Search Results (19,250)

The phenomenon of stepwise landslides, characterized by displacement exhibiting a step-like pattern, is often influenced by reservoir operations and seasonal rainfall. Traditional early warning models face challenges in accurately predicting the sudden initiation and cessation of displacement, primarily because conventional indicators such as rate or acceleration are ineffective in these scenarios. This underscores the urgent need for innovative early warning models and indicators. Viewing step-like displacement through the lens of three phases—stop, start, and acceleration—aligns with the green-yellow-red warning paradigm of the Traffic Light System (TLS). This study introduces a novel early warning model based on the TLS, incorporating jerk, the derivative of displacement acceleration, as a critical indicator. Empirical data and theoretical analysis validate jerk’s significance, demonstrating its clear pattern before and after step-like deformations and its temporal alignment with the deformation’s conclusion. A comprehensive threshold network encompassing rate, acceleration, and jerk is established for the TLS. The model’s application to the Shuiwenzhan landslide case illustrates its capability to signal in a timely manner the onset and acceleration of step-like deformations with yellow and red lights, respectively. It also uniquely determines the deformation’s end through jerk differential analysis, which is a feat seldom achieved by previous models. Furthermore, leveraging the C5.0 machine learning algorithm, a comparison between the predictive capabilities of the TLS model and a pure rate threshold model reveals that the TLS model achieves a 93% accuracy rate, outperforming the latter by 7 percentage points. Additionally, in response to the shortcomings of existing warning and emergency response strategies for this landslide, a closed-loop management framework is proposed, grounded in the TLS. This framework encompasses four critical stages: hazard monitoring, warning issuance, emergency response, and post-event analysis. We also suggest support measures to ensure implementation of the framework. Full article

Background: Hepatocyte ballooning (HB) is a significant histological characteristic linked to the advancement of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). Although clinicians now consider liver biopsy the most reliable method for identifying HB, its invasive nature and related dangers highlight the need for the development of non-invasive diagnostic options. Objective: This study aims to develop a novel methodology that combines deep learning and machine learning techniques to accurately identify and measure hepatobiliary abnormalities in liver ultrasound images. Methods: The research team expanded the dataset, consisting of ultrasound images, and used it for training deep convolutional neural networks (CNNs) such as InceptionV3, ResNet50, DenseNet121, and EfficientNetB0. A hybrid approach, combining InceptionV3 for feature extraction with a Random Forest classifier, emerged as the most accurate and stable method. An approach of dual dichotomy classification was used to categorize images into two stages: healthy vs. sick, and then mild versus severe ballooning.. Features obtained from CNNs were integrated with conventional machine learning classifiers like Random Forest and Support Vector Machines (SVM). Results: The hybrid approach achieved an accuracy of 97.40%, an area under the curve (AUC) of 0.99, and a sensitivity of 99% for the ‘Many’ class during the third phase of evaluation. The dual dichotomy classification enhanced the sensitivity in identifying severe instances of HB. The cross-validation process confirmed the strength and reliability of the suggested models. Conclusions: These results indicate that this combination method can decrease the need for invasive liver biopsies by providing a non-invasive and precise alternative for early identification and monitoring of NAFLD and NASH. Subsequent research will prioritize the validation of these models using larger datasets from multiple centers to evaluate their generalizability and incorporation into clinical practice. Full article

Scientific evaluation of pear maturity is important for commercial reasons. Near-infrared spectroscopy is a non-destructive method that could be used for rapid assessment of pear maturity. The aim of this study was to develop a reasonable and effective method for the assessment of Starkrimson pear maturity using near-infrared technology. Partial least squares regression and five classification methods were used for analysis of the data. Among the indices used with the competitive adaptive reweighting–partial least squares regression method for quantitation, the visual ripeness index had the best modeling effect (Rp2: 0.87; root mean square error of prediction: 0.39). The classification model constructed with the visual ripeness index and post-ripeness score gave a cross-validation neural network model with the best classification effect and the highest accuracy (classification accuracy: 88.7%). The results showed that combination of quality indices with near-infrared spectroscopy was effective for rapidly evaluating the maturity of Starkrimson pears. Full article

Accurate assessment of the aging state of transformer oil-barrier insulation is crucial for ensuring the safe and reliable operation of power systems. This study presents the development of indoor accelerated thermal aging experiments to simulate the degradation of oil-immersed barrier insulation within transformers. A series of samples reflecting various aging states was obtained and categorized into six distinct groups. Raman spectroscopy analytical technology was employed to characterize the information indicative of different aging states of the oil-immersed barrier insulation. The raw Raman spectra were processed using asymmetric reweighted penalty least squares to correct baseline shifts, Savitzky–Golay (S-G) smoothing to eliminate fluctuation noise, and principal component analysis (PCA) to reduce data dimensionality by extracting principal components. A support vector machine (SVM) classifier was developed to discriminate between the Raman spectra and category labels. The SVM parameters were optimized using grid search, particle swarm optimization (PSO), and genetic algorithm (GA), yielding the optimal parameters (C and gamma). Notably, the grid search method demonstrated high efficiency in identifying the best combination of SVM parameters (c and g). Comparative analyses with varying numbers of principal components in SVM classifiers revealed that incorporating an optimal subset of PCA features achieved the highest classification accuracy of 94.44% for external validation samples, with only eight samples being misclassified into adjacent categories. This study offers technical support and a theoretical foundation for the effective assessment of the aging state of oil-barrier type insulation in transformers, contributing to the advancement of condition monitoring and maintenance strategies in power systems. Full article

Soil tillage is essential for improving soil structure, enhancing fertility, promoting crop growth, and increasing yield. However, precise and efficient standardized methods for quantitatively evaluating post-tillage soil structure are still absent. This study aims to develop a general quantitative evaluation method for post-tillage soil structure using close-range photogrammetry. Six soil surface sample plots of different scales were selected, and two image acquisition methods and three platforms were chosen for image capture and 3D reconstruction. Geomagic Wrap was used for post-processing the models, with indicators such as clod sizes, surface flatness, and cumulative percentage used for quantitative description. Model accuracy was validated using traditional needle plate and vernier caliper measurements. The most effective combinations of image acquisition methods and 3D reconstruction platforms were identified based on modeling efficiency and quality. The results showed that combining image acquisition, 3D reconstruction platforms, and post-processing software enables high-precision 3D reconstruction and accurate digital information retrieval. Image Acquisition Method One and the AgisoftMetashape platform demonstrated the best combination in terms of model completeness, texture detail, and overall quality. This combination is recommended for the 3D reconstruction and digital information retrieval of soil surfaces. This study provides a method for evaluating post-tillage soil structure, including image acquisition, 3D reconstruction, model post-processing, and quantitative metrics. Full article

The fluid–structure interaction effect should not be disregarded when examining the vibration characteristics of hydraulic pipeline systems. The transfer matrix method (TMM) is an efficacious method for analyzing the vibration characteristics of hydraulic pipelines in the frequency domain, offering advantages such as simplicity and efficiency. However, the TMM suffers the problem of high frequency instability when dealing with long-span hydraulic pipelines, which restricts its practical application. Currently, several modified transfer matrix methods face challenges such as low computational efficiency and difficulties in handling complex boundaries. In response to these issues, this paper proposes a novel modified transfer matrix method known as the mixed variable transfer matrix method. This innovative method possesses clear physical significance and effectively prevents the transfer matrix from becoming singular without necessitating the subdivision of the pipeline length. Consequently, it addresses high-frequency instability while maintaining high computational efficiency. Moreover, this method is capable of addressing complex boundary problems by integrating boundary matrices, thereby demonstrating enhanced applicability compared to existing methods. The performance of the proposed method was validated through the utilization of classic Dubee pipeline impact test data, and the result shows maximum errors of 3.03% relative to the public data. Subsequently, an experiment was conducted on a section of hydraulic piping within a ship’s steering system. A hydraulic fluid noise generator was established to induce fluid pulsation excitation to the pipeline, thereby simulating the actual boundary conditions encountered in a ship’s hydraulic pipeline system so as to corroborate the efficacy of the proposed method in predicting the frequency domain vibration characteristics of a real hydraulic pipeline system. The experimental results indicate that the proposed method offers significant advantages in terms of high precision, efficiency, and stability, shows maximum errors of 4.35% relative to experimental data, and demonstrates promising prospects for engineering applications. Full article

This study examines a boreal peatland (the Sagnes peatland, Fanay, Limousin, France) with a depth of 1 m. This peatland is currently in the late stages of organic deposition, as evidenced by the growth of Carex species, along with Sphagnum mosses, in the uppermost level. To gain molecular insights, we conducted an analysis of the lignin and polyphenolic counterparts using HMDS (hexamethyldisilazane) thermochemolysis, enabling the identification of lignin degradation proxies. The goal was to develop characteristic indicators for the state of lignin degradation based on the relative distribution of lignin phenols, measured by gas chromatography coupled with mass spectrometry (GC-MS) after the HMDS thermochemolysis. For that purpose, the singular contribution of the 11 aromatic moieties yielded, along with SGC (sum of lignin moieties) and the most lignin degradation proxies, were applied. It has been shown that HMDS thermochemolysis exhibited the capacity to reveal oxidized and degraded lignin fractions, following the increasing trend yielded for most moieties and SGC proxy, in the mesotelm and catotelm layers. In addition, the C/G (Cinnamyl/Guaiacyl) and S/G (Syringyl/Guaiacyl) ratios showed their highest input in the upper half of the core. This bias in the aforementioned ratios could indicate that HMDS thermochemolysis is to be applied for geological samples, where low G-compounds exist. For the sake of validating HMDS thermochemolysis’ application, Principal Component Analysis (PCA) was then applied to the molecular fingerprint. For ratios and proxies of aromatic moieties of HMDS thermochemolysis, the PCA approach exhibited a higher contribution (79%). This indicates the efficiency of these ratios in describing the molecular fingerprint of peat depth records. In addition, a higher separation between the contributions of the investigated variables (molecular proxies) along the first two PCs was noticed. In other words, the variables that showed a high contribution towards PC₁ exhibited a low contribution towards PC₂, and vice versa. These findings indicate the high reliance of applying the ratios and proxies of HMDS thermochemolysis. Full article

In this study, a novel polyacrylate-co-vinyl imidazole hydrogel-supported palladium (Pd) catalyst (P(AA-co-VI)@Pd) was prepared through heat-initiated polymerization, starting with the formation of a complex between vinyl imidazole and palladium chloride, followed by the addition of 75% neutralized acrylic acid (AA), crosslinking agent, and initiator. The structure and morphology of the catalyst were characterized using ICP-OES, SEM, EDX, Mapping, FT-IR, TGA, XRD, XPS and TEM techniques. It was confirmed that the catalyst exhibited excellent compatibility with water solvent and uniform distribution of Pd. The P(AA-co-VI)@Pd hydrogel catalyst demonstrated remarkable catalytic activity and ease of separation. Notably, in a Tsuji–Trost reaction, employing water as the solvent, it achieved a conversion rate as high as 94% at very low catalyst dosages, indicating its superior catalytic performance. Moreover, after six consecutive cycles, the catalyst maintained good activity and structural stability, highlighting its exceptional reusability and environmental friendliness. Furthermore, the outstanding efficiency of the catalyst was also observed in a Suzuki coupling reaction where both conversion rate and yield reached 100% and 99%, respectively, within just one hour reaction time, thus further validating its universality and efficacy across various chemical reactions. Full article

Background/Objectives: Adenoid Cystic Carcinoma (AdCC) is a rare malignant salivary gland tumor, with high rates of recurrence and distant metastasis. This study aims to stratify patients Relapse-Free Survival (RFS) using a combined model of clinical and radiomic features from preoperative MRI. Methods: This retrospective study included patients with primary AdCC who underwent surgery and adjuvant radiotherapy. Segmentations were manually performed by two head and neck radiologists. Radiomic features were extracted using the 3D Slicer software. Descriptive statistics was performed. A Survival Random Forest model was employed to select which radiological feature predict RFS. Cox proportional hazards models were constructed using clinical, radiological variables or both. Synthetic data augmentation was applied to address the small sample size and improve model robustness. Models were validated on real data and compared using the C-index and Prediction Error Curves (PEC). Results: Three Cox models were developed: one with clinical features (C-index = 0.67), one with radiomic features (C-index = 0.68), and one combining both (C-index = 0.77). The combined clinical-radiomic model had the highest predictive accuracy and outperformed models based on clinical or radiomic features. The combined model also exhibited the lowest mean Brier score in PEC analysis, indicating better predictive performance. Conclusions: This study demonstrate that a combined radiomic-clinical model can predict RFS in AdCC patients. This model may provide clinicians a valuable tool in patient’s management and may aid in personalized treatment planning. Full article

Rapid urbanization and changing land use dynamics require robust tools for projecting and analyzing future land use scenarios to support sustainable urban development. This study introduces an integrated modeling framework that combines the Patch-generating Land Use Simulation (PLUS) model with Markov Chain (MC) analysis to simulate land use and land cover (LULC) changes for Montreal Island, Canada. This framework leverages historical data, scenario-based adjustments, and spatial drivers, providing urban planners and policymakers with a tool to evaluate the potential impacts of land use policies. Three scenarios—sustainable, industrial, and baseline—are developed to illustrate distinct pathways for Montreal’s urban development, each reflecting different policy priorities and economic emphases. The integrated MC-PLUS model achieved a high accuracy level, with an overall accuracy of 0.970 and a Kappa coefficient of 0.963 when validated against actual land use data from 2020. The findings indicate that sustainable policies foster more contiguous green spaces, enhancing ecological connectivity, while industrial-focused policies promote the clustering of commercial and industrial zones, often at the expense of green spaces. This study underscores the model’s potential as a valuable decision-support tool in urban planning, allowing for the scenario-driven exploration of LULC dynamics with high spatial precision. Future applications and enhancements could expand its relevance across diverse urban contexts globally. Full article

Background: The prevalence of food allergy is increasing, posing a significant health concern. Assessing health-related quality of life (HRQOL) in individuals with food allergies is crucial, and various questionnaires exist for this purpose. However, translation and validation of these tools are necessary to ensure cultural relevance. This study aimed to translate the FAQLQ-PF into Greek and perform a cross-sectional validation to assess its effectiveness in evaluating HRQOL among Greek children with food allergies. Methods: Parents of children aged 0 to 12 years diagnosed with food allergy completed the Greek version of the FAQLQ-PF, consisting of 30 items across three subscales. Socio-demographic and clinical data were collected. Statistical analyses included nonparametric tests, correlation analysis for validity, and Cronbach’s alpha for internal consistency. Results: Out of 85 participants, 81 were included. The FAQLQ-PF demonstrated excellent internal consistency (Cronbach’s α = 0.94). Validity evaluation indicated its ability to measure HRQOL in younger children. HRQOL in the Greek pediatric population was significantly correlated with the number of food allergies, symptoms, parental and child concerns, anxiety levels, and activity restrictions. However, sex and general health status were not significantly correlated with HRQOL. Conclusions: The Greek translation and validation of FAQLQ-PF provides insights into HRQOL among Greek children with food allergies. Factors such as anaphylactic reactions, epinephrine autoinjector usage, number of food allergies, and symptoms influenced HRQOL in this population. Full article

Radiogenomics is an emerging field that links imaging features with molecular characteristics of diseases. In clear cell renal cell carcinoma (ccRCC), metabolic reprogramming leads to lipid accumulation, influenced by the adipose differentiation-related protein (ADFP). This study aimed to investigate whether hepatic and tumoral Hounsfield Unit (HU) values could serve as noninvasive radiogenomic biomarkers for ADFP expression in ccRCC. We analyzed CT images of 185 ccRCC patients, comparing lipid-associated HU values in the liver and tumor across ADFP expression statuses. Patients with low-grade ccRCC expressing ADFP showed significantly lower minimum HU values in both liver and tumor tissue, indicating greater lipid accumulation. Additionally, ADFP expression correlated negatively with abdominal adipose tissue compartments and positively with minimum tumoral HU values, linking systemic lipid metabolism to tumor biology. These findings suggest that hepatic and tumoral HU measurements may serve as noninvasive markers of lipid accumulation related to ADFP, providing insight into metabolic alterations in ccRCC. While promising, these results require validation in larger, controlled studies due to sample size and variability limitations. This approach could enhance the radiogenomic assessment of ccRCC, supporting noninvasive insights into tumor metabolism and progression. Full article

Lithium plating may occur during charging, especially at high rates or overcharging conditions for lithium-ion batteries (LIBs), which would cause battery capacity degradation and even trigger thermal runaway. Thus, it is essential to detect lithium plating onset during the charging processes. Electrochemical impedance can reveal the dynamic electrode properties of the battery, which is promising for use in battery management systems for the online detection of lithium plating onset. In this article, the impedance at 1 Hz is measured during the over-discharge and fast discharge processes using lithium–graphite half-cells. For half-cells, the variation in graphite electrode potential vs. Li/Li⁺ during discharging is directly recorded. An equivalent circuit model is proposed and adopted to estimate the real lithium plating reaction overpotential, which is deemed the thermodynamic indicator of lithium plating and is used as validation for the detection of lithium plating onset. Through the auxiliary validation of the estimation of lithium plating overpotential and the shape of incremental capacity curves, the relationship between impedance changes at specific frequency and the lithium plating onset is revealed. The results lay a good foundation for proposing the online diagnostic method of lithium plating onset based on the in situ impedance. Full article

Hydroxyanthracene derivatives (HADs) are plant substances produced by a variety of plant species, including different Aloe, Rheum, and Rhamnus species and Cassia senna. These plants are often used in food supplements to improve bowel function. However, recently, the European Commission prohibited a number of HADs due to toxicological concerns. These HADs included aloin (aloin A and aloin B), aloe-emodin, emodin, and danthron. Most of the currently available analytical methods are restricted to the analysis of only these compounds and do not include other HADs. In this view, a multi-analyte method could be useful for both regulatory analysis and dietary intake studies. To this end, such a method, employing liquid chromatography–tandem mass spectrometry and targeting 16 different HADs, was developed and validated in this study. Limits of quantification were in the range from 0.025 mg kg⁻¹ to 1 mg kg⁻¹. The recovery of the method was within the acceptable range of 80% to 120%, with the exception of physcion. Repeatability varied from 0.5% to 11.6%, and the range for within-laboratory reproducibility was from 3.4% to 16.3%. The expanded measurement uncertainty was below 50% for all HADs. Subsequently, 24 commercial samples of food supplements and herbal infusions sourced in Belgium were analyzed. The results indicated that although the industry put a great effort into minimizing the amount of aloin and danthron present in food supplements, more than half of the products still exceeded the maximum tolerated levels suggested for aloe-emodin and emodin. Full article

This study describes the design, simulation, and development process of a rehabilitation glove driven by soft pneumatic actuators. A new, innovative finger soft actuator design has been developed through detailed kinematic and workspace analysis of anatomical fingers and their actuators. The actuator design combines cylindrical and ribbed geometries with a reinforcing element—a thicker, less extensible structure—resulting in an asymmetric cylindrical bellow actuator driven by positive pressure. The performance of the newly designed actuator for the rehabilitation glove was validated through numerical simulation in open-source software. The simulation results indicate actuators’ compatibility with human finger trajectories. Additionally, a rehabilitation glove was 3D-printed from soft materials, and the actuator’s flexibility and airtightness were analyzed across different wall thicknesses. The 0.8 mm wall thickness and thermoplastic polyurethane (TPU) material were chosen for the final design. Experiments confirmed a strong linear relationship between bending angle and pressure variations, as well as joint elongation and pressure changes. Next, pseudo-rigid kinematic models were developed for the index and little finger soft actuators, based solely on pressure and link lengths. The workspace of the soft actuator, derived through forward kinematics, was visually compared to that of the anatomical finger and experimentally recorded data. Finally, an ergonomic assessment of the complete rehabilitation glove in interaction with the human hand was conducted. Full article