Search Results (221)

During traditional aircraft landings, pilots face significant challenges in identifying runway numbers with the naked eye, particularly at decision height under adverse weather conditions. To address this issue, this study proposes a novel detection algorithm based on an optimized version of the YOLOv5s model (You Only Look Once, version 5) for recognizing runway markings during civil aircraft landings. By integrating a data augmentation strategy with external datasets, the method effectively reduces both false detections and missed targets through expanded feature representation. An Alpha Complete Intersection over Union (CIOU) Loss function is introduced in place of the original CIOU Loss function, offering improved gradient optimization. Additionally, the model incorporates several advanced modules and techniques, including a Convolutional Block Attention Module (CBAM), Soft Non-Maximum Suppression (Soft-NMS), cosine annealing learning rate scheduling, the FReLU activation function, and deformable convolutions into the backbone and neck of the YOLOv5 architecture. To further enhance detection, a specialized small-target detection layer is added to the head of the network and the resolution of feature maps is improved. These enhancements enable better feature extraction and more accurate identification of smaller targets. As a result, the optimized model shows significantly improved recall (R) and precision (P). Experimental results, visualized using custom-developed software, demonstrate that the proposed optimized YOLOv5s model achieved increases of 5.66% in P, 2.99% in R, and 2.74% in mean average precision (mAP) compared to the baseline model. This study provides valuable data and a theoretical foundation to support the accurate visual identification of runway numbers and other reference markings during aircraft landings. Full article

Background: Large language models (LLMs) such as ChatGPT, Google Gemini, and Microsoft Copilot are increasingly used by patients seeking medical information online. While these tools provide accessible and conversational explanations, their accuracy and safety in emotionally sensitive scenarios—such as an incidental cancer diagnosis—remain uncertain. Objective: To evaluate the quality, completeness, readability, and safety of responses generated by three state-of-the-art LLMs to common patient questions following the incidental discovery of a kidney tumor. Methods: A standardized use-case scenario was developed: a patient learns of a suspicious renal mass following a computed tomography (CT) scan for back pain. Ten plain-language prompts reflecting typical patient concerns were submitted to ChatGPT-4o, Microsoft Copilot, and Google Gemini 2.5 Pro without additional context. Responses were independently assessed by five board-certified urologists using a validated six-domain rubric (accuracy, completeness, clarity, currency, risk of harm, hallucinations), scored on a 1–5 Likert scale. Two statistical approaches were applied to calculate descriptive scores and inter-rater reliability (Fleiss’ Kappa). Readability was analyzed using the Flesch Reading Ease (FRE) and Flesch–Kincaid Grade Level (FKGL) metrics. Results: Google Gemini 2.5 Pro achieved the highest mean ratings across most domains, notably in accuracy (4.3), completeness (4.3), and low hallucination rate (4.6). Microsoft Copilot was noted for empathetic language and consistent disclaimers but showed slightly lower clarity and currency scores. ChatGPT-4o demonstrated strengths in conversational flow but displayed more variability in clinical precision. Overall, 14% of responses were flagged as potentially misleading or incomplete. Inter-rater agreement was substantial across all domains (κ = 0.68). Readability varied between models: ChatGPT responses were easiest to understand (FRE = 48.5; FKGL = 11.94), while Gemini’s were the most complex (FRE = 29.9; FKGL = 13.3). Conclusions: LLMs show promise in patient-facing communication but currently fall short of providing consistently accurate, complete, and guideline-conform information in high-stakes contexts such as incidental cancer diagnoses. While their tone and structure may support patient engagement, they should not be used autonomously for counseling. Further fine-tuning, clinical validation, and supervision are essential for safe integration into patient care. Full article

Background: Small airway dysfunction (SAD) plays a critical role in the management of severe asthma, particularly in patients at risk of requiring biological therapies (BTs). Short-term studies have shown that switching to single-inhaler triple therapy (SITT) with extrafine beclomethasone–formoterol–glycopyrronium improves outcomes and helps achieve quiet asthma, a state marked by symptom control, no exacerbations or oral steroids, reduced inflammation, and better small airway function. This study investigated whether, over one year, patients could maintain this state as Steady Quiet Asthma (SQA) and whether baseline measures could predict this sustained response. Methods: Twenty-six patients with severe asthma and SAD were transitioned from open triple-inhaler therapy to a closed, single-inhaler triple therapy containing extrafine beclomethasone–formoterol–glycopyrronium. Assessments at baseline (T0) and at one-year follow-up (T12) included clinical evaluations, spirometry, and impulse oscillometry, with a focus on Fres as a predictor for the need for BT. When prescribed, biologic therapies included mepolizumab, benralizumab, and dupilumab. Results: Of the 26 patients, 9 (34.6%) achieved SQA and did not require biologic therapy at the one-year follow-up, while 17 patients (65.4%) initiated biologic treatment. At T0, patients who required biologics had significantly higher median Fres (21 (19.47; 24.58) vs. 17.61 (15.82; 20.63); p = 0.049) compared to those who remained biologic-free. They also exhibited higher residual volume to total lung capacity ratio (%RV/TLC) values and lower forced expiratory volume in one second/forced vital capacity ratios (FEV₁/FVC). At T12, patients spared from BT showed significant reductions in Fres (p = 0.014) and improvements in small airway function (difference in airway resistance between 5 Hz and 20 Hz (R5–20), forced expiratory flow between 25% and 75% of FVC (%FEF25–75), and better asthma control (ACT). In contrast, patients on BT demonstrated less favorable changes in these parameters. Conclusions: Baseline Fres, FEV1/FVC ratio, and %FEV25–75 are valuable predictors of achieving Steady Quiet Asthma (SQA) and sparing biologic therapy. These findings support the use of SITT in severe asthma and highlight the importance of early functional assessments to guide personalized management. Full article

Background/Objectives: The increased accessibility of information has resulted in a rise in patients trying to self-diagnose and opting for self-medication, either as a primary treatment or as a supplement to medical care. Our objective was to evaluate the reliability, comprehensibility, and readability of the responses provided by ChatGPT 4.0 when queried about the most prevalent orthopaedic problems, thus ascertaining the occurrence of misguidance and the necessity for an audit of the disseminated information. Methods: ChatGPT 4.0 was presented with 26 open-ended questions. The responses were evaluated by two observers using a Likert scale in the categories of diagnosis, recommendation, and referral. The scores from the responses were subjected to subgroup analysis according to the area of interest (AoI) and anatomical region. The readability and comprehensibility of the chatbot’s responses were analyzed using the Flesch–Kincaid Reading Ease Score (FRES) and Flesch–Kincaid Grade Level (FKGL). Results: The majority of the responses were rated as either ‘adequate’ or ‘excellent’. However, in the diagnosis category, a significant difference was found in the evaluation made according to the AoI (p = 0.007), which is attributed to trauma-related questions. No significant difference was identified in any other category. The mean FKGL score was 7.8 ± 1.267, and the mean FRES was 52.68 ± 8.6. The average estimated reading level required to understand the text was considered as “high school”. Conclusions: ChatGPT 4.0 facilitates the self-diagnosis and self-treatment tendencies of patients with musculoskeletal disorders. However, it is imperative for patients to have a robust understanding of the limitations of chatbot-generated advice, particularly in trauma-related conditions. Full article

In this paper, a unique method for controlling the effects of nonlinear vibrational responses in a cantilever beam system under harmonic excitation is presented. The Nonlinear Integral Negative Derivative Feedback (NINDF) controller is used for this purpose in this study. With this method, the cantilever beam is represented by a three-DOF nonlinear system, and the NINDF controller is represented by a first-order and second-order filter. The authors derive analytical solutions for the autonomous system with the controller by utilising perturbation analysis on the linearised system model. This study aims to reduce vibration amplitudes in a nonlinear dynamic system, specifically when 1:1 internal resonance occurs. The stability of the system is assessed using the Routh–Hurwitz criterion. Moreover, symmetry is present in the frequency–response curves (FRCs) for a variety of parameter values. The results show that, when compared to other controllers, the effectiveness of vibration suppression is directly correlated with the product of the NINDF control signal. The amplitude response of the system is demonstrated, and the analytical solutions are validated through numerical simulations using the fourth-order Runge–Kutta method. The accuracy and reliability of the suggested approach are demonstrated via the significant correlation between the analytical and numerical results. Full article

This retrospective study assessed the potential of blood leukocyte ratios as diagnostic biomarkers in cats with chronic enteropathies (CE). Absolute neutrophil-to-lymphocyte (NLR), neutrophil-to-monocyte (NMR), and lymphocyte-to-monocyte (LMR) ratios were calculated from the hematological profiles of 221 cats, including healthy controls (n = 73), and those diagnosed with food-responsive enteropathy (FRE, n = 59), steroid-responsive enteropathy (SRE, n = 56), or small cell lymphoma (SCL, n = 33). SCL cats had higher NLRs than SRE (p = 0.002) and FRE (p = 0.028), and lower LMRs than SRE (p = 0.012) and FRE (p = 0.001). Healthy cats had lower NLRs compared to the FRE (p < 0.001), SRE (p < 0.001), and SCL (p < 0.001) cats and higher LMRs compared to the FRE (p < 0.001), SRE (p < 0.001), and SCL (p < 0.001) cats. Receiver operating characteristic (ROC) curve analysis demonstrated that NLR ≥ 11.6 differentiated SCL from SRE with 87.5% specificity but low sensitivity (39.4%). NMR ≥ 34.5 distinguished FRE from SRE with 52.5% sensitivity and 69.6% specificity. LMR ≥ 3.72 differentiated SRE from SCL with 67.9% sensitivity and 60.6% specificity. Although significant differences in leukocyte ratios were observed among groups, their diagnostic accuracy in differentiating CE phenotypes was suboptimal. These findings suggest that the utility of NLR, NMR, and LMR as standalone diagnostic tools is limited. Full article

Developing sustainable packaging materials has become a global priority in response to environmental concerns associated with conventional plastics. This study used a wheat flour byproduct (glue flour, GF) and poly(butylene adipate-co-terephthalate) (PBAT) to produce films via blown extrusion, incorporating rosemary extract (RE) at 2% (FRE2) and 4% (FRE4) (w/w). A control film (FCO) was formulated without RE. The physicochemical, thermal, mechanical, and biodegradation properties of the films were evaluated. FCO, FRE2, and FRE4 exhibited tensile strength (TS) values between 8.16 and 9.29 MPa and elongation at break (ELO) above 889%. Incorporating 4% RE decreased luminosity (91.38 to 80.89) and increased opacity (41.14 to 50.95%). A thermogravimetric analysis revealed a main degradation stage between 200 °C and 450 °C, with FRE2 showing the highest residual mass (~15% at 600 °C). Sorption isotherms indicated enhanced hydrophobicity with RE, thereby reducing the monolayer moisture content from 5.23% to 3.03%. Biodegradation tests revealed mass losses of 64%, 58%, and 66% for FCO, FRE2, and FRE4, respectively, after 180 days. These findings demonstrate that incorporating RE into GF/PBAT blends via blown extrusion is a promising strategy for developing biodegradable films with enhanced thermal behavior, mechanical integrity, and water resistance, contributing to the advancement of sustainable packaging materials. Full article

The big data collected from structural health monitoring systems (SHMs), combined with the rapid advances in machine learning (ML), have enabled data-driven methods in practical SHM applications. These methods typically use ML algorithms to identify patterns within features extracted from data representing structural conditions, thereby inferring damage from changes in these patterns. However, data-driven models often struggle to generalize effectively to unseen datasets. This study addresses this challenge through three key contributions: dataset augmentation, an efficient feature representation, and a probabilistic modeling approach. First, a data augmentation method leveraging the symmetric properties of bridge structures is introduced to enhance dataset diversity. Second, a novel damage indicator named Fre-GraRMSC1 is proposed, capable of distinguishing both damage locations and severity. Finally, a probabilistic generative model based on a deep belief network (DBN) is developed to predict damage locations and degrees. The proposed methods are validated using vibration data from a numerical three-span continuous bridge subjected to random vehicle excitations. Results demonstrate high accuracy in damage identification and improved generalization performance. Full article

This study addresses the limitations of South Korea’s Design for Safety (DfS) reports, which are a critical component of construction safety reports (CSRs) but rely heavily on text, limiting readability and visual comprehension. While previous studies have highlighted the readability challenges in construction safety documents, few have quantitatively combined layout and readability assessments using objective metrics. To enhance information delivery, this research proposes an improved CSR format and quantitatively evaluates its effectiveness compared to the conventional format. A two-step analysis was conducted using document layout analysis, pixel-based methods, and the Flesch Reading Ease Score (FRES) to assess layout and readability. The results showed that conventional CSRs consist of nearly 100% text, while the improved format integrates approximately 70% images and 30% text, enhancing visual clarity without altering content. The improved format achieved a higher average FRES score of 50.24 compared to 44.52 for the conventional format, indicating a 1.12-fold increase in readability. These findings suggest that the improved CSR format significantly enhances comprehension and information delivery. The proposed quantitative analysis method offers a practical approach for evaluating and improving document design in construction safety, and it can be applied to other fields to improve the effectiveness of written communication. Full article

In the present work, the adsorption of vitamin B12 (VB12) on sugarcane-derived activated carbon (AC) was investigated with the purpose of developing a hybrid material able to degrade highly toxic and recalcitrant chlordecone (CLD) for remediating the severe environmental issue of pesticide pollution of water and soil. The AC used is made from locally accessible sugarcane bagasse. The experimental kinetic and isothermic data of VB12 adsorption on AC were modeled using multiple models, including Pseudo-Order 1 (Lagergren), Pseudo-Order 2, Elovich, and Brouers–Sotolongo models for the kinetics. The isotherms models used were Langmuir, Freundlich, Hill–Sips, Brouers–Sotolongo (BS), Brouers-Gaspard (BG), General Brouers–Sotolongo (GBS), and Redlich–Peterson (RP) models. The results showed that the BG model is the most suitable to satisfactorily describe the adsorption of VB12 on the studied AC, involving a heterogeneous adsorption mechanism onto a heterogeneous surface and providing the maximum adsorption capacity, a convenient tool to estimate the saturation level of adsorbate (i.e., chlordecone (CLD)) onto the adsorbent (AC). Voltammetric studies confirm the interaction between VB12 and the AC. Finally, molecular modeling is used to provide atomic insights showing the entrapment of VB12 inside the porous system to form a new hybrid material. The calculations provide the conformations with the best binding energy in the GaudiMM environment. Full article

Cloud removal is a vital preprocessing step in optical remote sensing images (RSIs), directly enhancing image quality and providing a high-quality data foundation for downstream tasks, such as water body extraction and land cover classification. Existing methods attempt to combine spatial and frequency features for cloud removal, but they rely on shallow feature concatenation or simplistic addition operations, which fail to establish effective cross-domain synergistic mechanisms. These approaches lead to edge blurring and noticeable color distortions. To address this issue, we propose a spatial–frequency collaborative enhancement Transformer network named SFCRFormer, which significantly improves cloud removal performance. The core of SFCRFormer is the spatial–frequency combined Transformer (SFCT) block, which implements cross-domain feature reinforcement through a dual-branch spatial attention (DBSA) module and frequency self-attention (FreSA) module to effectively capture global context information. The DBSA module enhances the representation of spatial features by decoupling spatial-channel dependencies via parallelized feature refinement paths, surpassing the performance of traditional single-branch attention mechanisms in maintaining the overall structure of the image. FreSA leverages fast Fourier transform to convert features into the frequency domain, using frequency differences between object and cloud regions to achieve precise cloud detection and fine-grained removal. In order to further enhance the features extracted by DBSA and FreSA, we design the dual-domain feed-forward network (DDFFN), which effectively improves the detail fidelity of the restored image by multi-scale convolution for local refinement and frequency transformation for global structural optimization. A composite loss function, incorporating Charbonnier loss and Structural Similarity Index (SSIM) loss, is employed to optimize model training and balance pixel-level accuracy with structural fidelity. Experimental evaluations on the public datasets demonstrate that SFCRFormer outperforms state-of-the-art methods across various quantitative metrics, including PSNR and SSIM, while delivering superior visual results. Full article

The aims of this study were to characterise the faecal amino acid profile of dogs with different chronic digestive diseases (food-responsive enteropathy (FRE), immunosuppressant-responsive enteropathy (IRE)) prior to dietary change, and Giardia infection (GIA), compared to healthy control (HC), and to evaluate their discriminating potential. The HC group presented lower faecal tyrosine (Tyr) and aromatic amino acids (AAAs) compared to FRE or IRE dogs (p = 0.0001). Additionally, the HC group had lower levels of threonine (Thr) (p = 0.0005) than the IRE group, while FRE dogs showed intermediate values. No statistically significant differences in faecal amino acids were observed between FRE and IRE dogs. In contrast, the GIA group had higher faecal amino acid values (except glutamic acid (Glu)) compared to the other dogs. The most determinant variables contributing to the discriminant functions were Tyr, Glu, arginine, and phenylalanine. Validation results of the discriminant functions showed that 44% of stool samples were misclassified, resulting in a 56% success rate. The faecal amino acid profile did not accurately distinguish FRE from IRE dogs; however, faecal excretion of AAs was generally higher in dogs with GIA. Full article

Background/Objectives: Bladder cancer, the most common and heterogeneous malignancy of the urinary tract, presents with diverse types and treatment options, making comprehensive patient education essential. As large language models (LLMs) emerge as a promising resource for disseminating medical information, their accuracy and validity compared to traditional methods remain under-explored. This study aims to evaluate the effectiveness of LLMs in educating the public about bladder cancer. Methods: Frequently asked questions regarding bladder cancer were sourced from reputable educational materials and assessed for accuracy, comprehensiveness, readability, and consistency by two independent board-certified urologists, with a third resolving any discrepancies. The study utilized a 3-point Likert scale for accuracy, a 5-point Likert scale for comprehensiveness, and the Flesch–Kincaid (FK) Grade Level and Flesch Reading Ease (FRE) scores to gauge readability. Results: ChatGPT-3.5, ChatGPT-4, and Gemini were evaluated on 12 general questions, 6 questions related to diagnosis, 28 concerning treatment, and 7 focused on prevention. Across all categories, the correct response rate was notably high, with ChatGPT-3.5 and ChatGPT-4 achieving 92.5%, compared to 86.3% for Gemini, with no significant difference in accuracy. However, there was a significant difference in comprehensiveness (p = 0.011) across the models. Overall, a significant difference in performance was observed among the LLMs (p < 0.001), with ChatGPT-4 providing the most college-level responses, though these were the most challenging to read. Conclusions: In conclusion, our study adds value to the applications of Artificial Intelligence (AI) in bladder cancer education, with notable insights into the accuracy, comprehensiveness, and stability of the three LLMs. Full article

Purpose: This study examines GPT-4o’s ability to communicate effectively with relatives of patients undergoing decompressive hemicraniectomy (DHC) after malignant middle cerebral artery infarction (MMCAI). Methods: GPT-4o was asked 25 common questions from patients’ relatives about DHC for MMCAI, twice over a 7-day interval. Responses were rated for accuracy, clarity, relevance, completeness, sourcing, and usefulness by board-certified intensivist* (one), neurologists, and neurosurgeons using the Quality Analysis of Medical AI (QAMAI) tool. Interrater reliability and stability were measured using ICC and Pearson’s correlation. Results: The total QAMAI scores were 22.32 ± 3.08 for the intensivist, 24.68 ± 2.8 for the neurologist, 23.36 ± 2.86 and 26.32 ± 2.91 for the neurosurgeons, representing moderate-to-high accuracy. The evaluators reported moderate ICC (0.631, 95% CI: 0.321–0.821). The highest subscores were for the categories of accuracy, clarity, and relevance while the poorest were associated with completeness, usefulness, and sourcing. GPT-4o did not systematically provide references for their responses. The stability analysis reported moderate-to-high stability. The readability assessment revealed an FRE score of 7.23, an FKG score of 15.87 and a GF index of 18.15. Conclusions: GPT-4o provides moderate-to-high quality information related to DHC for MMCAI, with strengths in accuracy, clarity, and relevance. However, limitations in completeness, sourcing, and readability may impact its effectiveness in patient or their relatives’ education. Full article

This study aimed to investigate the effects of supplementing broiler feed with high-yield protease Bacillus subtilis strain FRE76 on growth performance, slaughter performance, apparent digestibility, intestinal morphology, and intestinal microbiota. One-day-old Arbor Acres broilers (n = 240) were randomly assigned to four groups (n = 6 replicates; 10 animals per replicate), in which their basal diet was supplemented with B. subtilis strain FRE76 at 0 CFU/kg (group C), 3.60 × 10⁸ CFU/kg (group L), 1.08 × 10⁹ CFU/kg (group M), and 1.80 × 10⁹ CFU/kg (group H). The experiment lasted for 42 d and comprised two phases: Days 1–21 and days 22–42. The broilers’ body weight at 21 d and the average daily gain at 1–21 d in the L and H groups increased significantly relative to those in group C (p < 0.05). The half-bore weight, half-bore percentage, and breast muscle percentage in group L were significantly higher (p < 0.05) than those in group C. The apparent digestibility of crude protein in group L and the ether extract in groups L, M, and H were significantly increased at 22–42 d compared with those in group C (p < 0.05). In group L, the jejunal villus height and villus height/crypt depth increased significantly relative to those in group C (p < 0.05). The chymotrypsin and trypsin activities tended to increase in the B. subtilis FRE76 groups (p = 0.072 and p = 0.056, respectively) relative to those in group C. Additionally, the abundance of Bacteroidota, Proteobacteria, Alistipes, Clostridia_vadinBB60_group, and Parabacteroides increased significantly in the B. subtilis FRE76 groups (p < 0.05). In conclusion, dietary B. subtilis FRE76 could improve broilers’ body weight, average daily gain, apparent digestibility, protease activity, intestinal morphology, and gut microbiota. Full article