Search Results (2,605)

Lung cancer is one of the most common and deadly forms of cancer worldwide, despite sustained efforts to encourage smoking cessation and raise awareness of the risk factors. In Romania, lung cancer is a significant health challenge, being the leading cause of death caused by cancer, especially amongst men. The incidence of lung cancer in connective tissue disease (CTD) varies in different studies from 4.5% in rheumatoid arthritis (RA), to 4.4% in polymyositis or dermatomyositis, and up to 11.1% in systemic sclerosis. However, older studies have shown an increased risk of cancer in patients with rheumatoid arthritis (RA), ranging from 10% to 30% compared to the general population, particularly in those undergoing methotrexate therapy. Rheumatoid arthritis affects approximately 40 per 100,000 people annually worldwide, with a three- to four-fold higher incidence in women. Non-small cell lung cancer (NSCLC), the most common lung cancer subtype, has been linked to RA, yet the association remains poorly defined, with limited insight into the underlying molecular mechanisms. We present the case of a 61-year-old male with a 49-pack-year smoking history and a known diagnosis of rheumatoid arthritis, currently managed with methotrexate therapy. He was admitted for evaluation due to a progressive decline in general condition, characterized by worsening dyspnea and chest pain, symptoms that had been longstanding but had markedly exacerbated over the past two weeks. Based on a chest CT performed prior to the patient’s admission to our clinic, subsequent diagnostic investigations established the diagnosis of pulmonary adenocarcinoma. The diagnostic process proved to be particularly challenging due to the presence of multiple comorbidities, which significantly impacted both the diagnostic approach and the overall clinical trajectory. Full article

The commercialization of selective broccoli harvesters, a critical response to agricultural labor shortages, is hampered by end effectors with large operational envelopes and poor adaptability to complex field conditions. To address these limitations, this study developed and evaluated a novel end-effector with an integrated transverse cutting mechanism and a foldable grasping cavity. Unlike conventional fixed cylindrical cavities, our design utilizes actuated grasping arms and a mechanical linkage system to significantly reduce the operational footprint and enhance maneuverability. Key design parameters were optimized based on broccoli morphological data and experimental measurements of the maximum stem cutting force. Furthermore, dynamic simulations were employed to validate the operational trajectory and ensure interference-free motion. Field tests demonstrated an operational success rate of 93.33% and a cutting success rate of 92.86%. The end effector successfully operated in dense planting environments, effectively avoiding interference with adjacent broccoli heads. This research provides a robust and promising solution that advances the automation of broccoli harvesting, paving the way for the commercial adoption of robotic harvesting technologies. Full article

The autonomous navigation of an Unmanned Underwater Vehicle (UUV) in unknown 3D underwater environments remains a challenging task due to the presence of complex terrain, uncertain obstacles, and strict kinematic constraints. This paper proposes a novel smooth path planning framework that integrates improved Rapidly-exploring Random Tree* (RRT*) with 3D Dubins curves to efficiently generate feasible and collision-free trajectories for nonholonomic UUVs. A fast curve-length estimation approach based on a backpropagation neural network is introduced to reduce computational burden during path evaluation. Furthermore, the improved RRT* algorithm incorporates pseudorandom sampling, terminal node backtracking, and goal-biased exploration strategies to enhance convergence and path quality. Extensive simulation results in unknown underwater scenarios with static and moving obstacles demonstrate that the proposed method significantly outperforms state-of-the-art planning algorithms in terms of smoothness, path length, and computational efficiency. Full article

Background: A growing body of evidence suggests that diet can modify Parkinson’s disease (PD) outcomes, although there is disagreement about what should be included and excluded in such a diet. Existing evidence suggests that adherence to the MIND and Mediterranean (MEDI) diets are associated with reduced PD symptoms, but only a few variables from the adherence scales are responsible for the statistically observed improvement. Objectives: The goal was to use patient-reported outcomes in a large cohort to identify the foods and dietary patterns (PRO diet) most strongly associated with the fewest PD symptoms over time, and to develop a composite adherence scale to enable comparisons between MEDI, MIND, and PRO. Methods: Data were obtained from the prospective longitudinal natural history study and from Modifiable Variables in Parkinsonism (MVP)—a study designed to identify behaviors associated with patient-reported outcomes (PRO-PD). Upon the completion of the binary and food frequency data collection, using various predictive models and considering congruence with historical data, the PRO diet was created via an iterative process. Our goal was to create a new scale and compare its performance to the existing MIND and MEDI scores. The comparison was made at baseline, using the regression models for PRO-PD and the different scales as the predictors. The models were compared via the Akaike Information Criterion (AIC). To examine whether baseline adherence levels predicted subsequent symptom trajectories, the baseline PRO diet adherence and subsequent slope of progression were evaluated. Results: Data from 2290 individuals with PD were available for this analysis. The Mediterranean and MIND diets showed almost identical effects. For both the diets, the effect they had on non-motor symptoms was about twice the effect on motor symptoms. The slopes for the total PRO-PD for MEDI, MIND, and PRO-21 were −64.20467, −64.04220, and −28.61995, respectively. The AIC value differences were substantial (>2), indicating meaningful improvements in the model fit for total PRO-PD, as follows: MEDI: 28,897.24, MIND: 28,793.08, and PRO-21: 27,500.71. The subset of individuals who were most adherent to the PRO-21 diet at baseline had the slowest subsequent progression, as measured by a 43% reduced PRO-PD slope, compared to the less adherent groups. Conclusions: The PRO-21 outperformed the MIND and MEDI diets in the model fit, overcoming the ceiling effects and showing orders of magnitude and superior explanatory power for variance in PD outcomes, despite the smaller per-unit effect sizes. However, its rigorous demands may introduce barriers related to cost, feasibility, and sustainability, underscoring the need for future intervention trials to assess real-world feasibility, adherence, side effects, and clinical impact. Full article

Enhancing quadrotor control to improve both precision and responsiveness is essential for expanding their deployment in complex and dynamic environments. These aerial vehicles are widely used in applications, such as aerial mapping, delivery, disaster response, and defense, where maintaining stability and accuracy is critical, especially under external disturbances like wind. This paper makes three key contributions. First, it develops a nonlinear mathematical model of a quadrotor and designs a backstepping controller for trajectory tracking. Second, the controller’s parameters are optimized using three nature-inspired algorithms: Gray Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and the Flower Pollination Algorithm (FPA), enabling performance comparisons in terms of their tracking precision and control effort. Third, the robustness of the best-performing optimized controller is evaluated by applying wind disturbances at the simulation level, modeled as external forces acting along the x-axis and summed with the control input. The simulation results highlight the comparative efficiency of the optimization methods and demonstrate the robustness of the selected controller in maintaining stability and accuracy under wind-induced perturbations. Full article

Steel lazy wave risers (SLWRs) are critical in offshore hydrocarbon transport for linking subsea wells to floating production facilities in deep-water environments. The incorporation of buoyancy modules reduces curvature-induced stress concentrations in the touchdown zone (TDZ); however, extended operational exposure under cyclic environmental and operational loads results in repeated seabed contact. This repeated interaction modifies the seabed soil over time, gradually forming a trench and altering the riser configuration, which significantly impacts stress patterns and contributes to fatigue degradation. Accurately reconstructing the riser’s evolving profile in the TDZ is essential for reliable fatigue life estimation and structural integrity evaluation. This study proposes a simulation-based framework for the autonomous tracking of SLWRs using a fin-actuated autonomous underwater vehicle (AUV) equipped with a monocular camera and multibeam echosounder. By fusing visual and acoustic data, the system continuously estimates the AUV’s relative position concerning the riser. A dedicated image processing pipeline, comprising bilateral filtering, edge detection, Hough transform, and K-means clustering, facilitates the extraction of the riser’s centerline and measures its displacement from nearby objects and seabed variations. The framework was developed and validated in the underwater unmanned vehicle (UUV) Simulator, a high-fidelity underwater robotics and pipeline inspection environment. Simulated scenarios included the riser’s dynamic lateral and vertical oscillations, in which the system demonstrated robust performance in capturing complex three-dimensional trajectories. The resulting riser profiles can be integrated into numerical models incorporating riser–soil interaction and non-linear hysteretic behavior, ultimately enhancing fatigue prediction accuracy and informing long-term infrastructure maintenance strategies. Full article

Trajectory planning is crucial for solar aircraft endurance. The multi-wing morphing solar aircraft can enhance solar energy acquisition through wing deflection, which simultaneously incurs aerodynamic losses, complicating energy coupling and challenging existing planning methods in efficiency and long-term optimization. This study presents an energy-optimal trajectory planning method based on Hierarchical Reinforcement Learning for morphing solar-powered Unmanned Aerial Vehicles (UAVs), exemplified by a Λ-shaped aircraft. This method aims to train a hierarchical policy to autonomously track energy peaks. It features a top-level decision policy selecting appropriate bottom-level policies based on energy factors, which generate control commands such as thrust, attitude angles, and wing deflection angles. Shaped properly by reward functions and training conditions, the hierarchical policy can enable the UAV to adapt to changing flight conditions and achieve autonomous flight with energy maximization. Evaluated through 24 h simulation flights on the summer solstice, the results demonstrate that the hierarchical policy can appropriately switch its bottom-level policies during daytime and generate real-time control commands that satisfy optimal energy power requirements. Compared with the minimum energy consumption benchmark case, the proposed hierarchical policy achieved 0.98 h more of full-charge high-altitude cruise duration and 1.92% more remaining battery energy after 24 h, demonstrating superior energy optimization capabilities. In addition, the strong adaptability of the hierarchical policy to different quarterly dates was demonstrated through generalization ability testing. Full article

This study investigates the relationship between sovereign credit rating transitions and domestic equity market performance, focusing on Greece from 2004 to 2024. Although credit ratings are central to sovereign risk assessment, their immediate influence on financial markets remains contested. This research adopts a multi-method analytical framework combining algebraic combinatorics and time-series econometrics. The methodology incorporates the construction of a directed credit rating transition graph, the partially ordered set representation of rating hierarchies, rolling-window correlation analysis, Granger causality testing, event study evaluation, and the formulation of a reward matrix with optimal rating path optimization. Empirical results indicate that credit rating announcements in Greece exert only modest short-term effects on the Athens Stock Exchange General Index, implying that markets often anticipate these changes. In contrast, sequential downgrade trajectories elicit more pronounced and persistent market responses. The reward matrix and path optimization approach reveal structured investor behavior that is sensitive to the cumulative pattern of rating changes. These findings offer a more nuanced interpretation of how sovereign credit risk is processed and priced in transparent and fiscally disciplined environments. By bridging network-based algebraic structures and economic data science, the study contributes a novel methodology for understanding systemic financial signals within sovereign credit systems. Full article

Background: Obesity is a growing global health concern and a modifiable risk factor for multiple pancreatic diseases, including acute pancreatitis (AP), chronic pancreatitis (CP), and pancreatic cancer (PC). While these conditions have distinct clinical courses, obesity contributes to their pathogenesis through shared mechanisms, such as visceral adiposity, systemic inflammation, insulin resistance, and ectopic pancreatic fat deposition. Methods: This narrative review synthesizes current evidence from clinical, epidemiological, and mechanistic studies exploring the relationship between obesity and pancreatic diseases. We also critically evaluate the effects of weight loss interventions—including lifestyle modifications, pharmacologic therapies, endoscopic approaches, and bariatric surgery—on the risk and progression of disease. Results: Obesity increases the risk and severity of AP via mechanisms such as gallstone formation, hypertriglyceridemia, and lipotoxicity. In CP, obesity-related intrapancreatic fat and metabolic dysfunction may influence disease progression, although some data suggest a paradoxical protective effect. In PC, obesity accelerates tumorigenesis through chronic inflammation, adipokine imbalance, and activation of oncogenic signaling pathways. Weight loss interventions, particularly bariatric surgery and incretin-based therapies (e.g., GLP-1 receptor agonists and dual agonists such as tirzepatide), show promising effects in reducing disease burden and improving metabolic and inflammatory profiles relevant to pancreatic pathology. Conclusions: Obesity plays a multifaceted role in the pathophysiology of pancreatic diseases. Therapeutic strategies targeting weight loss may alter disease trajectories, improve outcomes, and reduce cancer risk. Further research is needed to define optimal intervention strategies and to identify and validate biomarkers for personalized risk assessment and prevention. Full article

Food security constitutes a fundamental pillar of future sustainable development. A systematic evaluation of the impact of urban expansion on food security is critical to advancing the United Nations Sustainable Development Goals (SDGs), particularly “Zero Hunger” (SDG 2). Drawing on bibliographic data from the Web of Science Core Collection, this study employs the bibliometrix package in R to conduct a comprehensive bibliometric analysis of the literature on the “urban expansion–food security” nexus spanning from 1982 to 2024. The analysis focuses on knowledge production, collaborative structures, and thematic research trends. The results indicate the following: (1) The publication trajectory in this field exhibits a generally increasing trend with three distinct phases: an incubation period (1982–2000), a development phase (2001–2014), and a phase of rapid growth (2015–2024). Land Use Policy stands out as the most influential journal in the domain, with an average citation rate of 43.5 per article. (2) China and the United States are the leading contributors in terms of publication output, with 3491 and 1359 articles, respectively. However, their international collaboration rates remain relatively modest (0.19 and 0.35) and considerably lower than those observed for the United Kingdom (0.84) and Germany (0.76), suggesting significant potential for enhanced global research cooperation. (3) The major research hotspots cluster around four core areas: urban expansion and land use dynamics, agricultural systems and food security, environmental and climate change, and socio-economic and policy drivers. These focal areas reflect a high degree of interdisciplinary integration, particularly involving land system science, agroecology, and socio-economic studies. Collectively, the field has established a relatively robust academic network and coherent knowledge framework. Nonetheless, it still confronts several limitations, including geographical imbalances, fragmented research scales, and methodological heterogeneity. Future efforts should emphasize cross-regional, interdisciplinary, and multi-scalar integration to strengthen the systematic understanding of urban expansion–food security interactions, thereby informing global strategies for sustainable development. Full article

Background/Objectives: Children with above-average cognitive functioning often present complex developmental profiles, combining high cognitive potential with heterogeneous socio-emotional and learning trajectories. Although the cognitive and behavioral characteristics of giftedness have been widely studied in Anglophone countries, evidence remains limited in Southern Europe. This study aimed to investigate the cognitive, academic, and emotional–behavioral profiles of Italian children and adolescents with above-average cognitive functioning, using an inclusive, dimensional approach (IQ > 114). Methods: We analyzed a cross-sectional sample of 331 children and adolescents (ages 2.11–16.5 years), referred for clinical cognitive or behavioral evaluations. Participants were assessed using the WPPSI-III or WISC-IV for cognitive functioning, the MT battery for academic achievement, and the Child Behavior Checklist (CBCL) for emotional and behavioral symptoms. Comparative and correlational analyses were performed across age, gender, and functional domains. A correction for multiple testing was applied using the Benjamini–Hochberg procedure. Results: Gifted participants showed strong verbal comprehension (mean VCI: preschoolers = 118; school-aged = 121) and relative weaknesses in working memory (WM = 106) and processing speed (PS = 109). Males outperformed females in perceptual reasoning (PR = 121 vs. 118; p = 0.032), while females scored higher in processing speed (112 vs. 106; p = 0.021). Difficulties in writing and arithmetic were observed in 47.3% and 41.8% of school-aged participants, respectively. Subclinical internalizing problems were common in preschool and school-aged groups (mean CBCL T = 56.2–56.7). Working memory negatively correlated with total behavioral problems (r = −0.13, p = 0.046). Conclusions: These findings confirm the heterogeneity of gifted profiles and underscore the need for personalized educational and psychological interventions to support both strengths and vulnerabilities in gifted children. Caution is warranted when interpreting these associations, given their modest effect sizes and the exploratory nature of the study. Full article

Expressive imitation in the performing arts is typically trained through animal behavior imitation, aiming not only to reproduce action trajectories but also to recreate rhythm, style and emotional states. However, evaluation of such animal imitation behaviors relies heavily on teachers’ subjective judgments, lacking structured criteria, exhibiting low inter-rater consistency and being difficult to quantify. To enhance the objectivity and interpretability of the scoring process, this study develops a machine learning and structured pose data-based auxiliary evaluation framework for imitation quality. The proposed framework innovatively constructs three types of feature sets, namely baseline, ablation, and enhanced, and integrates recursive feature elimination with feature importance ranking to identify a stable and interpretable set of core structural features. This enables the training of machine learning models with strong capabilities in structured modeling and sensitivity to informative features. The analysis of the modeling results indicates that temporal–rhythm features play a significant role in score prediction and that only a small number of key feature values are required to model teachers’ ratings with high precision. The proposed framework not only lays a methodological foundation for standardized and AI-assisted evaluation in performing arts education but also expands the application boundaries of computer vision and machine learning in this field. Full article

Introduction: Human immunodeficiency virus (HIV)-associated neurocognitive impairment (NCI) remains a concern despite combination antiretroviral therapy (cART), with cognitive problems often persisting even after viral suppression. The mechanisms underlying neurocognitive deterioration in people living with HIV (PLWH) and the role of plasma biomarkers remain unclear. This study aims to evaluate neurocognitive trajectories and biomarker changes in a real-world cohort of newly diagnosed PLWH initiating cART in Greece. Methods: This prospective, single-center study assessed neuropsychological performance and plasma biomarkers in treatment-naïve PLWH at baseline and 18 months after cART initiation. HIV-associated neurocognitive disorder (HAND) was classified using the Frascati criteria, and plasma biomarkers of inflammation and monocyte activation were measured. Correlations between biomarkers and cognitive performance were analyzed. Results: A total of 39 treatment-naïve PLWH were enrolled in this study. At baseline, 45.7% of participants met criteria for HAND, predominantly, asymptomatic neurocognitive impairment (ANI). Over 18 months, neurocognitive function improved, particularly in speed of information processing, executive function, and visuospatial ability, while verbal fluency, fine motor dexterity, and attention/working memory remained unchanged. Biomarkers of inflammation and monocyte activation decreased following cART, except for neopterin, which increased (10.6 vs. 13 ng/mL, p = 0.002), and plasma NFL (7.5 vs. 7.2 pg/mL, p = 0.54), which remained stable. A negative correlation between monocyte activation markers and cognitive performance was observed only at follow-up, suggesting that systemic inflammation may mask these associations in untreated PLWH. Conclusions: Early cART initiation supports neurocognitive recovery and reduces immune activation in PLWH. The observed correlation between cognitive performance and monocyte activation markers after viral suppression highlights the potential utility of plasma biomarkers in predicting cognitive impairment. Full article

This paper presents the development and analysis of a novel 6-DOF Cartesian parallel mechanism intended for use as a haptic device for initial sonography training. The system integrates a manipulator designed for delivering force feedback in five degrees of freedom; however, in the current stage, only mechanical architecture and kinematic validation have been conducted. Future enhancements will focus on implementing and evaluating closed-loop force control to enable complete haptic feedback. To assess the kinematic performance of the mechanism, a detailed kinematic model was developed, and both the Kinematic Conditioning Index (KCI) and Global Conditioning Index (GCI) were computed to evaluate the system’s dexterity. A trajectory simulation was conducted to validate the mechanism’s movement, using motion patterns typical in sonography procedures. Quasi-static analysis was performed to study the transmission of force and torque for generating realistic haptic feedback, critical for simulating real-life sonography. The simulation results showed consistent performance, with dexterity and torque distribution confirming the suitability of the mechanism for haptic applications in sonography training. Additionally, structural analysis verified the robustness of key components under expected loads. In order to validate the proposed design, the prototype was constructed using a combination of aluminum components and 3D-printed ABS parts, with Igus^® linear guides for precise motion. The outcomes of this study provide a foundation for the further development of a low-cost, effective sonography training system. Full article

This study presents a systematic review of fatigue analysis methodologies and failure criteria for asphalt binders and mixtures employed in various cyclic fatigue testing configurations. The investigation focuses on two principal predictive approaches: phenomenological models and mechanistic frameworks, which are commonly utilized to forecast asphalt pavement fatigue life based on experimental data from different fatigue tests. A critical evaluation is conducted on the diverse failure criteria integrated within these analytical approaches, with particular emphasis on their respective merits and limitations. The current research findings reveal a notable absence of consensus regarding the precise definition of the fatigue failure criteria for asphalt materials. Furthermore, critical parameters including accuracy assessment, reliability verification, and sensitivity analysis of these failure criteria are identified as requiring enhanced research attention. This review recommends specific fatigue failure criteria classified according to fatigue testing methods and material types. This comprehensive analysis of fatigue failure mechanisms in asphalt composites aims to inform strategic refinements for future research trajectories and enhance durability-oriented pavement design practices. Full article