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Urban-rural development is an important driving force for regional economic growth. The existing researches have studied this issue from various perspectives, but they ignore the impact of big data on the economy. In the post pandemic era, big data, as an emerging production factor, has a significant indicative effect in promoting urban-rural economic recovery and fostering new business forms. Therefore, fully considering the factor of big data can help reveal its impact mechanism on urban-rural economic growth in the post-epidemic period. Based on the data of 30 provinces and cities in China, this paper introduced big data on the basis of traditional models and constructed a multi-dimensional factor indicator system. At the same time, the panel regression model was established by using unit root test, Hausman test and precision test. Through benchmark regression and heterogeneity analysis, the impact of urban-rural development factors on economic growth was discussed. The results showed that the panel model passed all tests, and its regression error was stable below 5 %. Transportation, technology, and the three major industries can all promote positive economic growth, with a significance of 1 %. The three industries' contribution to economic growth ranks the third, second and first industries in order. In addition, the good ecological environment contributes to the benign economic growth during the study period. A 1 % increase in forest cover would drive economic growth by 0.215 %. But the impact of public's attention on the overall economy was an indirect effect manifested through its physical industries.The regional heterogeneity indicated that each element had different effects on economic development in eastern, central and western regions. Based on its results, this paper proposed suggestions for each region. In addition, this study found that the Internet attention reflected by big data did not directly drive economic growth, but affected economic growth through indirect channels such as information flow and resource allocation of real industries. This study provided data support for the existing theoretical review, and provided policy reference for the rational planning and industrial layout of China's regional economy.

The accurate diagnosis and treatment of gliomas depends largely on the understanding of the P53 gene status. In our study, we presented a robust deep learning model, CTD-RegNet (improved RegNet integrating CNN, vision transformer, and truth discovery), tailored for predicting P53 gene status in gliomas. Our model addressed common challenges of existing deep learning models, such as incomplete feature extraction and uncertainty. First, the model used the RegNet network as a basis for predicting P53 gene mutations by skillfully extracting heterogeneous features. Next, the RegNet network was enhanced by integrating the CNN and ViT modules to optimise feature extraction and computational efficiency. Finally, using the truth discovery algorithm, we iteratively refined model uncertainties, thereby improving prediction accuracy. Our experiments demonstrated the effectiveness of the CTD-RegNet model, achieving an impressive accuracy of 95.57% and an AUC score of 0.9789, outperforming existing P53 gene status prediction models. The non-invasive nature of our model minimised the economic burden and physical and psychological stress on patients, while providing critical insights for accurate clinical diagnosis and treatment of gliomas.

Porcine epidemic diarrhea virus (PEDV) causes diarrhea and vomiting in piglets, leading to a mortality rate of 100%. Due to the high frequency of mutation, it is important to monitor the evolution of PEDV and develop potential vaccine candidates. In this study, two PEDV strains (ZJ2022 and ZQ2022) were identified by PCR. These strains were subsequently isolated, and their genome sequences, growth characteristics, and pathogenicity were compared. Phylogenetic and recombination analyses revealed that both strains belonged to GIIa-subgroup, and ZQ2022 was identified as a recombinant strain derived from ZJ2022. Further sequence analysis showed that the ZJ2022 strain had a modified top region of the S1 protein due to a three amino acid insertion (T380_Y380insGGE) in the S1 gene. According to the virus growth curve, ZJ2022 exhibited better cellular adaptation than ZQ2022, with higher viral titers from 8 hpi to 24 hpi. Additionally, ZQ2022 exhibited a high level of pathogenicity, causing severe diarrhea in piglets at 36 hpi and a 100% mortality rate by 96 hpi. In contrast, ZJ2022 showed lower pathogenicity, inducing severe diarrhea in piglets at 60 hpi, with a mortality rate of 60% at 96 hpi and 100% at 120 hpi. In summary, our findings provided evidence of the undergoing mutations in Chinese PEDV strains. Furthermore, the S gene insertion strain ZJ2022 exhibited strong cellular adaptability and low pathogenicity, making it a potential candidate strain for vaccine development.

Oxidative stress plays an important role in the pathogenesis of acute lung injury (ALI). As a typical post-translational modification triggered by oxidative stress, protein S-glutathionylation (PSSG) is regulated by redox signaling pathways and plays diverse roles in oxidative stress conditions. In this study, we found that GSTP downregulation exacerbated LPS-induced injury in human lung epithelial cells and in mice ALI models, confirming the protective effect of GSTP against ALI both in vitro and in vivo. Additionally, a positive correlation was observed between total PSSG level and GSTP expression level in cells and mice lung tissues. Further results demonstrated that GSTP inhibited KEAP1-NRF2 interaction by promoting PSSG process of KEAP1. By the integration of protein mass spectrometry, molecular docking, and site-mutation validation assays, we identified C434 in KEAP1 as the key PSSG site catalyzed by GSTP, which promoted the dissociation of KEAP1-NRF2 complex and activated the subsequent anti-oxidant genes. In vivo experiments with AAV-GSTP mice confirmed that GSTP inhibited LPS-induced lung inflammation by promoting PSSG of KEAP1 and activating the NRF2 downstream antioxidant pathways. Collectively, this study revealed the novel regulatory mechanism of GSTP in the anti-inflammatory function of lungs by modulating PSSG of KEAP1 and the subsequent KEAP1/NRF2 pathway. Targeting at manipulation of GSTP level or activity might be a promising therapeutic strategy for oxidative stress-induced ALI progression.

As the main form of new urbanization, the coordinated development of cities in metropolitan areas requires reliable and efficient rail transit skeleton support. However, in the rapid development of metropolitan areas, the layout and analysis of multi-level rail transit systems have a certain lag. Taking the Xi’an metropolitan area as an example, this study analyzes the comprehensive accessibility and resilience of the multi-level rail transit network, and proposes an expansion plan accordingly. The traffic analysis zone (TAZ) is divided by towns and streets, and the relationship between points of interest (POIs) and the regional average level is analyzed using DEA. The improved weighted average travel time model is built with the analysis results as regional weights; a site selection model based on multiple construction influencing factors is proposed, and four expansion plans, namely, economic optimal, environmental optimal, transport optimal, and integrated optimal, are designed. The peak passenger flow scenario and the “failure–reparation” scenario during the entire operation period are designed to analyze the resilience of four plans, and the resilience is quantified by the elasticity curve of the maximum connected subgraph ratio (MCSR) changing over time. The research results show that the transport optimal plan has the best comprehensive accessibility and resilience, reducing travel costs in Houzhenzi Town, which has the worst accessibility, by 34%. The expansion model and evaluation method in this study can provide an empirical example for the development of other metropolitan areas and provide a reasonable benchmark and guidance for the development of multi-level rail transit networks in future urban areas.

Selecting an appropriate bra product has long been a challenge for adolescent girls, whose breasts are rapidly growing. This challenge arises due to the absence of a rational benchmark for breast measurement. Traditional benchmarks are based on ergonomic principles, using reference points located on the human skeleton. However, the breast lacks a bone structure for support, leading to highly variable measurement results. In this study, the jugular notch and the xiphoid process were selected as breast measurement points according to academic principles. Their accuracy, stability, and deviation were experimentally investigated through tactile assessment of adolescent girls at different stages of development. Based on the experimental results, a novel breast measurement coordinate system was established, and its accuracy was verified by conversion calculations using a geodetic coordinate system. The results indicated that the numerical calculation of the breast shape was more accurate using the newly established breast measurement coordinate system.

The supply of electric power is vital for the daily lives of people, industrial production, and business services. At present, although enough electric power can be supplied to meet the power demand, there are still some challenges, especially in terms of long-distance power transmissions and long-term power storage. Consequently, if the power production capacity exceeds the immediate consumption requirements, i.e., the produced electric power cannot be consumed in a short period, and much electric power could be wasted. Evidently, to minimize the wastage of electric power, it is necessary to properly plan power production by accurately forecasting the future power load. Therefore, a preferable power load forecasting algorithm is crucial for the planning of power production. This paper proposes a novel deep learning model for the purpose of power load forecasting, termed the SSA-CNN-LSTM-ATT model, which combines the CNN-LSTM model with SSA optimization and attention mechanisms. In this model, the CNN module extracts the features from the sequential data, and then the features are passed to the LSTM module for modeling and capturing the long-term dependencies hidden in the sequences. Subsequently, an attention layer is employed to measure the importance of different features. Finally, the output is obtained through a fully connected layer, yielding the forecasting results of the power load. Extensive experiments have been conducted on a real-world dataset, and the metric R2 can reach 0.998, indicating that our proposed model can accurately forecast the power load.

Secondary vocational education (SVE) is responsible for cultivating talents with moral and technical skills, receiving widespread attention from scholars and the public. Studying the two attentions can broaden the research perspectives and promote the development of SVE. However, there are the following problems: 1) the public attention and academic attention of SVE cannot be accurately characterized; 2) the relationship between the public attention and academic attention of SVE cannot be clear; 3) the impact of public attention and academic attention on SVE cannot be predicted. To address the above issues, this paper puts forward the PLSH (Pearson correlation–Linear regression, Seasonal autoregressive integrated moving average (SARIMA), and Holt-winters model) framework. It involves four research steps: 1) public attention and academic attention are obtained for SVE; 2) the correlation between them is analyzed and a linear model is developed; 3) the performance of the SARIMA model and Holt-winters model are conducted, and the best model is adopted to predict the public attention; 4) academic attention is predicted using the results from the previous step. The study shows that the PLSH framework can characterize academic and public attention to SVE, effectively reflecting their correlation and predicting their growth trends.

The key to whether elderly individuals in the community can enjoy their later years peacefully lies in the service capabilities of community comprehensive elderly care service facilities (CCECSF) under the Public-Private Partnership (PPP) mode. To maintain a high level of service capability in community comprehensive elderly care service facilities under PPP mode, scientific evaluation of the effectiveness of these facilities is equally crucial. This article first constructs a post-occupancy evaluation index system of community comprehensive elderly care service facilities under PPP mode that includes three evaluation attributes and fifteen evaluation criteria based on the Chinese culture and lifestyle habits. Regarding the issue of direct users being unable to directly participate in evaluations, the uncertainty in evaluation information, and the volatility of evaluation results, a multi-stage post-occupancy evaluation model is constructed based on probabilistic linguistic term set, TOPSIS model and multi-stage decision theory. The above post-occupancy evaluation index system and evaluation model together constitute a multi-stage post-occupancy evaluation framework for community comprehensive elderly care service facilities under PPP mode. The outcomes of the case study indicate that the post-occupancy evaluation index system can offer a scientifically guided approach for evaluating the service level of community comprehensive elderly care service facilities under the PPP mode; meanwhile, the multi-stage evaluation model can enable direct user participation in the post-evaluation of facility usage and improve the robustness and reduce the fluctuation of the evaluation results, so as to improve the scientificity of the evaluation results.

In this article, multiplicity of nontrivial solutions for an inhomogeneous singular biharmonic equation with Rellich potential are studied. Firstly, a negative energy solution of the studied equations is achieved via the Ekeland's variational principle and Caffarelli–Kohn–Nirenberg inequality. Then by applying Mountain pass theorem lack of Palais–Smale conditions, the second solution with positive energy is also obtained.

[Introduction] The co-combustion of coal and solid waste can not only realize the energy recycling of solid waste, but also is one of the paths to realize carbon emission reduction of coal-fired power generation. [Method] This paper reviewed the study on co-combustion of coal and solid waste in coal-fired power plants, and mainly introduced the combustion application and technology development of co-combustion of coal and different solid wastes based on the current mainstream power plant boiler as a reactor; Evaluated the development of co-combustion technology of coal and solid waste from the perspectives of fuel economy, fly ash characteristics of mixed fuels, pollutant emissions, and carbon tax; Finally discussed the characteristics of direct and indirect mixing technologies. [Result] Direct co-combustion of coal and solid waste is required to minimize the impact on boiler operation, especially the emission of gas pollutants, the impact of fly ash on heat transfer surfaces, and the harmless disposal of fly ash. Indirect co-combustion can avoid the influence of mixed fuel combustion on the furnace, but requires high hardware cost investment and more complicated coupling technology. The oxygen-enriched combustion technology still needs to optimize the existing boiler structure to improve the applicability of the technology. [Conclusion] The direct co-combustion is better than the indirect co-combustion considering the realizability and the cost, and the extensive adaptability of circulating fluidized bed fuel is conducive to the application of direct co-combustion technology of coal and solid waste. With the development of the oxygen- enriched combustion technology based on the circulating fluidized bed, it is more conducive to realize carbon emission reduction in coal-fired power plant.

Low carbonization of the traction system is the key to low-carbon rail transit operation, and its preliminary plan decision plays a decisive role in whether low carbon can be achieved in later stages. Therefore, how to achieve scientific decisions of energy storage traction systems in a low-carbon background is a problem that needs to be solved. The innovation of this paper is as follows: first, aiming at the reality of the rail transit energy storage traction system, a decision index system of the energy storage traction system which contains seven attributes and 18 criteria is constructed; second, aiming at the uncertainty of decision information and the decision makers’ aversion to risk, the decision model adapted to the energy storage traction system decision is constructed based on the interval Pythagorean intuitionistic fuzzy number and VIKOR model principle. The decision index system and decision model together constitute the decision framework. The case study results show that the decision index system can provide scientific guidance for the decision of the energy storage traction system, and the decision model can provide risk aversion type decision results with good robustness.

The exploration of the correlation between subject network attention and literature research in China can aid in comprehending the development trend of Chinese scientific and technological journals. Currently, many scholars have done a lot of research based on the network media index, but the relationship between the discipline attention represented by it and literature research has not been fully verified. This paper used CNKI and Baidu Index as data sources to establish a RAPF experimental framework based on relationship analysis and prediction, and selected high school mathematics subjects in China for effective demonstration. First, RAPF extracted core keywords using text tools and word frequency statistics. Second, it constructed a relationship model between subject attention and literature research based on Spearman and LOOCV. Finally, it made predictions through time series and regression analysis. The results showed a correlation between subject attention and literature research, and the model fit R2 was 0.774, with a relative error of less than 2%. Short-term predictions found that some keywords received less online attention, and 2022–2024 may be the crucial development period for mathematical education research, with an annual literature research volume of approximately 380 articles. This paper summarized the mathematical subject themes centered on content, culture, literacy, and integration, and also provided a reference for the development of the subject through experimental prediction. In the next two years, China's mathematics literature research still needs to delve deeper, broaden its breadth, enhance its height, and ensure a steady improvement in the quality and quantity of literature research.

Cities play an essential role in promoting the development of the whole national economy. Many scholars have proposed different methods to study this problem, but all have ignored the critical influence of network attention on the economy. Therefore, this study uses big Network data to profoundly analyse the relationship between urban spatial development and economic growth in eastern, central, and western China, further study this issue, and provide theoretical support for urban economic development. This study first divides China into three regions: eastern, central, and western, then selects the indicators of the national economy and urban development from 2014 to 2019, and finally constructs a panel regression model to explore the relationship between urban space elements and national economic development. The results show that China’s national economic development is closely related to spatial factors such as urban transportation, environment, science and technology, energy consumption, and housing price. These findings will help to make full use of the natural advantages of each region and guide the rational planning of the urban economy. This study provides references for urban development planning and industrial transfer in various regions of China and offers feasible suggestions for China’s overall national economic growth.

Abstract In order to adapt to the development of modern education and provide education practitioners with decision‐making suggestions, it is necessary to understand the relationship between the public's attention to basic chemistry education (BCE) and academic attention. However, many existing research is based on a single platform to study social hot information, such as Google Index, Baidu Index, Web of Science, and so on. But they ignore the relationship that exists between the Baidu Index and related academic platforms, and ignore the common information reflected between them. This paper takes advantage of the big data method, through the big data of Baidu Index and the big data obtained from China National Knowledge Internet (CNKI) database, to study the network attention and academic attention of BCE, and propose a CVS‐LSP‐GP framework. It first selects keywords through correlation analysis, secondly uses the data obtained from the first step to construct a nonlinear regression model, and finally combines the results of gray prediction to predict the academic attention of CNKI related to BCE. The research results show that the BCE is mainly affected by the micro‐lecture teaching mode, and relevant education practitioners should integrate the micro‐lecture mode into teaching for more further research and practice.

This paper is concerned with the bifurcations of a susceptible-infectious-recovered-susceptible (SIRS) epidemic model with a general saturated incidence rate $ k I^p/(1+\alpha I^p) $. For general $ p > 1 $, it is shown that the model can undergo saddle-node bifurcation, Bogdanov-Takens bifurcation of codimension two, and degenerate Hopf bifurcation of codimension two with the change of parameters. Combining with the results in [1] for $ 0 < p\leq 1 $, this type of SIRS model has Hopf cyclicity $ 2 $ for any $ p > 0 $. These results also improve some previous ones in [2] and [3], which are dealt with the special case of $ p = 2 $.

Abstract Analyzing the relationship between Internet attention and teaching research can provide a reference for extracting and identifying research hot spots in the discipline. Currently, many kinds of literature obtain social hot‐spot information based on a single platform, such as the Baidu Index, China National Knowledge Infrastructure (CNKI), and Web of Science. Still, they ignore the common information reflected between the Baidu Index platform and the professional academic platform and the lack of relationship demonstration between them. Therefore, based on the CNKI database and Baidu Index platform, the most critical data statistical analysis service platform in the Internet era, this article proposes a relationship modeling and prediction framework (RMPF). First, RMPF analyzes the relationship between public attention and teaching research from the perspective of “high school information technology” by word frequency analysis and Pearson. Second, RMPF constructs the relationship model between them using leave‐one‐out cross‐validation. Third, RMPF predicts the development of subject teaching and research using the autoregressive integrated moving average model and multiple regressions. The results show that the correlation coefficient between public attention and teaching research is more than 0.65 from the perspective of “high school information technology,” showing a strong positive correlation. In recent years, the focus of high school information technology discipline tends to be integrating multimedia technology, cloud classroom, artificial intelligence, and other technologies and disciplines. In addition, discipline literature volume growth will slow in the next 2 years, with an average annual publication of 350 articles. The overall number of literature decreased by about 120 compared with 2020. Based on the proposed RMPF, this study clarifies the relationship between subject attention and teaching research through experimental demonstration and provides an implementation framework reference for researchers of relevant literature.

Cervical sagittal balance plays a pivotal role in spine surgeries as it has a significant impact on the clinical outcomes in cervical spine surgery. Image processing techniques have significantly improved the accuracy and precision of cervical surgical techniques. This study aims to investigate the effects of T1 slope (T1s) on the disappearance of cervical lordosis after posterior cervical double-door laminoplasty using medical informatics and radiographic measures. To do so, we determined and measured the loss of T1s and cervical lordosis during the postoperative follow-up period in patients with double-door posterior cervical laminoplasty. Patients (n = 40) who underwent posterior cervical double-door laminoplasty participated in this study. For all patients, the difference between the preoperative T1s (angle between the upper edge of T1 and the horizontal line) and preoperative and postoperative cervical lordosis (Cobb method) was estimated, and the linear relationship between the two was statistically analyzed to observe the influence of preoperative T1s on postoperative cervical lordosis disappearance. The average preoperative T1s was 23.54°, and the average preoperative cervical lordosis angle was 8.50°. After 1–20 months of follow-up (mean = 9.53 months), the average postoperative cervical lordosis was 8.50°, and the average loss of cervical lordosis was 0.22°. Twenty cases had different degrees of lordosis angle loss after the operation, with an average loss of 9.31°. All patients were divided into groups A and B, according to a mean value of T1s = 23.54°, of which T1S > 23.54° was group A and T1s < 23.54 was group B. Cervical lordosis was quantified by the C2–C7 Cobb angle. The Cobb angle difference of cervical lordosis was measured before and after the operation, and its correlation with preoperative T1s was assessed. The preoperative Cobb angle and cervical curvature changes in the two groups were statistically compared, and the difference between the two groups was statistically significant (p < 0.05). The group with a T1s > 23.54° had greater loss of preoperative Cobb angle and cervical curvature. In group A, the mean preoperative cervical disability index (NDI) was 32.4 ± 3.4, and the mean postoperative NDI score was 16.5 ± 2.1. The mean preoperative VAS scores of neck pain and neck pain were 5.41 ± 1.1 and 5.55 ± 0.3, respectively, and the improvement in neck pain was −0.2%. The mean preoperative NDI in group B was 30.1 ± 2.9, and the mean postoperative NDI score was 11.5 ± 3.1. The mean VAS score for preoperative neck pain was 5.11 ± 1.2, that for postoperative neck pain was 4.18 ± 0.7, and that for neck pain improved by 18%. There was a significant difference between the two groups (p < 0.05). The disappearance of cervical lordosis after posterior cervical double-door laminoplasty is an important cause of postoperative cervical spine pain. The T1s is meaningful for predicting the loss of postoperative curvature in patients undergoing posterior cervical double-door laminoplasty. This is especially true for patients with good preoperative cervical curvature without ankylosis and kyphosis but with a wide T1s.

Due to the dynamic and competitive market environment, it is widely recognized that the development of new products and processes has become the critical point of attention for many companies. The first step in the product development process is to define the nature and function of the product, which is to classify the new product. The traditional product classification process only focuses on the product and market, which is developed by designers on the basis of the products of past dynasties. This way is labor-consuming, inefficient, and has become a bottleneck or constraint for these enterprises to improve their productivities and regulate production. In recent years, Artificial Intelligence has been applied in a wide range of fields including product classification. How to apply machine learning technologies to solve the classification problem of product classification has been widely concerned by researchers. In this paper, a fast and effective product classification, called MdmNet, is proposed, which is based on a novel attempt that embeds the innovation idea of human in machine learning technologies. MdmNet includes three modules: a target customer modeling method based on the deep learning technologies a consumer information deduction method based on the MDM that builds a consumer feature closed loop and output the classification result of the consumers’ perspective, and a weighted fusion module. Experiments conducted on benchmark datasets Cars demonstrate the impressive performance of the proposed MdmNet. This paper first attempts to add consumer motivation analysis to traditional machine learning method, which has a strong application prospect.

Abstract Purpose To explore the feasibility of sacral-2-alar (S2-alar) screw placement by measuring the length, diameter, and angle of the screw trajectory on computed tomography (CT). Methods This study selected 100 Han-nationality adults in northern China with a normal spine and pelvis. CT data were imported into PHILIPS software for reconstructing the 3D digital images. The optimal S2-alar screw trajectory was imitated on CT. Parameters including the length of the screw trajectory, sagittal angle, coronal angle, distance between the entry point and the spinous process, and minimum diameter of the screw trajectory were measured to evaluate the application of S2-alar screws. Results In total, 48 males and 52 females were included. The average length of the left screw trajectory was 47.18 ± 3.91 mm. The sagittal angle was 29.06 ± 4.00°. The coronal angle was 13.31 ± 6.95°. The distance between the entry point and the spinous process was 21.0 (3.7) mm. The minimum diameter of the screw trajectory was 17.1 (2.3) mm. The average length of the right screw trajectory was 45.46 ± 4.37 mm. The sagittal angle was 23.33 ± 4.26°. The coronal angle was 14.88 ± 6.84°. The distance between the entry point and the spinous process was 22.8 (2.9) mm. The minimum diameter of the screw trajectory was 16.9 (3.1) mm. In women, the average length of the left screw trajectory was 44.80 ± 3.66 mm. The sagittal angle was 32.14 ± 5.48°. The coronal angle was 16.04 ± 7.74°. The distance between the entry point and the spinous process was 21.8 (2.8) mm. The minimum diameter of the screw trajectory was 17.1 (5) mm. The average length of the right screw trajectory was 44.01 ± 3.72 mm. The sagittal angle was 25.12 ± 5.19. The coronal angle was 16.67 ± 8.34°. The distance between the entry point and the spinous process was 21.6 (2.7) mm. The minimum diameter of the screw trajectory was 17 (4.5) mm. As seen from the data, there were significant differences in the minimum diameter of the screw trajectory in both males and females. In females, there were also significant differences between the left and right sides in the coronal angle. Between males and females, there were statistically significant differences in the length of the screw trajectory. There were no statistically significant differences in the other parameters between males and females. Conclusion The optimal screw trajectory of the S2-alar screw can be found on CT. The length and deflection angle of the screw meet the clinical requirements. This method is easy to perform and feasible for clinical application.

BackgroundCorynebacterium accolens (C. accolens) is a common nasal colonizer, whereas Staphylococcus aureus (S. aureus) is typically regarded a pathogenic organism in patients with chronic rhinosinusitis (CRS). This study aims to evaluate the interaction of the two bacteria in vitro.MethodsClinical isolates of C. accolens and S. aureus from sinonasal swabs, as well as primary human nasal epithelial cells (HNECs) cultured from cellular brushings of both healthy and CRS patients were used for this study. The cell-free culture supernatants of all isolates grown alone and in co-cultures were tested for their effects on transepithelial electrical resistance (TER), FITC-Dextran permeability, lactate dehydrogenase (LDH), and IL-6 and IL-8 secretion of HNECs. Confocal scanning laser microscopy and immunofluorescence were also used to visualize the apical junctional complexes. C. accolens cell-free culture supernatants were also tested for antimicrobial activity and growth on planktonic and biofilm S. aureus growth.ResultsThe cell-free culture supernatants of 3\C. accolens strains (at 60% for S. aureus reference strain and 30% concentration for S. aureus clinical strains) inhibited the growth of both the planktonic S. aureus reference and clinical strains significantly. The C. accolens cell-free culture supernatants caused no change in the TER or FITC-Dextran permeability of the HNEC-ALI cultures, while the cell-free culture supernatants of S. aureus strains had a detrimental effect. Cell-free culture supernatants of C. accolens co-cultured with both the clinical and reference strains of S. aureus delayed the S. aureus-dependent mucosal barrier damage in a dose-dependent manner.ConclusionCorynebacterium accolens cell-free culture supernatants appear to inhibit the growth of the S. aureus planktonic bacteria, and may reduce the mucosal barrier damage caused by S. aureus.

A low-profile broadband dual-polarized antenna is investigated for base station applications. It consists of two orthogonal dipoles, fork-shaped feeding lines, an artificial magnetic conductor (AMC), and parasitic strips. By utilizing the Brillouin dispersion diagram, the AMC is designed as the antenna reflector. It has a wide in-phase reflection bandwidth of 54.7% (1.54–2.70 GHz) and a surface-wave bound range of 0–2.65 GHz. This design effectively reduces the antenna profile by over 50% compared to traditional antennas without an AMC. For demonstration, a prototype is fabricated for 2G/3G/LTE base station applications. Good agreement between the simulations and measurements is observed. The measured −10-dB impedance bandwidth of our antenna is 55.4% (1.58–2.79 GHz), with a stable gain of 9.5 dBi and a high isolation of more than 30 dB across the impedance passband. As a result, this antenna is an excellent candidate for miniaturized base station antenna applications.

The tunability of conductance states of various emerging nonvolatile memristive devices emulates the plasticity of biological synapses, making it promising in the hardware realization of large‐scale neuromorphic systems. The inference of the neural network can be greatly accelerated by the vector‐matrix multiplication (VMM) performed within a crossbar array of memristive devices in one step. Nevertheless, the implementation of the VMM needs complex peripheral circuits, and the complexity further increases as non‐idealities of memristive devices prevent precise conductance tuning (especially for the online training) and largely degrade the performance of the deep neural networks (DNNs). Herein, an efficient online training method of the memristive deep belief net (DBN) is presented. The proposed memristive DBN uses stochastically binarized activations, reducing the complexity of peripheral circuits, and uses the contrastive divergence (CD)‐based gradient descent learning algorithm. The analog VMM and digital CD are performed separately in a mixed‐signal hardware arrangement, making the memristive DBN highly immune to non‐idealities of synaptic devices. The number of write operations on memristive devices is reduced by two orders of magnitude. The recognition accuracy of 95–97% can be achieved for the MNIST dataset using pulsed synaptic behaviors of various memristive synaptic devices.

Understanding the concentration of agricultural topsoil heavy metals and their spatial variability is crucial in terms of keeping a close track of the quality of regional environment. In this study, a total of 139 sites were sampled from the agricultural topsoils in Xuzhou area, Jiangsu, China. The contents of eight hazardous heavy metals (Cu, Zn, Ni, Pb, Cr, Hg, As, Cd) were measured for each soil sample. We used semivariogram to study the spatial patterns of these heavy metals, and obtained their spatial distribution maps in Xuzhou area. The descriptive statistics and the correlation between each heavy metal were examined. Our data show that the concentrations of all eight hazardous heavy metals do not exceed the threshold values that are recommended in the national soil quality standard (GB15618–2018). These new results provide a useful basis for the policy design regarding the agriculture, industry, topsoil contamination treatment, and regional environment in this region.

Physics in middle school plays an irreplaceable role in cultivating students’ scientific quality and creativity. It occupies an essential position in middle school teaching, which has always been the focus of academia (academic concern) and the public (public concern). The relationship between academic and public concern for physics in middle school has become an important research issue. To solve this problem, this article proposes the Pearson correlation-linear prediction and ARIMA (autoregressive integrated moving average) (PCLA) framework. It consists of four steps. Firstly, PCLA obtains the public concern and academic concern in middle school physics. Secondly, the Pearson correlation between the public concern and academic concern is analysed, and a linear model between the two concerns is established. Thirdly, for better predicting public concern, the evaluation index is used to choose the best model between the ARIMA model and the SARIMA model. Finally, based on the result of the third step and the linear model, academic concern can also be predicted. The results show that the PCLA framework can reflect the relationship between academic concern and public concern for physics in middle school and can accurately predict public concern and academic concern.

The aim of this study is to analyse the expression status of long non-coding RNA (lncRNA) H19 in nasopharyngeal carcinoma and to unravel its oncogenic properties at molecular level. The abundance of H19, let-7a, b, g, i and HRAS was quantified by real-time PCR. Cell viability was measured by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide method. Cell proliferation was evaluated by the cell counting. Cell migration and cell invasion were determined using transwell chamber and scattering colony formation. Tumour progression was monitored in xenograft tumour model and tail vein injection was adopted for lung metastasis assessment. Luciferase reporter assay was employed to interrogate the potential regulatory action of let-7 genes on H19 expression. The endogenous HRAS protein was quantified by western blotting. H19 was aberrantly over-expression in nasopharyngeal carcinoma, which intimately associated with poorer prognosis. H19-deficency significantly inhibited cell viability and suppressed cell proliferation. Furthermore, both migrative and invasive capacity were compromised by H19 knockdown. H19-silencing remarkably delayed xenograft tumour progression and lung metastasis. Mechanistically, H19 competitively sponged let-7 genes and therefore up-regulated HRAS, which consequently contributed to its oncogenic activity in nasopharyngeal carcinomas. Our study uncovered the oncogenic properties of H19 in nasopharyngeal carcinoma and highlighted the H19-let-7-HRAS signalling axis underlying the incidence and metastasis of this disease.

African swine fever (ASF) outbreak has caused serious economic losses in Asia since 2018. As ASF is a new emerging disease, many farmers hesitate to raise pigs before biosafety procedures were evaluated to be effective. To support small-scale farms in resuming pig production, a comprehensive procedure, called the quadruple protection procedure (QPP), was tested in 35 small farms which had been confirmed with African swine fever virus (ASFV). The QPP takes care of the farms' construction, environmental disinfection, regular immunization, and feed quality. Qualified daily management was supplemented as well. During a one-year survey four disinfectants and one piece of equipment were used in higher frequency. A 7- or 15-day empty period after the disinfection was suitable when it was combined with the rest of the protection measures from QPP. Totally 18,730 porkers and 3,006 sows were healthy by the end of the study with percentage of 100 and 98.8, respectively, indicating that QPP could protect pigs in small-scale farms from pathogens within China. This study developed an effective protective procedure system for small-scale farms to produce pigs under the risk of ASF outbreak.

Cancer cell-derived exosomes regulate tumor growth and progression. However, the effects of exosomes and its contents on head and neck squamous cell carcinoma (HNSCC) and its underlying mechanisms remain unclear. Here, we found HNSCC displayed a dysregulation of exosomes biogenesis. miR-185-3p was one of the most upregulated exosome-derived miRNAs in HNSCC. Functional assay showed that RAB25 is a direct downstream target of miR-185-3p. miR-185-3p/RAB25 signaling controlled tumor progression and correlated with disease prognosis. Targeting miR-185-3p/RAB25 significantly inhibited tumor growth and promoted drug response to chemotherapy. To conclude, the findings demonstrate exosomal miR-185-3p promotes tumor growth by mediating RAB25 that could be effectively targeted for HNSCC treatment.

Summary: G-protein-gated inwardly rectifying potassium (GIRK) channels are important for determining neuronal excitability. In addition to G proteins, GIRK channels are potentiated by membrane cholesterol, which is elevated in the brains of people with neurodegenerative diseases such as Alzheimer’s dementia and Parkinson’s disease. The structural mechanism of cholesterol modulation of GIRK channels is not well understood. In this study, we present cryo- electron microscopy (cryoEM) structures of GIRK2 in the presence and absence of the cholesterol analog cholesteryl hemisuccinate (CHS) and phosphatidylinositol 4,5-bisphosphate (PIP2). The structures reveal that CHS binds near PIP2 in lipid-facing hydrophobic pockets of the transmembrane domain. Our structural analysis suggests that CHS stabilizes PIP2 interaction with the channel and promotes engagement of the cytoplasmic domain onto the transmembrane region. Mutagenesis of one of the CHS binding pockets eliminates cholesterol-dependent potentiation of GIRK2. Elucidating the structural mechanisms underlying cholesterol modulation of GIRK2 channels could facilitate the development of therapeutics for treating neurological diseases. Video abstract:

Abstract Background Essential proteins are distinctly important for an organism’s survival and development and crucial to disease analysis and drug design as well. Large-scale protein-protein interaction (PPI) data sets exist in Saccharomyces cerevisiae, which provides us with a valuable opportunity to predict identify essential proteins from PPI networks. Many network topology-based computational methods have been designed to detect essential proteins. However, these methods are limited by the completeness of available PPI data. To break out of these restraints, some computational methods have been proposed by integrating PPI networks and multi-source biological data. Despite the progress in the research of multiple data fusion, it is still challenging to improve the prediction accuracy of the computational methods. Results In this paper, we design a novel iterative model for essential proteins prediction, named Randomly Walking in the Heterogeneous Network (RWHN). In RWHN, a weighted protein-protein interaction network and a domain-domain association network are constructed according to the original PPI network and the known protein-domain association network, firstly. And then, we establish a new heterogeneous matrix by combining the two constructed networks with the protein-domain association network. Based on the heterogeneous matrix, a transition probability matrix is established by normalized operation. Finally, an improved PageRank algorithm is adopted on the heterogeneous network for essential proteins prediction. In order to eliminate the influence of the false negative, information on orthologous proteins and the subcellular localization information of proteins are integrated to initialize the score vector of proteins. In RWHN, the topology, conservative and functional features of essential proteins are all taken into account in the prediction process. The experimental results show that RWHN obviously exceeds in predicting essential proteins ten other competing methods. Conclusions We demonstrated that integrating multi-source data into a heterogeneous network can preserve the complex relationship among multiple biological data and improve the prediction accuracy of essential proteins. RWHN, our proposed method, is effective for the prediction of essential proteins.

Abstract In this paper, we study a class of perturbed nonlinear fractional differential systems with two control parameters. Under some appropriate conditions on the primitive function of nonlinear terms Fu $F_{u}$ and Fv $F_{v}$, we prove the multiplicity of weak solutions by using the variational method and Ricceri’s critical points theorems.

Abstract Background microRNA (miR)-342–3p is frequently dysregulated in human cancers. In the present study, we aimed to explore the expression, prognostic significance, and biological relevance of miR-342-3p in nasopharyngeal carcinoma (NPC). Methods We examined miR-342-3p expression in 79 paired NPC specimens and corresponding normal tissues and analyzed its prognostic impact on overall survival of NPC patients. Gain- and loss-of-function experiments were conducted to determine the biological roles of miR-342-3p. Results Compared with matched normal nasopharyngeal tissues, miR-342-3p was significantly downregulated in NPC (P = 0.0038). Low miR-342-3p expression was significantly correlated with reduced overall survival (P = 0.0084). Ectopic expression of miR-342-3p significantly suppressed proliferation, colony formation, and invasion of NPC cells. In contrast, depletion of miR-342-3p facilitated NPC cell proliferation and invasion. In vivo xenograft studies confirmed that overexpression of miR-342-3p restrained the growth of NPC xenograft tumors. Mechanistically, FOXQ1 served as a functional target of miR-342-3p. There was a significantly negative correlation between miR-342-3p and FOXQ1 expression (r = − 0.487, P = 0.004) in NPC specimens. Overexpression of FOXQ1 rescued the inhibitory effects of miR-342-3p on NPC cell growth and invasion. Conclusions miR-342-3p downregulation predicts poor prognosis in NPC patients and shows suppressive activity against NPC growth and invasion through repression of FOXQ1. Restoration of miR-342-3p may represent a potential therapeutic strategy for NPC.

Probabilistic computing is an emerging computational paradigm that uses probabilistic circuits to efficiently solve optimization problems such as invertible logic, where traditional digital computations are difficult to solve. This paper proposes a true random number generator (TRNG) based on resistive random-access memory (RRAM), which is combined with an activation function implemented by a piecewise linear function to form a standard p-bit cell, one of the most important parts of a p-circuit. A p-bit multiplexing strategy is also applied to reduce the number of p-bits and improve resource utilization. To verify the superiority of the proposed probabilistic circuit, we implement the invertible p-circuit on a field-programmable gate array (FPGA), including AND gates, full adders, multi-bit adders, and multipliers. The results of the FPGA implementation show that our approach can significantly save the consumption of hardware resources.

Recently, many models based on the combination of graph convolutional networks and deep learning have attracted extensive attention for their superior performance in graph clustering tasks. However, the existing models have the following limitations: (1) Existing models are limited by the calculation method of graph convolution, and their computational cost will increase exponentially as the graph scale grows. (2) Stacking too many convolutional layers causes the over-smoothing issue and neglects the local graph structure. (3) Expanding the range of the neighborhood and the model depth together is difficult due to the orthogonal relationship between them. Inspired by personalized pagerank and auto-encoder, we conduct the node-wise graph clustering task in the undirected simple graph as the research direction and propose a Scalable Deep Network (SDN) for graph clustering via personalized pagerank. Specifically, we utilize the combination of multi-layer perceptrons and linear propagation layer based on personalized pagerank as the backbone network (i.e., the Quasi-GNN module) and employ a DNN module for auto-encoder to learn different dimensions embeddings. After that, SDN combines the two embeddings correspondingly; then, it utilizes a dual self-supervised module to constrain the training of the embedding and clustering process. Our proposed Quasi-GNN module reduces the computational costs of traditional GNN models in a decoupled approach and solves the orthogonal relationship between the model depth and the neighborhood range. Meanwhile, it also alleviates the degraded clustering effect caused by the over-smoothing issue. We conducted experiments on five widely used graph datasets. The experimental results demonstrate that our model achieves state-of-the-art performance.

Abstract Background Autosomal dominant hearing loss (ADHL) accounts for about 20% of all hereditary non‐syndromic HL. Truncating mutations of the EYA4 gene can cause either non‐syndromic ADHL or syndromic ADHL with cardiac abnormalities. It has been proposed that truncations of the C‐terminal Eya domain lead to non‐syndromic HL, whereas early truncations of the N‐terminal variable region cause syndromic HL with cardiac phenotype. Methods The proband and all the other hearing impaired members of the family underwent a thorough clinical and audiological evaluation. The cardiac phenotype was examined by ECG and echocardiography. Their DNA was subjected to target exome sequencing of 129 known deafness genes. The sequencing data were analyzed and the candidate variants were interpreted following the ACMG guidelines for clinical sequence interpretation. The effect of candidate variant on EYA4 gene expression was assessed by quantitative PCR and western blot of gene production in blood. Results We report a Chinese family cosegregating post‐lingual onset, progressive ADHL with a novel nonsense mutation NM_004100.4:c.543C>G (p.Tyr181Ter) of EYA4. Two affected members show no cardiac abnormalities at least until now revealed by electrocardiography and echocardiography. The overall expression level of the EYA4 gene in the proband was lower than that in his unaffected relative. Conclusion This report expands the mutational spectrum of the EYA4 gene and highlights the fact that more data are needed to elucidate the complex genotype–phenotype correlation of EYA4 mutations.

Summary: G-protein-gated inwardly rectifying K+ (GIRK) channels are essential effectors of inhibitory neurotransmission in the brain. GIRK channels have been implicated in diseases with abnormal neuronal excitability, including epilepsy and addiction. GIRK channels are tetramers composed of either the same subunit (e.g., homotetramers) or different subunits (e.g., heterotetramers). Compounds that specifically target subsets of GIRK channels in vivo are lacking. Previous studies have shown that alcohol directly activates GIRK channels through a hydrophobic pocket located in the cytoplasmic domain of the channel. Here, we report the identification and functional characterization of a GIRK1-selective activator, termed GiGA1, that targets the alcohol pocket. GiGA1 activates GIRK1/GIRK2 both in vitro and in vivo and, in turn, mitigates the effects of a convulsant in an acute epilepsy mouse model. These results shed light on the structure-based development of subunit-specific GIRK modulators that could provide potential treatments for brain disorders.

Cisplatin has been used as the first-line chemotherapy to treat advanced nasopharyngeal carcinoma (NPC), while acquired cisplatin resistance resulting from epigenetic regulation is not well understood. The relative expression of LINC00346 was detected in healthy persons, cisplatin-sensitive (CS) patients and cisplatin-resistant (CR) patients. The regulatory effect of LINC00346 on the proliferation was detected by cell-counting kit-8. Apoptosis was assayed by histone/DNA ELISA and Caspase-3 activity. Clonal formation and cisplatin resistance were also deciphered. Luciferase reporter and RNA immunoprecipitation assay were adopted to study the interaction between LINC00346 and miR-342-5p. LINC00346 was highly expressed in NPC patients, especially in CR patients, which was associated with cisplatin resistance and poor prognosis. Inhibition of LINC00346 expression promoted cisplatin sensitivity of NPC cells, while LINC00346 over-expression promoted cisplatin resistance of NPC cells. miR-342-5p expression was negatively correlated with cisplatin resistance, and microRNA-342-5p siRNAs treatment could rescue the cisplatin resistance diminished by LINC00346 inhibition. It was further found that miR-342-5p was negatively regulated by LINC00346. In conclusion, LINC00346 regulates the cisplatin resistance of NPC cells by inhibiting miR-342-5p, which could provide a potential target for chemotherapy resistance.

Abstract In this paper, we apply Morse theory, local linking arguments and the Clark theorem to a study of the multiplicity of nontrivial solutions for a class of impulsive fractional differential equations with Dirichlet boundary conditions.

Resistive random access memory (RRAM) is one of the most promising new nonvolatile memories because of its excellent properties. Moreover, due to fast read speed and low work voltage, it is suitable for seldom-write frequent-read applications. However, as technology nodes shrink, RRAM faces many issues, which can significantly degrade RRAM performance. Therefore, it is necessary to optimize the sensing schemes to improve the application range of RRAM. In this paper, the issues faced by RRAM in advanced technology nodes are summarized. Then, the advantages and weaknesses in the novel design and optimization methodologies of sensing schemes are introduced in detail from three aspects, the reference schemes, sensing amplifier schemes, and bit line (BL)-enhancing schemes, according to the development of technology in especially recent years, which can be the reference for designing the sensing schemes. Moreover, the waveforms and results of each method are illustrated to make the design easy to understand. With the development of technology, the sensing schemes of RRAM become higher speed and resolution, low power consumption, and are applied at advanced technology nodes and low working voltage. Now, the most advanced nodes the RRAM applied is 14 nm node, the lowest working voltage can reach 0.32 V, and the shortest access time can be only a few nanoseconds.

Abstract Metastatic spread is the mechanism in more than 90 percent of cancer deaths and current therapeutic options, such as systemic chemotherapy, are often ineffective. Here we provide a proof of principle for a novel two-pronged modality referred to as Synergistic Immuno Photothermal Nanotherapy (SYMPHONY) having the potential to safely eradicate both primary tumors and distant metastatic foci. Using a combination of immune-checkpoint inhibition and plasmonic gold nanostar (GNS)–mediated photothermal therapy, we were able to achieve complete eradication of primary treated tumors and distant untreated tumors in some mice implanted with the MB49 bladder cancer cells. Delayed rechallenge with MB49 cancer cells injection in mice that appeared cured by SYMPHONY did not lead to new tumor formation after 60 days observation, indicating that SYMPHONY treatment induced effective long-lasting immunity against MB49 cancer cells.

Abstract In this paper, we apply critical point theory and variational methods to study the multiple solutions of boundary value problems for an impulsive fractional differential equation with p-Laplacian. Some new criteria guaranteeing the existence of multiple solutions are established for the considered problem.

Ferroelectric capacitors (FeCAPs) with high process compatibility, high reliability, ultra-low programming current and fast operation speed are promising candidates to traditional volatile and nonvolatile memory. In addition, they have great potential in the fields of storage, computing, and memory logic. Nevertheless, effective methods to realize logic and memory in FeCAP devices are still lacking. This study proposes a 1T2C FeCAP-based in situ bitwise X(N)OR logic based on a charge-sharing function. First, using the 1T2C structure and a two-step write-back circuit, the nondestructive reading is realized with less complexity than the previous work. Second, a method of two-line activation is used during the operation of X(N)OR. The verification results show that the speed, area and power consumption of the proposed 1T2C FeCAP-based bitwise logic operations are significantly improved.

Studying the attention of “artificial intelligence + education” in ethnic areas is of great significance for China for promoting the integrated development of new educational modes and modern technology in the western region. Guizhou province is an area inhabited by ethnic minorities, located in the heart of Southwest China. The development of its intelligent education has strong enlightenment for the whole country and the region. Therefore, this paper selects the Baidu Index of “artificial intelligence (AI) + education” in Guizhou province from 2013 to 2020, analyzes the spatial–temporal characteristics of its network attention by using the elastic coefficient method, and builds the ARIMA model on this basis to predict future development. The results show that the public’s attention to “AI + education” differs significantly in time and space. Then, according to the prediction results, this paper puts forward relevant suggestions for the country to promote the sustainable development of education in western ethnic areas.

Information technology is a significant action to promote education informatization. Currently, there are numerous literature studies on information technology education, but existing research lacks a comparative summary of the research status, research hot spots, and research trend of information technology in China and abroad from an overall perspective. Therefore, this paper selected the information technology education literature collected by China National Knowledge Infrastructure (CNKI) and Web of Science Core Collection (WoS) from 2000 to 2021 as the research object and used bibliometrics to analyze the literature characteristics, core forces, research hot spot, and frontier trend of information technology education in China and abroad. The results showed that: (1) The discipline literature of information technology education showed a fluctuating growth trend, in which the growth rate of foreign literature was faster, and the scale of Chinese literature was slightly smaller; Both Chinese and foreign literature involved pedagogy, computer, and other disciplines, with the characteristics of multi-disciplinary integration. (2) The United States was the leading country in this field, followed by China, with the total literature accounting for more than 50%, ranking the leading position in the world; China Audio-Visual Education and Computers & Education were representative journals in China and abroad. (3) Foreign research focused on information technology, education, science, attitude, model, perception, experience, self-efficacy, Internet, etc. China's hotspots included information literacy, computational thinking, curriculum standards, application contexts, teaching models, subject integration, etc. Foreign research focused on both theory and practice, while China focused on the theoretical nature of the subject, but the degree of practice was insufficient. (4) Student participation, technology integration, teaching innovation, Internet +, core literacy, and artificial intelligence (AI) have been popular until now, constituting the discipline frontier in information technology education.

The MYB transcription factors (TFs) is a plant TF families, which involves in hormone signal transduction, and abiotic stress tolerance, etc. However, there are few studies on the MYB TFs family and its regulatory mechanism in Tamarix hispida. In this study, 14 MYB genes (named ThMYB1 - ThMYB14) were cloned and characterized from T. hispida. The transcription profiles of ThMYBs in T. hispida under different abiotic stress conditions were monitored using qRT-PCR. Most of studied ThMYBs were significantly downregulated and/or upregulated by salt and osmotic stress, ABA, GA3 and JA treatments in at least one organ. Especially, ThMYB13 was induced in the leaves and roots of T. hispida when exposed to NaCl treatment at all study periods, indicating that it may involve in salt stress. To further study ThMYB13 function, ThMYB13 overexpression and knock-down plants and control plants transformed with an empty pROKII were obtained using a transient transformation system. Overexpression of ThMYB13 in T. hispida displayed the lowest O2-, H2O2 and MDA accumulation, minimal cell death, the most stable K+/Na+ ratio and the lowest electrolyte leakage rate among the three kinds of transient expression in T. hispida. Conversely, the RNAi-silencing, transiently transformed plants displayed the opposite physiological changes. Therefore, ThMYB13 might play a role in salt stress tolerance in transgenic T. hispida plants.

Wave loads estimation and structural strength evaluation are the fundamental work at the ship design stage. The hydroelastic responses and slamming strength issues are also concerned especially for large-scale high-speed ships sailing in harsh waves. To accurately predict the wave-induced motions and loads acting on the ship sailing in regular waves, a fully coupled 3D time-domain nonlinear hydroelasticity theory is developed in this paper. The vibration modal characteristics of the flexible hull structure derived by the 3D finite element method (FEM) and simplified 1D nonuniform Timoshenko beam theory are firstly described. The hydrostatic restoring force and hydrodynamic wave force are calculated on the real-time wetted surface of hull to address geometric nonlinearity due to the steep wave and large amplitude motions. The bow slamming and green water loads acting on the ship in severe regular waves are estimated by the momentum impact method and dam-breaking method, respectively. Moreover, a small-scaled segmented ship model is designed, constructed, and tested in a laboratory wave basin to validate the hydroelasticity algorithm. The results predicted by theoretical and experimental approaches are systemically compared and analyzed. Finally, future work for predictions of ship hydroelasticity and slamming loads in irregular waves is prospected.

This paper is devoted to investigating a class of nonhomogeneous Choquard equations with perturbation involving p-Laplacian. Under suitable hypotheses about the perturbation term, the existence of at least two nontrivial solutions for the given problems is obtained using Nehari manifold and minimax methods.

This paper is devoted to studying the existence of solutions to a class of impulsive fractional differential equations with derivative dependence. The used technical approach is based on variational methods and iterative methods. In addition, an example is given to demonstrate the main results.