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Abstract Background sphingosine-1-phosphate (S1P), a naturally occurring sphingolipid, has been involved in pulmonary interstitial remodeling signaling. However, no study has examined its clinical merits for interstitial lung disease (ILD). This study aimed to investigate the serum level of S1P in ILD patients and its clinical correlation with the severity of disease in the two main types of ILDs: the IPF and the CTD-ILD patients. Methods This retrospective observational pilot study included 67 ILD patients and 26 healthy controls. These patients were stratified into the IPF group (35) and the CTD-ILD group (32). The severity of ILD was evaluated through pulmonary function indicators and the length of hospital stay. Results Serum S1P level was statistically higher in ILD patients than in health control (p = 0.002), while the Serum S1P levels in CTD-ILD and IPF patients were comparable. Serum S1P level further showed statistically negative correlation with pulmonary function indexes (TLC% pred, FVC% pred and FEV1% pred) and positive correlation with length of hospital stay (r = -0.38, p = 0.04; r = -0.41, p = 0.02, r = -0.37, p = 0.04; r = 0.42, p = 0.02, respectively) in CTD-ILD patients, although serum S1P level was not significantly correlated with inflammatory indexes. The IPF patients failed to exhibit a significant correlation of serum S1P level with pulmonary function and length of hospital stay. Conclusions Serum S1P level might be a clinically useful biomarker in evaluating the severity of CTD-ILD patients rather than IPF patients.

IntroductionIndividual leaves in the image are partly veiled by other leaves, which create shadows on another leaf. To eliminate the interference of soil and leaf shadows on cotton spectra and create reliable monitoring of cotton nitrogen content, one classification method to unmanned aerial vehicle (UAV) image pixels is proposed.MethodsIn this work, green light (550 nm) is divided into 10 levels to limit soil and leaf shadows (LS) on cotton spectrum. How many shadow has an influence on cotton spectra may be determined by the strong correlation between the vegetation index (VI) and leaf nitrogen content (LNC). Several machine learning methods were utilized to predict LNC using less disturbed VI. R-Square (R2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the performance of the model.Results(i) after the spectrum were preprocessed by gaussian filter (GF), SG smooth (SG), and combination of GF and SG (GF&SG), the significant relationship between VI and LNC was greatly improved, so the Standard deviation of datasets was also decreased greatly; (ii) the image pixels were classified twice sequentially. Following the first classification, the influence of soil on vegetation index (VI) decreased. Following secondary classification, the influence of soil and LS to VI can be minimized. The relationship between the VI and LNC had improved significantly; (iii) After classifying the image pixels, the VI of 2-3, 2-4, and 2-5 have a stronger relationship with LNC accordingly. Correlation coefficients (r) can reach to 0.5. That optimizes monitoring performance when combined with GF&SG to predict LNC, support vector machine regression (SVMR) has the better performance, R2, RMSE, and MAE up to 0.86, 1.01, and 0.71, respectively. The UAV image classification technique in this study can minimize the negative effects of soil and LS on cotton spectrum, allowing for efficient and timely predict LNC.

Timely and accurate estimation of Above-Ground-Biomass (AGB) in cotton is essential for precise production monitoring. The study was conducted in Shaya County, Aksu Region, Xinjiang, China. It employed an unmanned aerial vehicle (UAV) as a low-altitude monitoring platform to capture multispectral images of the cotton canopy. Subsequently, spectral features and textural features were extracted, and feature selection was conducted using Pearson’s correlation (P), Principal Component Analysis (PCA), Multivariate Stepwise Regression (MSR), and the ReliefF algorithm (RfF), combined with the machine learning algorithm to construct an estimation model of cotton AGB. The results indicate a high consistency between the mean (MEA) and the corresponding spectral bands in textural features with the AGB correlation. Moreover, spectral and textural feature fusion proved to be more stable than models utilizing single spectral features or textural features alone. Both the RfF algorithm and ANN model demonstrated optimization effects on features, and their combination effectively reduced the data redundancy while improving the model performance. The RfF-ANN-AGB model constructed based on the spectral and textural features fusion worked better, and using the features SIPI2, RESR, G_COR, and RE_DIS, exhibited the best performance, achieving a test sets R2 of 0.86, RMSE of 0.23 kg·m−2, MAE of 0.16 kg·m−2, and nRMSE of 0.39. The findings offer a comprehensive modeling strategy for the precise and rapid estimation of cotton AGB.

ObjectivesTo ensure the icebreaking navigation capability of polar bidirectional icebreaker ships, it is necessary to focus on the design of their stern line characteristics. MethodsThis paper surveys many polar ships in active service, summarizes the current standards, discusses the ice load modes of the pod propulsion units and ice breakage processes of ships, and analyzes the key design characteristic parameters and adaptability of the stern lines of pod-propelled polar ships.ResultsIt was found that the dip angle of the stern transom plate affects the horizontal and vertical forces on the sea ice by ships directly during the ice-breaking processes. This dip angle should be amplified properly to guide the bending fracture of sea ice effectively. The W type of stern plate transverse profile can resolve the contradiction between the optimization of wake field and the outflow efficiency of broken ice. The pod installation base could play a certain ice-breaking function, but it would affect the flow field of the pod. The tail fin would also affect the stern flow field and the outflow of broken ice obviously. Conclusions The reference for the adaptive optimization design of pod-propelled polar ship stern profile can be provided by the research results.

Abstract Aiming at the problems of low utilization rate of corn fertilizer, low precision of fertilization ratio, and time-consuming and laborious topdressing in the later stage, an U-shaped fertilization device with uniform fertilizer mechanism was designed. The device was mainly composed of uniform fertilizer mixing mechanism, fertilizer guide plate and fertilization plate. Compound fertilizer was applied on both sides and slow/controlled release fertilizer was applied at the bottom to form an U-shaped distribution of fertilizer around corn seeds. Through theoretical analysis and calculation, the structural parameters of the fertilization device were determined. Through the simulated soil tank test, the quadratic regression orthogonal rotation combination design was carried out on the main factors affecting the spatial stratification effect of fertilizer. The optimal parameters were obtained as follows: the stirring speed of the stirring structure was 300 r/min, the bending angle of the fertilization tube was 165°, and the operating speed of the fertilization device was 3 km/h. The results of bench verification test showed that under the optimized stirring speed and bending angle, the fertilizer particles were stirred evenly, and the average values of fertilizer flowing out of the fertilization tubes on both sides were 299.5 g and 297.4 g, respectively. The average fertilizer amounts of the three fertilizer outlets were 200.4 g, 203.2 g and 197.7 g, respectively, which met the agronomic requirements of 1:1:1 fertilization, and the variation coefficients of fertilizer amounts on both sides of the fertilizer pipe and each layer were less than 0.1% and 0.4%, respectively. The simulation results of the optimized U-shaped fertilization device can achieve the expected U-shaped fertilization effect around corn seeds. The results of field experiment showed that the U-shaped fertilization device could realize the U-shaped proportional application of fertilizer in soil. The distance between the upper end of fertilization on both sides and the distance between the base fertilizer and the surface were 87.3–95.2 mm and 197.8–206.0 mm, respectively. The transverse distance between the fertilizers on both sides was 84.3–99.4 mm, and the error with the designed theoretical fertilization was within 10 mm. Compared with the traditional side fertilization method, the number of corn roots increased by 5–6, the root length increased by 30–40 mm, and the yield increased by 9.9–14.8%.

The HIsmelt process is well suited for smelting vanadium-titanium magnetite and has significant potential. In this paper, the vanadium and titanium distribution behavior at 1450 °C in the basicity range of 0.3–1.2 during HIsmelt smelting vanadium titano-magnetite was investigated experimentally for the first time. According to thermodynamic calculations combined with experimental validation, lower C content increases the activity coefficients of vanadium and titanium in the metal. Higher basicity decreases the activity coefficients of TiO2 and SiO2 in the slag, improves the distribution ratio of vanadium and titanium. Increasing C content and reducing basicity could increase the content of vanadium in the metal and TiO2 in the slag, which is favorable to vanadium extraction and titanium recovery. Furthermore, the V and Ti content in the hot metal increased significantly with rising smelting temperature. Higher smelting temperatures promote vanadium distribution into hot metal but not titanium distribution into slag. Finally, The Si and Ti distribution ratios have a positive connection according to the fitted models.

Degradation of various organic pollutants by bacteria has been proved to be an economical and environmentally friendly method. The key challenge in making these technologies widely available is their low degradation efficiency. Here, we report a composite membrane composed of graphene oxide and polyvinyl alcohol (GO-PVA) which can markedly enhance the efficiency and rate of Shewanella bacteria to degrade Congo red (CR). The degradation efficiency of Shewanella bacteria alone on CR solution was about 42% at 72 h. After the addition of the GO-PVA membrane, the degradation efficiency reached 91% at the degradation time at 36 h. The degradation efficiency of CR was increased by two times and the degradation rate was increased by four times in the presence of GO-PVA membrane and Shewanella bacteria compared to Shewanella bacteria alone. This indicates that the CR could be rapidly and completely degraded by increasing the input amounts of GO-PVA membrane in the process of Shewanella algae degradation of CR. Moreover, the GO-PVA membrane showed good recyclability and reusability. The biocatalysts performance of the GO-PVA membrane did not decrease after 10 cycles. Furthermore, the role of the GO-PVA membrane in the degradation process and the degradation products of CR were discussed. HIGHLIGHTS The efficiency of Shewanella bacteria to degrade CR is about 42% at 72 h.; The GO-PVA can significantly improve the degradation rate and efficiency of CR.; The degradation rate was increased by four times and the efficiency was increased by about two times.; The GO in the GO-PVA plays as a redox mediator in the degradation of CR.; The GO-PVA shows good recyclability and reusability.;

Aiming at the problem that the path search rules in the traditional path planning methods are divorced from the actual maneuverability of an unmanned surface vehicle (USV), a motion planning method of state prediction rapidly exploring random tree (spRRT) is proposed. This method retains the discrete search of the original rules of RRT while adding the continuity of the motion of USV. Firstly, the state information for each movement (position, yaw angle, velocity, etc.), is calculated based on the mathematical model of USV’s motion which takes into account the complete dynamic constraints. Secondly, this information is added to the RRT path search rules to predict the state points that can be reached by the USV. Furthermore, in order to improve search efficiency and reduce cost, spRRT is enhanced by an elliptic sampling domain (spRRT-Informed). The simulation results indicate that spRRT can effectively plan smooth paths for smoothly navigating USV. The inclusion of the USV motion model has improved steering performance by an average of over 40%. Additionally, the spRRT-Informed enhanced with sampling optimization strategy improves performance by at least 10% over spRRT in terms of sailing time and distance of the path. The results of the simulation conducted in a realistic scenario validate that spRRT-Informed can be used as a reference for practical applications.

The sulfur content of hot metal in a blast furnace is an important index that reflects the production effects and quality of the hot metal. Establishing an accurate prediction model for hot metal sulfur content can guide the production process. In the present study, the blast furnace production data were collected and then preprocessed using box plotting. Cross-validation was used in the training process of the model to improve the generalization performance and robustness of the model. Two models for predicting the sulfur content in hot metal were established based on extreme gradient boosting (XGBoost) and multilayer perceptron (MLP) algorithms. The results show that coal consumption (CC), coal ratio (CLR), and sinter consumption (SC) are all positively correlated with hot metal sulfur content. The oxygen enrichment rate (OER) was negatively related to hot metal sulfur content. Both the extreme gradient boosting (XGBoost) and multilayer perceptron (MLP) models predicted hot metal sulfur content effectively; however, the extreme gradient boosting (XGBoost) model had a higher hit rate, accuracy, and stability, with the hit rate achieving 95.07%.

The domain of unknown function 668 (DUF668) is a gene family that plays a vital role in responses to adversity coercion stresses in plant. However, the function of the DUF668 gene family is not fully understood in sweet potato. In this study, bioinformatics methods were used to analyze the number, physicochemical properties, evolution, structure, and promoter cis-acting elements of the IbDUF668 family genes, and RNA-seq and qRT–PCR were performed to detect gene expression and their regulation under hormonal and abiotic stress. A total of 14 IbDUF668 proteins were identified in sweet potato, distributed on nine chromosomes. By phylogenetic analysis, IbDUF668 proteins can be divided into two subfamilies. Transcriptome expression profiling revealed that many genes from DUF668 in sweet potato showed specificity and differential expression under cold, heat, drought, salt and hormones (ABA, GA3 and IAA). Four genes (IbDUF668-6, 7, 11 and 13) of sweet potato were significantly upregulated by qRT-PCR under ABA, drought and NaCl stress. Results suggest that the DUF668 gene family is involved in drought and salt tolerance in sweet potato, and it will further provide the basic information of DUF668 gene mechanisms in plants.

Tight gas makes up a significant portion of the natural gas resources. There are tight gas reservoirs with great reserve and economic potential in the west Sichuan Basin, China. Due to the complex mineral component and heterogeneity of the thick tight sand formations, the reservoir parameters are challenging to evaluate from well logs using conventional methods, even the fundamental porosity. The mineral components must be considered. In this study, based on the analysis of different logging responses of varying lithologies, we introduced the complex reservoir analysis (CRA) method. CRA is always used in the carbonate reservoirs to calculate the different rock component volume fractions and can be used to classify the lithology and calculate the porosity simultaneously. By analyzing the component, a new equivalent component method (CRAE) is proposed based on the CRA method in this paper. In this method, the AC-CNL equation-calculated porosity is calibrated according to the core porosity data to set the rock components’ physical parameters. After calibration, the rock component fractions and porosity can be calculated accurately. Also, according to the relationship between the grain size and natural gamma-ray, a granularity median model was established. Six lithology types, including coarse-grained quartz sandstone and coarse-grained lithic sandstone, are distinguished, and the porosity is estimated in the study area. The identification results are compared with the mud logging data and other methods. It shows that this method is very well adequate in the tight sandstone gas reservoirs in the study area.

HIsmelt is well suited for smelting vanadium–titanium magnetite due to its flexibility in feedstock selection and tolerance to high viscosity slag, compared with the blast furnace. In this work, the effect of smelting time on the distribution behavior and recovery rates of vanadium and titanium in HIsmelt smelting of vanadium–titanium magnetite was investigated by experiment for the first time. The relationship between slag viscosity and temperature at different smelting times was further revealed by thermodynamic calculations. The experimental results show that extending the smelting time increases the FeO content in the slag, the LV rose from 0.66 to 5.02, the LTi declined from 206.90 to 114.86, the shorter smelting time is favorable for increasing the recovery ratio of vanadium and titanium in metal and slag. In addition, slag viscosity decreases with increasing smelting time. The precipitation of high melting point titania spinel and CaTiO3 phases is responsible for the significant increase in slag viscosity at 1300 °C.

The gas utilization rate (GUR) is an important indicator parameter for reflecting the energy consumption and smooth operation of a blast furnace (BF). In this study, the original data of a BF are pre-processed by two methods, i.e., box plot and 3σ criterion, and two data sets are obtained. Then, support vector regression (SVR) is used to construct a prediction model based on the two data sets, respectively. The state parameters of a BF are selected as input parameters of the model. Gas utilization after one hour (GUR-1h), two hours (GUR-2h), and three hours (GUR-3h) are selected as output parameters, respectively. The simulation result demonstrates that using the 3σ criterion to pre-process the raw data leads to better prediction of the model compared to using the box plot. Moreover, the model has the best predictive effect when the output parameter is selected as GUR-1h.

Classical dye absorption-based pH test papers are widely used to measure the degree of acidity of media for quantification or semi-quantification by the observation of the naked eye on the variation of colour. However, it remains a challenging task to extend portable and cheap methods to a wide range of analytes for accurate quantification. Here, we report a dosage-sensitive test paper for the assay of mercury ions (Hg2+) with wide colour evolution. The ratiometric fluorescent probe was prepared by mixing blue-emission silicon nanocrystals (Si NCs) and red-emission carbon dots (r-CDs). The Si NCs serve as the reaction site and the r-CDs as the internal reference. Upon the addition of Hg2+, the blue fluorescence of the Si NCs was quenched, while the red fluorescence of the r-CDs remained constant, resulting in the consecutive fluorescence colour changes from blue to orange red. The probe demonstrates high selectivity and sensitivity of the visualization assay of Hg2+ with a detection limit of 7.63 nM. Moreover, we printed the ratiometric fluorescence probe result onto a piece of filter paper to prepare a test paper, which represents an on-site, sensitive, rapid and cost-effective method for the visual determination of Hg2+ by the naked eye.

Biological receptors including enzymes, antibodies and active proteins have been widely used as the detection platform in a variety of chemo/biosensors and bioassays. However, the use of artificial host materials in chemical/biological detections has become increasingly attractive, because the synthetic recognition systems such as molecularly imprinted polymers (MIPs) usually have lower costs, higher physical/chemical stability, easier preparation and better engineering possibility than biological receptors. Molecular imprinting is one of the most efficient strategies to offer a synthetic route to artificial recognition systems by a template polymerization technique, and has attracted considerable efforts due to its importance in separation, chemo/biosensors, catalysis and biomedicine. Despite the fact that MIPs have molecular recognition ability similar to that of biological receptors, traditional bulky MIP materials usually exhibit a low binding capacity and slow binding kinetics to the target species. Moreover, the MIP materials lack the signal-output response to analyte binding events when used as recognition elements in chemo/biosensors or bioassays. Recently, various explorations have demonstrated that molecular imprinting nanotechniques may provide a potential solution to these difficulties. Many successful examples of the development of MIP-based sensors have also been reported during the past several decades. This review will begin with a brief introduction to the principle of molecular imprinting nanotechnology, and then mainly summarize various synthesis methodologies and recognition properties of MIP nanomaterials and their applications in MIP-based chemosensors. Finally, the future perspectives and efforts in MIP nanomaterials and MIP-based sensors are given.

The asymmetric structure expands the voltage window of supercapacitors in aqueous electrolyte solutions, thereby significantly increasing energy density. However, exploring high-performance negative electrode materials is of utmost importance, on the premise that positive electrode materials have been extensively developed. This study explores the homogeneous composition of iron oxide and graphene via in situ laser induction on the precursor film of polyimide foam containing Fe3+ ions. 3D interconnected graphene was prepared under laser direct writing, while Fe3+ ions were thermally reduced to obtain iron oxide (Fe3+/Fe2+). By adjusting the concentration of Fe3+ ions in the precursor polyimide foam and laser parameter settings, the Fe3+/Fe2+ ratio was regulated to exhibit optimal electrochemical performance. When employed as a negative electrode material for assessing the electrochemical performance, it displayed a notable area capacitance of 654.3 mF cm2 at a current density of 1 mA cm2. In the assembled aqueous asymmetric supercapacitors, the specific energy density was 33.25 mW h cm2 at a power density of 750 mW cm2 and retained 86.8% of its capacitance even after 10 000 charge-discharge cycles. This method offers a new approach for synthesizing laserinduced multivalent compounds and serves as a reference for efficiently producing negative electrodes of an asymmetric supercapacitor. [ABSTRACT FROM AUTHOR]