Search Results (6,307)

Grape stems are undervalued winemaking by-products that constitute a promising source of bioactive phenolics with notable antioxidant potential and diverse industrial applications, including food preservation, cosmetics, and pharmaceuticals. Effective valorisation of this resource requires not only efficient extraction strategies, but also the strategic selection of grape stem varieties to tailor phenolic profiles for specific high-value uses. In this study, a comparative assessment of three extraction techniques, pressurized liquid extraction (PLE), ultrasound-assisted extraction (UAE), and conventional solid–liquid extraction (SLE), across six grape stem varieties was conducted. By integrating spectrophotometric analyses of total phenolics and antioxidant capacity with HPLC-DAD profiling of individual phenolic compounds, the combined influence of extraction method and varietal composition on phenolic recovery was demonstrated. PLE and UAE significantly enhanced both yield and antioxidant capacity relative to SLE, with PLE providing the broadest spectrum of phenolic compounds. Varietal differences were also pronounced; e.g., Cabernet Sauvignon stems yielded higher antioxidant phenolic compound content, particularly under UAE, reinforcing the importance of aligning extraction technique and stem variety with the intended functional application. Full article

Rebalancing shared bikes poses a significant challenge for dockless bike-sharing (DLBS) operators, as inevitable spatiotemporal mismatches between demand and supply lead to high redistribution costs. Despite its operational significance, empirical research on the spatiotemporal imbalance of DLBS usage and its underlying drivers remain limited. Utilizing one month’s extensive trajectories of shared bikes in Shanghai, China, this study quantifies DLBS net flows at fine-grained grid level by hour to capture demand–supply imbalances across both spatial and temporal dimensions. To uncover dominant patterns in DLBS imbalance, we employ non-negative matrix factorization, a matrix decomposition technique, to extract latent structure of DLBS net flows. Four distinct patterns are identified: self-sustained balance, morning peak outflow, morning peak inflow, and metro-driven imbalance. We further apply multinomial logit models (MNL) to examine how these patterns are associated with different built environment characteristics. The results show that higher population density, greater diversity of points of interest, and proximity to city centers promote more balanced DLBS flows, whereas high road network density and concentrations of subway stations, residential communities, and firms intensify imbalances. These findings provide valuable insights for enhancing the operational efficiency of DLBS systems and supporting informed transportation management and urban planning practices. Full article

The abundant satellite data have enabled the study of the dynamics of forest logging and its corresponding carbon balance with remote sensing. Change detection techniques with moderate-resolution imagery have been widely developed. Yet the signal processing or machine learning methods are sample-dependent, lacking an understanding of spectral signals of forest growth and logging cycles, which is necessary to distinguish logging from other types of disturbance, and mechanism models addressing post-logging tree changes are too complex for parameter inversion. We therefore proposed an efficient physical-based model for spectral simulation of annual forest logging by coupling forest dynamic model ZELIG and the stochastic radiative transfer (SRT) model. The forest logging simulation was conducted and validated by Abies forest field data before and after logging in Wangqing County, Northeastern China (R² = 0.85, RMSE = 10.82 t/ha). The spectral changes in Abies forest stands with annual growth and varying logging intensities were simulated by the novel model. The annual Landsat-8 and Gaofen-1 fusion multispectral imagery of the study area from 2013 to 2016 was furtherly used to extract annual sequence spectral data of 350 forest plots and perform inversion of the annual difference in above-ground biomass (dAGB). With the inversion method combining the look-up table of the ZELIG-SRT model and the random forest regression, the retrieved dAGB of the 350 plots indicated consistency with the measured data on the whole (R² = 0.71, RMSE = 13.32 t/ha). The novel physical-based approach for AGB monitoring is more efficient than previous 3D computer models and less dependent on field samples than data-driven models. This study provides a theoretical basis for understanding the remote sensing response mechanism of forest logging and a methodological basis for improving forest logging monitoring algorithms. Full article

Thin-layer chromatography (TLC) remains a widely used, cost-effective and convenient method to separate small molecules, particularly in the field of natural products and (phospho)lipids. Despite advances in chromatographic methods such as high-performance liquid chromatography (HPLC), TLC retains several advantages, including simplicity and accessibility. However, a critical step is the visualization of the separated lipids on the TLC plate. Although the majority of the regularly used methods were established decades ago, there are still a number of potential pitfalls and widely unknown aspects. This review provides a concise overview about commonly used stationary phases and the solvent systems in TLC analysis of lipids. The main focus is on visualization techniques, spanning from non-specific, destructive (charring by semi-concentrated acids) to specific, non-destructive approaches (e.g., exposition to iodine to monitor unsaturated lipids). The advantages and disadvantages of the different methods will be critically discussed and frequently occurring problems highlighted. Furthermore, the combination of TLC with mass spectrometry (MS) detection will be introduced, covering both extraction-based electrospray ionization MS techniques as well as desorption techniques such as matrix-assisted laser desorption/ionization MS. MS detection, while generally more sensitive and offering molecular specificity, introduces higher technical and financial requirements compared to conventional staining. Nonetheless, the combination of TLC with MS holds significant potential for enhancing lipidomic workflows, particularly in complex biological samples. Full article

Face recognition has become a major research area due to the rapid growth of intelligent software applications. However, reliable face identification remains challenging because human facial features vary significantly under different conditions. Originating from pattern recognition, image processing, and computer vision, modern face recognition continues to advance through new algorithms and learning-based approaches. This paper describes and analyzes the existing literature regarding facial recognition and surveillance systems. It describes and explains the principles underlying facial recognition and surveillance in a general sense and analyzes the most significant application domains. Furthermore, it describes and analyzes the most relevant and widely used benchmark datasets that can be used to measure the recognition and surveillance performance of such systems. We also discuss and analyze the most relevant and significant issues related to existing systems and datasets. Two primary feature extraction categories are discussed in detail, followed by a comparison of appearance-based, model-based, and hybrid methods. Important components such as feature selection, distance measures, classification techniques, and evaluation protocols are also reviewed. Finally, the review summarizes current challenges and emerging research trends, offering insights into future directions for developing more accurate, robust, and practical face recognition systems. Full article

Accurate and efficient defect detection in printed circuit boards (PCBs) is critical for manufacturing quality control. Existing methods predominantly rely on manually extracted features such as surface texture, color, and shape for defect recognition and classification within small-dimensional feature datasets. A convolutional neural network (CNN) model was developed via transfer learning. Feature extraction involves diverse operations across different CNN layers. Essential features were selected, and dimensionality was reduced via either t-distributed stochastic neighbor embedding (t-SNE) or principal component analysis (PCA). Defect classification was subsequently performed by clustering the reduced features with either the K-means or K-nearest neighbors (KNN) algorithm. Compared with alternative model feature learning classifiers, the proposed small-dimensional CNN model performs significantly better. A defect recognition accuracy of 97.33% was achieved, with processing completed in approximately 60 s. This approach, which integrates transfer learning-based CNN feature extraction with dimensionality reduction and clustering techniques, provides a fast and effective method for high-precision defect detection and classification in PCBs. Full article

Traditional surgical techniques are based on the manual application of force using mallets and osteotomes, which often result in uncontrolled impact forces, procedural inconsistencies, and patient discomfort. Magnetic mallets (MMs), magnetodynamic devices, provide a controlled application of force using electromagnetism, aiming to achieve greater precision, reduced operating time, and improved surgical outcomes. The aim of the present systematic review was to evaluate the effectiveness of MMs compared to conventional surgical techniques in oral and implant surgery. The focused question was as follows: “Do magnetic mallets improve clinical outcomes in oral and implant surgery compared to traditional instruments?” Only clinical studies comparing the use of MMs with traditional techniques in oral surgery were included. The following databases were searched up to 27 November 2025: Pubmed, Scopus, Web of Science. For quality assessment, the Cochrane Risk of Bias 2 (RoB 2) tool was applied for randomized controlled trials (RCTs), while the Newcastle–Ottawa Scale (NOS) was used for non-randomized studies. Data were screened and synthesized by two reviewers. The systematic review was conducted based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement. In total, 347 studies were initially found and 6 matched the inclusion criteria and were included in the review, for a total of 282 patients. Five RCTs were included, as well as one retrospective study. The studies investigated were as follows: implant site preparation (two studies with a total of 86 patients), sinus lift and contextual implant insertion (three studies, total: 102 patients), dental extraction (two studies, total: 70 patients), and split-crest (one study with 46 patients). The outcomes suggest that MMs may serve as a potential alternative to traditional techniques, exhibiting promising although preliminary outcomes. The studies included reported a lower incidence of benign paroxysmal positional vertigo with the use of MMs compared to hand osteotomes. Regarding quality assessment, RCTs raised some concerns, while the retrospective study had a moderate risk of bias. Despite the promising results, the paucity of high-quality controlled trials limits definitive conclusions on the superiority of MM over conventional techniques. Further well-designed comparative trials are needed to confirm the clinical benefits, optimize protocols across different indications, and evaluate MMs’ potential role in the management of critical bone conditions and complex surgery. Full article

Background/Objectives: The ¹⁰³Pd/^103mRh in vivo generator represents a promising Auger electron-emitting system, in which both parent and daughter radionuclides emit predominantly Auger electrons with minimal accompanying radiation. This study investigates the release dynamics of daughter radionuclides from the ¹⁰³Pd/^103mRh in vivo generator and evaluates the underlying mechanisms governing bond rupture and daughter retention. Methods: Cyclotron irradiation of rhodium foils was performed in two separate batches, followed by radionuclide separation using conventional wet chemistry and a novel dry distillation technique. The purified ¹⁰³Pd radionuclide was used to radiolabel DOTA-TATE, phthalocyanine-TATE, and DOTA-TOC chelators. The resulting complexes were immobilized on Strata-X and Strata-C18 solid-phase extraction columns. Scheduled elution experiments were conducted to quantify the release of the ^103mRh daughter radionuclide. Results: The measured ^103mRh release rates were 9.8 ± 3.0% and 9.6 ± 2.7% from Strata-X columns with DOTA-TATE and phthalocyanine-TATE, respectively, and 10.5 ± 2.7% and 12.0 ± 0.5% from Strata-X and Strata-C18 columns, respectively, with DOTA-TOC. These values are significantly lower than the ~100% release predicted based on the reported Auger electron yield of 186%. One explanation for this difference could be potential inconsistencies in decay data that may require correction; this needs further investigation. The results further demonstrated that delocalized π-electrons, introduced via phthalocyanine-based chelation, did not mitigate daughter release. Conclusions: The low observed daughter nuclide release represents a favorable characteristic for the future clinical translation of the ¹⁰³Pd/^103mRh Auger emitter pair. The findings support the conclusion that Auger electron cascades, rather than nuclear recoil energy, dominate bond rupture processes. Full article

Sulfidized nano zero-valent iron (S-nZVI) particles are known to stimulate the reductive removal of various oxyanions due to enhanced electron selectivity and electron conductivity between the Fe(0) core and the target compound. Sulfidation creates a number of reactive sulfur species, the role of which has not yet been investigated in the context of S-nZVI. In this study, we investigated the contribution of reactive sulfur species to Se(VI) reduction by S-nZVI at different molar S/Fe ratios (0, 0.1 and 0.6) and Se(VI) concentrations (0, 5 and 50 mg L⁻¹). In the presence of S-nZVI, the rate of reduction was accelerated by a factor of up to ten. X-ray Absorption Near-Edge Structure (XANES) spectroscopy and surface-sensitive X-ray photoelectron spectroscopy (XPS) identified Se(0) as the predominant reduction product (~90%). The reduction reaction was accompanied by a loss of FeS and the formation of surface-bound Fe(II) polysulfide (FeSx) and S(0) species. Likewise, wet chemical extraction techniques suggested a direct involvement of acid volatile sulfide (AVS) species (surface-bound FeS) in the reduction of Se(IV) to Se(0) and formation of S(0). Mass balance estimates reveal that between 9 and 15% of the conversion of Se(0) originates from oxidation of FeS to FeSx. From these findings, we propose that surface-bound Fe sulfide species are important but previously overlooked reactants contributing to the reduction of oxyanions associated with S-nZVI particles, as well as in natural environments undergoing sulfidation reactions. Full article

Predicting internal quality parameters, such as Brix and water content, of apples, is essential for quality control. Existing near-infrared (NIR) and hyperspectral imaging (HSI)-based techniques have limited applicability due to their dependence on equipment and environmental sensitivity. In this study, a transportable quality assessment system was proposed using spatiotemporal domain analysis with long-wave infrared (LWIR)-based thermal diffusion phenomics, enabling non-destructive prediction of the internal Brix of apples during transport. After cooling, the thermal gradient of the apple surface during the cooling-to-equilibrium interval was extracted. This gradient was used as an input variable for multiple linear regression, Ridge, and Lasso models, and the prediction performance was assessed. Overall, 492 specimens of 5 cultivars of apple (Hongro, Arisoo, Sinano Gold, Stored Fuji, and Fuji) were included in the experiment. The thermal diffusion response of each specimen was imaged at a sampling frequency of 8.9 Hz using LWIR-based thermal imaging, and the temperature changes over time were compared. In cross-validation of the integrated model for all cultivars, the coefficient of determination (R²_cv) was 0.80, and the RMSEcv was 0.86 °Brix, demonstrating stable prediction accuracy within ±1 °Brix. In terms of cultivar, Arisoo (Cultivar 2) and Fuji (Cultivar 5) showed high prediction reliability (R²_cv = 0.74–0.77), while Hongro (Cultivar 1) and Stored Fuji (Cultivar 4) showed relatively weak correlations. This is thought to be due to differences in thermal diffusion characteristics between cultivars, depending on their tissue density and water content. The LWIR-based thermal diffusion analysis presented in this study is less sensitive to changes in reflectance and illuminance compared to conventional NIR and visible light spectrophotometry, as it enables real-time measurements during transport without requiring a separate light source. Surface heat distribution phenomics due to external heat sources serves as an index that proximally reflects changes in the internal Brix of apples. Later, this could be developed into a reliable commercial screening system to obtain extensive data accounting for diversity between cultivars and to elucidate the effects of interference using external environmental factors. Full article

Urban surface subsidence is primarily induced by intensive above-ground and underground construction activities and excessive groundwater extraction. Integrating InSAR techniques for safety monitoring of urban subway infrastructure is therefore of great significance for urban safety and sustainable development. However, single-track high-spatial-resolution SAR imagery is insufficient to achieve full coverage over large urban areas, and direct mosaicking of inter-track InSAR results may introduce systematic biases, thereby compromising the continuity and consistency of deformation fields at the regional scale. To address this issue, this study proposes an inter-track InSAR correction and mosaicking approach based on the mean vertical deformation difference within overlapping areas, aiming to mitigate the overall offset between deformation results derived from different tracks and to construct a spatially continuous urban surface deformation field. Based on the fused deformation results, subsidence characteristics along subway lines and in key urban infrastructures were further analyzed. The main urban area and the eastern and western new districts of Zhengzhou, a national central city in China, were selected as the study area. A total of 16 Radarsat-2 SAR scenes acquired from two tracks during 2022–2024, with a spatial resolution of 3 m, were processed using the SBAS-InSAR technique to retrieve surface deformation. The results indicate that the mean deformation rate difference in the overlapping areas between the two SAR tracks is approximately −5.54 mm/a. After applying the difference-constrained correction, the coefficient of determination (R²) between the mosaicked InSAR results and leveling observations increased to 0.739, while the MAE and RMSE decreased to 4.706 and 5.538 mm, respectively, demonstrating good stability in achieving inter-track consistency and continuous regional deformation representation. Analysis of the corrected InSAR results reveals that, during 2022–2024, areas exhibiting uplift and subsidence trends accounted for 37.6% and 62.4% of the study area, respectively, while the proportions of cumulative subsidence and uplift areas were 66.45% and 33.55%. In the main urban area, surface deformation rates are generally stable and predominantly within ±5 mm/a, whereas subsidence rates in the eastern new district are significantly higher than those in the main urban area and the western new district. Along subway lines, deformation rates are mainly within ±5 mm/a, with relatively larger deformation observed only in localized sections of the eastern segment of Line 1. Further analysis of typical zones along the subway corridors shows that densely built areas in the western part of the main urban area remain relatively stable, while building-concentrated areas in the eastern region exhibit a persistent relative subsidence trend. Overall, the results demonstrate that the proposed inter-track InSAR mosaicking method based on the mean deformation difference in overlapping areas can effectively support subsidence monitoring and spatial pattern identification along urban subway lines and key regions under relative calibration conditions, providing reliable remote sensing information for refined urban management and infrastructure risk assessment. Full article

This research explores the use of physiological signals derived from heart activity to assess mental effort during flight-related tasks. Data were collected through wearable sensors during simulations with varying cognitive demands. Specific indicators related to heart rate variability (HRV) were extracted and tested in different combinations to identify those most relevant for distinguishing levels of mental workload (WL). A Random Forest (RF) ensemble method is applied to classify two conditions, and its performance is examined under various settings, including model complexity and data partitioning strategies. Results showed that certain feature pairs significantly enhanced classification accuracy. The best features settings obtained from the RF are then used to train the other two decision trees-based classifiers, namely the AdaBoost and the XGBoost. Moreover, the decision trees models output is compared with predictions from a Kriging spatial interpolation technique, showing superior results in terms of reliability and consistency. This study highlights the potential of using heart-based physiological data and advanced classification techniques for developing intelligent support systems in aviation. Full article

The proliferation of Large Language Models (LLMs) has transformed natural language processing (NLP) applications across diverse domains. This paper presents a comprehensive comparative analysis of three state-of-the-art language models—GPT-4o, Claude-3, and Julius AI—evaluating their performance across systematic NLP tasks using standardized datasets and evaluation frameworks. We introduce a reusable evaluation methodology incorporating five distinct prompt engineering techniques (Prefix, Cloze, Anticipatory, Heuristic, and Chain of Thought) applied to three categories of linguistic challenges: data extraction, aggregation, and contextual reasoning. Using a bibliometric analysis use case as our evaluation domain, we demonstrate the framework’s application to structured data processing tasks common in academic research, business intelligence, and data analytics applications. Our experimental design utilized a curated Scopus bibliographic dataset containing 3212 academic publications to ensure reproducible and objective comparisons, representing structured data processing tasks. The results demonstrated significant performance variations across models and tasks, with GPT-4o achieving 89.3% average accuracy, Julius AI reaching 85.7%, and Claude-3 demonstrating 72.1%. The results demonstrated significant performance variations across models and tasks, with Claude-3 showing notably high prompt sensitivity (consistency score: 74.3%, compared with GPT-4o: 91.2% and Julius AI: 86.7%). This study revealed critical insights into prompt sensitivity, contextual understanding limitations, and the effectiveness of different prompting strategies for specific task categories. Statistical analysis using repeated measures ANOVA and pairwise t-tests with Bonferroni’s correction confirmed significant differences between models (F(2, 132) = 142.3, p < 0.001), with effect sizes ranging from 0.51 to 1.33. Response time analysis showed task-dependent latency patterns: for data extraction tasks, Claude-3 averaged 1.9 s (fastest), GPT-4o 2.1 s, and Julius AI 2.8 s; however, for contextual reasoning tasks, latency increased as follows for Claude-3: 3.8 s, GPT-4o: 4.5 s, and Julius AI: 5.8 s. Overall averages were as follows for GPT-4o: 3.2 s, Julius AI: 4.1 s, and Claude-3: 2.8 s. While specific performance metrics reflect current model versions (GPT-4o: gpt-4o-2024-05-13; Claude-3 Opus: 20240229; Julius AI: v2.1.4), the evaluation framework provides a reusable methodology for ongoing LLM assessment as new versions emerge. These findings provide practical guidance for researchers and practitioners in selecting appropriate LLMs for domain-specific applications and highlight areas requiring further development in language model capabilities. While demonstrated on bibliometric data, this evaluation framework is generalizable to other structured data processing domains. Full article

Aiming to address the core issue that the current intelligence evaluation for Unmanned Ground Vehicles (UGVs) overly rely on static performance metrics and lack dynamic quantitative characterization of learning internalization capability (LIC), this study proposes a dynamic evaluation framework based on time series analysis. The framework begins by constructing a multidimensional test scenario parameter system and collecting externally observable performance sequence data. It then introduces a sliding window-based slope-standard deviation collaborative analysis technique to achieve unsupervised division of learning phases, from which five core evaluation metrics are extracted to comprehensively quantify the multidimensional dynamic characteristics of LIC in terms of efficiency, stability, and overall effectiveness. Simulation experiments were carried out using UGVs equipped with three types of path-planning algorithms in low-, medium-, and high-difficulty scenarios. Results demonstrate that the proposed algorithm can effectively distinguish multi-dimensional differences in LIC among different UGVs, exhibiting strong discriminative power and interpretability. This study provides a standardized evaluation tool for UGV intelligent selection, algorithm iteration optimization, and training strategy design, and offering significant reference value for the evaluation of the learnability of autonomous driving systems. Full article

Background: Radiomics can provide quantitative descriptors of tumor phenotype, but translation is often limited by feature instability across scanners and protocols. We aimed to develop and internally validate a protocol-specific CT-radiomics model using preoperative imaging to predict 5-year recurrence in patients with stage I lung adenocarcinoma after complete surgical resection. Methods: The retrospective study included 270 patients with completely resected stage I lung adenocarcinoma from January 2010–December 2021, among whom 23 (8.5%) experienced recurrence within five years. Radiomic features were extracted from routine preoperative CT scans. After preprocessing to remove highly constant and highly correlated features, the Synthetic Minority Over-sampling Technique addressed class imbalance in the training set. Recursive Feature Elimination identified the most predictive radiomic features. An XGBoost classifier was trained using optimized hyperparameters identified through RandomizedSearchCV with cross-validation. Model performance was evaluated using the ROC curve and predictive metrics. Results: Five radiomic features differed significantly between recurrence groups (p = 0.007 to <0.001): Shape Sphericity, first-order 90Percentile, GLCM Autocorrelation, GLCM Cluster Shade, and GLDM Large Dependence Low Gray Level Emphasis. The radiomics model showed excellent discriminatory ability with AUC values of 0.99 (95% CI: 0.98–1.00), 0.97 (95% CI: 0.91–1.00), and 0.96 (95% CI: 0.85–1.00) on the training, validation, and test sets, respectively. On the test set, the model achieved sensitivity of 100% (95% CI: 51–100%), specificity of 94% (95% CI: 81–98%), PPV of 67% (95% CI: 30–90%), NPV of 100% (95% CI: 90–100%), and overall accuracy of 95% (95% CI: 83–99%). Conclusions: Under protocol-homogeneous imaging conditions, CT radiomics accurately predicted recurrence in patients with completely resected stage I lung adenocarcinoma. External multi-vendor validation is needed before broader deployment. Full article