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Graphene nanoplatelets (GNPs) have significant potential as reinforcement agents in metallic materials. Understanding the interfacial structure between GNPs and an aluminum matrix (AlM) is crucial for this composite (GNPs/AlMC) design and application. Studies on the effects of heat treatment on the interfacial character and its influence on mechanical properties at the atomic scale are, therefore, of high importance. This study presents molecular dynamics simulations investigating the effects of heat treatment on the interfacial structural evolution between GNPs and AlM and how such behavior tunes mechanical performance. The results show that the number of interfacial bonds increases significantly when the heat treatment temperature reaches 900 K, below which it stays at low level. Compared with the heat treatment temperature, the pressure has less effect on the interfacial bonds of GNPs/AlMCs. Moreover, defective GNPs with pores at certain heat treatment temperatures promote the formation of Al-C bonds at the interface and improve the mechanical properties of GNPs/AlMCs. These findings underscore the potential for utilizing heat treatment and defect engineering to improve interfacial bonding and achieve improved mechanical performance in GNPs/AlMCs. [ABSTRACT FROM AUTHOR]

The analysis of genomic variations in offspring after implantation has been infrequently studied. In this study, we aim to investigate the extent of de novo mutations in humans from developing fetus to birth. Using high-depth whole-genome sequencing, 443 parent-offspring trios were studied to compare the results of de novo mutations (DNMs) between different groups. The focus was on fetuses and newborns, with DNA samples obtained from the families' blood and the aspirated embryonic tissues subjected to deep sequencing. It was observed that the average number of total DNMs in the newborns group was 56.26 (54.17–58.35), which appeared to be lower than that the multifetal reduction group, which was 76.05 (69.70–82.40) (F = 2.42, P = 0.12). However, after adjusting for parental age and maternal pre-pregnancy body mass index (BMI), significant differences were found between the two groups. The analysis was further divided into single nucleotide variants (SNVs) and insertion/deletion of a small number of bases (indels), and it was discovered that the average number of de novo SNVs associated with the multifetal reduction group and the newborn group was 49.89 (45.59–54.20) and 51.09 (49.22–52.96), respectively. No significant differences were noted between the groups (F = 1.01, P = 0.32). However, a significant difference was observed for de novo indels, with a higher average number found in the multifetal reduction group compared to the newborn group (F = 194.17, P < 0.001). The average number of de novo indels among the multifetal reduction group and the newborn group was 26.26 (23.27–29.05) and 5.17 (4.82–5.52), respectively. To conclude, it has been observed that the quantity of de novo indels in the newborns experiences a significant decrease when compared to that in the aspirated embryonic tissues (7–9 weeks). This phenomenon is evident across all genomic regions, highlighting the adverse effects of de novo indels on the fetus and emphasizing the significance of embryonic implantation and intrauterine growth in human genetic selection mechanisms. [ABSTRACT FROM AUTHOR]

While many organizations have successfully leveraged big data analytics capabilities to improve their performance, our understanding is limited on whether and how big data analytics capabilities affect social innovation in organizations. Based on the organizational information processing theory and the organizational learning theory, this study aims to investigate how big data analytics capabilities support social innovation, and how knowledge ambidexterity mediates this relationship. A total of 354 high-tech companies in China, this study shows that big data analytics management, big data analytics technology, and big data analytics personnel capabilities all have positive effects on social innovation. In addition, both knowledge exploration and knowledge exploitation play a mediating role in this process. Furthermore, a polynomial regression and response surface analysis shows that social innovation increases when knowledge exploration and knowledge exploitation are highly consistent but declines when knowledge exploration and knowledge exploitation are inconsistent. This study not only provides new perspectives for understanding how big data analytics capabilities contribute to social innovation, complementing the existing literature on big data analytics capabilities and social innovation, but also provides important practical guidance on how organizations can develop big data analytics capabilities to improve social innovation and solve social problems in the digital age. [ABSTRACT FROM AUTHOR]

Background: Synonymous variants are non‐pathogenic due to non‐substitution of amino acids. However, synonymous exonic terminal nucleotide substitutions may affect splicing. Splicing variants are easily analyzed at RNA level for genes expressed in blood cells. Minigene analysis provides another method for splicing variant analysis of genes that are poorly or not expressed in peripheral blood. Methods: Whole exome sequencing was performed to screen for potential pathogenic mutations in the proband, which were validated within the family by Sanger sequencing. The pathogenicity of the synonymous mutation was analyzed using the minigene technology. Results: The proband harbored the compound heterogeneous variants c. [291G >A; 572‐50C >T] and c.681 + 1G >T in F7, of which the synonymous variant c.291G >A was located at the terminal position of exon 3. Minigene analysis revealed exon3 skipping due to this mutation, which may have subsequently affected protein sequence, structure, and function. Conclusion: Our finding confirmed the pathogenicity of c.291G >A, thus extending the pathogenic mutation spectrum of F7, and providing insights for effective reproductive counseling. [ABSTRACT FROM AUTHOR]

Credit risk assessment is an important process in bank financial risk management. Traditional machine-learning methods cannot solve the problem of data islands and the high error rate of two-way decisions, which is not conducive to banks' accurate credit risk assessment of users. To this end, this paper establishes a federated three-way decision incremental naive Bayes bank user credit risk assessment model (FTwNB) that supports asymmetric encryption, uses federated learning to break down data barriers between banks, and uses asymmetric encryption to protect data security for federated processes. At the same time, the model combines the three-way decision methods to realize the three-way classification of user credit (good, bad and delayed judgment), so as to avoid the loss of bank interests caused by the forced division of uncertain users. In addition, the model also incorporates incremental learning steps to eliminate training samples with poor data quality to further improve the model performance. This paper takes German Credit data and Default of Credit Card Clients data as examples to conduct simulation experiments. The result shows that the performance of the FTwNB model has been greatly improved, which verifies that it has good credit risk assessment capabilities. [ABSTRACT FROM AUTHOR]

The role of non-coding RNAs in regulating biological processes associated with cancer progression, such as proliferation, migration, and apoptosis, has been extensively studied. Long non-coding RNAs (lncRNAs) play a role in regulating these processes through various mechanisms, including transcriptional and post-transcriptional modifications. In post-transcriptional regulation, lncRNAs can bind to specific miRNAs and affect their function, which can either promote or inhibit cancer development. The interaction between lncRNAs, miRNAs, and mRNAs forms a network known as competitive endogenous RNA (ceRNA), which is involved in cancer progression or inhibition. One specific miRNA called miR-26a-5p has been identified as having tumor-suppressive properties. However, when lncRNAs bind to and inhibit miR-26a-5p, it can lead to cancer progression. Therefore, targeting this ceRNA network could be a promising strategy for preventing cancer development. This review will first discuss the anticancer effects of miR-26a-5p and then explore the involvement of the lncRNA-miR26a-5p-mRNA axis in cancer progression and potential targeted therapies. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the correlations of greenness exposure with test anxiety among university students during COVID-19 lockdowns and to explore their mechanisms. We conducted a cross-sectional study with 2609 university students in Anhui and Shandong provinces, China. We assessed perceived campus greenness using a five-point Likert scale for quality, visibility, abundance, usage, and accessibility. Objective greenness was estimated via average normalised difference vegetation index (NDVI) in 1,000-, 1,500-, and 2,000-m radius zones around each of the campuses. A generalised linear mixed model examined the associations between greenness and test anxiety and to evaluate the mediation effects of physical activity, body mass index (BMI), and air pollution. Results showed that higher NDVI1500-m correlated with lower test anxiety (OR = 0.871; 95% CI: 0.851, 0.891), physical activity may partially mediate this association. Increased campus greenness may alleviate test anxiety among Chinese university students. [ABSTRACT FROM AUTHOR]

The Grand Canal possesses a unique ecosystem as one of the world cultural heritage sites. However, its ecological roles and services have been underemphasized in heritage conservation efforts, leading to environmental pollution and the degradation of its heritage value, especially in the highly urbanized southern Jiangsu section downstream. This study examines the synergy between regulating ecosystem services (RESs) and cultural ecosystem services (CESs) along the highly urbanized Wuxi section of the Grand Canal, as well as the environmental drivers influencing this relationship. The findings reveal that the synergy between CESs and RESs does not occur at the urban–rural interface but rather in areas 4-6 km away from the Central Business District (CBD). Certain drivers, such as floor area ratio (FAR) and plot ratio (PR), enhance both RES and CES within specific sections. Beyond riparian green belts, various low-density, dispersed, and well-vegetated lands could serve as significant contributors to future ecosystem service (ES) synergy. These conclusions further elucidate the spatial distribution of the heritage cultural services and other ES synergy along the Grand Canal, providing scientific support for broader improvements guiding the sustainable co-development in similar urbanized areas. [ABSTRACT FROM AUTHOR]

Aims: Itch, a common uncomfortable sensory experience, occurs frequently in inflammatory or allergic disorders. In recent years, with the discovery of itch‐specific pathways in the peripheral and central nervous system, the association between immunology and neural pathways has gradually emerged as the main mechanism of itch. Although many studies have been conducted on itch, no bibliometric analysis study focusing on this topic has been conducted. This study aimed to explore the research hotspots and trends in the itch field from a bibliometric perspective. Methods: Publications relevant to itch, published from 2003 to 2022, were retrieved from the Science Citation Index‐Expanded of Web of Science Core Collection. Publications were critically reviewed and analyzed with CiteSpace software, Vosviewer, and the bibliometric online analysis platform. Visual maps were conducted in terms of annual production, collaborating countries or institutions, productive authors, core journals, co‐cited references, and keyword bursts. Results: 2395 articles on itch that met our criteria were identified and the quantity of publications has been increasing rapidly since 2012. The USA was the most influential country. University Hospital Münster was the institution with the most publications. Gil Yosipovitch was the most prolific author. Atopic dermatitis (AD), intradermal serotonin, chronic pruritus, mechanical itch, gastrin‐releasing peptide, substance p, interleukin‐31 receptor, histamine‐induced itch, bile acid, scratching behavior, and h‐4 receptor were the top 11 clusters in co‐citation cluster analysis. Keyword burst analysis suggested that treatment, inflammation, and AD are current research hotspots. Conclusion: Global publications on itch research have increased steadily and rapidly over the past 20 years. Inflammation and AD are current research hotspots. The neuroimmunological and neuroinflammatory mechanisms of itch, as well as clinical assessment methods and therapeutic targets, will be novel research directions in the future. This study provides guidance for further itch research. [ABSTRACT FROM AUTHOR]

Understanding the biotic mechanisms of community stability in variable environments has been a focal point of fundamental ecological research. A multitude of mechanisms, encompassing compensatory dynamics arising from negative species covariance, portfolio effect linked to species richness and evenness, and dominant species stability, have been found to collectively enhance community stability. However, it is not clear how their stabilizing effects change and contribute to the maintenance of community stability along environmental gradients. We performed a ten-year investigation in a large shallow lake with a eutrophic gradient across space. With the dataset, we quantified the role of the three stability mechanisms, and their changes in effect size along the eutrophic gradient to determine their relative importance in biomass stability. Our results showed that the biomass stability shifted from one stable state at eutrophic sites to another stable state at hypertrophic sites, and biomass stability was positively correlated with composition stability. In the relatively stable state, biomass stability exhibited a closely synchronized variation along with compositional stability in response to environmental changes. Conversely, in the unstable state, biomass stability displayed weaker sensitivity to environmental changes compared to compositional stability. The effect sizes of different biotic mechanisms of biomass stability varied across the eutrophic gradient. Compensatory dynamics emerged as the primary force governing biomass stability in eutrophic waters, overshadowing the relatively weak impact of the portfolio effect, which might help resist the shift from turbid state to clear state with decreasing nutrient concentrations. However, as nutrient levels increased, the primary force shifted from compensatory dynamics toward the dominant species stability. This study improves our understanding for the biotic mechanisms of phytoplankton community responding to nutrients mitigation in eutrophic waters, which might be one of the most important ecological components for managing communities to maintain ecosystem functioning. [ABSTRACT FROM AUTHOR]

SHEN Dan stands as one of the most significant and influential scholars in the humanities in contemporary China. She is also a famous scholar in the international academia of narrative studies, with significant contributions to rhetorical narratology in particular. Her theoretical innovation in rhetorical narratology extends beyond the Aristotelian tradition. Her main contributions in this field are the following: (1) championing the notion of "comprehensive structure-style analysis" by taking an interdisciplinary approach; (2) proposing an integrated model of intratextual-extratextual-intertextual "overall-extended close reading" through taking into consideration sociohistorical context and extending attention to intertextual comparison; (3) putting forward the concept of "covert progression," revealing the undercurrent paralleling the plot development; (4) pioneering the exploration of dual narrative dynamics and revealing the joint functioning of plot development and covert progression which contrast or conflict with each other. [ABSTRACT FROM AUTHOR]

Aiming at the fact that the moving target defense game model fails to accurately portray attack and defense gains, resulting in bias in attack and defense games and the inability to select effective defense strategies, we construct the moving target three-way evolutionary game defense model (MTTEGDM). Firstly, the model is defined and analyzed theoretically under the premise of uncertainty and irrationality. Then, combined with the three-way decisions, the attack intention is introduced into the target network loss calculation, and a dynamic weight adjustment algorithm based on the three-way decisions is proposed to accurately characterize the attack and defense gains from a multi-attribute perspective. Finally, the evolutionary game model is used to analyze the evolution trend of the multi-stage defense strategy, so as to carry out feasible and effective defense behavior. The simulation results show that the model can accurately predict the optimal defense strategy of moving targets in different stages. Through a Monte Carlo simulation experiment, the proposed algorithm is compared with the traditional evolutionary game model, and the effectiveness and security of the proposed algorithm are verified. [ABSTRACT FROM AUTHOR]

This study described electroencephalogram (EEG) parameters in children under general anesthesia, which could monitor patient-specific brain responses to anesthetics and assess the effects of anesthesia. The objective was to detect the patient state index (PSI) and associated factors. We analyzed EEG parameters in patients in the age range 1 to 36 months. Patients were stratified into 2 groups as those aged 1 to 12 months and 13 to 36 months. Sixty-two patients were involved. Spectral edge frequency (SEF), PSI, and blood pressure were lower, and burst suppression rate (BSR) and heart rate were higher in the 1 to 12 months group. The SEF was associated with PSI in both groups. Age and blood pressure were positively associated with PSI, and BSR was negatively related to PSI in children under 1 year of age. Blood pressure was not associated with PSI in the 13 to 36 months age group. We found that the PSI levels did not accurately assess the depth of anesthesia in children under 1 year of age. [ABSTRACT FROM AUTHOR]

With a large number of intrusion detection datasets and high feature dimensionality, the emergent nature of new attack types makes it impossible to collect network traffic data all at once. The modified three-way attribute reduction method is combined with a Self-Organizing Incremental learning Neural Network (SOINN) algorithm to propose a self-organizing incremental neural network intrusion detection model based on three-way attribute reduction. Attribute importance is used to perform attribute reduction, and the data after attribute reduction are fed into a self-organized incremental learning neural network algorithm, which generalizes the topology of the original data through self-organized competitive learning. When the streaming data are transferred into the model, the inter-class insertion or node fusion operation is performed by comparing the inter-node distance and similarity threshold to achieve incremental learning of the model streaming data. The inter-node distance value is introduced into the weight update formulation to replace the traditional learning rate and to optimize the topological structure adjustment operation. The experimental results show that T-SOINN achieves high precision and recall when processing intrusion detection data. [ABSTRACT FROM AUTHOR]

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is an accurate method for quantifying gene expression levels. Choosing appropriate reference genes to normalize the data is essential for reducing errors. Gelsemium elegans is a highly poisonous but important medicinal plant used for analgesic and anti-swelling purposes. Gelsenicine is one of the vital active ingredients, and its biosynthesis pathway remains to be determined. In this study, G. elegans leaf tissue with and without the application of one of four hormones (SA, MeJA, ETH, and ABA) known to affect gelsenicine synthesis, was analyzed using ten candidate reference genes. The gene stability was evaluated using GeNorm, NormFinder, BestKeeper, ∆CT, and RefFinder. The results showed that the optimal stable reference genes varied among the different treatments and that at least two reference genes were required for accurate quantification. The expression patterns of 15 genes related to the gelsenicine upstream biosynthesis pathway was determined by RT-qPCR using the relevant reference genes identified. Three genes 8-HGO, LAMT, and STR, were found to have a strong correlation with the amount of gelsenicine measured in the different samples. This research is the first study to examine the reference genes of G. elegans under different hormone treatments and will be useful for future molecular analyses of this medically important plant species. [ABSTRACT FROM AUTHOR]

Purpose: Online brand communities (OBCs) are important platforms to obtain consumers' ideas. The purpose of this study is to examine how peer influence and consumer contribution behavior simulate innovative behaviors in OBCs to increase idea quality. Design/methodology/approach: Using a firm-hosted popular online brand community – Xiaomi Community (MIUI), the authors collected a set of data from 6567 consumers and then used structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to empirically test the impact of peer influence and consumer contribution behaviors on idea quality in OBCs. Findings: The results of this study show that both peer influence breadth and depth have a positive effect on idea adoption and peer recognition, wherein proactive contribution behavior positively mediates these relationships, and responsive contribution behavior negatively mediates the impact of peer influence breadth and peer influence depth on peer recognition. A more detailed analysis using the fsQCA method further identifies four types of antecedent configurations for better idea quality. Originality/value: Based on the attention-based view and the theory of learning by feedback, this study explores the factors that affect idea quality in the context of social networks and extends the research of peer influence in the digital age. The paper helps improve our understanding of how to promote customer idea quality in OBCs. [ABSTRACT FROM AUTHOR]

Air pollution and lake eutrophication have led to a reduction in incident total radiation and water transparency in many lakes, resulting in a decrease in available underwater light. This reduction in available light depends significantly on the dynamics of spring phytoplankton communities. However, the process and mechanisms behind these effects are not yet well understood. In this study, we conducted a field mesocosm experiment to observe the responses of the phytoplankton community to varying levels of light intensity (100%, 85%, and 65% photosynthetically active radiation, PAR). Our study revealed that reducing PAR resulted in an earlier peak of cyanobacterial biomass in spring, while the biomass of chlorophytes and bacillariophytes declined with decreasing light intensity. The weakening of light intensity promoted the recovery of photosynthetic activity in cyanobacteria but reduced the photosynthetic activity in chlorophytes and bacillariophytes. Additionally, the decrease in light intensity reduced the diversity of phytoplankton communities, accelerating the rate of species turnover. However, the rate of species turnover slowed down as the dominance of cyanobacteria was established in the later stages of the experiment. Therefore, the weakening of light intensity is beneficial to the early establishment of the dominance of cyanobacteria in the phytoplankton community structure, accelerating the succession process of phytoplankton community. These findings contribute to the exploration of the effects of reduced light intensity on the establishment of cyanobacterial dominance in spring, providing valuable insights for the management of lake ecosystems. [ABSTRACT FROM AUTHOR]

In this paper, an improved Internet of Things (IoT) network security situation assessment model is designed to solve the problems arising from the existing IoT network security situation assessment approach regarding feature extraction, validity, and accuracy. Firstly, raw data are dimensionally reduced using independent component analysis (ICA), and the weights of all features are calculated and fused using the maximum relevance minimum redundancy (mRMR) algorithm, Spearman's rank correlation coefficient, and extreme gradient boosting (XGBoost) feature importance method to filter out the optimal subset of features. Piecewise chaotic mapping and firefly perturbation strategies are then used to optimize the sparrow search algorithm (SSA) to achieve fast convergence and prevent getting trapped in local optima, and then the optimized algorithm is used to improve the light gradient boosting machine (LightGBM) algorithm. Finally, the improved LightGBM method is used for training to calculate situation values based on a threat impact to assess the IoT network security situation. The research findings reveal that the model attained an evaluation accuracy of 99.34%, sustained a mean square error at the 0.00001 level, and reached its optimum convergence value by the 45th iteration with the fastest convergence speed. This enables the model to more effectively evaluate the IoT network security status. [ABSTRACT FROM AUTHOR]

Background: The genes of the RECQ DNA helicase family play a part in preserving the stability of the genome and controlling different disease mechanisms. However, the expression features of RECQs in relation to pan-cancer, their correlation with the immune microenvironment of tumors, and the landscape of prognostic power are still undisclosed. Methods: Various sequence and clinical data extracted from 33 cancers were utilized to generate a comprehensive overview of RECQs in the landscape. Afterward, we discovered variations in gene expression, potential enrichment of functions, genetic alterations, and analysis related to the immune response in tumors. Additionally, we explored the clinical characteristics and diagnostic significance of RECQs. And the important association of RECQL4 with liver hepatocellular carcinoma (LIHC) was investigated. Results: RECQs exhibited extensive mutations in different types of cancers. The expression of RECQ may be influenced by an oncogenic mutation in certain types of cancer, resulting in the observed genomic and epigenetic changes in diverse tumor formations. Furthermore, RECQs originating from tumors exhibited a significant association with the immune microenvironment of the tumor, indicating their potential as promising targets for therapy. Patient prognosis was significantly associated with the majority of genes in the RECQ family. In LIHC, RECQL4 eventually emerged as a separate prognostic determinant. Conclusions: To summarize, RECQs are essential for the regulation of the immune system in tumors, and RECQL4 serves as a prognostic indicator in LIHC. The results of our study offer fresh perspectives on RECQs from a bioinformatics perspective and emphasize the importance of RECQs in the diagnosis and treatment of cancer. [ABSTRACT FROM AUTHOR]

We explore the association of polymorphisms in Secreted protein acidic and rich in cysteine (SPARC) with ankylosing spondylitis (AS) and detect SPARC mRNA and protein expression in a Chinese Han population.Methods: Nine single-nucleotide polymorphisms (SNPs) of SPARC were genotyped in 768 AS patients and 768 controls by TaqMan genotyping assay. mRNA expression of SPARC was detected by real-time polymerase chain reaction (RT-PCR), and serum level of SPARC protein was detected by ELISA.Results: The frequency of A allele of rs171121187 was significantly higher in AS patients than in controls (Pc=0.003, odds ratio [OR]=1.45, 95% confidence interval [95% CI] = 1.18– 1.77), the AA and AC genotypes increased the risk of AS when compared with CC genotype (Pc=0.003, OR=3.96, 95% CI=1.80– 8.75, and Pc=0.003, OR=1.27, 95% CI=1.01– 1.61, respectively). The frequency of G allele of rs4958487 was significantly lower in AS than in controls (Pc=0.001, OR=0.60, 95% CI=0.47– 0.68), the GG and GA genotypes reduced the risk of AS when compared with AA genotype (Pc=0.005, OR=0.46, 95% CI 0.18– 1.14, and Pc=0.005, OR=0.60, 95% CI=0.45– 0.79, respectively). The haplotype AA of rs17112187/rs4958487 significantly increased the risk of AS (P=2.31E-5, OR=1.60, 95% CI=1.28– 1.98), while haplotype CG decreased the risk of AS (P=5.42E-5, OR=0.55, 95% CI=0.41– 0.74). Expression levels of SPARC mRNA were significantly lower in both Peripheral blood mononuclear cells (PBMC) and granulocytes in AS patients than in controls (P=0.008 and P=0.005, respectively). SPARC protein levels were also reduced in AS patients versus the controls (P= 0.002).Conclusion: This study indicates that polymorphisms in SPARC are associated with AS susceptibility, and both mRNA and protein levels of SPARC are decreased in AS patients in a Chinese Han population. [ABSTRACT FROM AUTHOR]

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Phenine nanotubes (PNTs) have recently been synthesized as a promising new one-dimensional material for high-performance electronics. The periodically distributed vacancy defects in PNTs result in novel semiconducting properties, but may also compromise their mechanical properties. However, the role of these defects in modifying the structural and mechanical properties is not yet well understood. To address this, we conducted systematic molecular dynamics simulations investigating the structural evolution and mechanical responses of PNTs under various conditions. Our results demonstrated that the twisting of linear carbon chains in both armchair and zigzag PNTs led to interesting structural transitions, which were sensitive to chiralities and diameters. Additionally, when subjected to tensile and compressive loading, PNTs' cross-sectional geometry and untwisting of linear carbon chains resulted in distinct mechanical properties compared to carbon nanotubes. Our findings provide comprehensive insights into the fundamental properties of these new structures while uncovering a new mechanism for modifying the mechanical properties of one-dimensional nanostructures through the twisting–untwisting of linear carbon chains. [ABSTRACT FROM AUTHOR]

Aims and Objectives: The aim of this study was to assess the sleep quality in dialysis patients during the COVID‐19 epidemic and explore the association between negative psychology (including depression, anxiety, and stress) and sleep quality in this population. Design: A cross‐sectional study including three centres. Methods (Patients or Public Contribution): This cross‐sectional study included 378 dialysis patients from April to May 2022 in three dialysis centres in Shanghai. Methods. Depression, anxiety, stress, and sleep quality were measured by the Hospital Anxiety and Depression Scale (HADS), Perceived Stress Scale‐14 (PSS‐14), and Pittsburgh sleep quality index (PSQI), respectively. With a threshold of 5 to classify participants into good and poor sleep quality, with HADS/PSS‐14 scores as independent variables (per standard deviation (SD) increment), respectively and binary Logistic regression model was constructed to explore the association between the three negative psychological aspects of depression, anxiety, and stress and sleep quality. Results: The median PSQI score was 11.0 (mean ± SD: 11.8 ± 4.8). Among them, poor sleep quality (i.e., PSQI >5) was reported by 90.2% of participants. After adjusting for sociodemographic and disease‐related information, HADS‐depression was associated with a significant 49% (odds ratio (OR): 1.49; 95% CI 1.02–2.18) increase in the risk of poor sleep quality for each additional SD (2.4). Correspondingly, for each SD (7.1) increase in PSS‐14, the risk of poor sleep quality was significantly increased by 95% (OR: 1.95; 95% CI 1.35–2.82). Conclusion: During the COVID‐19 pandemic, there was a significant negative association between negative psychology, such as depression and stress, and sleep quality in dialysis patients, and this relationship was independent of the dialysis modality. Relevance to Clinical Practice: In the context of the rampant COVID‐19, the vast majority of dialysis‐dependent chronic kidney disease presents with severe sleep quality problems, and negative psychology is a potential influencing factor. [ABSTRACT FROM AUTHOR]

Traditional image steganography techniques complete the steganography process by embedding secret information into cover images, but steganalysis tools can easily detect detectable pixel changes that lead to the leakage of confidential information. The use of a generative adversarial network (GAN) makes it possible to embed information using a combination of information and noise in generating images to achieve steganography. However, this approach is usually accompanied by issues such as poor image quality and low steganography capacity. To address these challenges, we propose a steganography model based on a novel information-driven generative adversarial network (IDGAN), which fuses a GAN, attention mechanisms, and image interpolation techniques. We introduced an attention mechanism on top of the original GAN model to improve image accuracy. In the generation model, we replaced some transposed convolution operations with image interpolation for better quality of dense images. In contrast to traditional steganographic methods, the IDGAN generates images containing confidential information without using cover images and utilizes GANs for information embedding, thus having better anti-detection capability. Moreover, the IDGAN uses an attention mechanism to improve the image details and clarity and optimizes the steganography effect through an image interpolation algorithm. Experimental results demonstrate that the IDGAN achieves an accuracy of 99.4%, 95.4%, 93.2%, and 100% on the MNIST, Intel Image Classification, Flowers, and Face datasets, respectively, with an embedding rate of 0.17 bpp. The model effectively protects confidential information while maintaining high image quality. [ABSTRACT FROM AUTHOR]

This paper considers the problem of minimising makespan on a single batch processing machine with flexible periodic preventive maintenance. This problem combines two sub-problems, scheduling on a batch processing machine with jobs' release dates considered and arranging the preventive maintenance activities on a batch processing machine. The preventive maintenance activities are flexible but the maximum continuous working time of the machine, which is allowed, is determined. A mathematical model for integrating flexible periodic preventive maintenance into batch processing machine problem is proposed, in which the grouping of jobs with incompatible job families, the starting time of batches and the preventive maintenance activities are optimised simultaneously. A method combining rules with the genetic algorithm is proposed to solve this model, in which a batching rule is proposed to group jobs with incompatible job families into batches and a modified genetic algorithm is proposed to schedule batches and arrange preventive maintenance activities. The computational results indicate the method is effective under practical problem sizes. In addition, the influences of jobs' parameters on the performance of the method are analyzed, such as the number of jobs, the number of job families, jobs' processing time and jobs' release time. [ABSTRACT FROM AUTHOR]

Background: Heart failure (HF) is a major complication following ischemic heart disease (IHD) and it adversely affects the outcome. Early prediction of HF risk in patients with IHD is beneficial for timely intervention and for reducing disease burden. Methods: Two cohorts, cases for patients first diagnosed with IHD and then with HF (N = 11,862) and control IHD patients without HF (N = 25,652), were established from the hospital discharge records in Sichuan, China during 2015-2019. Directed personal disease network (PDN) was constructed for each patient, and then these PDNs were merged to generate the baseline disease network (BDN) for the two cohorts, respectively, which identifies the health trajectories of patients and the complex progression patterns. The differences between the BDNs of the two cohort was represented as disease-specific network (DSN). Three novel network features were exacted from PDN and DSN to represent the similarity of disease patterns and specificity trends from IHD to HF. A stacking-based ensemble model DXLR was proposed to predict HF risk in IHD patients using the novel network features and basic demographic features (i.e., age and sex). The Shapley Addictive exPlanations method was applied to analyze the feature importance of the DXLR model. Results: Compared with the six traditional machine learning models, our DXLR model exhibited the highest AUC (0.934 ± 0.004), accuracy (0.857 ± 0.007), precision (0.723 ± 0.014), recall (0.892 ± 0.012) and F1 score (0.798 ± 0.010). The feature importance showed that the novel network features ranked as the top three features, playing a notable role in predicting HF risk of IHD patient. The feature comparison experiment also indicated that our novel network features were superior to those proposed by the state-of-the-art study in improving the performance of the prediction model, with an increase in AUC by 19.9%, in accuracy by 18.7%, in precision by 30.7%, in recall by 37.4%, and in F1 score by 33.7%. Conclusions: Our proposed approach that combines network analytics and ensemble learning effectively predicts HF risk in patients with IHD. This highlights the potential value of network-based machine learning in disease risk prediction field using administrative data. [ABSTRACT FROM AUTHOR]

Biocementation based on the microbially induced carbonate precipitation (MICP) process can be used as a soil improvement method to improve the mechanical strength of granular soils. In this study, triaxial consolidated drained (CD) tests and constant shear drained (CSD) tests were carried out to evaluate the applicability of MICP as a method to mitigate suffusion and improve the internal stability of silty sands under seepage conditions. The CD test results demonstrated that biocementation is efficient in terms of strength improvement and deformation control of silty sand. The shear strength and the slope of failure line in p'-q plane increased with the number of treatment cycles at various levels of soil density. The CSD tests simulated the concurrent seepage-constant shear drained conditions that can trigger suffusion and instability of slopes. Results showed that the stress ratio q/p' and the hydraulic gradient at which the biocemented samples became unstable were higher than those of the untreated samples, which suggests that biocementation can effectively improve the stability of soils under concurrent seepage-constant shear drained conditions. Additionally, the biocemented samples also have stronger resistance against suffusion compared with untreated samples showing lower amount of eroded fine particles. Suffusion resistance improved with the number of treatment cycles and soil density. [ABSTRACT FROM AUTHOR]

Theoretical calculation results unveil that the reconstructed Co(Ni)OOH on FeNiCo-MOF during OER processes is beneficial to improve the OER activity. Experimentally, to achieve 2D trimetallic FeNiCo-MOF nanosheets, a facile room-temperature dispersion approach is employed. Such 2D nanosheets reveal an OER overpotential as low as 239 mV at 10 mA cm−2 and excellent long-term stability in 1M KOH. Undoubtedly, this work highlights the great potential of directly utilizing MOF nanosheets as OER electrocatalysts. [ABSTRACT FROM AUTHOR]

With the application and development of Internet technology, network traffic is growing rapidly, and the situation of network security is becoming more and more serious. As an important way to protect network security, abnormal traffic detection has been paid more and more attention. In this paper, the uncertainty of the samples in the abnormal traffic detection dataset is studied. Combining the three-way decision idea with the random forest algorithm, a three-way selection random forest optimization model for abnormal traffic detection is proposed. Firstly, the three-way decision idea is integrated into the random selection process of feature attributes, and the attribute importance based on decision boundary entropy is calculated. The feature attributes are divided into the normal domain, abnormal domain, and uncertain domain, and the three-way attribute random selection rules are designed to randomly select the feature attributes that conform to the rules from different domains. Secondly, the classifier evaluation function is constructed by combining pure accuracy and diversity, and the anomaly traffic detection base classifier with a high evaluation value is selected for integration to eliminate the unstable factors caused by randomness in the process of base classifier generation. Thirdly, the optimal node weight combination of the base classifier is obtained by iterative calculation of the gray wolf optimization algorithm to further improve the prediction effect and robustness of the model. Finally, the model is applied to the abnormal traffic detection dataset. The experimental results show that the prediction accuracy of the three-way selection random forest optimization model on CIC-IDS2017, KDDCUP99, and NSLKDD datasets is 96.1%, 95.2%, and 95.3%, respectively, which has a better detection effect than other machine learning algorithms. [ABSTRACT FROM AUTHOR]

Emerging spatial technologies, including spatial transcriptomics and spatial epigenomics, are becoming powerful tools for profiling of cellular states in the tissue context1–5. However, current methods capture only one layer of omics information at a time, precluding the possibility of examining the mechanistic relationship across the central dogma of molecular biology. Here, we present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications (H3K27me3, H3K27ac or H3K4me3) and gene expression on the same tissue section at near-single-cell resolution. These were applied to embryonic and juvenile mouse brain, as well as adult human brain, to map how epigenetic mechanisms control transcriptional phenotype and cell dynamics in tissue. Although highly concordant tissue features were identified by either spatial epigenome or spatial transcriptome we also observed distinct patterns, suggesting their differential roles in defining cell states. Linking epigenome to transcriptome pixel by pixel allows the uncovering of new insights in spatial epigenetic priming, differentiation and gene regulation within the tissue architecture. These technologies are of great interest in life science and biomedical research.The authors present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications and gene expression on the same tissue section at near-single-cell resolution. [ABSTRACT FROM AUTHOR]

Aiming at the problems of the dynamic increase in data in real life and that the naive Bayes (NB) classifier only accepts or rejects the sample processing results, resulting in a high error rate when dealing with uncertain data, this paper combines three-way decision and incremental learning, and a new three-way incremental naive Bayes classifier (3WD-INB) is proposed. First, the NB classifier is established, and the distribution fitting is carried out according to the minimum residual sum of squares (RSS) for continuous data, so that 3WD-INB can process both discrete data and continuous data, then carry out an incremental learning operation, select the samples with higher data quality according to the confidence of the samples in the incremental training set for incremental learning, solve the problem of data dynamics and filter the poor samples. Then we construct the 3WD-INB classifier and determine the classification rules of the positive, negative and boundary domains of the 3WD-INB classifier, so that the three-way classification of samples can be realized and better decisions can be made when dealing with uncertain data. Finally, five discrete data and five continuous data are selected for comparative experimental analysis with traditional classification methods. The results show that 3WD-INB has high accuracy and recall rate on different types of datasets, and the classification performance is also relatively stable. [ABSTRACT FROM AUTHOR]

In recent years, deep learning models have been more and more widely used in various fields and have become a research hotspot for various tasks in artificial intelligence, but there are significant limitations in non-convex optimization problems. As a model training strategy for non-convex optimization, curriculum learning advocates that models learn in the order of easier to more difficult data, mimicking the basic idea of gradual human learning as they learn curriculum. This strategy has been widely used in the fields of computer vision, natural language processing, and reinforcement learning; it can effectively solve the non-convex optimization problem and improve the generalization ability and convergence speed of models. This paper first introduces the application of curriculum learning at three major levels: data, task, and model, and summarizes the evaluators designed using curriculum learning methods in various domains, including difficulty evaluators, training schedulers, and loss evaluators, which correspond to the three stages of difficulty evaluation, training schedule, and loss evaluation in the application of curriculum learning to model training. We also discuss how to choose an appropriate evaluation system and the differences between terms used in different types of research. Finally, we summarize five methods similar to curriculum learning in the field of machine learning and provide a summary and outlook of the curriculum learning evaluation system. [ABSTRACT FROM AUTHOR]

A complex network in reality contains a large amount of information, but some information cannot be obtained accurately or is missing due to various reasons. An uncertain complex network is an effective mathematical model to deal with this problem, but its related research is still in its infancy. In order to facilitate the research into uncertainty theory in complex network modeling, this paper summarizes and analyzes the research hotspots of set pair analysis, rough set theory and fuzzy set theory in complex network modeling. This paper firstly introduces three kinds of uncertainty theories: the basic definition of set pair analysis, rough sets and fuzzy sets, as well as their basic theory of modeling in complex networks. Secondly, we aim at the three uncertainty theories and the establishment of specific models. The latest research progress in complex networks is reviewed, and the main application fields of the three uncertainty theories are discussed, respectively: community discovery, link prediction, influence maximization and decision-making problems. Finally, the prospect of the modeling and development of uncertain complex networks is put forward. [ABSTRACT FROM AUTHOR]

In order to study the controlled factors and variation of gas content in deep low-rank coal reservoirs, taking No.4 coal of Jurassic Yan'an Formation in Huanglong coalfield as an example, collects the production data and prepares the coal samples. Carrying out the coal rock and coal quality test and multi-temperature methane isothermal adsorption experiment to explore the influence of different temperature and pressure conditions on coal adsorption capacity, and analyzes the variation characteristics of gas content with burial depth combined with geological conditions. The results show that the sedimentary, structural and hydrogeological conditions have a certain influence on the formation and preservation of coalbed methane. The gas content increases with the increase of coalification degree and vitrinite content, and decreases with the increase of inertinite content, ash yield and volatile yield. With the increase of moisture, the gas content increased first and then decreased. When the pressure is less than 4 MPa, the adsorption capacity increases about 4-4.7m3/t with the increase of pressure, and the increasing trend slows down when the pressure is greater than 4 MPa. The adsorption capacity decreased more obviously with the increase of temperature when the temperature increased from 25°C to 35°C. Based on the prediction model of saturated adsorption capacity of deep coal reservoirs, it is found that there is a gas content critical conversion depth 800-900 m. The gas content shows a process of rapid increase (<800 m)-slow increase (800–900 m)-gradual decrease (>900 m) with the increase of burial depth. Below 800 m, the positive effect of reservoir pressure is dominant, and the gas content increases with burial depth. Above 900 m, the negative effect of reservoir temperature is dominant, and the gas content decreases with burial depth. The study results provide a theoretical basis for the development of deep low-rank coalbed methane resources. [ABSTRACT FROM AUTHOR]

In recent years, some research results have been achieved in the field of image steganalysis. However, there are still problems of difficulty in extracting steganographic features from images with low embedding rates and unsatisfactory detection performance of steganalysis. In this paper, we propose an image steganalysis method based on the attention mechanism and transfer learning. The method constructs a network model based on a convolutional neural network, including a preprocessing layer, a transposed convolutional layer, an ordinary convolutional layer, and a fully connected layer. We introduce the efficient channel attention module after the ordinary convolutional layer to focus on the steganographic region of the image, capture the local cross-channel interaction information, realize the adaptive adjustment of feature weights, and enhance the ability to extract steganographic features. Meanwhile, we apply the transfer learning method to use the training model parameters of high embedding rate images as the initialization parameters of the training model of the low embedding rate to achieve feature migration and further improve the steganalysis performance of the low embedding rate. The experimental results show that compared to the typical Xu-Net and Yedroudj-Net models, the detection accuracy of the proposed method is improved by 16.36% to 30.66% and by 35.59 to 37.83% for the embedding rates of 0.05 bpp, 0.1 bpp, and 0.2 bpp, respectively. Compared to the state-of-the-art Shen-Net model with low embedding rates, the detection accuracy is improved by 3.43% to 6.41%. This demonstrates the higher detection performance of the proposed method for steganalysis of low embedding rate images. [ABSTRACT FROM AUTHOR]

(1) Background: Preclinical and clinical studies on the anti-aging effect of α-Klotho are emerging. Urinary albumin excretion (UAE) is a well-known biomarker of kidney injury and generalized damage in the cardiovascular system. However, the potential relationship between α-Klotho and UAE is limited and controversial. This study aimed to quantify this relationship in the general middle-aged and elderly population from the National Health and Nutrition Survey (NHANES) 2007–2016. (2) Methods: Serum α-Klotho was measured by enzyme-linked immunosorbent assay. UAE was assessed by the albumin-to-creatinine ratio (ACR). After adjusting for several confounding variables, the relationship between α-Klotho and ACR was analyzed by weighted multivariable logistic regression, subgroup analysis, and interaction tests. A generalized additive model (GAM) with smooth functions using the two-piecewise linear regression model was used to examine the potential nonlinear relationship between α-Klotho and ACR. (3) Results: Among 13,584 participants aged 40–79 years, we observed an independent and significant negative correlation between α-Klotho and ACR (β = −12.22; 95% CI, −23.91, −0.53, p = 0.0448) by multivariable logistic regression analysis, especially in those with age ≥ 60 years, pulse pressure (PP) ≥ 60 mmHg, hypertension or diabetes. We further discovered the nonlinear relationship between α-Klotho and ACR by GAM, revealing the first negative and then positive correlations with an inflection point of 9.91 pg/mL between α-Klotho and ACR. (4) Conclusions: A dose-response relationship between α-Klotho and ACR was demonstrated, and the negative correlation therein indicated that α-Klotho has potential as a serum marker and prophylactic or therapeutic agent despite its metabolic and effective mechanisms needing to be further explored. [ABSTRACT FROM AUTHOR]

Motivation In recent years, a growing number of spatial epigenome datasets have been generated, presenting rich opportunities for studying the regulation mechanisms in solid tissue sections. However, visual exploration of these datasets requires extensive computational processing of raw data, presenting a challenge for researchers without advanced computational skills to fully explore and analyze such datasets. Results Here, we introduce AtlasXplore, a web-based platform that enables scientists to interactively navigate a growing collection of spatial epigenome data using an expanding set of tools. Availability and implementation https://web.atlasxomics.com [ABSTRACT FROM AUTHOR]

Metal ions play an important role in many metabolic processes in all living organisms. At low concentrations, heavy metals such as Fe2+, Cu2+ and Zn2+ are essential cofactors for many enzymes. However, at high concentrations they are toxic. Mesorhizobium species belong to the class α-proteobacteria and have high tolerance to soil acidity, salinity, temperature extremes, and metallicolous conditions. To identify factors responsible for this tolerance we have studied the effects of metal ions on Mesorhizobium delmotii thymidylate kinase (MdTMPK), an essential enzyme in the synthesis of dTTP, thus being vital for cell growth. We show that Mg2+ and Mn2+ are the divalent metal ions required for catalysis and that Mn2+ gives the highest catalytic efficiency. MdTMPK activity in the presence of Mg2+ was strongly inhibited by the co-presence of Zn2+, Ni2+ and Co2+. However, the addition of Cs+ caused >2-fold enhanced MdTMPK activity. For TMPK from Bacilus anthracis and humans, the effects of Mg2+ and Mn2+ were similar, whereas the effects of other divalent metal ions were different, and no stimulatory effect of Cs+ was observed. Together, our results demonstrate that MdTMPK and BaTMPK function well in the presence of high concentrations of heavy metal ions, introducing a potential contribution of these enzymes to the heavy metal tolerance of Mesorhizobium delmotii and Bacillus anthracis [ABSTRACT FROM AUTHOR]

Network intrusion detection has the problems of large amounts of data, numerous attributes, and different levels of importance for each attribute in detection. However, in random forests, the detection results have large deviations due to the random selection of attributes. Therefore, aiming at the current problems, considering increasing the probability of essential features being selected, a network intrusion detection model based on three-way selected random forest (IDTSRF) is proposed, which integrates three decision branches and random forest. Firstly, according to the characteristics of attributes, it is proposed to evaluate the importance of attributes by combining decision boundary entropy, and using three decision rules to divide attributes; secondly, to keep the randomness of attributes, three attribute random selection rules based on attribute randomness are established, and a certain number of attributes are randomly selected from three candidate fields according to conditions; finally, the training sample set is formed by using autonomous sampling method to select samples and combining three randomly selected attribute sets randomly, and multiple decision trees are trained to form a random forest. The experimental results show that the model has high precision and recall. [ABSTRACT FROM AUTHOR]

Twisted van der Waals heterostructures were recently found to possess unique physical properties, such as superconductivity in magic angle bilayer graphene. Owing to the nonhomogeneous stacking, the energy of twisted van der Waals heterostructures are often higher than their AA or AB stacking counterpart, therefore, fabricating such structures remains a great challenge in experiments. On the other hand, one dimensional (1D) coaxial van der Waals structures has less freedom to undergo phase transition, thus offer opportunity for fabricating the 1D cousin of twisted bilayer graphene. In this work, we show by molecular dynamic simulations that graphene nanoribbons can self-assemble onto the surface of carbon nanotubes driven by van der Waals interactions. By modifying the size of the carbon nanotubes or graphene nanoribbons, the resultant configurations can be controlled. Of particular interest is the formation of twisted double walled carbon nanotubes whose chiral angle difference can be tuned, including the 1.1° magic angle. Upon the longitudinal unzipping of such structures, twisted bilayer graphene nanoribbons can be obtained. As the longitudinal unzipping of carbon nanotubes is a mature technique, we expect the strategy proposed in this study to stimulate experimental efforts and promote the fast growing research in twistronics. [ABSTRACT FROM AUTHOR]

Several studies have demonstrated that exercise preconditioning is an effective means of alleviating poststroke cognitive impairment (PSCI). Mechanisms of regulating cognitive function have not been fully elucidated. Herein, the present study is aimed at exploring the effect of the microbiota-gut-inflammasome-brain axis in the process of exercise preconditioning moderating cognitive impairment after ischemic stroke. We observed that exercise preconditioning decreased infarct size, reduced the degree of neuronal damage, and alleviated cognitive impairment in mice with ischemic stroke. In addition, exercise preconditioning also reduced the expression of inflammatory cytokines, as well as NLRP3, Caspase-1, IL-18, and IL-1β protein expressions. Ischemic stroke could downregulate the abundance of Roseburia while increasing the abundance of the Helicobacter at the level of genus. As a comparison, exercise preconditioning increased the abundance of the Lactobacillus, which was beneficial for mice at the genus level. In conclusion, exercise preconditioning can improve cognitive dysfunction after ischemic stroke through alleviating inflammation and regulating the composition and diversity of the gut microbiota, which might provide a new strategy for the prevention of PSCI. [ABSTRACT FROM AUTHOR]

Background: Ovarian mature cystic teratomas (MCTs) are the most common tumors in pregnant women. The premature rupture of membranes (PROM) is a typical complication of pregnancy; however, the relationship between MCT and PROM is unknown. Therefore, we aimed to determine whether MCT is associated with the occurrence of PROM during pregnancy. Methods: The data of patients with adnexal masses during pregnancy between January 2017 and August 2021 were retrospectively analyzed. Ovarian cystectomy was performed during cesarean delivery or after vaginal delivery. Univariate and multivariate logistic regression models were used for statistical analysis. Results: A total of 234 patients with histopathological results were included. Among these patients, 21 occurred PROM during pregnancy, of which 11 were diagnosed with MCT. Compared with other subtypes, MCT (p=0.025) showed a stronger correlation with PROM and was an independent risk factor for PROM (odds ratio [OR], 2.811; 95% confidence interval [CI], 1.096– 7.215; p=0.032). Furthermore, we found that MCT with a diameter > 5 cm (p=0.0037) was more likely to promote the development of PROM than those that with a diameter < 5 cm. Conclusion: MCT was an independent risk factor for PROM during pregnancy. Positive actions and preventative clinical treatments should be fully taken into consideration by clinicians for pregnant women with MCTs, especially those ≥ 5 cm in diameter, to reduce the clinical complications related to MCT-associated PROM. [ABSTRACT FROM AUTHOR]

A SEIARN compartment model with the asymptomatic infection and secondary infection is proposed to predict the trend of COVID-19 more accurately. The model is extended according to the propagation characteristics of the novel coronavirus, the concepts of the asymptomatic infected compartment and secondary infection are introduced, and the contact rate parameters of the improved model are updated in real time by using the LSTM trajectory, in order to make accurate predictions. This SEIARN model first builds on the traditional SEIR compartment model, taking into account the asymptomatic infection compartment and secondary infection. Secondly, it considers the disorder of the trajectory and uses the improved LSTM model to predict the future trajectory of the current patients and cross-track with the susceptible patients to obtain the contact rate. Then, we conduct real-time updating of exposure rates in the SEIARN model and simulation of epidemic trends in Tianjin, Xi'an, and Shijiazhuang. Finally, the comparison experiments show that the SEIARN model performs better in prediction accuracy, MSE, and RMSE. [ABSTRACT FROM AUTHOR]

Microbially induced calcium carbonate precipitation (MICP) is a novel and potentially sustainable bio-mediated soil stabilization technology for geotechnical engineering applications, which can be promoted by many processes, including denitrification and urea hydrolysis. Compared to urea hydrolysis-based MICP, no ammonium is produced in denitrification-based MICP, but the amount of precipitation induced by the conventionally adopted methods (semi-stagnant column and continuous flow) reported in the early studies is small. This paper presented a large-volume circulation method to induce calcium carbonate precipitation into the sands via denitrification. The sands were treated with denitrification to different levels, and a series of consolidated drained (CD) triaxial tests were conducted after the treated sands were fully saturated to investigate the mechanical response. The results show that the peak drained strength and dilatancy are all improved by the denitrification-based MICP for samples treated. The cohesion and peak strength effective friction angle can be improved by 22 kPa and 3° with 4.48% calcium carbonate contents for denitrification-based MICP. However, the critical state line of soil cannot be shifted. The resulting calcium carbonate precipitation amount and rate can be as high as 4.61% by weight and 0.129% per day. The precipitation rate is increased by at least 5 times compared to the conventional adopted methods for denitrification-based MICP. In addition to CaCO3 precipitation, biofilms were also produced during denitrification. The high conversion ratio and precipitation-substrates ratio also indicate the high efficiency of this method. [ABSTRACT FROM AUTHOR]

Multitarget threat evaluation of warship air attacks is one of the most urgent problems in warship defense operations. To evaluate the target threat quickly and accurately, an air attack multitarget threat evaluation method based on improved TOPSIS gray relational analysis is proposed. This method establishes threat assessment system of five attributes of target type, anti-jamming ability, heading angle, altitude, and speed. The weight coefficient of each index of the warship is obtained by combining the entropy weight method with the analytic hierarchy process. Topsis can make full use of the information of the original data, and its results can accurately reflect the gap between various evaluation schemes. The weighted Mahalanobis distance and comprehensive gray correlation between the attribute to be evaluated and the positive and negative ideal states are calculated by the improved TOPSIS gray correlation method. The target threat degree to be evaluated is obtained by combining the two methods. Finally, an example is given to prove the effectiveness of the evaluation model. [ABSTRACT FROM AUTHOR]

Water content and water–coal interface wettability are always the difficult issues of coalbed methane adsorption/desorption. In order to study the effects of the pore structure and wettability of different macro coal components on methane adsorption and desorption, we compared and analyzed the wettability difference between vitrain and durain, and revealed the influence of wettability on methane adsorption and desorption through a pore structure analysis, wettability measurements, an adsorption–desorption experiment and adsorption heat calculations under different conditions, taking the No. 4 coal in Dafosi Coal Mine of the Huanglong coalfield as the research object. The results show that both vitrain and durain are relatively hydrophilic substances. However, vitrain has a low ash content, high volatility, and less oxygen, and the pores are mainly semi-closed pores compared with dark coal. Vitrain also has poor connectivity, poor sorting, a small pore diameter, and a coarser surface, resulting in poor surface wettability. The large specific surface area (SSA) and relatively poor wettability of vitrain leads to more adsorption sites in methane, which makes the adsorption capacity of vitrain greater than that of durain, but the good pore connectivity of durain causes the strong desorption capacity of durain. The isosteric adsorption heat of the adsorption process is greater than that of the desorption process, indicating that there is a desorption hysteresis phenomenon which is essentially due to the lack of energy in desorption. Surfactants change the wettability of the coal surface, and different surfactants have different effects on methane adsorption and desorption. Relatively speaking, the methane desorption of coal samples treated with G502 and 6501 are better. The research results provide scientific reference for the study of gas–water transport in the desorption process of low-rank CBM, and provide evidence for the methane desorption model of vitrain and durain. [ABSTRACT FROM AUTHOR]