Search Results (4,942)

Stability plays essential roles for Vertical Take-Off and Landing (VTOL) vehicles. This paper investigates the stability characteristics of a novel tail-sitter VTOL vehicle employing vector thrust control, specifically focusing on nonlinear modeling and parameter optimization. Firstly, the tail-sitter VTOL which employs vector thrust controlling principles, is designed, and manufactured using 3D printing and carbon-fiber reinforced techniques, with a customized flight controller implemented on the PX4 architecture. To address the nonlinear dynamic characteristics introduced by the vector thrust mechanism, a nonlinear dynamic model for cruise flight is established based on an offline database and validated against cruise flight test data. Flight tests show that the vector-thrust-based pitch control provides rapid response and accurate tracking during cruise flight. Furthermore, based on the validated model, a hybrid optimization strategy combining pattern search and sequential quadratic programming (SQP) is used to tune the cascaded control parameters. Simulation results demonstrate that the optimized controller reduces the rise time from 6.8 s to 0.2 s and the settling time from 10.1 s to 0.9 s under the tested cruise-condition step response, indicating a marked improvement in dynamic response performance. This study provides a practical framework for cruise-flight modeling, pitch-stability analysis, and control-parameter optimization of vector-thrust tail-sitter UAVs. Full article

To address the stability challenges of surrounding rock in large-span open-off cuts within weakly cemented strata of western China, this study investigated the 1219 open-off cut at the Shila Wusu Coal Mine. An analytical elastic model for rectangular roadway stress was developed using complex variable function theory to examine the influence of the lateral pressure coefficient on stress distribution. Furthermore, numerical simulations were employed to characterize plastic zone evolution and evaluate support effectiveness. The results demonstrate that the lateral pressure coefficient significantly dictates the stress field: circumferential stress at the ribs intensifies with the increasing lateral pressure coefficient, while stress in the roof and floor decreases accordingly. Notably, tensile stresses develop in the roof and floor when the lateral pressure coefficient is less than 1. Stress extremes are concentrated at the roadway shoulders, exhibiting a distribution pattern where the ribs experience higher concentration than the roof and floor. The circumferential stress concentration coefficient exhibits a marked positive correlation with the lateral pressure coefficient. Numerical results indicate that post-support compressive stress at the shoulders reaches 39.24 MPa, with plastic zone widths of 1.64~2.06 m at the ribs, 2.70 m at the roof, and a significant 5.33 m at the floor, highlighting a pronounced risk of floor heave. Field loosening zone measurements of 1.08 m in the roof and 2.49 m in the rib align closely with numerical findings, confirming that the implemented support effectively constrains plastic zone development. By integrating theoretical derivation, numerical modeling, and in situ observations, this study establishes a robust theoretical and technical framework for the support design of large-span roadways in similar geological settings. Full article

Small-scale inland fisheries in the Amazon are critical for food security, yet their sustainability is increasingly threatened by overexploitation and environmental degradation. In data-limited contexts such as the northern Ecuadorian Amazon, the absence of continuous monitoring constrains the development of adaptive management strategies. This study develops an integrated socio-ecological baseline to support the establishment of fisheries agreements in five Indigenous communities of the Napo and Aguarico rivers. Through a participatory monitoring approach, we generated reproductive parameters (gonadosomatic index, fecundity, size at first maturity), population structure metrics, and length–weight relationships for key subsistence species across three hydrological phases. Reproductive investment exhibited marked seasonality, with peak gonadosomatic indices during rising waters in most species, identifying a critical period for protection. Life-history strategies ranged from high-fecundity periodic strategists to low-fecundity equilibrium species, implying differentiated vulnerability to harvesting. Community perceptions prioritized large migratory catfish and floodplain habitats, aligning with biological indicators of vulnerability. High performance in technical training demonstrated the feasibility of long-term local monitoring systems. By linking biological indicators with local ecological knowledge, this study proposes a pathway from baseline assessment to adaptive co-management. The framework presented here provides a transferable model for strengthening sustainability, governance, and food security in tropical small-scale fisheries facing persistent data limitations. Full article

The introduction of multibeam echosounders has marked a turning point in bathymetric data acquisition, providing precise and detailed digital bathymetric models. These instruments not only enhance our understanding of underwater terrain dynamics but also reveal the presence of complex sedimentary structures, such as submarine dunes. Dunes play an important role in the preservation of the environment but can also be obstacles to safe navigation, requiring dragging operations. Hence, it is important to detect them from bathymetric models. Although information about these dunes has numerous applications, their identification methods remain poorly automated. This paper aims to leverage deep learning to develop a segmentation method for submarine dunes. Several challenges must be overcome. Dunes are complex objects with irregular, highly variable shapes, while bathymetric data are noisy and lack detailed information. Furthermore, in the fluvio-marine context, no labeled datasets exist for training purposes. Starting from a small pre-labeled dataset, this paper proposes a systematic approach to train a Mask R-CNN network. First, data augmentation techniques are applied to expand the dataset significantly and introduce meaningful variations. By relying on transfer learning with a carefully selected pre-trained backbone, feature extraction is optimized, reducing training time while enhancing model performance. The adaptation of the Mask R-CNN model to our submarine dune segmentation task has led to a significant improvement in detection performance, with a pixel-level F1-score reaching 89%. Additionally, the mean Average Precision has exceeded 50%, demonstrating the model’s effectiveness in identifying and delineating dunes despite their varied shapes and blurred contours. These results confirm the relevance of our approach for achieving more reliable dune segmentation in a complex fluvio-marine environment. Full article

Artificial intelligence (AI) has become an indispensable tool in orthopedic surgery. It provides new methods to increase surgical precision, improve patient safety, and support personalized treatment plans. This review presents a comprehensive analysis of AI-assisted orthopedic surgery across three core domains. Based on 89 recent studies, this review organizes findings around a perception–decision–execution framework. It groups diverse AI applications into certain categories while highlighting the mutuality across domains. Perception systems have progressed from basic CNN-based segmentation models to advanced transformer architectures. They support multi-modal data fusion and enable uncertainty quantification. Decision systems have moved far beyond rigid rule-based methods and evolve into data-driven models that support surgical planning, accurate risk prediction and continuous outcome optimization. And execution systems have advanced from passive navigation tools to active robotic assistance systems with real-time adaptive capabilities. Beyond mapping technological advances, this review also identifies pivotal challenges that hinder clinical translation and concludes with a clear roadmap for future research, which marks closed-loop surgical assistance systems as the next key development direction. Building on these findings, this review illuminates the potential of AI-assisted orthopedic surgery and guides future research toward innovations that can be translated into clinical practice. Full article

The global aging process has increased the number of older individuals providing care for relatives with severe mental disorders (SMD). This population faces unique health challenges. The present cross-sectional study examined the relationship between self-perceived health (SPH) and clinical, functional, and sociodemographic variables among 71 older caregivers (median age: 65 years) in Bogotá, Colombia. SPH was assessed by answering the question: “How would you describe your overall health status?” and dichotomized into good versus poor perception. Comorbidity was measured as the number of self-reported chronic conditions. Caregiver burden was evaluated using the Zarit Caregiver Burden Interview, and health-related quality of life (HRQoL) was assessed using the SF-36, including dimensions such as physical functioning, emotional well-being, bodily pain, and general health. Descriptive analyses, non-parametric comparisons, and logistic regression models were conducted. The results revealed a marked feminization of caregiving (92.96%) and a high prevalence of good SPH (70.42%), despite a substantial burden of physical comorbidities (mean: 3.21). Dimensions such as physical functioning, emotional well-being, and pain were significant in univariate analyses. However, the multivariate model identified general health as the only independent predictor of good SPH (adjusted odds ratio [OR]: 1.112; 95% confidence interval [CI]: 1.053–1.174; p < 0.001). These findings suggest that subjective health assessment may transcend objective disease counts for older caregivers. Public health policies could prioritize wellness-based interventions and emotional support over traditional disease-centered approaches to improve the quality of life of this growing, active, socially valuable, yet vulnerable population. Full article

Background: Cellular senescence is a key regulatory mechanism in tumor initiation and progression, influencing cancer development through modulation of the cell cycle, the immune microenvironment, and inflammatory responses. However, the molecular characteristics and potential clinical value of senescence-related genes in lung adenocarcinoma (LUAD) have not been systematically elucidated. This study aimed to comprehensively characterize the expression patterns, molecular subtypes, and prognostic significance of cellular senescence-related genes in LUAD, and to identify key regulatory determinants. Methods: Transcriptomic data of cellular senescence-related genes were obtained from The Cancer Genome Atlas (TCGA) cohort, and integrated analyses were performed to characterize their mutational landscape, copy number variations, and differential expression profiles. Senescence-related molecular subtypes were established using consensus clustering, followed by gene set variation analysis (GSVA) for pathway enrichment and immune infiltration analyses. A prognostic risk model was subsequently constructed using LASSO-penalized Cox regression, and its predictive performance was systematically evaluated. Candidate key regulators were further prioritized through bioinformatic screening, identifying FOLR1 as a hub gene. The biological function of FOLR1 was validated by qRT–PCR, Western blotting, assessment in clinical specimens, and a subcutaneous xenograft tumor model in mice. Results: Cellular senescence-related genes in LUAD exhibited a high frequency of somatic mutations and copy number alterations, accompanied by marked transcriptional dysregulation. Based on the expression profiles of these genes, LUAD patients could be stratified into three distinct molecular subtypes with significantly different clinical outcomes. These subtypes displayed pronounced heterogeneity in pathway enrichment patterns and immune cell infiltration. The subsequently developed prognostic signature demonstrated robust predictive performance in both the training and validation cohorts. Functional assays showed that FOLR1 was significantly downregulated in LUAD tissues and cell lines; FOLR1 knockdown promoted tumor cell proliferation, whereas restoration of its expression or pharmacological intervention markedly suppressed tumor progression. Consistently, in vivo xenograft experiments further corroborated the tumor-suppressive role of FOLR1 in lung adenocarcinoma. Conclusions: This study systematically delineated the molecular landscape of cellular senescence-related genes in LUAD and elucidated their associations with the tumor immune microenvironment and patient prognosis. Moreover, FOLR1 was identified as a potential tumor suppressor and therapeutic target. These findings provide a theoretical basis for senescence-informed molecular stratification and the development of precision treatment strategies in lung adenocarcinoma. Full article

Financial distress prediction remains a central topic in corporate finance and risk management, with extensive research devoted to improving classification accuracy through increasingly sophisticated statistical and machine learning techniques. Nevertheless, the influence of data preparation on predictive performance has received comparatively less systematic attention. This study examines how an economically grounded data-preparation process affects the predictive performance of selected statistical and machine-learning models dedicated to predicting corporate financial distress. Using the chosen financial ratios, generally accepted indicators of corporate financial stability and economic performance, financial distress models are estimated on both raw, unprocessed input data and pre-processed data involving the exclusion of economically implausible accounting values, treatment of missing observations, and class balancing. In light of the above, the study adopts a structured methodological approach to assess the predictive performance of selected classification models, namely decision tree algorithms (CART, CHAID, and C5.0), artificial neural networks (ANNs), logistic regression (LR), and linear discriminant analysis (DA), using confusion-matrix–based evaluation and a comprehensive set of evaluation measures. The results suggest that the process of input data preparation is a critical factor, significantly improving the predictive performance of financial distress prediction models across most modelling techniques employed. The most pronounced gains are observed in decision tree models. ANNs also demonstrate marked improvement after input data preparation, whereas LR benefits more moderately, and linear DA remains limited despite preprocessing. The average gain in accuracy across all six modelling techniques, calculated as the difference between pre-processed and raw performance for each method and averaged across methods, was approximately 15.6 percentage points, with specificity improving by approximately 26.9 percentage points on average, amounting to roughly half the performance variation attributable to algorithm choice, which underscores that data preparation is a primary determinant of model reliability alongside algorithm selection. A step-level detailed analysis further shows that missing value imputation is the dominant driver of improvement for tree-based models, while class balancing contributes most for ANNs and logistic regression. The findings highlight that reliable financial distress prediction depends not only on technique selection but also on the consistency and economic plausibility of the input data, underscoring the central role of structured data preparation in developing robust early-warning models. Full article

Improving the dissolution and solubility of poorly water-soluble drugs remains a major challenge in drug development. Solid dispersion (SD) techniques offer an effective strategy by which to enhance the bioavailability of BCS Class II drugs such as ivermectin (IVM). This study aimed to develop and validate stability-indicating analytical methods for the quantification of IVM and to evaluate the performance of the formulated SDs. A novel RP-HPLC and a UV spectrophotometric method were developed and validated in accordance with ICH guidelines. IVM SDs were prepared via physical mixing (PM), solvent evaporation (SE), and melt fusion (MF) using Poloxamer 188, Kollicoat^® IR, and PEG 6000 at respective ratios of 1:1, 1:3, and 1:5. Dissolution studies showed a marked enhancement in drug release from SDs prepared by SE and MF methods compared with pure IVM. Among all formulations, the Poloxamer 188-based binary SD prepared by the SE method at a 1:5 ratio exhibited the highest dissolution (98.55% at 60 min), with release kinetics following anomalous (non-Fickian) transport (n = 0.681) according to the Korsmeyer–Peppas model. Solid-state characterization evidenced by FTIR, DSC, TGA, and SEM confirmed the transformation of IVM from its crystalline form to an amorphous state. Future studies will focus on the in vivo evaluation of the optimized IVM SD formulations. Full article

Background and Objectives: Intraperitoneal onlay mesh (IPOM) reduces ventral/incisional hernia recurrence but raises concern for adhesions and infection, particularly when the operative field is not strictly clean. We aimed to determine how contamination severity modulates the peritoneal response to intraperitoneal polypropylene mesh. Materials and Methods: In a prospective, randomized, blinded rat study, 60 male Wistar rats were allocated to three groups (n = 20/group) and evaluated at postoperative day (POD) 4 and POD 8 (n = 10/timepoint): A, clean mesh placement; B, small-bowel resection with end-to-end anastomosis without spillage (“potentially septic”); and C, mesh placement followed by intraperitoneal inoculation with Escherichia coli and Staphylococcus aureus (“controlled contamination”). The primary outcome was adhesion severity (Van der Ham scale, 0–3). Secondary outcomes included semi-quantitative histological scores (0–4) for neutrophil infiltration, fibroblast proliferation, neoangiogenesis, and collagen deposition. Prespecified non-parametric analyses were applied. Results: All animals completed follow-up; no pre-sacrifice deaths occurred. Adhesion severity showed no statistically significant differences between Groups A and B at either timepoint (mean POD4: 0.3 vs. 0.6; POD8: 0.4 vs. 0.8; p > 0.05). In contrast, Group C demonstrated markedly higher adhesion scores (mean POD4: 2.3; POD8: 2.4; both p < 0.001 vs. Groups A and B), with a substantially greater proportion of grade 2–3 adhesions. Histological parameters paralleled these findings: at both POD4 and POD8, Group C showed significantly higher neutrophil, fibroblast, neoangiogenesis, and collagen scores compared with Groups A and B (all p < 0.001). No statistically significant within-group temporal differences were observed between POD4 and POD8. Conclusions: In this experimental model, intraperitoneal polypropylene mesh demonstrated similar early biological response patterns in clean and controlled contamination settings, whereas established intra-abdominal sepsis was associated with a marked escalation of inflammation, fibroproliferation, and adhesion formation. These findings suggest that selective use of synthetic intraperitoneal mesh may be considered when contamination is controlled, while caution is warranted in frankly septic environments. Full article

Background: The clinical benefit of percutaneous coronary intervention (PCI) for chronic total occlusion (CTO) remains controversial, particularly regarding left ventricular (LV) functional recovery. Global longitudinal strain (GLS) has emerged as a more sensitive marker of myocardial function than left ventricular ejection fraction (LVEF). This study aimed to evaluate the effect of CTO revascularization on LV function using GLS. Methods: This systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. A comprehensive literature search was performed in the PubMed/MEDLINE database from inception through March 2026 using predefined search terms and Boolean operators. Reference lists of relevant articles were also screened to ensure completeness. Studies evaluating GLS before and after PCI for CTO and reporting quantitative strain data were included. Pooled effect estimates were calculated as mean differences (MDs) with 95% confidence intervals (CIs) using a random-effects model. Subgroup and sensitivity analyses were performed to explore heterogeneity and assess the robustness of the findings. Results: Six studies involving 376 patients were included. Successful CTO-PCI may be associated with an improvement in GLS (MD = 1.69; 95% CI: 1.09–2.29; p < 0.001), with substantial heterogeneity (I² = 81%). Subgroup analysis demonstrated greater GLS improvement in studies with longer follow-up durations. Sensitivity analyses confirmed the robustness of the results. Conclusions: CTO revascularization may be associated with an improvement in LV myocardial function as assessed by GLS, even in the absence of marked changes in conventional parameters such as LVEF. These findings support the clinical utility of GLS as a sensitive imaging biomarker for detecting early myocardial recovery and for guiding risk stratification in patients undergoing CTO-PCI. Full article

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of all cases. Isorhamnetin (ISO), a natural dietary flavonoid, has demonstrated potent anti-lung cancer activity in cell models. However, its precise mechanism of action within the complex landscape of NSCLC remains to be fully elucidated. Methods: The effects of ISO on NSCLC cell viability, apoptosis, and cell cycle distribution were assessed in A549 and H1650 cells using the MTT assay, Annexin V-FITC/PI staining, and flow cytometry. Wound healing and Transwell assays were employed to evaluate the isorhamnetin impact on cell migration, invasion, and adhesion. To investigate the underlying molecular mechanisms, RNA sequencing (RNA-seq) was performed, followed by validation of key target genes and proteins using qRT-PCR and Western blot analysis. Results: ISO treatment elicited a significant, dose- and time-dependent inhibition of NSCLC cell viability, which coincided with a marked induction of apoptosis. Cell cycle analysis revealed that ISO triggered an S-phase arrest. Transcriptomic profiling identified ELFN1 and TMEM186 as significantly upregulated genes, while SETDB1 was downregulated in a concentration-dependent manner; this was accompanied by a concomitant upregulation of FGFBP1 protein expression. Functionally, ISO effectively suppressed the migratory, invasive, and adhesive capabilities of both cell lines. Conclusions: Our findings demonstrate that ISO exerts a potent anti-proliferative and anti-metastatic effect on NSCLC cells. The underlying mechanism is multifaceted, involving the induction of apoptosis and cell cycle arrest, coupled with the modulation of a novel regulatory network centered on ELFN1, TMEM186, SETDB1, and FGFBP1. These results provide new mechanistic insights into the anti-tumor pharmacology of isorhamnetin and highlight its potential as a therapeutic agent targeting both cancer cells and their supporting microenvironments. Full article

Background: Diabetes mellitus is a metabolic disturbance characterized by chronic hyperglycemia, which stems from defective secretion and/or action of insulin. D-Limonene has been studied for the confirmation of its antidiabetic and antioxidant effects. This paper aims to investigate the antidiabetic and antioxidants effects of D-Limonene in an experimental model of DM1. Methods: Female Wistar rats (180–250g) received streptozotocin (STZ, 45 mg/kg) intraperitoneally. Animals with capillary glycemia ≥ 250 mg/dL were considered diabetic. D-Limonene at oral doses of 12.5 mg/kg, 25 mg/kg and 50 mg/kg was administered during 28-day treatment. Water and food intake, weight gain and capillary glycemia were evaluated. At the end of the treatment, the following biochemical parameters were assessed: serum glucose, HbA1c, urea, creatinine, AST, ALT, GGT, ALP and albumin. The oxidative stress markers were determined in plasma, erythrocytes, and aortic homogenates: malondialdehyde, nitrite, myeloperoxidase, superoxide dismutase and catalase. Results: D-Limonene (25 and 50 mg/kg) significantly reduced serum glucose, HbA1c, AST, ALT, GGT and ALP when compared to DC, as well as plasma MDA and nitrite concentrations. Interestingly, D-Limonene (25 and 50 mg/kg) decreased both plasma and aortic myeloperoxidase activities, as well as increased both erythrocytic and aortic catalase activities. Conclusions: These findings, besides a marked D-Limonene-induced hypoglycemic effect, pave the way for further studies comprising a multi-target treatment by providing benefits on hepatic and vascular complications related to the diabetic condition. Full article

A multi-task network for runway lines and runway markings based on deep learning was designed to address the issue of prior information dependence on runway width in unmanned aerial vehicle visual autonomous landing application scenarios. By detecting runway images captured at different positions during flight, the parameters of the runway start line, left and right boundary lines, and runway markings were obtained. On this basis, a runway width estimation model and visual positioning algorithm based on line features were designed. In standard runway scenarios, the recognition of runway signs provides valuable prior information about the runway width. For simplified runways or cases where signs are missing, we have devised a width estimation model based on the left/right boundary lines. Furthermore, considering the variation in pitch angle during the UAV’s landing process, we have analyzed and refined the width estimation model to ensure its applicability throughout the entire landing process. Additionally, we have developed a visual positioning algorithm that utilizes the runway width and runway line parameters to calculate the relative position between the UAV and the runway. Considering the limitations of a single visual positioning algorithm, we adopt a visual and inertial navigation fusion positioning algorithm to enhance the reliability of landing positioning. To validate our algorithms, we have constructed a visual simulation platform and flight test. These tests confirm the effectiveness and accuracy of our detection algorithm and width estimation model. Furthermore, by utilizing the estimated runway width and the detected runway line parameters, we have successfully calculated the relative position, further validating the effectiveness of our positioning algorithm. Full article

The increasing demand for sustainable pest management has positioned essential oils (EOs) as viable bio-based alternatives to synthetic pesticides. This study investigates the insecticidal potential of Haplopappus foliosus EO, a Chilean endemic medicinal plant, against Drosophila melanogaster as a key toxicological model for fruit fly control. Chemical characterization identified 56 compounds, with 4-terpineol (27.27%) and α-bisabolol (10.40%) as the primary constituents, marking the first report of α-bisabolol in this species. To enhance bioavailability and overcome EO volatility, a nanoemulsion was developed, achieving an exceptionally small and stable particle size of 2.10 nm that remained consistent for over 90 days. Nanoencapsulation significantly optimized the EO’s efficacy, reducing the median lethal concentration (LC₅₀) from 120.26 µg/mL to a potent 54.57 µg/mL. While in vitro assays showed the free oil as a more potent acetylcholinesterase (AChE) inhibitor, molecular docking confirmed the high affinity of 4-terpineol and α-bisabolol for the enzyme’s active site, elucidating the neurotoxic mechanism at a molecular level. In silico analysis predicted a favorable human safety profile within GHS classes 4 and 5. Overall, this stable nanoformulation represents a sustainable biotechnological strategy for agricultural pest management, leveraging the synergistic effects and enhanced delivery of natural products. Full article