Search Results (52)

To address the low drilling efficiency of the composite rod drill in gas extraction boreholes, key drilling parameters are optimized using coal-seam hardness grading tests and response surface methodology. By conducting mechanical tests on coal samples from the Sangshuping, Zhangcun, and Wangzhuang coal mines, the coal seams are classified into three categories: soft (Pus coefficient 0.87), medium–hard (2.16), and hard (3.47). Multi-factor and multi-level field tests were then performed at different working faces, using Design Expert software to analyze the response surface of three factors: pump pressure, flow rate, and feed pressure. The response surface method was used to determine the influence of drilling factors on drilling time under different coal-seam hardness conditions and the optimal drilling parameters. The results indicate that the technology is not suitable for soft coal seams due to frequent bit jamming. The optimal parameters for medium–hard coal seams are a pump pressure of 4 MPa, a flow rate of 180 L/min, and a feed pressure of 6 MPa (time per 100 m: 62 min 33 s). For hard coal seams, the optimal parameters are a pump pressure of 6 MPa, a flow rate of 200 L/min, and a feed pressure of 8 MPa (time per 100 m: 55 min 27 s). This study provides a theoretical basis for efficient coal seam drilling. Full article

Background: Pyoderma is a superficial bacterial infection that is considered the formation of pus-containing lesions on the skin occurring in animals. Staphylococci, including Staphylococcus aureus and Staphylococcus pseudintermedius, that cause pyoderma in pet animals is a global health concern. The objectives of this study were to investigate antibiotic-resistant staphylococci isolated from pyoderma in dogs and cats and to analyse whole genome sequences of multidrug-resistant (MDR) staphylococci. Methods: A total of 56 pyoderma swabbing samples from 42 dogs and 14 cats located in Southern Thailand was collected to isolate staphylococci. Antibiotic susceptibility and antibiotic-resistant genes of staphylococcal isolates were investigated. Furthermore, the representative MDR isolates were investigated using whole genome sequence analysis. Results: 61 isolates were identified as staphylococci, which can be classified into 12 different species, mostly including 13 S. intermedius (13.26%), 13 S. saprophyticus (13.26%), 8 S. sciuri (8.16%), and Staphylococcus cohnii (8.16%). Remarkably, the main pyoderma-causing species that were isolated in this study were S. aureus (5.10%) and S. pseudintermedius (3.06%). Most staphylococci were resistant to penicillin G (30%), and the blaZ gene was found to be the highest prevalence of the resistance genes. Both MDR-S. aureus WU1-1 and MDR-S. pseudintermedius WU48-1 carried capsule-related genes as main virulence factor genes. Interestingly, MDR-S. pseudintermedius WU48-1 was resistant to seven antibiotic classes, which simultaneously carried blaZ, mecA, aac, dfrK, aph3, and tetM. Genes related to antibiotic efflux were the highest proportion of the mechanism found in both representatives. Remarkably, SCCmec cassette genes were found in both isolates; however, the mecA gene was found only in MDR-S. pseudintermedius WU48-1. In addition, these were mostly carried by macrolide- and tetracycline-resistance genes. Mobile gene transfer and horizontal gene transfer events frequently contain genes involved in the antibiotic target alteration mechanism. Conclusions: This study found that MDR staphylococci, especially S. aureus and S. pseudintermedius, are important in animals and owners in terms of One Health concern. The information on whole genome sequences of these MDR staphylococci, particularly antimicrobial resistance genes, mobile genetic elements, and horizontal gene transfer events, can help to understand gene transmission and be applied for antibiotic resistance surveillance in veterinary medicine. Full article

Elevators are a vital part of urban infrastructure, playing a key role in smart cities where increasing population density has driven the rise in taller buildings. As an essential means of vertical transportation, elevators have become an integral part of daily life, making their design, construction, and maintenance crucial to ensuring safety and compliance with evolving industry standards. The safety of elevator systems depends on the continuous monitoring and fault-free operation of Permanent Magnet Synchronous Motor (PMSM) drives, which are critical to their performance. Furthermore, the fault-free operation of PMSM drives reduces operating costs, increases service life, and improves reliability. The PMSM drive components may be susceptible to electrical, mechanical, and thermal faults that, if undetected, can lead to operational disruptions or safety risks. The integration of artificial intelligence and Internet of Things (IoT) technologies can enhance fault prediction, reducing downtime and improving efficiency. Ongoing challenges such as managing machine thermal load and developing more durable materials for PMSMs require the development of suitable models that are adapted to existing drive systems. The proposed framework for fault prediction is validated on a real residential elevator equipped with a PMSM drive. Multimodal signal data is processed through a Generative Adversarial Network (GAN)-enhanced Positive Unlabeled (PU) classifier and a Reinforcement Learning (RL)-based adaptive decision engine, enabling robust and scalable fault prediction in a non-intrusive fashion. Full article

This study investigates the use of advanced convolutional neural networks (CNNs) to analyze and classify the fracture behavior of U-shaped concrete modified with polyurethane (PU) under repeated drop-weight impact loads. A total of 17 U-shaped specimens were tested under multiple drop-weight impact loads for each PU binder content (0%, 10%, 20%, and 30%) by weight of cement. By integrating digital image correlation (DIC) with dynamic and static mechanical testing, this research evaluates the concrete’s impact resistance and flexural behavior with varying PU binder content. Three CNN architectures, InceptionV3, MobileNet, and DenseNet121, were trained on a dataset comprising 1655 high-resolution crack images to classify the failure stages into no crack, initial crack, and advanced failure. Experimental results revealed that 20% PU content optimally enhances impact resistance and flexural strength, while mechanical properties declined significantly with 30% PU content. The strain localization in DIC analysis indicated reduced matrix cohesion, which was measured by the extent of strain concentration in the material, highlighting the importance of maintaining PU content below 20% to avoid compromising structural integrity. Among the models, InceptionV3 demonstrated superior accuracy (96.67%), precision, and recall, outperforming MobileNet (94.56%) and DenseNet121 (90.03%). The combination of DIC and deep learning offers a robust, automated framework for crack assessment, significantly improving accuracy and efficiency over traditional methods such as visual inspections, which are time-consuming and reliant on expert judgment. Full article

Medication-related osteonecrosis of the jaw (MRONJ) is a multifactorial condition defined as an adverse drug reaction that results in progressive jawbone destruction and necrosis in individuals treated with certain medications, occurring without a history of prior radiotherapy. These drugs are mainly bisphosphonates, denosumab, and other bone-modifying agents, anti-angiogenic agents such as anti-endothelial growth factor, tyrosine kinase inhibitors, and proteins classified as mammalian targets of rapamycin. The diagnosis of MRONJ is based on clinical (exposed jawbone, fistula with pus, hyperplasia of the mucosa overlying the necrotic bone tissue) and radiological evaluation. We report four cases of clinical and radiological evidence of osteonecrosis of the jaw that are unrelated to the use of antiresorptive or anti-angiogenic agents. In two instances, histological and microbiological evidence was also found (high concentration of Actinomyces, the microbe most commonly found in oral sites affected by MRONJ). These atypical cases are reported to highlight the possibility that other, previously undocumented, drugs may also contribute to the development of ONJ Full article

As a unique class of plant-specific transcription factors, the GROWTH-REGULATING FACTORs (GRFs) play pivotal roles in regulating plant growth, development, and stress responses. In this study, the woody plant Populus ussuriensis was taken as the research object. Nineteen PuGRFs were identified and classified into six clades, and their potential evolutionary relationships were analyzed. The possible biological functions of PuGRFs were speculated through bioinformatics analysis. Combining real-time fluorescence quantitative PCR, PuGRFs were determined to be actively expressed in young tissues, and there are distinct tissue-specific expressions in the mature tissues of woody plants. We also conducted RT-qPCR of PuGRFs under different abiotic stresses and phytohormone treatments, most of the family members were induced under the treatments of methyl jasmonate (MEJA) and salicylic acid (SA), and we also found that 4 of 19 PuGRFs might participate in abscisic acid (ABA)-mediated osmotic stress in roots. Protein–protein interaction prediction analysis showed that six PuGRFs can interact with two types of growth-regulating interaction factors (GIFs). Further prediction and verification revealed that PuGRF1/2c and PuGRF1/2d, which belong to the same clade and have highly similar sequences, exhibited divergent interaction capabilities with GIFs, indicating evolutionary fine-tuning and functional redundancy within the GRF family. These findings lay a foundation for studying the molecular mechanisms of PuGRFs in P. ussuriensis, suggest that PuGRFs play important roles in responding to hormones and environmental changes, and the potential interaction relationships are worthy of exploration. Full article

Investigating spatio-temporal differentiation patterns of land-use conflicts in mountainous and flatland regions provides critical insights for optimizing spatial regulation strategies and advancing sustainable regional development. Using the Urban Agglomeration in Central Yunnan (UACY) as a case study, the production–living–ecological space (PLES) was classified through land-use functional dominance analysis based on 2010–2020 geospatial datasets. Spatio-temporal evolution patterns and mountain–dam differentiation were analyzed using spatial superposition, dynamic degree analysis, transfer matrices, and geospatial TuPu methods. A multi-scale conflict index incorporating landscape metrics was developed to assess PLES conflict intensities across spatial scales, with contribution indices identifying key conflict-prone spatial types. Analysis revealed distinct regional differentiation in PLES distribution and evolutionary trajectories during 2010–2020. Forest Ecological Space (FES) and Agricultural Production Space (APS) dominated both the entire study area and mountainous zones, with APS exhibiting particular dominance in dam regions. Grassland Ecological Space (GES) and Other Ecological Space (OES) experienced rapid conversion rates, contrasting with stable or gradual expansion trends in other space types. Change intensity was significantly greater in mountainous zones compared to flatland area (FA). PLES conflict exhibited marked spatial heterogeneity. FA demonstrated substantially higher conflict levels than mountainous zones, with evident scale-dependent variations. Maximum conflict intensity occurred at the 4000 m scale, with all spatial scales demonstrating consistent escalation trends during the study period. ULS, FES, and WES predominantly occurred in low-conflict zones characterized by stability, whereas APS, Industrial and Mining Production Space (IMPS), RLS, GES, and OES were primarily associated with high-conflict areas, constituting principal conflict sources. Full article

Forest mapping using remote sensing has made considerable progress over the past decade, but substantial uncertainties remain in complex regions, particularly where terrain and climate vary dramatically. Yunnan Province, China, represents such a challenging case, with its diverse climatic zones ranging from tropical to temperate and its topography spanning over 6500 m in elevation. These factors contribute to substantial variation in vegetation types, complicating the accurate identification of forest cover through remote sensing. This study aims to enhance forest mapping in Yunnan by leveraging multi-temporal remote sensing data from Sentinel-2 and Landsat 8/9 imagery, incorporating key phenological stages—such as the leaf greening (GRN) period, as well as the senescence, defoliation, and foliation (SDF) stages of deciduous forests—along with kNDVI and terrain factors. A random forest (RF) classifier was applied on the Google Earth Engine (GEE) platform to create a 10 m resolution forest map (LS2-RF). This map achieved an overall accuracy of 96.35% when validated with 1572 ground samples, significantly outperforming existing global datasets, such as Dynamic World (73.88%) and WorldCover (87.66%). These maps agreed well in extensive forested areas; discrepancies were noted in mixed land types, including farmland, urban areas, and regions with fragmented landscapes. In 2022, Yunnan’s forest cover was 60.40%, with higher coverage in the southwestern region and lower in the northeast. The largest forested area was found in Pu’er City, while the smallest was in Yuxi City. Forests were most abundant at elevations between 1500 and 2500 m (occupying 52.29% of the total forest area) and slopes of 15° to 25° (occupying 39.19% of the total forest area). Conversely, forest cover was lowest in areas below 500 m elevation (occupying 0.64% of the total forest area) and on slopes less than 5° (occupying 2.40% of the total forest area). The analysis also revealed a general trend of increasing forest cover with decreasing latitude and longitude, with peak forest coverage at mid-elevations and slopes, followed by a decline at higher elevations. The resultant forest map provides valuable data for ecological assessments, forest conservation initiatives, and informed policy decision-making. Full article

Background/Objectives: Toxoplasmosis is a zoonotic disease caused by Toxoplasma gondii. The parasite infection in humans continues to rise due to an increasing seroprevalence rate in domestic and wild warm-blooded animals that serve as a major reservoir of the parasite. There are fewer drugs available for the treatment of toxoplasmosis. However, these drugs are limited in efficacy against tachyzoites and bradyzoites. Also, there are clinical side effects and geographical barriers to their use, especially in immunocompromised patients, children, and pregnant women. Tannins, a class of natural products, are known to have antimicrobial properties. However, little is known about the effects of Corilagin (CG) and Punicalagin (PU), which are classified as tannins, on T. gondii growth and their possible mechanism of action in vitro. We hypothesize that CG and PU could inhibit T. gondii growth in vitro and cause mitochondria membrane disruption via oxidative stress. Methods: Here, we investigated the anti-T. gondii activity of the two named tannins using a fluorescent-based reporter assay. Results: The 50% effective concentrations (EC_50s) values for CG and PU that inhibited T. gondii parasites growth in vitro were determined to be 3.09 and 19.33 µM, respectively. Pyrimethamine (PY) was used as a standard control which gave an EC₅₀ value of 0.25 µM. Interestingly, CG and PU were observed to cause high reactive oxygen species (ROS) and mitochondrial superoxide (MitoSOX) production in tachyzoites. This resulted in a strong mitochondria membrane potential (MMP) disruption in T. gondii tachyzoites. Conclusions: Therefore, the possible mechanism(s) of action of CG and PU against T. gondii is associated with the disruption of the mitochondria redox biology. Thus, the high ROS and MitoSOX produced as a result of these compounds created high oxidative stress, leading to mitochondrial dysfunction. Full article

Accurate monitoring of aboveground biomass (AGB) in subtropical forests plays an important role in maintaining biodiversity and the balance of forest ecosystems. It is of high importance to explore how machine learning models can improve the ability and accuracy of AGB estimation of different types of subtropical forests under the conditions of active and passive open-source remote sensing (RS) data. In this study, the subtropical forests in the Pu’er region of Yunnan Province were used as the research object, and backscattering coefficients, mean reflectance, and textural features from Sentinel-1, Sentinel-2, and Landsat 8 OLI open-source RS data were used as the data source. We classified the subtropical forests into three basic forest types: broadleaf forest, coniferous forest, and mixed forest. Based on filtering and analyzing RS features, we performed forest AGB inversion using Random Forest (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost). The results show that: (1) VH-related texture features in Sentinel-1, and red-edge band features, IR band features, and texture features in Sentinel-2 and Landsat 8 OLI are sensitive to changes in forest AGB. (2) Among the three nonparametric methods, the XGBoost algorithm had the highest estimation accuracy with an MAE of 10.05 t/ha and RMSE of 12.43 t/ha in coniferous forests; the second estimation accuracy in mixed forests with an MAE of 20.18 t/ha and RMSE of 25.33 t/ha; and the estimation accuracy in broad-leaved forests with an MAE of 25.22 t/ha and RMSE of 32.32 t/ha. (3) The accuracy of estimating forest AGB by combining multiple RS data is higher than the estimation results using a single RS data. We found that the VH features of SAR data contribute more to the inversion of high-precision forest AGB; the XGBoost model has the strongest robustness and the highest accuracy in the AGB inversion of subtropical forests using multisource RS data. (4) The spatial autocorrelation of the samples themselves also needs to be taken into account when modeling forest AGB estimates. Full article

To investigate the flavour evolution mechanism of raw Pu-erh tea (RPT) during storage, the volatile and non-volatile compounds of RPT with different storage years (1–10 years) from the same raw material origin, manufacturer, and storage location in Wenshan Prefecture, Yunnan Province, were systematically analysed by HPLC, HS-SPME-GC-MS, and OAV. The results showed that both cluster analyses based on non-volatile and volatile compounds could classify RPT of different storage years into three ageing cycles, with key turning points in the third and eighth years of storage, which is also accompanied by the colour changing from green to orange or brown, the aroma changing from a faint scent to woody and ageing, the astringency diminishing, and the sweet and mellow increasing. Theophylline was identified as the potential marker of RPT stored 1–3 years, while (−)-catechin gallate, (−)-gallocatechin gallate, quercetin, and rutin as those for a storage of 9–10 years. The volatile compounds indicate a general trend of an initial increase followed by a decrease. Forty-four key aroma compounds (OAV ≥ 1) were identified. Eucalyptol, β-Caryophyllene, 2-Amylfuran, Copaene, Estragole, and α-Terpinene originated as potential markers for RPT stored 1–3 years, while (Z)-Linalool oxide (furanoid), α-Terpineol, Terpinen-4-ol, and cis-Anethol were for RPT stored 8–10 years. This study revealed the flavour characteristics and quality changes of RPT over the course of storage, and constructed a sensory flavour wheel, providing theoretical underpinnings for the quality control and assessment of RPT. Full article

This paper presents an experimental study on the elastic support in a discrete rail fastening system used in a ballastless tram track structure. The study focuses on the elastic support of the anchor element, specifically the Pm49 baseplate. These elements significantly influence environmental pollution along tram routes, such as vibration (at low frequencies) or noise (at high frequencies), as well as static and dynamic rail deflections. The authors outline a methodology for identifying the static and dynamic characteristics of the discrete elastic support in laboratory conditions. The procedure follows the European standard EN 13146-9 for track category A (tramway), as classified according to the European standard EN 13481-5. The study analyzes how the thickness and density of the tested materials affect stiffness. Additionally, it examines the correlation between parameters identified easily on-site (thickness, Shore hardness and density) and laboratory-determined parameters (static and dynamic stiffness), which are costly and time-consuming to measure. The research confirms that prototype prefabricated vibration isolation baseplate pads made of styrene butadiene rubber (SBR) granules, recycled from end-of-life car tires, can achieve equivalent basic static and dynamic parameters, compared to underlays made of two-component polyurethane (PU) resin. This aligns with the strategy of promoting sustainable materials in construction. The innovative and prefabricated SBR rubber baseplate pads can also be used in repair and maintenance works (regardless of weather conditions), as they enable the quick launch of tram traffic. The results of the research included in this article can be used by other scientists, recycled rubber producers, tram track designers or construction site engineers. Full article

The cost of hyperspectral image (HSI) classification primarily stems from the annotation of image pixels. In real-world classification scenarios, the measurement and annotation process is both time-consuming and labor-intensive. Therefore, reducing the number of labeled pixels while maintaining classification accuracy is a key research focus in HSI classification. This paper introduces a multi-strategy triple network classifier (MSTNC) to address the issue of limited labeled data in HSI classification by improving learning strategies. First, we use the contrast learning strategy to design a lightweight triple network classifier (TNC) with low sample dependence. Due to the construction of triple sample pairs, the number of labeled samples can be increased, which is beneficial for extracting intra-class and inter-class features of pixels. Second, an active learning strategy is used to label the most valuable pixels, improving the quality of the labeled data. To address the difficulty of sampling effectively under extremely limited labeling budgets, we propose a new feature-mixed active learning (FMAL) method to query valuable samples. Fine-tuning is then used to help the MSTNC learn a more comprehensive feature distribution, reducing the model’s dependence on accuracy when querying samples. Therefore, the sample quality is improved. Finally, we propose an innovative dual-threshold pseudo-active learning (DSPAL) strategy, filtering out pseudo-label samples with both high confidence and uncertainty. Extending the training set without increasing the labeling cost further improves the classification accuracy of the model. Extensive experiments are conducted on three benchmark HSI datasets. Across various labeling ratios, the MSTNC outperforms several state-of-the-art methods. In particular, under extreme small-sample conditions (five samples per class), the overall accuracy reaches 82.97% (IP), 87.94% (PU), and 86.57% (WHU). Full article

One primary concern in the field of remote-sensing image processing is the precise classification of hyperspectral images (HSIs). Lately, deep-learning models have demonstrated cutting-edge results in HSI classification. Despite this, researchers continue to study and propose simpler, more robust models. This study presents a novel deep-learning approach, the iterative convolutional neural network (ICNN), which combines spectral–fractal features and classifier probability maps iteratively, aiming to enhance the HSI classification accuracy. Experiments are conducted to prove the accuracy enhancement of the proposed method using HSI benchmark datasets of Indian pine (IP) and the University of Pavia (PU) to evaluate the performance of the proposed technique. The final results show that the proposed approach reaches overall accuracies of 99.16% and 95.5% on the IP and PU datasets, respectively, which are better than some basic methods. Additionally, the end findings demonstrate that greater accuracy levels might be achieved using a primary CNN network that employs the iteration loop than with certain current state-of-the-art spatial–spectral HSI classification techniques. Full article

Due to the deficiency of radio spectrum resources caused by the progress in technology, cognitive radio networks (CRNs) have made significant progress. CRNs have two types of users, namely, primary users (PUs) and secondary users (SUs). Considering that PUs have a higher priority and diversified data transmission requirements, this study divides PUs into two levels, namely, PU1s with a higher priority and PU2s with a lower priority. On the other hand, the occurrence of failures is inevitable in CRNs, which affects the data transmission of users. In this paper, combined with an adjustable PU packets transmission rate mechanism, a communication failure and repair mechanism with classified PUs based on the single-channel CRNs is proposed, and different preemption principles are set according to different system states. A queueing model is established and analyzed with a Markov chain, the performance index expressions that need targeted research are listed, numerical experiments are conducted, and the system performance change trends are obtained. The comparison experiment shows that the proposed communication failure and repair mechanism with classified PUs can improve the throughput of PU1 packets and reduce the blocking rate of PU1 packets compared with the conventional communication failure and repair mechanisms with unclassified PUs. Full article