Search Results (556)

Depressive symptoms are increasingly common in middle-aged and older adults and have become a major public health problem. People may experience transitions across different underlying states due to symptom variability over a course of many years. And risk factors may have different impact on different symptom states. However, existing research rarely considers the identification of important factors related to symptom conversion. The purpose of this study was to examine the risk associated with transitioning between various stages of depressive symptoms and their influencing factors, utilizing a multi-state model with a simultaneous feature selection method. We used the four waves of data from the China Health and Retirement Longitudinal Study (CHARLS) and 3916 participants were selected after screening. Five states of depressive symptoms were defined including no symptom, new symptom episode, symptom persistence, remission and relapse. We included 13 variables on demographic background, health status and functioning, and family and social connectivity, along with their interactions. Multi-state models were used to evaluate the risks of state transitions. The regularized (adaptive Lasso) partial likelihood approach was employed to simultaneously identify the important risk factors, estimate their impact on the state transition rates and determine their statistical significance. There were 1392 new depressive episodes events, 402 symptom persistence events, 639 remission events and 118 relapse events. We identified nine significant risk factors for the new onset of depressive symptoms: urban–rural residence, sex, retirement status, income, body pain, difficulty with basic daily activities, social engagement, education by income interaction and number of conditions by income interaction. The effects of the identified risk factors on new symptom episode weakened as those symptoms became persistent or went into remission. In terms of symptom relapse, sex by age was identified as a significant influencing factor. This study identified key factors and explored their effects on the various depressive symptom states among older Chinese adults. The findings could serve as a foundation for the development and implementation of targeted policies aimed at enhancing the mental well-being of China’s elderly population. Full article

In this paper, a generic computational framework, based on the generalized-mode approach, is developed for the fully coupled time-domain aero-hydro-servo-elastic analysis of Hybrid Offshore Wind and Wave Energy Systems (HOWiWaESs), consisting of a Floating Offshore Wind Turbine (FOWT) and several wave energy converters (WECs) mechanically connected to it. The FOWT’s platform and the WECs of the HOWiWaES are modeled as a single floating body with conventional rigid-body modes, while the motions of the WECs relative to the FOWT are described as additional generalized modes of motion. A numerical tool is established by appropriately modifying/extending the OpenFAST source code. The frequency-dependent exciting forces and hydrodynamic coefficients, as well as hydrostatic stiffness terms, are obtained using the traditional boundary integral equation method, whilst the generalized-mode shapes are determined by developing appropriate 3D vector shape functions. The tool is applied for a 5 MW FOWT with a spar-type floating platform and a conic WEC buoy hinged on it via a mechanical arm, and results are compared with those of other investigators utilizing the multi-body approach. Two distinctive cases of a pitching and a heaving WEC are considered. A quite good agreement is established, indicating the potential of the developed tool to model floating HOWiWaESs efficiently. Full article

This study proposes a high-contrast photoacoustic (PA) imaging methodology based on a dual-wavelength confocal metalens, designed to monitor the dissemination of cancer cells and to inform subsequent cancer treatment strategies. The metalens is composed of two metasurfaces that perform filtering and focusing functions, effectively reducing the cross-talk between the two wavelengths of light in space and achieving a confocal effect. Furthermore, to minimize process complexity, a uniform material system of silicon dioxide (SiO₂) and titanium dioxide (TiO₂) is employed across the different metasurfaces of the metalens. The designed metalens has a radius of 25 µm and an operational focal length of 98.5 µm. The results confirm that this dual-metasurface design achieves high focusing efficiency alongside precise focusing capability, with the deviations of the actual focal lengths for both beams from the design values being within 1.5 µm. Additionally, this study developed a skin tissue model and simulated multi-wavelength photoacoustic imaging of cancer cells within the human body by integrating theories of radiative transfer, photothermal conversion, and the wave equation. The results demonstrate that the enhancement trend of the reconstructed signal closely matches the original signal, confirming the model’s excellent fitting performance. The sound pressure values generated by cancer cells are significantly higher than those of normal cells, proving that this method can effectively distinguish cancerous tissue from healthy tissue. This research provides new theoretical support and methodological foundations for the clinical application of multi-wavelength photoacoustic imaging technology. Full article

As a key form of deep foundation in civil engineering, the concrete pouring quality of cast-in-place piles directly determines the integrity and long-term bearing performance of the pile body. Accurate monitoring of the pouring interface is critical to preventing defects such as mud inclusion and pile breakage. To address the limitations of existing monitoring methods for concrete pouring interfaces, this paper proposes a full-pulse acoustic monitoring method for the concrete pouring interface of cast-in-place piles. Firstly, by constructing a hardware system platform consisting of “multi-level in-borehole sound sources + interface acoustic wave sensors + orifice full-pulse receivers + ground processors”, differential capture of signals propagating at different depths is achieved through multi-frequency excitation. Subsequently, a waveform data processing method is proposed to realize denoising, enhancement, and frequency discrimination of different signals, and a target feature recognition model that integrates cross-correlation functions and signal similarity analysis is established. Finally, by leveraging the differential characteristics of measurement signals at different depths, a near-field measurement mode and a far-field measurement mode are developed, thereby establishing a calculation model for the elevation position of the pouring interface under different scenarios. Meanwhile, the feasibility of the proposed method is verified through practical engineering cases. The results indicate that the proposed full pulse acoustic monitoring method can achieve non-destructive, real-time, and high-precision monitoring of the pouring interface, providing an effective technical approach for quality control in pile foundation construction and exhibiting broad application prospects. Full article

To address the issues of traditional mooring lines, such as high self-weight, low strength, and poor durability, Carbon-Fiber-Reinforced Polymer (CFRP) was investigated as a material for mooring lines of offshore floating wind turbines, aiming to achieve high performance, lightweight design, and long service life for mooring systems. Based on a “chain–cable–chain” configuration, a CFRP mooring line design is proposed in this study. Taking a 5 MW offshore floating wind turbine as the research object, the dynamic performance of offshore floating wind turbines with steel chains, steel cables, polyester ropes, and CFRP mooring lines under combined wind, wave, and current loads was compared and analyzed to demonstrate the feasibility of applying CFRP mooring lines by combining the potential flow theory and the rigid–flexible coupling multi-body model. The research results indicate that, compared to traditional mooring systems such as steel chains, steel cables, and polyester ropes, (1) under static water, the CFRP mooring system exhibits a larger static water free decay response and longer free decay duration; (2) under operating sea conditions, the motion response and mooring tension of the offshore floating wind turbine with CFRP mooring lines are smaller than those with steel cables and steel chains but greater than those with polyester ropes; and (3) under extreme sea conditions, the motion responses of the offshore floating wind turbine with CFRP mooring lines are smaller than those with steel wire ropes and steel chains but close to the displacement responses of the polyester rope system, while the increase in mooring tension is relatively moderate and the safety factor is the highest. Full article

Recently, there has been significant progress in the area of semantic classification of water bodies at global scales with deep learning. For the key purposes of water inventory and change detection, advanced deep learning classifiers such as UNets and Vision Transformers have been shown to be both accurate and flexible when applied to large-scale, or even global, satellite image datasets from optical (e.g., Sentinel-2) and radar sensors (e.g., Sentinel-1). Most of this work is conducted with optical sensors, which usually have better image quality, but their obvious limitation is cloud cover, which is why radar imagery is an important complementary dataset. However, radar imagery is generally more sensitive to soil moisture than optical data. Furthermore, topography and wind-ripple effects can alter the reflected intensity of radar waves, which can induce errors in water classification models that fundamentally rely on the fact that water is darker than the surrounding landscape. In this paper, we develop a solution to the use of Sentinel-1 radar images for the semantic classification of water bodies that uses style transfer with multi-modal and multi-temporal image fusion. Instead of developing new semantic classification models that work directly on Sentinel-1 images, we develop a global style transfer model that produces synthetic Sentinel-2 images from Sentinel-1 input. The resulting synthetic Sentinel-2 imagery can then be classified with existing models. This has the advantage of obviating the need for large volumes of manually labeled Sentinel-1 water masks. Next, we show that fusing an 8-year cloud-free composite of the near-infrared band 8 of Sentinel-2 to the input Sentinel-1 image improves the classification performance. Style transfer models were trained and validated with global scale data covering the years 2017 to 2024, and include every month of the year. When tested against a global independent benchmark, S1S2-Water, the semantic classifications produced from our synthetic imagery show a marked improvement with the use of image fusion. When we use only Sentinel-1 data, we find an overall IoU (Intersection over Union) score of 0.70, but when we add image fusion, the overall IoU score rises to 0.93. Full article

Research demonstrates a positive correlation between experiencing adverse childhood experiences (ACEs) and negative outcomes in emerging adulthood. However, relatively little research has examined the potential effects of ACEs on a common experience in emerging adulthood: dating and establishing romantic relationships. This is especially true for troubled relationships. We extend this literature by examining a potential mechanism that might moderate the association between ACEs and intimate partner violence (IPV): family social capital. A large body of research establishes family social capital as a protective factor for positive child and youth development. We expand this research by examining how studying family systems might inform efforts to prevent IPV. However, the information, obligations, norms, and connections that make up family social capital may have more tenuous relationships with intimate partner violence, especially for people who have experienced ACEs. We developed a model to analyze this interaction using the National Longitudinal Study of Adolescent to Adult Health (Add Health). Add Health is a nationally representative study from the United States that initially sampled 20,745 adolescents in Wave 1. We use demographic and data from Wave 1 and IPV measures from Wave 3 data when respondents are in emerging adulthood (ages 18–26) (n = 15,701). We examine whether family social capital is associated with exposure to IPV, as well as whether family social capital can moderate the relationship between experiencing ACEs and exposure to IPV. Our results suggest some protective effects of family social capital on the emergence of IPV for both maternal and paternal social capital, but that family social capital does not fully moderate the damaging effects of ACEs. Full article

Background/Objectives: Cerebral palsy (CP) is a leading cause of motor disability in children and is commonly associated with spasticity. Treatment with radial extracorporeal shock waves (rESWs) is an established non-invasive therapy for spasticity, although its underlying mechanisms remain poorly understood. Caenorhabditis elegans (C. elegans) represents a powerful model for neuromuscular research due to its fully mapped nervous system, conserved cholinergic pathways and suitability for high-throughput behavioral analysis. This study aimed to test whether rESWs modulate cholinergic signaling at the neuromuscular junction (NMJ) in C. elegans. Methods: Wild-type and acr-16 mutant C. elegans were exposed in liquid to varying doses of rESWs, nicotine and carbachol in different combinations. Locomotor behavior was recorded using high-resolution video tracking, and parameters including peristaltic speed, body wavelength, reversals and omega bends were quantified. Results: Exposure to rESWs transiently altered locomotion, most notably by reducing forward speed and increasing the frequency of reversals. However, rESWs did not consistently modify behavioral responses to nicotine or carbachol, and these effects were not clearly dependent on NMJ-associated nicotinic receptors. Conclusions: Exploring C. elegans as a model for rESW effects on spasticity proved informative but also revealed important limitations. Results indicate that rESWs act on the nervous system more broadly, extending beyond neuromuscular structures. This contrasts with the clinical situation, where rESWs primarily target muscles and connective tissues. While this precludes C. elegans as a direct model for CP-related spasticity, the observation that rESWs influence nervous-system function at a systemic level points to potential therapeutic avenues for neurological diseases. Full article

As the environmental regulations of the International Maritime Organization (IMO) become more stringent, the accurate prediction of ship propulsion performance has become essential. Under ballast conditions where the draft is shallow, the propeller approaches the free surface, causing complex phenomena such as ventilation and surface piercing, which reduce propulsion efficiency. The conventional virtual disk (VD) method cannot adequately capture these free-surface effects, leading to deviations from model propeller results. To resolve this, a correction formula that accounts for the advance ratio (J) and submergence ratio ($h/D$) has been proposed in previous studies. In this study, the correction formula was simplified and implemented in a CFD environment using a field function, enabling dynamic adjustment of body force based on time-varying submergence depth. A comparative analysis was conducted between the conventional VD, modified VD, and model propeller using POW and self-propulsion simulations for an MR tanker and SP598M propeller. The improved method was validated in calm and regular wave conditions. The results showed that the modified VD method closely matched the performance trends of the model propeller, especially in free surface-interference conditions (e.g., $h/D$ < 0.5). Furthermore, additional validations in wave-induced self-propulsion confirmed that the modified VD method accurately reproduced the reductions in wake fraction and thrust deduction coefficient, unlike the overestimations observed with the conventional VD. These results demonstrate that the modified VD method can reliably predict propulsion performance under real sea states and serve as a practical tool in the early design stage. Full article

Background: Air pollution is a well-established risk factor for cardiovascular and metabolic diseases. Although Southern Switzerland is considered a relatively low-pollution area, levels of nitrogen dioxide (NO₂) and particulate matter (PM₁₀) still exceed the latest WHO air quality guidelines. This study aimed to assess the association between long-term exposure to air pollutants, vascular aging, and body composition in a Swiss population sample. Methods: A cross-sectional analysis was conducted on 1202 participants from the Ticino Epidemiological Stiffness Study (2017–2018). Vascular health was assessed via pulse wave velocity (PWV), used to estimate vascular age. Body composition was evaluated through bioimpedance analysis, yielding fat mass index (FMI), body cellular mass (BCM), and body cellular mass index (BCMI). Individual exposure to NO₂ and PM₁₀ was estimated, using geocoded residential data and environmental monitoring records from 2002 to 2017. Statistical models were adjusted for major cardiovascular risk factors. Results: Higher exposure to NO₂ was significantly associated with increased vascular age (mean delta age: +0.53 years in the high exposure group) and adverse body composition markers, including higher FMI and lower BCM/BCMI. These associations remained significant after adjusting for confounders. PM₁₀ showed weaker associations, significant only in unadjusted models. Conclusions: Even in a relatively clean environment, exposure to NO₂ is linked to early vascular aging and unfavorable body composition. These findings reinforce the need for stricter air quality standards and underline the importance of continuous environmental health surveillance, even in regions considered low risk. Full article

Floating two-body wave energy converters (WECs) exhibit advantages, including insensitivity to water depth and tidal range, along with adaptability to multi-level sea states. However, WECs suffer from drawbacks, including unstable power generation and low wave energy capture efficiency. To enhance the hydrodynamic performance and energy capture efficiency, a dish-shaped buoy and perforated damping plate configuration was designed based on conventional two-body WECs. First, four two-body WECs were developed according to these configurations. Second, a numerical model based on potential flow theory and the boundary element method (BEM) was established, with its accuracy validated through sea trials. Finally, the frequency domain response, motion response, mooring tension and power absorption effect of the WECs under wave excitation of grades 3, 4 and 5 were analyzed. The results demonstrate that both the dish-shaped buoy and perforated damping plate significantly improve the device stability and energy capture potential. Regarding the motion response, both configurations reduced the peak response amplitudes in heave and roll, enhancing the device stability. For mooring tension, both configurations reduced the mooring line tension. For power absorption, the perforated damping plate effectively increased the energy capture efficiency, while the dish-shaped buoy also demonstrated superior performance under higher-energy wave conditions. Overall, this study provides a theoretical foundation and design guidance for floating two-body WECs. Full article

Spin waves represent an important class of low-energy excitations in magnetic solids, which influence the thermodynamic properties and play a major role in technical applications, such as spintronics or magnetic data storage. Despite the enormous advances of ab initio simulations in materials science, quantitative calculations of spin-wave spectra still pose a significant challenge, because the collective nature of the spin dynamics requires an accurate treatment of the Coulomb interaction between the electrons. As a consequence, simple lattice models like the Heisenberg Hamiltonian are still widespread in practical investigations, but modern techniques like time-dependent density-functional theory or many-body perturbation theory also open a route to material-specific spin-wave calculations from first principles. Although both are in principle exact, actual implementations necessarily employ approximations for electronic exchange and correlation as well as additional numerical simplifications. In this review, we recapitulate the theoretical foundations of ab initio spin-wave calculations and analyze the common approximations that underlie present implementations. In addition, we survey the available results for spin-wave dispersions of various magnetic materials and compare the performance of different computational approaches. In this way, we provide an overview of the present state of the art and identify directions for further developments. Full article

Entrepreneurship has proven to be a dynamic force for societal change in urgent global transformation toward sustainability. While governments and established institutions often move slowly, a new wave of entrepreneurs—guided by passion, purpose, and a holistic worldview—are actively shaping alternative paths to sustainable development. This study investigates how entrepreneurs integrate personal purpose, well-being, and a holistic approach into their businesses and lifestyles. Drawing on observations and interviews with selected entrepreneurs, the research employs thematic analysis to explore their motivations, decision-making processes, and the role of environments in shaping their ventures. The findings highlight a distinctive approach of conscious entrepreneurs: a shift from profit maximisation to values-based operations, educational marketing to foster authentic client engagement, and a regenerative leadership style that prioritises balance, relational intelligence, and co-creation. Unlike digital nomads or traditional influencers, these entrepreneurs represent a grounded, intentional lifestyle rooted in inner development and collective transformations. The results of this study contribute to a growing body of interdisciplinary literature on sustainability and entrepreneurship by offering an expanded understanding of purpose and passion in sustainable/conscious entrepreneurship and proposing an updated business model canvas for such entrepreneurs. Full article

During walking, the human lower limbs primarily support the body and drive forward motion, while the arms exhibit greater variability and flexibility without bearing such loads. In gait-based target recognition, collecting exhaustive arm-motion data for training is challenging, and unseen arm movements during testing may degrade the performance. This paper investigates the impact of arm movements on radar-based gait recognition and proposes a gait recognition method using extracted lower limb motion data to mitigate interference from different arm motions. Gait data is collected via a millimeter-wave radar sensor encompassing four kinds of common arm movements, including natural arm swings, object-holding states, and irregular arm motions, from 11 volunteers. Using extracted lower limb motion data, millimeter wave point-cloud gait datasets covering diverse arm motions are generated. Three gait recognition experiments are conducted for comparing the performances of our proposed method using only lower limb data and existing method using all limb data, both based the on PointNet++ model. And the experimental results show that our method consistently outperforms existing methods, with a 22.9-percent improvement in accuracy. Results also show that the proposed method can enhance feature extraction, accelerate convergence, and achieve higher accuracy, especially with limited samples, and the highest recognition accuracy reaches 96.9%. In addition, in unseen arm movement cases, our method significantly outperforms existing methods, demonstrating superior robustness and recognition accuracy. Full article

In medical applications, microwave sensors are usually employed to work in direct contact with the human body, therefore requiring an accurate prediction of the electromagnetic interactions with biological tissues. While full-wave simulations can be useful to achieve the above task, they are computationally expensive, especially for iterative sensor optimization. Analytical models may offer a more efficient alternative, but they are often complex, and they must be formulated in a practical way to be useful. As a result, approximate approaches can be advantageous. Traditional approaches, such as plane-wave approximations and transmission-line models, often fail to capture key sensing features. This paper presents an approximate analytical model for standard-aperture sensor configurations to predict the sensor response in terms of the reflection coefficient when placed above a layered medium. The model is based on the assumption that the electromagnetic interaction is primarily governed by the sensor’s dominant mode. Full-wave simulations in the 2–3 GHz frequency range (relevant for medical applications) demonstrate strong agreement with the analytical model, thereby validating its effectiveness as a first-order approximation for sensor–tissue interactions. This provides a reliable and computationally efficient tool to properly manage microwave sensors design in medical applications. Full article