Search Results (242)

Background: Sleep deprivation is one of the major public health and performance risk factors, with documented effects on vigilance, executive function, emotional regulation, and safety-critical behaviour. This review examines how event-related potentials (ERPs)—which provide millisecond-level resolution of cognitive processing stages—can clarify which neural processes are most affected by sleep loss, from early sensory encoding to later evaluative and control-related stages. Materials and Methods: This study was conducted as a systematic review of human studies on sleep deprivation and ERPs. Eligible studies included human participants, focused primarily on acute/total sleep deprivation, and reported ERP outcomes (e.g., amplitude, latency, topography, or related event-locked EEG measures). Searches were performed in major biomedical/psychology databases using sleep deprivation and ERP terms, with additional forward/backward citation searching. Data was extracted in a structured format (participant characteristics, deprivation protocol, ERP methods, behavioural outcomes, ERP findings, and recovery/countermeasure effects). Due to substantial heterogeneity in paradigms, protocols, and ERP methods, findings were synthesised narratively rather than meta-analysed. Risk of bias was assessed with RoB 2 and ROBINS-I. Results: The search identified 854 records, of which 82 studies were included following deduplication, screening, full-text review, and citation chasing. Samples were typically small, highly selected, and dominated by healthy young adults, with frequent attrition related to prolonged wakefulness and EEG data-quality constraints. Across studies, sleep deprivation produced stage-specific and task-dependent ERP effects rather than a single uniform pattern. The most consistent findings involved mid-to-late components. These components typically showed prolonged latency and reduced amplitude. In some cases, amplitude increases were observed and interpreted as compensatory recruitment. Early sensory/pre-attentive components (e.g., P1/N1/MMN/P50) were often relatively preserved, but showed selective vulnerability, including latency slowing, reduced filtering/gating, or decreased phase locking. A recurring observation was a behaviour–ERP dissociation, where ERP abnormalities were detectable even when behavioural impairment was modest, indicating covert neural inefficiency or compensation. Recovery sleep, naps, and countermeasures (e.g., modafinil, caffeine) produced partial, component-specific recovery, with amplitude and latency often recovering at different rates. Conclusions: The evidence indicates that sleep deprivation primarily disrupts higher-order, late-stage, and temporally coordinated neural processing, while earlier sensory processing is often preserved but becomes slower and less stable. Among ERP markers, the P300/P3 family is the most robust and informative signature of sleep loss effects and recovery. ERPs are therefore a sensitive tool for detecting neural dysfunction and compensation under sleep deprivation, including changes that may precede overt behavioural decline. Future research must improve the generalisability and reproducibility of ERP findings by employing larger, more diverse samples, alongside more standardised methodological, recording, and reporting practices. Full article

To address carrier-phase loss of lock and long-term drift in frequency-offset estimation that may arise from time-of-arrival (TOA) measurements in enhanced Loran (eLoran) timing receivers under low signal-to-noise ratio (SNR) and moderate-to-high dynamics, this paper proposes a joint TOA and carrier-phase measurement and tracking method. First, transmitter identification and group repetition interval (GRI) lock are achieved by exploiting the periodic repetition of pulse groups, and epoch folding is applied to enhance effective SNR. Then, a sub-sample TOA observation is constructed via a three-stage progressive refinement procedure: energy-matching coarse estimation, coherent cross-correlation, and parabolic peak interpolation. In parallel, baseband phase observations are obtained through coherent downconversion and accumulation. A unified state-space model incorporating TOA bias, TOA drift rate, baseband phase, and frequency offset is further established to enable joint Kalman filtering of TOA and phase. Moreover, an innovation-likelihood-weighted parallel multiple-model filter combined with measurement-noise covariance inflation is introduced to suppress outlier observations. Simulations show that the TOA estimate converges within about 1 s while maintaining phase continuity and stable frequency-offset estimation, and that the proposed method achieves superior overall robustness and long-term stability compared with a conventional Costas loop. Full article

Background: Evidence on the role of synthetic biomimetic bone substitutes in the surgical management of proximal humerus fractures remains limited. This study aimed to evaluate the clinical, radiographic, and safety outcomes of a porous hydroxyapatite bone substitute used as an adjunct to locking-plate fixation in proximal humerus fractures with metaphyseal bone loss. Methods: We performed a retrospective comparative cohort study including 45 patients treated with locking-plate fixation and porous hydroxyapatite scaffold augmentation and 40 comparable control patients treated with locking-plate fixation without scaffold augmentation. Patients were evaluated clinically and radiographically at 1, 3, 6, and 12 months after surgery. Functional outcome was assessed with the Constant–Murley Score (CMS), and pain was assessed using the Visual Analogue Scale (VAS). Longitudinal changes over time were analyzed using mixed-effects models for repeated measures. Results: CMS improved progressively over follow-up, whereas VAS pain scores decreased significantly over time. No cases of device migration or radiographic resorption were observed during follow-up. Adverse events were recorded, but no complication was considered directly attributable to the implanted biomaterial. Functional recovery and pain reduction followed a similar trajectory in both groups, with no significant group-by-time interaction. Conclusions: In this retrospective series, graft augmentation with a porous hydroxyapatite scaffold during locking-plate fixation of proximal humerus fractures with bone void was associated with progressive functional improvement and pain reduction, without evident device-related safety concerns. Owing to the retrospective, non-randomized design, limited sample size, potential selection bias, and incomplete follow-up in part of the cohort, these findings should be interpreted as supportive of feasibility and short- to mid-term safety rather than as definitive evidence of biomaterial efficacy. Level of Evidence: Level III, retrospective cohort study. Full article

GNSS signals are extremely weak at the Earth’s surface and are highly vulnerable to in-band interference, particularly high-dynamic linear frequency-modulated (LFM) jamming, which may lead to receiver loss of lock. Existing anti-jamming techniques struggle to balance real-time constraints with high-precision frequency estimation. This paper proposes a Newton-iteration-enhanced three-spectral-line RIFE algorithm implemented on a heterogeneous FPGA platform (Zynq-7000 SoC). The method performs coarse frequency estimation using the three-spectral-line RIFE to mitigate FFT fence effects, followed by Newton-based quadratic refinement, enabling high estimation accuracy with reduced FFT size. A fast–slow loop architecture is adopted, where the FPGA (PL) performs real-time interference suppression and the ARM (PS) handles system control and parameter updates. Experimental results show that, under static interference, the proposed method achieves a 10.9 dB improvement over direct estimation algorithms. Under chirp interference, it significantly outperforms both direct estimation and conventional iterative methods. In GNSS closed-loop tests, the proposed approach extends the anti-jamming margin to 82 dB J/S. Overall, the proposed method effectively balances estimation accuracy and processing latency, providing a practical solution for GNSS anti-jamming in high-dynamic environments. Full article

Shape memory polymers (SMPs) can undergo reversible shape transformations, yet most conventional one-way SMPs recover only a single programmed shape. Reported bidirectional SMPs frequently rely on complex chemistries or continuous external loads or tolerate pronounced losses in mechanical robustness, largely because microphase separation, crystallization and internal stress are difficult to regulate in an integrated fashion. Here, we propose a UV-programmed internal-stress-locking strategy to construct a crosslinked polyurethane (UV-SMPU) that simultaneously achieves high toughness and stable, stress-free bidirectional actuation. Using polycaprolactone (PCL) as the soft segment, hexamethylene diisocyanate (HDI) as the hard segment and triallyl isocyanurate (TAIC) as a photocrosslinker, in-situ UV curing under pre-stretch fixes a tunable three-dimensional network while “freezing” the microphase-separated morphology and pre-oriented internal stress. Covalent crosslinks stabilize PCL crystallites as reversible actuation domains, whereas hydrogen-bonded hard segments provide elastic restoring force; the coordinated regulation of crosslink density, crystallinity and locked-in internal stress enables efficient CIE/MIC-type transitions without compromising mechanical integrity. The optimized UV-SMPU (3 wt% TAIC, 10 min UV) exhibits excellent thermal stability, a rare strength–ductility balance (26.6 MPa tensile strength; ~1700% elongation) and robust bidirectional actuation, with reversible strain stabilizing at 15.73% after six cycles. This work offers a simple, scalable route to tough bidirectional SMPUs and furnishes mechanistic design principles for next-generation adaptive and soft-actuated materials. Full article

The gap dimension of a railway switch machine is a critical physical quantity for determining the locking status of railway turnouts. Under operating conditions characterized by heavy oil contamination, complex illumination, and equipment vibration, existing visual measurement methods often struggle to maintain stability and achieve sub-pixel precision. To address this issue, this paper proposes a gap measurement method based on the fusion of vision and geometric features (G-VFM). The method first utilizes a confidence-aware optimized YOLOv8 model to achieve robust localization of the gap region. Subsequently, an improved multi-channel U-Net is employed to extract soft-edge probability maps, based on which a 20-dimensional structured geometric descriptor is constructed. Finally, visual semantic features and geometric priors are fused for regression through an R34-Fusion two-stream residual network, and systematic errors are corrected using a weighted Huber loss combined with a piecewise linear calibration strategy. Test results on a constructed field dataset show that the proposed method achieves a Mean Absolute Error (MAE) of 0.0076 mm and a maximum error of 0.0193 mm. It achieves a 100% pass rate under an industrial tolerance of 0.02 mm, with an end-to-end inference time of 52.23 ms (~19.15 FPS), balancing both precision and efficiency. Further tests on illumination degradation, noise interference, and cross-batch evaluations indicate that the method maintains relatively stable performance across various complex scenarios. However, performance decreases significantly under extremely low-light conditions, suggesting that actual deployment may require integration with active lighting or multi-sensor fusion to ensure system reliability across all working conditions. Overall, this method achieves high-precision gap measurement under current experimental conditions and provides a feasible solution for vision-based switch machine status monitoring. Full article

Background/Objectives: Immediate provisionalization in the esthetic zone is a well-documented but technique-sensitive procedure, and the choice of provisional connection geometry, with or without an antirotational index, remains debated. The aim of this retrospective single-arm cohort clinical study was to evaluate the clinical performance of a digitally planned, guide-delivered provisionalization protocol using prefabricated provisional crowns connected to 5-degree Morse taper implants without an antirotational index, with emphasis on emergence profile shaping and peri-implant tissue stability at one year; Methods: Twenty consecutive single-implant cases treated according to the standardized protocol from January 2024 onward and completing at least one year of follow-up after definitive crown delivery by the February 2026 data-lock date were included (19 female, 1 male; mean age 38.1 ± 12.7 years; 18 anterior and 2 premolar sites). All implants were placed with primary insertion torque ≥ 30 N·cm (mean 34.75 ± 2.55 N·cm) and immediately restored with a digitally designed, non-antirotational provisional crown. Primary outcome was provisional retention without major intervention; secondary outcomes included biologic complications, papilla score, marginal bone change at T0–T3 and T3–T4, and buccal contour change (T0 vs. T2 intraoral scan superimposition). Wilson 95% confidence intervals, Fisher’s exact test, and Mann–Whitney U test were used (α = 0.05); Results: Provisional retention without major intervention was 75.0% (15/20; 95% CI 53.1–88.8). Biologic complications were uncommon (bleeding on probing, suppuration, midfacial recession, and chairside adjustment, each 5.0%). Mean total marginal bone loss at one year was 0.37 ± 0.20 mm; mean buccal contour gain was 1.41 ± 0.48 mm. A complete papilla was preserved in 70.0% of cases. Conclusions: Digitally planned, guide-delivered provisionalization on a non-antirotational 5-degree Morse taper interface appears clinically feasible for emergence profile shaping in the esthetic zone, with favorable peri-implant tissue outcomes at one year. Full article

Coastal urbanization is increasingly constrained by legacy land-use patterns and escalating climate risks, yet long-term morphological trajectories remain poorly quantified due to the absence of multispectral data in pre-satellite archives. This study introduces a scalable deep learning pipeline that bridges a century-scale domain gap through an attention-enhanced Pix2Pix colorization stage and a few-shot U-Net++ segmentation stage, enabling automated reconstruction of urban expansion from panchromatic historical aerial imagery (1920–1971) and digital aerial photographs (1997) to contemporary very-high-resolution satellite data (2024) in Les Sables-d’Olonne, France. The novelty of the approach lies in coupling generative colorization with epoch-specific fine-tuning to overcome radiometric and annotation bottlenecks that have historically prevented quantitative urban reconstruction from pre-satellite archives. The colorization stage achieved high spectral fidelity (PSNR 35.21 dB, SSIM 0.9762), and segmentation performed strongly on modern imagery (mIoU 0.9789). While the segmentation model performed strongly on modern imagery, direct transfer to historical data exhibited substantial domain shift due to radiometric discrepancies. Few-shot adaptation on year-specific calibration sets recovered reliable building footprints (mIoU 0.53–0.65) across the full timeline. Multi-scalar analysis of the reconstructed footprints revealed constrained anisotropic expansion: early saturation of the coastal historic core, followed by rapid inland peri-urbanization post-1971 driven by geographic barriers. This spatiotemporal shift has entrenched spatial lock-in, placing recent development in retro-littoral zones that are vulnerable to submersion and characterized by severe vegetation loss. The framework unlocks previously inaccessible historical archives for quantitative urban monitoring, providing critical insights into legacy effects of unconstrained growth and informing resilient coastal planning under climate change. Full article

Land-use change substantially contributes to carbon emissions, yet systematic research on complex human–environment interactions in old industrial bases remains scarce. Here, we integrated multi-temporal land-use data and socio-economic statistics from 1990 to 2023 to analyze the spatiotemporal dynamics and drivers of land-use carbon emissions in Northeast China. The land-use transfer matrix, the carbon emission coefficient, exploratory spatiotemporal data analysis (ESTDA), standard deviational ellipses, and modified Kaya–LMDI models were applied. Construction land area expanded by 120%, with its share of total emissions increasing from 87% to 95%. Meanwhile, forest and grassland shrank, reducing their carbon sink capacity and increasing their net carbon emissions 1.9-fold. Spatially, emissions showed a weak global correlation but strong local lock-in (i.e., persistent stability of local spatial patterns over time), with the emission center of gravity shifting southwestward. Economic development was the dominant positive driver (provincial contribution rates: 275–529%), whereas energy intensity was the main mitigating factor (up to −409%). Population loss exerted a slight negative contribution, while energy structure showed only a weak inhibitory effect (−9.1%), reflecting the region’s path-dependent lock-in to fossil fuels. This study provides a scientific basis for differentiated carbon management strategies in Northeast China and analogous regions. Full article

Electro-optic frequency combs (EOFCs), generated through the microwave-driven modulation of continuous-wave lasers, have emerged as a highly reconfigurable and system-compatible class of optical frequency combs with growing importance in microwave photonics, coherent communications, spectroscopy, and precision metrology. In contrast to mode-locked lasers and Kerr microresonator combs, EOFCs offer electrically programmable repetition rates, deterministic phase coherence, and intrinsic compatibility with radiofrequency electronic systems, making them particularly attractive for integrated and application-oriented implementations. As EOFCs evolve toward broader bandwidths, lower power consumption, and full on-chip integration, their achievable performance is increasingly constrained by the interplay between electro-optic physical mechanisms, modulator architectures, and material platform properties. This review establishes a unified analytical framework that systematically connects EOFC generation mechanisms, device configurations, key performance metrics, and platform-level limitations. We first summarize the fundamental electro-optic effects underpinning EOFC generation and analytically examine representative modulator architectures, including phase modulators, Mach–Zehnder modulators, and microresonator-based schemes, to clarify their respective comb-generation characteristics. Key performance determinants, such as modulation depth, bandwidth, electro-optic efficiency, and optical loss, are then discussed to elucidate their coupled influence on comb-line count, spectral flatness, output power, and phase noise. Subsequently, the performance of EOFCs implemented on major integrated platforms, including Silicon on Insulator (SOI), Indium Phosphide on Insulator (InPOI), Lithium Niobate on Insulator (LNOI), and Lithium Tantalate on Insulator (LTOI), is comparatively reviewed to highlight the material-dependent advantages and constraints. Finally, emerging directions based on heterogeneous integration and ferroelectric materials with ultrahigh electro-optic coefficients are discussed as promising pathways to overcome the current performance bottlenecks. This review provides clear physical insights and engineering guidance for the future development of high-performance, integrated EOFC systems. Full article

Coastal bottom dissolved oxygen (DO) depletion poses a serious threat to marine ecosystems and aquaculture, and hypoxic events in the semi-enclosed Jinhae Bay, Korea, repeatedly cause large-scale damage to fish farms. Accurate DO prediction models are therefore crucial for ecosystem management and loss mitigation. This study analyzes how different tidal input representations affect the performance of data-driven DO prediction models in a tide-dominated coastal environment. Using time-series data of oceanographic and meteorological variables from nearby observation sites, we develop an long short-term memory (LSTM)-based neural network ensemble model with four experimental configurations. These include not only water level but also tidal envelope, tidal-intensity proxy, and temporal differences in water level and DO (Δtide, ΔDO) as additional inputs. Compared with the baseline configuration, the full tide-informed input case reduced the 72 h mean root mean square error (RMSE) from 1.16 to 1.12 and increased the Pearson correlation coefficient from 0.873 to 0.883. It also improved the representation of intraday variability and prediction stability. These results show that tide-derived variables help the model more effectively capture tidal-phase-locked DO fluctuations, while temporal-difference inputs further strengthen short-term variability and sensitivity to DO changes. These results indicate that properly representing tidal forcing is essential for learning the temporal structure and variability of coastal bottom DO. Full article

Ultrafast fiber lasers operating near 2 μm have emerged as a critical platform for advancing mid-infrared photonics due to their narrow pulse durations, high peak powers, and broad tunability. These sources exploit the rich energy-level structures of Tm³⁺ and Ho³⁺ doped fibers and reside within an atmospheric transmission window, enabling applications spanning nonlinear microscopy, precision micromachining, optical frequency metrology, biophotonics, and free-space optical communication. Recent progress in low-loss fiber fabrication, dispersion-engineered cavity design, and mode-locking technologies has significantly expanded the performance boundaries of 2 μm ultrafast fiber lasers. This review systematically examines the underlying pulse-formation mechanisms and categorizes state-of-the-art mode-locking approaches. Representative laser architectures are compared with respect to pulse duration, energy scalability, repetition-rate enhancement, spectral characteristics, and environmental stability. Key application pathways in high-resolution spectroscopy, biomedical diagnostics, and mid-IR supercontinuum generation are highlighted. Finally, the remaining challenges and prospective research directions are discussed to inform the development of next-generation ultrafast photonic sources in the 2 μm band. Full article

Deep-sea mining operations demand continuous, drift-free positioning over multi-day missions—a requirement that traditional acoustic dead-reckoning systems struggle to meet due to cumulative error accumulation and frequent DVL bottom-lock loss in sediment plume environments. Inspired by Google Cartographer’s 2D grid mapping paradigm, we present a prior map-based visual localization framework that decouples offline mapping from real-time localization, fundamentally eliminating drift through absolute image registration against pre-built seabed mosaics. By integrating adaptive keyframe selection, Multi-Scale Retinex (MSR) enhancement, and the AD-LG deep feature matching architecture, our system constructs globally consistent seabed maps for absolute positioning. The framework leverages deformable convolutions and LightGlue to effectively mitigate challenges such as low texture and non-rigid distortion. Quantitative validation on tank simulation datasets demonstrates significant superiority over IMU-only and standard fusion schemes; qualitative deployment on real Pacific CCZ imagery confirms near-real-time operational feasibility on an embedded Jetson Orin NX platform. This system establishes visual navigation as a viable backup to acoustic systems, addressing a critical gap in deep-sea mining vehicle autonomy. Full article

Rapid urbanization and agricultural expansion in arid regions have profoundly altered carbon cycles and landscape stability. Focusing on the Hexi Corridor, China, this study integrates multi-source geospatial data (1990–2020) to analyze the spatiotemporal evolution and driving factors of land-use carbon emissions (LUCE) and landscape ecological risks (LER). By integrating carbon accounting, LER assessment, bivariate spatial autocorrelation, and the Optimal Parameter Geographic Detector (OPGD), we quantify the intricate relationship between carbon dynamics and landscape integrity. Results indicate a transformative pattern of anthropogenic expansion and natural contraction, with a 2315.49 km² net loss of unused land. Net carbon emissions surged 4.6-fold, while forest and grassland sinks exhibited a significant “lock-in effect” due to fragile ecological foundations. Simultaneously, LER followed an “inverted U-shaped” trajectory; the refined 5 × 5 km grid scale revealed a significant drop in high-risk areas from 44.65% to 10.96% following ecological restoration. Spatial analysis reveals a significant “spatial mismatch” between LUCE and LER, with oases manifesting “high carbon–low risk” clustering. Driver detection confirms a driving asymmetry. LUCE is dominated by anthropogenic factors (nighttime light, q > 0.90), whereas LER is profoundly constrained by natural backgrounds. Future governance must shift toward a collaborative system centered on source-based emission control and precise regional management to synergize low-carbon transition with landscape security. Full article

The millisecond-level volatile fluctuations in workloads in large-scale intelligent computing clusters pose significant challenges to traditional electricity markets. Constrained by optical–electrical–optical conversion bottlenecks, these markets struggle to achieve real-time response and risk substantial social welfare loss. Leveraging existing fiber-optic infrastructure to build All-Optical Networks (AONs) presents a cost-effective evolutionary path. This paper develops a distributed energy trading strategy based on all-optical multicast. By utilizing the physical multicast properties of the underlying light-tree architecture instead of traditional protocols, the proposed strategy bypasses end-to-end latency constraints. This enables rapid transaction synchronization and dynamic tracking of social welfare optima within millisecond-level time-slots. Simulation results demonstrate that the proposed scheme elevates the transaction saturation threshold by two orders of magnitude compared with traditional strategies, effectively breaking the physical locking effect of latency on system throughput. Across various topologies, the social welfare gains exceed those of conventional schemes by more than 20 times. This study validates the engineering value of all-optical architectures for high-frequency trading and provides critical technical support for ultra-dynamic power trading algorithms. Full article