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The rational selection of non-geological hazard samples is of great significance to improve the accuracy of geological hazard susceptibility prediction. This study uses Liulin County as a case study, where appropriate impact factors were selected, and the random forest (RF) model was employed for susceptibility assessment based on GIS technology. A total of twenty sets of models were created by varying the ratio of geological hazard to non-geological hazard points (1∶1, 1∶1.5, 1∶3, 1∶5 and 1∶10) and the distance from non-geological hazard points to known hazard points (100,500,800,1000 m). The results demonstrate that: (1) Through error index, confusion matrix, and ROC curve tests, the sample proportion and distance from the known hazard point significantly influenced the geological hazard susceptibility evaluation. As the sample proportion decreased and the distance from known hazard points increased, the overall MAE and RMSE of the models decreased, while the overall ACC increased. All models achieved AUC value greater than 0.8, indicating excellent predictive performance. When the sample proportion was less than 1∶3, the increasing distance from the known hazard points on model error and accuracy became less pronounced, stabilizing the results. The most suitable model for the study area was found to have a sample ratio of 1∶10 and a distance of 1000 m from known hazard points. (2) High and very high susceptibility areas were primarily located in the central and northern regions, adjacent to roads and rivers, making them key areas for hazard prevention and reduction in Liulin County. (3) Differences in sample selection led to varying susceptibility results mainly due to changes in the RF model's data feature collection and judgment during the modeling process, as well as the representativeness of the samples. These research findings hold significant implications for the implementation of hazard prevention and reduction measures.

Abstract The lasso peptide microcin Y (MccY) effectively inhibits various serotypes of Salmonella in vitro, but the antibacterial effect against S. Pullorum in poultry is still unclear. This study was the first to evaluate the safety and anti-S. Pullorum infection of MccY in specific pathogen-free (SPF) chicks. The safety test showed that the body weight, IgA and IgM levels of serum, and cecal microbiota structure of 3 groups of chicks orally administrated with different doses of MccY (5 mg/kg, 10 mg/kg, 20 mg/kg) for 14 days were not significantly different from those of the control group. Then, the chicks were randomized into 3 groups for the experiment of anti-S. Pullorum infection: (I) negative control group (NC), (II) S. Pullorum-challenged group (SP, 5 × 108 CFU/bird), (III) MccY-treated group (MccY, 20 mg/kg). The results indicated that compared to the SP group, treatment of MccY increased body weight and average daily gain (P

Lung adenocarcinoma, a chronic non-small cell lung cancer, needs to be detected early. Tumor gene expression data analysis is effective for early detection, yet its challenges lie in a small sample size, high dimensionality, and multi-noise characteristics. In this study, we propose a lung adenocarcinoma convolutional neural network (LATCNN), a deep learning model tailored for accurate lung adenocarcinoma prediction and identification of key genes. During the feature selection stage, we introduce a hybrid algorithm. Initially, the fast correlation-based filter (FCBF) algorithm swiftly filters out irrelevant features, followed by applying the k-means-synthetic minority over-sampling technique (k-means-SMOTE) method to address category imbalance. Subsequently, we enhance the particle swarm optimization (PSO) algorithm by incorporating fast-decay dynamic inertia weights and utilizing the classification and regression tree (CART) as the fitness function for the second stage of feature selection, aiming to further eliminate redundant features. In the classifier construction stage, we present an attention convolutional neural network (atCNN) that incorporates an attention mechanism. This improved model conducts feature selection post lung adenocarcinoma gene expression data analysis for classification and prediction. The results show that LATCNN effectively reduces the feature dimensions and accurately identifies 12 key genes with accuracy, recall, F1 score, and MCC of 99.70%, 99.33%, 99.98%, and 98.67%, respectively. These performance metrics surpass those of other comparative models, highlighting the significance of this research for advancing lung adenocarcinoma treatment.

The shrinkage issue has posed a significant challenge for the practical application of biodegradable foam materials. The varying temperature-pressure mode (VT-PM) was adopted to fabricate poly (butylene adipate-butylene terephthalate) (PBAT)/Polybutylene succinate (PBS) foams. Foam shrinkage was significantly improved by the synergistic effect of temperature and pressure, and the shrinkage resistance mechanism was investigated by analyzing the cell structure. The crystallization behavior of PBS was investigated using differential scanning calorimetry (DSC) and polarized optical microscope (POM). Small size PBS crystals were obtained during the temperature change process, and the heterogeneous nucleation of PBS crystals increased the foaming expansion ratio. It was also found that the thick cell wall was able to act as a skeleton to support the structure of large cell size (＞100 μm) foams, ensuring larger compression stress and modulus.

Background: Emerging evidence has demonstrated the impact of psychological stress on intestinal microbiota, however, the precise mechanisms are not fully understood. Enteric glia, a unique type of peripheral glia found within the enteric nervous system (ENS), play an active role in enteric neural circuits and have profound effects on gut functions. In the present study, we tested the hypothesis that enteric glia are involved in the alterations in the intestinal microflora and barrier induced by chronic water-avoidance stress (WAS) in the gut. Methods and results: Western blotting and immunohistochemical (IHC) staining were used to examine the expression of glial fibrillary acidic protein (GFAP), nitric oxide synthetase (NOS) and choline acety1transferase (ChAT) in colon tissues. 16S rDNA sequencing was performed to analyse the composition of the intestinal microbiota in rats. Changes in the tight junction proteins Occludin, Claudin1 and proliferating cell nuclear antigen (PCNA) in the colon tissues were detected after WAS. The abundance of Firmicutes, Proteobacteria, Lactobacillus and Lachnospiraceae_NK4A136 decreased significantly, whereas the abundance of Actinobacteria, Ruminococcaceae_UCG−005 and Christensenellaceae-R-7 increased significantly in stressed rats. Meanwhile, the expression of Occludin, Claudin1 and PCNA significantly decreased after WAS. Treatment with L-A-aminohexanedioic acid (L-AA), a gliotoxin that blunts astrocytic function, obviously decreased the abundance of Actinobacteria, Ruminococcaceae_UCG−005 and Christensenel-laceae_R-7 in stressed rats and significantly increased the abundance of Proteobacteria, Lactobacillus and Lachnospiraceae_NK4A136. In addition, the protein expression of colon Occludin, Claudin1, and PCNA increased after intraperitoneal injection of L-AA. Furthermore, the expression level of NOS in colon tissues was significantly decreased, whereas that of ChAT was significantly increased following L-AA treatment. Conclusions: Our results showed that enteric glial cells may contribute to WAS-induced changes in the intestinal microbiota and barrier function by modulating the activity of NOS and cholinergic neurones in the ENS.

Mineralization characterized by Au, Hg, and Tl enrichment is rare, and research on Au, Hg, and Tl mineralization is limited. The Lanmuchang Au–Hg–Tl deposit is located in the “Golden Triangle” of Yunnan, Guizhou, and Guangxi Provinces in China. In this study, we used scanning electron microscopy (SEM), electron microprobe analysis (EPMA), and a Tescan integrated mineral analyzer (TIMA) to analyze the mineral composition and distribution of the different types of ores and identify the occurrence state and enrichment mechanism of ore-forming elements in the Lanmuchang deposit. The results show that the primary ore minerals in the Lanmuchang deposit are pyrite, cinnabar, and lorandite. Cinnabar is the primary carrier of Hg (>90%), and pyrite is the primary carrier of Tl (>60%). Gold, Hg, and Tl primarily occur as solid solutions in hydrothermal pyrite, whereas they primarily occur as nano-scale particles in diagenetic pyrite. The substitution of As for S in hydrothermal pyrite promotes Au enrichment. The coupled substitution of 2Fe2+ ⇔ Tl+ + As3+ may be a significant Tl incorporation mechanism and promotes the occurrence of Hg in pyrite. The As and Se contents and Cu/Au and Co/Ni ratios of the hydrothermal pyrite demonstrate that the ore-forming fluid was mostly in a low-temperature, low-salinity, almost-neutral pH, and nearly reducing environment. The results show that the mineralization of the Lanmuchang deposit is associated with the cooling, oxidation, water–rock interaction, and boiling processes of the ore-forming fluid(s).

Block compressed sensing (BCS) is a promising method for resource-constrained image/video coding applications. However, the quantization of BCS measurements has posed a challenge, leading to significant quantization errors and encoding redundancy. In this paper, we propose a quantization method for BCS measurements using convolutional neural networks (CNN). The quantization process maps measurements to quantized data that follow a uniform distribution based on the measurements’ distribution, which aims to maximize the amount of information carried by the quantized data. The dequantization process restores the quantized data to data that conform to the measurements’ distribution. The restored data are then modified by the correlation information of the measurements drawn from the quantized data, with the goal of minimizing the quantization errors. The proposed method uses CNNs to construct quantization and dequantization processes, and the networks are trained jointly. The distribution parameters of each block are used as side information, which is quantized with 1 bit by the same method. Extensive experiments on four public datasets showed that, compared with uniform quantization and entropy coding, the proposed method can improve the PSNR by an average of 0.48 dB without using entropy coding when the compression bit rate is 0.1 bpp.

The efficiency of household dehumidifiers is affected by air temperature and the temperature used for regeneration. A regeneration temperature that is too high can lead to increased energy use, heat build-up in the desiccant wheel, and lower dehumidification efficiency. In this study, we developed a LiCl@Al-Fum composite material and evaluated it through physical characterization and module testing. The results show that the LiCl@Al-Fum composite with a 20% mass fraction is particularly effective as a desiccant material. Additionally, a 15% volume concentration of neutral silica sol was identified as the optimal binder concentration. A comparative analysis of the effects of glass-fiber desiccant wheels (GF DWs), aluminum desiccant wheels (Al DWs), and commercial desiccant wheels (CM DWs) on household dehumidifier performance revealed that the Al DWs outperformed the CM DWs, showing a 13% improvement in the dehumidification rate and a 12.56% increase in the DCPP. An increase in the dehumidifier structure led to increases in the dehumidification rate by 11.8%, 11.9%, and 10% and in the DCPP by 11.6%, 12.1%, and 10%, respectively. Moreover, the modifications resulted in a 3.85 °C, 3.34 °C, and 3.8 °C decrease in the temperature.

Abstract A simple and effective method was reported for the rapid synthesis of zeolite X from kaolin by adding a template agent. The results of X‐ray diffraction confirm the good crystallinity of the synthesized X‐zeolite with lamellar structure. Transmission electron microscopy images clearly demonstrate the lamellar structure of the synthesized X‐zeolite. The corresponding selective electron diffraction pattern suggests that the crystal structure of X‐type zeolite is octahedral. The co‐existence and uniform distribution of Si, Al, O, and Na in the layered structure of the synthesized X‐type zeolite are detected by element mapping. In addition, N2 adsorption–desorption measurement indicates that the specific surface area of the lamellar zeolite X is 153.4625 m2 g−1. The micropore surface area is 148.8374 m2 g−1, and the micropore volume is 0.073377 m2 g−1. After exploring the influence of different temperatures on the adsorption of carbon dioxide, the research shows that two‐dimensional zeolite X has the best adsorption capacity for carbon dioxide. The adsorption and separation performance of CO2/CH4 was investigated. The study showed that the adsorption and separation coefficient of two‐dimensional zeolite X was the highest.

ABSTRACT Non-typhoidal salmonellae-induced zoonotic intestinal bleeding infections are a crucial threat to public health worldwide, and the development of new medications is required to prevent and treat non-typhoid Salmonella (NTS) infections. MccY, a class of lasso peptide, is effective in biological sterilization against Salmonella and Shigella. In this study, the therapeutic effect of MccY on S. Typhimurium-infected BALB/c mice has been discussed. specific pathogen free (SPF) mice were infected with the ST53 strain with bacterial loads of 106 and 103 CFU/mL, respectively, and then treated with 9.92 mg/kg BW MccY. Results revealed that MccY significantly reduced mouse mortality by 20%–40% and effectively alleviated weight loss and diarrhea. The integrity of the intestinal structure was protected, and the expression of TNF-α, IL-6, IL-10, and IL-18 was inhibited. Particularly, MccY exerted multiple effects on gut microbiota ecology, including direct elimination of Salmonella, showing promise in limiting infection to the gut level and thereby halting the transmission of NTS. MccY indirectly inhibited other opportunistic pathogens and partially restored the microbiota, promoting an increase in Bacteroidetes, Lactobacillus, Muribaculum, and Clostridium. Consequently, this work provides an understanding of the link between host infection and therapeutic application of MccY. IMPORTANCE Diseases caused by Enterobacteriaceae multidrug-resistant strains have become increasingly difficult to manage. It is necessary to verify the new antibacterial drug MccY effect on non-typhoid Salmonella infection in mice since it is regarded as a promising microcin. The results demonstrated that MccY has a potential therapeutic application value in the protection against Salmonella-induced intestinal damage and alleviating related intestinal dysbiosis and metabolic disorders. MccY could be a promising candidate as an antimicrobial or anti-inflammatory agent for treating infectious diseases.

ABSTRACTIn this paper, hybrid models of Kalman filter and neural network for state estimation are reviewed of their corresponding academic achievements, the creation of which is a noteworthy development in state estimation. This paper aims to provide a summary of research progress on such hybrid models and emphasize their functions and advantages. First of all, the concept and feature are paid attention to about Kalman filter, and its transmutative modes are taken into consideration. Then several popular neural network algorithms are introduced in brief. Subsequently, research results on hybrid models are analysed and discussed comprehensively. Not only Kalman filter and neural network can be adopted in succession, but also can be mixed in structure. The mixed models can also be divided into two types, the equations or parameters of state–space model are trained by neural network for Kalman filter and the parameters of neural network are updated by Kalman filter. It is proved that the hybrid models outperform than single model of Kalman filter or neural network in accuracy and generalization. Last but not least, the effectiveness of state–space equations of Kalman filter can be established by neural network in nonlinear systems is verified.

Solid hygroscopic materials are extensively utilized in diverse fields, including adsorption heat transfer, adsorption heat storage, atmospheric water harvesting (AWH), and air conditioning dehumidification. The efficacy and energy efficiency of these materials in practical applications are significantly influenced by their adsorption and desorption properties. Yet, the introduction of inorganic salts to boost adsorption performance can result in issues like salt leakage. In this research, we prepared a polyacrylamide hydrogel through free radical polymerization, and its water-absorbing capabilities were improved by incorporating the hygroscopic salt lithium chloride. We compared it to a salt-based porous adsorbent, AlFum-LiCl, which also exhibited strong water adsorption properties and the potential for large-scale production. While AlFum-LiCl suffered from limited pores and salt leakage during high water uptake, the optimized PAM-LiCl displayed superior water sorption capabilities, showing no salt leakage even at water uptake of up to 3.5 g/g. At 25 °C, PAM-LiCl achieved equilibrium water uptake of 1.26 g/g at 30% RH and 3.15 g/g at 75% RH. In this context, utilizing 20 g of PAM-LiCl for the AWH experiment yielded daily water outputs of 8.34 L/kg at 30% RH and 16.86 L/kg at 75% RH. The salt-optimized PAM-LiCl hydrogel offers the benefit of application in higher relative humidity environments without the risk of deliquescence, underscoring its promise for atmospheric water harvesting.

Accurate engine gas path component fault diagnosis methods are key to ensuring the reliability and safety of engine operations. At present, the effectiveness of the data-driven gas path component fault diagnosis methods has been widely verified in engineering applications. The deep stack neural network (DSN), as a common deep learning neural network, has been gaining more attention in gas path fault diagnosis studies. However, various gas path component faults with strong coupling effects could occur simultaneously, resulting the DSN method less effective for engine gas path fault diagnosis. In order to improve the prediction performance of the DSN handling multiple gas path component fault diagnosis, a sparse regularization and representation method was proposed. The sparse regularization term is used to expand the traditional deep stacking neural network in the sparse representation, and the predicted output tag is close to the target output tag through this term. The diagnosis performance of six different neural network methods were compared by various engine gas path component fault diagnosis types. The results show that the proposed sparse regularization method significantly improves the prediction performance of the DSN, with an accuracy rate 99.9% under various gas path component fault conditions, which is higher than other methods. The proposed engine gas path component fault diagnosis method can handle multiple coupling gas path faults, and help engine operators to develop maintenance plans for the purpose of engine health management.

Experiments of premixed syngas/air explosion inside parallel narrow channels were conducted. The flame propagation, explosion pressure and temperature inside the narrow channel are investigated to find out the effect of narrow channel spacing on the explosion. The experimental results show that the narrow channel spacing has an influence on the flame before and inside the parallel narrow channels. The flame inside the narrow channels accelerates and reaches a peak value at the middle or rear of the channel. The flame front velocity, the maximum explosion Pmax and the maximum explosion flame temperature Tmax decrease as the channel spacing decreases. The channel wall has an effect of heat dissipation on the flame, resulting in the explosion inside the channel weakening. The explosion pressure in rear of the narrow channel is larger than that in the front of the narrow channel.

Abstract This research aimed to analyze the regulatory effect of chicken telomerase reverse transcriptase (chTERT) on the Wnt/β-catenin signaling pathway and its effect on the tumorigenicity of avian leukosis virus subgroup J (ALV-J) through in vivo experiments. The chTERT eukaryotic expression plasmid and its recombinant lentivirus particles were constructed for in vivo transfection of chTERT to analyze the effect of chTERT continuously overexpressed in chickens on the tumorigenicity of ALV-J. During 156 days of the artificial ALV-J tumor-inducing process, 7 solid tumors developed in 3 chickens in the chTERT-overexpression group (n = 26*2) and no tumors developed in the control group (n = 26*2). Another 18 tumors induced by ALV-J were confirmed and collected from breeding poultry farms. And we confirmed that chTERT was significantly highly expressed in ALV-J tumors. The ELISA data suggested that the protein levels of β-catenin and c-Myc in the chicken plasma of the chTERT-overexpressing group with ALV-J infected were consistently and significantly higher than those of the control group. Compared with that of the tumor-adjacent tissues, the activity of the Wnt/β-catenin signaling pathway and expression of the c-Myc was significantly increased in ALV-J tumors. And the percentage of apoptosis in ALV-J tumors significantly lower than that in tumor-adjacent tissues. Immunohistochemistry, Western blot and RT-qPCR suggested that the replication level of ALV-J in tumors was significantly higher than that in tumor-adjacent tissues. This study suggests that chTERT plays a critical role in the tumorigenicity of ALV-J by enhancing the Wnt/β-catenin signaling pathway, which will contribute to further elucidating the tumor-inducing mechanism of ALV-J.

Improving the detection accuracy of hyperbola in B-scan images has been a considerable challenge for ground penetrating radar (GPR) to detect tree roots. In this paper, a method for data enhancement and target detection, both based on deep learning was proposed to identify hyperbolas in GPR B-scan images. First, the authors used a cyclic consistent adversarial network (CycleGAN) to augment the original data. In this procedure, the hyperbolic features of the images were preserved and created a wider variety of training samples. Then, the authors could apply the enhanced dataset to the YOLOv5 detection model to evaluate the effectiveness of their method. Meanwhile, the detection effects of Yolov3, Yolov5, Faster R-CNN, and CenterNet detection models on the enhanced dataset were compared. The results showed that applying the enhanced dataset to the Yolov5 detection model exhibited better detection accuracy compared to other combinations of datasets and detection models. The authors demonstrate that the proposed method increases data diversity and the number of samples, improving the precision and recall of hyperbolic curves. These results provide a new method for tree root localization with important effects.

To explore the function of phosphatidylinositol 4-phosphate 5-kinase (PIP5K) in tomatoes, members of the tomato PIP5K family were identified and characterized using bioinformatic methods, and their expression patterns were also analyzed under salt stress and in different tissues. Twenty-one PIP5K members—namely, SlPIP5K1–SlPIP5K21—were identified from ten chromosomes, and these were divided into three groups according to a phylogenetic analysis. Further bioinformatic analysis showed four pairs of collinear relationships and fragment replication events among the SlPIP5K family members. To understand the possible roles of the SlPIP5Ks, a cis-acting element analysis was conducted, which indicated that tomato PIP5Ks could be associated with plant growth, hormones, and stress responses. We further validated the results of the in silico analysis by integrating RNA-seq and qRT-PCR techniques for salt- and hormone-treated tomato plants. Our results showed that SlPIP5K genes exhibited tissue- and treatment-specific patterns, and some of the SlPIP5Ks exhibited significantly altered expressions after our treatments, suggesting that they might be involved in these stresses. We selected one of the SlPIP5Ks that responded to our treatments, SlPIP5K2, to further understand its subcellular localization. Our results showed that SlPIP5K2 was located on the membrane. This study lays a foundation for the analysis of the biological functions of the tomato SlPIP5K genes and can also provide a theoretical basis for the selection and breeding of new tomato varieties and germplasm innovation, especially under salt stress.

We have particularly investigated the correlation law of the effect of different carbon black fillings on the hyper-elastic mechanical behavior of natural rubber by conducting uniaxial tensile tests over a wide range of deformations with different volume fractions of carbon black fillings (0%, 4.7%, 8.9%, 12.8%, 16.4%, 19.7%, 22.7% and 25.2%). The results show that the stress-strain curve for carbon black filled rubber increases with the amount of filling, meaning that the rubber gradually becomes “harder”. We explore the correlation between the carbon black filling volume and the parameters of the Yeoh constitutive model by examining the Yeoh constitutive model to characterize the hyper-elastic mechanical behavior of rubber with different carbon black fillings. A quantitative relationship between the material parameters and the carbon black filling volume in the Yeoh constitutive model is presented. A method for calculating the material parameters of the Yeoh constitutive model is developed, and it predicts the correlation between the hyper-elastic properties of rubber and the volume fraction of the carbon black filling.

To control well the surface fluctuation of liquid metal in a slab mold, a new type of combined permanent magnets braking system, namely a permanent magnet flow control mold (PMFC-Mold) is proposed by our research group, of which its main feature is that the device can control the flow of molten steel in the mold without additional energy. To observe the fluctuation state of the alloy with the PMFC-Mold, instantaneous surface fluctuations were recorded by a laser level meter and camera. To study the effect of various casting speeds and permanent magnet placement on surface fluctuations, the three measurement points, which were 7, 18, and 36 mm away from the narrow surface of the mold, were selected to record the trend of level fluctuation. Three types of permanent magnet placement were designed by setting the differences between the height center of the permanent magnet and the free surface in the slab mold, which were H1 = 0 mm, H2 = −25 mm, and H3 = −75 mm. The experimental results indicated that with the acceleration of the casting speed, the average height and standard deviation of surface fluctuation at the measurement point increased, but the surface fluctuation pattern remained. When the permanent magnets were arranged at H1 = 0 mm and H2 = −25 mm, the position of the magnetic field was reasonable and the surface fluctuation could be effectively suppressed. In contrast, when the permanent magnets were arranged at H3 = −75 mm, the level fluctuation was intensified.

Aeroengine performance diagnosis technology is essential for ensuring flight safety and reliability. The complexity of engine performance and the strong coupling of fault characteristics make it challenging to develop accurate and efficient gas path diagnosis methods. To address these issues, this study proposes a novel digital twin framework for aeroengines that achieves the digitalization of physical systems. The mechanism model is constructed at the component level. The data-driven model is built using a particle swarm optimization–extreme gradient boosting algorithm (PSO-XGBoost). These two models are fused using the low-rank multimodal fusion method (LWF) and combined with the sparse stacked autoencoder (SSAE) to form a digital twin framework of the engine for performance diagnosis. Compared to methods that are solely based on mechanism or data, the proposed digital twin framework can effectively use mechanism and data information to improve the accuracy and reliability. The research results show that the proposed digital twin framework has an error rate of 0.125% in predicting gas path parameters and has a gas path fault diagnosis accuracy of 98.6%. Considering that the degradation cost of a typical flight mission for only one aircraft engine after 3000 flight cycles is approximately USD 209.5, the proposed method has good economic efficiency. This framework can be used to improve engine reliability, availability, and efficiency, and has significant value in engineering applications.

In practical applications, the accuracy of domain terminology translation is an important criterion for the performance evaluation of domain machine translation models. Aiming at the problem of phrase mismatch and improper translation caused by word-by-word translation of English terminology phrases, this paper constructs a dictionary of terminology phrases in the field of electrical engineering and proposes three schemes to integrate the dictionary knowledge into the translation model. Scheme 1 replaces the terminology phrases of the source language. Scheme 2 uses the residual connection at the encoder end after the terminology phrase is replaced. Scheme 3 uses a segmentation method of combining character segmentation and terminology segmentation for the target language and uses an additional loss module in the training process. The results show that all three schemes are superior to the baseline model in two aspects: BLEU value and correct translation rate of terminology words. In the test set, the highest accuracy of terminology words was 48.3% higher than that of the baseline model. The BLEU value is up to 3.6 higher than the baseline model. The phenomenon is also analyzed and discussed in this paper.

The water sorption and desorption properties of solid adsorbent materials are crucial in rotary dehumidification systems. Metal organic frameworks (MOFs) and hydrogels are mostly at the laboratory stage due to factors like the synthesis process and yield. In this study, we utilized an eco-friendly and large-scale synthesis method to prepare polyacrylamide (PAM) hydrogels (yielding approximately 500 mL from a single polymerization). Subsequently, PAM was then coated onto glass fiber paper (GFP), which serves as a commonly employed substrate in desiccant wheels. By incorporating the hygroscopic salt LiCl and optimizing the content of each component, the water sorption performance of the composite was notably improved. The water sorption and desorption performances, as well as cycling stability, were evaluated and compared with composites containing aluminum fumarate, LiCl, and GFP (AlFum-LiCl&GFP). The results revealed that PAM-LiCl&GFP outperformed AlFum-LiCl&GFP in terms of sorption capacity throughout various relative humidity (RH) levels. It achieved a water uptake of 1.06 g·g−1 at 25 °C and 30% RH, corresponding to a water sorption rate coefficient K of 15.32 × 10−4 s−1. Furthermore, the lower desorption temperature (60 °C) resulting in a desorption ratio of 82.6%, along with the excellent cycling stability and effective performance as a desiccant wheel module, provide evidence for the potential application of PAM-LiCl&GFP in desiccant wheels.

With the advent of the era of big data, data mining methods show their powerful information mining ability in various fields, seeking the association information hidden in the data, which is convenient for people to make scientific decisions. This paper analyses the butterfly effect in the agricultural product industry chain from the perspective of producer and consumer by using multidimensional time and space theory and proposes a new price forecasting method. We consider that the price change of agricultural products is not only affected by the balance of market supply and demand but also by the factors of time and space. Taking the pig industry chain of Sichuan Province as an example, this paper explores and excavates the data from 2010 to 2020 in the time dimension. Interestingly, we found that the price changes in pork in the market are generally highly correlated with the prices of slaughtered pigs, piglets a few weeks ago and the prices of multiple feed a few months ago. Based on the precise time-space factors, we improved the price forecasting model, greatly improved the accuracy of price prediction, and proved the effectiveness of multidimensional spatiotemporal association mining. The research in this paper is helpful to establish a brand-new agricultural product price prediction theory, which is of great significance to the development of the agricultural economy and global poverty alleviation.

The PLATZ (plant AT protein and zinc-binding protein) transcription factor family is involved in the regulation of plant growth and development and plant stress response. In this study, 24 SlPLATZs were identified from the cultivated tomato genome and classified into four groups based on the similarity of conserved patterns among members of the same subfamily. Fragment duplication was an important way to expand the SlPLATZ gene family in tomatoes, and the sequential order of tomato PLATZ genes in the evolution of monocotyledonous and dicotyledonous plants and the roles they played were hypothesized. Expression profiles based on quantitative real-time reverse transcription PCR showed that SlPLATZ was involved in the growth of different tissues in tomatoes. SlPLATZ21 acts mainly in the leaves. SlPLATZ9, SlPLATZ21, and SlPLATZ23 were primarily involved in the red ripening, expanding, and mature green periods of fruit, respectively. In addition, SlPLATZ1 was found to play an important role in salt stress. This study will lay the foundation for the analysis of the biological functions of SlPLATZ genes and will also provide a theoretical basis for the selection and breeding of new tomato varieties and germplasm innovation.

Aiming to improve the performance of the solar adsorption cooling system using the silica gel-water as the working pair, a series of fin-enhanced vacuum tube bed have been developed and tested experimentally. The characteristics of the system were analyzed as the fin number changed between zero to eight in the bed. It is revealed that longer time was needed for the preheating and desorption of the bed in a cycle because of the increase of the heat capacity of finned tube bed. Nevertheless, the fast heat transfer of the finned tube has resulted in a significant reduction of the pre-cooling time of the bed, which usually took the biggest part in the whole cycle time originally. The comprehensive result was the cycle time of the solar adsorption cooling system diminished, and the general performance of the system was improved. As evaluated under the criterion of the specific cooling power (SCP), it is found that the SCP2 of the finned system, which is defined by the cycle time, increased 37.4%–80.7% in comparison to the situation of the smooth tube bed. On the other hand, analyzing the coefficient of performance (COP) of the system has revealed that not always the more the internal fins, the better the performance result. The experimental results told us that the COP of the system reached the optimum as the fin number was equal to four. Over enhanced tube bed caused the performance of the adsorption cooling system to deteriorate.

Construction of superhydrophobic woods with high abrasion resistance is still a major challenge, and micro analysis for abrasion resistance is scarce. To improve these issues, cellulose nanocrystals (CNC)@SiO2@phosphorylated lignin (PL) rods were prepared by SiO2 in situ generated on CNC, and then the modified lignin attached to the CNC@SiO2 rods surface. Subsequently, the superhydrophobic coating was constructed using hydrophobic modified CNC@SiO2@PL rods as the main structural substance by simple spraying or rolling them onto wood surfaces, and both polydimethylsiloxane (PDMS) and epoxy resin were used as the adhesives. The resulting coating had excellent superhydrophobic properties with a water contact angle (WCA) of 157.4° and a slide angle (SA) of 6°. The introduced PL could enhance ultraviolet (UV) resistance of the coating due to the presence of these groups that absorbed UV light in lignin. In the abrasion resistance test, compared with the SiO2/PL coating, the abrasion resistance of the one with CNC was much higher, suggesting that CNC could improve the abrasion resistance of the coating due to its high crystallinity and excellent mechanical strength. The coating with PDMS performed better than the one with epoxy resin because the soft surface could offset part of the external impact by deformation in the abrasion process. This was also consistent with the results of the nanoindentation (NI) tests. In view of the simple preparation and good performance, this superhydrophobic wood will have broad application potential.

The exhaust gas temperature (EGT) baseline of an aeroengine is key to accurately analyzing engine health, formulating maintenance decisions and ensuring flight safety. However, due to the complex performance characteristics of aeroengine and the constraints of many external factors, it is difficult to obtain accurate non-linear features between various operating factors and EGT. In order to diagnose and forecast aeroengine performance quickly and accurately, four data-driven baseline prediction frameworks for EGT are proposed. These baseline frameworks took engine operating conditions and operating state control parameters as input variables and EGT as predicted output variables. The original data were collected from CFM56-5B engine ACARS flight data. Four typical machine learning methods, including Generalized Regression Neural Network (GRNN), Radial Basis Neural Network (RBF), Support Vector Regression (SVR) and Random Forest (RF) are trained to develop the models. Four aeroengine EGT baseline models were validated by comparing the after-flight data of another engine. The results show that the developed GRNN models have the best accuracy and computational efficiency compared with other models, and their RE and CPU calculation time on the verification set are 1.132 × 10−3 and 3.512 × 10−3 s, respectively. The developed baseline prediction frameworks can meet the needs of practical engineering applications for airlines. The methodologies developed can be employed by airlines to predict the EGT baseline for the purpose of engine performance monitoring and health management.

The thermomechanical and tribological properties of graphene (GNS)-reinforced NR were investigated using molecular dynamics (MD) simulations. The amorphous molecular dynamics models of two nanocomposites, i.e., natural rubber (pure NR) and graphene/natural rubber (GNS/NR), were established. In addition, the thermodynamic properties of the two materials, before and after the incorporation of graphene into the natural rubber matrix, were investigated through analytical comparison. The results showed that after the graphene was added to the rubber matrix as a reinforcing material, the elastic modulus and shear modulus were increased by 110% and 94.8%, respectively, the tensile property was increased by 178%, the overall thermal conductivity of the composite system was increased by 59%, the glass transition temperature increased from 223 K to 236 K, and the rigidity of the material matrix was significantly improved. The inherent interactions and wear mechanisms of the polymer nanocomposites were discussed at the atomic scale by analyzing the changes in temperature, atomic velocity, relative atomic concentration, and radial distribution functions at the friction interface in the thickness direction.

This study reports a promising method of solid-phase extraction for determining the toluene, ethylbenzene, p-xylene, m-xylene, o-xylene, 1,3,5-trimethylbenzene, and 1,2,4-trimethylbenzene in water samples by gas chromatography–mass spectrometry (GC–MS). Prior to this procedure, the magnesium–aluminum bimetallic hydroxides modified with sodium dodecylbenzenesulfonate (Mg/Al-SDBS-LDH) were prepared and served as the novel solid-phase extractant. The Mg/Al-SDBS-LDH has advantage of good hydrophobicity and larger spacing which facilitates the monoaromatic hydrocarbons (MAHCs) into the interlayer for adsorption. As a result, the seven MAHCs in 500 mL water samples were enriched greatly, and the theoretical enrichment factor reached to 125 times. Under the optimized conditions of solid-phase extraction (SPE) and GC–MS, the mass concentration of each MAHC (0.005–10, 0.01–10, or 0.05–10 ng/mL) had a fine linear relationship with peak area. The correlation coefficients were more than 0.995. The detection limits were between 0.001 and 0.01 ng/mL, and the RSD were between 3.1% and 6.6%. The method had been applied to determine the seven MAHCs in the Dongfengqu river water and laboratory wastewater of Chengdu University of Technology successfully. Keywords: Solid-phase extraction, Monoaromatic hydrocarbons, Gas chromatography–mass spectrometry, SDBS-modified layered bimetallic hydroxide

Based on the electronic universal testing machine with a temperature chamber, this paper investigated the temperature and filler effects on the hyper-elastic behavior of reinforced rubbers and revealed the regulation of the stress and strain of the natural rubber and filled rubber with temperature. The experimental results showed that the hyper-elastic behavior of the filled rubber was temperature-dependent in a wide range. Comparing the adaptability of different models to the stress–strain variation with temperature, the Yeoh model was proven to reasonably characterize the experimental data at different temperatures. Based on the Yeoh model, an explicit temperature-dependent constitutive model was developed to describe the stress–strain response of the filled rubber in a relatively large temperature range. The prediction data of this proposed constitutive model fit well with the test data of the mechanical experiments, indicating that the model is suitable to characterize the large deformation behavior of filled rubbers at different temperatures to a certain degree. The proposed model can be used to obtain the material parameters and has been successfully applied to finite element analysis (FEA), suggesting a high application value. Notably, the model has a simple form and can be conveniently applied in related performance tests of actual production or finite element analysis.

The Yunnan–Guizhou–Guangxi district (also known as the Dian–Qian–Gui “Golden Triangle”) in southwestern China contains numerous Carlin-type Au deposits (CTGDs). However, the sources of Au and Au-bearing fluids in these deposits remain controversial. The Tangshang Au deposit is a middle-sized CTGD in southeastern Yunnan Province. This study involved in situ chemical and S isotope analyses of sulfides and in situ trace elemental and Sr isotope analyses of ore-related calcite; these data were used to trace the sources of fluids and Au, as well as the genesis of this deposit. Four pyrite types (Py1, Py2, Py3, and Py4) and two arsenopyrite types (Apy1 and Apy2) were identified based on their textural characteristics. It was found that Py1 contains relatively lower Au, Sb, Cu, and Tl contents than those of Py2, Py3, and Py4. Py1 is wrapped by rim-Py2 and Py3, which indicates an early-ore-stage genesis. The Carlin-type mineralization elements are elevated in the pyrites (Au = 3.04–38.1 ppm; As = 40,932–65,833 ppm; Tl = 0 to 3.3 ppm; Sb = 1.2 to 343 ppm; and Cu = 10 to 102 ppm), and the average Co/Ni ratio is 0.54. Additionally, Au has a positive correlation with Tl and Cu. The high concentrations of As and Au in all types of pyrite indicate that the ore-forming fluids are rich in both elements. The sulfides in the ores were shown to produce similar S isotope ratios, which are obviously higher than the S isotope value of sulfide (~0‰) in Emeishan basalt; therefore, the integration of these and elemental composition data indicated that all pyrites (Py1, Py2, Py3, and Py4) form during the ore stage. These results also demonstrate that the δ34S values of the Au-bearing fluids are higher than those of basalt wall rocks. The flat chondrite-normalized REEs pattern and positive Eu anomaly of the calcite were similar to those obtained from Emeishan basalt, which suggests a reducing characteristic of hydrothermal fluids. The 87Sr/86Sr ratios (0.70557–0.70622) of calcite were also comparable to the range obtained from Emeishan basalt. Some slightly higher 87Sr/86Sr ratios, which ranged between those obtained from Emeishan basalt and limestone from the Maokou Formation, indicated that the Sr isotope ratios of the Au-bearing fluids are higher than those of Emeishan basalt. Based on data generated in the present study and the regional geology of this area, a genetic model involving a metamorphic fluid system was proposed for the Tangshang gold deposit, and a gold mineralization event related to metamorphic fluid in the south of the Dian–Qian–Gui “Golden Triangle” was indicated.

Study of the adsorption property and mechanism about the environmental functional material can provide theoretical guidance for its actual application. The sulfhydryl attapulgite (SH-ATP) was prepared by solution blending method. The saturated adsorption capacity to Pb2+ and Cd2+ in the experiment were 65.57 and 22.71 mg/g, which increased by 57.74% and 31.96% comparing with the attapulgite (ATP), respectively. The adsorption kinetics experiments illustrated that the adsorption of Pb2+ and Cd2+ onto SH-ATP accorded with multiple kinetic models. Among them, the pseudo second-order model had the best correlation, the correlation coefficients were all above 0.998. The adsorption thermodynamics experiments testified that the adsorption of Pb2+ and Cd2+ onto SH-ATP conformed to the Langmuir, Tempkin and Dubinin-Radushkevich models, those models indicated that the adsorption processes belonged to the monolayer adsorption, the adsorbent surface was homogeneous, and the micropore distribution of the adsorbent was also relatively uniform. Various thermodynamic parameters (ΔH > 0, ΔS > 0, ΔG

The fault current calculation model of photovoltaic (PV) power stations is usually treated as a capacity weighted equivalent model of a single PV generation unit (PVGU). However, in the same PV power station, different PVGUs have various fault current characteristics. As a result, there are significant differences in fault current characteristics between a PVGU and a PV power station. It means that the existing capacity weighted equivalent model cannot be used for accurately describing the fault current contributions from a practical PV power station. In this paper, the fault behaviors of the PVGUs located at different access points of a PV power station are firstly analyzed. The difference in PVGUs’ fault current contributions is identified and reflected with the activation states of current limiters that are employed for PV inverters. The activation states are represented by a theoretical expression so as to distinguish the PVGUs’ fault contributions. Further, based on the proposed theoretical expression, a novel algorithm is developed for sorting all PVGUs included in a PV power station. The multi-machine calculation model is deduced in order to exactly express the fault current contribution from a PV station. Finally, some simulation tests are conducted. The tested results verify the effectiveness of the proposed calculation model. It can provide support for calculating the protection setting of power grid connected with large-scale PV stations.

The outer contours of living trees are often considered as a standard circle during non-destructive testing (NDT) of internal defects using ground-penetrating radar (GPR). However, the detection of classical cross-sections (circular) lacks consideration of irregular contours, making it difficult to accurately locate the radar image of the target. In this paper, we propose a method based on the image affine transformation and the Riemann mapping principle to analyze the effect of irregular detection routes on the geometric characteristics of target reflection hyperbola. First, for the similar output phenomenon in the “hyperbola fitting”, geometric analysis and numerical simulation were performed. Then, the conversion of irregular trunk radar images and physical domain radar images was implemented using the method of image affine transformation and the Riemann mapping principle. Finally, the influence of irregular detection routes on the geometry of the target reflection curve was investigated in detail through numerical simulations and actual experiments. The numerical simulation and measurement results demonstrated that the method in this study could better reflect the imaging characteristics of the target reflection hyperbola under the irregular detection pattern. This method provides assistance to further study the defects of irregular living trees and prevents the misjudgment of targets as a result of hyperbolic distortion, resulting in a greater prospect of application.

Ground penetrating radar (GPR), as a newly nondestructive testing technology (NDT), has been adopted to explore the spatial position and the structure of the tree roots. Due to the complexity of soil distribution and the randomness of the root position in the natural environment, it is difficult to locate the root in the GPR B-Scan image. In this study, a novel method for root detection in the B-scan image by considering both multidirectional features and symmetry of hyperbola was proposed. Firstly, a mixed dataset B-Scan images were employed to train Faster RCNN (Regions with CNN features) to obtain the potential hyperbola region. Then, the peak area and its connected region were filtered from the four directional gradient graphs in the proposed region. The symmetry test was applied to segment the intersecting hyperbolas. Finally, two rounds of coordinate transformation and line detection based on Hough transform were employed for the hyperbola recognition and root radius and position estimation. To validate the effectiveness of this approach for tree root detection, a mixed dataset was made, including synthetic data from gprMax as well as field data collected from 35 ancient tree roots and fresh grapevine controlled experiments. From the results of hyperbola recognition as well as the estimation for the radius and position of the root, our method shows a significant effect in root detection.

Taxi drivers always look for strategies to locate passengers quickly and therefore increase their profit margin. In reality, the passenger seeking strategies are mostly empirical and substantially vary among taxi drivers. From the history taxi data, the top performing taxi drivers can earn 25% more than the ones with mediocre seeking strategy in the same period of time. A better strategy not only helps taxi drivers earn more with less effort, but also reduce fuel consumption and carbon emissions. It is interesting to examine the influential factors in passenger seeking strategies and find algorithms to guide taxi drivers to passenger hotspots with the right timing. With the abundant availability of history taxicab traces, the existing methods of doing taxi business have been radically changed. This paper focuses on the problem of mining efficient operation strategies from a large-scale history taxi traces collected over one year. Our approach presents generic insights into the dynamics of taxicab services with the objective of maximizing the profit margins for the concerned parties. We propose important metrics, such as trip frequency, hot spots, and taxi mileage, and provide valuable insights toward more efficient operation strategies. We analyze these metrics using techniques, such as Newton's polynomial interpolation and Gamma distribution, to understand their dynamics. Our strategies use the real taxicab traces from the city of Changsha (P.R.China), may predict the taxi rides at different times by 90.68% per day, and increase the taxi drivers income levels up to 19.38% by controlling appropriate mileage per trip and following the route across more urban hot spots.

The title compound, C21H15N3O3, features an essentially planar molecule (r.m.s. deviation for all non-H atoms = 0.090 Å). An intramolecular C—H...N hydrogen bond occurs. In the crystal, the molecules are connected by C—H...N and C—H...O hydrogen bonds into layers parallel to (102).

Southwestern Guizhou, China, is famous for hosting clusters of Carlin-type Au, Sb, and Hg-Tl deposits. These deposits are thought to be the products of a low-temperature hydrothermal metallogenic event. Calcite and fluorite are common and widespread gangue minerals in Au and Sb deposits, respectively. Ore-related calcite commonly coexists with stibnite, realgar, and orpiment at the periphery of high-grade orebodies in Au deposits, while ore-related fluorite is generally intergrown with stibnite in Sb deposits. In this study, ore-related calcite and fluorite samples from representative Au (Zimudang) and Sb (Dachang) deposits, respectively, were separated, and the rare earth element (REE) concentrations, Sm/Nd isotope ratios, and Sm–Nd isochron ages were analyzed. This study aims to determine the formation ages of the calcite and fluorite and to constrain the age of low-temperature metallogenic event in Southwestern Guizhou. The calcite and fluorite samples contain relatively high total concentrations of REEs (8.21–22.5 μg/g for calcite, 21.7–36.6 μg/g for fluorite), exhibit variable Sm/Nd ratios (0.51–1.01 for calcite, 0.35–0.49 for fluorite), and yield Sm–Nd isochron ages of 148.4 ± 4.8 and 141 ± 20 Ma, respectively. These ages are consistent with the age range constrained by the low-temperature thermochronology of zircon (132–160 Ma), crosscutting relationships of stratigraphy or intrusions (96–160 Ma), and previous dating results (135–150 Ma) in Southwestern Guizhou. Collectively, the ages obtained in this study add new evidence to previous geochronology studies, such that the low-temperature hydrothermal mineralization in Southwestern Guizhou can be constrained to 135–150 Ma, corresponding to the Yanshanian orogeny, which was associated with a weak extensional tectonic environment.

In this paper, some representative hyperelastic constitutive models of rubber materials were reviewed from the perspectives of molecular chain network statistical mechanics and continuum mechanics. Based on the advantages of existing models, an improved constitutive model was developed, and the stress–strain relationship was derived. Uniaxial tensile tests were performed on two types of filled tire compounds at different temperatures. The physical phenomena related to rubber deformation were analyzed, and the temperature dependence of the mechanical behavior of filled rubber in a larger deformation range (150% strain) was revealed from multiple angles. Based on the experimental data, the ability of several models to describe the stress–strain mechanical response of carbon black filled compound was studied, and the application limitations of some constitutive models were revealed. Combined with the experimental data, the ability of Yeoh model, Ogden model (n = 3), and improved eight-chain model to characterize the temperature dependence was studied, and the laws of temperature dependence of their parameters were revealed. By fitting the uniaxial tensile test data and comparing it with the Yeoh model, the improved eight-chain model was proved to have a better ability to predict the hyperelastic behavior of rubber materials under different deformation states. Finally, the improved eight-chain model was successfully applied to finite element analysis (FEA) and compared with the experimental data. It was found that the improved eight-chain model can accurately describe the stress–strain characteristics of filled rubber.

Rubber-based materials are widely used in a variety of industrial applications. In these applications, rubber components withstand various loading conditions over a range of temperatures. It is of great significance to study the mechanical behavior of vulcanized rubber at different temperatures, especially in a range of high temperatures. The temperature dependence of the constitutive behavior of filled rubber is important for the performance of the rubber. However, only a few constitutive models have been reported that investigate the stress–temperature relationship. In this paper, based on an analysis of experimental data, the effects of temperature on the hyperelastic behaviors of both natural rubber and filled rubber, with different mass fractions of carbon black, were studied. The regulation of stress and strain of natural rubber and filled rubber with temperature was revealed. In addition, an eight-chain model that can reasonably characterize the experimental data at different temperatures was proved. An explicit temperature-dependent constitutive model was developed based on the Arruda-Boyce model to describe the stress–strain response of filled rubber in a relatively large temperature range. Meanwhile, it was proved that the model can predict the effect of temperature on the hyperelastic behavior of filled rubber. Finally, the improved Arruda-Boyce model was used to obtain the material parameters and was then successfully applied to finite element analysis (FEA), which showed that the model has high application value. In addition, the model had a simple form and could be conveniently applied in related performance test of actual production or finite element analysis.

The Shuiyindong Gold Mine hosts one of the largest and highest-grade, strata-bound Carlin-type gold deposits discovered to date in Southwestern China. The outcrop stratigraphy and drill core data of the deposit reveal Middle–Upper Permian and Lower Triassic formations. The ore is mainly hosted in Upper Permian bioclastic limestone near the axis of an anticline. The gold is mainly hosted in arsenian pyrite and arsenopyrite, mainly existing in the form of crystal lattice gold, submicroscopic particles and nanoparticles. Fluorite commonly occurs at the vicinity of an unconformity between the Middle–Upper Permian formations, which is proposed to be the structural conduit that fed the ore fluids. Calcite commonly fills fractures at the periphery of decarbonated rocks, which contain high grade orebodies. This study aimed to verify the occurrence of two distinct hydrothermal events at the Shuiyindong, based on Sm–Nd isotope dating of the fluorite and calcite. For this purpose, rare-earth element (REE) concentrations, Sm/Nd isotope ratios, and Sm–Nd isochron ages of the fluorite and calcite were determined. The fluorite and calcite contain relatively high total concentrations of REE (12.3–25.6 μg/g and 5.71–31.7 μg/g, respectively), exhibit variable Sm/Nd ratios (0.52–1.03 and 0.57–1.71, respectively), and yield Sm–Nd isochron ages of 200.1 ± 8.6 Ma and 150.2 ± 2.2 Ma, with slightly different initial ε Nd ( t ) values of −4.4 and −1.1, respectively. These two groups of Sm–Nd isochron ages suggest two episodes of hydrothermal events at the Shuiyindong gold deposit. The age of the calcite probably represents the late stage of the gold mineralization period. The initial ε Nd ( t ) values of the fluorite and calcite indicate that the Nd was probably derived from mixtures of basaltic volcanic tuff and bioclastic limestone from the Permian formations.