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The northwest Yunnan rift zone develops along the western boundary of the Sichuan-Yunnan block. The N-S trending eastern foothill fault of Yulong Snow Mountain on the northern edge of the rift zone extends between the Yulong Snow Mountain and the Lijiang Basin. It is the innermost part of the western boundary of the Sichuan-Yunnan block. The formation mechanism and activity characteristics of the eastern foothill fault of Yulong Snow Mountain are not only related to the complex structural boundary issues on the western side of Sichuan and Yunnan, but also can explain the uplift process of the Yulong Mountain and the Haba Mountain. After the MS7.0 earthquake in Lijiang in 1996, the eastern foothill fault of Yulong Snow Mountain widely attracted attention. However, previous research results about the Late Quaternary activity of this fault were diverse, including normal faulting, strike-slip faulting, and thrusting. It indicates that although this fault is only 60 kilometers long, it belongs to a complex tectonic system. Understanding the activity characteristics and earthquake-generating behavior of this fault is of great significance for establishing a complex structural model in the western Sichuan-Yunnan block. This article summarizes previous research data, reinterprets the fault based on satellite images, analyzes the typical fault-related landforms and identifies clear evidence of a combination of normal faulting and sinistral strike-slip along the fault. Field investigations have further validated these interpretations. Based on the conclusions and analysis presented in this paper, we have summarized different regional tectonic models corresponding to the different activity properties of eastern foothill fault of Yulong Snow Mountain. Meanwhile, from the perspective of plateau expansion, it is believed that the complex fault combination in the western Sichuan-Yunnan block is new and is forming a new western boundary of the Sichuan-Yunnan block.

Abstract Off-line leachate collection from agricultural landscapes cannot guarantee precise evaluation of agricultural non-point source (ANPS) due to geospatial variations, time, and transportation from the field to the laboratory. Implementing an in-situ nitrogen and phosphorous monitoring system with a robust photochemical flow analysis is imperative for precision agriculture, enabling real-time intervention to minimize non-point source pollution and overcome the limitations posed by conventional analysis in laboratory. A reliable, robust and in-situ approach was proposed to monitor nitrogen and phosphorous for determining ANPS pollution. In this study, a home-made porous ceramic probe and the frequency domain reflectometer (FDR) based water content sensors were strategically placed at different soil depths to facilitate the collection of leachates. These solutions were subsequently analyzed by in-situ photochemical flow analysis monitoring system built across the field to estimate the concentrations of phosphorus and nitrogen. After applying both natural and artificial irrigation to the agricultural landscape, at least 10 mL of soil leachates was consistently collected using the porous ceramic probe within 20 min, regardless of the depth of the soil layers when the volumetric soil water contents are greater than 19%. The experimental results showed that under different weather conditions and irrigation conditions, the soil water content of 50 cm and 90 cm below the soil surface was 19.58% and 26.08%, respectively. The average concentrations of NH4 +-N, NO3 −-N, PO4 3− are 0.584 mg/L, 15.7 mg/L, 0.844 mg/L, and 0.562 mg/L, 16.828 mg/L and 0.878 mg/L at depths of 50 cm and 90 cm below the soil surface, respectively. Moreover, the comparison with conventional laboratory spectroscopic analysis confirmed R2 values of 0.9951, 0.9943, 0.9947 average concentration ranges of NH4 +-N, NO3 −-N, and PO4 3−, showcasing the accuracy and reliability of robust photochemical flow analysis in-situ monitoring system. The suggested monitoring system can be helpful in the assessment of soil nutrition for precision agriculture.

The Lijiang–Jinpingshan fault (LJF) is an important secondary boundary fault that obliquely cuts the Sichuan–Yunnan rhombic block. It is of great significance for understanding the tectonic evolution of the Sichuan–Yunnan rhombic block and even the southeastern margin of the Tibet Plateau. Based on a digital elevation model (DEM), this work combines ArcGIS with MATLAB script programs to extract geomorphic indices including slope, the relief degree of the land surface (RDLS), hypsometric integral (HI), and channel steepness index (ksn) of 593 sub–watersheds and strip terrain profiles around the LJF. By analyzing the spatial distribution characteristics of the geomorphic indices and combining the regional lithology and precipitation conditions, the spatial distribution of the geomorphic indices around the study area was analyzed to reveal the implications of the LJF’s activity. The results of this work indicate that (1) the distribution of geomorphic indices around the LJF may not be controlled by climate and lithological conditions, and the LJF is the dominant factor controlling the geomorphic evolution of the region. (2) The spatial distribution patterns of geomorphic indices and strip terrain profiles reveal that the vertical movement of the LJF resulted in a pronounced uplift on its northwest side, with tectonic activity gradually diminishing from northeast to southwest. Furthermore, based on the spatial distribution characteristics of these geomorphic indices, the activity intensity of the LJF can be categorized into four distinct segments: Jianchuan–Lijiang, Lijiang–Ninglang, Ninglang–Muli, and Muli–Shimian. (3) The activity of the LJF obtained from tectonic geomorphology is consistent with the conclusions obtained in previous geological and geodesic studies. This work provides evidence of the activity and segmentation of the LJF in tectonic geomorphology. The results provide insight for the discussion of tectonic deformation and earthquake disaster mechanisms in the southeastern margin of the Tibet Plateau.

Garlic constitutes a significant small-scale agricultural commodity in China. A key factor influencing garlic prices is the planted area, which can be accurately and efficiently determined using remote sensing technology. However, the spectral characteristics of garlic and winter wheat are easily confused, and the widespread intercropping of these crops in the study area exacerbates this issue, leading to significant challenges in remote sensing image analysis. Additionally, remote sensing data are often affected by weather conditions, spatial resolution, and revisit frequency, which can result in delayed and inaccurate area extraction. In this study, historical data were utilized to restore Sentinel-2 remote sensing images, aimed at mitigating cloud and rain interference. Feature combinations were devised, incorporating two vegetation indices into a comprehensive time series, along with Sentinel-1 synthetic aperture radar (SAR) time series and other temporal datasets. Multiple classification combinations were employed to extract garlic within the study area, and the accuracy of the classification results was systematically analyzed. First, we used passive satellite imagery to extract winter crops (garlic, winter wheat, and others) with high accuracy. Second, we identified garlic by applying various combinations of time series features derived from both active and passive remote sensing data. Third, we evaluated the classification outcomes of various feature combinations to generate an optimal garlic cultivation distribution map for each region. Fourth, we developed a garlic fragmentation index to assess the impact of landscape fragmentation on garlic extraction accuracy. The findings reveal that: (1) Better results in garlic extraction can be achieved using active–passive time series remote sensing. The performance of the classification model can be further enhanced by incorporating short-wave infrared bands or spliced time series data into the classification features. (2) Examination of garlic cultivation fragmentation using the garlic fragmentation index aids in elucidating variations in accuracy across the study area’s six counties. (3) Comparative analysis with validation samples demonstrated superior garlic extraction outcomes from the six primary garlic-producing counties of the North China Plain in 2021, achieving an overall precision exceeding 90%. This study offers a practical exploration of target crop identification using multi-source remote sensing data in mixed cropping areas. The methodology presented here demonstrates the potential for efficient, cost-effective, and accurate garlic classification, which is crucial for improving garlic production management and optimizing agricultural practices. Moreover, this approach holds promise for broader applications, such as nationwide garlic mapping.

The transformation of ecosystem structure leads to changes in ecosystem services (ESs) and their relationship. However, most research in this area has focused on particular scales and timeframes, often overlooking the significance of spatial and temporal variations. Therefore, we used the equivalent value method to evaluate seven typical ESs in the Yangtze River Delta (YRD) between 2000 and 2020: food production (FP), water supply (WS), climate regulation (CR), environmental purification (EP), soil conservation (SC), biodiversity maintenance (BM), and aesthetic landscape (AL). We further employed the Spearman correlation coefficient and bivariate Moran’s I to evaluate the relationship of ESs and their spatial heterogeneity at grid, township, county and city scales. Our results show that (1) All ESs except AL exhibited a fluctuating upward trend from 2000 to 2020, resulting in a total increase in ecosystem service (ES) value of RMB 650.63 billion. (2) Approximately 70% of the ES pairs demonstrated a synergistic relationship, with the exception of FP and other ESs, which mainly showed a trade-off. (3) With the scale increased from grid to city level, the degree of trade-off between FP and other ESs strengthened at different levels, while the synergy degree of among other ESs gradually decreased. (4) The relationship between ESs demonstrated strong spatial heterogeneity, with FP and other ESs exhibiting trade-offs primarily in the northern and southern YRD, while other ES pairs exhibited mostly synergy in these regions. This study provides scientific information for governments to optimize land use distribution and improve ESs.

Oilseed rape is an important oilseed crop planted worldwide. Maturity classification plays a crucial role in enhancing yield and expediting breeding research. Conventional methods of maturity classification are laborious and destructive in nature. In this study, a nondestructive classification model was established on the basis of hyperspectral imaging combined with machine learning algorithms. Initially, hyperspectral images were captured for 3 distinct ripeness stages of rapeseed, and raw spectral data were extracted from the hyperspectral images. The raw spectral data underwent preprocessing using 5 pretreatment methods, namely, Savitzky–Golay, first derivative, second derivative (D2nd), standard normal variate, and detrend, as well as various combinations of these methods. Subsequently, the feature wavelengths were extracted from the processed spectra using competitive adaptive reweighted sampling, successive projection algorithm (SPA), iterative spatial shrinkage of interval variables (IVISSA), and their combination algorithms, respectively. The classification models were constructed using the following algorithms: extreme learning machine, k-nearest neighbor, random forest, partial least-squares discriminant analysis, and support vector machine (SVM) algorithms, applied separately to the full wavelength and the feature wavelengths. A comparative analysis was conducted to evaluate the performance of diverse preprocessing methods, feature wavelength selection algorithms, and classification models, and the results showed that the model based on preprocessing-feature wavelength selection-machine learning could effectively predict the maturity of rapeseed. The D2nd-IVISSA-SPA-SVM model exhibited the highest modeling performance, attaining an accuracy rate of 97.86%. The findings suggest that rapeseed maturity can be rapidly and nondestructively ascertained through hyperspectral imaging.

Antibacterial properties are critical for implants, while general pure titanium implants are bioinert. Adding nano Ag to metals is an effective strategy to obtain antibacterial properties. However, the comprehensive properties of Ti–Ag alloy prepared by traditional methods are not satisfactory. In this paper, Ti–5Ag alloy with an antibacterial rate close to 100 % was synthesized in situ by laser powder bed fusion (LPBF), and its microstructure and properties were studied systematically. Phase analysis demonstrated the existence of Ti2Ag which played an important role in gaining excellent antibacterial properties. Benefiting from in situ laser alloying, the elements were homogeneously distributed, which endowed the Ti–5Ag alloy with excellent mechanical properties and corrosion resistance. The tensile strength and elongation reached 716 MPa and 33.51 %, respectively. Furthermore, through the design of triply periodic minimal surface (TPMS) structures, mechanical properties matching human bone were obtained. Based on LPBF-printed Ti–5Ag alloy and TPMS structures, this paper provides a feasible method for the manufacturing of bone implants with excellent comprehensive properties.

Abstract Background Post-infectious bronchiolitis obliterans (PIBO) is the most common sequelae in children with adenovirus pneumonia (ADVP). However, there are few studies on the risk factors for PIBO occurrence. This study aims to investigate the risk factors for PIBO in pediatric patients with severe ADVP, especially after invasive mechanical ventilation (IMV), as well as to build a nomogram prediction model. Methods The clinical data, laboratory and imaging features, and treatment of 863 children with ADVP under 3 years old who were admitted to our hospital from January to December 2019 were retrospectively analyzed. Among them, 66 children with severe ADVP received IMV treatment. The situation and the influencing factors of PIBO in children with severe ADVP were explored, and a nomogram prediction model was constructed. Results Among the 863 cases of ADVP, 46 cases (5.33%) developed PIBO. Duration of fever, IMV, complications, and neutrophil percentage were independent risk factors for PIBO in children with ADVP. Among the 66 patients with ADVP who underwent IMV, 33 patients (50.0%) developed PIBO. Gender, duration of fever, adenovirus (ADV) load, and mixed fungal coinfections were independent risk factors for PIBO. In the nomogram prediction model analysis, the area under the curve (AUC) was 0.857; in addition, Hosmer‒Lemeshow (H–L) detection reflected good alignment (χ2 = 68.75, P

IntroductionThe association between long working hours and cumulative fatigue is widely acknowledged in the literature. However, there are few studies on the mediating effect of working hours on cumulative fatigue using occupational stress as a mediating variable. The present study aimed at investigating the mediating role of occupational stress in the relationship between working hours and cumulative fatigue in a sample of 1,327 primary health care professionals.MethodsThe Core Occupational Stress Scale and the Workers’ Fatigue Accumulation Self-Diagnosis Scale were utilized in this study. The mediating effect of occupational stress was examined using hierarchical regression analysis and the Bootstrap test.ResultsWorking hours were positively associated with cumulative fatigue via occupational stress (p

Abstract Background Multifocal atrophic gastritis and intestinal metaplasia are considered to be important links in the gastric precancerous cascade. However, there are no specific drugs for these conditions. Although many studies have shown that traditional Chinese medicine is effective with no serious side effects, these studies have not been scientifically rigorous trials. Our aim is to design a high-quality trial for a Chinese patent medicine, Elian Granules, to investigate its efficacy and safety in treating patients with chronic atrophic gastritis with or without intestinal metaplasia. Methods This is a phase II, randomized, double-blind, placebo-controlled, multicenter clinical trial. A total of 240 participants will be assigned to a treatment or placebo control group in a 1:1 ratio. The experimental drug or placebo will be taken with boiling water, two small bags (24.2 g) each time, twice a day, half an hour after a meal, for 24 weeks. The primary outcome is the observation of histological changes in the gastric mucosa of patients with atrophic gastritis with or without intestinal metaplasia after 6 months based on the OLGA/OLGIM staging systems. The secondary outcomes include the assessment of dyspepsia and quality of life based on the dyspepsia symptom score and the quality-of-life scale. Discussion This study is designed to evaluate the efficacy and safety of Elian Granules in a randomized, double-blind, placebo-controlled, multicenter manner. This trial may not only provide evidence for a phase III clinical trial, but also an alternative option for the treatment of chronic atrophic gastritis (CAG). Trial registration Registry Platform For Evidence-Based Traditional Chinese Medicine ChiMCTR2000003929 . Registered on 13 September 2020

In the emerging applications of optoelectronics and mobile, it has always been an urgent problem in fire safety field to ensure the excellent transparency, water resistance and mechanical properties of flame retardant polycarbonate (PC). Unfortunately, the high-efficiency flame retardants used in PC often endow it with satisfied flame retardancy at the cost of the mechanical properties, transparency of composites. Hence, in this work, the high-quality pentaerythritol phosphate flame retardant (PBPP) was successfully prepared by novel synthesis method. The results indicated that the introduction of only 2 wt% PBPP and 0.1 wt% polytetrafluoroethylene (PTFE) enabled PC to obtain a UL-94 V-0 rating and the melt dripping of PC composites was inhibited during combustion. Meanwhile, the PC/PBPP composites still maintained its original transparency. With the introduction of PBPP, the PHRR and THR of PC/PBPP were reduced by 36.0% and 11.4%, respectively. This was mainly attributed to the catalytic charring and gas-phase suppression effect of PBPP. To our surprise, the introduction of PBPP obviously increased the tensile properties and ductility of PC composites. Besides, the PC/PBPP composites still retained excellent flame retardancy and mechanical performance before and after water resistance tests due to the hydrophobicity of PBPP and its excellent compatibility with the PC matrix. This work provided a simple and effective method to prepare PC composites with excellent water resistance, flame retardancy, transparency and mechanical enhancement, which possessed great significance for the application of PC composites in emerging fields.

Heteroatom doping, especially nitrogen doping, has been regarded as an efficient strategy to break through the capacity limitation of carbonaceous anode materials in potassium-ion batteries (PIBs). Constructing edge-nitrogen-rich carbon skeleton with highly exposed active sites and efficient charge transfer is critical for the high performance of nitrogen-doped carbonaceous anode materials. Herein, a kind of ultrahigh edge-nitrogen (up to 16.2 at%) doped carbon nanosheets (ENCNS) has been developed by an efficient assembly of high-nitrogen-ratio melamine (MA) with polyacrylic acid grafted graphene oxide (GO-g-PAA) molecular brushes. The assembled PAA/MA structure facilitates the formation of an edge-nitrogen-rich carbon skeleton during heat treatment, while the highly conductive graphene backbone with a 2D nanomorphology enables shortened ion diffusion pathways and numerous exposed active surfaces. As a result, the ENCNS demonstrate excellent rate performance (up to 144 mAh g−1 at 10 A g−1) and good cycle stability (136 and 100 mAh g−1 after 400 cycles at 5 and 10 A g−1, respectively).

A rapid and efficient method using an alkyl-functionalized magnetic nanoparticles-based extraction technique combined with Ultra-High Performance Liquid Chromatography was developed for the detection of trace amounts of polycyclic aromatic hydrocarbons in tea leaves. As a popular coating for chromatographic column packing materials, C18-alkyl has been demonstrated to be effective in separating polycyclic aromatic hydrocarbons. Additionally, the magnetism of the nanomaterials accelerates the extraction process while their high surface ratio enables desirable dispersity in the sample matrix. Meanwhile, the adsorbents can be washed and reused 30 times without compromising recovery, which greatly reduces the budget. The effects of various parameters were investigated and optimized, and the recoveries for five analytes were in the range of 84.8–105.4%. The RSD of intra-day and inter-day were below 11.9% and 6.8%, respectively. The limits of detection and limits of quantification ranged from 1.69–9.97 ng g−1 and 5.12–30.21 ng g−1, indicating satisfactory sensitivity. Thus, the proposed methodology is rapid, highly efficient, and economical, and it expands the application of magnetic cleanup approaches in complex food matrices.

To further improve the security of SHA-256, a compression function construction scheme based on chaotic S-box is proposed. Through the reasonable design of the iteration mode and using the nonlinearity, confusion, and anti-difference of chaotic S-box, the relationship between the plaintext and the hash value becomes more complicated and unpredictable, by the diffusion of the linear transformation P-box, the dependence between the plaintext and the hash value is further improved. The compression function is applied to the SHA-256 Hash Algorithm to test and analyze the distribution, sensitivity, confusion, diffusion, and collision resistance, and compared with the original SHA-256 and the mainstream Hash Algorithms. The results show that the proposed scheme can effectively improve the collision resistance of the SHA-256 Hash Algorithm and enhance the stability of confusion and diffusion with fewer cryptographic components and lower operational consumption to provide more reliable security guarantee for its application.

Background. The study aimed to explore risk factors for bronchial mucus plugs (BMP) formation in children with adenovirus (AdV) pneumonia. Methods. A retrospective study was conducted on children with AdV pneumonia who underwent bronchoscopy from January 2019 to December 2019. Children were divided into the BMP group and the control group, depending on whether BMP was formed or not. The clinical information and treatment proposals of the two groups of children were counted and analyzed via multiple logistic regression analysis, ROC curve analysis, and correlation analysis. Results. Among 453 patients with AdV pneumonia, 185 (40.84%) were in the BMP group. Among all the cases, there were 188 patients with a single AdV infection, including 64 (34.04%) in the BMP group and 124 (65.96%) in the control group. The incidence of dyspnea, poor spirits, mixed infections, and other symptoms in the BMP group was higher than in the control group. Children in the BMP group had a longer heat range. C-reactive protein (CRP), lactate dehydrogenase (LDH), D-dimer (DD), and AdV load levels were higher in the MBP group. AdV load, Mycoplasma coinfection, DD, heat range, and LDH were independent risk factors for BMP, among which AdV load was the most significant (AUC = 0.819). AdV load was positively correlated with other risk factors, respectively. AdV load and heat range were independent risk factors for BMP patients with a single AdV infection. Conclusion. AdV load might have important clinical value in predicting BMP development in AdV pneumonia.

Abstract Carbon‐based nanomaterials have been regarded as promising non‐noble metal catalysts for renewable energy conversion system (e.g., fuel cells and metal–air batteries). In general, graphitic skeleton and porous structure are both critical for the performances of carbon‐based catalysts. However, the pursuit of high surface area while maintaining high graphitization degree remains an arduous challenge because of the trade‐off relationship between these two key characteristics. Herein, a simple yet efficient approach is demonstrated to fabricate a class of 2D N‐doped graphitized porous carbon nanosheets (GPCNSs) featuring both high crystallinity and high specific surface area by utilizing amine aromatic organoalkoxysilane as an all‐in‐one precursor and FeCl3·6H2O as an active salt template. The highly porous structure of the as‐obtained GPCNSs is mainly attributed to the alkoxysilane‐derived SiOx nanodomains that function as micro/mesopore templates; meanwhile, the highly crystalline graphitic skeleton is synergistically contributed by the aromatic nucleus of the precursor and FeCl3·6H2O. The unusual integration of graphitic skeleton with porous structure endows GPCNSs with superior catalytic activity and long‐term stability when used as electrocatalysts for oxygen reduction reaction and Zn–air batteries. These findings will shed new light on the facile fabrication of highly porous carbon materials with desired graphitic structure for numerous applications.

Pulmonary arterial hypertension (PAH) is a serious disease with reduced systemic circulation and low bioavailability associated with conventional and dosed therapy, which inhaled drugs can avoid. A mean pulmonary artery pressure (mPAP) of ≥25 mmHg (1 mmHg=0.133 kPa) at rest or ≥30 mmHg during exercise and a pulmonary capillary pressure or left atrial pressure (PLA) of ≤15 mmHg can be diagnosed with PAH. Pulmonary hypertension is classified into primary PAH and secondary PAH according to the presence or absence of principles or risk factors. The main symptoms of pulmonary hypertension include dyspnoea, syncope, weakness, chest pain, and the presence of varying degrees of peripheral oedema. It is a highly pathogenic and life-threatening disease and can lead to delays in treatment if not diagnosed in time. In the past few years, the studies related to this progressed slowly, which brought great harm to patients with PAH. Reports showed that patients diagnosed with PAH should receive routine preventative care, such as pneumococcal and influenza vaccinations. Inhalation therapy is mainly used for the treatment of respiratory diseases and is of great interest due to the concentration of the drug in the airways and lung tissues. Therefore, the present situation of pulmonary hypertension and the characteristics of inhalation preparation were reviewed in this paper to provide some related cue for the treatment of pulmonary hypertension. In the future, it is necessary to develop more treatment methods for pulmonary hypertension.

BPL is a special Chinese long-wave time service system constructed and maintained by the National Time Service Center of the Chinese Academy of Sciences. Two experimental paths were designed to analyze the impact of the annular solar eclipse that occurred on June 21, 2020, on the BPL time service performance. In the first path, the receiving point was located in the ring eclipse zone, whereas in the other path, the sky-wave reflection point was located in the ring eclipse zone. The time of arrival (TOA) data during 19–23 June 2020 at the Sanya test point (sky-wave reflection point located in the ring eclipse zone) and field strength from 21 to 22 June 2020 at the Xiamen test point (receiving point located in the ring eclipse zone) were measured and analyzed. The experimental results show that under normal conditions of BPL time service signal transmission, the TOA and field strength undergo significant changes during the annular solar eclipse, the TOA data change up to 5.17 µs, and the field strength data change up to 17.72 dBμV. The data tend to increase initially and then decrease. This change is consistent with the ionospheric change caused by the annular solar eclipse. The BPL time service performance is affected at the receiving points located in the south of the BPL time service station with the great circle distance ranging from 1426 to 1844 km.

Background: By implementing aggressive control measures, China has rapidly and effectively controlled the Coronavirus disease 2019 (COVID-19) pandemic. However, the neglected homeless population may become victims of that perceived success. Due to political sensitivity, we know little about them.Aims: This study aimed to investigate how the pandemic and the pandemic control strategies of the government affected the lives of the homeless people in mainland China.Methods: A total of 103 eligible participants experiencing absolute homelessness were recruited from Guangzhou City during July and August 2020. Surveys measured demographic characteristics including health status, changes in daily living, and actions of the government toward the homeless during the pandemic. Sankey diagrams and ordered logit regression models were used to examine the impact on the homeless of inhumane government efforts to drive the homeless away. Qualitative materials were analyzed by using an inductive approach to provide more details.Results: First, the homeless people in Guangzhou tended to be male, aged 40 to 64 years, less well-educated, and they originated from outside Guangdong although they were living in the city center. Most had little connection with their families. After a long period of homelessness, almost half of the participants were in poor health with various conditions, which made them extremely vulnerable to COVID-19. Second, the pandemic caused a substantial decline in incomes of homeless people, had less of a negative impact on their food intake, and affected sleep time in different ways. Third, during the COVID-19 pandemic, humanitarian aid from local governments of China decreased, whereas inhumane efforts to drive the homeless away intensified. Fourth, quantitative models and qualitative materials demonstrate the devastating effect of the strict pandemic control strategy of the government on the lives of homeless people, which may further cause their health problems.Conclusion: This study for the first time illustrated the characteristic features of the street homeless population in mainland China and their living situation during the COVID-19 pandemic, and most importantly demonstrated the devastating effect of the strict pandemic control of the government, which has been considered a great success in previous studies, on lives of homeless people. Urgent measures should be taken to ensure the protection of the homeless population and prevent an impending humanitarian crisis.

Abstract Background Diffusion weighted imaging (DWI) has a good diagnostic value for malignant thyroid nodules, but the published protocols suffer from flaws and focus on the apparent diffusion coefficient (ADC). This study investigated the diagnostic performance of multiple MRI parameters in differentiating malignant from benign thyroid nodules. Methods This was a retrospective study of 181 consecutive patients (148 benign and 111 malignant nodules, confirmed by pathological results). The patients underwent conventional MRI, DWI, and dynamic contrast-enhanced MRI before surgery. The chi-square test and the Student t test were used to compare the conventional features and ADC value between malignant and benign groups. Multivariate logistic regression was used to identify the independent predictors and to construct a model. Receiver operator characteristic (ROC) curve analysis was used to assess the diagnostic performance of the independent variables and model. Results Tumor diameter, ADC value, cystic degeneration, pseudocapsule sign, high signal cystic area on T1-weighted imaging, ring sign in the delayed phase, and irregular shape showed significant differences between two groups (all P

To provide real-time prediction of wastewater treatment plant (WWTP) effluent water quality, a machine learning (ML) model was developed by combining an improved feedforward neural network (IFFNN) with an optimization algorithm. Data used as input variables of the IFFNN included hourly influent water quality parameters, influent flow rate and WWTP process monitoring and operational parameters. Additionally, input variables included historical effluent water quality parameters for future prediction. The model was demonstrated in a WWTP in Jiangsu Province, China, where prediction of effluent chemical oxygen demand (COD) and total nitrogen (TN) with large variations were tested. Relative to the traditional feedforward neural network (FFNN) model without considering historical effluent water quality parameter input, the IFFNN enhanced prediction performance by 52.3% (COD) and 72.6% (TN) based on the mean absolute percentage errors of test datasets, after its model structure was optimized with a genetic algorithm (GA). The problem of over-fitting could also be overcome through the use of the IFFNN, with the determination of coefficient increased from 0.20 to 0.76 for test datasets of effluent COD. The GA-IFFNN model, which was efficient in capturing complex non-linear relationships and extrapolation, could be a useful tool for real-time direction of regulatory changes in WWTP operations.

PURPOSE:We aimed to evaluate preoperative diffusion-weighted magnetic resonance imaging (DWI) for predicting aggressive histological features in papillary thyroid cancer (PTC).METHODS:This prospective study included 141 PTC patients, who underwent DWI prior to thyroidectomy; 88 patients with 88 PTC lesions were finally analyzed. Multiple comparisons of mean and minimum apparent diffusion coefficient (ADC) values (ADCmean and ADCmin) and ADC of the solid component (ADCsolid) between the lowly aggressive PTC, highly aggressive PTC without hobnail, and hobnail variant PTC groups were performed by one-way ANOVA or the Welch test. The nonparametric Kruskal-Wallis H test was used to assess lesion size differences. Receiver operating characteristic (ROC) curve analysis was also performed.RESULTS:ADC values in the lowly aggressive PTC group were found to be significantly higher than those in the highly aggressive PTC without hobnail group (ADCmean: 1.35±0.20×10-3 mm2/s vs. 1.16±0.17×10-3 mm2/s, P = 0.003; ADCmin: 1.10±0.17×10-3 mm2/s vs. 0.88±0.16×10-3 mm2/s, P < 0.001; ADCsolid: 1.26±0.23×10-3 mm2/s vs. 1.04±0.17×10-3 mm2/s, P < 0.001). No significant differences for the ADCmean, ADCmin, and ADCsolid were observed between the lowly aggressive and hobnail variant PTC groups (all P > 0.05). Lesion sizes in the hobnail variant PTC group was significantly elevated compared with the lowly aggressive PTC group (2.19±1.21 cm vs. 0.93±0.37 cm, P < 0.001). Areas under the curves (AUCs) for ADCmean, ADCmin, and ADCsolid between the lowly aggressive PTC and highly aggressive PTC group without hobnail were 0.758, 0.851, and 0.787, respectively. The AUC for size between the lowly aggressive and hobnail variant PTC group was 0.896.CONCLUSION:ADCmin from DWI could potentially provide quantitative information to differentiate lowly aggressive PTC from highly aggressive PTC lesions without hobnail variants.

Use of folic acid (FA) during early pregnancy protects against birth defects. However, excess FA has shown gender-specific neurodevelopmental toxicity. Previously, we fed the mice with 2.5 times the recommended amount of FA one week prior to mating and during the pregnancy and lactation periods, and detected the activated expression of Fos and related genes in the brains of weaning male offspring, as well as behavioral abnormalities in the adults. Here, we studied whether female offspring were affected by the same dosage of FA. An open field test, three-chamber social approach and social novelty test, an elevated plus-maze, rotarod test and the Morris water maze task were used to evaluate their behaviors. RNA sequencing was performed to identify differentially expressed genes in the brains. Quantitative real time-PCR (qRT-PCR) and Western blots were applied to verify the changes in gene expression. We found increased anxiety and impaired exploratory behavior, motor coordination and spatial memory in FA-exposed females. The brain transcriptome revealed 36 up-regulated and 79 down-regulated genes in their brains at weaning. The increase of Tlr1; Sult1a1; Tph2; Acacb; Etnppl; Angptl4 and Apold1, as well as a decrease of Ppara mRNA were confirmed by qRT-PCR. Among these genes; the mRNA levels of Etnppl; Angptl4andApold1 were increased in the both FA-exposed female and male brains. The elevation of Sult1a1 protein was confirmed by Western blots. Our data suggest that excess FA alteres brain gene expression and behaviors in female offspring, of which certain genes show apparent gender specificity.

We use the movement velocities of GPS stations in western Sichuan province, China, to determine the distribution of horizontal strain accumulation before the 2008 Wenchuan Ms8.0 earthquake with a piecewise approximation approach and the coseismic displacements to determine the fault slips of the earthquake with an inversion analysis method. The results show that the distribution of the principal strain rates is strongly related to the active faults in the region, but along Longmenshan fault where the earthquake occurred, the strain rates are much lower than the others. The fault slip distribution shows mainly a thrusting with dextral striking, and the fault slips in the upper parts of the fault plane are in general bigger. Using the current strain accumulation rate and the released energy by the earthquake, we predict such a big earthquake in Longmenshan fault zone will happen in 460 to 1380 years.

This paper addresses challenges in extracting effective information from rolling bearing fault signals and handling strong correlations and information redundancy in high-dimensional feature samples post-extraction. A rolling bearing fault diagnosis method is proposed on the basis of hierarchical discrete entropy (HDE) combined with semi-supervised local Fisher discriminant analysis (SELF). Firstly, hierarchical discrete entropy is extracted from signals preprocessed via variational mode decomposition. We assess entropy stability under different parameters using the coefficient of variation and select optimal parameters accordingly. Secondly, we employ the SELF method to remap the multidimensional feature sample set extracted, performing dimensionality reduction. Finally, a fault diagnosis model classifies the dimensionality-reduced feature samples for fault identification. Experimental results demonstrate that entropy samples extracted via HDE achieve higher diagnostic accuracy after dimensionality reduction with the SELF method. Specifically, accuracy rates of 100 % and 98.2 % are achieved for two types of fault samples, respectively, validating the feasibility and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Aiming at monitoring of gearbox faults, a gear fault feature extraction method based on variational mode decomposition (VMD) and multi-scale discrete entropy (MDE) is proposed in this paper. Firstly, the gear fault signal is decomposed into a series of intrinsic modal function (IMF) by VMD with selected parameters; Secondly, the decomposed IMF are extracted by MDE feature extraction method to form a feature sample set; Finally, the least square support vector machine (LSSVM) is used to classify the data set after feature extraction. The experiment results show that the proposed method owns the higher fault diagnosis accuracy than the traditional multi-scale entropy methods. [ABSTRACT FROM AUTHOR]