• Local and circular initiatives are often small and fragmented. • Little is known about how to effectively scale local circular supply chains. • We present 17 factors for scaling across 8 established amplification processes. • We contribute factors to increase the size and transformative impact of initiatives. The fashion and textiles industry operates a global supply chain that causes harm to the environment, emitting pollution and generating waste across all stages of production, consumption and disposal. The implementation of a local circular economy would reduce impact, shorten import and export distances while retaining the value of materials through practices like reuse and recycling. However, current local and circular initiatives operate in a system that is incomplete to provide sufficient support structures for their operations, meaning they are often small, fragmented, and at risk of cessation. To address this, five focus groups with fashion industry stakeholders were conducted where factors for scaling at a local and national level were discussed. Data analysis revealed 17 factors for scaling, two of which were unique to scaling nationally. These offer ways for local circular clothing supply chains to stabilise, speed up, grow, replicate, transfer, spread, scale up and scale deep. The results provide a new insight on how to increase the scale and transformative impact of local circular initiatives emerging in towns and cities across the UK. The research is useful for the industry and policymakers in informing the development of purposive interventions that seek to step beyond current niche and fragmented solutions and build circular systems that can overtake the current linear, energy intensive, pollutive and waste generating system. Significantly, this will facilitate a shift away from global production and distribution, to grow more locally and systemically robust initiatives at the local and national scale.

Synthesis and physical properties characterization of artificial seawater samples.

• Physics-informed Lucas-Washburn equation captures nanoscale capillary imbibition. • Coupled interfacial and thermal effects integrated through a scaling factor f . • Model validated across minerals and experiments with R 2 up to 0.95. The classical Lucas-Washburn (LW) equation fails to accurately describe capillary imbibition phenomena in soil nanopores, where nanoconfinement, temperature, and surface charge substantially alter interfacial water behavior. A generalized form of the LW equation is proposed, which integrates nanoscale effects through a physics-informed scaling factor f . This new formulation explicitly incorporates interfacial and thermal influences that are previously treated in isolation and whose coupled effects are neglected, significantly extending the applicability of the LW model to nanoconfined soil media. Using molecular dynamics (MD) simulations across 45 representative montmorillonite slit-pore systems (2–10 nm wide, 0–104.91 cmol·kg −1 CEC, and 300–360 K), the water interfacial structure is identified via a density-based clustering algorithm (DBSCAN) and extracted imbibition slopes are used to calibrate the model through systematic analysis of the interfacial and thermal effects on capillary transport. The generalizability of the revised framework is further demonstrated through 18 additional simulations for different soil mineral systems, including kaolinite and quartz. The novel LW equation significantly enhances predictive performance, increasing the coefficient of determination from 0.19 to 0.92. Cross-mineral comparisons demonstrate its broad applicability, with R 2 = 0.95 for kaolinite and 0.92 for quartz. Experimental validation using red-layer mudstone data confirms the accuracy of the proposed model, reducing RMSE from 71.63 mm to 5.54 mm. This work bridges interfacial molecular-level insights and practical continuum-scale predictions, offering a robust tool for assessing capillary-driven water transport in mineral-rich environments.

Does size really matter? Examining the role of scale factors and community perceptions in drilling project acceptance

Abstracts: To improve the zoom speed of electrowetting lenses, a method using vibration to realise high-speed zoom is proposed. The method involves the utilisation of an alternating electric field to drive the liquid medium to carry out periodic vibration, in accordance with the dynamics of the liquid–gas interface and the change curve of focal length, to determine the imaging moment corresponding to different focal lengths, and to complete the zoom. Molecular dynamics simulations show that the shape of the liquid–gas interface, i.e. the focal length of the lens, is time-dependent in one cycle when the frequency is constant. Different frequencies produce different shapes of liquid–gas interface. Under this high-speed zoom approach, the shape change of the liquid–gas interface introduces complex distortions in the lens during the imaging process, making it difficult to adequately characterise such distortions with conventional distortion models. To this end, a collaborative correction method that combines the Enhanced Zernike model and neural network is proposed. The method improves the model’s ability to model complex distortions by introducing a radial scaling factor S(r) and a radial–angular coupling bias term β(r,θ) associated with the normalised radius r in the Zernike model. Furthermore, it establishes a global coordinate mapping model for global correction. On this basis, a two-branch neural network is designed, and the features of the Zernike model in the previous stage are introduced to learn the displacement residuals in the radial and angular directions, respectively, to realise the compensation of the distortion in the local region of the image. Experiments have demonstrated that controlling the frequency so that the droplets appear to resonate or increasing the voltage will help to increase the zoom speed. For a 5-μL deionised water droplet, good zoom performance and image quality can be achieved at 110 Hz, 190 V AC drive. The focal length is adjusted from 0.821 mm to infinity in 2.250 ms, with a minimum instantaneous zoom speed of 0.512 mm/ms, which is threefold faster than those of existing lenses. Finally, after the collaborative correction method for distortion compensation, the average straightness of the image is at a minimum of ∼0.609 pixel, and the root-mean-square error (RMSE) at the key point is at a minimum of ∼1.007 pixel, indicating an average reduction of 29.377% in the error compared with the traditional distortion model, and it effectively suppressing the complex distortions in the image.

• Optimal scaling 5.5–8.4% K −1 varies with event duration and return period. • High-elevation basins show strong CPM–scaling agreement; lowlands weak links. • Seasonal shifts degrade scaling reliability, especially for longer-duration storms. • Event-based temperature outperforms mean annual temperature in T-P scaling. Quantifying future changes in sub-daily extreme precipitation in mountainous basins is essential for adapting to increasingly intense flash floods and debris flows. Often, extreme precipitation-temperature scaling rates are used to anticipate future extremes. In this study, we evaluate the reliability of temperature-scaling approaches by comparing their projections of extreme precipitation to outputs from convection-permitting models (CPMs) across a complex mountainous region in northeastern Italy. We use hourly data from an ensemble of five CPMs at a 3 km resolution, comparing historical (1996–2005) and future (2090–2099, RCP8.5) periods and using average temperatures in the 24 h prior to the precipitation event to compute the scaling rates. Our analysis employs a robust methodological framework to identify optimal scaling factors that replicate the extreme precipitation estimates produced by CPM simulations. We find that the optimal scaling rates vary with duration and return period. For moderate extremes (2- and 5-year return period), the optimal scaling rates are typically between 5.5 and 6% K −1 , while more severe events scale with higher rates, up to 8.4% K −1 for 3‐hour, 100‐year events. Future shift in precipitation seasonality also plays a role. In high‐elevation areas, where extremes are prevalent in the summer in both present and future conditions, estimates based on temperature-scaling rates align closely with CPM outputs. Conversely, poorer performances are found in areas where large shifts in the seasonality of extremes are expected. Overall, careful consideration of local variability, seasonality, duration, and return period is necessary to avoid potential inaccuracies when deriving projections from temperature-scaling relationships.

Multilevel inverters (MLIs) are widely adopted in high power density systems for their efficiency and waveform quality. Conventional capacitor-based MLIs, however, face persistent challenges such as voltage imbalance and device stress induced by neutral currents. To address these limitations, the neutral-point-less H-type (NPL.H) inverter has been introduced, eliminating the static neutral point altogether. Unlike conventional designs, the NPL.H achieves its multilevel outputs through impedance-based voltage division rather than stacked capacitors, thereby offering a fundamentally different mechanism of voltage generation. Since voltage generation relies on the load impedance, the resulting waveforms exhibit inherent sensitivity to load balance. This paper develops an analytical framework for understanding multilevel voltage generation in NPL.H inverters and examines how load imbalance influences voltage symmetry and reliability. Theoretical derivations establish line-to-line voltage levels and neutral point dynamics for each switching state, demonstrating consistent production of half the dc-link voltage via impedance division in the load. An equivalent impedance model is further employed to quantify imbalance effects, introducing a scaling factor that predicts voltage behavior under asymmetric conditions. Simulation and experimental validations confirm the analysis, showing that eliminating the neutral point and accommodating up to 12% load imbalance only cause brief voltage overshoot before stabilizing at expected levels.

ADCSim: Software for attitude determination and control system design and simulation

Antiplatelet Therapy After Endovascular Intracranial Aneurysm Surgery: Comparative Effectiveness and Safety Insights from Systematic Review and Meta-Analysis.

High-resolution images of non-cooperative spacecraft are essential for on-board autonomous operations. Hardware bandwidth limits and continuously changing observation distances mean that a practical super-resolution (SR) system must handle arbitrary, non-integer magnification factors without retraining, a setting known as arbitrary-scale SR (ASSR). Recent 2D Gaussian splatting (2DGS) methods represent image content with explicit anisotropic Gaussian primitives and render at any continuous coordinate, offering substantially faster inference than implicit neural representation (INR) approaches. Yet spacecraft imagery presents a structural mismatch for uniform 2DGS regression: the target occupies a small, densely structured region within a vast, featureless deep-space background, so a network that minimizes average reconstruction loss inevitably over-invests capacity in the irrelevant background and smears the fine edges of antennas and solar panels. We propose S3R-GS, a saliency-guided framework that embeds semantic spatial priors into the 2DGS pipeline at three levels: an encoder-level module that suppresses background noise before it reaches the splatting stage; a discrete Gaussian routing mechanism that assigns each spatial location to a semantically appropriate kernel group and reformulates Gaussian modeling as semantic prototype selection; and a saliency-weighted training strategy that concentrates the optimization gradient on the spacecraft target. Experiments on the SPEED and SPEED+ benchmarks show that S3R-GS achieves strong PSNR performance, competitive SSIM, and improved perceptual quality across scale factors from ×2 to ×12; additional ablation, extreme-lighting, and efficiency analyses further support the robustness and practicality of the proposed design.

In traditional spin-exchange relaxation-free (SERF) gyroscopes, open-loop detection leaves the scale factor vulnerable to fluctuations in probe laser intensity and environmental temperature. To address this, an external-modulator-free closed-loop atomic spin gyroscope (ASG) is demonstrated using direct probe laser injection current modulation. A magnetic feedback loop maintains the total optical rotation at a zero point, decoupling the inertial signal from intensity and optical depth variations. Experimental results demonstrate a closed-loop rotation sensitivity of 2.8×10-6°/s/Hz1/2 at 1 Hz. Compared to open-loop measurements under identical conditions (1×10-5∘/s/Hz1/2), low-frequency noise is significantly suppressed. By eliminating bulky external modulators and effectively suppressing low-frequency noise, this design offers a highly competitive architecture for miniaturized atomic inertial sensors.

ABSTRACTAnion binding to nanographenes is governed by noncovalent interactions, particularly anion–π interactions in electron‐deficient aromatic regions and CH‐‐‐anion hydrogen bonding in electron‐rich domains. These interactions are primarily driven by electrostatic effects, with the quadrupole moment of the aromatic system playing a central role in determining the strength and directionality of anion–π binding. The perpendicular component of the quadrupole moment (Qzz) correlates with binding energies for both anion–π and CH‐‐‐anion interactions, though polycyclic systems present challenges due to competing interaction modes. In this study, we investigate the role of the local quadrupole moment in anion binding across 171 Cl−–aromatic complexes, comparing various descriptors including aromaticity indices, Fukui functions, and electron density at ring critical points. We find that electrostatic descriptors, particularly the local quadrupole moment, provide a more consistent and robust explanation for binding energies than conventional descriptors. Specifically, two geometric descriptors derived from the local quadrupole moment—the scale factor (SRmax) and the ellipticity (ec′)—show good correlation with binding strength, with SRmax reflecting π‐acidity and ellipticity quantifying charge distribution anisotropy. These descriptors are validated across fluorinated naphthalenes and nanographenes, demonstrating their general applicability. Regression models based on SRmax and ec′ effectively predict binding energies, with enhanced accuracy when combined with polarization‐dependent penalty functions, especially for larger nanographene systems. While the predictive model is still somewhat constrained by polarization effects, its simplicity, robustness, and transferability across a wide range of systems offer distinct advantages over more complex, multilayered machine learning models. These results underscore the critical role of quadrupole moment anisotropy in anion–π interactions and offer a practical framework for predicting anion binding affinities and designing π‐acidic receptors.

Background and Purpose: The Social Ecological Model provides a comprehensive perspective on the physical, social, environmental, and individual factors that influence physical activity. This study aimed to evaluate the Turkish validity and reliability of the Physical Activity Correlates Questionnaire from the Social Ecological Model (SEM-PACQ) among university students. Methods: This study was conducted between October 2023 and June 2024 at the faculties of health sciences at state universities in two provinces of Türkiye (N = 335) using a cross-sectional design to evaluate the validity and reliability of the instrument. The scale was translated using translation/back-translation procedures. Content validity was assessed using the content validity index. Following pilot testing, minor wording revisions were made to enhance item clarity. Reliability was evaluated using coefficient alpha, McDonald's omega, item-total correlations, and internal consistency coefficients, and construct validity was examined using confirmatory factor analyses. Convergent and discriminant validity were also examined. Results: The scale consists of 22 items and retains its original, six-factor structure. The content validity index was .95. The item-level content validity ratios ranged from .75 to 1. The coefficient alpha and McDonald's omega values for the scale were .90. The item-total correlation coefficients ranged between .41 and .60, and all items had values greater than .40. Confirmatory factor analysis supported the scale's factor structure. The convergent and discriminant validity results indicated acceptable construct validity across the subscales. The total SEM-PACQ and social support subscale scores were significantly higher among those who engaged in vigorous and moderate physical activity than among those who did not (p < .001). Conclusions: The SEM-PACQ was found to be valid and reliable for Turkish university students.

In seismic reliability analysis of engineering structures, stochastic ground-motion time histories are routinely required as input excitation. The stochastic finite-fault method has become a cornerstone tool for high-frequency ground-motion simulation in engineering seismology. However, the conventional EXSIM implementation can be further improved in its treatment of corner frequency, source duration, and phase representation. In this study, several representative source- and phase-related updates previously reported in the literature were integrated within the Ground Motion Simulation System (GMSS), a MATLAB-based stochastic finite-fault simulation framework. Compared with EXSIM, the GMSS incorporates a rupture-velocity-dependent corner frequency and source duration model that physically links normalized rupture velocity (Vrup/β₀) to subfault corner frequency. An improved scaling factor Hij is introduced by weighting each subfault's contribution according to its relative slip within the total rupture, thereby achieving greater theoretical consistency in the seismological model that governs subfault Fourier amplitude spectra. Furthermore, the phase spectrum is refined to honor propagation-induced frequency-dependent characteristics. The enhanced method is rigorously validated using the well-constrained finite-fault slip model of the 2013 MW 6.7 Lushan thrust earthquake, together with regionalized attenuation and site parameters, against recorded strong-motion data at 18 near-field stations. Results demonstrate that GMSS accurately reproduces observed Fourier amplitude spectra (FAS), 5%-damped Pseudo-acceleration response spectra (PSA), and peak ground acceleration (PGA). The incorporation of non-stationary phase characteristics markedly improves the time-domain envelope and phase realism of synthetic waveforms, enhancing the physical fidelity and predictive accuracy of broadband ground-motion simulation. The GMSS framework therefore provides a practical tool for stochastic finite-fault simulation and seismic hazard assessment in regions lacking abundant strong-motion records. Nevertheless, both methods still show limited performance in the low-frequency range below 1Hz.

Over the past decade, the phase equilibria of CO2 hydrates have been the subject of numerous molecular simulation studies. However, inconsistencies among these works-stemming from disparate molecular models, box geometries, and simulation protocols-have hindered direct comparison. Moreover, prior simulations have generally not reported the three-phase dissociation temperature, T3, under conditions that reliably suppress finite-size and intermolecular potential truncation artifacts, often because of limited system sizes and/or short cutoff distances. Here, we compute T3 using molecular simulations specifically designed to minimize these effects, employing systems containing more than 3000 molecules and a long cutoff of 1.6nm. The hydrate-aqueous-liquid CO2 coexistence temperature is obtained via the solubility method: at fixed pressure, we evaluate the CO2 solubility in the aqueous phase in contact with a CO2 hydrate phase and, separately, with a pure liquid CO2 phase over a range of temperatures. The intersection of the two solubility curves defines T3. This approach has emerged in recent years as an alternative and complementary method to direct coexistence. We apply this procedure from 100 to 5000bar. In addition, we refine the unlike CO2-water interactions to reproduce the dissociation line quantitatively. We find that modifying the Berthelot combining rule for CO2-water cross-interactions with a scaling factor ξ = 1.085 yields excellent agreement between simulated T3 values and experimental ones, providing a qualitative description of the dissociation enthalpy along the line. Furthermore, we provide, to the best of our knowledge, the first physical explanation for the unusual reentrant behavior of the CO2 hydrate dissociation line. We show that this behavior originates from a pressure-induced change in the signof the reaction molar volume, caused primarily by the strong compressibility of the fluid CO2 phase relative to the hydrate and aqueous phases.

Discrete event simulation (DES) is a flexible and computationally efficient approach for modeling diverse processes; however, DES remains underutilized in health care and medical decision making due to a lack of reliable and reproducible implementations. We developed an open-source DES framework to simulate individual-level state-transition models (iSTMs) in continuous time accounting for treatment effects, time dependence on state residence, and age-dependent mortality. Our DES implementation employs a modular and easily adaptable structure, with each module corresponding to a unique transition between health states. To simulate the evolution of the process (i.e., individual state transitions), we adapted the next-reaction algorithm from the stochastic chemical reactions literature. Simulation-time dependence (age-dependent mortality) and state residence time dependence (transition from sick to sicker) are seamlessly incorporated into the DES framework via validated nonparametric and parametric sampling routines (e.g., inversion method) of event times. Treatment effects are integrated as scaling factors of the hazard functions (proportional hazards). We illustrate the framework's benefits by implementing the Sick-Sicker Model in R and conduct a cost-effectiveness analysis and probabilistic analysis. We also obtain epidemiological outcomes of interest from the DES output, such as disease prevalence, survival probabilities, and distributions of state-specific dwell times. Our DES framework offers a reliable and accessible alternative that enables the simulation of more realistic dynamics of state-transition processes at potentially lower implementation and computational costs than discrete-time iSTMs.HighlightsDiscrete event simulation (DES) is a flexible and efficient approach to simulate diverse processes in model-based decision analysis.The tutorial presents an open-source DES framework to simulate individual-level state-transition models (iSTMs) in continuous time.The modular structure of our DES framework accommodates treatment effects, time-dependent transitions, and age-dependent mortality using validated sampling methods.The coded example in R uses the Sick-Sicker Model to compute a cost-effectiveness analysis, epidemiological outcomes, probabilistic analysis, and value-of-information analysis.

Image processing forms the foundation of computer vision and is widely used in many applications. In scaling operations, images are resized by performing computations on the values of pixel points. The success of computer vision processes is directly related to the use of accurate and high-quality images. Ensuring that source images have the same dimensions is of great importance for effective processing. The use of source images with different sizes adversely affects the performance of computer vision algorithms. Therefore, image scaling operations must be performed both accurately and efficiently. In this study, processing time values are evaluated and compared using Bicubic and Lanczos interpolation-based image scaling methods under different scaling factors (0.5× and 2×) and various parallelization models. Using the developed multithreaded console application, thread configurations are compared under single-core and multi-core execution scenarios. Experimental results have shown that a speed performance increase of up to 176.4% can be achieved with the multi-core parallel model. In addition, when all the results were evaluated, it was concluded that the Bicubic interpolation method was faster than the Lanczos method.

The American Heart Association's PREVENT equations estimate risk of total cardiovascular disease (CVD), atherosclerotic CVD (ASCVD), and heart failure (HF) to guide lipid and blood pressure-lowering therapy in people ages 30 to 79 years in the United States. The SCORE2 risk algorithm is used to estimate CVD risk for similar purposes in people ages 40 and older in Europe. Neither set of equations has been comprehensively validated in global observational cohorts and randomized trials. Here, in 44 observational cohorts and 18 randomized trials, we assessed discrimination and calibration of the two risk algorithms across geographical regions (North America, Europe, Asia/other, multi-region trials). We also created scaling factors for risk prediction over 1-9 years using the PREVENT equations, enabling shorter-term risk prediction for research purposes or to facilitate clinical trial enrolment. Over 5.1 years of mean follow-up, 293,737 PREVENT total CVD events (fatal and non-fatal ASCVD or HF) and 258,086 SCORE2 CVD events (myocardial infarction, stroke, or cardiovascular death) were observed among 6,422,714 and 5,437,384 individuals, respectively. Despite differences in CVD outcome definitions, target populations and predictor variables, overall discrimination and calibration were similar for both equations, with generally good performance across regions, including in multi-regional randomized trials. These findings lend support for adoption of PREVENT or SCORE2 for cardiovascular risk stratification across diverse settings.

We present a Wilson-Cowan reservoir computer (WC-RC) that treats a retinotopic excitatory-inhibitory neural field as a structured reservoir. Travelling waves and bounded oscillations provide an interpretable spatiotemporal basis, while a two-stage sampler (40 sites × 200 steps) exports 8000 features per input-a [Formula: see text] reduction with unchanged integration cost. On MNIST and Fashion-MNIST, recurrent readouts trained on these features achieve strong performance within this fixed export budget: Att-LSTM reaches [Formula: see text], while GRU and vanilla LSTM yield similar accuracies (all with tight Wilson 95% confidence intervals). A simple MLP readout performs markedly worse ([Formula: see text]), whereas a compact ridge classifier still attains non-trivial performance ([Formula: see text]), indicating that the exported WC-RC representation is partially linearly decodable but benefits further from temporal modelling. Selective suppression of lateral couplings ([Formula: see text]) shows that reinstating wave dynamics improves recurrent models while degrading the MLP, supporting a functional role for propagating dynamics. A complementary neighbour-coupling ablation, implemented via a diffusion-like scaling factor ψ, shows that increasing lateral spread beyond the baseline regime reduces late-time spatial variance and degrades recurrent-readout accuracy, indicating that useful computation depends on balanced wave dynamics rather than maximal smoothing. At matched exported-feature and readout budgets, ESN baselines underperform ([Formula: see text]), whereas compact CNNs achieve higher accuracy but with larger parameter and MAC budgets and without wave interpretability. Supplementary analyses confirm numerical fidelity and relate performance gains to propagation coherence. We outline a fixed-point streaming-convolution mapping for FPGA/ASIC deployment, positioning WC-RC as an interpretable, energy-aware reservoir for neuromorphic vision.

The IEC exposure index (EI), deviation index (DI), and target exposure index (EIT), represent critical standardized metrics for the evaluation of exposure and quality in radiographic imaging. This work develops and validates a systematic procedure to estimate the EIT for eight of the most common radiography imaging protocols utilizing automatic exposure control (AEC) from measurements acquired under reference conditions. A model was developed to define the relationship between a systems AEC logic, and an estimation of the EI under flat-field conditions (EIFFC). Separately, clinical data and acquisition protocol information for resultant EI during patient studies were also collected for the eight protocols studied: Chest posteroanterior (PA), Chest lateral, Abdomen anteroposterior (AP), Pelvis AP, L-Spine AP, C-Spine AP, T-Spine AP, and Ribs AP. Data were collected from 41 x-ray units spanning seven institutions. For each protocol on each unit the EIFFC was computed based on the acquisition protocol, as well as median EI from clinical exams to produce a scaling factor (SF). Kruskal-Wallis statistical tests were used to compare SF's between vendors and AEC cell configurations. SFs for eight radiographic imaging protocols have been produced per vendor and per AEC cell selection. A workflow has been established for end-users to follow to apply these SFs to flat-field measurements taken at their own locations to establish local EIT. The study results show that in seven of the eight imaging protocols, the SFs for most units included in the study report SFs within 1 DI(±25%) of their respective final vendor reported SF (218/228). The ribs protocol is the exception to this finding (n=26). SFs have high utility for establishing EIT values on individual x-ray units and normalizing EI value distributions for quality assurance purposes.