Search Results (154)

Transformer-based speech enhancement (SE) architectures suffer from high computational complexity, while existing lightweight state space model (SSM) approaches are constrained to fixed one-dimensional scanning that cannot fully exploit the two-dimensional time–frequency structure of speech spectrograms. To address these limitations, we propose DOM-MUSE, a lightweight U-Net-style SE framework built upon the Mamba-2 SSM with four targeted innovations. First, a Deformable Feature Extractor (DFE) predicts per location spatial offsets that warp the feature sampling grid to align with speech formant trajectories and harmonic structures, providing geometrically coherent inputs to the state space model. Second, a DOM Mamba Block with Cross-Dimensional Gated Fusion (CDGF) deploys two parallel Mamba-2 instances scanning the time and frequency axes independently, and uses Taylor Channel Attention (TCA) to derive semantic gates that modulate each SSM output before fusion. Third, a Phase-Guided Feature Conditioner (PGFC) computes local phase-gradient gates that suppress noise-dominated activations prior to the SSM stage, making the feature extraction pathway implicitly phase-aware. Fourth, an Attention-Based Skip Connection (ABSC) replaces conventional concatenation skip connections with a learned channel gate, adaptively controlling the information flow from the encoder to the decoder. Experiments on the VoiceBank-DEMAND benchmark demonstrate that DOM-MUSE outperforms the reproduced MUSE baseline on all five evaluation metrics—including PESQ (+0.077), CSIG (+0.058), CBAK (+0.026), COVL (+0.070), and STOI (+0.002)—while reducing the parameter count by 24% (0.51 M to 0.39 M). Notably, DOM-MUSE also surpasses MUSE++ on perceptual quality metrics (PESQ +0.061, COVL +0.032) despite MUSE++ employing dynamic SNR augmentation and an augmented multi-objective loss that DOM-MUSE deliberately omits, demonstrating that the proposed architectural innovations yield genuine improvements independent of training strategy. When DOM-MUSE is additionally trained under the same augmented protocol as MUSE++, it achieves PESQ of 3.46 and COVL of 4.22, further confirming the complementary nature of architectural and training improvements. Full article

Regional electricity interconnections are increasingly recognised as enablers of cost-effective power system expansion, resilience and energy security in emerging economies. In East Africa, Kenya and neighbouring countries, namely Tanzania, Ethiopia, and Uganda, operate relatively low-carbon electricity systems; however, rapidly growing electricity demand and expanding thermal generation are placing upward pressure on grid emissions intensity. This study examines whether planned cross-border interconnections can mitigate this trajectory using OSeMOSYS Global v1.0.0, an open-source least-cost capacity expansion model, comparing stand-alone national power systems against an interconnected regional grid over 2022–2045. Results show that interconnection enables access to low-cost renewable electricity and facilitates surplus generation exports, maintaining system-wide carbon intensity within climate finance eligibility thresholds of 100 gCO₂/kWh. Outcomes are heterogeneous: Ethiopia and Kenya incur cost increases (+USD 481 million and +USD 568 million, respectively) attributable to transmission capital expenditure, whereas Tanzania and Uganda achieve net cost savings (−USD 590 million and −USD 891 million) alongside substantial emissions intensity reductions of 141.9 and 280.5 gCO₂/kWh, respectively. Regional emissions equity is preserved, with modest intensity increases in Ethiopia and Kenya offset by large reductions elsewhere. These findings strengthen the case for climate-financed regional transmission as a scalable and equitable mitigation strategy in East Africa. Full article

Universities worldwide are increasingly committing to carbon neutrality; however, most institutional climate strategies treat operational emissions forecasting and ecosystem-based carbon sequestration as separate analytical domains, leading to inconsistencies in accounting boundaries, temporal alignment, and verification practices. This study develops and demonstrates an integrated LEAP–InVEST framework that explicitly links energy-system modeling with spatial ecosystem carbon accounting within a unified monitoring, reporting, and verification (MRV)-aligned structure. The framework combines the Low Emissions Analysis Platform (LEAP) for scenario-based greenhouse gas emissions modeling with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for spatial carbon storage assessment. A key methodological contribution lies in reconciling emission flows and carbon stock changes by converting carbon stock variations into annualized removal flows, thereby enabling consistent estimation of gross emissions, carbon removals, and net emissions while avoiding double counting across scopes. Using a university campus in Taiwan as a case study, a baseline inventory was established following ISO 14064-1 standards, and future emissions trajectories were simulated under Business-as-Usual and mitigation pathways through 2040. In parallel, land-use and land-cover data were used to quantify historical and projected carbon stocks across forest, grassland, agricultural, and built-up areas. Results indicate that electricity consumption constitutes the dominant emissions source, and that energy efficiency improvements, photovoltaic deployment, and green power procurement provide the largest mitigation potential. Although ecosystem carbon stocks remain substantial, their annual sequestration capacity offsets only a limited portion of projected emissions, reinforcing the importance of prioritizing emissions reduction before applying nature-based removals. The proposed framework provides a transferable methodological approach for institutional carbon neutrality planning by integrating emissions reduction and carbon sequestration within a coherent analytical system. By aligning energy modeling, ecosystem dynamics, and MRV principles, the framework enhances the transparency, credibility, and robustness of net-zero pathway assessment and is applicable to universities and compact urban systems seeking data-driven and verifiable decarbonization strategies. Full article

Point-the-bit rotary steerable systems (RSSs) achieve trajectory build-up through the coupled action of internal steering offset, bit attitude change, bottom hole assembly (BHA) flexure, and nonlinear wellbore interaction. Unlike conventional rigid or quasi-static BUR models, this study developed a 3D dynamic finite element model for point-the-bit RSS. The drill string was discretized using Euler–Bernoulli beam elements, with an equivalent “hinge-deflection angle” constraint introduced at the steering unit. Relative angle loading was imposed using the penalty function method, with nonlinear boundary conditions (bit–formation interaction and borehole friction) coupled into the model. Based on the established model, the effects of deflection angle, weight on bit (WOB), and rotary speed were systematically quantified. The results show that when the deflection angle increases from 0.5° to 1.5°, the average BUR rises from 1.452°/30 m to 4.251°/30 m; when the WOB increases from 60 kN to 100 kN, the average BUR increases from 2.281°/30 m to 2.814°/30 m. Within the range of 50–90 r/min, rotary speed has a limited effect on the average BUR, but it can alter the characteristics of transient fluctuations. This approach provides a robust theoretical basis for BUR evaluation, parameter optimization, and control strategy design for rotary steerable tools. Full article

This study examines the time–frequency dynamics between CO₂ emissions and their determinants—oil prices, renewable energy deployment, and climate policy uncertainty—in Türkiye from 1987Q2 to 2024Q1. We integrate a rolling-window Nonlinear Autoregressive Distributed Lag (NARDL) model with wavelet coherence analysis to capture evolving asymmetric effects and multi-scale transmission mechanisms. Our findings reveal pronounced, persistent asymmetries. Oil price decreases stimulate CO₂ emissions substantially more than equivalent price increases reduce them, yielding a negative asymmetry effect. Renewable energy demonstrates a stable, negative long-run relationship with emissions, with wavelet analysis indicating this effect concentrates over medium-to-long-term horizons, underscoring its structural decarbonization role. Climate policy uncertainty exerts fragmented, episodic influences, disrupting short-to-medium-term emission trajectories. Rolling-window estimates confirm these asymmetric relationships shift markedly around structural breaks, including the 2001 domestic crisis and the 2008 global financial crisis. The study concludes that effective decarbonization requires temporally calibrated policies: counter-cyclical carbon pricing to offset oil price asymmetries, and credible long-term frameworks to sustain renewable energy investments. Methodologically, the results demonstrate the value of combining time-domain and frequency-domain techniques to diagnose complex, evolving interactions in the energy–environment nexus. Full article

Advances in implantable and wearable cardiac monitoring technologies have led to widespread detection of brief, often asymptomatic atrial high-rate episodes, frequently labelled as device-detected atrial fibrillation (AF). While detection has increased substantially, the clinical interpretation of these findings remains uncertain. Observational studies demonstrate associations between AF burden and stroke risk but reveal marked inter-individual heterogeneity and no consistent temporal threshold below which risk is eliminated. Recent randomised controlled trials show that anticoagulation guided solely by arrhythmia duration confers limited net clinical benefit, with modest reductions in ischaemic stroke offset by increased bleeding. These findings challenge the biological and clinical validity of rigid time-based thresholds for intervention. Increasing evidence suggests that AF may act primarily as a marker of underlying atrial disease rather than the sole mechanistic cause of thromboembolism. This article provides an evidence-informed perspective on the interpretation of device-detected AF in contemporary clinical practice and argues for a shift away from duration-based triggers toward a longitudinal, risk-adapted approach that integrates AF trajectory, atrial substrate, and clinical context. Emerging tools such as artificial intelligence-enhanced electrocardiography may help identify occult atrial pathology but must augment rather than replace clinical judgement. Proportionate, individualised care should supersede reflexive treatment strategies in the management of device-detected AF. Full article

The rotary steerable system (RSS) is the core equipment for precise wellbore trajectory control in deep oil and gas drilling, and its performance is directly determined by the coordination and adaptability of the tool’s offset actuator and control platform. To overcome the limitations of complex control architectures and low positioning accuracy of conventional offset actuators for rotary steering drilling tools, a novel three hydraulic cylinder synchronous steering offset actuator driven by a drilling fluid rotary valve distributor, along with its dedicated control strategy, is proposed. Laboratory experiments and numerical simulations are performed to analyze the piston displacement characteristics of the three hydraulic cylinder under different drilling fluid flow rates and rotary valve rotational speeds. The results demonstrate that the proposed actuator exhibits controllable piston displacement behavior. The simulated and experimental data show consistent variation tendencies with a relative error of less than 8%, thus validating the reliability of the proposed numerical model. Increasing the flow rate from 1 to 1.5 L/s increases the cycle-averaged peak-to-peak piston displacement by 14.5 mm, while raising the rotational speed from 60 rpm to 120 rpm reduces it by 25.3 mm, corresponding to a dogleg severity variation of approximately 1.9–3.1°/30 m. Piston displacement deviations are mainly attributed to valve port machining tolerance, drilling fluid compressibility, pipeline pressure loss, and internal leakage, and these discrepancies are exacerbated as the rotary valve speed or flow rate increases. Finally, optimization strategies for improving synchronization performance are proposed, thereby providing theoretical and technical support for the engineering implementation and parameter optimization of the proposed actuator. Full article

Fiber-optic shape sensing enables real-time monitoring of structural deformation across a wide range of applications. For large-scale structures, Brillouin-based distributed sensing, typically implemented through Brillouin Optical Time Domain Analysis (BOTDA), offers an extended range for quasi-static measurements, albeit its limited spatial resolution degrades reconstruction accuracy. This study addresses this fundamental limitation through the introduction of a novel error compensation algorithm, particularly suited for a Brillouin-based shape sensing system, yet agnostic with respect to the sensing technology. The method leverages both the initial and final points of the sensing path, performing both forward and backward reconstructions and fusing the two trajectories by testing several polynomial and exponential weighting strategies. The algorithm is experimentally validated on a 28.91 m four-core shape sensing fiber cable (length = L), interrogated through BOTDA operating at 50 cm spatial resolution, and reconstructed through the Frenet–Serret frame formulation. Calibration procedures include radial-offset tuning and segment alignment via a hotspot reference. A non-trivial S-shaped geometry is adopted as a case study, specifically addressing curvature discontinuities arising from mixed straight and curved segments. Reconstruction accuracy is quantified through a Euclidean-distance-based Figure of Merit (FOMs). The cubic weighting strategy demonstrates improvements exceeding 86% in all FOMs compared to classical methods without compensation. Specifically, it achieves an RMSE of 0.145 m (0.50% of L), a MAE of 0.109 m (0.38% of L), and a maximum error of 0.341 m (1.18% of L). Remarkably, these percentage errors are of the same order of magnitude as those reported in the literature for Fiber Bragg Grating (FBG) and Optical Frequency Domain Reflectometry (OFDR) systems, indicating that the proposed compensation strategy enables BOTDA-based shape sensing to achieve comparable reconstruction accuracy despite its lower spatial resolution. Full article

Roadside perception systems, also known as roadside units (RSUs), are critical in Vehicle-to-Everything (V2X) applications, yet spatio-temporal asynchrony between multiple sensors severely compromises the accuracy of fusion. In this paper, a spatio-temporal synchronization method for millimeter-wave (MMW) radar and camera fusion is proposed, integrating target matching based on dynamic time warping (DTW) with spatio-temporal parameter estimation. Leveraging the advantages of DTW in time-series alignment to calculate the similarity between radar and visual trajectories enables target matching and parameter estimation in sparse scenes. This method was validated on a real-world dataset containing over 30 pedestrian trajectories, covering scenarios with varying densities ranging from one to six pedestrians. The results indicate a temporal offset of 0.116 s between the camera and radar. Following synchronization, the average spatial deviation decreased from 1.4358 to 0.1074 m in the x-direction (i.e., across the road) and from 3.0732 to 0.1775 m in the y-direction (i.e., along the road). Consequently, this method provides an efficient solution for deploying roadside perception systems in sparse traffic environments. Full article

The vertical-wheel PDC bit adds a rotatable wheel cutter to conventional fixed PDC blades, creating a dual-structure cooperative rock-breaking system. A synergistic design theory is established through the following consecutive steps. Firstly, a fully coupled digital model of the wheel cutters, fixed blades and rock was built; load-calculation methods for each cutter type were derived, enabling the WOB distribution to be predicted by simulation. Secondly, for complex drilling modes, such as mixed-mode rotary steering, the wheel must be located at the instantaneous resultant force point of the bit to maximize buffering and torque mitigation; the locus of this point was traced while drilling. Thirdly, a proportional relationship between relative cutter exposure and weight on bit share was validated and used to synchronize the cutting trajectories of the two structures. Finally, systematic design criteria for wheel diameter, shaft inclination, normal offset, offset distance, cutter shape and wheel count were formulated. The results provide a theoretical basis and a technical roadmap for high-efficiency, long-life VW-PDC bit design. Full article

In smart campus construction, behavior pattern recognition and perturbation analysis serve as the cornerstones for achieving a transition from passive response to dynamic regulation, with intelligent perception and anomaly diagnosis methods based on campus traffic flow underpinning transportation system resilience. Traditional research methods suffer from issues such as privacy risks, coarse modeling, and limitations from single data formats, labeling difficulties, and coverage gaps. This study proposes a refined semantic trajectory construction method that integrates multi-source data (e.g., mobile signaling data, maps and weather conditions), known as the Campus Transportation Semantic Trajectories Space (CTSTSpace) framework. It enables the precise identification of semantic origin–destination points from dynamic personnel trajectories, quantifies service performance through real-time road network mapping, and models multidimensional perturbations, achieving full campus coverage without complex labeling while ensuring robust privacy protection. Under clear weather conditions, the analysis demonstrates accurate recognition of travel behavior patterns (dwelling, aggregation, mobility, and congestion) that synchronize with class schedules, where vehicle speeds drop by over 50% during peak hours. Under rainy weather perturbations, it captured demand shifts (e.g., peak hour offsets of 30–60 min and a 6.8–9.2% reduction in long-distance dining trips) and speed reductions (52.15–73.74%). This approach provides critical insights for resilient smart campus traffic management. Full article

Lane departure can cause lateral vehicle collisions and, in severe cases, lead to vehicles running off the road. Such incidents often occur on curved sections and ramps. This study focuses on loop ramps. To quantify the impact of geometric alignment characteristics of loop ramps on lane departure behaviors, unmanned aerial vehicle (UAVs) aerial photography was used to collect operation videos of 10 loop ramps at 6 interchanges, and 762 pieces of vehicle trajectory data under free-flow conditions were extracted based on DataFromSky. Combined with the indicators of equivalent radius and trajectory design curvature difference, vehicle trajectories were systematically classified into three patterns via k-means clustering: in the direction of centrifugal force (IDCF), against the direction of centrifugal force (ADCF), and no-offset normal driving (NOND). A multinomial logistic regression model was constructed to analyze the influence of loop ramp geometric alignment characteristics on departure behaviors. The results show that for the horizontal alignment elements of loop ramps, an increase in circular curve radius, a decrease in circular curve length, and a decrease in the length of the transition curve entering the circular curve all increase the risk of IDCF; conversely, the increase in these geometric parameters tend to increase the risk of ADCF. For the vertical alignment elements, there is a significant nonlinear negative correlation between the adjacent maximum gradient difference and lane departure behaviors. For the cross-section of loop ramps, widening can significantly suppress the risk of IDCF but slightly increase the risk of ADCF. This study reveals the synergistic influence mechanism of the three-dimensional (horizontal, vertical, and cross-sectional) geometric characteristics of combined alignments on lane departure behaviors at interchange loop ramps. Full article

In ultrasonic nondestructive testing, maintaining the ultrasonic sensor in normal contact with curved surfaces is pivotal for acquiring valid defect signals. Replacing manual operation with a robotic arm ensures stable signal collection, while stable and fast trajectory planning for complex curved-surface tracking remains a key challenge. This research investigates gesture-driven robotic trajectory planning and impact optimization via the particle swarm optimization (PSO) algorithm in the robot joint space for rapid and smooth movement. Gesture trajectories are acquired via a Leap Motion device, with unified mapping established through spatial transformations among gesture, simulation, and experimental robot spaces. PSO is utilized to optimize trajectories, enhancing accuracy and controllability. Median filtering is applied to trajectory coordinate data to suppress errors from hand tremor and sensor limitations, followed by introducing a surface normal offset to generate pose matrices at each trajectory point. Systematic comparison of interpolation methods (polynomial, cubic spline, circular, cubic B-spline) reveals that cubic B-spline interpolation achieves the shortest execution time under angular acceleration constraints. The results show that PSO optimizes point-to-point trajectories based on 5-5-5 polynomial interpolation, with impact force and execution time as objectives, yielding the optimal trajectory with minimal time under acceleration constraints. This research provides valuable methodological references for robotic manipulator trajectory planning and optimization in complex curved-surface ultrasonic testing. Full article

Background: Meal timing has emerged as a potential determinant of healthy aging; however, evidence linking temporal dietary patterns (TDPs) with frailty remains limited. We aimed to identify distinct TDPs among older adults and examine their associations with frailty and its components. Methods: In this cross-sectional study, 4184 adults aged ≥ 65 years from the Korea National Health and Nutrition Examination Survey (2016–2018) were analyzed. Temporal energy-intake trajectories from 24 h recalls were clustered using dynamic time warping-based kernel k-means. Frailty was defined using a modified Fried phenotype, and diet quality was assessed employing the Healthy Eating Index. Survey-weighted logistic regression and mediation analyses were performed. Results: Five distinct patterns were identified as follows: balanced (n = 1665, 38.8%), steady (n = 735, 17.8%), midday (n = 737, 18.0%), evening (n = 627, 15.2%), and morning–evening (n = 420, 10.2%). Among these, the evening-skewed (characterized by a disproportionate concentration of energy intake in the late evening; adjusted odds ratio [OR] = 1.48, 95% confidence interval [CI] = 1.03–2.10) and morning–evening (characterized by higher energy intake in both the morning and evening; OR = 1.43, 95% CI = 1.01–2.03) patterns were associated with higher frailty risk than the balanced pattern. Mediation analysis showed that higher total energy intake had a protective role in the evening-skewed pattern; however, this benefit was offset by the adverse impact of late-night eating, resulting in increased frailty risk. In the morning–evening pattern, both a direct association with frailty and an indirect pathway mediated by lower energy intake and poorer diet quality contributed to the increased frailty risk. Conclusions: Older adults with evening-skewed or morning–evening TDPs had greater frailty risk than those with balanced eating patterns. Optimizing meal timing—by reducing late-day energy loading and ensuring adequate overall intake and dietary quality—may represent a feasible chrono-nutrition strategy for frailty prevention. Full article

Background: Laparoscopic living donor nephrectomy is a standard approach for kidney procurement, yet optimal technique and learning curve trajectories remain incompletely characterized. We present a high-volume single-center experience with standardized transperitoneal laparoscopic donor nephrectomy and CUSUM-based learning curve analysis. Methods: Retrospective analysis of 1446 consecutive laparoscopic living donor nephrectomies performed by six surgeons between January 2015 and December 2024. Learning curve analysis used the cumulative sum (CUSUM) methodology to identify proficiency phases. The most recent 200 consecutive cases, representing mature institutional performance, were analyzed for detailed outcomes. The surgical technique employed a transperitoneal approach with the GelPOINT^® Advanced Access Platform for kidney extraction via an offset Pfannenstiel incision. Results: CUSUM analysis identified case 669 as the inflection point, defining four phases: Phase I (initial learning, cases 1–250, n = 250, 154.6 ± 35.9 min), Phase II (rapid improvement, cases 251–669, n = 419, 136.7 ± 32.6 min), Phase III (consolidation, cases 670–1000, n = 331, 118.0 ± 30.1 min), and Phase IV (mastery, cases 1001–1446, n = 446, 101.5 ± 26.2 min). Overall operative time decreased from 154.6 to 96.8 min (37.4% reduction, p < 0.001). In the 200-case mastery-phase cohort, mean operative time was 96.8 ± 25.5 min with warm ischemia time of 3.8 ± 1.2 min. There were no conversions to open surgery (0%), no intraoperative complications, and one major postoperative complication (0.5%, Clavien–Dindo ≥ IIIa). Left kidney procurement was performed in 99.5% of cases. Among male donors (n = 86), systematic orchalgia surveillance demonstrated 46.5% prevalence at 1 month, declining to 36.0% at 1 year, and 7.0% at a 5-year follow-up. Conclusions: This high-volume single-center experience demonstrates favorable outcomes in laparoscopic living donor nephrectomy with CUSUM-defined proficiency phases extending beyond 1000 cases. The outcomes observed likely reflect the combined effects of institutional volume, team experience, and standardized technique. Multi-center validation is required before generalizing these results. Full article