Search Results (86)

Background: Infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have generated major public health concerns worldwide. Young adults represent a critical group for viral transmission due to their high proportion of asymptomatic infections. Objective: To characterize the dynamics of SARS-CoV-2-specific antibodies in individuals aged 20–29 years from Bogotá, Colombia, across two longitudinal phases. Methods: Phase I assessed seroprevalence, seroconversion, spatial clustering, symptoms associated with seropositivity and antibody kinetics following natural infection. Phase II evaluated vaccine-induced antibodies, immune memory, and neutralizing capacity. Analyses included Functional Principal Component Analysis, survival analysis, clustering, and predictive modeling. Results: In Phase I, a seroprevalence of 15.59% (17/109 participants enrolled) was observed, while seroconversion among those who completed all six sampling points was 30.18% (16/53), with clusters of positive cases in different areas of Bogotá. The symptoms most associated with seropositivity included mucus hypersecretion, fever, and respiratory difficulty. Antibody responses were heterogeneous: naturally infected individuals generally showed high titers during the first 1–2 months, remaining detectable up to 4 months. The reduction in dimensionality suggested dominant humoral patterns, and clustering revealed two immune profiles differing in the risk of seroconversion. Predictive modeling indicated diverse antibody trajectories over 12 months. In Phase II (2024), three long-term immune memory clusters (low, medium, high) were observed; post-vaccination IgG titers were observed, although in most cases they lacked neutralizing activity. Conclusions: This longitudinal exploratory observational study provides an initial characterization of antibody dynamics in young adults, suggesting their potential epidemiological relevance and offering preliminary insights into post-infection and post-vaccination immunity. Full article

Background and Objectives: Postoperative pain after posterior spinal fusion (PSF) for adolescent idiopathic scoliosis (AIS) shows substantial interindividual variability, particularly during early mobilization. Although preoperative psychological vulnerability has been associated with less favorable pain trajectories in prior AIS research, evidence focused on the acute postoperative phase remains limited. This preliminary study evaluated whether preoperative psychological factors are associated with acute postoperative pain intensity, with separate assessment of resting and standing pain. Materials and Methods: A single-center retrospective cohort study included consecutive adolescents with AIS (<18 years) who underwent primary elective posterior instrumented spinal fusion between 1 January 2024 and 31 December 2025. Preoperative psychological variables were collected using validated instruments (STAIC-State, STAIC-Trait, Pain Catastrophizing Scale, HAQ/FDI, and inverted SRS-22). Pain intensity (VAS 0–10) was recorded at postoperative day (POD) 1, POD2, POD3, discharge, and 2-week follow-up in supine and standing positions. Derived endpoints included peak in-hospital standing pain, in-hospital standing pain burden (AUC), and standing–rest pain gaps. The prespecified inferential analysis used a linear mixed-effects model with fixed effects for time, position, preoperative STAIC-State, and position × STAIC-State interaction, with a patient-level random intercept. Results: Thirty-five patients were analyzed (mean age 15.2 ± 3.4 years; 62.9% female), with complete pain data at all timepoints. During hospitalization, standing pain was descriptively higher than resting pain (largest mean difference at POD2: 0.73 VAS points), with convergence at week 2 (both 1.52). In mixed-model analysis, pain significantly decreased at week 2 versus POD1 (β = −1.261, 95% CI −1.853 to −0.669; p < 0.001). Preoperative STAIC-State was not independently associated with postoperative pain (β = 0.030, 95% CI −0.065 to 0.124; p = 0.545), and no significant position × STAIC-State interaction was found (β = −0.008, 95% CI −0.079 to 0.064; p = 0.836). Conclusions: In this retrospective preliminary AIS cohort, postoperative pain improved significantly over time, while movement-evoked pain remained relevant during early recovery. In this preliminary cohort, no clear association was detected between preoperative state anxiety and acute postoperative pain intensity, supporting the need for broader multidimensional prognostic models in future prospective multicenter studies. Full article

Background: This study examined age-related differences and interrelationships among psychological symptoms, personality traits, and emotional expression styles in a community sample of 151 participants aged 10–77 years, spanning four age groups: adolescents, young adults, middle-aged adults, and older adults. Methods: Psychological symptoms were assessed using the SCL-90, personality traits using the Big Five Inventory-2 (BFI-2), and emotional expression patterns were derived from facial expression recognition via a convolutional neural network (CNN) model. Kruskal–Wallis H tests were used to examine age-related differences. K-means cluster analysis was applied to identify emotional expression patterns, and logistic regression was used to construct a mental health risk screening model. Results: The young adult group (19–35 years) achieved the highest scores on the depression (M = 1.73) and anxiety (M = 1.61) dimensions, indicating a higher level of psychological distress during this life stage. Personality traits showed a significant developmental trajectory: neuroticism decreased with age (H(3) = 17.09, p < 0.001, η² = 0.11), declining from 2.69 in the young adult group to 2.17 in the older adult group; conscientiousness increased with age (H(3) = 37.39, p < 0.001, η² = 0.24), representing the most substantial age-related effect. K-means clustering identified three distinct emotional expression patterns: Cluster 1 was characterised by happiness, Cluster 2 by anger, disgust, and fear, and Cluster 3 by neutrality, sadness, and surprise. Cluster 2 exhibited the highest scores on neuroticism, anxiety, depression, and mood swings, and scored significantly higher than the other two clusters on interpersonal sensitivity, depression, anxiety, and hostility (p < 0.05). Mental health risk screening indicated that 26.5% of participants were classified as high-risk. Logistic regression analysis (AUC = 0.742) showed that neuroticism was the strongest predictor of elevated mental health risk (OR = 4.58), while extraversion (OR = 0.41) and conscientiousness (OR = 0.57) were significant protective factors. Conclusions: These findings provide exploratory evidence regarding age-related patterns of psychological symptoms and personality traits in a convenience sample and offer preliminary support for personality-based mental health risk screening. Notably, the SCL-90 was employed as a screening tool rather than for clinical diagnosis. Given the unequal age group sizes, particularly the small young adult subgroup, generalisability across the lifespan should not be assumed. Full article

The evolution towards “more-electric” aircraft has accelerated in the last decade, motivated by environmental concerns and the development of new market frontiers such as urban air mobility. This transition is affecting both propulsion and aircraft systems, with electro-mechanical brakes (E-Brakes) representing a promising alternative to traditional hydraulic solutions. While E-Brakes offer advantages such as reduced system complexity and elimination of hydraulic leakage issues, they remain a relatively unproven technology in civil aviation. In this context, the development of Prognostics and Health Management (PHM) solutions aligns with the need for continuous monitoring of novel components while also providing the benefits typically associated with prognostic techniques. This paper presents the preliminary stages of the development of a PHM framework for an E-Brake intended for future executive-class aircraft. Since experimental activities are not yet available, the analysis was carried out on simulated data generated through a high-fidelity model of the system. The study focuses on brake pad wear as the primary degradation mechanism and proposes a particle-filtering approach to estimate the health state and predict the Remaining Useful Life (RUL). Early results obtained from simulated fault-to-failure trajectories prove the ability of the algorithm to track degradation and to provide reliable prognostic forecasts, paving the way for future validation with real-world data. Full article

Background: Hematologic malignancies pose a critical threat to global health, with their pathological progression intrinsically linked to metabolic dysregulation and nutrient imbalance. Malnutrition accelerates the trajectory of adverse outcomes while substantially diminishing the quality of survival. Although several nutritional assessment tools are currently used in clinical practice, a significant evidence gap persists regarding their validation in populations with hematologic neoplasms. This study systematically evaluates the prognostic performance of existing nutritional assessment instruments in this cohort. Based on these findings, we further explored the feasibility of a preliminary framework that reflects metabolic characteristics specific to this population. Methods: This prospective cohort study analyzed nutritional assessment data from 1067 patients with hematologic malignancies enrolled in the INSCOC registry. Eight assessment systems were examined: Patient-Generated Subjective Global Assessment (PG-SGA), Modified PGSGA (mPG-SGA), PGSGA Short Form (PG-SGA SF), Abbreviated PGSGA (abPG-SGA), Nutritional Risk Screening-2002 (NRS-2002), Global Leadership Initiative on Malnutrition criteria (GLIM), Scored-GLIM, and Karnofsky Performance Status Scale (KPS). Kaplan–Meier survival curves and multivariate Cox regression analyses were conducted to investigate the association between nutritional status and overall survival (OS) and to determine the prognostic weight of individual components within the nutritional assessment tools. Linear regression models were applied to examine the relationships between nutritional assessment tools, length of hospital stay (LOS), and EORTC QLQ-C30 scores. The predictive performance of the tools was evaluated using the area under the receiver operating characteristic curve (AUC) and the concordance index (C-index). Least absolute shrinkage and selection operator (LASSO) regression was applied to optimize the selection of inflammation-related biomarkers. Results: A total of 1067 participants were analyzed (mean [SD] age, 55.54 [17.4] years; 625 were male [58.6%]). Cox proportional hazards regression demonstrated statistically significant associations for all eight nutritional assessment tools (p ≤ 0.05). However, their prognostic discrimination was limited, as indicated by the AUC analysis. Specifically, the area under the curve (AUC) values for each tool were as follows: mPG-SGA, 0.561; NRS-2002, 0.557; PG-SGA, 0.550; KPS, 0.544; PG-SGA SF, 0.542; abPG-SGA, 0.528; Scored-GLIM, 0.489; and GLIM, 0.473. The concordance index validation further corroborated these findings. Prognostically significant components and inflammation-related biomarkers identified by Cox and LASSO regression were combined to explore a composite assessment approach, termed the Hematologic Marker–Patient Generated Subjective Global Assessment (HMPG-SGA), incorporating the albumin–globulin ratio (AGR). The HMPG-SGA was significantly associated with overall survival (p < 0.001), with an AUC of 0.616 and a C-index of 0.605. Conclusions: Multidimensional validation demonstrated limited prognostic discrimination of eight conventional nutritional assessment tools for overall survival in patients with hematologic malignancies. Based on existing assessment tools and integrated hematologic indicators, the HMPG-SGA was preliminarily explored as a prognostic assessment tool in hematologic malignancies. Full article

This paper presents a comparative study of the applicability and accuracy of optimal control methods and neural-network-based estimators in the context of porkchop plots for preliminary asteroid rendezvous mission design. The scenario considered involves a deep-space CubeSat equipped with a low-thrust engine, departing from Earth and rendezvousing with a near-Earth asteroid within a three-year launch window. A low-thrust trajectory optimization model is formulated, incorporating variable specific impulse, maximum thrust, and path constraints. The optimal control problem is efficiently solved using Sequential Convex Programming (SCP) combined with a solution continuation strategy. The neural network framework consists of two models: one predicts the minimum fuel consumption ($\Delta v$), while the other estimates the minimum flight time ($\Delta t$) which is used to assess transfer feasibility. Case results demonstrate that, in simplified scenarios without path constraints, the neural network approach achieves low relative errors across most of the design space and successfully captures the main structural features of the porkchop plots. In cases where the SCP-based continuation method fails due to the presence of multiple local optima, the neural network still provides smooth and globally consistent predictions, significantly improving the efficiency of early-stage asteroid candidate screening. However, the deformation of the feasible region caused by path constraints leads to noticeable discrepancies in certain boundary regions, thereby limiting the applicability of the network in detailed mission design phases. Overall, the integration of neural networks with porkchop plot analysis offers an effective decision-making tool for mission designers and planetary scientists, with significant potential for engineering applications. Full article

Dyslexia is not easily observed from outward appearance alone; differences typically emerge through learning performance and certain behavioral indicators. This study introduces the Direction Game, a computer-based task that uses mouse-tracking to capture behavioral signals related to directional perception, a common challenge among children at risk for dyslexia. The prototype consists of language-independent mini-games targeting three main types of directional confusion and was piloted with 102 primary school students. Analyses showed that concentration-related variables, particularly attentional control and visuo-motor planning, may provide more informative indicators of risk than simple accuracy scores. Machine learning models demonstrated promising classification performance relative to standardized school screening protocols. Additionally, an exploratory analysis of mouse trajectories revealed five tentative interaction profiles: hesitation, impulsivity, deliberate processing, fluent performance, and disengagement. Together, these findings highlight the potential of a simple, game-based mouse-tracking tool to support accessible and preliminary dyslexia risk assessment in classroom environments. Full article

This study presents a comprehensive numerical and theoretical analysis comparing the aerodynamic performance of a racetrack trajectory vertical axis wind turbine with a baseline VAWT. The racetrack trajectory comprises two parallel straight segments connected by semicircular arcs. However, two critical research gaps remain: the aerodynamic performance of this non-axisymmetric rotor, especially its sensitivity to inflow direction, is not well understood, and a computationally efficient theoretical model for its rapid design is lacking. Using unsteady Reynolds-Averaged Navier–Stokes (URANS) simulations to systematically quantify this sensitivity, and developing an adapted double multiple streamtube (DMST) model, the performance of both turbines is evaluated across tip speed ratios (TSRs) of 1.5–4 and inflow angles β = 0–90°. Results indicate that the racetrack turbine achieves a peak power coefficient of 0.49 at TSR = 2.5 and β = 90°, 16.7% higher than the baseline VAWT. Its performance is highly sensitive to inflow direction, whereas the baseline operates more uniformly across angles. Flow field and wake analyses reveal that the racetrack turbine exhibits faster wake recovery and lower turbulence intensity downstream under optimal inflow. This study demonstrates the potential of racetrack turbines for enhanced directional efficiency in marine wind conditions and validates the adapted DMST model as a reliable tool for preliminary design. Full article

ESG (Environmental, Social, and Governance) considerations are increasingly influencing REIT (real estate investment trust) investment decisions; however, empirical evidence on the ESG–financial performance nexus in infrastructure REITs remains scarce. Given China’s nascent highway REIT market, this exploratory study proposes a hybrid modeling framework that integrates static econometric analysis with dynamic system simulation to examine how ESG factors affect investment risk. Using VaR (Value at Risk) analysis and an ESG-adjusted CAPM (Capital Asset Pricing Model) on 10 Chinese highway REITs (2021Q2–2025Q2), we constructed a composite ESG indicator via a weighted proxy approach. We identified three key findings testing hypotheses linked to ESG finance theory; these findings support H1 (non-monotonic VaR reduction) and partially confirm H2 (inverted-U path with lag): (1) the ESG-adjusted weighted average cost of capital (WACC) exhibits an inverted U-shaped trajectory with post-peak oscillations and an overall 20-month implementation lag (derived from system dynamics simulations) to efficiency realization; (2) the results suggest initial evidence showing that an ESG investment intensity (IEP ≈ 0.40, representing moderate ESG resource allocation) may indicate potential outperformance over both under-investment (−5.0% deviation in risk-adjusted returns) and over-investment (−8.0% deviation in risk-adjusted returns), though with uncertainty in static estimates; and (3) system dynamics validation suggests potential predictive accuracy. These preliminary findings challenge linear ESG–performance assumptions and offer dynamic risk assessment tools; nevertheless, as an exploratory study, they warrant replication in larger and more diverse samples. Thus, the results should be regarded as preliminary guidance rather than conclusive evidence, with further validation needed to confirm generalizability. Full article

Captive Asian elephants (Elephas maximus) frequently present to hospitals with complex, multisystemic diseases, yet veterinarians lack objective tools to predict and classify clinical outcomes. Decision-making often relies on experience or anecdote, and few studies have applied data-driven approaches in wildlife medicine. This study developed a machine learning–based classification model using routinely collected clinical data. A total of 467 medical records from hospitalized elephants at Thailand’s National Elephant Institute (2019–2024) were retrospectively analyzed. Four variables (age, sex, disease group, and length of stay [LOS]) were used to train four classification algorithms: Random Forest, eXtreme Gradient Boosting, Naïve Bayes, and multinomial logistic regression. The Random Forest model achieved the highest classification performance (accuracy = 86.3%; log-loss = 0.374), with disease group, LOS, and age as key predictors. Survival analysis revealed distinct hospitalization trajectories across disease groups: acute conditions like elephant endotheliotropic herpesvirus-hemorrhagic disease and toxicosis showed rapid early declines, whereas dental and renal cases followed more prolonged courses. Our findings demonstrate the preliminary feasibility of outcome classification in elephant care and highlight the potential of clinical data science to improve in-hospital prognostication, monitoring, and treatment planning in zoological and wildlife medicine. Full article

To mitigate the degradation of positioning accuracy in sixth-generation mobile communication systems under dynamic line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, this paper proposes an improved adaptive Kalman filter positioning method based on Orthogonal Time Frequency Space (OTFS)-modulated signals. Firstly, the distance can be measured by using the OTFS-modulated signals transmitted between base stations and nodes. Secondly, the distance information is converted into the distance difference information to establish the time difference of arrival (TDOA) positioning equation, which is preliminarily solved using the Chan algorithm. Thirdly, residuals are calculated based on the preliminary positioning results, dividing the complex environment into distinct regions and adaptively determining corresponding genetic factors for each region. Finally, the selected genetic parameters are substituted into the Sage–Husa adaptive Kalman filter equations to estimate positioning results. The simulation analysis demonstrates that in complex environments featuring both line-of-sight and non-line-of-sight conditions, the vehicle motion trajectories estimated using this method more closely approximate actual trajectories. Additionally, both the accuracy and stability of positioning results show significant improvement compared to traditional methods. Full article

This paper presents a comparative mathematical analysis of electrode configurations used in active fog water harvesting systems based on electrostatic ionization. The study begins with a brief overview of fog formation and typology. It also addresses the global relevance of fog as a decentralized water resource. It also outlines the main methods and collector designs currently employed for fog water capture, both passive and active. The core of the work involves solving the Laplace equation for various electrode geometries to compute electrostatic field distributions and analyze field line density patterns as a proxy for potential water collection efficiency. The evaluated configurations include centered rod–cylinder, symmetric parallel multi-rod, and asymmetric wire–plate layouts, with emphasis on identifying spatial regions of high field line convergence. These regions are interpreted as likely trajectories of charged droplets under Coulombic force influence. The modeling approach enables preliminary assessment of design efficiency without relying on time-consuming droplet-level simulations. The results serve as a theoretical foundation prior to the construction of electrode layouts in the portable HygroCatch experimental harvester and provide insight into how field structure correlates with fog water harvesting performance. Full article

Background and Objectives: Progressive pulmonary fibrosis (PPF) presents significant clinical challenges due to irreversible lung damage and declining respiratory function. Nintedanib has demonstrated antifibrotic effects, yet there is a lack of sensitive tools to assess treatment efficacy quantitatively. This study evaluated the potential of artificial intelligence (AI)-powered quantitative computed tomography (QCT) to monitor lung changes and predict treatment outcomes in patients with PPF undergoing nintedanib therapy. Materials and Methods: This retrospective study analysed 37 patients diagnosed with PPF who were treated with nintedanib for one year. AI-powered QCT was performed using the 3D Slicer software version 5.2.2, which quantified lung infiltration, collapse, and vessel volumes. These data were then correlated with pulmonary function tests. Receiver operating characteristic (ROC) analysis was used to assess baseline AI-powered QCT predictors for progression. Results: AI-powered QCT demonstrated a significant reduction in post-treatment right lung infiltration (5.56 ± 3.08 cm³ to 4.88 ± 2.77 cm³, p = 0.041), whereas total lung infiltration decreased non-significantly. Functional parameters, including forced vital capacity (FVC) and diffusion capacity for carbon monoxide (DLCO), showed no significant changes. ROC analysis identified a baseline infiltrated lung volume greater than 21.90% as predictive of continued disease progression (AUC = 0.767; sensitivity, 91.70%; specificity, 68.00%). Conclusions: AI-powered QCT identified diverse parenchymal responses to nintedanib in PPF and showed preliminary prognostic value for clinical trajectory. Imaging biomarkers enhance functional measures and may reveal early treatment effects. Prospective, multicentre validation is necessary to confirm usefulness and establish actionable thresholds for clinical application. Full article

This paper aims to present a set of possible transfer trajectories for a rendezvous mission with asteroid 2024 YR4, using a spacecraft propelled by a photonic solar sail. Asteroid 2024 YR4 was discovered in late December 2024 and was briefly classified as Torino Scale 3 for three weeks in early 2025, before being downgraded to zero at the end of February. In this study, rapid Earth-to-asteroid transfers are analyzed by solving a typical optimal control problem, in which the thrust vector generated by the solar sail is modeled using the optical force approach. Numerical simulations are carried out assuming a low-to-medium performance solar sail, considering both a simplified orbit-to-orbit transfer and a more accurate scenario that incorporates the actual ephemerides of the celestial bodies. The numerical results indicate that a medium-performance solar sail can reach asteroid 2024 YR4, achieving the global minimum flight time and arriving before its perihelion passage in late December 2032. Full article

The ten-year fishing ban on the Yangtze River aims to restore aquatic biodiversity and rebuild fishery resources. Megalobrama skolkovii, a key species in the basin, was investigated using 2024 data to provide a preliminary assessment of its population structure, stock dynamics, and early recovery. Age analysis (n = 243) showed that 1–6-year-olds were dominated by fish aged 3 (35%), with few older than 4, indicating moderate structural truncation. Growth parameters modeled by the von Bertalanffy Growth Function yielded L_∞ = 61.89 cm and k = 0.25 year⁻¹, with a weight–growth inflection age of 4.4 years. Natural mortality (M = 0.48 year⁻¹) was estimated using Pauly’s empirical formula, and total mortality (Z = 0.55 year⁻¹) was estimated from the catch curve analysis. While fishing mortality (F) was statistically indistinguishable from zero, a plausible low-intensity fishing scenario was explored to assess potential impacts of residual activities. Length-based indicators (LBIs) showed P_mat = 46.05%, P_opt = 9.51%, and P_mega = 6.88%, suggesting reproductive recovery but incomplete structural restoration. These preliminary findings reveal an asymmetrical recovery trajectory, whereby physiological improvements and enhanced recruitment have occurred, yet full structural restoration remains incomplete. This underscores the need for continued, long-term conservation and monitoring to support population resilience. Full article