detoxification. Although these processes are not directly linked to aggression, they may have downstream effects on neurodevelopmental and hormonal regulation that could indirectly influence behavioral phenotypes. Experimental validation is required to confirm these signals and to clarify their functional and biological significance. Full article

This paper presents a high-efficiency wireless power transfer (WPT) architecture employing a resonant inductive coupling to power smart sensor nodes in remote or sealed environments, where conventional power delivery is unfeasible. The system integrates a photovoltaic (PV) energy source with a step-down DC-DC converter based on the LM2596 buck regulator to adjust the voltage from the PV. The proposed conditioned power system supplies the entire electronic circuit consisting of a PWM modulator based on an NE555, which drives an IR2110 gate driver connected to a Class D power amplifier. The amplifier excites a pair of high-Q resonant coils designed for mid-range inductive coupling. On the receiver side, the inductively coupled AC signal is rectified and regulated through an AC-DC conversion stage to charge a secondary energy storage unit. The design eliminates the need for physical electrical connections, ensuring efficient, contactless energy transfer. The proposed system operates at a resonant frequency of 24.46 kHz and achieves up to 80% transmission efficiency at a distance of 113 mm. The receiver provides a regulated DC output between 4.80 V and 4.97 V, sufficient to power low-consumption smart sensors. Full article

Glutamate delta 1 receptor (GluD1) has various functional roles in the brain, such as high-frequency hearing, synapse formation and maintenance, and regulation of cognition disorders and neurodevelopmental disease. However, the underlying molecular mechanism, especially at the genetic level, remains to be elucidated. In this study, we use transcriptomics analysis to define the genetic impact of GluD1 across the brain regions in GluD1 knockout mice. Our results show that GluD1 deletion induced pronounced differences in gene expression both across the four brain regions (cortex, cerebellum, hippocampus, and striatum) and the distinct hippocampal subregions. Despite differences in transcriptional profiles, the differentially expressed genes (DEGs) across all four brain regions show significant enrichment in synaptic signaling pathways, highlighting the critical role of GluD1 in synaptic function. The GluD1 interaction network and its downstream target genes are closely linked to the pathogenesis of intellectual disability (ID) and autism spectrum disorders (ASDs). In conclusion, our work reveals that GluD1 deletion leads to brain-region-specific transcriptional changes and establishes a genetic link between the interaction network with GluD1 and the risk genes for ID and ASD. Full article

To address the torque ripple and speed fluctuation issues in high-frequency square-wave injection-based sensorless control of interior permanent magnet synchronous motors (IPMSM) caused by low-order stator current harmonics (primarily the fifth and seventh), this paper proposes a harmonic voltage compensation strategy based on a sixth-order quasi-proportional resonant (QPR) controller, which effectively suppresses these specific harmonic disturbances. The proposed method, building upon conventional high-frequency square-wave injection, introduces a harmonic current extraction technique based on multiple synchronous reference frame transformations to separate the fifth and seventh harmonic components accurately; then, according to the established harmonic voltage compensation equation, generates targeted compensation voltage commands; finally, further precisely suppresses the corresponding harmonic currents through a sixth-order QPR controller connected in parallel with the current proportional-integral (PI) controller. This paper comprehensively establishes the mathematical models for harmonic extraction and voltage compensation, and conducts a detailed analysis of the parameter design of the sixth-order QPR controller. Simulation results demonstrate that the proposed strategy can significantly suppress stator current distortion, effectively reduce torque and speed ripples, and substantially improve rotor position estimation accuracy, thereby verifying the superiority of the novel harmonic-suppression-based sensorless control strategy. Full article

This study examines how the education sector in one Canadian province has navigated post-pandemic recovery between 2023 and 2025, drawing on cross-sectional survey data from a convenience sample of sector employees in the 2023–2024 (n = 1411) and 2024–2025 (n = 742) school years. The findings revealed selective improvement over time, including increased organizational commitment among teachers and other education workforce members, alongside reduced perceptions of students’ academic, social, and behavioural needs. Teachers indicated no corresponding increases in their perceived frequency of meeting students’ needs, whereas other educator sector employees indicated improvement in this area. In contrast, no year-over-year differences were found in the education workforce members’ and teachers’ stress, coping, well-being or connectedness, and job search behaviours remained high, ranging from 14 to 43%. Guided by the Job Demands–Resources (JD-R) model, these findings suggest that post-pandemic recovery reflects the establishment of a new and complex baseline. The coexistence of improved commitment with persistent job strain signals a paradox of stability amid depletion. Qualitative responses reinforced this interpretation, revealing how educators adapt to ongoing organizational resource constraints and shifting student needs. Together, the findings extend JD-R-informed understandings of post-crisis workforce adaptation and identify implications for leadership and policy aimed at supporting organizational health and workforce sustainability. Full article

Aiming to understand the genetic diversity of Culter and Cultrichthys fishes in West Dongting Lake following the implementation of fishing ban, this study analyzed the genetic diversity and phylogeny of five Cultrinae species based on mitochondrial Cytb and COI gene sequences. For Cytb, 50 haplotypes were identified with high overall genetic diversity (H_d = 0.954, π = 0.04765, K = 53.506). Similarly, COI yielded 48 haplotypes (H_d = 0.950, π = 0.03251, K = 36.414). Interspecific comparisons revealed that Culter mongolicus and Culter alburnus exhibited the highest genetic diversity, while Cultrichthys erythropterus showed the lowest. Demographic inferences from neutrality tests and mismatch distribution analyses differed between markers. Based on Cytb, populations of C. erythropterus and C. oxycephaloides appeared to be at neutrality, whereas C. mongolicus, C. alburnus, and C. dabryi significantly deviated, suggesting historical expansion. Mismatch distributions supported recent expansion in C. mongolicus and C. dabryi. In contrast, COI-based neutrality tests indicated expansion in C. mongolicus, C. alburnus, and C. dabryi, with mismatch results confirming this signal for C. dabryi. The negative values of Tajima’s D and Fu’s Fs observed across all five species reflect an accumulation of low-frequency alleles within populations, which may indicate a recent rapid demographic recovery. These findings provide genetic evidence that the genetic diversity of these Cultrinae species has likely been recovering since the fishing ban took effect on 1 January 2020. Further conservation strategies are therefore recommended to promote population recovery and enhance genetic diversity. Full article

Brain–Computer Interface (BCI) systems are rapidly evolving and increasingly integrated into interactive environments such as gaming and Virtual/Augmented Reality. In such applications, user adaptability and engagement are critical. This study applies deep learning to predict user performance in a 3D BCI-controlled game using pre-game Motor Imagery (MI) electroencephalographic (EEG) recordings. A total of 72 EEG recordings were collected from 36 participants, 17 using the Muse 2 headset and 19 using the Emotiv Insight device, during left and right hand MI tasks. The signals were preprocessed and transformed into time–frequency spectrograms, which served as inputs to a custom convolutional neural network (CNN) designed to classify users into three performance levels: low, medium, and high. The model achieved classification accuracies of 83% and 95% on Muse 2 and Emotiv Insight data, respectively, at the epoch level, and 75% and 84% at the subject level, using LOSO-CV. These findings demonstrate the feasibility of using deep learning on MI EEG data to forecast user performance in BCI gaming, enabling adaptive systems that enhance both usability and user experience. Full article

Photoacoustic (PA) velocimetry offers a promising solution to the limitations of conventional techniques for measuring blood flow velocity. Given its moderate penetration depth and high spatial resolution, PA imaging is considered suitable for measuring low-velocity blood flow in capillaries located at moderate depths. High-resolution measurements based on PA signals from individual blood cells can be achieved using a high-frequency transducer. However, high-frequency signals attenuate rapidly within biological tissue, restricting the measurable depth. Consequently, low-frequency transducers are required for deeper measurements. To date, PA flow velocimetry employing low-frequency transducers remains insufficiently explored. In this study, we investigated the effect of the concentration of particles that mimic blood cells within vessels under low-concentration conditions. The performance of flow velocity measurement was evaluated using an ultrasonic transducer (UST) with a center frequency of 10 MHz. The volume fraction of particles in the solution was systematically varied, and the spatially averaged flow velocity was assessed using two different distinct analysis methods. One method employed a time-shift approach based on cross-correlation analysis. Flow velocity was estimated from PA signal redpairs generated by particles dispersed in the fluid, using consecutive pulsed laser irradiations at fixed time intervals. The other method employed a pulsed Doppler method in the frequency domain, widely applied in ultrasound Doppler measurements. In this method, flow velocity redwas estimated from the Doppler-shifted frequency between the transmitted and received signals of the UST. For the initial analysis, numerical simulations were performed, followed by experiments based on displacement measurements equivalent to velocity measurements. The target was a capillary tube filled with an aqueous solution containing particles at different concentration levels. The time–domain method tended to underestimate flow velocity as particle concentration increased, whereas the pulsed Doppler method yielded estimates consistent with theoretical values, demonstrating its potential for measurements at high concentrations. Full article

This paper proposes a small-signal model of a DC–DC parallel resonant converter operating in continuous conduction mode based on asymmetric pulse-width modulation (APWM) under light-load conditions. The parallel resonant converter enables soft switching and no-load control over a wide load range because the resonant capacitor is connected in parallel with the load. However, the resonant energy required for soft switching is already sufficient, and the current flowing through the resonant tank is independent of the load magnitude; therefore, as the load decreases, the energy that is not delivered to the load and instead circulates meaninglessly inside the resonant tank increases. This results in conduction loss and reduced efficiency. To address this issue, APWM with a fixed switching frequency is required, which reduces circulating energy and improves efficiency under light-load conditions. Precise small-signal modeling is required to optimize the APWM controller. Unlike PFM or PSFB, APWM includes not only sine components but also DC and cosine components in the control signal due to its asymmetric switching characteristics, and this study proposes a small-signal model that can relatively accurately reflect these multi-harmonic characteristics. The proposed model is derived based on the Extended Describing Function (EDF) concept, and the derived transfer function is useful for systematically analyzing the dynamic characteristics of the APWM-based parallel resonant converter. In addition, it provides information that can systematically analyze the dynamic characteristics of various APWM-based resonant converters and control signals that reflect various harmonic characteristics, and it can be widely applied to future control design and analysis studies. The validity of the model is verified through MATLAB (R2025b) and PLECS (4.7.5) switching-model simulations and experimental results, confirming its high accuracy and practicality. Full article