Fine-grained mortality forecasting has gained momentum in actuarial research due to its ability to capture localized, short-term fluctuations in death rates. This paper introduces MortFCNet, a deep-learning method that predicts weekly death rates using region-specific weather inputs. Unlike traditional Serfling-based methods and gradient-boosting models that rely on predefined fixed Fourier terms and manual feature engineering, MortFCNet automatically learns patterns from raw time-series data without needing explicitly defined Fourier terms or manual feature engineering. Extensive experiments across over 200 NUTS-3 regions in France, Italy, and Switzerland demonstrate that MortFCNet consistently outperforms both a standard Serfling-type baseline and XGBoost in terms of predictive accuracy. Our ablation studies further confirm its ability to uncover complex relationships in the data without feature engineering. Moreover, this work underscores a new perspective on exploring deep learning for advancing fine-grained mortality forecasting.

Diastasis of rectus abdominis (DRA) is a common pathological condition in postpartum rehabilitation, but with limited treatment strategies. This study aimed to explore the effect of using a trunk-wearable neuromuscular electrical stimulation (NMES) device on postpartum women with moderate and severe DRA. A total of 84 postpartum women with an inter-rectus distance (IRD) of ≥3 cm were randomly assigned to two equal groups. The treatment group received a trunk-wearable NMES device and exercise therapy, whereas the control group received exercise only. We measured IRD and calculated treatment response proportion, improvement of trunk muscle strength, and low-back pain in both groups. Additionally, we evaluated quality of life (QoL) using the SF-36 questionnaire and Hernia-related Quality of Life Survey (HerQLes). Statistical analysis was performed using SAS 9.4. After 8-week treatment, the IRD of the umbilical (M3) sector showed a greater reduction in the treatment group (−10.6 [−17.9 to −3.3]%, p < 0.05). Patients in the treatment group had higher treatment response proportions (p = 0.0031 and p = 0.0010, W2 and W3, respectively). Additionally, the treatment group had higher Janda assessment scores and greater reduction in low-back pain (both p < 0.0001). QoL evaluation indicated greater improvements in the SF-36 questionnaire (pain and role-emotional scales,p < 0.05) and HerQLes (p < 0.0001) in the treatment group. The application of a trunk-wearable NMES device on DRA patients, accompanied by exercise therapy, significantly reduced IRD and increased the treatment response proportion. Moreover, we observed positive improvements in trunk muscle strength, low-back pain, and QoL.

Drawing on attribution theory and impression management research, we investigate when and how abused employees engage in different coping strategies and what the interpersonal consequences of the coping strategies are for employees. Specifically, from an employee actor–based perspective, we develop and test a dual-path-mediated moderation model that represents the double-edged sword effect of abusive supervision. Using data from 444 front-line employees, we find that injury initiation motives attribution enhances the positive relationship between abusive supervision and revenge motivation, which in turn is positively related to intimidation, exemplification, and supplication. Conversely, performance promotion motives attribution strengthens the positive relationship between abusive supervision and motivation to reconcile, which in turn is positively associated with ingratiation, self-promotion, and exemplification. Intimidation and supplication are then related to increased interpersonal conflict with leaders, while ingratiation is related to reduced interpersonal conflict with leaders. Theoretical contributions, practical implications, and limitations are discussed.

The menopausal period in women is characterized by neuroendocrine alterations, which is in part mediated by the reduction in circulating estrogen. During this transition, many perimenopausal and menopausal women experience sleep disturbances and increased susceptibility to sleep-related disorders. Sleep disruptions are partially attributed to nighttime vasomotor symptoms (VMS), which exacerbates the insomnia risk in the menopausal woman. Converging data implicate the orexin system in the pathophysiology of insomnia and VMS, particularly through regulation of arousal, thermoregulation, and sympathetic outputs. Estrogen decline due to menopause is postulated to modulate orexin signaling, thereby heightening sympathetic drive and thermoregulatory instability. Given this potential mechanistic framework, orexin receptor antagonists, notably dual orexin receptor antagonists (DORAs), have been proposed as alternative menopausal therapeutics. Herein, we aim to examine preclinical, translation, and clinical literature assessing the therapeutic potentials of DORAs as a nonhormonal intervention for the mitigation of insomnia and VMS in midlife women.

This paper investigates the welfare implications of the rise of shadow banking in China, driven by regulatory arbitrage and implicit guarantees. Although shadow banking can improve social welfare by relaxing constraints on banks’ capacity to expand credit, it may also hurt social welfare due to the risk-taking behavior induced by implicit guarantees. We study the optimal level of guarantees and shadow banking in a model that balances these benefits and costs. Our findings suggest that reducing the existing degree of guarantees and shrinking the shadow banking sector could enhance social welfare in China.

Traditional studies examining caffeine intake and age-related eye diseases (ARED) have shown inconsistent results, potentially related to variations in caffeine assessment methods. This two-sample Mendelian randomisation study investigated associations between plasma caffeine and four ARED: senile cataract, diabetic retinopathy (DR) and glaucoma and age-related macular degeneration (AMD). Summary data on genetically predicted plasma caffeine came from a genome-wide association study of 9876 European-ancestry participants across six population-based studies. ARED data were extracted from FinnGen Consortium clinical records. We further examined causal effects on glaucoma subtypes: primary open-angle glaucoma (POAG) and primary angle closure glaucoma (PACG) and assessed intraocular pressure (IOP) as a potential mediator. Higher genetically predicted plasma caffeine levels were associated with reduced risk of senile cataract (OR 0·84, 95 % CI 0·78, 0·90, P < 0·001), DR (OR 0·81, 95 % CI 0·74, 0·88, P < 0·001), glaucoma (OR 0·83, 95 % CI 0·73, 0·95, P = 0·008) and PACG (OR 0·74, 95 % CI 0·54, 0·99, P = 0·046). No associations were observed with AMD or POAG. Mediation analysis suggested that 41 % (95 % CI −0·14, −0·01) of caffeine’s effect on glaucoma was mediated by IOP. Our findings indicate that elevated plasma caffeine may protect against senile cataract, DR and glaucoma, but not AMD. Effects differed by glaucoma subtype, with IOP partially explaining the overall association. This study provides genetic evidence supporting caffeine’s role in mitigating ARED risk, highlighting its potential therapeutic implications.

Vertical thermal convection exhibits weak turbulence and spatio-temporally chaotic behaviour. For this configuration, we report seven new equilibria and 26 new periodic orbits. These orbits, together with four previously studied in Zheng et al. (J. Fluid Mech., 2024b, vol. 1000, p. A29) bring the number of periodic-orbit branches computed so far to 30, all solutions to the fully nonlinear three-dimensional Navier–Stokes equations. These new and unstable invariant solutions capture intricate spatio-temporal flow patterns including straight, oblique, wavy, skewed and distorted convection rolls, as well as bursts and defects. These interesting and important fluid mechanical processes in a small flow unit are shown to also appear locally and instantaneously in a chaotic simulation in a large domain. Most of the solution branches show rich spatial and/or spatio-temporal symmetries. The bifurcation-theoretic organisation of these solutions is discussed; the bifurcation scenarios include Hopf, pitchfork, saddle-node, period-doubling, period-halving, global homoclinic and heteroclinic bifurcations, as well as isolas. Furthermore, these orbits are shown to be able to reconstruct statistically the core part of the attractor, so that these results may contribute to a quantitative description of transitional fluid turbulence using periodic orbit theory.

Understanding the flow behaviour of wet granular materials is essential for comprehending the dynamics of numerous geological and physical phenomena, but remains a significant challenge, especially the transition of these flow regimes. In this study, we perform a series of rotating drum experiments to systematically investigate the dynamic observables and flow regimes of wet mono-dispersed particles. Two typical continuous flows including rolling and cascading regimes are identified and analysed, concentrating on the impact of fluid density and rotation speed. The probability density functions of surface angles, $\theta _{\textit{top}}$ and $\theta _{\textit{lo}w\textit{er}}$, reveal distinct patterns for these two flow regimes. A morphological parameter thus proposed, termed angle divergence, is used to characterise the rolling–cascading regime transition quantitatively. By integrating quantitative observables, we construct the flow phase diagram and flow curve to delineate the transition rules governing these regimes. Notably, the resulting nonlinear phase boundary demonstrates that higher fluid densities significantly enhance the likelihood of the system transitioning into the cascading regime. This finding is further supported by corresponding variations in flow fluctuations. Our results provide new insights into the fundamental dynamics of wet granular matter, offering valuable implications for understanding the complex rheology of underwater landslides and related phenomena.

We numerically investigate the cellular detonation dynamics in ethylene/oxygen/ozone/nitrogen mixtures considering detailed chemical kinetics. The aim is to elucidate emergent detonation structures and reveal the transition mechanism from single- to double-cellular structures. Ozone is used to induce two-stage reactions within the mixture. Through systematic initiation strength analysis, we demonstrate two distinct propagation regimes: (i) under strong initiation, a stable double-cellular detonation is established; (ii) weak initiation triggers a multi-stage evolutionary process, beginning with a low-speed single-cellular detonation in the initiation zone. During the initial weak stage, the detonation propagates at a quasi-steady velocity with uniform cellular patterning. The subsequent transition phase features spontaneous acceleration accompanied by structural bifurcation into double cells, ultimately stabilising in a normal stage with sustained double-cellular structures. Further analysis reveals that the weak-stage dynamics is governed exclusively by first-stage chemical reactions, resulting in a single-cellular structure propagating at a velocity much lower than the Chapman–Jouguet speed. In contrast, the double-cellular structure observed at the normal stage results from the two-stage exothermic reactions. Thermodynamic perturbations arising from cellular instability and fluid dynamic instability are identified as critical drivers for the transition from single- to double-cellular detonation. Besides, conditions for the formation of double-cellular detonation are explored, and two qualitative requirements are summarised: the reactions of the two stages must proceed as independently as possible, and both heat releases from the two stages must be high enough to sustain the triple-shock configurations.

Monitoring fluid flow and pollutant transport is important in many geophysical, environmental and industrial processes, such as geological $\textrm {CO}_2$ sequestration, waste water disposal, oil and gas recovery and sea water invasion. But it can also be challenging. Recent studies revealed a series of self-similar solutions to describe the interface shape evolution between the injecting and the ambient fluids during fluid injection into a confined porous layer. The present work focuses further on the pressure evolution. In particular, we present self-similar solutions for the pressure evolution at both the early and late times. Two dimensionless parameters are recognised, including the viscosity ratio $M$ and the rescaled buoyancy $G$, and their specific role on the pressure evolution is clarified. Laboratory experiments are also performed to measure the pressure evolution at two specific locations during the propagation of a viscous gravity current within a vertically placed Hele-Shaw cell, with a favourable comparison with the model prediction in the unconfined regime. The obtained pressure solutions are also used to explain the field data of bottom-hole-pressure (BHP) evolution from a geological $\textrm {CO}_2$ sequestration project, considering both fluid injection and shut-in operations. The model and solutions might also be of use to assess reservoir injectivity and develop pressure-based monitoring technologies at well bores.

In the fields of meal-assisting robotics and human–robot interaction (HRI), real-time and accurate mouth pose estimation is critical for ensuring interaction safety and improving user experience. The complexity arises from the diverse opening degrees of mouths, variations in orientation, and external factors such as lighting conditions and occlusions, which pose significant challenges for real-time and accurate posture estimation of mouths. In response to the above-mentioned issues, this paper proposes a novel method for point cloud fitting and posture estimation of mouth opening degrees (FP-MODs). The proposed method leverages both RGB and depth images captured from a single viewpoint, integrating geometric modeling with advanced point cloud processing techniques to achieve robust and accurate mouth posture estimation. The innovation of this work lies in the hypothesis that different states of mouth openings can be effectively described by distinct geometric shapes: closed mouths are modeled by spatial quadratic surfaces, half-open mouths by spatial ellipses, and fully open mouths by spatial circles. Then, based on these hypotheses, we developed algorithms for fitting geometric models to point clouds obtained from mouth regions, respectively. Specifically, for the closed mouth state, we employ an algorithm based on least squares optimization to fit a spatial quadratic surface to the point cloud data. For the half-open or fully open mouth states, we combine inverse projection methods with least squares fitting to model the contour as a spatial ellipse and circle, respectively. Finally, to evaluate the effectiveness of the proposed FP-MODs method, extensive actual experiments were conducted under varying conditions, including different orientations and various types of mouths. The results demonstrate that the proposed FP-MODs method achieves high accuracy and robustness. This study can provide a theoretical foundation and technical support for improving HRI and food delivery safety in the field of robotics.

In this paper, the upper bounds of non-real eigenvalues of indefinite Sturm–Liouville (S-L) problems with boundary conditions depend on the eigenparameter are studied. The upper bounds of real parts, imaginary parts and absolute values of non-real eigenvalues are given under the condition that the coefficients are integrable.