Search Results (29,097)

LiDAR (Light Detection and Ranging) and RGB-D camera imaging have emerged as essential tools in agricultural applications, particularly for plant size and distance measurements, enabling non-destructive, cost-effective, and precise estimation. The objective of this study was to measure the plant canopy dimensions and distance between apples using commercial LiDAR, and an RGB-D camera with a speed sprayer platform was used to determine whether LiDAR provides a higher measurement accuracy under field conditions. Data were collected in an apple orchard in Muju, Republic of Korea. Commercial 3D LiDAR, a terminal box, an RGB-D camera, a microcontroller, a power supply, and individual display monitors were integrated into a customized data acquisition (DAQ) box for LiDAR point cloud (PCD), RGB, and depth imagery data collection. Commercial software was used for data acquisition, data conversion (pcap to PCD), segmentation of regions of interest (ROI), and pre-processing of data. PCD processing and measurement consisted of data frame selection, data conversion, outlier removal, downsampling, denoising, ground point removal by filtering, voxelization, and density map generation using an open access programming language script. Depth image processing included importing raw data, shaping metadata using intrinsic camera parameters, visualizing depth images, extracting depth points, and measuring the plant canopy at the pixel level. RGB image analysis involved grayscale conversion, thresholding, segmentation of ROI, contour preparation, noise removal, and binary masking for eliminating the background. Estimated results were compared to measured results. LiDAR measurements showed the closest agreement with the measured results for plant height, canopy volume, plant spacing, and row distance, outperforming both RGB and depth imaging. Under field conditions, plant spacing and row distance were estimated with accuracies of 97.5% and 94.7%, respectively, exhibiting higher measurement accuracies than RGB and depth imagery data results. Despite some discrepancies due to complex plant geometry and dynamic data collection, the results support data collection strategies critical for precision horticulture. Full article

Detecting a few hundred fraudulent transactions among hundreds of thousands is an extreme class-imbalance problem where one miss can cost a full transaction value. Stacking heterogeneous classifiers is the standard recipe, yet under a leakage-free, precision–recall evaluation, its ranking gain over the best single model is inconsistent: sizable when the base learners are diverse, and negligible when they are redundant. The ensemble’s dependable value lies elsewhere: member disagreement is a usable, threshold-free estimate of epistemic uncertainty. We propose Daunt (Disagreement As UNcertainty for Triage), which turns this disagreement into three deployment layers over one ensemble: (i) routing the most uncertain transactions to human review by ranking them on base-learner disagreement, (ii) deciding alarms by an example-dependent rule that weighs the fraud score against the transaction amount, and (iii) attaching interpretations verified for faithfulness and stability. On two contrasting datasets, the anonymized ULB ($0.17\%$ fraud) and feature-rich IEEE-CIS ($3.5\%$ fraud), deferring the $5\%$ most uncertain transactions raises system recall from $0.76$ to $0.91$ (ULB) and $0.66$ to $0.75$ (IEEE-CIS) while removing every automated false alarm, and the example-dependent rule recovers more fraudulent money than any global threshold. Daunt reframes the heterogeneous ensemble as a source of actionable uncertainty rather than an end in itself. Full article

This study aimed to isolate Bacillus strains producing poly-γ-glutamic acid (γ-PGA), a natural antioxidant biopolymer, from Cheonggukjang, optimize fermentation conditions, improve γ-PGA production in the food by applying the selected strain, and investigate its potential association with antioxidant activity. Among 157 isolates, Bacillus velezensis CH7P29 demonstrated the highest γ-PGA production (602.80 ± 6.55 μg/mL) and was selected for fermentation. Optimal in vitro conditions for γ-PGA production were determined as 40 °C and 0–2% NaCl. In Cheonggukjang fermentation under optimal conditions, the CH7P29-inoculated group exhibited a marked increase in γ-PGA content (16.02 mg/g on day 4), which was significantly higher than the uninoculated control (not detected) and reference strain groups (12.25 and 11.38 mg/g, respectively). Simultaneously, its DPPH radical scavenging activity exceeded 60%, and γ-PGA content strongly correlated with antioxidant activity (R = 0.95). Conversely, total polyphenol content remained stable, indicating that γ-PGA and microbial metabolites contributed primarily to antioxidant activity. Further analysis supported this by showing that γ-PGA accounted for 37.40% of the total DPPH radical scavenging activity in the CH7P29-inoculated group at the end of fermentation. These data emphasize that B. velezensis CH7P29 is a promising functional starter culture for enhancing the biofunctional properties of Cheonggukjang and fermented soybean food. Full article

Background/Objectives: In laboratories using coagulation analyzers with different clot-detection principles and platform-specific reference intervals (RIs), numerical agreement does not necessarily ensure concordant clinical interpretation. We evaluated how analytical agreement and RI-based interpretation diverge between mechanical and optical coagulation analyzers. Methods: Paired duplicate measurements were performed on the STA R Max (Stago) and ACL TOP 750 LAS (Werfen) for prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), fibrinogen (FIB), international normalized ratio (INR), and antithrombin (AT). Numerical agreement was assessed by Passing–Bablok regression and log-ratio Bland–Altman analysis against predefined total allowable error (TEa) limits. Each result was standardized using its platform-specific RI, and the between-platform zlog difference (Δzlog) described differences in RI-relative position. Results: Stago yielded higher values than Werfen for PT, APTT, TT, and FIB, with mean biases of +15.6% to +20.4%, whereas INR and AT showed close agreement (−1.1% and +1.6%). Interpretive concordance among the primary assays ranged from 61.9% for APTT to 89.2% for FIB. The analytical-by-interpretive matrix identified specimens that were within TEa but classified differently and specimens that were beyond TEa but retained the same RI classification. Median Δzlog values were −0.43 for PT, +0.09 for APTT, −0.66 for TT, and +0.77 for FIB. Conclusions: Numerical and interpretive comparability are distinct dimensions. Combining the analytical-by-interpretive matrix with Δzlog can reveal clinically relevant divergence not apparent from bias-versus-TEa assessment alone. In this purposively selected dataset, divergence arose mainly from RI offset for TT and analytical dispersion for APTT; the sample-specific discordance rates are not population prevalence estimates. Full article

Low back pain (LBP) is one of the most common causes of medical consultation worldwide and is typically attributed to degenerative spinal disorders. However, a subset of patients presents with LBP related to extra-spinal conditions, including disorders of the lumbar plexus. These conditions are often underrecognized and may mimic lumbar radiculopathy, leading to delayed diagnosis or inappropriate management. The lumbar plexus, formed by the anterior rami of L1–L4 nerve roots, lies deep within the psoas muscle and can be affected by a wide range of pathological processes, including traumatic, compressive, inflammatory, infectious, and neoplastic conditions. A targeted literature search of the PubMed/MEDLINE database was performed to identify relevant English-language articles on lumbar plexus disorders, their imaging features, and diagnostic strategies published from inception to June 2026; representative imaging examples were selected from the co-authors’ clinical archives. The review summarizes the relevant anatomy, clinical presentation, and key imaging features of the most common plexus pathologies, including retroperitoneal tumors, psoas hematomas, inflammatory plexopathies, and iatrogenic injuries. Advances in imaging techniques, particularly magnetic resonance imaging (MRI) and magnetic resonance neurography (MRN), have significantly improved the ability to visualize the lumbar plexus and detect pathological changes involving peripheral nerves and surrounding structures. Additionally, a practical diagnostic imaging approach is proposed to assist radiologists and clinicians in recognizing lumbar plexus involvement in patients with atypical low back pain. Full article

Heme oxygenase-1 (HO-1), encoded by HMOX1, catalyzes the rate-limiting step of heme degradation and generates biliverdin, carbon monoxide, and ferrous iron, thereby linking heme turnover with redox regulation and stress-associated signaling. In birds, biliverdin is retained as a major heme-derived product, but the cellular consequences of HMOX1 perturbation remain insufficiently defined. Here, CRISPR/Cas9-mediated editing was used to generate a heterogeneous HMOX1-edited population in Chicken hepatocellular carcinoma-derived cells. The selected sgRNA reduced HO-1 protein abundance by approximately 47%, and no detectable cleavage was observed at the seven predicted high-risk off-target loci examined. Compared with vector-control cells, HMOX1-edited cells exhibited intracellular heme accumulation, reduced biliverdin levels, increased oxidation-sensitive fluorescence, and reduced CCK-8 absorbance values, indicating disruption of heme–biliverdin metabolic and redox homeostasis. RNA sequencing identified 2650 differentially expressed genes, including 951 upregulated and 1699 downregulated genes. Downregulated genes were mainly enriched in immune, cytokine, MAPK/stress, and extracellular signaling-associated pathways, whereas DNA replication and cell-cycle-related genes were increased. Enrichment-term association and STRING functional-association analyses further identified a coordinated module involving IL1B, JUN, NFKBIA, IRF1, TGFB1, IL10, CCL5, and PTGS2. Independent RT-qPCR analysis confirmed selected expression trends. These findings show that heterogeneous HMOX1 editing and reduced HO-1 abundance are associated with disruption of the avian heme–biliverdin metabolic axis and coordinated remodeling of basal immune, stress, extracellular signaling, and cell-cycle-associated transcriptional programs in Chicken hepatocellular carcinoma-derived cells. Full article

Open-pit mines contain rapidly changing terrain, discontinuous bench structures, and mixed artificial–natural objects, which complicate automated three-dimensional mapping. This study presents a dual-module workflow for UAV LiDAR point clouds. Module A characterizes local geometry using normal and curvature descriptors, constructs local plane support through RANSAC fitting, and detects candidate bench-line points using an angular-gap criterion, followed by regional grouping and Kalman-filter refinement. Qualitative overlay with the orthophoto showed coherent correspondence with principal platform–slope transitions. Module B segments buildings, roads, and vegetation using a PointNet++ network enhanced by local Transformer self-attention and inverted residual feature transformation. Under a fixed spatial hold-out setting, the network achieved an overall accuracy of 97.6% and a mean intersection over union of 96.4%. It obtained the highest overall accuracy, mean intersection over union, and class-wise intersection over union among the selected baselines, whereas Point Transformer achieved a slightly higher mean class accuracy. The two independently operated modules provide complementary structural and semantic information for open-pit mine mapping. Broader applicability requires reference-based bench-line assessment and evaluation across additional mines and survey periods. Full article

Alternaria leaf spot caused by Alternaria alternata affects Ocimum basilicum L. worldwide, yet its occurrence and impact in Serbia have remained unknown. This study identified the causal agent of leaf spot on O. basilicum cv. Genovese using molecular, morphological, and pathogenic analyses, and assessed the effects of disease and meteorological conditions on herb yield and morphometric traits. From 109 sampled plants in the preliminary survey (2021 and 2022) and field experiment (2023–2025), 59 isolates were obtained from symptomatic leaves, stems, branches, and seeds. Isolates were preliminarily identified based on the ITS rDNA region. Pathogenicity tests were done on detached leaves, stems, and seedlings. For selected A. alternata isolates, identification was confirmed based on additional regions (Alt a1, ATP, and CAL) and morphology (PDA, PCA, and V8 media), while pathogenicity was confirmed on whole plants. Disease incidence varied from 0% (2023) to 34.87% (2024) under warm and favourable moisture conditions. Significant reductions in plant height, plant weight, number of branches, and leaf size indicated the combined effects of disease and unfavourable meteorological conditions, while dry herb yield was reduced by up to 59.8%. Temperature and precipitation between harvests played a key role in disease development and yield loss. This study provides the first comprehensive molecular, morphological, and pathogenic characterization of A. alternata causing basil leaf spot in Serbia and demonstrates the importance of climate–disease interactions for basil production. These findings provide a basis for targeted disease monitoring, early detection, and climate-informed disease management strategies. Future studies should validate these findings in additional basil cultivars and production environments. Full article

Industrial Internet of Things (IIoT) environments are increasingly exposed to sophisticated cyberattacks, creating a growing need for intrusion detection systems (IDSs) that balance high accuracy with computational efficiency. Although existing studies primarily focus on classification performance, they often overlook deployment costs and model interpretability. To address these challenges, this study presents BOSE (Bayesian-Optimized Stacked Ensemble), an intrusion detection system that combines hybrid feature selection, Bayesian hyperparameter optimization, Out-of-Fold (OOF) stacking with leakage-free meta-feature generation, and SHAP-based explainability. Hybrid feature selection reduces the original feature space from 84 to 46 informative features, while Bayesian Optimization is used to determine the hyperparameter configuration of the ensemble models and leakage-free OOF stacking enables reliable meta-learning. Experimental results on the 50-class DataSense benchmark show that BOSE achieves a Macro-F1 score of 88.43% over ten independent runs, outperforming all directly comparable baseline models evaluated under the same end-to-end 50-class classification setting while remaining competitive with recent hierarchical multi-stage frameworks. In addition, the proposed model achieves an inference latency of 0.1421 ms, a throughput of 7035 packets/s, a runtime memory footprint of 19.11 MB, and a model size of 309.12 MB. These results demonstrate the CPU-based inference feasibility of the suggested framework under the evaluated workstation configuration and indicate its potential applicability to industrial edge gateways and industrial PCs. The proposed dual-level SHAP solution improves model transparency by explaining both feature-level contributions and ensemble-level decisions, making BOSE an accurate, computationally efficient, and interpretable solution for fine-grained IIoT intrusion detection. Full article

Azelaic acid (AZA) and salicylic acid (SA) are widely used active ingredients in pharmaceutical and cosmetic formulations for the treatment of acne, rosacea, hyperpigmentation, and other dermatological conditions. Despite their frequent co-administration, analytical methods for their simultaneous determination remain limited. In the present study, a capillary electrophoresis (CE) method with indirect UV detection was developed and validated for the simultaneous determination of AZA and SA in pharmaceutical and cosmetic preparations. Preliminary experiments demonstrated that direct UV detection was unsuitable because of the weak UV absorbance of AZA; therefore, indirect UV detection based on a sodium benzoate background electrolyte (BGE) was used. Following an initial one-factor-at-a-time (OFAT) screening, method optimization was performed using a face-centered central composite design (CCD) to evaluate the effects of BGE concentration, BGE pH, and separation voltage on the separation. The optimum separation was achieved using a 30 mM sodium benzoate BGE at pH 6.5 containing 5% (v/v) methanol, a separation voltage of +18 kV, a capillary temperature of 20 °C, and indirect UV detection at 230 nm. Baseline separation of both analytes was achieved within 5 min. The method was validated according to the ICH Q2(R2) guideline and demonstrated satisfactory accuracy, linearity, precision, selectivity, sensitivity, and robustness. The developed procedure was successfully applied to the analysis of commercial cosmetic formulations containing AZA, SA, or both active ingredients, providing assay results consistent with the declared contents. The proposed CE method provides a simple, rapid, cost-effective, and environmentally friendly alternative for the routine quality control of pharmaceutical and cosmetic formulations containing AZA and SA. Full article

Background: Mismatch repair deficiency (MMRd) is a central molecular determinant of colorectal cancer (CRC) biology, prognosis, and treatment response, and Universal Tumour Screening (UTS) is advocated for Lynch syndrome (LS) detection; yet real-world performance across clinical subgroups remains limited. We evaluated MMRd distribution and UTS-based LS detection in a large consecutive surgical cohort. Methods: We retrospectively analyzed 1022 consecutive CRC patients undergoing surgical resection at the University Hospital of Padua (2015–2023). MMR status was assessed by immunohistochemistry; MMRd cases underwent reflex BRAF mutation testing and, when available, MLH1 promoter methylation analysis, followed by germline multigene panel testing for suspected LS. Clinicopathological features were compared by MMR status, age at onset, and tumour location. Results: MMR testing was performed in 875 patients (85.6%), rising from 67.0% (2015–2017) to 97.4% (2021–2023). MMRd was identified in 139 tumors (15.9%) and was independently associated with age ≥ 70 years, colonic location, and stage 0–II. Of 22 patients with confirmed LS, 13 (59.1%) were newly identified through UTS; family history showed no significant univariate association with LS status and was not independently associated with MMRd after multivariable adjustment. MMRd prevalence was numerically higher in early- than late-onset CRC (20.0% vs. 15.4%), approaching significance after multivariable adjustment (OR 1.90, 95% CI 0.99–3.64; p = 0.054); hereditary syndromes were also more frequent in early-onset disease. MMRd was markedly rarer in rectal than colonic cancer (4.1% vs. 22.5%; p < 0.0001), though MMRd rectal cancers arose in younger patients. Conclusions: UTS identified a substantial proportion of LS carriers missed by age- or family-history criteria. The relationship between age and MMRd prevalence proved more nuanced than a simple comparison would suggest, reinforcing the value of universal over selective testing across the age spectrum, while MMRd rectal cancer shows a distinct younger profile relevant to immunotherapy-based organ preservation. Full article

Ground-penetrating radar (GPR) is a rapid and non-destructive technique for road sub-surface distress (RSD) detection. However, reliable interpretation of GPR images remains challenging because subsurface anomalies often present weak boundaries, ambiguous textures, and high similarity to non-distress targets. This study proposes a cross-verification intelligent algorithm that exploits complementary information from different views of 3D GPR data. Three YOLO-based detectors are trained on view-specific GPR images to identify RSD-related targets, including voids, loose structures, and manholes. By sequentially verifying detection results across different views, the proposed method improves recognition reliability under vague subsurface imaging conditions. The models are trained and evaluated on an expert-annotated field 3D GPR dataset containing 2134 location-level multi-view samples. At the selected operational thresholds, the complete cross-verification procedure achieved 95.9% precision and 98.6% recall for RSD detection in the testing subset. In a field evaluation on 15 roads, all 69 RSD locations in the expert-identified reference set were matched by automatic indications. When integrated into an automatic detection system, the method reduced manual inspection workloads by approximately 90% while maintaining high field reliability. These results demonstrate the potential of multi-view cross-verification for post-survey RSD screening and expert-assisted review. Full article

Background and Objectives: Arterial hypertension is a major cause of systemic microvascular damage involving target organs such as the kidneys and retina. Because the retinal and renal microcirculations share common pathogenic mechanisms, retinal imaging may provide non-invasive biomarkers of hypertensive microvascular injury. This study investigated the association between the renal resistive index (RRI), systemic oxidative stress biomarkers, and retinal abnormalities in patients with essential hypertension. Retinal structural and microvascular parameters were evaluated using optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA). Materials and Methods: In this cross-sectional exploratory study, 32 patients with essential hypertension (64 eyes) underwent comprehensive ophthalmic examination, OCT/OCTA imaging, renal Doppler ultrasonography for RRI assessment, and evaluation of systemic oxidative stress biomarkers. Associations between renal, ocular, and biochemical parameters were analyzed. Results: No significant differences were observed in most global OCT/OCTA parameters. However, higher RRI values were associated with localized macular thinning and reduced optic nerve head perfusion metrics. Patients with RRI values ≥ the 75th percentile showed significantly increased hydrogen peroxide levels (p = 0.004) and reduced nitric oxide bioavailability (p = 0.033), together with thinning of selected inner macular sectors. A trend toward reduced optic nerve head flow index was also observed, suggesting early microvascular impairment that may not be detected by conventional global OCT measurements. Conclusions: These exploratory findings suggest a possible association among increased renal vascular resistance, oxidative stress, and localized retinal structural alterations in patients with essential hypertension. The coexistence of systemic redox imbalance and sectorial retinal changes supports the hypothesis of an eye–kidney–redox interplay and indicates that advanced retinal imaging, particularly detailed sectorial OCT analysis, may represent a complementary non-invasive tool for the early detection of subclinical microvascular damage. Larger prospective studies are warranted to validate these findings. Full article

Rock fissure parameters, such as length, width, and density, are essential for analyzing the progressive instability of open-pit mine slopes. Under the combined effects of engineering disturbance, geological conditions, and environmental factors, slope fissures continuously propagate and evolve. However, large variations in fissure scale, complex rock-surface textures, blurred boundaries, and weak micro-fissure features increase the difficulty of intelligent fissure segmentation, identification, and parameter extraction. Consequently, many mining enterprises still rely on manual interpretation, which is time-consuming and susceptible to subjective errors. To address these challenges, this study develops Crack-YOLO, a task-oriented fissure detection and instance-segmentation model based on YOLOv8-Seg. A total of 500 original UAV images were collected from multiple open-pit mines and processed to construct a dataset containing 3600 fissure image patches, including 3240 images for training and 360 images for testing. In Crack-YOLO, selected C2f modules are replaced with contextual semantic enhancement modules (CoT Blocks), and a texture information enhancement module (SM Block) is incorporated to strengthen contextual semantic representation and fine-grained texture-feature extraction. The model achieved segmentation precision, recall, mAP50, and mAP50:95 values of 0.896, 0.787, 0.854, and 0.392, respectively. For object detection, the corresponding values were 0.968, 0.862, 0.959, and 0.773, respectively. The segmentation results were further processed using K3M skeleton extraction and physical-scale calibration to quantitatively extract geometric parameters, including fissure length, equivalent average width, and azimuth. Validation using an image containing seven representative fissures yielded mean absolute errors of 0.016 m, 0.010 m, and 0.90° for fissure length, equivalent average width, and azimuth, respectively, indicating the feasibility of the proposed parameter-quantification workflow. In an application test conducted in a typical open-pit mine scene, the proposed workflow identified 196 fissures within approximately 22 s and quantitatively analyzed their geometric parameters and distribution characteristics. The results indicate that the proposed method has potential for fissure identification and geometric-parameter quantification in open-pit mine slopes and may provide quantitative data support for slope-fissure monitoring and stability analysis. Full article

Motion perception in biological vision is influenced by experience-dependent processes, during which direction-selective responses emerge and are further refined over time, although the underlying mechanisms remain partially understood. This study proposes a biologically inspired Hierarchical Agglomerative Clustering (HAC)-based unsupervised artificial visual system (AVS) for motion-direction detection. The model consists of a local motion-processing layer and a global motion inference layer. At the local level, motion-direction responses are extracted using retina-inspired local motion detection neurons (LMDN) to capture pixel-level spatiotemporal variation. These local direction responses are clustered using prototype-aware HAC to infer the global motion direction at the global level. Additionally, an unsupervised learning mechanism inspired by the concepts of neural plasticity and critical periods has been established. Extensive computer simulations are conducted under varying noise conditions and object scales. The results indicate that the proposed AVS maintained robust global motion-direction detection accuracy across diverse test conditions and exhibited biologically interpretable features resembling selected features of biological visual systems. In conclusion, this HAC-based unsupervised AVS provides a computational model for studying the formation of motion representations in visual systems and supports the development of artificial vision systems. Full article