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Abstract This retrospective study analyzed a large population of gastric cancer (GC) patients treated between 2010 and 2015 to investigate the clinical features and predictive risk factors for developing secondary primary malignancies (SPMs). The cumulative incidence of SPM was assessed using Kaplan–Meier analysis. Competing risk analyses adjusted for mortality were conducted using stratified Cox proportional hazard regression models and multivariate analyses to identify independent predictors of SPM. A total of 3289 out of 167,747 GC patients were included in the analytic cohort, with 155 patients diagnosed with SPM. Patients whose histologic type other than adenocarcinomas (AC) and signet ring cell carcinoma (SRCC) emerged as an independent risk factor for developing SPM (hazard ratio [HR] 2.262, 95% confidence interval [CI] 1.146–4.465, P = 0.019) in multivariate Cox regression analysis. The surgical method, including biopsy/local excision (HR 2.3, [CI] 1.291–4.095, P = 0.005) and subtotal/total resection ([HR] 1.947, [CI] 1.028–3.687, P = 0.041), chemotherapy ([HR] 1.527, [CI] 1.006–2.316, P = 0.047), and histologic type ([HR] 2.318, [CI] 1.193–4.504, P = 0.013)), were identified as independent risk factors in the competitive risk model. Subgroup analyses, stratified by chemotherapy, revealed an increased risk of SPM among older patients. Furthermore, a nomogram was developed and internally validated to predict the cumulative incidence of SPM in GC patients (C-index = 0.73 for 72 months). These findings suggested that in specific histologic types of GC, the lymph node infiltration region missed after local surgical resection, and concomitant chemotherapy would have an increased risk of SPM for cancer survivors.

Geothermal energy is a renewable and environmentally sustainable resource of increasing importance. However, areas with geothermal potential are not easily detected by traditional field investigations, requiring the development of new, robust, and reliable models for detection. In this study, remote sensing data and ground-based variables were used to detect and analyze geothermal resource potential areas. General Land Surface Temperature (GLST) was integrated using 5 years of remote sensing data. Landsat 8 daytime GLST (Landsat-GLST), Moderate Resolution Imaging Spectroradiometer (MODIS) daytime GLST (MODIS-DLST), and MODIS nighttime GLST (MODIS-NLST) data were integrated with Landsat Nighttime Land Surface Temperature (Night-LST), which not only filled the gap of Landsat Night-LST but also improved the spatial resolution of MODIS nighttime temperatures. Specifically, three independent variables (Night-LST, Distance From Known Geothermal Resource Points [DFGP], and Distance From Geological Faults [DFF]) were used to develop a weighted model to form a Geothermal Detection Index (GDI) based on Principal Component Analysis (PCA). Along with field verification, the GDI was successfully used to identify three geothermal activity areas in Tengchong City, Yunnan Province. Overall, this work provides a novel method for detecting geothermal potential to support the successful exploitation of geothermal resources.

The Ms 5.5 earthquake struck on 24 October 2023, in Subei County, Gansu Province, China, occurring along the eastern segment of the Altyn Tagh fault. It raises the question of whether this earthquake is linked to the ongoing shortening slip rate along this segment or triggered by other seismic events. Analyzing the fault geometry of the Subei earthquake and understanding the significance of the weakening activity rate for seismic hazards in neighboring regions is crucial. The surface deformation from small- and medium-sized earthquakes (magnitudes less than Mw5.5) is often subtle, and the coseismic deformation detected by interferometric synthetic aperture radar (InSAR) is vulnerable to atmospheric disturbances, leading to significant measurement errors. Moreover, inaccuracies in the regional crustal velocity structure can cause errors in earthquake localization based on seismic data. These challenges complicate the establishment of a rupture model for seismogenic faults and hinder the inversion of fault slip models. To overcome these limitations, we employed the time-series InSAR stacking method and aftershock relocation to determine the fault geometry of the Subei earthquake. A two-step inversion method was utilized to ascertain both the fault geometry and slip distribution. Our modeling indicates that the 2023 Subei earthquake had a thrust mechanism with a component of strike-slip. The rupture did not reach the surface, with the maximum fault slip measuring 0.45 m at a depth of 2.5–3.5 km. The fault dips westward, and the moment magnitude is calculated at 5.4. This earthquake is associated with the ongoing weakening of the left-lateral strike-slip rupture along the Altyn Tagh fault in the Subei region. Furthermore, retrograde thrust tectonics significantly contribute to the absorption of accumulated stress during this process.Our findings highlight the potential of utilizing time-series InSAR images to enhance earthquake catalogs with geodetic observations, offering valuable data for further studies of the earthquake cycle and active tectonics. This approach is also applicable in other tectonically active regions, enhancing understanding of seismic hazards and risk assessment.

Low scattering terrain areas introduce complex phase interference, which reduces the accuracy of deformation signal estimation in InSAR(Interferometric Synthetic Aperture Radar) techniques. Existing covariance matrix-based InSAR phase calculation methods often fail to account for translational offset relations between scatterers leading to inaccuracies, and pixels with zero spatial coherence exist. To address this issue, this paper proposes a distributed scatterer InSAR phase estimation method based on the Cross-Correlation complex coherence matrix. The effectiveness and superiority of the algorithm are verified through simulation and actual data. The results show that: (i) The simulation analysis shows that, compared to the traditional covariance matrix method, the optimal Cross-Correlation matrix improves the interferometric phase, coherence, and accuracy by 21.51%, 15.24%, and 6.52%, respectively. (ii) The actual experimental data show that the interferometric phase optimal by the Cross-Correlation matrix can effectively overcome the pseudo-signal caused by spatial hopping and make the phase more continuous. Compared with the traditional covariance matrix, the average a posteriori coherence and average coherence of arbitrary interference combinations in the Cross-Correlation matrix are improved by 18.12% and 58.10%, respectively. (iii) The number of DS points selected by the Cross-Correlation matrix algorithm is more than that of the covariance matrix algorithm. PS-InSAR (Persistent Scatterer Interferometric Synthetic Aperture Radar) achieved more accurate deformation rates compared to the covariance and correlation matrices, with errors of 9.34, 17.21, and 16.28 mm∙a-1 when compared against GNSS data, respectively. (iv) The Cross-Correlation matrix reduces the deformation rate error by 5.43 % relative to the covariance matrix. The algorithm provides reliable phase estimation for accurate monitoring of surface deformation in low-scattering regions, supporting geological disaster early warning and resource and environmental management.

Fluoride exposure is widespread worldwide and poses a significant threat to organisms, particularly to their gastrointestinal tracts. However, due to limited knowledge of the mechanism of fluoride induced intestinal injury, it has been challenging to develop an effective treatment. To address this issue, we used a series of molecular biology in vitro and in vivo experiments. NaF triggered m6A mediated ferroptosis to cause intestinal damage. Mechanistically, NaF exposure increased the m6A level of SLC7A11 mRNA, promoted YTHDF2 binding to m6A-modified SLC7A11 mRNA, drove the degradation of SLC7A11 mRNA, and led to a decrease in its protein expression, which eventually triggers ferroptosis. Moreover, NaF aggravated ferroptosis of the colon after antibiotics destroyed the composition of gut microbiota. 16 S rRNA sequencing and SPEC-OCCU plots, Zi-Pi relationships, and Spearman correlation coefficients verified that Lactobacillus murinus (ASV54, ASV58, and ASV82) plays a key role in the response to NaF-induced ferroptosis. Collectively, NaF-induced gut microbiota alteration mediates severe intestinal cell injury by inducing m6A modification-mediated ferroptosis. Our results highlight a key mechanism of the gut in response to NaF exposure and suggest a valuable theoretical basis for its prevention and treatment.

Incorporating Interferometric Synthetic Aperture Radar (InSAR) data sources into the landslide susceptibility evaluation process has yielded favorable outcomes in some studies. However, there are fewer analyses of the applicability of InSAR data monitoring, and a framework of accurate and effective grading criteria needs to be developed. To overcome these limitations, this study presents a novel and precise approach for landslide susceptibility assessment in alpine valley regions. This method incorporates the suitability of InSAR monitoring and introduces a grading system based on deformation rates, enhancing accuracy and efficiency. Taking the Dongchuan district, the most typical high mountain valley area in southwest China, as the research object, the SAR is quantitatively simulated and analyzed in this study using the improved R-index shadow layover method. Then, the optimal Synthetic Aperture Radar (SAR) monitoring scheme is derived, calculate deformation rates using LiCSBA (small baseline subset with the automated sentinel-1 InSAR processor) technology, and accurately establish the extent of landslide hazards (including potential landslides) by incorporating high-resolution images. The criteria for grading susceptibility within the landslide hazard range are based on deformation rates. Evaluation factors for corresponding grid cells are obtained. The best evaluation factors are selected using covariance diagnosis and gray correlation analysis. The landslide susceptibility model is developed utilizing the Particle Swarm Optimization-Back Propagation (PSO-BP) algorithm. It includes evaluation techniques for regions without deformation rates. The study findings demonstrate that: (i) Analyzing SAR suitability in alpine and canyon areas is crucial. Complementary monitoring with SAR lift tracks may only sometimes resolve geometric distortion issues in all these regions. (ii) The InSAR deformation rate can be an essential evaluation factor for landslide susceptibility evaluation. (iii) The proposed method effectively addresses low coherence challenges in certain alpine valley regions, where grading based on deformation rate is complex. The landslide susceptibility evaluation model is validated using performance evaluation indexes (Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-Square (R2)), confirming its reliability and effectiveness. (iv) The proposed method improves the grading accuracy by 37.84 ~ 60.91%. Overall, our proposed new landslide susceptibility method brings a new way of evaluating landslide susceptibility in alpine valley regions.

The advancement of remote sensing technology aids geologists in obtaining lithological maps more quickly, comprehensively, and accurately. However, key challenges in lithological mapping include the limited spectral information from individual sensors and the difficulties in visually interpreting lithological samples. In this study, we integrated 241 scenes of optical data and 106 scenes of radar data on the Google Earth Engine (GEE) platform, proposing a rapid lithological identification framework that combines an automatic lithological sample data generation strategy with multi-source data. Using various machine learning algorithms, we evaluated the classification capabilities of heterogeneous predictive factors, feature optimization algorithms, and object-based algorithms. Results indicate that: (1) Combining optical and radar data improves prediction accuracy, with terrain data further enhancing mapping capabilities; (2) Terrain factors contribute most to classification, but SWIR and TIR bands of optical data are critical for lithological identification; (3) The feature optimization algorithm reduces feature redundancy and efficiency issues from multi-source data, achieving 96.51% accuracy with the optimal feature model, an improvement of 0.1%−2.02% over original features; (4) Object-based algorithms show significant potential in mapping areas with large rock outcrops. This study offers new insights for medium- to large-scale lithological maps and provides essential data support for geological work.

ABSTRACTIncreased tourism forms subdivide the tourism market. Therefore, distinguishing spatiotemporal behaviors of different types of tourists is of great significance for tourism marketing and planning. In this study, we constructed a Spatial–Temporal-Preference model based on spatial clustering, seasonal-trend decomposition and statistical analysis, using multi-source data. It provides a novel frame for comparing tourist behavioral characteristics from a new perspective. Meanwhile, providing a method to rapidly identify the Tourism Infrastructure Supply and Demand Pattern (TISDP) fusing nighttime light and social media data. Then the framework was applied to analyze the differences between sightseeing tourists (STs) and cycling tourists (CTs) in Yunnan Province, China. The results showed clear spatial differences in location popularity and flow data with STs favoring northwestern Yunnan, and CTs favoring eastern Yunnan. Tourist behavior varied also seasonally and tourists preferred visiting minority areas or areas with better infrastructure. The tourism industry in the study area developed rapidly after 2013, although still with an unbalanced infrastructure supply and demand. The CTs loss areas were greater in northeastern and northwestern Yunnan and south of Kunming. The proposed methods help to identify differences in tourism activities and has important implications to tourism infrastructure planning and sustainable development.

Interferometry synthetic aperture radar (InSAR) is essential for monitoring surface deformation and geological hazards. InSAR applications in complex mountainous regions are challenging due to high noise levels and large phase gradient variations, which make it difficult to obtain accurate and efficient phase unwrapping results. As a result, the development of fast and high-precision phase unwrapping methods in these areas has become an urgent research topic in the current InSAR field. In response to this challenge, this study proposes a fast and high-precision phase unwrapping method specifically designed for complex mountainous regions. The method includes a sliding window singular value denoising phase gradient correction unwrapping algorithm (SDPC) for obtaining precise phase unwrapping results. A fast-phase unwrapping algorithm based on FPUNet networks is proposed by integrating deep learning techniques with the SDPC results. By integrating these two methods, the phase unwrapping speed can be accelerated while maintaining the unwrapping accuracy. The Dongchuan District of Kunming City, Yunnan Province, is used as the subject of this article. Because the true phase cannot be known using the measured interferometric phase data, simulated data are introduced to verify and demonstrate the validity and accuracy of the method presented in this article. Experimental results demonstrate the effectiveness of the proposed method for phase unwrapping, even in complex mountainous regions characterized by high noise and large gradient variations. Moreover, this approach significantly enhances the phase unwrapping speed. The presented method addresses the inefficiency and inaccuracy of InSAR in phase unwrapping in complex mountainous regions.

The Kunming Changshui International Airport was built on complex mountainous terrain with significant fluctuations in Southwest China. The site contains large areas of high fill susceptible to uneven ground deformation from filling material consolidation, dynamic loading from aircraft, and other factors. Thus, monitoring and analyzing the causes and characteristics of postconstruction deformation of high-fill foundations are crucial to the airport's safe operation. In this study, we obtained a total of 149 Sentinel-1A ascending SAR images from March 2017 to March 2022. We estimated the elevation error and corrected the topographic phase to mitigate the impact of large topographic fluctuations on monitoring. Then, we extracted ground subsidence results based on time-series InSAR technology. The results show that deformation mainly occurred in the high-fill areas during the monitoring period, whereas settlement was more pronounced when the fill height was over 30 m. The deformation rate is influenced by the height of the fill, ground reinforcement measures, and dynamic loading of aircraft. Using the Mann-Kendall trend analysis and Pettitt mutation test methods to detect temporal information from time-series points, we found that the time required for the foundation to reach a stable state after construction is not directly related to its residual subsidence. Other factors, such as construction measures also influence it.

Due to a heavy reliance on groundwater, Bangladesh is experiencing a severe decline in groundwater storage, with some areas even facing land subsidence. This study aims to investigate the relationship between groundwater storage changes and land subsidence in Bangladesh, utilizing a combination of GRACE and InSAR technologies. To clarify this relationship from a macro perspective, the study employs GRACE data merged with GLDAS to analyze changes in groundwater storage and SBAS-InSAR technology to assess land subsidence. The Dynamic Time Warping (DTW) method calculates the similarity between groundwater storage and land subsidence time series, incorporating precipitation and land cover types into the data analysis. The findings reveal the following: (1) Groundwater storage in Bangladesh is declining at an average rate of −5.55 mm/year, with the most significant declines occurring in Rangpur, Mymensingh, and Rajshahi. Notably, subsidence areas closely match regions with deeper groundwater levels; (2) The similarity coefficient between the time series of groundwater storage and land subsidence changes exceeds 0.85. Additionally, land subsidence in different regions shows an average lagged response of 2 to 6 months to changes in groundwater storage. This study confirms a connection between groundwater dynamics and land subsidence in Bangladesh, providing essential knowledge and theoretical support for further research.

To address the subjectivity of traditional factor attribute grading methods and the weak predictive capabilities of single-model classifications, this study focused on Yulong County; the Contribution Degree Clustering Method (CDCM) utilizes the Certainty Factor (CF) as the contribution index to partition continuous factor attribute intervals. Additionally, the Sparrow Search Optimization Algorithm (SSA) is employed for hyperparameter tuning. The CF is incorporated into Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), and Random Forest (RF) models to form the CF-SSA-SVM, CF-SSA-BPNN, and CF-SSA-RF coupling models, respectively. These basic coupling models are further integrated using the Stacking algorithm to create the CF-SSA-Stacking integrated coupling model for constructing a landslide susceptibility assessment system. The results indicate that the CF-SSA-Stacking integrated coupling model achieves the highest accuracy, F1 score, Kappa coefficient, and AUC value, with values of 0.89375, 0.89172, 0.787500, and 0.9522, respectively. These metrics are significantly superior to those of the three basic coupling models, demonstrating better generalization capability and reliability. This suggests that the model can identify more historical landslide occurrences using fewer grid areas classified as extremely-high- or high-susceptibility zones. It is suitable as an effective regional landslide susceptibility assessment method for practical disaster prevention and mitigation applications. Further studies could explore the model’s performance across varying geological settings or with different datasets, providing a roadmap for future research and development in landslide susceptibility assessment.

BackgroundHyperuricemia (HUA) is a prevalent metabolic disorder whose development is associated with intestinal microbiota. Therefore, probiotics have emerged as a potential and safe approach for lowering uric acid (UA) levels. However, the underlying mechanisms of many effective probiotic strains remain unknown.Methods and resultsC57BL/6 mice were randomly divided into two groups: control and model groups. The model group received 12 weeks of potassium oxonate. Through 16s sequencing we found that HUA resulted in a significant decrease in the total diversity of all intestinal segments. When each intestinal segment was analyzed individually, the reduction in diversity was only significant in the cecum and colon sections. RDA analysis showed that lactobacilli in the rat colon exhibited a strong correlation with model group, suggesting that Lactobacillus may play an important role in HUA. Consequently, the preventive effects of Lactobacillus johnsonii YH1136 against HUA were investigated. C57BL/6 mice were randomly divided into three groups: control, model and YH1136 groups. The results showed that administering Lactobacillus johnsonii YH1136 effectively reduced serum UA levels in vivo by inhibiting hepatic xanthine oxidase (XOD) activity and promoting renal ABCG2 transporter expression. Moreover, supplementation with Lactobacillus johnsonii YH1136 significantly ameliorated pathological damage in the kidney and liver, thereby reducing UA accumulation.ConclusionHyperuricemia is accompanied by an altered composition of multiple gut bacteria, of which Lactobacillus is a key genus. Lactobacillus johnsonii YH1136 may ameliorate renal involvement in HUA via the gut-kidney axis.

Surface subsidence hazards in mining areas are common geological disasters involving issues such as vegetation degradation and ground collapse during the mining process, which also raise safety concerns. To address the accuracy issues of traditional prediction models and study methods for predicting subsidence in open-pit mining areas, this study first employed 91 scenes of Sentinel-1A ascending and descending orbits images to monitor long-term deformations of a phosphate mine in Anning City, Yunnan Province, southwestern China. It obtained annual average subsidence rates and cumulative surface deformation values for the study area. Subsequently, a two-dimensional deformation decomposition was conducted using a time-series registration interpolation method to determine the distribution of vertical and east–west deformations. Finally, three prediction models were employed: Back Propagation Neural Network (BPNN), BPNN optimized by Genetic Algorithm (GA-BP), and BPNN optimized by Artificial Bee Colony Algorithm (ABC-BP). These models were used to forecast six selected time series points. The results indicate that the BPNN model had Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) within 7.6 mm, while the GA-BP model errors were within 3.5 mm, and the ABC-BP model errors were within 3.7 mm. Both optimized models demonstrated significantly improved accuracy and good predictive capabilities.

Geohazards that have developed in densely vegetated alpine gorges exhibit characteristics such as remote occurrence, high concealment, and cascading effects. Utilizing a single remote sensing datum for their identification has limitations, while utilizing multiple remote sensing data obtained based on different sensors can allow comprehensive and accurate identification of geohazards in such areas. This study takes the Latudi River valley, a tributary of the Nujiang River in the Hengduan Mountains, as the research area, and comprehensively uses three techniques of remote sensing: unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR), Small Baseline Subset interferometric synthetic aperture radar (SBAS-InSAR), and UAV optical remote sensing. These techniques are applied to comprehensively identify and analyze landslides, rockfalls, and debris flows in the valley. The results show that a total of 32 geohazards were identified, including 18 landslides, 8 rockfalls, and 6 debris flows. These hazards are distributed along the banks of the Latudi River, significantly influenced by rainfall and distribution of water systems, with deformation variables fluctuating with rainfall. The three types of geohazards cause cascading disasters, and exhibit different characteristics in the 0.5 m resolution hillshade map extracted from LiDAR data. UAV LiDAR has advantages in densely vegetated alpine gorges: after the selection of suitable filtering algorithms and parameters of the point cloud, it can obtain detailed terrain and geomorphological information on geohazards. The different remote sensing technologies used in this study can mutually confirm and complement each other, enhancing the capability to identify geohazards and their associated hazard cascades in densely vegetated alpine gorges, thereby providing valuable references for government departments in disaster prevention and reduction work.

Abstract FERONIA (FER) is a member of the Catharanthus roseus receptor-like kinase 1-like (CrRLK1L) protein subfamily, which participates in reproduction, abiotic stress, biotic stress, cell growth, hormone response, and other molecular mechanisms of plants. However, the mechanism by which a single RLK is capable of mediating multiple signals and activating multiple cellular responses remains unclear. Here, we summarize research progress revealing the spatial–temporal expression of FER, along with its co-receptors and ligands determined the function of FER signaling pathway in multiple organs. The specificity of the FER signaling pathway is proposed to operate under a four-layered mechanism: (1) Spatial–temporal expression of FER, co-receptors, and ligands specify diverse functions, (2) Specific ligands or ligand combinations trigger variable FER signaling pathways, (3) Diverse co-receptors confer diverse FER perception and response modes, and (4) Unique downstream components that modify FER signaling and responses. Moreover, the regulation mechanism of the signaling pathway- appears to depend on the interaction among the ligands, RLK receptors, co-receptors, and downstream components, which may be a general mechanism of RLKs to maintain signal specificity. This review will provide a insight into understanding the specificity determination of RLKs signaling in both model and horticultural crops.

BackgroundThis study aimed to understand the hospital-acquired COVID-19 infection rate and infection prevention and control status of emergency support frontline healthcare workers (ESFHCWs) under closed-loop management, and to explore the related factors affecting hospital-acquired COVID-19 prevention and control status.MethodsThe study site was a provincial-level tertiary hospital in the Xinjiang Uygur Autonomous Region specializing in treating COVID-19 patients. ESFHCWs were assigned from different hospitals in Zhejiang Province to provide emergency medical support in this specialized hospital. All ESFHCWs were managed using a closed loop. A self-designed questionnaire was used to estimate basic information, work experience, and the status of infection prevention and control (SIPC). A total of 269 ESFHCWs responded to the questionnaire. A generalized linear regression model was used to estimate the factors influencing SIPC.ResultsThere were six hospital-acquired COVID-19 cases, with an infection rate of 2.23%. The independent risk factors influencing COVID-19 prevention and control status were work seniority, anxiety disorder, and consumption of gastrointestinal, anti-inflammatory and anti-asthmatic, and hypnotic sedative drugs. Compared with ESFHCWs with more than 10 years of work seniority, ESFHCWs with less than 5 years of work seniority and 5–10 years of work seniority had lower COVID-19 SIPC scores. Among ESFHCWs with anxiety disorder, the SIPC score was significantly lower than that of ESFHCWs without anxiety disorder. The SIPC scores of ESFHCWs taking other medications (gastrointestinal, anti-inflammatory and anti-asthmatic, and hypnotic sedative drugs) were lower than those of ESFHCWs who did not.ConclusionThe closed-loop management method may be effective in reducing the infection rate of hospital-acquired COVID-19 among ESFHCWs. HCWs with less than 10 years of work seniority, anxiety disorder, and other medications (gastrointestinal, anti-inflammatory and anti-asthmatic, and hypnotic sedative drugs) were probably not suitable for participating in emergency assistant actions because of their poor SIPC scores. Further studies are needed to develop the selection criteria for ESFHCWs.

Introduction: Nowadays, the widespread use of plastic products has significantly contributed towards environmental pollution caused by waste plastics. Laser-induced breakdown spectroscopy (LIBS), an emerging spectroscopic technology, has shown great potential for rapid sorting and recycling of plastics. However, the poor robustness of the classification model severely limits the large-scale application of LIBS technology in plastic sorting and recycling.Methods: In this research, we used spectral preprocessing combined with feature selection to improve the robustness of the support vector machine (SVM) classification model for four typical plastic samples (ABS, nylon, 3240, and its modified product FR-4). LIBS spectral data were collected under different experimental conditions, then we defined robustness over time (ROT), robustness over time and different focusing lenses (ROT&RFL), and robustness over time and different manufacturers (ROT&RDM) to assess model performance. The feature importance of the preprocessed spectra was evaluated using the Relief-F algorithm, and the maximum accuracy of the validation set was 92.6% when inputting the first 19 most important features. Eventually, the optimal model was used for the prediction of the test set.Results and discussion: The ROT of the original spectrum, spectrum preprocessing, and spectral preprocessing combined with feature selection were 58.4%, 79.1%, and 98.47%, respectively. Similarly, ROT&RFL for the same methods were 65.54%, 75%, and 95.25%, respectively. ROT&RDM were 65.5%, 67%, and 93.92%, respectively. The results demonstrate that spectral preprocessing combined with feature selection can significantly improve the robustness of the classification model, and the proposed method is feasible for plastic sorting and recycling.

Background: Colorectal cancer (CRC) is a harmful cancer with high morbidity and poor prognosis. There is growing evidence that RNA methylation is closely related to the occurrence of cancer and its malignant biological behavior. N6-methyladenosine (m6A) methylation is the most common RNA modification in eukaryotes, and its multiple regulatory mechanisms in CRC have been elucidated from multiple perspectives. At the same time, the role of 5-methylcytosine (m5C), another important and widely distributed methylation modification, in CRC is far from being elucidated.Methods: In this study, we used RNA immunoprecipitation sequencing combined with bioinformatics methods to identify the m5C peaks on messenger RNA (mRNA) in HCT15 cells and sh-NSUN2 HCT15 cells, understand which transcripts are modified by m5C, and characterize the distribution of m5C modifications. In addition, we performed further bioinformatics analysis of the detected data to initially clarify the potential function of these m5C-modified transcripts.Results: We found significant differences in the distribution of m5C between HCT15 cells and sh-NSUN2 HCT15 cells, suggesting that m5C is likely to play a key role in the occurrence and development of CRC. Furthermore, Gene Ontology (GO) enrichment analysis showed that genes altered by m5C were mainly enriched in phylogeny, synaptic membrane, and transcription factor binding. The Kyoto Encyclopedia of Genes and Genomes (KEGG)pathway analysis showed that the genes altered by m5C are enriched in ECM receptor interaction pathway, the circadian pathway, and the cAMP signaling pathway.Conclusion: Here, our study preliminarily revealed the different distribution patterns of m5C between HCT15 cell and sh-NSUN2 HCT15 cell. Our results open a new window to understand the role of m5C RNA methylation of mRNA in the development of CRC.

The Asian elephant (Elephas maximus) have a wide range of foraging needs and heterogeneous habitat preferences that bring them close to the forest edge. Currently, most of the range of wild elephants is located outside protected areas (PA). Nighttime light data (NTL) has been used as a component of human footprint due to its characteristics of characterizing the intensity of human activities and spatio-temporal continuity. We chose Xishuangbanna National Nature Reserve, the main distribution area of Asian elephants, as the study area and used NTL to monitor human activities, supplemented by land cover (LC) type data, to study the Asian elephant sanctuary and its surrounding areas, and found that: 1) NTL features inside and outside the PA are approaching, and the boundary effect of the sanctuary is weakening; 2) The NTL gathering area in the study area is mainly distributed in the south of the PA, 86.21% of the area’s nighttime lights have a significant increase trend, and the human activity range is evolving toward the southeast in the past decade; 3) the percentage of areas with a significant increasing trend of NTL under different LC types is greater than decreasing, nearly 50% of the forest showed an increasing trend of NTL. LC types with reduced forest have been converted to cropland and building in the last decade.

Environmental variables are crucial factors affecting the development and distribution of landslides, and they also provide vitally important information for statistically-based landslide susceptibility mapping (SLSM). The acquisition and utilization of appropriate and the most influential environmental variables and their combinations are crucial for improving the quality of SLSM results. However, compared with the construction of SLSM models based on machine learning, the acquisition and utilization of high-quality environmental variables have received very little attention. In order to further clarify the research status of the application of environmental variables and possible development directions in future research, this study systematically analyzed the application of environmental variables in SLSM. To this end, a literature database was constructed by collecting 261 peer-reviewed articles (from 2002 to 2021) on SLSM from the Web of Science and CNKI platform (www.cnki.net) based on the keywords of “landslide susceptibility” and “environmental variable.” We found that existing methods for determining environmental variables do not consider the regional representativeness and geomorphological significance of the variables. We also found that at present, environmental variables are utilized generally without the realization and understanding of their spatial heterogeneity. Accordingly, this study raises two major scientific issues: 1) Effective identification of important environmental variables required in SLSM. 2) Effective representation of the spatial heterogeneity of environmental variables in SLSM modeling. From the perspective of the identification of dominant variables and their geospatial pattern of heterogeneity, targeted solutions for future research are also preliminarily discussed, including the method for identifying dominant variables from qualitative and quantitative perspectives and SLSM model construction considering the specific geospatial patterns. In addition, the applicability and limitation of the mentioned methods are discussed.

BackgroundThe widely accepted microbiome-gut-brain axis (MGBA) hypothesis may be essential for explaining the impact of high-altitude exposure on the human body, especially brain function. However, studies on this topic are limited, and the underlying mechanism remains unclear. Therefore, this study aimed to determine whether high-altitude-induced working memory dysfunction could be exacerbated with gut microbiota disruption.Methods and resultsC57BL/6 mice were randomly divided into three groups: control, high-altitude exposed (HAE), and high-altitude exposed with antibiotic treatment (HAE-A). The HAE and HAE-A groups were exposed to a low-pressure oxygen chamber (60–65 kPa) simulating the altitude of 3,500–4,000 m for 14 days, The air pressure level for the control group was maintained at 94.5 kPa. Antibiotic water (mixed with 0.2 g/L of ciprofloxacin and 1 g/L of metronidazole) was provided to the HAE-A group. Based on the results of the novel object test and P300 in the oddball behavioral paradigm training test, working memory dysfunction was aggravated by antibiotic treatment. We determined the antioxidant capacity in the prefrontal cortex and found a significant negative influence (p

The North Konawe region in Indonesia, known for its lateritic nickel (Ni) deposits, holds significant potential for obtaining Ni resources. However, the complex topographic conditions of this area pose challenges. Exploring the application of remote sensing technology to reveal the spectral response mechanism of Ni grade from high-precision multispectral data and inversion of Ni grade represents a novel direction in future Ni resource exploration. Traditional remote sensing inversion methods solely consider the spectral characteristics of sample data and ignore vital geospatial location information. As a result, efficiently obtaining regional details of target substance content over large areas has become challenging. The introduction of the geographically weighted regression (GWR) method offers an opportunity for fine-grained Ni grade inversion based on remote sensing. This study focused on the E and K blocks within the lateritic Ni mining area in North Konawe. Through utilizing the WordView-3 multispectral data which exhibits immense potential in quantitative remote sensing inversion studies, GWR was employed to integrate spectral features and spatial information. The goal was to reveal the correlation between multispectral remote sensing data and Ni grade. The obtained results were then compared and analyzed with multiple linear regression (MLR) and back propagation neural network (BPNN) models. The findings revealed that GWR achieved the highest coefficient of determination R2 of 0.96, surpassing MLR and BPNN values of 0.05 and 0.17, respectively. Additionally, GWR exhibited the lowest root mean square error of 0.04, which was lower than those of MLR and BPNN with the values of 0.25 and 0.23, respectively. These results confirmed the enhanced stability and accuracy of the GWR method compared to MLR and BPNN. Furthermore, GWR effectively mapped the spatial distribution trends of Ni grades in the study area, providing evidence of the method’s effectiveness in Ni grade inversion. The study also delved into the inversion effect of the GWR method in areas with varying weathering crust thickness and vegetation cover. The research revealed that higher values of weathering crust thickness negatively impacted the inversion effect. However, the influence mechanism of vegetation cover on Ni grade inversion necessitated further investigation. These results served as a significant demonstration of the remote sensing inversion of mineral resource grades in similar areas. They provided valuable insights for future exploration and decision-making processes.

Fritillaria has a long history in China, and it can be consumed as medicine and food. Owing to the high cost of Fritillaria cirrhosa, traders sometimes mix it with the cheaper Fritillaria thunbergii powder to make profit. Herein, we proposed a laser-induced breakdown spectroscopy (LIBS) technique to test the adulteration present in the sample of Fritillaria cirrhosa powder. Experimental samples with different adulteration levels were prepared, and their LIBS spectra were obtained. Partial least squares regression (PLSR) was adopted as the quantitative analysis model to compare the effects of four data standardization methods, namely, mean centring, normalization by total area, standard normal variable, and normalization by the maximum, on the performance of the PLSR model. Principal component analysis and least absolute shrinkage and selection operator (LASSO) were utilized for feature extraction and feature selection, and the performance of the PLSR model was determined based on its quantitative analysis. Subsequently, the optimal number of features was determined. The residuals were corrected using support vector regression (SVR). The mean absolute error and root mean square error of prediction obtained from the quantitative analysis results of the combined LASSO-PLSR-SVR model for the test set data were 5.0396% and 7.2491%, respectively, and the coefficient of determination R2 was 0.9983. The results showed that the LIBS technique can be adopted to test adulteration in the sample of Fritillaria cirrhosa powder and has potential applications in drug quality control.

Abstract Cracking caused by shrinkage deformation of cement-based materials at early age is a major problem leading to material failure in restrained conditions. Carbon nanotubes (CNTs) are incorporated into cement-based materials, and the autogenous shrinkage and crack resistance of the new composite materials obtained by linear shrinkage and ring tests are studied to solve the destruction of the materials caused by the shrinkage of cement-based materials. The results showed that addition of CNTs significantly inhibited the autogenous shrinkage of cement-based materials with maximum reduction rate above 40%. CNTs also significantly improved the cracking resistance of cement-based materials. The optimal effect was noticed at CNTs content of 0.1 wt%. The incorporation of CNTs not only inhibits the autogenous shrinkage of cement-based materials, but also inhibits the drying shrinkage of cement-based materials to some extent. Therefore, carbon nanotubes have the potential to solve the destruction of materials caused by shrinkage of cement-based materials.

A variety of factors are involved in inter-city transportation route selection in the areas of complex terrain. With the help of Geographic Information System (GIS), three quantitative methods were employed to determine the transport routes between 44 cities in Northern Yunnan (China), an area with alpine valleys, Karst mountains, and plateau basins. The network analysis in GIS was used to find the routes based on the Transport Suitability Evaluation (TSE) map, which was produced from several factors, including population density, terrain slope, vertical terrain dissection, landslide and mud-flock area, land cover types, and Normalized Difference Vegetation Index (NDVI). Analytic Hierarchy Process (AHP), Grey Relational Analysis (GRA), and Delphi analysis were used to collect and calculate suitability values of these factors. Finally, all the routes connecting 44 cities of Northern Yunnan formed a network which could provide reference for route selection planning in the area.

Due to the new demonstrations of Laser-induced breakdown spectroscopy (LIBS) applicability in a surprisingly wide variety of applications, the use of LIBS as a medical diagnostic tool is steadily gaining momentum. Especially in different cancer diseases, LIBS has the potential to become a fast and valuable analytical tool. We addressed LIBS equipment and quantitative analytical procedures, and signal enhancement techniques for improving element detection. For detailed aspects of applications, we reviewed the recent progress of LIBS in different cancer diseases diagnoses by using different tissues and medical fluid as samples. To fulfill the high demands in the medical industry and overcome the severe tissue sample problem, it is proposed that the chemometric and signal amplification techniques for quantitative analysis should be employed, and robust and effective LIBS devices should be developed. This overview of the different cancers by LIBS is meant to summarize the research performed to date and suggest some suitable advanced chemometrics techniques and effective LIBS devices, if successfully implemented, would be significantly beneficial to the medical field in the future.

On 2 January 2022, an earthquake of Ms 5.5 occurred in Ninglang County, Lijiang City, the earthquake-prone area of northwestern Yunnan. Whether this earthquake caused significant deformation and thermal anomalies and whether there is a relationship between them needs further investigation. Currently, multi-source remote sensing technology has become a powerful tool for long-time-series monitoring of earthquakes and active ruptures which mainly focuses on single crustal deformation and thermal anomaly. This study aims to reveal the crustal deformation and thermal anomaly characteristics of the Ninglang earthquake by using both Interferometric Synthetic Aperture Radar (InSAR) and Robust Satellite Techniques (RST). First, Sentinel-1A satellite SAR data were selected to obtain the coseismic deformation field based on Differential InSAR (D-InSAR), and the Small Baseline Set InSAR (SBAS-InSAR) technique was exploited to invert the pre- and post-earthquake displacement sequences. Then, RST was used to extract the thermal anomalies before and after the earthquake by using Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST). The results indicate that the seismic crustal deformation is dominated by subsidence, with 23 thermal anomalies before and after the earthquake. It is speculated that the Yongning Fault in the deformation area is the main seismogenic fault of the Ninglang earthquake, which is dominated by positive fault dip-slip motion. Meanwhile, the seismic fault system composed of NE- and NW-oriented faults is an important factor in the formation of thermal anomalies, which are accompanied by changes in stress at different stages before and after the earthquake. Moreover, the crustal deformation and seismic thermal anomalies are correlated in time and space, and the active rupture activities in the region produce deformation accompanied by changes in thermal radiation. This study provides clues from remote sensing observations for analyzing the Ninglang earthquake and provides a reference for the joint application of InSAR and RST for earthquake monitoring.

Taking the effect of actual surface topography under elastohydrodynamic lubrication (EHL) conditions on the contact state of gear pairs into consideration, a combination model with the analytical sliced method and two-dimensional (2D) EHL model is proposed to characterize the three-dimensional (3D) meshing characteristics of spur gears. Firstly, the surface topology of gears is tested by a surface profiler, which reflects that the topography of tooth surface accords with fractal characteristics. Thus, by adopting the Weierstrass–Mandelbrot (W-M) fractal function, the gear surface is characterized. Secondly, the numerical 2D EHL model with fractal roughness is established, and distributions of oil film pressure (OFP) and oil film thickness (OFT) at different meshing positions are obtained. Finally, considering the different topography distributions in the direction of face width, time-varying mesh stiffness (TVMS) is calculated based on the analytical sliced method. Thus, the influence of 3D surface topography can be considered. The Hertz contact stiffness is substituted by the time-varying lubricating oil film stiffness (OFS). The influences of tooth surface topography and lubricant film characteristics on meshing characteristics are investigated. The results show that the 3D rough tooth surface may be well characterized by a fractal function with random phase. Moreover, there is a great difference in the distribution of OFP and OFT between rough and smooth surfaces, which certainly influences the gear meshing characteristics.

Due to the differences in raw materials and production processes, the quality of the same type of drug produced by different manufacturers is different. In drug supervision, determining the manufacturer can help to trace drug quality issues. In this study, a method for the quick identification of drug manufacturers based on laser-induced breakdown spectroscopy (LIBS) was proposed for the first time. We obtained the LIBS spectra from 12 samples of three types of penicillin (phenoxymethylpenicillin potassium tablets, amoxicillin capsules, and amoxicillin and clavulanate potassium tablets) produced by 10 manufacturers. The LIBS characteristic lines of the three types of penicillin were ranked by importance based on the decrease in the Gini index of random forest (RF). Three classifiers—the linear discriminant analysis (LDA), support vector machine (SVM) and artificial neural network (ANN)—were used to identify the different manufacturers of the three types of penicillin. RF-ANN provided the best classification result and an accuracy of 100% in penicillin manufacturer identification. The results show that LIBS combined with chemometrics could be used in the identification of penicillin manufacturers, and this method has application potential in drug quality supervision.

The existing neural network model in urban land-subsidence prediction is over-reliant on historical subsidence data. It cannot accurately capture or predict the fluctuation in the sequence deformation, while the improper selection of training samples directly affects its final prediction accuracy for large-scale urban land subsidence. In response to the shortcomings of previous urban land-subsidence predictions, a subsidence prediction method based on a neural network algorithm was constructed in this study, from a multi-factorial perspective. Furthermore, the scientific selection of a large range of training samples was controlled using a K-shape clustering algorithm in order to produce this high-precision urban land subsidence prediction method. Specifically, the main urban area of Kunming city was taken as the research object, LiCSBAS technology was adopted to obtain the information on the land-subsidence deformation in the main urban area of Kunming city from 2018–2021, and the relationship between the land subsidence and its influencing factors was revealed through a grey correlation analysis. Hydrogeology, geological structure, fault, groundwater, high-speed railways, and high-rise buildings were selected as the influencing factors. Reliable subsidence training samples were obtained by using the time-series clustering K-shape algorithm. Particle swarm optimization–back propagation (PSO-BP) was constructed from a multi-factorial perspective. Additionally, after the neural network algorithm was employed to predict the urban land subsidence, the fluctuation in the urban land-subsidence sequence deformation was predicted with the LSTM neural network from a multi-factorial perspective. Finally, the large-scale urban land-subsidence prediction was performed. The results demonstrate that the maximum subsidence rate in the main urban area of Kunming reached −30.591 mm⋅a−1 between 2018 and 2021. Moreover, there were four main significant subsidence areas in the whole region, with uneven distribution characteristics along Dianchi: within the range of 200–600 m from large commercial areas and high-rise buildings, within the range of 400–1200 m from the under-construction subway, and within the annual average. The land subsidence tended to occur within the range of 109–117 mm of annual average rainfall. Furthermore, the development of faults destroys the stability of the soil structure and further aggravates the land subsidence. Hydrogeology, geological structure, and groundwater also influence the land subsidence in the main urban area of Kunming. The reliability of the training sample selection can be improved by clustering the subsidence data with the K-shape algorithm, and the constructed multi-factorial PSO-BP method can effectively predict the subsidence rate with a mean squared error (MSE) of 4.820 mm. The prediction accuracy was slightly improved compared to the non-clustered prediction. We used the constructed multi-factorial long short-term memory (LSTM) model to predict the next ten periods of any time-series subsidence data in the three types of cluster data (Cluster 1, Cluster 2, and Cluster 3). The root mean square errors (RMSE) were 0.445, 1.475, and 1.468 mm; the absolute error ranges were 0.007–1.030, 0–3.001, and 0.401–3.679 mm; the errors (mean absolute error, MAE) were 0.319, 1.214, and 1.167 mm, respectively. Their prediction accuracy was significantly improved, and the predictions met the measurement specifications. Overall, the prediction method proposed from the multi-factorial perspective improves large-scale, high-accuracy urban land-subsidence prediction.

As a popular food, Chinese yam (CY) powder is widely used for healthy and commercial purposes. Detecting adulteration of CY powder has become essential. In this work, chemometric methods combined with laser-induced breakdown spectroscopy (LIBS) were developed for identification and quantification of CY powder adulteration. Pure powders (CY, rhizome of winged yam (RY) and cassava (CS)) and adulterated powders (CY adulterated with CS) were pressed into pellets to obtain LIBS spectra for identification and quantification experiments, respectively. After variable number optimization by principal component analysis and random forest (RF), the best model random forest-support vector machine (RF-SVM) decreased 48.57% of the input variables and improved the accuracy to 100% in identification. Following the better feature extraction method RF, the Gaussian process regression (GPR) method performed the best in the prediction of the adulteration rate, with a correlation coefficient of prediction (Rp2) of 0.9570 and a root-mean-square error of prediction (RMSEP) of 7.6243%. Besides, the variable importance of metal elements analyzed by RF revealed that Na and K were significant due to the high metabolic activity and maximum metal content of CY powder, respectively. These results demonstrated that chemometric methods combined with LIBS can identify and quantify CY powder adulteration accurately.

Hydrothermal alteration minerals are an effective prospecting indicator. Advanced spaceborne thermal emission and reflection radiometer (ASTER) satellite data are some of the most commonly adopted multispectral data for the mapping of hydrothermal alteration minerals. Compared to multispectral data, hyperspectral data have stronger ground object recognition ability. Chinese Gaofen-5 (GF-5) is the first hyperspectral satellite independently developed by China that has the advantages of both wide-width and high-spectral-resolution technology. However, the mapping ability of GF5 data for hydrothermal alteration minerals requires further study. In this study, ASTER and GF-5 satellite data were implemented to map hydrothermal alteration minerals in the Longtoushan Pb-Zn deposit, SW China. Selective principal component analysis (SPCA) technology was employed to map iron oxide/hydroxides, argillic, quartz, and carbonate minerals at the pixel level using ASTER data, and the mixture tuned matched filtering (MTMF) method was implemented for the extracted hematite, kaolinite, calcite, and dolomite at the sub-pixel level using GF-5 data. When mapping the hydrothermal alteration minerals, the distribution features of the hydrothermal alteration minerals from the Longtoushan Pb-Zn deposit were systematically revealed. A comprehensive field investigation and petrographic study were conducted to verify the extraction accuracy of the hydrothermal alteration minerals. The results showed that the overall accuracies for the ASTER and GF-5 data were 82.6 and 92.9 and that the kappa coefficients were 0.78 and 0.90, respectively. This indicates that the GF-5 data are able to map hydrothermal alteration minerals well and that they can be promoted as a hyperspectral data source for mapping systematic hydrothermal alteration minerals in the future.

Objective. To investigate the diagnostic value of interferon-gamma release assays combined with multiple indicators for tuberculous peritonitis. Methods. Patients who were admitted to the hospital due to suspected tuberculous peritonitis were prospectively included during the 30-month study period. Moreover, healthy individuals were recruited and included in the control group. All the study participants were assessed using various indexes, such as interferon-gamma release assays. Results. A total of 180 patients with suspected tuberculous peritonitis were enrolled, and 24 were excluded. 73 patients with a confirmed diagnosis of tuberculous peritonitis were included in the tuberculous peritonitis group, 83 patients with other diseases in the other-disease control group, and 52 healthy individuals in the control group. Moreover, 83 patients in the other-disease control group and 52 participants in the control group were identified as 135 nontuberculous peritonitis patients. The area under the receiver operating characteristics curve for the QuantiFERON-TB test was 0.851 (95% confidence interval: 0.799–0.903), and the optimal cutoff value was 0.55 IU/mL, which corresponds to a sensitivity and specificity of 86.30% and 80.00%, respectively. The receiver operating characteristic curves for the combination of the QuantiFERON-TB test and the use of erythrocyte sedimentation rate, serum adenosine deaminase level, serum cancer antigen 125 level, and hypersensitive C-reactive protein level had an area under the curve of 0.859 (95% confidence interval: 0.809–0.909), with a sensitivity and specificity of 97.26% and 62.96%, respectively. Conclusions. The combined use of the QuantiFERON-TB test and multiple indexes can significantly improve the accuracy of diagnosing tuberculous peritonitis.

The gold (Au) geochemical anomaly is an important indicator of gold mineralization. While the traditional field geochemical exploration method is time-consuming and expensive, the hyperspectral remote sensing technique serves as a robust technique for the delineation and mapping of hydrothermally altered and weathered mineral deposits. Nonetheless, mineralization element anomaly detection was still seldomly used in previous hyperspectral remote sensing applications in mineralization. This study explored the coupling relationship between Gaofen-5 (GF-5) hyperspectral data and Au geochemical anomalies through several models. The Au geochemical anomalies in the Chahuazhai mining area, Qiubei County, Yunnan Province, SW China, was studied in detail. First, several noise reduction methods including radiometric calibration, Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Savitzky–Golay filter, and endmember choosing methods including Minimum Noise Fraction (MNF) transformation, matched filtering, and Fast Fourier Transform (FFT) transformation were applied to the Gaofen-5 (GF-5) hyperspectral data processing. The Spectrum-Area (S-A) method was introduced to build an FFT filter to highlight the spectral abnormal characteristics associated with Au geochemical anomaly information. Specifically, the Matched Filtering (MF) technique was applied to the dataset to find the Au geochemical anomaly abundances of endmembers with innovative large-sample learning. Then, Multiple Linear Regression (MLR), Partial Least Squares (PLS) regression, a Back Propagation (BP) network, and Geographically Weighted Regression (GWR) were used to reveal the coupling relationship between the spectra of the processed hyperspectral data and the Au geochemical anomalies. The results show that the GWR analysis has a much higher coefficient of determination, which implies that the Au geochemical anomalies and the spectral information are highly related to spatial locations. GWR works especially well for showing the regional Au geochemical anomaly trend and simulating the Au concentrated areas. The GWR model with application of the S-A method is applicable to the detection of Au geochemical anomalies, which could provide a potential method for Au deposit exploration using GF-5 hyperspectral data.

A coastline is the boundary zone between land and sea, an active zone of human social production activities and an area where the ecology is fragile and easy to change. The traditional method to analyze temporal and spatial changes in the coastline is to extract the coastline through remote sensing, LiDAR, and field sampling and analyze the temporal and spatial changes with statistical data. The coastline extracted by these methods has high spatial and temporal resolution, but it requires remote sensing images and data obtained by other sensors, so it is impossible to extract coastlines from before the emergence of remote sensing technology. This paper improves the coastline generation algorithm. Firstly, a triangulated irregular network is used to generate the preliminary rough coastline, and then, each line segment is optimized with Python language according to the influence range of the place names to further approach the real coastline. The accuracy of the coastline extracted by this method can reach 80% within 500 m, which is of great significance in the mapping and analysis of small- and medium-scale coastlines. This paper analyzes the changes in the coastline of Hainan Island before the founding of China (pre-founding) and in modern times and analyzes the impact of coastal development on coastline change. Through the analysis, it is found that, from before the founding of the People’s Republic of China to the present, the natural coastline of Hainan Island has become shorter, the artificial coastline has become longer, and the coastline generally presents a trend of advancing toward the ocean. This method realizes coastline construction under the condition of missing remote sensing images and puts forward a new way to study historical coastline changes.

While remote sensing methods have long been used for coastal and desert sand dune studies, few methods have been developed for the automated measurement of dune migration in large dune fields. To overcome a major limitation of an existing method named “pairs of source and target points (PSTP)”, this paper proposes a toe line tracking (TLT) method for the automated measurement of dune migration rate and direction using multi-temporal digital elevation models (DEM) derived from light detection and ranging (LiDAR) data. Based on a few simple parameters, the TLT method automatically extracts the base level of a dune field and toe lines of individual dunes. The toe line polygons derived from two DEMs are processed using logical operators and other spatial analysis methods implemented in the Python programming language in a geographic information system. By generating thousands of random sampling points along source toe lines, dune migration distances and directions are calculated and saved with the sampling point feature class. The application of the TLT method was demonstrated using multi-temporal LiDAR-derived DEMs for a 9 km by 2.4 km area in the White Sands Dune Field in New Mexico (USA). Dune migration distances and directions for three periods (24 January 2009–26 September 2009, 26 September 2009–6 June 2010, and 24 January 2009–6 January 2010) were calculated. Sensitivity analyses were carried out using different window sizes and toe heights. The results suggest that both PSTP and TLT produce similar sand dune migration rates and directions, but TLT is a more generic method that works for dunes with or without slipfaces that reach the angle of repose.

The Pulang porphyry copper deposit (PCD), one of the main potential areas for copper resource exploration in China, exhibits typical porphyry alteration zoning. However, further investigation of the indicative significance of alteration minerals, additional insight into metallogenic characteristics, and prospecting guidelines continue to be challenging. In this study, ASTER and WorldView-3 data were used to map hydrothermal alteration minerals by employing band ratios, principal component analysis, and spectrum-area techniques; and subsequently, the indication significance of alteration minerals was studied in-depth. The following new insights into the metallogenic structure and spatial distribution of alteration zoning in Pulang PCD were obtained and verified. (1) A new NE trending normal fault, passing through the northeast of Pulang PCD, was discovered. (2) Two mineralization alteration centers, exhibiting alteration zoning characteristics of potassic-silicified, phyllic, and propylitic zones from the inside to the outside, were observed on both sides of the fault. (3) At the junction of the redivided potassic-silicification and phyllic zones, favorable prospecting potential areas were delineated. This study shows that the spectral/multi-sensor satellite data are valuable and cost-effective tools for the preliminary stages of porphyry copper exploration in inaccessible and remote areas around the world.

Tensile creep is an important factor affecting the early cracking resistance of concrete. The tensile creep model can effectively predict the development of tensile creep. In order to establish an appropriate tensile creep model, a temperature–stress testing machine (TSTM) was employed to test the development of temperature, deformation and restraint stress of benchmark concrete and concrete mixed with the MgO under different temperature curing modes. The development law of early age stress, strain and creep was analyzed via the test data of the TSTM. The early age tensile creep of concrete was predicted with the existing Kelvin creep model. The effect of variable temperature on creep was considered in this study, and an improved Kelvin creep model was proposed. The prediction accuracy of the two models was compared and analyzed. The results indicate that MgO has little influence on the creep and specific creep of concrete. The early age cracking resistance of MgO concrete is better than benchmark concrete. The improved Kelvin model based on the microprestress-solidification (MPS) theory predicts the early tensile creep of concrete more accurately in variable temperature conditions. These are significantly helpful for the application of the MgO expansion agent in dam engineering.

Strong background noise and complicated interfering signatures when implementing vibration-based monitoring make it difficult to extract the weak diagnostic features due to incipient faults in a multistage gearbox. This can be more challenging when multiple faults coexist. This paper proposes an effective approach to extract multi-fault features of a wind turbine gearbox based on an integration of minimum entropy deconvolution (MED) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA). By using simulated periodic transient signals with different noise to signal ratios (SNR), it evaluates the outstanding performance of MED in noise suppression and reveals the deficient in extract multiple impulses. On the other hand, MOMEDA can performs better in extracting multiple pulses but not robust to noise influences. To compromise the merits of them, therefore the diagnostic approach is formalized by extracting the multiple weak features with MOMEDA based on the MED denoised signals. Experimental verification based on vibrations from a wind turbine gearbox test bed shows that the approach allows successful identification of multiple faults occurring simultaneously on the shaft and bearing in the high speed transmission stage of the gearbox.

In view of the problem that the fault signal of the rolling bearing is weak and the fault feature is difficult to extract in the strong noise environment, a method based on minimum entropy deconvolution (MED) and local mean deconvolution (LMD) is proposed to extract the weak fault features of the rolling bearing. Through the analysis of the simulation signal, we find that LMD has many limitations for the feature extraction of weak signals under strong background noise. In order to eliminate the noise interference and extract the characteristics of the weak fault, MED is employed as the pre-filter to remove noise. This method is applied to the weak fault feature extraction of rolling bearings; that is, using MED to reduce the noise of the wind turbine gearbox test bench under strong background noise, and then using the LMD method to decompose the denoised signals into several product functions (PFs), and finally analyzing the PF components that have strong correlation by a cyclic autocorrelation function. The finding is that the failure of the wind power gearbox is generated from the micro-bending of the high-speed shaft and the pitting of the #10 bearing outer race at the output end of the high-speed shaft. This method is compared with LMD, which shows the effectiveness of this method. This paper provides a new method for the extraction of multiple faults and weak features in strong background noise.

Performing a multiscale assessment of water resource vulnerability on the basis of political boundaries and watersheds is necessary for adaptive water resources management. Using the Risk-Exposure-Sensitivity-Adaptability model (RESC model), the water resource vulnerability of the Huai River Basin was assessed using four scales, namely, Class II, Class III, Province-Class II, and Municipality-Class III WRR (Water Resources Region). Following this, the spatial heterogeneity of the vulnerability of the above four scales was evaluated with the Theil and the Shannon-Weaver index. The results demonstrate that, instead of moving towards convergence, water resource vulnerability presents different grades which change together with the change in scale, and in turn, tend to weaken from east to west. Of the four scales, the scale of Municipality-Class III WRR shows the most significant spatial diversity, whereas that of Class II WRR shows the least diversity. With spatial downscaling, the vulnerability demonstrates high spatial heterogeneity and diversity. Herein, an innovative cross-scales vulnerability assessment is proposed and the RESC model characteristics and uncertainties as well as the employment of cross-scale water resource vulnerability are discussed.

Deep vein thrombosis (DVT) is a common complication of skeletal surgery, which can cause disability and death in severe cases. Here, we have compared the preventive effects of rivaroxaban and low molecular weight heparin on DVT of lower extremity after total hip arthroplasty and the safety. A total of 310 patients who received total hip arthroplasty from May 2014 to June 2016 were divided into a rivaroxaban group (n=153) and a low molecular weight heparin group (n=157). The rivaroxaban group was orally administered with rivaroxaban (10 mg, qd) 12 h after surgery for 30 consecutive days, and the other group was subcutaneously injected with low molecular weight heparin calcium injection (0.6 mL, ad) for 7 consecutive days. The incidence rate of lower extremity DVT, drainage blood volume, hemoglobin decline, as well as preoperative and postoperative 7-d prothrombin time (PT), activated partial thromboplastin time (APTT), platelet (PLT) count and D-dimer level of the two groups were compared. The two groups had similar incidence rates of lower extremity DVT, drainage blood volumes and extents of hemoglobin decline (P >0.05). There were no significant differences in the preoperative and postoperative 7-d PT, APTT, PLT counts and D-dimer levels between the two groups (P >0.05). Rivaroxaban and low molecular weight heparin show comparable preventive effects on lower extremity DVT after total hip arthroplasty. Results suggest that rivaroxaban is superior than the low molecular weight heparin in terms of convenient use (oral administration), good compliance and absence of dose adjustment. [ABSTRACT FROM AUTHOR]

In this study, carbon nanofibers (CNFs) were uniformly dispersed into cement-based materials by the ultrasonic dispersion method to prepare cement-based materials modified by CNFs, and the deformation performance and fracture toughness of the CNF-modified cement-based materials were studied. The results showed that CNFs could inhibit the autogenous shrinkage and drying shrinkage of cement paste, and significantly delay the cracking time of cement mortar. When the dosage of CNFs was 0.05 wt%, the cracking time of cement mortar was extended by nearly 1.5 times longer than that of the control group. The fracture performance of the CNF-modified cement mortar was studied by the fracture test of a three-point bending beam, and the fracture toughness of the mortar was evaluated by double-K fracture parameters. The results showed that at 0.05 wt% dosage of CNFs, the unstable fracture toughness and fracture energy of the mortar increased by 53.5% and 17.2%, respectively. Scanning electron microscopy images illustrated that CNFs could produce bridging and pullout effects in the cement-based materials and retard crack propagation, thus reducing shrinkage deformation and improving fracture toughness. [ABSTRACT FROM AUTHOR]

Traveling Salesman Problem (TSP) is widely applied in various fields and can also be used to evaluate the merits of an algorithm, where there are many applications in real life situations that can be transformed into a TSP problem. Thus solving the problem has both theoretical and practical significance. Many methods can be used to solve TSP problems, such as branch limit method, dynamic programming, ant colony algorithm, etc. In this paper, we consider TSP problems based on genetic algorithms, as follows: i) we explain the principles of genetic algorithm, ii) we write C++ program to solve the TSP problem using genetic algorithms, iii) we analyze factors such as population size and crossover rate influence on the results, and iv) make comparisons between ant colony algorithms and genetic algorithms. Our results demonstrate that genetic algorithms used to solve TSP problems have good advantage in certain circumstances. [ABSTRACT FROM AUTHOR]