Search Results (207)

The Tibetan Plateau ozone valley is a prominent summertime ozone minimum in the Northern Hemisphere upper troposphere–lower stratosphere (UTLS), with implications for regional radiative balance and climate. Although previous studies emphasized large-scale circulation during the Asian summer monsoon, the contributions of local topographic and thermodynamic processes remain less well quantified, in part because most analyses rely on coarse-resolution data. Here we develop a refined multi-source remote sensing framework integrating high-resolution TROPOMI observations (total column ozone, NO₂, and HCHO), Microwave Limb Sounder (MLS) ozone profiles, ERA5 reanalysis, and SRTM topography. Within a physically informed scheme, local factors are treated as pixel-level predictors and large-scale circulation as regional-mean background fields, complemented by interpretable machine-learning attribution (XGBoost-SHAP). The ozone valley exhibits a pronounced horizontal and vertical structure, with its core anomaly confined to the UTLS and spatially anchored to plateau topography. The covariability between the South Asian High (SAH) and ozone mainly reflects a shared response to thermal forcing, while circulation modulates its amplitude. Attribution shows that topography (~41%) and stratosphere–troposphere exchange (~16%) together dominate the spatial organization of the ozone minimum (~58% of the explained variance; anomaly–elevation slope −0.0035 DU m⁻¹, R² = 0.89), whereas large-scale circulation (~13%) mainly drives its seasonal amplification. Mechanistically, the elevated plateau and intense summer heating lift the tropopause, injecting ozone-poor tropospheric air into the stratospheric reservoir and thereby diluting its ozone content. Consequently, while topographic and thermodynamic factors dictate the spatial pattern of ozone, large-scale circulation primarily modulates its seasonal amplitude over complex terrain. The ozone valley thus represents a quasi-stationary climatic imprint, and our framework advances refined satellite-based atmospheric observation over complex terrain. Full article

In land-scarce urban regeneration, small public green spaces (SPGSs) are increasingly used to improve everyday green-space access. However, their contribution to spatial provision equity in urban regeneration depends not only on how many small green spaces are built, but also on whether they compensate communities with greater spatial relief needs. Taking Shenzhen, China, as a case, this study develops a community-scale need–provision matching framework to evaluate the compensatory performance of 537 post-2016 SPGSs across 582 valid residential communities. Spatial relief needs were measured using a composite Spatial Relief Need Index (SRNI) based on traditional park deficiency, built-up intensity, and residential land concentration; the analysis therefore addresses spatial provision equity rather than broader social equity. SPGS provision was measured by distinguishing network-accessible SPGSs, evaluated through pedestrian-network accessibility, from residential-embedded SPGSs, evaluated as internal residential green-space supplementation. Communities were then classified into four compensation typologies: effective compensation, compensation failure, potential amplification (a diagnostic low-need–high-provision category), and low need–low provision. The results show that SPGS provision in Shenzhen is spatially fragmented and does not consistently align with high-need communities. Effective compensation occurred in 128 communities, while 163 high-need communities remained underprovided, and 163 lower-need communities received relatively high provision. Compensation failure and potential amplification together accounted for 56.0% of valid residential communities, exceeding the 50.0% random-label expectation (two-sided permutation p = 0.005), although the association was small (|φ| = 0.120). The traditional park deficit-supplementation and need-oriented compensation indicators yielded 58.8% and 44.0%, respectively; because their definitions and thresholds differ, this contrast is descriptive rather than a direct scale-effect estimate. The findings indicate that SPGS policy should move beyond project counts and coverage expansion toward community-scale, need-oriented allocation so that incremental greening can better support spatially responsive and sustainable urban regeneration. Full article

Small unmanned aerial vehicles (UAVs) are widely used in civil and military fields, and their noise problem has always been the industry’s focus. Compared with a traditional propeller fan, a ducted fan offers higher aerodynamic efficiency, lower aerodynamic noise, and greater safety. It has become the key power component of small UAVs. However, due to the rigid restriction on tip clearance, the traditional integral ducted fan is prone to generating a tip leakage vortex, which produces high-intensity aerodynamic noise and significantly reduces propulsion efficiency. To address the above key problem restricting the quiet flight of small UAVs, this paper designs a segmented ducted fan (SDF). It preliminarily explores the influence of the segmented clearance on the fan’s flow field structure and acoustic radiation characteristics. Specifically, the k-ω SST (shear stress transport) turbulence model and the broadband noise source model were used to establish a computational fluid dynamics model, and the effects of fan speed (20,000–40,000 rpm) and duct spacing (0–20 mm) on its aeroacoustic characteristics were systematically studied. The results showed that the SDF’s acoustic power level maximum (APL_max) was significantly higher than that of the traditional integral structure, especially at high speed. At 40,000 rpm, increasing the duct spacing to 20 mm resulted in a sudden increase in APL_max to 194.5 dB, 61.3 dB higher than that of the integral type. Its essence was derived from the three-stage chain amplification mechanism: (1) strong tip leakage vortex induced by geometric clearance; (2) broadband noise caused by vortex impacting the duct wall; (3) resonant coupling of leakage vortex harmonic frequency and duct cavity standing wave. Based on this, a collaborative noise reduction path was proposed: compressing the spacing to ≤10 mm to suppress the intensity of leakage vortex, designing the periodicity of failure vortex combined with the serrated blade tip/inner wall rubber strip, and blocking the acoustic cavity resonance with non-uniform wall stiffness or 8–10 kHz Helmholtz resonator, providing a solution for the low-noise design of UAV propulsion system. Unfortunately, our study cannot currently resolve transient characteristics; only time-averaged velocity/pressure flow-field contours and total acoustic power distribution are obtained for qualitative analysis of macroscopic noise variation laws and flow-sound correlation. Full article

Mediterranean islands face increasing climate risks from rising temperatures, prolonged droughts, extreme precipitation, and sea-level rise, while seasonal tourism intensifies water and energy demand during the most vulnerable periods of the year. This study examines whether incorporating tourism as an intrinsic component of the Water–Energy–Food nexus changes the assessment of climate risks in desalination-dependent island water systems. To address this question, the Water–Energy–Food (WEF) nexus is extended to Water–Energy–Food–Tourism (WEFT) and integrated into an EU-aligned Climate Risk and Vulnerability Assessment framework. The approach is applied to the Hermoupolis Water Supply System on Syros Island, Greece, where potable water supply depends largely on energy-intensive desalination. A technically bounded climate risk assessment is compared with a WEFT-adjusted assessment that accounts for tourism-driven demand amplification and water–energy interdependencies while keeping hazard exposure and likelihood climate-driven. The results show that heatwaves constitute the dominant near-term risk because they coincide with peak water demand and high electricity requirements for desalination. When tourism amplification is included, drought-related risks shift from medium to high already in the near future for key production and pumping components, indicating earlier emergence of critical risk conditions without changes in hazard probability. Coastal risks become more important toward the end of the century, especially under high-emission scenarios. The main contribution of the study is to show that tourism-driven amplification can be operationally incorporated into sensitivity and impact assessment while preserving comparability with a conventional CRVA. The proposed WEFT–KTM framework provides a transferable basis for assessing and prioritizing adaptation in desalination-dependent, tourism-driven Mediterranean island systems. Full article

This study presents an unsteady numerical investigation of multi-port lateral jet interaction phenomena on a hypersonic vehicle configuration. An unsteady RANS approach with Menter’s SST k-ω model is implemented to investigate the transient interference mechanisms among single-, triple-, and quintuple-port arrangements, focusing on jet initiation and termination transients. Upstream jets establish bow shocks and a separation zone that progressively degrade the effective pressure ratio for downstream ports. This aerodynamic shielding manifests as nonlinear escalation in coupling intensity, with the quintuple-port configuration exhibiting complex multi-level shock systems distinct from simple superposition of single-port effects. Flow field development completes within approximately 0.5 ms, yet jet-induced vortical structures exhibit pronounced temporal hysteresis during the decay phase, with the high-pressure zone dissipating progressively from upstream to downstream regions. Under steady-state conditions, the quintuple-port arrangement attains a normal force amplification coefficient of 1.044 alongside a pitching moment amplification coefficient of 4.387, illustrating substantial moment augmentation potential inherent to multi-port interference effects. These findings furnish theoretical foundations for Reaction Control System (RCS) port layout optimization and control strategy development in hypersonic flight vehicles. Full article

This paper presents a constant-temperature, single-heat-source continuous-flow PCR (CF-PCR) microdevice that achieves stable thermal control for denaturation, annealing, and extension on a single platform. Key innovations include: (1) a metal-powder/PDMS thermal conduction block with trapezoidal geometry that generates a programmable temperature gradient and tunable residence times under one heat source; and (2) a thermoelectric cooler (TEC)-based Peltier system that creates distinct high- and low-temperature zones by co-optimizing the hot/cold side temperature difference, spacer material (92% alumina), and input voltage (3.6 V). A self-pressurized gas-diffusion micropump, enabled by a capillary quartz tube at the outlet, drives continuous sample flow without external actuation. The platform features three configurations: an on-chip zoned-heating design, an off-chip coiled-tube setup, and a battery-powered handheld system (727 g, 6 W, ~4 h runtime). Using CNC-machined and thermally bonded PMMA microchips with BSA passivation, the on-chip device achieves ~80% amplification efficiency relative to commercial instruments for H7N9 and pGEM-3Zf(+); the off-chip version reaches ~75%. The portable system yields HPV and RUBV amplification intensities comparable to benchtop devices. This approach provides a practical, scalable solution for “sample-in–answer-out” nucleic acid testing in point-of-care settings. Full article

The Atlantic Meridional Overturning Circulation (AMOC), as the core component of the global thermohaline circulation, exerts a profound influence on the Northern Hemisphere climate. Recent observations show that AMOC intensity has weakened by approximately 15% over the past 40 years, yet the traditional theoretical framework dominated by the North Atlantic Oscillation (NAO) cannot fully explain its spatial heterogeneity. This study systematically quantifies the independent driving mechanism of the Arctic Oscillation (AO) on AMOC decline for the first time by integrating multi-source reanalysis data (ERA5, ORAS5) and CMIP6 model output. Theoretical analysis shows that the AO positive phase regulates the stability of AMOC through two coupled pathways: (1) anomalous wind stress curl leads to the weakening of Ekman suction in the subpolar seas (contribution: 42 ± 6%), inhibiting deep-water formation in the Labrador Sea; and (2) increased freshwater flux through the Fram Strait triggers a negative salinity advection feedback, which leads to shoaling of the North Atlantic high-latitude mixed layer by up to 30 m. The cross-scale interaction reveals that the AO interannual variability amplifies the modulation of the AMOC interdecadal trend. This amplification occurs through the positive feedback of sea-ice albedo. When AO and NAO are locked in opposite phases (AO+/NAO−), the AMOC weakening rate increases to 1.8 Sv/decade (1 Sv = 10⁶ m³/s), whereas the same-phase negative condition (AO−/NAO−) yields a moderate decline of 0.5 Sv/decade. This mechanism corrects the underestimation of the traditional wind-driven circulation theory for high-latitude processes and provides a physical attribution for the CMIP6 models’ systematic underestimation of AMOC sensitivity. The study further constructs the “Arctic Oscillation–subpolar basin–AMOC” three-pole coupling theoretical model and confirms that the Arctic amplification effect enhances the AO–AMOC coupling strength by a factor of 2.3 over the full study period (1979–2020; R² = 0.71, p < 0.01), with an even more pronounced enhancement of 2.1 times during the recent two decades (2000–2020; R² increased from 0.28 to 0.59). These findings have direct implications for coastal risk assessment, as AMOC weakening may accelerate sea-level rise along the North American East Coast and increase the frequency of extreme winter storm surges in European coastal areas. The results provide a dynamic basis for IPCC climate risk assessment and have practical application value for the early warning of extreme cold-wave events. Full article

The long-term reliability of stay cables is essential to the structural integrity of cable-stayed bridges, particularly under the coupled effects of high stress ratios, progressive corrosion, and local stress concentrations. Conventional fatigue formulations—such as S–N curves or static Weibull models—are inadequate for representing the nonlinear and stochastic nature of corrosion-fatigue deterioration. This study develops a time-dependent reliability model formulated within a four-parameter Weibull framework, where the shape and scale parameters evolve as functions of the corrosion rate and stress ratio. The corrosion evolution is modeled by an exponential function of exposure time, establishing a temporal coupling between mechanical loading and environmental degradation. Analytical derivations yield closed-form expressions for the time-dependent hazard function ℎ(t) and survival function R(t), providing explicit reliability evaluation without iterative computation. At the system level, a series–parallel reliability model is constructed by integrating wire-level degradation with a load redistribution function that captures sequential wire failures. Model parameters are estimated using a maximum-likelihood method based on 99 experimental datasets, and Monte Carlo simulations are performed to assess stochastic reliability evolution under varying corrosion intensities. The findings show that models incorporating corrosion evolution and stress-amplification effects consistently predict earlier fatigue failure than those based on the conventional assumption of constant corrosion, thereby offering a more conservative and realistic representation of structural degradation. The proposed framework is mathematically tractable and broadly applicable, enabling rigorous corrosion–fatigue reliability assessment for cable-stayed structures and other complex multi-component systems. Full article

Pediatric respiratory infections remain among the leading causes of emergency department visits, hospitalization and pediatric intensive care unit (PICU) admission. Although most acute respiratory infections in children are viral, clinical manifestations overlap substantially among viral, bacterial and atypical pathogens, creating diagnostic uncertainty and promoting empirical antimicrobial use. Rapid antigen tests, nucleic acid amplification tests, multiplex respiratory panels and metagenomic sequencing have expanded the ability to detect pathogens within clinically actionable timeframes. However, evidence from pediatric emergency trials indicates that rapid pathogen detection alone does not necessarily reduce antibiotic prescribing or healthcare costs. These findings suggest that the value of rapid diagnostics depends less on analytical breadth than on whether testing is applied to the right child, in the right clinical scenario and within a predefined decision pathway. This narrative review reorganizes the evidence around a scenario-driven top-pathogen framework. Top pathogens are defined as organisms that, in a specific age group, syndrome, season or care setting, have high prevalence, severe disease potential, transmissibility, treatment implications, antimicrobial resistance relevance or infection-control value. We discuss how top-pathogen testing should differ across emergency triage, inpatient ward management, severe pneumonia, PICU care, hospital-acquired pneumonia, ventilator-associated pneumonia and outbreak settings. We further examine the economic mechanisms through which rapid testing may generate value, including reduced unnecessary antibiotics, timely antiviral therapy, optimized isolation, shorter length of stay, reduced repeated testing and prevention of healthcare-associated transmission. Finally, we propose implementation principles centered on diagnostic stewardship, antimicrobial stewardship, local epidemiology and real-world cost-effectiveness evaluation. A scenario-driven top-pathogen strategy may provide a practical bridge between broad syndromic testing and precision infectious disease management in children. Full article

Landfalling typhoons are the dominant trigger of short-duration extreme rainfall along the Zhejiang coast. It is necessary to estimate the recurrence of future typhoon rainfall at the city scale under the global-warming scenarios. Using Super Typhoon Lekima (2019) as a representative high-impact event, this study develops an event-based assessment framework for Taizhou city by combining the Weather Research and Forecast (WRF) model simulation, pseudo-global-warming (PGW) perturbation experiments, and generalized extreme value analysis. The historical simulation is first evaluated against the China Meteorological Administration best track, storm intensity evolution, and station rainfall observations. Future counterparts of the same event are then generated using CMIP6-derived thermodynamic perturbations under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Finally, scenario-dependent rainfall totals are projected onto a historical GEV curve to identify equivalent historical rainfall return periods. Results show that the WRF setup reproduces the main track, intensity tendency, and rainfall timing of Lekima with reasonable fidelity. The ensemble-mean cumulative rainfall over the Taizhou area increases from 204.75 mm in the historical simulation to 335.85, 366.72, 400.79, and 464.08 mm under the four SSPs, respectively. These increases translate into equivalent historical rainfall return periods of 47.40, 84.61, 164.28, and 604.05 years, compared with 5.24 years for the historical case. The results indicate that the moderate thermodynamic rainfall amplification produces a highly nonlinear escalation of event rarity based on historical frequency statistics. This implies that future typhoon rainfall should be interpreted using scenario-aware benchmarks within the historical reference framework. Full article

Reliable monitoring of spontaneous affect is essential in biomedical sensing, where involuntary facial signals serve as objective indicators of physiological states. Micro expression recognition (MER) is particularly challenging due to the sub-second, low amplitude nature of these signals. Many existing MER methods rely on apex (peak) frame detection, making them sensitive to temporal localization errors and difficult to deploy in unconstrained settings. To address this, we propose an apex-free framework that analyzes facial dynamics by structuring motion-magnified features along a newly introduced magnification intensity axis. By applying Eulerian motion magnification across multiple discrete levels and collapsing the sequences into single accumulation images, we generate a multi-level representation of subtle facial dynamics without requiring frame-level annotations. A lightweight shared temporal mixer (STM) is employed to analyze the dynamic evolution of motion across the magnification intensity axis. Subsequently, a dual-branch convolutional neural network (CNN), processing low- and high-amplification regimes respectively, integrates a convolutional block attention module (CBAM) to capture subtle facial motion while effectively filtering out irrelevant noise. Our model is highly efficient, requiring only 0.94 M parameters and 262 MFLOPs, which is significantly lower than the computational demands of standard backbones such as ResNet18 or VGG16. To ensure the model generalizes to new individuals, we evaluated it by testing on subjects whose data was entirely excluded from the training process. Under this rigorous setup, the proposed method achieves approximately 80% and 70% accuracy on the CASME II and SMIC datasets respectively, showing performance comparable to, or in some cases, slightly above current state-of-the-art methods. Considering both the competitive accuracy and high computational efficiency, the proposed framework holds significant potential for practical integration into real-time affect monitoring systems, particularly within biomedical applications. Full article

The wide application of liquid hydrogen as a key energy carrier is severely limited by the reliability of high-pressure and low-temperature pumps. The traditional research on liquid hydrogen pumps relies on empirical analysis of isolated components, but fails to reveal the fundamental failure mechanism of these pumps. This review argues for a paradigm shift in the understanding and design of liquid hydrogen pumps. We systematically decomposed the failure of the liquid hydrogen pump into a thermodynamic-driven, cross-scale cascading process rather than the failure of isolated components. At the molecular level, the extreme thermal physical properties of liquid hydrogen (ultra-low latent heat and surface tension) can lead to widespread nucleation under slight thermal disturbances. At the mesoscopic scale, the initial perturbation is significantly amplified through the nonlinear dynamics of bubble clusters. This amplification is characterized by intense collapse and strong energy concentration due to the low density and low viscosity of liquid hydrogen. At the component level, this enhanced destructive energy will cause faults similar to phase transitions; namely, the liquid lubrication in the bearings will disappear, the seals will shift from viscous blockage to gas diffusion, and at the same time, the damage caused by low-temperature hydrogen cavitation and corrosion to the materials will also occur simultaneously. At the system level, the strong dynamic coupling among the subsystems has led to a nonlinear performance collapse. This cross-scale failure chain reveals the flaws in the classical cavitation theory, which is based on the assumptions of isothermal and inertia dominance. We have expounded the thermodynamic-dominated cavitation state in liquid hydrogen. This state is quantified by the Σ parameter and governs the multimodal behavior of low-temperature cavitation phenomena. To address this complexity, we have proposed a comprehensive framework that integrates multi-scale collaborative simulation and digital twin, combining molecular dynamics, CFD, system dynamics, and targeted experiments. This review proposes a candidate physical framework for addressing the reliability challenges of liquid hydrogen pumps. It also provides a clear roadmap for the next generation of inherently robust cryogenic fluid machinery, and offers a reference for the design of energy systems under other extreme conditions. Full article

Phase misalignment between periodic pollutant emissions and receptor inhalation can fundamentally bias exposure estimations, yet it is rarely quantified in transient assessments. Here we propose Phase-Independent Pollutant Exposure (PIPE), a general exposure metric that removes this temporal randomness by integrating phase-resolved exposure over a phase-difference probability distribution. The phase-dependent exposure function is reconstructed efficiently using a Fourier series surrogate built from sparse samples, enabling deterministic calculation of the expected exposure without resource-demanding Monte Carlo sampling. We demonstrate the framework using a short-range indoor exposure case representative of periodic human emissions resolved by transient computational fluid dynamics (CFD). Results showed that across multiple breathing intensities, breathing/coughing waveforms, interpersonal distances (0.5–1.5 m), and exposure durations, phase-dependent variability was consistently pronounced and accurately captured by the proposed model. Phase differences increased cumulative inhaled exposure by up to 9.58 times, with the largest amplification occurring at close range (0.5 m) under intense breathing. Flow-field analysis indicates that specific phase relationships can suppress turbulent kinetic energy in the inter-person region, limiting dispersion and thereby elevating near-field concentrations and intake. Although phase effects attenuate with time due to accumulation and mixing, they remain non-negligible even over extended contact (up to 60 s). Notably, PIPE is generally lower than exposure under perfectly synchronized phases, but becomes 1.20–4.97 times higher in close-range, high-intensity scenarios. By explicitly accounting for phase uncertainty, PIPE provides a transferable and computationally efficient methodology to stabilize exposure assessment for periodic sources, improving the robustness of process-based risk metrics relevant to environmental and human exposure evaluation. Full article

The rapid growth of data-intensive applications has increased the demand for efficient storage in large-scale key-value (KV) stores. Disaggregated memory architectures provide a scalable solution by separating compute and memory resources via RDMA. However, existing indexing schemes in these environments suffer from poor read efficiency, significantly degrading overall system throughput and scalability. Specifically, learned indexes often encounter substantial read amplification during remote data retrieval due to prediction errors. In addition, caching full keys incurs a high cache footprint, limiting the effective cache capacity on compute nodes and leading to additional remote memory accesses. This paper presents FPCache, a fingerprint-rectified learned index cache for disaggregated memory. We propose a fingerprint-assisted two-stage read approach to mitigate read amplification. FPCache first retrieves a compact fingerprint array for local matching. It then converts range reads into precise point accesses and directly reads the corresponding data item, thereby avoiding reading the entire range and reducing extra data transfers. Next, we design a fingerprint-offset compression strategy to maximize cache density. Leveraging fixed-length fingerprints and position offsets enables compute nodes to retain significantly more hotspot data within limited memory resources. Experimental evaluations using various YCSB workloads demonstrate that FPCache consistently outperforms state-of-the-art methods. Compared to systems like CHIME and ROLEX, FPCache improves system throughput by up to 62% and effectively maintains stable access efficiency under diverse data distributions. Full article

In this work, we explore a piezoelectric neuron model in deterministic perturbations and stochastic forcing due to its use in mechanically driven sensing systems and neuromorphic sensor design. The model comprises of fast activation and slow recovery behaviors and constitutes a multiscale excitable system, converting external mechanical perturbations into nonlinear electrical responses. We initially examine the deterministic dynamics with phase-space reconstruction, basin of attraction mapping, return map analysis and sensitivity to initial conditions. These findings demonstrate stable limit-cycle oscillations and high nonlinear sensitivity that are crucial to high-resolution sensing and signal amplification. Stochastic forcing is added in order to include realistic environmental effects, and solved numerically with the Euler-Maruyama scheme. Time-series statistics, phase portraits, and recurrence quantification analysis are used to analyze the resulting ensemble dynamics, making it possible to characterize the variability and loss of predictability caused by noise. Comparison of deterministic and stochastic regimes indicates that the intensity of noise can considerably alter the firing patterns and recurrence structures. Full article