Search Results (19,499)

This study focuses on the geometric and geophysical characterization of sedimentary landslides in the Laguna del Amor area, located in Chota-Cajamarca (Peru). The main objective was to identify key static factors related to landslide susceptibility, including slope angle, soil composition, and groundwater flow, prioritizing the areas affected by landslides. Electrical Resistivity Tomography (ERT) was the geophysical method selected because of its effectiveness in delineating subsurface geometries, detecting water content, and assessing mass movements. The methodology combined geophysical analysis (ERT), field geology, and photogrammetry to develop a detailed subsurface model. The results indicate a rotational landslide mainly composed of weathered shales and limestones, with highly saturated zones that increase the area’s hazard level. The investigation also identified significant variability in landslide depth throughout the study area, highlighting the importance of these factors in geotechnical risk assessment. This interdisciplinary approach not only contributes to geological knowledge of the area but also provides critical information for mitigation and risk management strategies in landslide-prone areas. Full article

A self-developed microwave moisture detection system (ranged from 2.00 GHz to 10.00 GHz) based on multi-frequency sweep technology was used to quickly determine the moisture content of tea seed kernels without breaking the shells. A multi-frequency evaluation method combined cross-validation and majority voting rules was proposed to select the optimal microwave features from the original microwave signals. Firstly, the moisture content of tea seed kernels was detected by the moisture detection system, and the determination coefficients of the ANN model established based on seven attenuations and six phase-shifts were over 0.999. Then, the moisture content of unshelled tea seeds was detected, and the determination coefficients of the ANN model based on 13 preferred frequency features were over 0.995. Moreover, the predicted moisture values of unshelled tea seeds were calibrated accurately by a moisture function (y = −0.017x² + 1.431x − 1.019). Above all, the self-developed system could achieve non-destructive moisture content prediction of tea seed kernels. Full article

This article presents the results of a study on the rheological and lubricating properties of selected alcohol fuels. Methanol, ethanol, and 2-propanol are investigated, for which density, kinematic, and dynamic viscosity are determined at selected temperatures in the range of 15–60 °C. In addition, the water content of the studied fuels is determined. Based on the measurements, the coefficient of temperature change for density and the relative percentage decrease in kinematic viscosity with increasing temperature are calculated. Subsequently, regression models are built to describe the value of density and viscosity of the tested liquid alcohol fuels as a function of temperature. Next, the fuels under study are subjected to the evaluation of antiwear properties using a high-frequency reciprocating rig (HFRR). For each fuel, the corrected wear scar size WS_1.4, which is a measure of lubricity, the average coefficient of friction, and the relative percentage decrease in oil FILM thickness during the conduct of the HFRR test under standardized conditions, are determined. The measurements are carried out at a standardized temperature of 25 °C in accordance with standardized methods for a time equal to 75 min. Due to the low lubricity of the tested fuels, additional tests are performed at a reduced time equal to 30 min. In this case, all fuels show a similar WS_1.4 value, which ranges from 384 μm for methanol through 422 μm for 2-propanol to 426 μm for ethanol. The wear marks on the samples after the execution of the test are used to draw additional qualitative conclusions about the lubricating properties of the tested alcohols. The results obtained are summarized, and possibilities for their use in further research are provided. Full article

The most important control parameters in the methanol distillation process, which are directly related to product quality and yield, are the temperature, pressure and water content of the finished product at the top of the column. In order to adapt to the development trend of modern industrial technology to be more accurate, faster and more stable, the fusion of multi-sensor data puts forward higher requirements. Traditional control methods, such as PID control and fuzzy control, have the disadvantages of low heterogeneous data processing capability, poor response speed and low control accuracy when dealing with complex industrial process detection and control. For the control of tower top temperature and pressure in the methanol distillation industry, this study innovatively combines generative artificial intelligence and a type II fuzzy neural network, using a GAN for data preprocessing and a type II fuzzy neural network for steady-state inverse prediction to construct the GAN-T2FNN temperature and pressure control model for an atmospheric pressure tower. Comparison experiments with other neural network models and traditional PID control models show that the GAN-T2FNN model has a better performance in terms of prediction accuracy and fitting effect, with a minimum MAE value of 0.1828, which is more robust, and an R² Score of 0.9854, which is closer to 1, for the best overall model performance. Finally, the SHAP model was used to analyze the influence mechanism of various parameters on the temperature and pressure at the top of the atmospheric column, which provides a more comprehensive reference and guidance for the precise control of the methanol distillation process. Full article

Large language models (LLMs) have demonstrated remarkable capabilities in text generation, which also raise numerous concerns about their potential misuse, especially in educational exercises and academic writing. Accurately identifying and tracing the origins of LLM-generated content is crucial for accountability and transparency, ensuring the responsible use of LLMs in educational and academic environments. Previous methods utilize binary classifiers to discriminate whether a piece of text was written by a human or generated by a specific LLM or employ multi-class classifiers to trace the source LLM from a fixed set. These methods, however, are restricted to one or several pre-specified LLMs and cannot generalize to new LLMs, which are continually emerging. This study formulates source LLM tracing in a class-incremental learning (CIL) fashion, where new LLMs continually emerge, and a model incrementally learns to identify new LLMs without forgetting old ones. A training-free continual learning method is further devised for the task, the idea of which is to continually extract prototypes for emerging LLMs, using a frozen encoder, and then to perform origin tracing via prototype matching after a delicate decorrelation process. For evaluation, two datasets are constructed, one in English and one in Chinese. These datasets simulate a scenario where six LLMs emerge over time and are used to generate student essays, and an LLM detector has to incrementally expand its recognition scope as new LLMs appear. Experimental results show that the proposed method achieves an average accuracy of 97.04% on the English dataset and 91.23% on the Chinese dataset. These results validate the feasibility of continual origin tracing of LLM-generated text and verify its effectiveness in detecting cheating in student coursework. Full article

The settlement and deformation of abandoned mining tunnels can lead to cracking, deformation, or even the collapse of surface structures. Recently, a dual-direction, four-lane expressway, designed a speed of 100 km/h, is planned to be constructed between Yuanling County and Chenxi County. This expressway will pass through a long-abandoned refractory clay mining area in Chenxi County. This study focuses on this abandoned mining area and employs the Electrical Resistivity Tomography (ERT) method to investigate the underground conditions, aiming to determine the location and scale of the subterranean goaf. A total of five survey lines were deployed for the investigation. The inversion results indicate the presence of five low-resistivity anomalies in the underground structure (with six low-resistivity anomalies identified along line L1). These low-resistivity anomalies are preliminarily interpreted as subsurface cavities. Subsequent borehole verification revealed that the five low-resistivity anomalies correspond to a total of eight water-filled cavities, including six abandoned mining tunnels and two karst caves. At the location K33+260~K33+350, a large low-resistivity anomaly was identified which actually consisted of three closely spaced water-filled abandoned mining tunnels. Additionally, the surrounding strata primarily consisted of fractured mudstone, which has a high water content and thus exhibits low resistivity. These two factors combined resulted in the three water-filled abandoned mining tunnels appearing as a single large low-resistivity anomaly in the inversion profile. Meanwhile, at K33+50~K33+110, two water-filled abandoned mining tunnels were found. These tunnels are far apart along line L1 but are relatively close to each other on the other four survey lines. Consequently, in the inversion results, line L1 displays these as two separate low-resistivity anomalies, while the other four survey lines show them as a single large low-resistivity anomaly. Based on the 2D inversion results, a 3D model of the study area was constructed. This model provides a more intuitive visualization of the underground cavity structures in the study area. The findings not only serve as a reference for the subsequent remediation of the goaf area but also offer new insights into the detection of abandoned mining tunnels. Full article

Background/Objectives: Fat deposition traits in pigs directly influence pork flavor, tenderness, and juiciness and are closely linked to overall pork quality. The Tibetan pig, an indigenous breed in China, not only possesses a high intramuscular fat content but also exhibits a unique fat metabolism pattern due to long-term adaptation to harsh environments. This makes it an excellent genetic and physiological model for investigating fat deposition characteristics. Adipose tissue from different body regions displays varying morphologies, cytokines, and adipokines. This study aimed to examine adipose tissue deposition characteristics in different parts of Tibetan pigs and provide additional data to explore the underlying mechanisms of differential fat deposition. Methods: Our research identified significant differences in the morphology and gene expression patterns between subcutaneous fat (abdominal fat [AF] and back fat [BF]) and intramuscular fat (IMF) in Tibetan pigs. Results: Histological observations revealed that subcutaneous fat cells were significantly larger in area and diameter compared to IMF cells. The transcriptomic analysis further identified differentially expressed genes (DEGs) between subcutaneous fat and IMF, with a total of 65 DEGs in BF vs. IMF and 347 DEGs in AF vs. IMF, including 25 DEGs common to both comparisons. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that these genes were significantly associated with lipid metabolism-related signaling pathways, such as the Wnt, mTOR, and PI3K-Akt signaling pathways. Several DEGs, including DDAH1, ADRA1B, SLCO3A1, and THBS3, may be linked to the differences in fat deposition in different parts of Tibetan pigs, thereby affecting meat quality and nutritional value. Conclusions: These findings provide new insights into the unique fat distribution and deposition characteristics of Tibetan pigs and establish a foundation for breeding strategies aimed at improving pork quality. Full article

Two different diets able to induce dietary hyperammonaemia (a methionine–choline-deficient (MCD) diet and a methionine-deficient diet enriched with ammonium acetate (MAD + 20% ammonium acetate)) were tested in a rat model. The diets were shown to have different modes of action, inducing significant hyperammonaemia (HA) and growth retardation in the rats, with different metabolic consequences. The MCD diet resulted in the development of endogenous HA, with a decrease in bilirubin levels and an increase in hepatic fat content. In contrast, the MAD + 20% ammonium acetate diet increased circulating ALP and haptoglobin levels and decreased liver mass. The above results suggest that the MCD diet deteriorated the liver function of the rats, resulting in the development of endogenous HA, while the MAD diet caused moderate changes in liver metabolism, resulting in the development of exogenous HA. Interestingly, the commonly used oral treatments Lactulose and Rifaximin did not ameliorate hyperammonaemia during or after the treatment period. In conclusion, even though the diets used in the current study caused somewhat similar hyperammonaemia, they seemed to provoke different metabolic consequences. The latter can have an impact on the severity of the resulting hyperammonaemia and thus on the hyperammonaemia-induced encephalopathy, resulting in the development of distinguishing cognitive and metabolic (liver) effects compared to other forms of encephalopathy. We hypothesized that these rat models, with significantly increased serum ammonia levels, along with different liver injuries, could serve as a suitable double animal model for the testing of new, oral enzyme therapies for hepatic encephalopathy in future studies. Full article

Rapid advances in text-to-image (T2I) generative models have significantly enhanced visual content creation. However, evaluating these models remains challenging, particularly when assessing their ability to handle complex textual content. The primary aim of this research is to develop a systematic evaluation framework for assessing T2I models’ capabilities in generating specialized content, with emphasis on measuring text rendering accuracy and identifying model limitations across diverse domains. The framework utilizes carefully crafted prompts that require precise formatting, semantic alignment, and compositional reasoning to evaluate model performance. Our evaluation methodology encompasses a comprehensive assessment across many critical domains: mathematical equations, chemical diagrams, programming code, flowcharts, multi-line text, and paragraphs, with each domain tested through specifically designed challenge sets. GPT-4 serves as an automated evaluator, assessing outputs based on key metrics such as text accuracy, readability, formatting consistency, visual design, contextual relevance, and error recovery. Weighted scores generated by GPT-4 are compared with human evaluations to measure alignment and reliability. The results reveal that current T2I models face significant challenges with tasks requiring structural precision and domain-specific accuracy. Notable difficulties include symbol alignment in equations, bond angles in chemical diagrams, syntactical correctness in code, and the generation of coherent multi-line text and paragraphs. This study advances our understanding of fundamental limitations in T2I model architectures while establishing a novel framework for the systematic evaluation of text rendering capabilities. Despite these limitations, the proposed benchmark provides a clear pathway for evaluating and tracking improvements in T2I models, establishing a standardized framework for assessing their ability to generate accurate and reliable structured content for specialized applications. Full article

Morus nigra L., or mulberry, is a susceptible fleshy fruit due to its high respiratory rate and low storage stability, which shortens its shelf life and makes it difficult to commercialize in natura. Edible coatings, thin membranes produced directly on the desired surface, could improve food preservation, among other properties. Annatto (Bixa orellana L.) seeds are natural pigments with high antioxidant activity. This work aimed to develop a pectin-based edible coating with annatto extract to increase the shelf life of fruits, using mulberries as a study model. The mulberries were randomly separated into five groups: without coating, coated with different extract concentrations (0%, 5%, and 10%), and a layer-by-layer treatment consisting of a pectin layer under a 10% extract layer. The samples were evaluated for the following parameters: titratable acidity, maturity index, mass loss, pH, soluble solids, moisture contents, and bioactive compounds. The coated group with 10% annatto extract had the best result for the maturity index (25.52), while the group with 5% showed better mass loss and moisture (37.28% and 83.66%, respectively). Herein, it was demonstrated that pectin-based edible coatings with annatto extract delay the maturation and senescence of mulberries, preserving the bioactive compounds and increasing shelf life. Full article

Accurately partitioning evapotranspiration (ET) of cropland into productive plant transpiration (T) and non-productive soil evaporation (E) is important for improving crop water use efficiency. Many methods, including machine learning methods, have been developed for ET partitioning. However, the applicability of machine learning models in cropland ET partitioning with diverse crop rotations is not clear. In this study, machine learning models are used to predict E, and T is obtained by calculating the difference between ET and E, leading to the derivation of the ratio of transpiration to evapotranspiration (T/ET). We evaluated six machine learning models (i.e., artificial neural networks (ANN), extremely randomized trees (ExtraTrees), gradient boosting decision tree (GBDT), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost)) on partitioning ET at 16 cropland flux sites during the period from 2000 to 2020. The evaluation results showed that the XGBoost model had the best performance (R = 0.88, RMSE = 6.87 W/m², NSE = 0.77, and MAE = 3.41 W/m²) when considering the meteorological data, ecosystem sensible heat flux, ecosystem respiration, soil water content, and remote sensing vegetation indices as input variables. Due to the unavailability of observed E or T data at the 16 cropland sites, we used three other widely used ET partitioning methods to indirectly validate the accuracy of our ET partitioning results based on XGBoost. The results showed that our T estimation results were highly consistent with their T estimation results (R = 0.83–0.91). Moreover, based on the XGBoost model and the three other ET partitioning methods, we estimated the ratio of transpiration to evapotranspiration (T/ET) for different crops. On average, maize had the highest T/ET of 0.619 ± 0.119, followed by soybean (0.618 ± 0.085), winter wheat (0.614 ± 0.08), and sugar beet (0.611 ± 0.065). Lower T/ET was found for paddy rice (0.505 ± 0.055), winter barley (0.590 ± 0.058), potato (0.540 ± 0.088), and rapeseed (0.522 ± 0.107). These results suggest the machine learning models are easy and applicable for cropland T/ET estimation with different crop rotations and reveal obvious differences in water use among different crops, which is crucial for the sustainability of water resources and improvements in cropland water use efficiency. Full article

E-participation platforms, such as iVoting and Join in Taiwan, provide digital spaces for citizens to engage in deliberation, voting, and oversight. As a forerunner in Asia, Taiwan has implemented these platforms to enhance participatory democracy. However, there is still limited research on the specific content debated on these platforms. Utilising recent advancements in Natural Language Processing, the content of proposals that users have submitted between 2015 and 2025 is explored. In this study, a pipeline for mining text corpora scraped from these platforms in the context of political analysis is proposed. The pipeline is applied to two datasets which have different characteristics. A topic model for each of the two platforms is generated and later evaluated with OCTIS (Optimizing and Comparing Topic Models Is Simple) and compared to different baselines. Our research highlights the trade-offs between model performance and processing time, emphasizing the balance between accuracy and meaningful topic creation. By integrating a translation pipeline from Chinese to English within the text-mining process, our method also demonstrates a solid approach to overcome language barriers. Consequently, our method is adaptable to e-participation platforms in various languages, providing decision-makers with a more comprehensive tool to understand citizens’ needs and enabling the formulation of more informed and effective policies. Full article

The experiments were divided into two groups to establish a drying shrinkage model suitable for modified ceramsite geopolymer concrete (MCGC). In the first experimental group, via comparison with dry ceramsite (untreated), a method for modifying the ceramsite surface with a 6% silicone resin was proposed which could reduce its water absorption, enhance the compressive strength and slump of the corresponding concrete, and decrease the drying shrinkage. The second group systematically explored the influences of control factors on MCGC prepared from modified ceramsite. Different water/binder (w/b) ratios, [Na₂O]/b ratios, and metakaolin content (MK/b) ratios were used in the experiment. Compressive strength and drying shrinkage tests were performed for 90 d. High w/b and Na₂O/b ratios could enhance drying shrinkage. Moreover, 8% Na₂O/b enhanced the compressive strength. Low compressive strength was obtained using 10% Na₂O/b. A high MK/b ratio reduced drying shrinkage. However, high w/b and MK/b ratios hindered strength development. Finally, a model predicting drying shrinkage for MCGC with a high prediction accuracy was proposed by considering three control factors. Due to the variety of ceramsite pretreatment methods and the considered factor limitations, the model had potential for additional enhancements. Full article

Style transfer is a technique that learns style features from different domains and applies these features to other images. It can not only play a role in the field of artistic creation but also has important significance in image processing, video processing, and other fields. However, at present, style transfer still faces some challenges, such as the balance between style and content, the model generalization ability, and diversity. This article first introduces the origin and development process of style transfer and provides a brief overview of existing methods. Next, this article explores research work related to style transfer, introduces some metrics used to evaluate the effect of style transfer, and summarizes datasets. Subsequently, this article focuses on the application of the currently popular deep learning technology for style transfer and also mentions the application of style transfer in video. Finally, the article discusses possible future directions for this field. Full article

This study examines the impact of soil hydraulic parameterization on simulating soil water content in a drip-irrigated grapefruit orchard (Citrus paradisi Mac.) using precise laboratory measurements and the HYDRUS 2D/3D model. Undisturbed soil samples were analyzed for water retention and saturated hydraulic conductivity using high-precision instruments, and parameters were estimated with unimodal and bimodal Van Genuchten functions. Soil water dynamics under deficit (80% of crop evapotranspiration, ET_C) and full irrigation (100% ET_C) were simulated, accounting for circular drip emitters. Calibration relied on soil water content data collected at varying depths and distances from the emitters. Results from the fitting process with laboratory-measured data for water retention and hydraulic conductivity indicate that the bimodal function provided more accurate parameter estimates, yielding lower RMSE for soil water content (0.0026 cm³ cm⁻³) and hydraulic conductivity (0.1143 cm day⁻¹), compared to the unimodal (0.0047 cm³ cm⁻³ and 0.1586 cm day⁻¹). HYDRUS simulations also demonstrated superior calibration metrics for the bimodal function with RMSE, MAE, and NSE values of 0.024 cm³ cm⁻³, 0.016 cm³ cm⁻³, and 0.892 respectively, compared to 0.025 cm³ cm⁻³, 0.017 cm³ cm⁻³, and 0.883 for the unimodal function. Although differences between the functions were small, the bimodal model’s slight performance gain comes with added complexity and uncertainty in parameter estimation. These findings highlight the critical role of precise parameterization in refining irrigation strategies and ensuring sustainable water use in citrus orchards. Full article