Search Results (2,476)

Background/Objectives: Stunting and weight faltering (WF) remain pressing public health challenges in low- and middle-income countries, with long-term consequences for child growth, development, and survival. While the role of gut health in early growth is increasingly recognized, evidence on how the gut microbiome and metabolome respond to nutritional interventions in WF infants is scarce. This study explored gut microbiome and metabolome changes in Indonesian infants aged 6–12 months who overcame WF following a one-month intervention. Methods: Infants were assigned to either a Nutritional Advice (NA) group or a Nutritional Advice plus Oral Nutritional Supplements (NAONS) group. Stool samples were collected before and after the intervention for microbiome (16S rRNA sequencing) and metabolome (LC-MS) analysis. Results: Significant shifts in gut microbial composition (beta diversity) and species richness (Chao1 index) were observed in both groups, suggesting enhanced microbial diversity and gut resilience. Within-group analysis revealed increases in beneficial genera such as Faecalibacterium and Peptostreptococcus, and a reduction in pro-inflammatory Fusobacterium in the NA group. The NAONS group showed a notable decrease in Proteus, a potentially pathogenic genus. Between-group comparisons indicated higher abundances of Lactococcus and Leuconostoc in the NAONS group, likely reflecting the influence of milk protein-rich supplements on microbial colonization, favoring lactic acid bacteria over SCFA-producing taxa, leading to better gut health. Metabolome analysis revealed significant changes in the NA group, increases in metabolites like Threonine, Tryptophan, and Xylose pointed to improved energy metabolism and gut health, while a decrease in Oxalic Acid suggested better metabolic efficiency. In contrast, the NAONS group, while benefiting from rapid weight gain, displayed a distinct metabolic profile influenced by high milk protein intake. No significant correlations were found between microbiome and metabolome changes, highlighting the complexity of gut-host interactions, suggesting that the interventions led to independent shifts in the aforementioned profiles. Conclusions: Overall, the findings suggest that nutritional interventions may enhance gut health and support recovery from weight faltering, providing insights into strategies that may contribute to restoring healthy growth trajectories and preventing stunting by modulating gut health. Full article

In road cycling, developing bike handling skills can prevent crashes and falls. Nevertheless, bike handling remains largely unexplored in the world of road cycling. The goal of this research was to develop a methodology to assess bike handling during races and training by estimating the rider–bicycle roll angle and road-plane accelerations from global positioning system (GPS) data only. A multi-dimensional bike-rider mathematical model was included in an optimal control framework to follow a reference trajectory generated from GPS data points. Estimated variables and experimental data collected with a cost-effective setup showed good agreement, i.e., root mean square error (RMSE) of 12° and 0.1 g for roll angle and both longitudinal and lateral accelerations, respectively, in the worst-case scenarios. This methodology might allow for the estimation of key bike handling variables during fast segments with cost-effective instrumentation. It can therefore constitute a tool for objectively assessing bike handling in road cycling training and racing. Full article

Background: Population ageing is a global challenge, prompting ageing-in-place policies in Hong Kong to support community-dwelling older adults while reducing healthcare costs. Yet, their impact on health-related quality of life (HRQoL) remains underexplored amid Hong Kong’s long life expectancy and growing older population. Traditional surveys are costly and time-consuming, while routinely collected registration data offers a large, efficient source for health insights. This study uses enhanced administrative data to track HRQoL trajectories and inform policy. Methods: This is a prospective, open-ended longitudinal study, enrolling adults aged 50 or older from a collaborating non-governmental organization in Hong Kong’s Southern District. Data collection, started in February 2021, occurs annually via phone and face-to-face interviews by trained social workers and volunteers using a standardized questionnaire to assess individual (e.g., socio-demographics), environmental (e.g., social support via Lubben Social Network Scale-6), biological (e.g., chronic illnesses), functional (e.g., cognition via Montreal Cognitive Assessment), and HRQoL (e.g., EQ-5D-5L) factors. A secure online system links health and service use data (e.g., service utilization like community care visits). Analysis employs descriptive statistics, group comparisons, correlations, growth modelling to identify health trajectories, and structural equation modelling to test a revised quality-of-life framework. Sample size (projected 470–580 after two follow-ups from a 2321 baseline) is based on power calculations: 300–500 for latent class growth analysis (LCGA) class detection and 200–400 for structural equation modelling (SEM) fit (e.g., RMSEA < 0.06) at 80% power/α = 0.05, simulated via Monte Carlo with a 50–55% attrition. Discussion: This is the first longitudinal HRQoL study in Hong Kong using enhanced non-governmental organization (NGO) administrative data, integrating social–ecological and HRQoL models to predict trajectories (e.g., stable vs. declining mobility) and project care demands (e.g., increase in in-home care for frailty). Unlike prior cross-sectional or inpatient studies, it offers a scalable model for NGOs, informing ageing-in-place policy effectiveness and equitable geriatric care. Full article

Although trauma exposure (TE) has been shown to be a robust predictor of youth involvement in the juvenile justice system, evidence regarding the role of TE amongst youth who recidivate has been more mixed. Recidivist youth are a population of particular concern, given evidence of declining mental health and diminished likelihood of returning to an adaptive developmental course. One way in which TE may contribute to these negative outcomes over time is through potentiating or “driving” mental health problems, which are especially prevalent among trauma-exposed youth in the justice system. To examine this hypothesis, longitudinal data were obtained over a 10-year period from a sample of 5615 juvenile justice-involved youth (1499 girls and 4116 boys) who completed a mental health screening at each admission to detention. Results of analyses assessing the associations among trauma exposure, linear and quadratic time, and mental health problems were consistent with the hypothesis that increases in TE were associated with increasing anger/irritability, depression/anxiety, somatic complaints, and suicidal ideation across repeat admissions. With the exception of alcohol/drug use, all mental health outcomes followed a quadratic trajectory over the course of multiple admissions. Rates of mental health problems were consistently highest for girls and White youth across all waves. These results add to our understanding of the role of trauma in mental health problems among persistent offenders and may help to inform interventions designed to reduce youth contact with the potentially iatrogenic effects of justice system involvement. Full article

Model Predictive Control (MPC) predicts the vehicle’s motion within a fixed time window, known as the prediction horizon, and calculates potential collision risks with obstacles in advance. It then determines the optimal steering input to guide the vehicle safely around obstacles. For example, when a sudden obstacle appears, sensors detect it, and MPC uses the vehicle’s current speed, position, and heading to predict its driving trajectory over the next few hundred milliseconds to several seconds. If a collision is predicted, MPC computes the optimal steering path among possible avoidance trajectories that are feasible within the vehicle’s dynamics. The vehicle then follows this input to steer away from the obstacle. In the proposed method, MPC is combined with Adaptive Artificial Potential Field (APF). The APF dynamically adjusts the repulsive force based on the distance and relative speed to the obstacle. MPC predicts the optimal driving path and generates control inputs, while the avoidance vector from APF is integrated into MPC’s constraints or cost function. Simulation results demonstrate that the proposed method significantly improves obstacle avoidance response, steering smoothness, and path stability compared to the baseline MPC approach. Full article

The root of Zanthoxylum nitidum is used in traditional Chinese medicine, whereas its leaves remain an under-exploited resource rich in essential oil (EO). By integrating cytological, analytical–chemical, and chemometric approaches, we have dissected the ontogeny of secretory cavities and the temporal accumulation of EO in Z. nitidum leaves for the first time. Cytological analyses revealed marginal-tooth-slit secretory cavities consisting solely of a spherical domain formed via a schizogenous mechanism. The EO yield followed a unimodal trajectory, peaking at growth stages ZN-2 and ZN-3. Gas chromatography–mass spectrometry (GC-MS) profiling identified 60 constituents; sesquiterpenoids reached maximal abundance at ZN-3, whereas monoterpenoids predominated at ZN-2. Second-derivative Fourier transform infrared spectroscopy (FTIR) spectra exhibited pronounced stage-specific differences, and hierarchical cluster analysis coupled with principal component analysis reliably discriminated developmental stages based on their chemical fingerprints. These findings provide a robust cytological and analytical framework for quality control and rational utilization of Z. nitidum leaves, laying the groundwork for their full medicinal exploitation. Full article

Background/Objectives: Optic neuropathies (ON) are a clinically heterogeneous group of disorders that can cause profound and lasting visual disability, with wide-ranging effects on patients’ quality of life. Although the NEI-VFQ-25 is an instrument for assessing vision-related quality of life (VRQoL), few studies have systematically compared patient-reported outcomes across multiple ON subtypes, especially in underrepresented populations. We aimed to delineate how etiological differences and longitudinal visual acuity trajectories shape VRQoL in a diverse Portuguese cohort with ON. Methods: This retrospective, cross-sectional study included 152 patients diagnosed with ON and followed at São João University Hospital, Portugal. All participants completed the validated NEI-VFQ-25. Diagnosis-specific differences in VRQoL were interrogated using ANCOVA and linear mixed-effects models, controlling for age and sex. Visual acuity changes over time were analyzed in relation to patient-reported outcomes. Results: Substantial heterogeneity in VRQoL was observed across ON subtypes. Patients with MS-related ON (MS-RON) and idiopathic ON reported significantly higher NEI-VFQ-25 scores in domains such as general vision, mental health, and dependency (F = 3.30, p = 0.013; ηp² = 0.08), while those with ischemic or other inflammatory etiologies showed persistently lower scores. Notably, both final visual acuity and diagnosis were independently associated with NEI-VFQ-25 composite scores, highlighting the correlation between objective and subjective measures of visual function. Age and diagnosis independently predicted poorer VRQoL. Conclusions: This study provides the first comprehensive evaluation of vision-related quality of life (VRQoL) across a diverse cohort of optic neuropathy patients in a Portuguese tertiary center, using the NEI-VFQ-25. Our results underscore the heterogeneity of functional impact across ON subtypes, emphasizing the value of integrating sensitive, multidimensional assessment tools into neuro-ophthalmic clinical care, especially in populations historically underrepresented in research. Full article

Enhancing the positioning stability and accuracy of autonomous following systems poses a significant challenge, particularly in dynamic indoor environments susceptible to occlusion and interference. This paper proposes an innovative approach that integrates Ultra-Wideband (UWB) technology with computer vision-based gait analysis to overcome these limitations. First, a low-power, high-update-rate UWB positioning network is established based on an optimized Double-Sided Two-Way Ranging (DS-TWR) protocol. To compensate for UWB’s deficiencies under Non-Line-of-Sight (NLOS) conditions, a visual gait recognition process utilizing the GaitPart framework is introduced for target identification and relative motion estimation. Subsequently, an Extended Kalman Filter (EKF) is developed to seamlessly fuse absolute UWB measurements with gait-based relative kinematic information, thereby generating precise and robust estimates of the leader’s trajectory. This estimated path is tracked by a differentially driven mobile platform via a Model Predictive Controller (MPC). Experimental results demonstrate that the tracking deviation for most trajectory points remains within 50 mm, with a maximum observed deviation of 115 mm during turns, confirming its strong robustness and practical utility in real-world intelligent vehicle applications. Full article

While the inclusiveness of digital finance is widely acknowledged, existing research predominantly focuses on its developmental level, with limited attention to its spatial correlation network and structural characteristics. A city’s centrality within this network governs the flow and allocation of digital financial resources, thereby influencing interregional and urban-rural efficiency in resource allocation and income distribution, which ultimately shapes the trajectory of common prosperity. Based on panel data from 280 Chinese cities (2011–2021), this study employs social network analysis to measure urban centrality in the digital financial spatial correlation network and empirically investigates its impact and mechanisms on common prosperity. The main findings are as follows: (1) Benchmark regressions confirm that overall network centrality and its three dimensions—degree, betweenness, and closeness centrality—significantly promote common prosperity, specifically by enhancing the “wealth” dimension and reducing regional development disparities, with the growth effect currently surpassing the inclusion effect. (2) Robustness checks, including instrumental variable approaches addressing endogeneity, affirm the reliability of the core findings. (3) Heterogeneity analysis reveals that the positive effect is more pronounced in cities that are less developed or have weaker financial foundations, such as those in Western China, non-financial centers, cities with no presence of formal financial institutions in antiquity, fifth-tier cities, and small and medium-sized cities, suggesting that network centrality serves as a catalytic tool for urban catch-up strategies. (4) Mechanism analysis identifies that fostering entrepreneurship, particularly among self-employed individuals and wholesale/retail enterprises characterized by decentralized operations and abundant transaction data, is the primary channel through which centrality advances common prosperity. This study provides insights into promoting balanced regional development and common prosperity by optimizing the spatial structure of digital finance. Full article

There have been many attempts to model the flow of vehicular traffic in analogy to the flow of fluids. Given the evident change in distance between vehicles driving in platoons, the compressibility of traffic flow is inferred and, considering the reaction time-scales of the driver (human or autonomous), it is argued that this compressibility is increased as relative velocities increase—giving the lag in imposed redirection by the driver and the controller units a higher relative importance. Therefore, a supersonic compressible flow field has been opted for as the most analogous base flow. On this point, added to by the overall extreme similarities of the two above-mentioned flows, the non-dimensional group of the traffic Mach number M_T has been defined in the present research, providing the possibility of calculating a suggested flow field and its corresponding shockwave systems, for any given obstacle ahead of the traffic flow. This suggested flow field is then taken as the basis to obtain trajectories designed for avoiding collision with the obstacle, and in compliance with the physics of the underlying analogous fluid flow phenomena, namely the internal supersonic compressible flow around a double wedge. It should be noted that herein we do not model the traffic flow but propose these trajectories for more optimal collision avoidance, and therefore the above-mentioned similarities (explained in detail in the manuscript) suffice, without the need to rely on full analogies between the two flows. The manuscript further analyzes the applicability of the proposed analogy in the path-planning process for an autonomous passenger vehicle, through dynamics and control of a full-planar vehicle model with an autonomous path-tracking controller. Simulations are performed using realistic vehicle parameters and the results show that the fluid flow analogy is compatible with the vehicle dynamics, as it is able to follow the target path generated by fluid flow calculations with minor deviations. Simulation results demonstrate that the proposed method produces smooth and dynamically consistent trajectories that remain stable under varying traffic scenarios. The controller achieves accurate path tracking and rapid convergence, confirming the feasibility of the fluid-flow analogy for real-time vehicle control. Full article

The emergence of surgical robots has revolutionized complex operations, improving precision, lowering operating risks, and shortening recovery periods. Given the merits, an eight degrees of freedom (DOF) hybrid surgical robot (HSR) has been proposed, which leverages the benefits of both serial and parallel manipulators. However, its performance is hindered by the constrained range of motion of its parallel platform. To address the issue, this research presents a systematic approach for designing and optimizing the proposed HSR. The first step is the design of the HSR, followed by a multi-stage design analysis of its parallel platform, concentrating on kinematic, geometrical, and singularity analysis. Higher values of the condition number indicate singular configurations in the platform’s workspace, highlighting the need for an optimized design. For optimization of the platform, performance parameters like global condition number (GCN), actuator forces, and stiffness are identified. Initially, the design is optimized by targeting GCN only through a genetic algorithm (GA). This approach compromised the other parameters and raised the need for simultaneous optimization employing a non-dominated sorting genetic algorithm (NSGA II). It offered a better trade-off between performance parameters. To further assess the working of the optimized parallel platform, workspace analysis and motion planning of a predefined trajectory have been performed. Full article

Vessel trajectory prediction (VTP) plays a critical role in maritime safety and intelligent navigation. Existing methods struggle to simultaneously capture long-term dependencies and nonlinear dynamic patterns in vessel movements. To address this challenge, we propose MKAIS, a novel trajectory prediction model that integrates the selective state space modeling capability of Mamba with the strong nonlinear representation power of Kolmogorov–Arnold Networks (KAN). Specifically, we design a feature-separated embedding strategy for AIS inputs (longitude, latitude, speed over ground, course over ground), followed by an MKAN module that jointly models global temporal dependencies and nonlinear dynamics. Experiments on the public ct_dma dataset demonstrate that MKAIS outperforms state-of-the-art baselines (LSTM, Transformer, TrAISformer, Mamba), achieving up to 16.65% improvement in the Haversine distance over 3 h prediction horizons. These results highlight the effectiveness and robustness of MKAIS for both short-term and long-term vessel trajectory prediction. Full article

This paper introduces a novel observer-based, fully distributed fault-tolerant consensus control algorithm for model-free adaptive control, specifically designed to tackle the consensus problem in nonlinear multi-agent systems. The method addresses the issue of followers lacking direct access to the leader’s state by employing a distributed observer that estimates the leader’s state using only local information from the agents. This transforms the consensus control challenge into multiple independent tracking tasks, where each agent can independently follow the leader’s trajectory. Additionally, an extended state observer based on a data-driven model is utilized to estimate unknown actuator faults, with a particular focus on brake faults. Integrated into the model-free adaptive control framework, this observer enables real-time fault detection and compensation. The proposed algorithm is supported by rigorous theoretical analysis, which ensures the boundedness of both the observer and tracking errors. Simulation results further validate the algorithm’s effectiveness, demonstrating its robustness and practical viability in real-time fault-tolerant control applications. Full article

The severe degradation of alpine grasslands on the Qinghai–Tibet Plateau poses a significant threat to regional ecological security. While insects are critical for ecosystem functions, their responses to restoration measures in these fragile habitats are poorly documented. This study assessed the initial impacts of four restoration approaches—grazing exclusion fencing (FE), no-till reseeding (FR), planting grass (GC), and grazing control (CK)—on insect trophic guilds (herbivores, predators, saprophagous, and omnivores) in the Qilian Mountains. Using a multi-dimensional indicator (alpha, zeta, and dark diversity), we systematically assessed community assembly and recovery potential. The results revealed the following: (1) FE supported the highest insect abundance, dominated by phytophagous insects. FR significantly enhanced species’ richness and diversity across multiple functional groups (p < 0.05). GC significantly increased the richness of omnivorous insects, but caused a significant decrease in the Shannon–Wiener index for saprophagous insects (p < 0.05). (2) Zeta diversity revealed stable, widespread-species-dominated communities under FR and FE, while CK and GC favored rare-species-driven succession. Dark diversity analysis indicated high recovery potential for phytophagous insects under FR and FE, while GC enhanced saprophagous latent diversity. However, we emphasize that mechanistic interpretations require further validation. Our findings highlight no-till reseeding as a promising initial strategy, though longer-term studies are essential to evaluate successional trajectories and establish definitive management protocols for alpine grassland restoration. Full article

Electoral abstention has emerged as a critical challenge to democratic legitimacy, with rising rates observed globally. For example, in Portugal, the turnout declined from 91.5% in 1975 to 51.4% in 2022. This systematic review synthesizes multidisciplinary literature to identify key determinants of voter nonparticipation and their interactions, aiming to inform adaptive strategies to enhance civic engagement amid social, organizational, and technological changes. Following PRISMA guidelines, we searched five databases (Academic Search Complete, MEDLINE, Psychology and Behavioral Sciences Collection, PsycINFO, and Web of Science) from 2000 to August 2025 using terms such as “electoral abstention” and “non-voting.” Inclusion criteria prioritized quantitative empirical studies in peer-reviewed journals in English, Portuguese, Spanish, or French, yielding 23 high-quality studies (assessed via MMAT, with scores ≥ 60%) from 13 countries, predominantly the USA and France. Results reveal abstention as a multidimensional phenomenon driven by three interconnected categories: individual factors (e.g., health issues like smoking and mental health trajectories, institutional distrust); institutional factors (e.g., electoral reforms such as biometric registration reducing abstention by up to 50% in local contexts, but with mixed outcomes in voluntary voting systems); and contextual factors (e.g., economic inequalities and urbanization correlating with lower turnout, exacerbated by events like COVID-19). This review underscores the need for integrated public policies addressing these factors to boost participation, particularly among youth and marginalized groups. By framing abstention as an adaptive response to contemporary challenges, this work contributes to the political psychology and democratic reform literature, advocating interdisciplinary approaches to resilient electoral systems. Full article