A bstract The measurement of the Higgs self-coupling is considered the next milestone in the study of the Higgs boson properties. At future e + e − facilities below the double Higgs production threshold, this is extracted from the Zh production cross-section, which is sensitive to the trilinear coupling at the one-loop level. At the same perturbative order, potential effects beyond the Standard Model (SM) may affect the Higgstrahlung rate and distort the self-coupling determination. We study the question focusing especially on contact interactions containing two electron and two top-quark fields. We conclude that, in the context of FCC-ee and its planned runs at different energies, eett interactions change the Higgs self-coupling sensitivity below the percent level. Even in the most pessimistic scenarios, we confirm a robust sensitivity of the order of 17% at the 1 σ confidence level under the assumption of otherwise SM-like Higgs couplings. A crucial role in these results is played by the measurement of fermion pair production above the Z resonance.

Assessment of double anaerobic threshold in trained subjects.

A Joint Maintenance strategy for offshore wind turbines based on multiple opportunity windows and dynamic double thresholds

Abstract The present study investigated the feasibility of applying pulsed electric field (PEF) treatment in anodic electrodeposition (AED) paints without inducing undesired paint deposition on electrode surfaces. PEF treatment is a promising non-thermal technique for microbial inactivation and control in E-coating lines. However, paint deposition on treatment electrodes can cause operational limitations. This study investigates paint stability, coating performance, and electrodeposition mechanisms during PEF treatment of industrial AED emulsions. Using both unipolar and bipolar square-wave pulses at varying durations and charge symmetry, deposition behavior and paint quality were evaluated under continuous flow for PEF treatments known to induce at least a 99% bacterial inactivation on bacteria isolated from E-coating lines. Intensive and repeated PEF treatment of AED paint did not impair the quality of the subsequent coating and did not change the properties of the paint. Paint quality remained unaffected across all trials. Regarding the coagulation and paint deposition on the PEF treatment electrodes, results show that unipolar pulses induce paint coagulation at the anode, even at short durations (when pulses are in the range of the double layer threshold time ≥ 1 µs). In contrast, bipolar pulses with nominal pulse durations ≤ 1.5 µs (0.75 + 0.75 µs) prevented electrode fouling and allowed uninterrupted treatment for over 10 h, without electrode fouling. Furthermore, the application of charge-symmetrical bipolar (CS-bipolar) pulses eliminated deposition entirely, even at nominal durations up to 2.9 µs (1.48 + 1.41 µs). Findings demonstrate that pulse polarity, charge balance, and field symmetry are critical for deposition-free PEF operation in AED systems. These insights provide a technical foundation for scaling PEF in industrial paint processing.

Abstract. Glacial lakes are critical indicators of the effects climate change and significant sources of natural hazards, such as Glacial Lake Outburst Floods (GLOFs), cascading events, etc. Monitoring their formation and evolution is essential for understanding cryospheric dynamics and supporting risk management, yet systematic mapping is hindered by the complexity of high-mountain environments. Developing robust, automated methods using remote sensing remains challenging due to rugged topography, snow, ice, and shadows causing misclassification.This paper proposes a multi-sensor methodology for glacial lake detection and monitoring, integrating optical data from Sentinel-2 and Synthetic Aperture Radar (SAR) data from Sentinel-1. The study focuses on the Western Alps using data from 2022 to 2024. The methodology applies optical indices using a double thresholding strategies and tests machine learning algorithms. On the other hand, it investigates the potential of the recently developed OASIS index for SAR-based detection, aiming to overcome cloud cover and illumination limitations inherent in optical imagery.Preliminary results show that optical indices perform well but require dynamic thresholding, as snowmelt and shadows remain major sources of uncertainty. Machine learning approaches demonstrate good potential in mitigating these limitations. The OASIS index (SAR) proves to be a promising complementary tool, especially under cloudy conditions, though still challenged by surface roughness. The integration of optical and radar data significantly increases the robustness of lake detection and reduces temporal gaps in monitoring. This methodology contributes to advancing automated systems for hazard assessment and climate change effects monitoring in alpine regions.

Federated learning (FL) allows collaborative machine learning (ML) model training among decentralized clients’ information, ensuring data privacy. The decentralized nature of FL deals with non-independent and identically distributed (non-IID) data. This open problem has notable consequences, such as decreased model performance and longer convergence times. Despite its importance, experimental studies systematically addressing all types of data heterogeneity (a.k.a. non-IIDness) remain scarce. This paper aims to fill this gap by assessing and quantifying the non-IID effect through an empirical analysis. We use the Hellinger Distance ( HD ) to measure differences in distribution among clients. Our study benchmarks five state-of-the-art strategies for handling non-IID data, including label, feature, quantity, and spatiotemporal skews, under realistic and controlled conditions. This is the first comprehensive analysis of the spatiotemporal skew effect in FL. Our findings highlight the significant impact of label and spatiotemporal skew non-IID types on FL model performance, with notable performance drops occurring at specific HD thresholds. The FL performance is also heavily affected, mainly when the non-IIDness is extreme. Thus, we provide recommendations for FL research to tackle data heterogeneity effectively. Our work represents the most extensive examination of non-IIDness in FL, offering a robust foundation for future research. • Label skew and spatiotemporal skew have the most significant impact on the model’s performance. • The drop in the model’s performance for label skew appears in a double threshold. A notable performance decline is immediately evident when the Hellinger Distance exceeds 0.5 and 0.75. • Feature skew does not alter the model’s performance nor the convergence point. • The quantity skew in the client’s data does not affect the model’s performance. • The higher the non-IIDness level in time and space (spatiotemporal skew), the worse the model’s performance.

Marine oil film identification based on GLOH, K-Means and adaptive threshold.

Classical rupture is attributed to molecular (van der Waals) forces acting at nanometric thicknesses. Nonetheless, micron-thick liquid sheets routinely perforate far above the scale where these molecular forces act, yet the mechanism that selects opening versus healing has remained unclear. Using direct numerical simulations of a draining sheet with an entrained air bubble (cavity), we show that irreversible rupture occurs only when a deterministic double threshold is crossed: (i)the outward driving (from airflow or inertia) is strong enough and (ii)the cavity is distorted enough. If either condition falls short, surface tension heals the cavity and the sheet reseals. The time for this process is set by the balance between inertia and viscosity-fast for inertia-dominated sheets and slower for viscous ones. This double-threshold mechanism explains why micrometer-thick films perforate and offers practical control options-driving strength and defect geometry-for predicting and controlling breakup in spray formation processes, wave breaking, and respiratory films.

The suggestion by K.L. Cooke (1967) that infected individuals become infective if they are exposed often enough for a natural disease resistance to be overcome is built into a Kermack-McKendrick type epidemic model with post-latency age. Both the case that the resistance may be the same for all hosts and the case that it is distributed among the host population are considered. In addition to the familiar threshold behavior of the final size of the epidemic with respect to a basic reproductive number, an Allee effect is generated for the final cumulative force of infection by the final cumulative primary force of infection. This offers a deterministic explanation why geographic areas that appear to be epidemiologically similar have epidemic outbreaks of quite different severity.

A double threshold detection of range-spread target using order statistic based on Rao test

With the widespread application of technologies such as artificial intelligence (AI), big data, cloud computing, and the Internet of Things (IoT) in the insurance sector, InsurTech is regarded as a potential solution to address the challenges of underwriting, pricing, and claims processing in agricultural insurance. This paper explores the role of InsurTech in the development of agricultural insurance through the lenses of information asymmetry, technological diffusion, and transaction costs. By integrating theoretical frameworks such as the Bayesian Signal Extraction Model, insurance pricing models, and demand functions, the paper proposes the mechanisms through which InsurTech influences agricultural insurance development and formulates testable hypotheses. Utilizing provincial-level panel data from 31 provinces in China from 2011 to 2023, a two-way fixed effects model is employed to identify the impact of InsurTech on agricultural insurance development. Robustness and endogeneity tests are conducted, including alternative dependent variables, instrumental variable methods, exclusion of pandemic period samples, and propensity score matching (PSM). Furthermore, regional heterogeneity is analyzed by distinguishing between major and non-major grain-producing areas, and a Hansen threshold model is used to examine the nonlinear effect of farmers' disposable income. The results reveal: (1) InsurTech significantly promotes agricultural insurance development; (2) the promotion effect is more pronounced in non-major grain-producing areas; and (3) farmers' disposable income exhibits a “double threshold” effect—when income exceeds approximately 6,008 RMB and 12,842 RMB, the marginal effect of InsurTech's promotion gradually intensifies. This study provides empirical evidence for enhancing the digital infrastructure of agricultural insurance and the development of regionally differentiated policies.

Under the impetus of the digital economy, the digital transformation of industrial chains and supply chains has become inevitable. Data assets effectively enhance the operational efficiency of industrial chains and supply chains, digital technologies can effectively enhance the competitiveness of enterprises in industrial chains and supply chains, and digital technologies effectively promote the transformation and upgrading of industrial chains and supply chains. This paper constructs an evaluation indicator system from four dimensions: research and design modernization, production and manufacturing modernization, logistics and transportation modernization, and sales and marketing modernization. Based on data from 30 provinces in China from 2015 to 2024, the paper uses the entropy method and spatial Moran index to analyze the digital economic development levels and spatial correlations of the 30 provinces, and examines the impact of the digital economy on the modernization level of the housing industrial chain and supply chain, as well as its underlying mechanisms. The research results show that the development of the digital economy exhibits a stable growth trend, while the modernization level of the housing industry chain and supply chain shows an overall fluctuating upward trend, with the overall modernization level of the housing industry chain and supply chain first declining and then rising. Additionally, the promotional effect of the digital economy exhibits significant spatial heterogeneity, with the development level of the digital economy generally showing a single threshold effect on the modernization level of the housing industry chain and supply chain, and a double threshold effect in western regions.

We considered the following parabolic system: $ \begin{equation*} \left\{ \begin{array}{llll} &u_t = d_1\Delta u-a(x)\cdot \nabla u+f(u,v),\quad x\in\Omega,\ t>0,\\ &v_t = d_2\Delta v-b(x) \cdot \nabla v+g(u,v),\quad x\in\Omega,\ t>0,\\ &u(x,0) = u_0(x),\quad v(x,0) = v_0(x),\quad x\in\Omega, \end{array}\right. \end{equation*} $ subject to Dirichlet (or Neumann) boundary conditions. Here $ \Omega\subset \mathbb{R}^N(N\geq 1) $ is a bounded smooth domain. In addition to some results on blowup and global existence of the solution, we found some more interesting results as follows: (1) There exists double thresholds for blowup and global existence of the solution. Under certain conditions, if $ f(u, v) = f_1(v)g_1(u) $ and $ g(u, v) = f_2(v)g_2(u) $, then the first watershed is $ \int^{+\infty}_{c_1}\frac{du}{g_1(u)} = +\infty\quad {\rm and}\quad \int^{+\infty}_{c_2}\frac{dv}{f_2(v)} = +\infty, $ and the second watershed is $ \int_{\tilde{c}_1}^{+\infty}\frac{dU}{\tilde{f}(\tilde{F}^{-1}(K\tilde{G}(U)))} = +\infty\quad{\rm and}\quad \int_{\tilde{c}_2}^{+\infty}\frac{dV}{\tilde{g}(\tilde{G}^{-1}(\frac{1}{\epsilon}\tilde{F}(V)))} = +\infty. $ Here $ \tilde{f}, \tilde{g}, \tilde{F} $ and $ \tilde{G} $ will be defined in Section 2.2. (2) If there exist nonnegative smooth functions $ h(u) $, $ l(v) $ and $ H(s) $ such that $ f(u,v)h'(u)l(v)+g(u,v)h(u)l'(v) = H[h(u)l(v)]\geq 0, $ then the watershed for blowup in finite time and global existence of the solution is$ \int^{+\infty}_0\frac{ds}{H(s)} = +\infty. $

In order to improve the precision and reliability of the objective evaluation of vehicle vibration, a multi-condition drivability test is designed based on the GB/T4970-2009 standard. The three-axis vibration and acceleration data in the speed range of 30–120 km/h are collected from urban roads, national highways, and motorways. Combined with the improved empirical modal decomposition (EMD) method, a vibration signal optimization strategy with dynamic frequency band adjustment and double threshold screening is proposed. The empirical modal decomposition algorithm solves the problem of modal aliasing, establishes a speed-based frequency band adjustment mechanism and a suspension intrinsic frequency model, and constructs a dual-threshold filtering mechanism with instantaneous frequency probability density (≥85%) and energy entropy criterion, which effectively separates the driver operation from the noise interference. Compared with various improvement methods, the EMD dynamic dual-threshold method has the best correction effect, and the correction error of the integrated weighted acceleration root mean square value is reduced to 0.191%. The method breaks through the severe limitations of the traditional test conditions and provides a highly robust analysis framework for vehicle vibration comfort evaluation.

The study examines the relationship between FDI spillover and green total factor productivity (GTFP) at the provincial level, as well as exploring the roles of digital transformation and human capital upgrading in this relationship using the global Malmquist-Luenberger index, system generalized methods of moments (S-GMM) combined with threshold model. Using a panel data from 63 Vietnamese provinces from 2011 to 2022, the research findings show that the FDI spillover has contributed significantly to province-level GTFP enhancement and both digital transformation, human capital upgrading positively moderate the effect of FDI spillover on provincial GTFP. Besides, the positive influence of FDI spillover on GTFP has a significant double threshold effect when advanced human capital is used as the threshold variable. These findings emphasize the significance of encouraging FDI spillover, enhancing digital transformation, and strengthening high-quality human capital as viable strategies for improving GTFP.

Double thresholding GeoAI prediction to fill gaps in extracted watercourse Network

IntroductionThe sports and health industries play a crucial role in fulfilling individuals' aspirations for an enhanced quality of life and in supporting the development of a Healthy China.MethodsTo explore the impact mechanism of the sports industry (SID) on the development of the health industry (HID), this study employed a rigorous empirical framework utilizing panel data from 30 provinces in China over the period 2014–2023. Using fixed-effects and mediation-effect models, this study examined the influence mechanism and identified the key pathways through which the sports industry impacts the development of the health industry.ResultsThe results demonstrate that the sports industry positively influences the development of the health industry. This conclusion remains robust across a series of robustness tests. The heterogeneous analysis reveals regional variation in the impact of the sports industry on the health industry development. Among the regions, the sports industry has the strongest promotional effect on health industry's development in the western regions, followed by the eastern and central regions. Conversely, the impact in the northeastern regions is relatively negligible. Mediation effect analysis reveals that the sports industry can effectively stimulate the development of the health industry by significantly increasing R&D intensity. Furthermore, testing the threshold effect indicates that the impact of the sports industry on the health industry exhibits a double threshold and non-linear diminishing marginal effects.DiscussionThis study contributes to a comprehensive understanding of the sports industry's impact on the health industry. Based on these findings, we proposed a series of targeted recommendations.

We investigated resonant multiphoton excitation of high-angular-momentum doubly excited states of the strontium atom, a quasi-three-body Coulomb system, both experimentally and theoretically. A series of highly doubly excited states converging to the double ionization threshold was identified and reproduced by first-principles calculations, which provided access to the two-electron wave functions of the resonances. In these states, the outer electron is dynamically localized at large distances by pendulumlike oscillations of the inner electron across the nucleus. A complementary simple molecularlike adiabatic model offers a clear interpretation of the correlated two-electron motion and suggests that the series is a general feature of highly doubly excited states of atoms and molecules.

Innovation capability serves as a crucial driving force for regional economic growth. In this empirical study, we investigate if and how government funding for science and technology ( GFS ) can promote regional innovation capability ( RIC ), by taking Chengdu-Chongqing Economic Circle as an example. The Circle is a key economic zone in China, with a population of more than 98 million and GDP of more than 1.1 billion USD. Using the panel data of all 44 prefecture-level cities (districts and counties) in the zone from 2011 to 2020, a systematic research methodology, consisting of panel model, mediating effect model, and threshold effect model, is adopted in the analysis. The results demonstrate that GFS significantly promotes RIC , as confirmed by multiple robustness tests. GFS exerts a more substantial driving effect on RIC in less innovative regions, with heterogeneity observed. Specifically, GFS in the Chongqing region exerts a stronger positive effect on RIC than the other part in the Circle. In terms of mediating mechanisms, GFS enhances RIC through R&D human capital accumulation and increased R&D capital investment. Further testing identifies a single threshold effect for R&D human capital and a double threshold effect for R&D capital investment in the relationship between GFS and RIC .

BackgroundDigital mobility outcomes (DMOs) have emerged as novel biomarkers offering objective, quantitative, and examiner-independent outcome measures for clinical studies. Unfortunately, research efforts on DMOs have not yet investigated the domain of clinical utility in Parkinson’s disease, i.e. providing evidence of improvements in health outcomes, diagnosis, decision-making, or prevention when compared to e.g. standard-of-care procedures. This manuscript, via a consensus building approach, aims to create a structured conceptual framework to map the knowledge generated by DMOs with clinical domains that could benefit from it.MethodsWe conducted a three-round consensus-building study with 12 experts recruited from the Mobilise-D consortium’s Parkinson’s Disease Working Group. The experts designed and ranked different aspects of the conceptual framework via a 5-level Likert scale for level of agreement. Consensus for the different points evaluated was based on a double threshold: the simultaneous presence of a high level of agreement had to be accompanied by a low level of disagreement. As secondary objectives, the experts were asked to rate the practical application of DMOs by evaluating the timeline to applicability, the foreseen challenges for their implementation in clinical settings, and their main role in the decision-making process.ResultsA full consensus on the clinical utility framework was achieved after three rounds. The final framework consisted of three main categories (Disease Diagnosis, Patient Evaluation, and Treatment Evaluation) and six underlying domains (Enhancing Diagnostic Procedure, Predicting Risk, Timely Detecting Deterioration, Enhancing Clinical Judgment, Selecting Treatment, and Monitoring Treatment Response). The experts believed in the next 1–5 years DMOs will play a relevant role in clinical decision making, complementing care knowledge with useful digital biomarkers information. However, the main challenge to address is the definition of clear reference value for DMOs interpretability.ConclusionsThis framework provides a structure for subsequent studies to build into by diversifying expert cohorts and expand our findings beyond PD. Additionally, our results support researchers planning future clinical trials where DMOs can play a valuable role for clinical decision support. Ultimately, this is the first step toward developing guidelines to assess DMOs’ clinical utility and support their integration into Real World clinical practice.Supplementary InformationThe online version contains supplementary material available at 10.1186/s42466-025-00426-8.