With the growing of university chemistry experiment projects, scientific research personnel, specialized equipment, laboratory accident is increasing yearly. And accident data lacks a safety platform to store related information and cannot be guaranteed for efficient conditional sharing. To solve these problems, we designed a laboratory accident system to store, share related data and predict risk level. In this paper, we manually collected chemistry laboratory accidents by python software and class assignments, then analyses risk factor variables using Spsspro, finally established a prediction model using Stata. We intended to register laboratory related data into proposed chemistry accident system based on data ownership safety architecture. The chemistry accident system can break data barriers using confirmation and authorization key technology to trace non-tampered data source in real time when emergency accident happens. Meanwhile, our proposed system can use our designed accident risk model to predict risk level of any experiment project. It can also recommend appropriate safety education models.

The task of unsupervised anomalous sound detection (ASD) is challenging for detecting anomalous sounds from a large audio database without any annotated anomalous training data. Many unsupervised methods were proposed, but previous works have confirmed that the classification-based models far exceeds the unsupervised models in ASD. In this paper, we adopt two classification-based anomaly detection models: (1) Outlier classifier is to distinguish anomalous sounds or outliers from the normal; (2) ID classifier identifies anomalies using both the confidence of classification and the similarity of hidden embeddings. We conduct experiments in task 2 of DCASE 2020 challenge, and our ensemble method achieves an averaged area under the curve (AUC) of 95.82% and averaged partial AUC (pAUC) of 92.32%, which outperforms the state-of-the-art models.

During their growth, bacteria respond to complex environmental changes through equally com-plex and orderly regulatory mechanisms. sRNA has an important role as a post-transcriptional regulator in the environmental adaptation of Bacillus. In this study, we identified an sRNA Bvs091 located in the intergenic region of the Bacillus velezensis, which is widely present in the genus Bacillus and whose expression is upregulated by salt treatment. The deletion of bvs091 in B. ve-lezensis PEBA20 led to a reduced growth rate and a change in the phenotypes, with simple colony structures and low and easily degradable biofilm. Salt tolerance tests indicated that the deletion of bvs091 results in a reduced salt tolerance. galT2 mRNA is supposed to be a direct target of Bvs091 according to results obtained by using the bioinformatic method and performing qRT-PCR analysis. The microscale thermophoresis (MST) analysis showed that Bvs091 could directly bind 28‒51 bases in front of the promoter of galT2. Subsequently, the deletion of galT2 in B. velezensis resulted in increased salt tolerance. Based on the techniques of double plasmids and a half-life period, sRNA Bvs091 is confirmed as a trans-encoded sRNA that negatively regulates the mRNA expression of galT2. In brief, the trans-encoded sRNA Bvs091 acts by pairing the incomplete complementary bases of galT2 mRNA, reducing galT2 mRNA’s stability and affecting the trans-lation efficiency.

After reviewing the history of the struggle between human beings and plant diseases, insects and weeds, especially reviewing the thoughts of plant protection in ancient Chinese agricultural books, the target recognition of plant pests and the six types of plant protection methods and 36 subdi-vision measures were summarized. Then the development overview of pesticide application technology was focused and reviewed systematically by combining the development timeline of pesticide application and the key technologies including the performance measurement and the simulation and modeling of pesticide spraying system. And three suggestions for further research were proposed from the perspectives of human-being and environmental health, the sustainable and eco-friendly application medium, and the efficiently application equipment system in plant protection.

Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder characterized by cognitive decline and neuronal loss, representing a most challenging health issue. We present a computational analysis of transcriptomic data of AD tissues vs. healthy controls, focused on elucidation of functional roles played by long non-coding RNAs (lncRNAs) throughout the AD progression. We first assembled our own lncRNA transcripts from the raw RNA-Seq data generated from 527 samples of dorsolateral prefrontal cortex, resulting in the identification of 31,574 novel lncRNA genes. Based on co-expression analyses between mRNAs and lncRNAs, a co-expression network is constructed. Maximal subnetworks with dense connections are identified as functional clusters. Pathway enrichment analyses are conducted over mRNAs and lncRNAs in each cluster, which serve as the basis for the inference of functional roles played by lncRNAs involved in each of the key steps in an AD development model that we have previously build based on transcriptomic data of protein-encoding genes. Detailed information is presented about the functional roles by lncRNAs in activities related to stress response, reprogrammed metabolism, cell-polarity, and development. Our analyses have also revealed that lncRNAs have discerning power in distinguishing between AD samples of each stage and healthy controls. This study represents the first of its kind.

The low and unstable performance is still one of the major obstacles for anaerobic digestion, which provokes researchers to develop some strategies to improve it. In addition to traditional approaches such as co-digestion, pretreatment, and recirculation, some emerging strategies, namely additive and microaeration have also been recognized and developed in recent years due to the update of knowledge. Many studies have evaluated the effect of those strategies on digestion performance. However, it lacks a unified analysis and induction, especially the mechanism for different strategies. Therefore, this review presents a comprehensive view of research progress on the strategies based on latest researches considering five main strategies. Critical think and summary of their mechanism, reactor performance, and availability of those strategies were executed. The results demonstrated that the contribution of microaeration is mainly to balance the composition and activity of hydrolysis, acidogenesis, and methanogenic archaea. Recirculation and co-digestion balance mass and reaction environments. Pretreatment makes the structure of the substrate (such as removing lignin, reducing cellulose crystallinity, and increasing the substrate-specific surface area) more conducive to microbial utilization. The mechanism of the additive strategy varies greatly depending on the type of additive, such as enhancing interspecies electrons transfer through conductive materials, resisting adverse digestion conditions through functional microbial additives, and accelerating nutrient absorption by regulating the bioavailability of trace elements. Although these strategies have different mechanisms for promoting digestion performance, their ultimate goal is to make the parameters of the reactor to an ideal status, and then to achieve a balance among substance, microorganisms and water in anaerobic reactor.

Aims: To assess the occurrence of Chemotherapy-induced nausea and vomiting (CINV) after standard antiemetic therapy in the acute (24 h post-chemotherapy) and delayed (2–5 days post-chemotherapy) phases, as well as to identify risk factors for CINV in the acute and delayed phases. Methods: This prospective longitudinal and observational study analyzed the data of 400 breast cancer patients scheduled for chemotherapy over two cycles in two hospitals. The self-report survey was developed to assess the occurrence of CINV and their associated factors. CINV was evaluated with a Multinational Association of Supportive Care in Cancer Antiemetic Tool (MAT) on days 2 and 6 of chemotherapy. The incidence of acute and delayed CINV were presented by frequency and percentage. Generalized equation estimates (GEE) was used to identify risk factors of acute and delayed CINV. Results: There were 400 evaluable patients with complete Round 1 data, 334 for Round 2 data. Among 400 patients, 29.8% and 23.5% experienced acute and delayed CINV, respectively. Risk factors associated with for acute CINV were pain/insomnia, history of CINV, history of motion sickness (MS), and highly emetogenic chemotherapy regimen, while history of MS, CINV history, number of completed chemotherapy cycle number < 3, and the incidence of acute CINV were risk factors of delayed CINV (all p < 0.05). Conclusions: The findings may help nurses working for Chinese population in identifying patients at risk for CINV and in planning effective program to reduce the occurrence of CINV.

The metastasis of lung cancer can spread to the lymph nodes around the lungs. Metastasis, rather than the primary cancer, judges patients survival. Wherefore, a more detailed study on transcriptome of metastatic lung adenocarcinoma including primary carcinoma was carried out. LUAD RNA-seq data and the corresponding clinical information were available from The Cancer Genome Atlas (TCGA), which included 522 cases but only 515 cases have transcriptome data. Differential expression analyses between cases and controls, between primary cancer and metastasis subgroup, or between TNM stages, were respectively carried out using edgeR package. Then, the Kruskal-Wallis tests were used to verify the gradient changes of cancer metastasis or staging with the differential expression genes. The survival analyses were calculated using the Kaplan-Meier algorithm and log-rank test. The functional predictions for the differentially expressed genes were porformed with the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (GO/KEGG). Single gene set enrichment analysis (single GSEA) was run to explore the biological pathways associated with the expressions of RN7SL494P gene based on the Molecular Signatures Database (MSigDB). 406 and 439 differentially expressed genes were identified respectively in lymph node metastasis or TNM stages. 112/296 intersection genes were associated with nodal metastasis and/or staging, among them only 25 genes were associated with the nodal metastasis, 13 genes were associated with the staging with gradient changes. Only one gene (RN7SL494P) was found to be associated with prognosis. But RN7SL494P was not found joining any biological functions or processes or cellular components with GO/KEGG analyses. Finally, single GSEA enrichment and pathway analyses showed that RN7SL494P might be involving in cancer development process and poor outcome in lung adenocarcinoma. These findings highlight the potential applications of RN7SL494P as a promising molecular predictor not only in nodal metastasis but prognosis evalution in lung adenocarcinoma patients.

For a fiber optic gyroscope, thermal deformation of the fiber coil can introduce additional ther-mal-induced phase errors, commonly referred to as thermal errors. Thermal error compensation techniques are effective means of addressing this issue. The principle behind these techniques involves real-time sensing of thermal errors and correcting them within the output signal. Since it is challenging to directly separate thermal errors from the output signal of the fiber optic gyro-scope, it is necessary to predict thermal errors based on temperature. To establish a mathematical model between temperature and thermal errors, this paper measured synchronized data of phase errors and angular velocity for the fiber coil under different temperature conditions and aimed to model it using data-driven methods. Due to the difficulty of conducting tests and the limited number of data samples, an algorithm called TD-model modeling is proposed to address the issue of overfitting, which can reduce the model's generalization ability. First, a theoretical analysis of the phase errors caused by thermal deformation of the fiber coil is performed. Subsequently, the critical parameters, such as the thermal expansion coefficient, are determined, and a theoretical model is established. Finally, the theoretical analysis model is incorporated as a regularization term and combined with the test data to jointly participate in the regression of model coefficients. Through experimental comparative analysis, it is shown that, relative to ordinary regression models, the TD-model effectively mitigates overfitting caused by the limited number of samples, leading to a 58% improvement in predictive accuracy.

Transit-oriented development (TOD), such as metro depot and over-track building complexes, has expanded rapidly in China. Over-track building construction has the advantage of comprehensive utilization of land resources, ease of commuting to work, and provide funds for subway construction. However, there still has a pending problem, disturbance of railway vibration, for TOD. Excessive noise and vibration seriously affect people’s life. To address this challenge, field measurements are used to obtain the vibration source characteristics and vibration propagation of the ground and the superstructure in this study. And the typical metro depot with over-track building in Wuhan was selected. The effects of vibration on the surrounding ground and adjacent buildings caused by the operation of trains have been measured, and the propagation law of train-induced ground vibration acceleration in the time and frequency domains has been analyzed. The distribution and propagation laws of vibration noise, structural noise and secondary structure noise in sensitive areas such as the throat area, depot area, test section and access section of the over-track buildings and their impact laws on the over-track buildings are obtained. And the effectiveness of the vibration and noise reduction methods used were evaluated. Results show that within 43 m from the train running track, the train running induced vertical vibration acceleration level in the high-rise building peak size of 58.8 dB, the average value of 55.62 dB; vibration frequency components are mainly 40-60 Hz. The findings might provide useful insight for designing vibration mitigation systems in new metro depots with over-track buildings.

A spinning gyroscope precesses about the vertical due to a torque acting upon the wheel. The torque is generated by the shift of moment of force by gravity and it points to the vertical instead of the tangential direction of precession. This intuition offers an alternative and straightforward view of precession dynamics in comparison with the literature. It also presumes a dynamic balance of momentum between circular motions of the wheel spin and precession. Accordingly, the gyroscopic dynamics is then applied to the study of galactic motion of the solar system in space and the Galactic mass is calculated with the inclusion of gyroscopic effect of the solar planets. Results indicate that the gyroscopic effect of Mercury orbiting around the Sun can increase the calculated Galactic mass by 23% in comparison with the result obtained by the classic approach.

(1) Background: Previous systematic review suggested a beneficial effect of progressive muscle relaxation (PMR) for cancer patients receiving chemotherapy. However, poor quality of eligible studies impaired the reliability and validity of findings. Moreover, additional potential studies with good quality published in Chinese language have been published recently. The aim of the present systematic review was to investigate the value of PMR training in preventing and alleviating nausea and vomiting caused by chemotherapy among cancer patients. (2) Methods: We assigned two independent investigators to search all potential studies in PubMed, Cochrane Controlled Register of Trial (CENTRAL), Cumulative Index to Nursing and Allied Health Literature (CINAHL), China Biomedical Literature database (CBM), China National Knowledge Infrastructure (CNKI), and Wanfang data. We used data extraction sheet extracted all essential information, and used the Cochrane risk of bias assessment tool to appraise the quality of eligible studies. Finally, we qualitatively summarized the results of all included studies. (3) Results: Six studies enrolling 288 patients were considered to meet our selection criteria finally. Of these 6 studies, three were labeled as moderate quality, and the remaining studies were low quality. All included studies consistently suggested that PMR has a positive impact on nausea and vomiting caused by chemotherapy among cancer patients especially alleviating the incidence, frequency and degree of delayed nausea and vomiting. (4) Conclusions: Independent studies indicated that PMR was a beneficial approach to prevent and alleviate nausea and vomiting caused by chemotherapy among cancer patients. However, further studies considering other types of primary tumors should be designed in order to increase the generality of PMR because studies included in the present systematic review mainly enrolled lung cancer and breast cancer.

Human papillomavirus testing is increasingly central to screening programs for cervical cancer. Although HPV testing has excellent clinical sensitivity it suffers from modest clinical specificity. Cytology has taken an important role as a triage test, while more recently HPV16/18 genotyping has provided modest incremental benefits. Despite such progress issues remain to be addressed, some of which are how to: 1) improve access to the best methods, 2) decrease costs so more women can be screened, 3) simplify management algorithms, 4) Recognize the evolving diagnostic landscape created by widespread HPV vaccination. Part of the solution lies in improving the positive predictive value of the initial screen and reflex triage. An aspirational goal should be that most women attending colposcopy have true precancer; DNA methylation testing has a role to play here. Cervical cancers and CIN2/3 generally have high levels of methylation in target human and HPV genes. In contrast normal and healthy women with no intraepithelial lesions or malignancy usually have low methylation. Furthermore, there is an interesting and useful secular gradient of increasing cervical DNA methylation as women progress toward precancer and cancer. Recent results from clinical studies using commercially available routine qPCR-based DNA methylation tests have been encouraging, demonstrating the potential for higher sensitivity and specificity than current triage methods.

In the past decade, we have witnessed a dramatically increasing volume of data collected from varied sources. The explosion of data has transformed the world as more information is available for collection and analysis than ever before. To maximize the utilization, various machine and deep learning models have been developed, e.g. CNN [1] and RNN [2], to study data and extract valuable information from different perspectives. While data-driven applications improve countless products, training models for hyperparameter tuning are still time-consuming and resource-intensive. Cloud computing provides infrastructure support for the training of deep learning applications. The cloud service providers, such as Amazon Web Services [3], create an isolated virtual environment (virtual machines and containers) for clients, who share physical resources, e.g., CPU and memory. On the cloud, resource management schemes are implemented to enable better sharing among users and boost the system-wide performance. However, general scheduling approaches, such as spread priority and balanced resource schedulers, do not work well with deep learning workloads. In this project, we propose SpeCon, a novel container scheduler that is optimized for shortlived deep learning applications. Based on virtualized containers, such as Kubernetes [4] and Docker [5], SpeCon analyzes the common characteristics of training processes. We design a suite of algorithms to monitor the progress of the training and speculatively migrate the slow-growing models to release resources for fast-growing ones. Specifically, the extensive experiments demonstrate that SpeCon improves the completion time of an individual job by up to 41.5%, 14.8% system-wide and 24.7% in terms of makespan.

Concrete-filled steel tube (CFST) members have been widely used in the field of civil engineering due to their advanced superior mechanical properties. However, internal defects such as concrete core voids and interface debonding are likely to weaken the load-carrying capacity and stiffness of these members, which affects safety and serviceability of CFST structures. Visualizing the inner defects of concrete core in CFST members have been a critical need in civil engineering construction, a travel time tomography (TTT) is introduced to quantitatively identify and visualize the sizes and positions of CFST members in this paper. Moreover, a parameter analysis is performed to investigate the relationship between TTT imaging qualities and influence factors, e.g. inversion parameters, defect sizes and positions. The effectiveness and accuracy of the TTT algorithm are verified by several numerical examples and the results demonstrate that TTT can identify the sizes and positions of concrete core void defects in CFST members efficiently and several inversion parameters including model weighting matrix and inversion grid size really pose a significant impact on the imaging results of CFST members. In addition, several optimum parameters are recommended to benefit the future study of the promising TTT approach for CFST members.

Inflammation associated endothelial dysfunction represents a pivotal contributor to atherosclerosis. Increasingly evidence has demonstrated that interleukin 1 receptor (IL1-R) / toll-like receptor (TLR) signaling participated in the development of atherosclerosis. Previous studies indicated the therapeutic potential of anti-inflammatory therapy in anti-atherosclerosis. The present study examined the effect of IL-1R-associated kinase 1 and 4 inhibitors (IRAK1/4i) in regulating endothelial dysfunction. IRAK1/4i showed little endothelial toxicity at concentrations from 1 to 10 μM. Inhibition of IRAK1/4 alleviated endothelial activation induced by LPS in vitro evidenced by attenuated monocyte adhesion to the endothelium. Mechanistically, blockade of IRAK1/4 ameliorated the transcriptional activity of NF-κB. Taken together, our findings demonstrated that dual inhibition of IRAK1 and IRAK4 attenuates endothelial dysfunction, suggesting pharmaceutical inhibition of IRAK1/4 might be a potential strategy to combat endothelial dysfunction and atherosclerosis.

Abstract: Female pelvic floor dysfunction (FPFD) is a series of conditions caused by the displacement or dysfunction of the patient's pelvic organs due to defects, injuries, and deterioration of the pelvic floor supportive structures, with stress urinary incontinence and pelvic organ prolapse being the most common. The mechanism of pelvic floor dysfunction is complex and not yet fully understood, and is often related to one or more functional abnormalities of muscles, connective tissues, fascia, nerves and blood vessels. Specialized clinicians are often unable to cover the comprehensive medical professional information of various specialties such as gynecology and obstetrics, urology, anus and intestines, imaging, etc., and the systematic and comprehensive diagnosis and treatment of the disease will be limited. The application of artificial intelligence concepts in the medical field not only improves the diagnostic efficiency of physicians, but also provides reference and basis for the development of comprehensive treatment programs. This article follows and draws on the diagnosis and treatment process of pelvic floor dysfunction recommended in various guidelines, incorporates physical therapy techniques into the conventional non-surgical treatment of pelvic floor dysfunction, provides guidance for the diagnosis and treatment of pelvic floor dysfunction, and briefly introduces the clinical application and value of pelvic floor intelligent diagnosis and treatment in pelvic floor dysfunction.

As a high fatal disease, fat embolism syndrome is complication, which brought great pain to patient and their family served as a serious social burden. The mechanism of FES remains unclear. Autophagy controls the cell survival and homeostasis by removing the mis-folded proteins and damaged organelles as well as intracellular pathogens through a lysosomal degradation pathway. Increasing research documented that autophagy was wildly involved in variety of human diseases and had huge therapeutic potential. In our study, we first established the rat model of FES with the liquid fat by tail vein injection. We observed the up-regulated MPO expression and activity, increased Wet-to-Dry (W/D) lung weight, promoted quantity of fat granules, and the dominant disorder in the lung rat model of FES, compared to the control group. These observations demonstrated that we successfully build the rat model of FES. Then, we sought to dissect the role of the autophagy in the rat model of FES. The western blots analysis showed that the autophagy was inhibited in the rat model of FES constructed with liquid fat. Furthermore, Rapamycin could restore the repression of autophagy in rat model of FES. These investigations illustrated that autophagy was involved in FES. In addition, our experiments showed that Rapamycin could alleviate the symptoms of FES. Taken together, our study demonstrated the participation of autophagy in FES and further, as a potential therapeutic target, the modulation of autophagy could affect the symptom of rat model of FES.

In recent years, both domestic and international research on quadruped robots has advanced towards high dynamics and agility, with a focus on high-speed locomotion as a representative motion in high-dynamic activities. Quadruped animals like cheetahs exhibit high-speed running capabilities, attributed to the indispensable role played by their flexible spines during the flight phase motion. This paper establishes dynamic models of quadruped robots with different degrees of spine flexibility, providing a parameterized description of the flight phase motion for both rigid trunks and flexible spine quadruped robots. By setting different initial values for the spine joint and calculating the flight phase results for both types of robots at various initial velocities, the study compares and analyzes the impact of a flexible spine on the flight phase motion of quadruped robots. Through comparative experiments, the research aims to validate the influence of a flexible spine during the flight phase motion, providing insights into how spine flexibility affects the flight phase motion of quadruped robots.

The optimization of the production process of metal mines has been traditionally driven only by economic benefits while ignoring resource efficiency. However, it has become increasingly aware of the importance of resource efficiency since mineral resource reserves continue to decrease while the demand continues to grow. To better utilize the mineral resources for sustainable development, this paper proposes a multi-objective optimization model of the production process of metal mines considering both economic benefits and resource efficiency. Specifically, the goals of the proposed model are to maximize the profit and resource utilization rate. Then, the fast and elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) is used to optimize the multi-objective optimization model. The proposed model has been applied to the optimization of the production process of a stage in the Huogeqi Copper Mine. The optimization results provide a set of Pareto-optimal solutions that can meet varying needs of decision makers. Moreover, compared with those of the current production indicators, the profit and resource utilization rate of some points in the optimization results can increase respectively by 2.99% and 2.64%. Additionally, the effects of the decision variables (geological cut-off grade, minimum industrial grade and loss ratio) on objective functions (profit and resource utilization rate) were discussed using variance analysis. The sensitivities of the Pareto-optimal solutions to the unit copper concentrate price were studied. The results show that the Pareto-optimal solutions at higher profits (with lower resource utilization rates) are more sensitive to the unit copper concentrate prices than those obtained in regions with lower profits.

The optimization of guide vane closing schemes (OGVCS) of pumped storage hydro unit (PSHU) is the research field of cooperative control and optimal operation of pumped storage, wind power and solar power generation. This paper presents a OGVCS model of PSHU considering the rise rate of the unit rotational speed and the specific node pressure of each hydraulic unit, as well as various complicated hydraulic and mechanical constraints. OGVCS model is formulated as a multi-objective optimization problem to optimize conflictive objectives, i.e., unit rotational speed and water hammer pressure criteria. In order to realize the efficient solution of the OGVCS model, an enhanced multi-objective bacterial-foraging chemotaxis gravitational search algorithm (EMOBCGSA) is proposed to solve this problem, which adopts population reconstruction, adaptive selection chemotaxis operator of local searching strategy and Elite archive set to efficiently solve the multi-objective problem. Especially, novel constraints-handling strategy with eliminating and local search based on violation ranking is used to balance various hydraulic and mechanical constraints. Finally, simulation cases of complex extreme operating conditions (i.e., load rejection and pump outage) of ‘single tube-double units’ type PSHU system are conducted to verify the feasibility and effectiveness of the proposed EMOBCGSA in solving OGVCS problem. The simulation results indicate that the proposed EMOBCGSA can provide lower rise rate of the unit rotational speed and smaller water hammer pressure than other method established recently while considering various complex constraints in OGVCS problem.

Wencheng waxy yam is famous for its glutinous and resilient taste, similar to waxy rice, but there is currently a lack of systematic research on the quality of this featured product, and little is known about its pesticide residues. We carried out a 2-year investigation of Wencheng waxy yam at seven sites from 2021 to 2022 to determine the oxidase content and phytochemical characteristics, namely amylose, amylopectin, protein, reducing sugar, and mineral contents such as K, Fe, and Zn, , including the status of pesticide residues. The results showed that oxidase content was affected by rainfall, and adequate water reduced the production of oxidase, including polyphenol oxidase (PPO), peroxidase (POD), and superoxide dismutase (SOD), during the late growth stage of waxy yam, which was beneficial for reducing browning in yam processing. Radar map analysis showed that, with comprehensive evaluation, standardized production sites 1 and 2 had a relatively higher quality than 3–7 with small farmers. The results of pesticide multi-residue testing showed that no pesticides were detected in 64.29% samples, and the detected residues samples were very low, making the consumption of yam safe for consumers. These findings could be beneficial for the exploitation of the health benefits of waxy yam tubers and the innovation of yam-based functional products.

In order to further clarify the growth-promoting effect of non-core Metarhizium spp. M. marquandii on plants, M. marquandii SGSF043, which was obtained from the pre-screening in the laboratory, was selected as a test strain, and the effects of the fungus fermentation broth during the stabilization period of the growth of this strain on the growth and growth of maize seeds were investigated by using fermentation broth as a submerged seed. germination, seedling growth, and inter-root soil enzyme activity of seedlings. The results are as follows: In the seed germination test, M. marquandii SGSF043 fermentation liquid had a certain inhibitory effect on the germination of maize seeds, and the 10-times dilution of the fermentation liquid significantly promoted the seed germination, and the effect was the best; After the germination test, the growth of maize germ was promoted by the treatment groups of 10 times and 1,000 times diluent; In the field plot experiment, the 100-fold dilution had the best growth promotion effect, which could promote the growth of cabbage seedlings and increase the seedling biomass. The results show that M. marquandii SGSF043 has the potential to promote the growth of maize and improve the soil environment, which provides a theoretical basis for the research and application of M. marquandii in farmland.

The plant graft healing process is an intricate development influenced by numerous endogenous and environmental factors. This process involves the histological changes, physiological and biochemical reactions, signal transduction, and hormone exchanges in the grafting junction. Studies have shown that applying exogenous plant growth regulators can effectively promote the graft healing process and improve the quality of grafted plantlets. However, the physiological and molecular mechanism of graft healing formation remains unclear. In our present study, transcriptome changes in the melon and cucurbita genomes were analyzed between control and NAA treatment, and we provided the first view of complex networks to regulate graft healing under exogenous NAA application. The results showed that the exogenous NAA application could accelerate the graft healing process of oriental melon scion grafted onto squash rootstock through histological observation, increase the SOD, POD, PAL, and PPO activities during graft union development and enhance the contents of IAA, GA3, and ZR except for the IL stage. The DEGs were identified in the plant hormone signal-transduction, phenylpropanoid biosynthesis, and phenylalanine metabolism through transcriptome analysis of CK vs. NAA at the IL, CA, and VB stage by KEGG pathway enrichment analysis. Moreover, the exogenous NAA application significantly promoted the expression of genes involved in the hormone signal-transduction pathway, ROS scavenging system, and vascular bundle formation.

Background: The present retrospective study was designed to evaluate the relative diagnostic utility of breast-specific gamma imaging (BSGI) and breast magnetic resonance imaging (MRI) as means of evaluating female breast cancer patients in China. Methods: A total of 229 malignant breast cancer patients underwent ultrasound, mammography, BSGI, and MRI between January 2015 and December 2018 for initial tumor staging. Of these patients, 73 were subsequently treated via definitive breast surgery following neoadjuvant chemotherapy (NAC), of whom 17 exhibited a complete pathologic response (pCR) to NAC. Results: BSGI and MRI were associated with respective 76.8% and 69.6% sensitivity values as a means of detecting residual tumors following NAC, while both of these approaches exhibited comparable specificity in this diagnostic context (58.8% vs 70.6%, P=0.473). Conclusion: These results demonstrate that BSGI is a useful auxiliary approach to evaluating pCR to NAC treatment.

An algorithm is proposed for discriminating the fatigue state of air traffic controllers based on ap-plying multispeech feature fusion using an FSVM to voice data, and for extracting eye-fatigue-state discrimination features based on PERCLOS eye data. For the speech algorithm and an eye-fatigue index, a new controller fatigue-state evaluation index based on the entropy weight method is proposed based on decision-level fusion of fatigue discrimination results for speech and the eyes. Experimental results show that the fatigue-state recognition accuracy rate was 84.81% for the fatigue state evaluation index, which was 3.36% and 1.86% higher than those for speech and eye assessments, respectively. The comprehensive fatigue evaluation index provides important reference values for controller scheduling and mental-state evaluations.

In 2019, Chinese government implemented the first round of National Centralized Drug Procurement (NCDP) pilot (so-called "4+7" policy) in mainland China, achieved a prominent price reduction of 52% on average for 25 bidding winning products. Under cross-price elasticity theory, the price behavior of pharmaceutical enterprises for policy-related drugs might change. This study used drug purchasing data from the Centralized Drug Procurement Survey in Shenzhen 2019, and applied single-group Interruption Time Series (ITS) design to examine the impact of "4+7" policy on the drug price index (DPI) of policy-related drugs. The ITS analysis showed that the DPI of winning (-0.183 per month, p&lt;0.0001) and non-winning (-0.034 per month, p=0.046) products significantly decreased after the implementation of "4+7" policy. No significant difference was found for the immediate change of DPI for alternative drugs (p=0.537), while a significant decrease in change trend was detected in the post-"4+7" policy period (-0.003 per month, p=0.014). The DPI of the overall policy-related drugs significantly decreased (-0.261 per month, p&lt;0.0001) after "4+7" policy. These findings indicate that the price behavior of pharmaceutical enterprises changed under NCDP policy, while the price linkage effect is still limited. It is necessary to further expand the scope of centralized purchased drugs and strengthen the monitoring of related drugs regarding price change and consumption structure.

Abstract: A multitude of biotic and abiotic stress factors do harm to plants by bringing about diseases and inhibiting normal growth and development. As an omnipotent signaling molecule, salicylic acid (SA) plays crucial roles in plants' tolerance responses to both biotic and abiotic stresses, thereby maintaining plants’ normal growth and improving yields under stresses. In view of this, this paper mainly discusses the role of SA in both biotic stresses, and abiotic stresses of plants. SA regulates the expression of genes involved in defense signaling pathways, thus enhancing plants’ immunity. In addition, SA mitigates the negative effects of abiotic stresses, and acts as a signaling molecule to induce the expression of stress-responsive genes and the synthesis of stress-related proteins. In addition, SA also improves certain yield-related photosynthetic indexes, thereby enhancing crop yield under stresses. On the other hand, SA acts with other signaling molecules in regulating plants’ growth and improving tolerances under stresses. This paper reviews recent advances in SA’s roles in plant stress tolerances, so as to provide theoretical references for further studies concerning the decryption of molecular mechanisms for SA’s roles and the improvement of crop management under stresses.

This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.

The origin of the nucleus remains a great mystery in life science, although nearly two centuries have passed since the discovery of nuclei. To date, studies of eukaryogenesis have focused largely on micro-evolutionary explanations. Here, we examined macro-patterns of C-values (the total amount of DNA within the haploid chromosome set of an organism) for over 110,000 species and the chromosome numbers for over 11,000 species and their potential links with the state of atmospheric oxidation over geological time. Eukaryogenesis was in sync with an over 2.5 order-of-magnitude increase in genome size from prokaryote to eukaryote, and also with a rapid rise of atmospheric oxidation, suggesting that eukaryogenesis would have resulted from a regime shift of genomes driven by the oxidation-driven complexification and structuralization (e.g. chromatin packing).

Early diagnosis of paroxysmal atrial fibrillation (PAF) could suggest patients to receive timely interventions in clinical practice. Various PAF onset prediction algorithms might benefit from accurate heart rate variability (HRV) analysis and machine learning classification but to be challenged in real-time monitoring scenarios. The aim of this study is to present an end-to-end deep learning based PAFNet model that integrates a sliding window technique on raw R-R interval of electrocardiogram (ECG) segments to achieve a real-time prediction of PAF onsets. This integration enable the deep convolutional neural network (CNN) to be customized as a light-weight architecture that accommodates the size of sliding windows simply by altering the input layer, specifically its effectiveness in making a new prediction with each new heartbeat. Catering to potential influence of input sizes, three CNN models were trained using 50, 100, and 200 R-R intervals, respectively. For each model, the performance of automated algorithms was evaluated for PAF prediction using a ten-fold cross-validation. As a results, a total of 56,381 PAFN-type and 56,900 N-type R-R interval segments was collected from publicly accessible ECG databases, and a promising prediction performance of the automated algorithm with a 100 R-R intervals was achieved in terms of the sensitivity of 97.12%, the specificity of 97.77%, and the accuracy of 97.45%, respectively. Importantly, the automated algorithm with a sliding windows step of 1 could process one sample in only 23.1 milliseconds and identify the onset of PAF at least 45 minutes in advance. The present results suggest that the sliding window technique on raw R-R interval sequences along by deep learning based algorithms may offers the possibility of providing an accurate, real-time, and end-to-end clinical tool for mass monitoring of PAF.

This paper introduces the AltumView Sentinare smart activity sensor for senior care and patient monitoring. The sensor uses an AI chip and deep learning algorithms to monitor the activity of people, collect activity statistics, and notify caregivers when emergencies such as falls are detected. To protect privacy, only skeleton (stick figure) animations are transmitted instead of videos. The sensor is highly affordable, accessible, and versatile. It was a CES 2021 Innovation Award Honoree, and has been selected by Amazon as one of only three fall detection devices integrated into its Alexa Together urgent response service, and has received very positive reviews from Amazon customers. It has also been used in different senior care settings in about ten different countries. The paper presents the main features of the system, the evidences and lessons learned from its practical applications, and future directions.

Image fusion is the process of combining multiple input images from single or multiple imaging modalities into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. In this paper, we propose a novel method based on deep learning for fusing infrared images and visible images, named the LBP-based proportional input generative adversarial network (LPGAN). In the image fusion task, the preservation of structural similarity and image gradient information is contradictory, and it is difficult for both to achieve good performance at the same time. To solve this problem, we innovatively introduce Local Binary Patterns (LBP) into Generative Adversarial Networks (GANs), which effectively utilize the texture features of the source images, so that the network has stronger feature extraction ability and anti-interference ability. In the feature extraction stage, we introduce a pseudo-siamese network for the generator to extract the detailed features and the contrast features. At the same time, considering the characteristic distribution of different modal images, we propose a 1:4 scale input mode. Extensive experiments on the publicly available TNO dataset and CVC14 dataset show that the proposed method achieves the state-of-the-art performance. We also test the universality of LPGAN through the fusion of RGB and infrared images on the RoadScene dataset. In addition, LPGAN is applied to multi-spectral remote sensing image fusion. Both qualitative and quantitative experiments demonstrate that our LPGAN can not only achieve good structural similarity, but also retain rich detailed information.

An adaptive operation strategy for on-demand control of DMFC system is proposed as an alternative method to enhance the voltage stability. Based on a single-cell DMFC stack, a newly simplified semi-empirical model is developed from the uniform-designed experimental results to describe the I-V relationship. Integrated with this model, the multi-objective optimization method is utilized to develop an adaptive operation strategy. Although the voltage instability is frequently encountered in unoptimized operations, the voltage deviation is successfully decreased to a required level by adaptive operations with operational adjustments. Moreover, the adaptive operations are also found to be able to extend the range of operating current density or to decrease the voltage deviation according to ones requirements. Numerical simulations are implemented to investigate the underlying mechanisms of the proposed adaptive operation strategy, and experimental adaptive operations are also performed on another DMFC system to validate the adaptive operation strategy. Preliminary experimental study shows a rapid response of DMFC system to the operational adjustment, which further validates the effectiveness and feasibility of the adaptive operation strategy in practical applications. The proposed strategy contributes to a guideline for the better control of output voltage from operating DMFC systems.

The extracts of Garcinia mangostana L. (mangosteen) are traditionally used for medicinal purposes in Asia. Phenols and xanthones are the two main phenolic compounds with anti-inflammatory activities in the pericarps of mangosteen. A simple and economic reverse-phase high-performance liquid chromatography (RP-HPLC) method has been developed for the simultaneous quantification of phenols (catechin, epicatechin, and procyanidin B2) in mangosteen. The mobile phase was acetonitrile: water (12:88) at 30°C, the flow rate was 1 mL/min, and the detection wavelength was 230 nm. The results showed good linear relationships for catechin from 50-250 ng and for epicatechin and procyanidin B2 from 250-1250 ng. The RP-HPLC fingerprint determination method of total xanthones in mangosteen was also established to provide a new method for quality control of mangosteen extract. The mobile phase was a methanol-water gradient elution at 30°C, the flow rate was 1 mL/min, and the detection wavelength was 320 nm. The similarity of fingerprints of 10 batches of total xanthones was more than 0.90. These methods had good precision, reproducibility, and stability and can be applied in the quality control of mangosteen extracts for medicinal purposes.

Polychlorinated phenoxathiins (PCPTs), polychlorinated dibenzothiophenes (PCDTs), and polychlorinated thianthrenes (PCTAs) are sulfur analogues of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/DFs). Chlorothiophenols(CTPs) and chlorophenols (CPs) are key precursors to form PCTA/PT/DTs, which can form chloro(thio)phenoxy radical, sulfydryl/hydryl-substituted phenyl radical and (thio)phenoxyl diradicals. The available radical/radical PCTA/DT formation mechanism failed to explain the higher concentration of PCDTs than that of PCTAs under the pyrolysis or combustion conditions. Thus in this work, a detailed thermodynamics and kinetic calculations were carried out to investigate the pre-intermediates formation for PCTA/PT/DTs from radial/molecule coupling of 2-C(T)P with their key radical species. Our study found that the radial/molecule mechanism can thermodynamically and kinetically contribute to the gas-phase formation of PCTA/PT/DT/s. The S/C coupling modes to form thioether-(thio)enol intermediats are preferable over the O/C coupling modes to form ether-(thio)enol intermediats. Thus, although the radial/molecule coupling of chlorophenoxy radical with 2-C(T)P have no effect on the PCDD/PTs formation, the radial/molecule coupling of chlorothiophenoxy radical with 2-C(T)P play an important role in the PCDT/PT formation. Most importantly, the pre-PCDT intermediates formation pathways from the coupling of sulfydryl/hydryl-substituted phenyl radical with 2-C(T)P and the coupling of (thio)phenoxyl diradicals with 2-C(T)P are more favorable to pre-PCTA/PT intermediates formation pathways from the coupling of chlorothiophenoxy radical with 2-C(T)P, which can give reasonable explanation for the high PCDT-to-PCTA ratio in the environment.

The subtropical forest soil plays a pivotal role in terrestrial carbon (C) cycling. Although global warming is expected to influence subtropical soil C cycling, a consensus on its impact on soil C dynamics remains elusive. Given the significant response of soil microbial respiration to temperature in subtropical soils, understanding its behavior under varying temperature regimes is critical for predicting soil organic C (SOC) responses to climate warming. We conducted a short-term laboratory soil warming incubation experiment, sampling both warmed and un-warmed soils from a subtropical plantation in southeastern China, incubating them at 20 °C, 30 °C, and 40 °C. Our aim was to study the SOC mineralization response to increasing temperatures. Our findings revealed that the temperature sensitivity (Q10) of SOC mineralization to short-term experimental warming varied between the warmed soil and the un-warmed soil. The Q10 of the un-warmed soil escalated with the temperature treatment (20-30 ℃: 1.31, 30-40℃: 1.63). Conversely, the Q10 of the warmed soil decreased (20-30 ℃:1.57, 30-40 ℃:1.41). Increasing temperature treatments enhanced hydrolytic enzyme activity but decreased soil substrate availability in both un-warmed and warmed soil. The Q10 of un-warmed soil was positively correlated with the response ratio of DOC. Additionally, soil microbial biomass exhibited a significant decline, leading to a reduced total amount of PLFAs and a decreased abundance of bacteria, fungi, and Gram-negative bacteria (GN). The changes in temperature also considerably altered the composition of the warmed soil microbial community. A drop in the microbial quotient coupled with a rise in the metabolic quotient indicated that warming amplified microbial respiration over microbial growth. The differential Q10 of SOC mineralization, in response to temperature across varying soil, can largely be attributed to shifts in soil nutrients, C-degrading enzyme activities, and microbial communities (the ratio of fungal to bacterial PLFAs).

In this work, a tailored extreme learning machine (ELM) algorithm to enhance the overall robustness of gas analyzer based on the tunable diode laser absorption spectroscopy (TDLAS) method is presented. The ELM model is tailored through activation function selection, input weight and bias searching, and cross validation method to address the analyzer robustness issues for industrial process analysis field application. The two particular issues are the inaccurate gas concentration measurement caused by the process gas background components variation, and the inaccurate spectra shift calculation caused by spectral interference. By using our algorithm, the concentration error is reduced by one order of magnitude over a much larger stream pressure and component range compared with that obtained by classical least square (CLS) fitting methods based on reference curves. Additionally, it is shown that with our algorithm, the wavelength shift accuracy is improved to less than 1 count over 1000 counts spectra length. In order to test the viability of our algorithm, a trace ethylene (C₂H₄) TDLAS analyzer with coexisting methane was implemented, and its experimental measurements support analyzing robustness enhancement effect.

Existing evidence suggest that lactoferrin might be beneficial for Alzheimer’s disease. We aimed to determine the effects of lactoferrin intervention on cognitive function from APP/PS1 mice, and possible mechanisms involved in. Both young and middle-aged male APP/PS1 mice were divided into control and lactoferrin group with 16 weeks’ intervention. Lactoferrin intervention had no effects on cognitive function from both young and middle-aged mice, and no key markers involved in Aβ, tau pathology, neuro-inflammation and synaptic plasticity were altered post lactoferrin intervention. In regards to gut microbiota profiles, in the young mice, lactoferrin elevated α diversity index including ACE and Chao 1, and reduced the relative abundance of the genera Bacteroides and Alistipes and elevated Oscillibacter, in addition, Oscillibacter, Anaerotruncus, EF096579_g, EU454405_g, Mollicutes_RF39, EU474361_g, EU774448_g, and EF096976_g were specifically abundant post Lf intervention via LEfSe analysis. In the middle-aged mice, the relative abundance of the phylum Proteobacteria, as well as the genera Oscillospira, Coprococcus and Ruminococcus was significantly reduced post Lf intervention, additionally, S24_7, Bacteroidia, Bacteroidetes and Methylobacterium were specific via LEfSe analysis post lactoferrin intervention. In conclusion, dietary lactoferrin might be beneficial for gut microbiota homeostasis although might have no effects on cognition.

This paper aims to quantify the influence of hydrogen embrittlement on Wierzbicki and Xue (W-X) model parameters. This model has 4 parameters which have been obtained by tensile tests on smooth specimens, tensile notch specimens, single notch tensile specimens (SENT) and pure shear specimens. Hydrogen embrittlement has been obtained by electrolytic method. Application of the (W-X) model has been done to get the local resistance used to predict failure by the Volumetric Method (VM). An example is given for the case of an embedded pipe submitted to service pressure and lateral seismic displacement. The effect of this displacement is then assimilated to the shearing of the straight section of the pipe. The pipe shows a corrosion defect considered a semi-elliptical crack. The influence of hydrogen embrittlement on local strain resistance and safety factors is discussed.

The current pneumonia epidemic could evolve into a pandemic on a global scale if not effectively contained. The COVID-19 virus possesses a 61-amino acid open reading frame resembling SARS-CoV virulence factor - ORF6 peptide. The isoleucine content is 15.9% in ORF6 of SARS-CoV versus 16.4% of that in SARS-CoV-2. Given the proton affinity in the carbonyl oxygen in isoleucine, augmented proton traffic can enhance proton-ion antiport and prompt cell swelling. Calorie restriction has been confirmed in animal studies to extend lifespan, and its underlying mechanism is not fully known. As the content of essential amino acids in the open reading frame of SARS-CoV-2 reaches 57.4%, a starch/vitamin diet served for short period of time does not give rise to essential amino acids and halts virion production, which could be adopted as prophylactic approach of many viral infections. Plant-based diet or fasting/boiled rice water can also minimize the intake of essential amino acids or all amino acids respectively. Furthermore, several proteins of SARS-CoV-2 possess high valine plus glycine content which is implicated in heart disease, justifying the aforementioned approaches.

It is always a difficult task to assign the peaks recorded from vibrational spectrum. Herein we explored a new pathway of density functional theory (DFT) simulation to present three kinds of spectra of ice XIV and can be referenced as inelastic neutron scattering (INS), IR, and Raman experimental spectrum. The INS spectrum is proportional to phonon density of states (PDOS) while the photon scattering signals reflect the normal vibration frequencies near the Brillouin zone (BZ) center. Based on good agreements with experimental data, we identified the relative frequency and made scientific assignments by normal vibration modes analysis. The debating two hydrogen bond (H-bond) peaks among ice phases from INS have been discussed and the dynamic process of H-bond vibrations were found to be classified into two basic modes. we deduce that two H-bond modes are a general rule among ice family and more works are ongoing to investigate this subject.

Ginkgolic acids (GA) have been reported to exhibit anticancer properties, however, the mechanisms remain unclear. This study aims to investigate the mechanisms of GA C13:0 that was isolated from Ginkgo biloba exocarp (GBE) for anti-proliferation, pro-apoptosis and anti-migration effects in human MCF-7 and mouse 4T-1 breast cancer cells. The cytotoxic effect, apoptosis induction and migration inhibition were measured using MTT, TUNEL and Wound healing assays. The expression of mRNA and protein were determined using qPCR and Western blot. Our results showed that no cytotoxicity was found at concentrations of C13:0 below 100µM. The effects of GA C13:0 was further demonstrated by up-regulation of the Bax/Bcl-2 apoptosis pathway and the expression of Apaf-1 protein in the mitochondria. In addition, GA C13:0 also suppressed cell migration and epithelial to mesenchymal transition (EMT) with the increase of E-cadherin expression accompanied by the decrease of Snail, MMP-2, MMP-9 and Vimentin expression. Moreover, GA C13:0 induced cytochrome P450 (CYP) 1B1 expression in aryl hydrocarbon receptor (AhR) pathway. Notably, the up-regulation of CYP1B1 also might play a pivotal regulatory role in mitochondrial and EMT pathways in MCF-7 and 4T-1 cells. Our results may have implications for the development of anticancer agents containing GA as functional additives.

The straightness error of guideway is one of the key indicators of ultra-precision machine, which plays an important role in the machining accuracy of work-piece. In order to measure straightness error of long distance ultra-precision guideway accurately, a splicing measurement for straightness error of guideway using high-precision flat mirror and displacement sensor was proposed in this paper, and the data splicing processing algorithm based on coordinate transformation was studied. Then comparative experiments on splicing measurement and direct measurement of straightness error were carried out on a hydrostatic guideway grinder. The maximum difference between the two measurements was about 0.3μm, which was far less than the straightness error of 5.8μm. The experiment demonstrated the correctness of the proposed splicing measurement method and data processing algorithm. To suppress the influence of straightness error on machining accuracy, a straightness error compensation algorithm based on error rotation transformation and vertical axis position correction was proposed, and the grinding experiment of a plane optics with size of 1400mm×500mm was carried out, whose flatness error after grinding was about 2.98μm. The straightness error of the grinding machine had been well suppressed.

The most recent outbreak of 2019 novel coronavirus, named as COVID-19, caused pneumonia epidemic in Wuhan with 2,121 deaths cases as of February 20^th 2020. Identification of effective antiviral agents to combat the novel coronavirus is urgently needed. Citrus fruit peel or wild citrus are rich in flavonoid, and is clinically documented for roles in relief of cough and promotion of digestive health. Therefore, citrus fruits are assumed to possess antivirus activities or enhance the host immunity. A previous study found that hesperetin could act as a high potent inhibitor of SARS-CoV 3CLpro. We determined six flavonoid compounds content of in three citrus species by using LC-MS technique. The content of naringin and naringenin was at higher levels in pummelo. Hesperetin and hesperidin were highly accumulated in mandarin and sweet orange. The subsequent in vitro and in vivo experiments indicated that naringin could inhibit the expression of the proinflammatory cytokines (COX-2, iNOS, IL-1β and IL-6) induced by LPS in Raw macrophage cell line, and may restrain cytokine through inhibiting HMGB1 expression in a mouse model. The results revealed that naringin may have a potential application for preventing cytokine storm. We simulated molecular docking to predict the binding affinity of those flavonoids to bind Angiotensin-converting enzyme 2 (ACE 2), which is a receptor of the coronavirus. Consideration of the potential anti-coronavirus and anti-inflammatory activity of flavonoids, the citrus fruit or its derived phytochemicals are promising in the use of prevention and treatment of 2019-nCoV infection.

Digital twin technology is revolutionizing traditional manufacturing paradigms. In modern manufacturing systems, digital twin technology is fraught with challenges due to the scarcity of labeled data. Specifically, existing supervised machine learning algorithms, with their reliance on voluminous training data, find their applicability constrained in real-world production settings. This paper introduces an unsupervised 3D reconstruction approach tailored for industrial applications, aimed at bridging the data void in creating digital twin models. Our proposed model, by ingesting high-resolution 2D images, autonomously reconstructs precise 3D digital twin models without the need for manual annotations or prior knowledge. Through comparisons with multiple baseline models, we demonstrate the superiority of our method in terms of accuracy, speed, and generalization capabilities. This research not only offers an efficient approach to industrial 3D reconstruction but also paves the way for the widespread adoption of digital twin technology in manufacturing.

Warm cloud-precipitation plays a vital role in the hydrological cycle, weather, and climate. Comprehensive observation and study of warm cloud-precipitation can advance our understanding of the internal physical processes and provide valuable information for developing the numerical models. This paper mainly focused on a study of characteristics of warm cloud-precipitation in South China during the pre-flood season using datasets observed from a Ka-band cloud radar, laser ceilometer and disdrometer. Eighteen kinds of quantities from these three instruments were used to precisely elucidate the distribution, diurnal variation, vertical structure, and physical property of warm cloud-precipitation. The results showed that the occurrence of aloft cloud-precipitation decreased with the increase of height, and most of the hydrometeors were distributed below 2 km. During the observation period, the ground rainfall mainly came from light precipitation; however, short-time and sharp showers contributed to the majority of rain amounts. Most of the cloud layers were single-layer, with base heights below 2.2 km, thickness thinner than 2.1 km, and top heights within 0.6-4.2 km. Warm cloud-precipitation owned certain diurnal variations, with a rising trend of cloud base heights in the afternoon and midnight. During 0230-1100, 1200-1800, and 2100-2300, the convections were relatively active with higher cloud tops, thicker cloud thickness, and higher rainfall occurrences. Separation and statistical results of cloud and precipitation indicated that they owned different vertical structures and physical properties, exhibiting different value ranges and changes of radar reflectivity, vertical air motion, particle size, number concentration, liquid water, and rain rate at different height levels. The particle size distributions of cloud and precipitation both were exponential. Radar-derived raindrop size distribution was very coherent with the ground measurement when the reflectivity of precipitation was within 10-20 dBZ. However, for other reflectivity regimes, instrument sensitivity, sampling height, attenuation, and non-precipitating weak targets can affect the comparison.

Piceatannol is a naturally occurring polyphenolic compound that is widely found in grapes, blueberries, mushrooms, passion fruit and other edible fruits and vegetables. It has a variety of biological activities and pharmacological effects, including anti-inflammatory, antioxidation, antiaging, antiviral, antitumor, anticancer, antiatherosclerosis, antiparasitic, immunoregulatory, and cardiovascular protection effects. The aim of this study was to investigate the antiherpesvirus effects of piceatannol. Pseudorabies virus (PRV) belongs to the family Herpesviridae. PRV has a wide host range and can cause cytopathic effects (CPEs) in PK-15 cells. Therefore, PRV was used as a model to study the antiherpesvirus effect of piceatannol. In this study, we evaluated the antiviral activity of piceatannol against PRV in vitro and in vivo. The results showed that piceatannol could reatrain PRV multiplication in PK-15 cells in a dose-dependent manner. The 50% inhibitory concentration (IC50) was 0.0307 mg/mL, the IC50 was 0.0307 mg/mL, and the selectivity index (SI, CC50/IC50) was 3.68. The addition of piceatannol at different stages of PRV infection inhibited the viral adsorption and intracellular replication phases of PRV. Piceatannol significantly reduced the expression levels of the IE180, EP0, UL29, UL44, US6 and UL27 genes of PRV within 48 h, significantly reduced the expression levels of the PRV gB and gD proteins, and reduced PRV-induced apoptosis. Molecular docking showed that piceatannol had good binding activity with the PRV gB and gD proteins. The results of animal experiments in vitro indicated that piceatannol could delay the onset of disease, improve the survival rate of the mice (14.3%), reduce the viral load in the brain and kidney of the mice, alleviate the pathological changes in the mouse tissues and organs, and increase the levels of TNF-α, IEN-γ and IL-4 in the serum of the mice. These data indicate that piceatannol has good anti-PRV activity in vitro and in vivo, indicating that it could be a novel antiherpesvirus infection agent in the future.

With the introduction of policies to promote intangible cultural heritage into schools, intangible cultural heritage plays an increasingly important role in primary education. However, the implementation of the intangible cultural heritage policy in primary schools is mostly formalized, which leads to many problems in the introduction of intangible cultural heritage, such as mainly staying at the level of visual display and public opinion publicity, carrying out a small number of intangible cultural experience activities, and insufficient materialization and scenization of intangible cultural heritage, so that students cannot feel the inherent charm of intangible cultural heritage. In order to promote the development of intangible cultural heritage education, this paper uses the AGIL model of Parsons, literature analysis, case analysis and interview methods to study the promotion path of intangible cultural heritage in primary schools, and proposes solutions such as setting educational goals for school segments, setting up intangible cultural experience activities in schools, combining intangible cultural heritage with subject courses, and establishing a good maintenance mode. After the program is applied into practice, it is found that the use of storytelling by teachers to popularize the relevant knowledge of intangible cultural heritage is conducive to stimulating students' interest and prompting students to think. In addition, teaching and life practice should be combined, and students' five education should be emphasized at the same time to avoid teaching uniformity.

A series of penta-1,4-dien-3-one containing a H-phosphonate scaffold were designed and synthesized. The structures of all title compounds were determined by 1H-NMR, 13C-NMR, 31P-NMR, and HRMS. Bioassay results showed that several of the title compounds exhibited remarkable antibacterial and antiviral activities. Among these, compounds 3c and 3o exhibited substantial antibacterial activities against Xanthomonas oryzae pv. Oryzae (Xoo) and Xanthomonas axonopodis pv. citri (Xac). In addition, compounds 3c, 3f, and 3r showed remarkable curative activities against tobacco mosaic virus (TMV), with 50% effective concentration (EC50) values of 290.0, 234.0, and 373.6 μg/mL, respectively. These were superior to that of ningnanmycin (386.2 μg/mL). Compound 3r exhibited comparative protective activity against TMV, with an EC50 value of 291.1 μg/mL, which was better than that of ningnanmycin (297.1 μg/mL). Notably, the solubility of all title compounds improved relative to the lead compound curcumin. These results suggest that penta-1,4-dien-3-one containing a H-phosphonate scaffold may be considered as an activator for antibacterial and antiviral agents.

Trehalose is a non-reducing disaccharide and participates in physiological activities such as or-gan formation, energy metabolism, and stress resistance of insects. In the present study, phylo-genetic analysis divided 21 Bmtret1 orthologs into three clades. These genes are equally distrib-uted on the nine chromosomes. Cis-elements in the promoter regions of Bmtret1s indicated the possible function of Bmtret1s in response to hormones and environmental stimulus. The qPCR analysis showed the significantly different expression levels of Bmtret1s in different tissues and organs, indicating possible functional divergence. In addition, most Bmtret1s showed disturbed expression levels in response to BmNPV stresses. Our results provide a foundation for further functional dissection of Tret1s in Bombyx mori and implicate them as potential regulators for an-tiviral responses.

Polyphenols have been shown to possess outstanding anti-obesity properties. In this study, the effect of blue honeysuckle berry extract (BHBE) with high polyphenol content on lipid accumulation in adipocytes and the underlying mechanism were investigated for the first time. Composition analysis demonstrated that flavonoids (mainly flavonols and anthocyanins) were the primary polyphenols in BHBE, which contributed to its biological functions. The results of Oil Red O staining combined with triglyceride (TG) content determination showed that BHBE exhibited an obvious inhibitory effect on intracellular lipid accumulation in a dose-dependent manner. BHBE also reduced the protein level of fatty acid synthase (FAS) and increased the phosphorylation level of acetyl-CoA carboxylase (ACC), indicating that lipogenesis was suppressed by BHBE treatment. Moreover, BHBE was found to significantly promote the phosphorylation of AMP-activated protein kinase (AMPK) and further reduce the expression of key transcription factors (PPARγ, C/EBPα, and SREBP-1c) that regulate lipogenesis. In addition, the expression of beige adipocyte markers (Tmem26 and Cd137) and uncoupling protein 1 (UCP1) was increased in BHBE-treated adipocytes. In summary, we consider that BHBE inhibits lipid accumulation in adipocytes by suppressing lipogenesis as well as by promoting beiging of adipocytes. These results support blue honeysuckle berry as a candidate functional food against obesity.

Developing and utilising of underground space is an vital direction for urban growth. Underground commercial streets, as a significant component of underground space accommodating extensive human social activities, and consequently necessitate the creation of human-scale spaces. In the evolution of urban design development towards greater refinement, applying architectural theories and excessively subjective designs has resulted in a deficiency of human-centered design and a disordered spatial environment. This study merges environmental psychology and architectural theory to determine the appropriate length of spatial scale. Two experiments focusing on spatial perception evaluation were conducted using a virtual experimental platform that featured varying dimensions of spatial scale combinations. These quantified combinations were correlated with perception evaluation, and regression analysis was employed to identify appropriate scale ranges, which were superimposed on the range of length selection. Finally, the optimal length and scale combination for underground commercial street spaces were established, providing a reference for the human-centered design of these environments.

The images captured by UAVs during inspection often contain numerous small targets related to transmission lines, which are critical and vulnerable elements for ensuring the safe operation of these lines. However, due to various factors such as the small size of the targets, low resolution, complex background, and potential line aggregation, achieving accurate and real-time detection becomes challenging. To address these issues, this paper proposes a detection algorithm called P2-ECA-EIOU-YOLOv5 (P2E-YOLOv5). Firstly, in order to address the challenges posed by the complex background and environmental interference that impact small targets, an ECA attention module is integrated into the network. This module effectively enhances the network's focus on small targets while concurrently mitigating the influence of environmental interference. Secondly, considering the characteristics of small target size and low resolution, a new high-resolution detection head is introduced, which is more sensitive to small targets. Lastly, the network utilizes the EIOU-Loss as the regression Loss function to improve the positioning accuracy of small targets, as they tend to aggregate. Experimental results demonstrate that the proposed P2E-YOLOv5 detection algorithm achieves an accuracy P of 96.0% and an average accuracy (mAP) of 97.0% for small target detection in transmission lines.

In the present study, the corrosion resistance of amorphous coating and composite coatings in 3.5 wt.% NaCl, 0.5 M H₂SO₄ and 10 wt.% NaOH solution were studied. The composite coatings exhibit superior corrosion resistance. When the content of AT13 （Al₂O₃–13 wt.% TiO₂）was 15 wt.%, the composite coating has the lowest corrosion current density (1.75×10^-6 A cm^-2), which is 5.14×10^-5 A cm^-2 for Fe-based metallic glassy coating, and the highest corrosion potential (-411 mV), which is -580 mV for Fe-based metallic glassy coating. The breakdown potential of the passivation film in 3.5 wt.% NaCl solution was much higher than that of 316L.The long-time immersion corrosion tests carried out on different coatings showed that the corrosion protection effect of coating was enhanced with the increase of the amount of AT13 added.

Accurate forecast of tail water level (TWL) is of great importance for the safe and economic operation and management of hydropower stations. The predictive performance is significantly influenced by the backwater effect of downstream hydropower stations and tributaries, but the explicit quantification method of the backwater effect is lacked. In this study, a deep learning model based forecasting framework for TWL predictions is established and applied to forecast TWL of Xiangjiaba (XJB) hydropower stations, which is influenced by the backwater effect of downstream tributaries including Hengjiang River (HJR) and Minjiang River (MJR). Firstly, the lag time of the backwater effect of HJR and MJR is analyzed based on the permutation importance. The results demonstrate that the lag time of backwater effect on the TWL of XJB is 5-7 hours for the HJR and 1-2 hours for the MJR. Then, the runoff thresholds of the HJR and MJR for impacting the TWL of the XJB station are obtained by scenario comparison, and the results show that the thresholds of HJR and MJR are 700 m3/s and 7000 m3/s respectively. Finally, the deep learning methods based TWL forecasting model is established based on the lag time and threshold analysis. The model is used to forecast the TWL in future 48 hours. The results show that the forecasting model has a good predictive performance with 98.22% of absolute errors less than 20 cm. The mean absolute error over the validation dataset is 5.27 cm and the maximum absolute error is 63.35 cm.

Based on the data of A-share listed companies in Shanghai and Shenzhen of China from 2011 to 2018 and the digital inclusive finance index of Peking University (2011-2018), this paper empirically tests the impact of digital finance development on corporate social responsibility in various provinces of China and its impact mechanism. The results show that: (1) the development of digital finance helps to promote the fulfillment of corporate social responsibility; (2) the influence mechanism of the development of digital finance to promote the fulfillment of corporate social responsibility lies in that it reduces the cost of debt financing and leads to the improvement of corporate social responsibility.(3) Further research shows that the positive relationship between digital finance and corporate social responsibility is more significant in private enterprises. At the same time, the impact is more significant in areas with poor market environment. The above research shows that the development of digital finance has a significant positive effect, which will improve the level of corporate social responsibility.

With the improvement of software copyright protection awareness, code obfuscation technology plays a crucial role in protecting key code segments. As the obfuscation technology becomes more and more complex and diverse, it has spawned a large number of malware variants, which make it easy to evade the detection of anti-virus software. Malicious code detection mainly depends on binary code similarity analysis. However, the existing software analysis technologies are difficult to deal with the growing complex obfuscation technologies. To solve this problem, this paper proposes a new obfuscation-resilient program analysis method, which is based on the data flow transformation relationship of the intermediate representation and the graph network model. In our approach, we first construct the data transformation graph based on LLVM IR. Then, we design a novel intermediate language representation model based on graph networks, named DFSGraph, to learn the data flow semantics from DTG. DFSGraph can detect the similarity of obfuscated code by extracting the semantic information of program data flow without deobfuscation. Extensive experiments prove that our approach is more accurate than existing deobfuscation tools when searching for similar functions from obfuscated code.

In order to obtain stable super-hydrophobicity, suitable hydrophobic treatment agent should be selected according to different materials. In this paper, cotton and poly (-ethylene terephthalate) (PET) fabric was respectively coated by dodecyl methacrylate (LMA) via argon combined capacitively coupled plasma (CCP), and the surface hydrophobicity and durability of treated cotton and polyester fabrics were also discussed. An interesting phenomenon was happened that LMA coated cotton fabric (Cotton-g-LMA) had better water repellency and mechanical durability than LMA coated PET fabric (PET-g-LMA), and LMA coated hydroxyl grafted PET fabrics (PET fabrics were successively coated with polyethylene glycol (PEG) and LMA, PET-g-PEG&amp;LMA) had similar performance to those of cotton fabrics. The water contact angle (WCA) of Cotton-g-LMA, PET-g-LMA and PET-g-PEG&amp;LMA was 156 °, 153 ° and 155 °, respectively, and after 45 washing cycles or 1000 rubbing cycles, the corresponding WCA was decreased to 145 °, 88 °, 134 °and 146 °, 127 °, 143 °, respectively. Also, thermoplastic polyurethane (TPU) and polyamides-6 (PA6) fabrics were all exhibited the same properties to PET fabric. Therefore, the grafting of hydroxyl can improve the hydrophobic effect of LMA coating and the binding property between LMA and fabrics effectively without changing the wearing comfort..

Background: Many randomized controlled trials (RCTs) and systematic reviews (SRs) on acupuncture treatment for post-acute stroke dysphagia have been published. Due to conflicting results an overview of SRs to summarize and assess the quality of this evidence to determine whether acupuncture is effective for this disease was conducted. Methods: Seven databases were searched for SRs and/or Meta-analysis of RCTs and quasi-RCTs on acupuncture for post-acute stroke dysphagia. Two authors independently identified SRs and meta-analyses, collected data to assess the quality of included SRs and meta analyses according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and the revised Assessment of Multiple Systematic Reviews (AMSTAR 2). Results: 31 SRs were identified. Quality of 22 SRs was critically low, 5 SRs were low, and 4 Cochrane SRs were moderate when evaluated by AMSTAR2. 17 SRs reported 85.2-96.3% items of PRISMA. Five SRs included explanatory RCTs, 16 SRs included pragmatic RCTs, and 10 SRs included both. Conclusion: Currently evidence on the effectiveness of acupuncture on post-acute stroke dysphagia is low quality. Type of study appeared to have no direct influence on the result, but the primary outcome measures showed a relationship with the quality of SRs. High quality trials with large sample sizes should be the focus of future research. PROSPERO REGISTRATION NUMBER: CRD42019134163

Traditional numerical techniques such as sliding mesh, dynamic grid mesh, and others, have many limitations in dealing with flow simulation with large-scale movement of solid boundaries; which is the case for complex-shaped hydraulic turbomachinery such as propellers, pumps and turbines. The immersed boundary (IB) method provides a new approach to solve the above limitations. Therefore, this study proposes an sharp-interface IB method based on the level-set function that is suitable for simulating the flow through turbomachinery with complex geometries. This method is applied to actual three-dimensional numerical simulations of high-Reynolds number propellers using an in-house computational fluid dynamics solver. The results show that the proposed method can provide comparatively accurate predictions of unsteady load coefficients within the propeller flow passage, and capture the correct propeller wake characteristics as well as the interaction between the propeller wake and free surface. This study aims at providing a theoretical basis and engineering reference for the application of the IB method in engineering numerical simulations.

In recent years, there has been a growing interest in remote sensing image-text cross-modal retrieval due to the rapid development of space information technology and the significant increase in remote sensing image data volume. One approach that has shown promising results in cross-modal retrieval of natural images is the multimodal fusion encoding method. However, remote sensing images have unique characteristics that make the retrieval task challenging. Firstly, the semantic features of remote sensing images are fine-grained, meaning they can be divided into multiple basic units of semantic expression. Additionally, these images exhibit variations in resolution, color, and perspective. Different combinations of basic units of semantic expression can generate diverse text descriptions. These characteristics pose considerable challenges for cross-modal retrieval. To address these challenges, this paper proposes a multi-task guided fusion encoder (MTGFE) based on the multimodal fusion encoding method. The model incorporates three tasks: image-text matching (ITM), masked language modeling (MLM), and the newly introduced multi-view joint representations contrast (MVJRC) task. By jointly training the model with these tasks, we aim to enhance its capability to capture fine-grained correlations between remote sensing images and texts. Specifically, the MVJRC task is designed to improve the model’s consistency in feature expression and fine-grained correlation, particularly for remote sensing images with significant differences in resolution, color, and angle. Furthermore, to address the computational complexity associated with large-scale fusion models and improve retrieval efficiency, this paper proposes a retrieval filtering method. This method achieves higher retrieval efficiency while minimizing accuracy loss. Extensive experiments were conducted on four public datasets to evaluate the proposed method, and the results validate its effectiveness. Overall, this study focuses on remote sensing image-text cross-modal retrieval and introduces the MTGFE model, which combines multimodal fusion encoding with multiple tasks to enhance the model’s ability to capture fine-grained correlations. Additionally, a retrieval filtering method is proposed to improve retrieval efficiency. Experimental results demonstrate the effectiveness of the proposed method.

Recently, ice with the stacking disorder structure, consisting of random sequences of cubic ice (Ic) and hexagonal ice (Ih) layers, is reported to be more stable than pure Ih/Ic. While, due to a much lower free energy barrier of heterogeneous nucleation, in practice, the freezing process of water is usually controlled by heterogeneous nucleation which is triggered by an external medium. Herein, molecular dynamic simulations were carried out to explore the polymorph dependence of ice on the lattice structure of substrates. It turns out that, during the nucleation stage, the polymorph of ice nuclei can be severely altered by the graphene substrate, on which the Ih was found to occupy an absolute majority in new-formed ice. This can be attributed to the structure similarity between graphene and basal face of Ih. Besides the nucleation stage, our results suggest that the substrate can not affect the polymorph of ice which is far from the graphene surface. The polymorph selectivity of graphene to Ih will diminish with the growth of ice layer.

The working environment of mining electric locomotives is wet and muddy coal mine 1 roadway. There may be idling or slipping between the wheels and rails of mining electric locomotives 2 due to low friction between the wheel and rail and insufficient utilization of creep rate. Therefore, it 3 is necessary to control the creep rate within a reasonable range. In this paper, the autonomous control 4 algorithm for mining electric locomotives based on improved ε-greedy is theoretically proven to be 5 convergent and effective firstly. Secondly, after analyzing the contact state between the wheel and rail 6 under wet and slippery road conditions, it was concluded that the value of creep rate is an important 7 factor affecting the autonomous driving of mining electric locomotives. Therefore, the autonomous 8 control method for mining electric locomotives based on creep control is proposed in this paper. 9 Finally, the effectiveness of the proposed method was verified through simulation. The problem of 10 wheel slipping and idling caused by insufficient friction of mining electric locomotives in coal mining 11 environments is effectively suppressed. Autonomous operation of vehicles with optimal driving 12 efficiency can be achieved through quantitative control and utilization of the creep rate between 13 wheels and rails

Chronic heat stress (CHS) has been exerting great pressure on the poultry industry due to the global warming. CHS-induced negative impacts deeply change the physiology, metabolism, egg production and meat quality of ducks. To reduce the increasing economic loss, different environmental strategies have been adopted. Here, we comprehensively compared three rearing modes for Cherry-Valley ducks (CVds) mainly by changing the environmental temperature. Plus, the transcriptome analysis and physiological anatomy were implied to investigate the underlying molecular biology and pneumonocyte dynamics. It turns out that the CVds affected by CHS have poor growth performance and bad carcass traits. In addition, the irreversible damage caused by CHS in the lung tissue of CVds was observed. Furthermore, several potential genes related to CHS have been found, such as HSPA8, IGF1, FGFR1, ACSL3, ACSL6, NR1H3 and GAPDH. This study strongly indicates the severe injury of CHS from the high-temperature environment and points out one of the adopting directions for duck rearing.

Nowadays, it is highly desired to develop highly active and humidity resistive ozone decomposition catalyst to eliminate the ozone contaminant, one of the primary pollutants in air. In this work, a series of Cu2S hollow structured materials are rapidly synthesized using different structured Cu2O templates. The Cu2S from porous Cu2O shows the highest ozone catalytic decomposition efficiency of >95% to 400 ppm ozone with a weight hourly space velocity of 480,000 cm3g-1h-1 in dry air. Importantly, the conversion still keeps >85% in a high relative humidity of 90%. The mechanism is explored by diffusive reflectance infrared spectroscopy which shows the decomposition intermediate of O22-, and the X-ray photoelectron spectroscopy reveals the dual active site of both Cu and S. All these results show the effective decomposition of ozone by Cu2S especially in harsh environments, promising for active ozone elimination.

The shear stress generated by wall turbulence is the main cause of wall friction resistance in turbulent flow through pipes. This paper investigates the impact of inserting rest periods (regions of constant Reynolds number) within the pulsating operating cycle of velocity on the fully turbulent flow at large time-averaged Reynolds numbers, using the Large Eddy Simulation (LES) method. The study aims to explore the effect of increasing rest periods within pulsations on drag resistance. The dimensionless shear stress and drag reduction rates during different time periods of rest were analyzed. Numerical simulation results indicate that the pulsating velocity operation mode does not necessarily lead to drag reduction; it may even result in increased resistance. Inserting rest periods within the pulsation cycle can achieve drag reduction effects, with the maximum drag reduction rate reaching 21.8\%. Comparisons with experimental data and Direct Numerical Simulation (DNS) from the literature validate the feasibility of using the LES method for pipe pulsating operation modes.

Characterization of cancer cells and neural stem cells indicates that tumorigenicity and pluripotency are coupled cell properties determined by neural stemness, and tumorigenesis represents a process of progressive loss of original cell identity and gain of neural stemness. This reminds of a most fundamental process required for the development of the nervous system and body axis during embryogenesis, i.e., embryonic neural induction. Neural induction is that, in response to extracellular signals that are secreted by the Spemann-Mangold organizer in amphibians or the node in mammals and inhibit epidermal fate in ectoderm, the ectodermal cells lose their epidermal fate and assume the neural default fate and consequently, turn into neuroectodermal cells. They further differentiate into the nervous system and also some non-neural cells via interaction with adjacent tissues. Failure in neural induction leads to failure of embryogenesis, and ectopic neural induction due to ectopic organizer or node activity or activation of embryonic neural genes causes a formation of secondary body axis or a conjoined twin. During tumorigenesis, cells progressively lose their original cell identity and gain of neural stemness, and consequently, gain of tumorigenicity and pluripotency, due to various intra-/extracellular insults in cells of a postnatal animal. Tumorigenic cells can be induced to differentiation into normal cells and integrate into normal embryonic development within an embryo. However, they form tumors and cannot integrate into animal tissues/organs in a postnatal animal because of lack of embryonic inducing signals. Combination of studies of developmental and cancer biology indicates that neural induction drives embryogenesis in gastrulating embryos but a similar process drives tumorigenesis in a postnatal animal. Tumorigenicity is the manifestation of aberrant occurrence of pluripotent state in a postnatal animal. Pluripotency and tumorigenicity are both but different manifestations of neural stemness in pre- and postnatal stage, respectively, of animal life. The unique property of neural stemness is derived from the evolutionary advantage of neural genes and the neural-biased state of the last common unicellular ancestors of metazoan. Based on these findings, I discuss about some confusion in cancer research, propose to distinguish the causality and associations and discriminate causal and supporting factors involved in tumorigenesis, and suggest revisiting the focus of cancer research.

Since the implementation of the Materials Genome Project by the Obama administration in the United States, the development of various computational materials databases has fundamentally expanded the choices of industries such as materials and energy. In the field of thermoelectric materials, the thermoelectric figure of merit ZT quantifies the performance of the material. From the viewpoint of calculations for vast materials, the ZT values are not easily obtained due to their computational complexity. Here, we show how to build a database of thermoelectric materials based on first-principles calculations for the electronic and heat transport of materials. Firstly, the initial structures are classified according to the values of bandgap and other basic properties using the clustering algorithm K-means in machine learning, and high-throughput first principles calculations are carried out for narrow-bandgap semiconductors which exhibiting potential thermoelectric application. The present framework of calculations mainly includes deformation potential module, electrical transport performance module, mechanical and thermodynamic properties module. We have also set up a search webpage for the calculated database of thermoelectric materials, providing searching and viewing the related physical properties of materials. Our work may inspire the construction of more computational databases of first-principle thermoelectric materials and accelerate research progress in the field of thermoelectrics.

The molecular clusters ((HO)3Si-O-Si(OH)3, and (HO)3Al-O-Si(OH)3 ) representative of aluminosilicate mineral surface were employed to study the dissolution of aluminosilicate in acidic condition via density functional theory with the M06-2X+G(d,p) methodology. The surface termination sites (Si and Al) were both tetra-coordinated and the terminal oxygen was protonated in acidic condition. In the dissolution reaction, the calculated barrier height of the six-membered ring transition state complex containing two water molecules was predicted to be lower than that of four-membered ring transition state complex containing one water molecule. In addition, the calculated barrier heights for Al-terminated sites were predicted to be lower than those for the Si-terminated sites, suggesting that breaking the Al-O bond is easier than Si-O bond in the aluminosilicate mineral surface; the barrier heights of the surface termination sites with protonated terminal oxygen were lower than those without protonated terminal oxygen. With the fracture of Si-O and Al-O bonds, the Si and Al release from the aluminosilicate. These results confirm the experimental conclusions that the acidic condition facilitates the release of Si and Al from the aluminosilicate, and the concentration of Al leaching from aluminosilicate is higher than Si.

Background: It remains unclear whether transfer RNA-derived small RNAs (tsRNAs) play a role in pathological cardiac hypertrophy (PCH). We aimed to clarify the expression profile of tsRNAs and disclose their relationship to the clinical phenotype of PCH and the putative role. Methods: Small RNA sequencing was performed in the plasma of PCH patients and healthy volunteers. In a larger sample size and angiotensin â…¡ (Ang II)-stimulated H9c2 cells, the data were validated by real-time qPCR. The atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP) were examined in Ang II-stimulated H9c2 cells. The role of tsRNAs in the pathogenesis of PCH was explored by bioinformatics analysis. Results: A total of 4185 differentially expressed tsRNAs were identified, of which 4 and 5 tsRNAs were observed to be significantly differentially upregulated and downregulated expressed. Of the 5 down-regulated tsRNAs, 4 of them were verified to be significantly down-regulated in the larger sample group, among which tRF-30-3JVIJMRPFQ5D, tRF-16-R29P4PE, tRF-21-NB8PLML3E, and tRF-21-SWRYVMMV0 had areas under the curve to diagnose concentric hypertrophy. The 4 down-regulated tsRNAs were negatively correlated with left ventricular posterior wall dimensions in PCH patients (r=-0.4227; r=-0.4517; r=-0.5567; r=-0.4223). The levels of ANP and BNP as well as cell size were decreased in Ang II-stimulated H9c2 cells with 21-NB8PLML3E mimic transfection. Bioinformatics analysis revealed that the target genes of tRF-21-NB8PLML3E were mainly enriched in the metabolic pathway and involved in the regulation of ribosomes. Conclusion: The plasma tsRNAs tRF-21-NB8PLML3E might be considered biomarkers in patients with PCH with early screening potential.

In aerospace, robotics, network engineering and mechanical engineering, there exist complex and urgent challenges that can be abstracted into mathematical optimization problems. Therefore, exploring solutions to these optimization problems is a crucial task. Metaheuristic algorithms have been applied across various fields.The Snake Optimization Algorithm (SO) is an innovative metaheuristic method recognized for its efficient solving capabilities.However, this algorithm encounters challenges such as diminished search efficiency in the later stages and a propensity to become ensnared in local optima. To mitigate these issues, this paper introduces an enhanced Snake Optimization Algorithm (ISO). ISO integrates the RIME Algorithm(RIME)and introduces two effective enhancement strategies to improve its capability to evade local optima.To assess the effectiveness of ISO,the population distribution state was first measured using three algorithms, including Star Discrepancy, and the exploration and exploitation performance was analyzed using the CEC2017 test functions. Subsequently, utilized 23 benchmark functions for testing.The evaluation outcomes indicate that ISO exhibits outstanding performance in terms of convergence velocity and robustness.Additionally, it was applied to engineering fields including UAV path planning,Robot path planning, Wireless sensor network node deployment and Pressure vessel design.In comparison to the SO algorithm, the ISO algorithm demonstrates superior stability, converges earlier, and improves its ability to solve for optimal .These findings highlight the significant potential of the ISO algorithm in diverse test functions and a wide range of interdisciplinary problems.

This paper comprehensively explores the integration of cloud computing and advanced recommendation systems, emphasizing their pivotal roles in enhancing user experiences and operational efficiencies across digital platforms. It reviews the evolution of recommendation algorithms, highlighting their application in diverse domains such as e-commerce and media. The study evaluates the performance of advanced models like UniLLMRec against traditional counterparts using datasets from news and e-commerce domains. Additionally, the paper discusses the infrastructure architecture of cloud computing, demonstrating its capability to support scalable and efficient data processing. Through experimental insights and methodology, the research underscores the transformative impact of cloud technologies on optimizing recommendation system performance, thereby advancing digital engagement and competitiveness.

In cosmology, the cosmic curvature $K$ and the cosmological constant $\Lambda$ are two most important parameters, whose values have strong influence on the behavior of the universe. By analyzing the energy-momentum tensor and equations of state of ideal gas, scalar, spinor and vector potential in detail, we find that the total mass density of all matter is always positive, and the initial total pressure is negative. Under these conditions, by qualitatively analyzing the global behavior of the dynamical equation of cosmological model, we get the following results: (i) $K= 1$, namely, the global spatial structure of the universe should be a 3-dimensional sphere $S^3$. (ii) $0\le\Lambda < 10 ^ {-24} {\rm ly} ^ {-2}$, the cosmological constant should be zero or an infinitesimal. (iii) $a(t)>0$, the initial singularity of the universe is unreachable, and the evolution of universe should be cyclic in time. This means that the initial Big Bang is impossible at all. Since the matter components considered are quite complete and the proof is very elementary and strict, these logical conclusions should be quite reliable. Obviously, these conclusions will be much helpful to correct some popular misconceptions and bring great convenience to further research other problems in cosmology such as property of dark matter and dark energy.

The description of the microscopic world in quantum mechanics is very different from that in classical physics, and there are some points of view that are contrary to intuition and logic. The first is the loss of reality, the behavior of micro particles shows randomness and hopping. The second is the loss of certainty, the conjugate physical variables of a system cannot be determined synchronously, they satisfy the Heisenberg uncertainty principle. The third is the non-local correlation. The measurement of one particle in the quantum entanglement pair will change the state of the other entangled particle simultaneously. In this paper, some concepts related to quantum entanglement, such as EPR correlation, quantum entanglement correlation function, Bell's inequality and so on, are analyzed in detail. Analysis shows that the mystery and confusion in quantum theory may be caused by the logical problems in its basic framework. Bell's inequality is only a mathematical theorem, but its physical meaning is actually unclear. The Bell state of quantum entangled pair may not satisfy the dynamic equation of quantum theory, so it cannot describe the true state of microscopic particles. In this paper, the correct correlation functions of spin entanglement pair and photonic entanglement pair are strictly derived according to normal logic. Quantum theory is a more fundamental theory than classical mechanics, and they are not parallel relation in logic. However, there are still some unreasonable contents in the framework of quantum theory, which need to be improved. In order to disclose the real relationship between quantum theory and classical mechanics, we propose some experiments which show the wave-particle duality simultaneously and provide intuitionistic teaching materials for the new interpretation of quantum theory.

Tumorigenesis is a complex biological phenomenon that includes extensive genetic and phenotypic heterogeneities and complicated regulatory mechanisms. In the recent few years, our studies demonstrate that tumor-initiating cells are similar to neural stem/progenitor cells in regulatory networks, tumorigenicity and pluripotent differentiation potential. In the review, I will make further discussion on these observations and propose a rule of cell biology by integrating these findings with evidence from developmental biology, tumor biology and evolution, which suggests that neural stemness underlies two coupled cell properties, tumorigenicity and pluripotent differentiation potential. Tumorigenicity and phenotypic heterogeneity in tumor is a result of acquirement of neural stemness in cells. The neural stemness property of tumor-initiating cells can hopefully integrate different concepts/hypotheses underlying tumorigenesis. Neural stem cells/neural progenitors and tumor-initiating cells share regulatory networks; both exhibit neural stemness, tumorigenicity and differentiation potential; both are dependent on expression or activation of ancestral genes (the atavistic effect); both rely primarily on aerobic glycolytic metabolism; both can differentiate into various cells or tissues that are derived from three germ layers, resembling severely disorganized or more severely degenerated process of embryonic development; both are enriched in long genes with more splice variants that provide more plastic scaffolds for cell differentiation, etc. The property of neural stemness might be a key point to understand tumorigenesis and pluripotent differentiation potential, and possibly explain certain pathological observations in tumors that have been inexplicable. Therefore, behind the complexity of tumorigenesis might be a general rule of cell biology, i.e., neural stemness represents the ground state of cell tumorigenicity and pluripotent differentiation potential.

The magnetic field of the earth plays an important role in the ecosystem, and the magnetic field of celestial bodies is also important in the formation of cosmic large-scale structures, but the origin and evolution of the celestial magnetic field is still an unresolved mystery. Many hypotheses to explain the origin have been proposed, but there are some insurmountable difficulties for each one. At present, the theory widely accepted in scientific society is the dynamo model, it says that, the movement of magnetofluid inside celestial bodies, which can overcome the Ohmic dissipative effect and generate persistent weak electric current and macroscopic magnetic field. However, this model needs an initial seed magnetic field, and the true values of many physical parameters inside the celestial body are difficult to obtain, and there is no stable solution to the large range of fluid motion. These are all difficulties for the dynamo model. Furthermore, it is difficult for the dynamo to explain the correlation between the dipole magnetic field and angular momentum of a celestial body. In this paper, by calculating the interaction between spin of particles and gravity of celestial body according to Clifford algebra, we find that a rotational celestial body provides a field $\Omega^\alpha$ for spins, which is similar to the magnetic field of a dipole, and the spins of charged particles within the celestial body are arranged along the flux line of $\Omega^\alpha$, then a macroscopic magnetic field is induced. The calculation shows that the strength of $\Omega^\alpha$ is proportional to the angular momentum of the celestial body, which explains the correlation between the magnetic intensity and angular momentum. The results of this paper suggest that further study of the effects of internal variables such as density, velocity, pressure and temperature of a celestial body on $\Omega^\alpha$ may provide some new insights into the origin and evolution of celestial magnetic field.

This paper draws upon the theory of turbulence in physics to explore the similarities and differences between volatility in financial markets and turbulent phenomena at a statistical physics level. By analogizing the dynamics of financial markets with fluid turbulence, we have developed an innovative analytical framework aimed at deepening the understanding of the complexity inherent in financial markets. Our research methodology includes a comparative analysis of simulated time series data for the Standard & Poor's 500 Index and fully developed turbulent velocity time series. We focus on the similarities in key statistical physical properties, including probability distributions, correlation structures, and power spectral densities. Furthermore, we have established a model of capital flow in financial markets and proposed corresponding solutions. Through computational simulations and data analysis, we find that while financial market volatility shares some statistical characteristics with turbulent phenomena, significant differences exist in the shapes of probability distributions and the temporal scales of correlations. This suggests that although financial markets exhibit patterns akin to turbulence, the behavior of this complex, multi-variable driven system does not completely correspond to natural turbulence, highlighting the limitations of directly applying turbulence theory to financial market analysis. Our study offers a new perspective for the theories of financial markets and the field of econophysics, providing fresh insights into the complexity of financial markets and contributing to the prevention and management of financial risk.

This study establishes a multivariate function model for natural human load carrying walking in some typical scenarios like college equipments and materials relocation for students. For classified materials relocation needs and constraints, we obtain the relationship between walking speed and load weight for single person, as well as the time cost for different round trips. By establishing an integer programming model with the minimum total transportation time cost and quality assurance as the objective function and the requirements of negative weight and speed as the constraints conditions, we reach the optimal item allocation methods considering time cost and shelf life were obtained. We discover that there is an approximate linear relationship between the change in natural walking speed and travel time when the load is small, thus obtaining the time cost of student transportation under different round-trip situations. The Monte Carlo simulation algorithm is used to obtain the optimal allocation scheme that meets the efficiency and quality requirements. The analysis methods and results can be used as guidance for task scheduling optimization for material relocation in educational organizations as well as commercial agencies.

The Fengcheng Formation in the Mahu Sag of the Junggar Basin was generally composed of detritus, pyroclastic material, carbonates, and evaporites. During the process of sedimentation, the climate was arid or semi-arid. Due to the effect of high concentration and sodium-rich source, an alkaline lake was formed, and alkaline minerals were also preserved in the stratum. After the sediments were buried, three mineral assemblages were formed in the Fengcheng Formation, which are carbonate mineral assemblages (i.e., calcite + ferreous dolomite), reedmergnerite and carbonate mineral assemblages (i.e., reedmergnerite + calcite + ferreous dolomite), and reedmergnerite and alkaline mineral assemblages (i.e., reedmergnerite + shortite + trona), respectively. According to the homogenization temperature of reedmergnerite primary fluid inclusions, alkaline diagenesis of Fengcheng Formation is divided into early stage（≤100℃） and middle stage（＞100℃）, respectively. The former is characterized by the generation of ferreous saddle dolomite, quartz dissolution, and agglutination of the laumontite, which occurred under normal burial conditions. The latter is marked by the reedmergnerite’s appearance, which is correlated to deep hydrothermal activity controlled by faults. Finally, based on sedimentary and diagenetic factors, including climate, provenance, diagenetic surroundings, and the action of subsurface fluid, the alkaline deposition-diagenesis model for shale series in four stages of the Fengcheng Formation was established.

Carbazole is one of the typical heterocyclic aromatic compounds (NSO-HETs) observed in pol-luted urban atmosphere, which has become a serious environmental concern. The most im-portant atmospheric loss process of carbazole is the reaction with OH radical. The present work investigated the mechanism of OH-initiated atmospheric oxidation degradation of carbazole by using density functional theory (DFT) calculations at the M06-2X/6-311++G(3df,2p)//M06-2X/6-311+G(d,p) level. The rate constants were determined by the Rice-Ramsperger-Kassel-Marcus (RRKM) theory. The lifetime of carbazole determined by OH was compared with other typical NSO-HETs. The theoretical results show that the degradation of carbazole initiated by OH radical includes four types of reactions: OH additions to “bend” C atoms, OH additions to “benzene ring” C atoms, H abstractions from C-H bonds and the H ab-straction from N-H bond. The OH addition to C1 atom and the H abstraction from N-H bond are energetically favorable. The main oxidation products are hydroxycarbazole, dialdehyde, carba-zolequinone, carbazole-ol, hydroxy-carbazole-one and hydroperoxyl-carbazole-one. The calcu-lated overall rate constant of carbazole oxidation by OH radical is 6.52 × 10−12 cm3 molecule−1 s−1 and the atmospheric lifetime is 43.92 h under the condition of 298 K and 1 atm. The lifetime of carbazole determined by OH radical is similar with that of dibenzothiophene oxidation but longer than those of pyrrole, indole, dibenzofuran and fluorene. This work provides a better understanding of the reactivity of carbazole in the atmospheric environment, the formation of secondary organic aerosols, and the chemical degradation and removal of carbazole in the at-mospheric environment.

In this paper, the sol−gel method was used to synthesize powders of LaMnO3 (LMO), La0.85Ca0.15MnO3 (LCM), and La0.85Sr0.15MnO3 (LSM). We investigated the impact of substituting Ca and Sr at the A−site on the perovskite crystal structure and electrochemical capabilities of LMO. LCM retained its orthogonal structure in comparison to the parent LMO components, whereas LSM transitioned to a rhombic structure. At 0.5 A/g, the specific capacitance of LCM and LSM electrodes is 185.5 F/g and 248 F/g, respectively.The specific capacitance of LCM was more three times than that of the LMO electrode. Among the three samples (LMO, 22.25 m2 g−1; LSM, 31.56 m2 g−1), the LCM sample exhibited the highest specific surface area of 38.79 m2 g−1. The charge transfer resistances of the LMO, LCM, and LSM are 0.48 Ω, 0.36 Ω, and 0.38 Ω, correspondingly. The LCM electrode exhibits the greatest capacitance performance due to its more refined morphology, increased concentration of oxygen vacancy, and more complete utilization of the perovskite bulk structure. The above results demonstrate that Ca or Sr substitution of A−site compounds has great potential for supercapacitor applications.

Bearing fault diagnosis is crucial for ensuring the stable operation of mechanical equipment. With the continuous development of deep learning technology, Convolutional Neural Networks (CNNs) have demonstrated significant advantages in the field of fault diagnosis. This paper proposes a new method that combines various CNN architectures to improve the accuracy of bearing fault diagnosis. We designed five different convolutional network structures, including SerConv, ResConv, One-Shot Aggregation Convolution (OSAConv), Cross-Stage Aggregation Convolution (CSAConv), and MD-DAConv. Experimental results on the Case Western Reserve University (CWRU) bearing dataset show that the proposed method exhibits high accuracy and robustness in fault diagnosis. The results indicate that strategies such as multi-directional, multi-scale, and residual connections play a crucial role in enhancing the depth and breadth of feature extraction, while simple and effective feature fusion and information transmission mechanisms are key to ensuring the robustness and generalization ability of the model.

Vaccination has proven highly effective against severe acute res-piratory syndrome coronavirus 2 (SARS-CoV-2), but the long-term immunogenicity and the functional preserved im-mune responses of vaccines are needed to inform evolving evi-dence-based guidelines for boosting schedules. We enrolled 205 healthcare workers into a cohort study; all had received three doses of BBIBP-CorV (China Sinopharm Bio-Beijing Company) inactivated vaccine. We assessed SARS-CoV-2 specific binding antibodies, neutralizing antibody, and peripheral T and B cell responses. We demonstrated that more robust antibody re-sponses to SARS-CoV-2 were elicited by booster immunization compared to primary vaccination. Neutralizing antibody titers to SARS-CoV-2 Omicron variant were also efficiently elevated post homologous vaccine booster despite in a lower titer compared to the prototype stain. In addition to S specific humoral and cellular immunity, BBIBP-CorV also induced N-specific antibody and ef-fector T cell responses. The third-dose vaccination led to further expansion of critical polyfunctional T cell responses, likely an essential element for vaccine protection. In particular, a func-tional role for Tfh cell subsets in immunity was suggested by the correlation between both CD4+Tfh and CD8+Tfh with total anti-body, IgG, B cell responses and neutralizing antibodies. Our study details the humoral and cellular responses generated by the BBIBP-CorV booster vaccination in a seven-month follow up study. There is a clear immunologic boosting value of homolo-gous inactivated SARS-CoV-2 vaccine boosters, a consideration for future vaccine strategies.

Currently, urban renewal activities in China are booming. And promoting the renovation of public buildings is the key due to its large scale, high cost and significant impact to the nature and social environment. To reduce the ambiguity and uncertainty in evaluating the potential for renewal of existing public buildings, a renewal potential evaluation model combining game theory combination weighting method and cloud model theory is proposed. The paper constructs a comprehensive evaluation index system based on relevant standards and literature. Game theory is used to optimize the weights obtained by AHP and entropy weight methods to obtain a combined weight. MATLAB programming is used to calculate the comprehensive cloud parameters of the evaluation index for the potential renewal of existing public buildings and therefore generate cloud Graphs. Through case study in Nanjing of China, it has been demonstrated that the combination empowerment cloud model can objectively reflect the relationship between the fuzziness and randomness of evaluation indicators for public building renewal potential. The expression of cloud Graphs can intuitively reflect the magnitude of renewal and renovation potential and the degree of uncertainty in evaluation results. The research result provides useful references for the sustainable utilization of building resources in the era of building.

MADS-box genes play a crucial role in fruit ripening, yet limited research has been conducted on mango. Based on the conserved domains of this gene family, 84 MADS-box genes were identified in the mango genome, including 22 type I and 62 type II MADS-box genes. Gene duplication analysis revealed that both tandem duplication and segmental replication significantly contributed to the expansion of MADS-box genes in the mango genome, with purifying selection playing a vital role in the segmental duplication events within the MiMADS gene family. Cis-acting element analysis demonstrated that most MiMADS genes were hormonally regulated and participated in the growth, development, and stress resistance of mango fruit. Moreover, through expression pattern analysis and phylogenetic tree construction, we identified six MiMADS genes belonging to the SEP1 subfamily and two belonging to the AG subfamily as potential candidates involved in mango ripening regulation. Notably, Mi08g17750 and Mi04g18430 from the SEP1 subfamily were identified as key regulators inhibiting mango fruit maturation; their interaction network was also analyzed. These findings provide a foundation for further investigation into the regulatory mechanisms underlying mango ripening.

The fungal family Microthyriaceae is represented by relatively few mycelial cultures and DNA sequences. As a result, the taxonomy and classification of this group of organisms remain poorly understood. Here, based on DNA sequences at four gene fragments (nuLSU rDNA, nuSSU rDNA, TEF1 and RPB2) in our analyses of aquatic hyphomycetes from southern China, we identify and report four new genera (Antidactylaria, Isthmomyces, Keqinzhangia, Pseudocoronospora) and thirteen new species (Antidactylaria minifimbriata, Pseudocoronospora hainanensis, Isthmomyces oxysporus, I. dissimilis, I. macrosporus, I. relanceatus, Keqinzhangia aquatica, Triscelophorus anakonajensis, T. anisopterioides, T. guizhouensis, T. mugecuoensis, T. multibrachiatus, T. neoseptatus; new combinations Isthmomyces asymmetrica, I. basitruncata, I. geniculata, I. lanceata, I. minima, I. rotundata) belonging to Microthyriaceae. Our results provided the first molecular evidence of asexual morph of this family and strengthened the phylogenetic placement of the family in class Dothideomycetes. The addition of these new taxa made Microthyriaceae the largest family comprising freshwater asexual genera in Pleosporomycetidae. In addition, we confirmed the monophyly of the genus Triscelophorus, the paraphyly of the genus Isthmolongispora, and revised 6 new combinations in Isthmolongispora. ITS barcoding of 13 species were also provided to help identify aquatic hyphomycetes in the future. Our results suggest that the asexual genera and sexual genera identified so far within this family have completely different ecological niches.

This study examines the differential impact of proactive and reactive job crafting behaviors on job well-being, drawing from the perspective of boundary theory. The increasing popularity of work connectivity behavior after-hours (WCBA) after work has attracted widespread attention from scholars on the relationship between WCBA and employee well-being. One view suggests that the impact of WCBA is negative, while another view suggests it is positive. Obviously, the impact of (WCBA) on well-being at work is still contradictory. To clarify the complexity of the impact of WCBA on well-being at work, based on boundary theory, we divided WCBA into proactive WCBA (PC) and reactive WCBA (RC), and examined the double-edged sword effect of WCBA on well-being at work, as well as the mediating mechanisms of job control and work-to-home conflict, and the moderated effects of boundary segmentation preferences. This study uses empirical sampling method to collect data from 125 employees for a period of five days for quantitative research. The results show that, first, PC has an inverted U-shaped effect on job control, and job control is negatively related to well-being at work. Thus the mediating effect of job control is significant. Second, RC has a negative impact on job control, and there is also a negative relationship between job control and well-being at work. Therefore, the mediating effect of job control is significant. Third, PC and RC are positively correlated with work-to-home conflict, and work-to-home conflict has a significant positive impact on well-being at work. Therefore, the mediating effect of work-family conflict is significant. Fourth, the study also found that integration preference moderates the relationship between work-to-home conflict and PC on well-being at work, that is, the mediating effect of work-to-home conflict is stronger for employees with low integration preference. This study enriches our understanding of WCBA, PC and RC from the perspective of proactive and passive employee behaviors. The study also provides a new interpretation of the impact of WCBA on well-being at work and offers valuable insights for the sustainable development in digital social transformation and the application of boundary theory and the theory of empowerment-subjugation in achieving the United Nations Sustainable Development Goals. In addition, the study deepens our understanding of the heterogeneous regulatory role played by work-family integration preferences in influencing well-being at work under different types of WCBA.

Four previously undescribed highly oxygenated diterpenoids (1–4), zeylleucapenoids A–D, characterized by halimane and labdane skeletons, were isolated from the aerial parts of Leucas zeylanica. Their structures were elucidated primarily by NMR experiments. The absolute configuration of 1 was established by theoretical ECD calculations and X-ray crystallographic analysis, whereas those for 2−4 were assigned by theoretical ORD calculations. Zeylleucapenoid D (4), with an IC₅₀ value of 38.45 μM in RAW264.7 macrophages and nontoxic activity for zebrafish embryo, and obviously inhibited pro-inflammatory cytokines TNF-α and IL-6 in a dose-dependent manner. Compound 4 inhibited of the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2). Furthermore, molecular docking analysis indicated the possible mechanism of action for 4 may be bind to targets by hydrogen- and hydrophobic- bond interactions.

Electrically conductive polymer composites are in high demand for modern technologies, however, the intrinsic brittleness of conducting conjugated polymers and the moderate electrical conductivity of engineering polymer/carbon composites have highly constrained their applications. In this work, super high electrical conductive polymer composites were produced by a novel hot embossing design. The polydimethylsiloxane (PDMS) composites containing short carbon fiber (SCF) exhibited an electrical percolation threshold at 0.45 wt%, and reached a saturated electrical conductivity of 49 S/m at 8 wt% of SCF. When reduced the sample thickness from 1.0 mm to 0.1 mm by the hot embossing process, a compression-induced percolation threshold occurred at 0.3 wt%, while the electrical conductivity was further enhanced to 378 S/m at 8 wt% SCF. Furthermore, the additional of a second nanofiller of 1 wt%, such as carbon nanotube or conducting carbon black further increased the electrical conductivity of the PDMS/SCF (8 wt%) composites to 909 S/m and 657 S/m, respectively. The synergy of the densified conducting filler network by the mechanical compression and the hierarchical micro-/nanoscale filler approach has realize super high electrical conductive yet mechanical flexible polymer composites for modern flexible electronics applications.

This study presents a vibration control using actively tunable vibration absorbers (ATVA) to suppress vibration of a thin plate. The ATVA’s is made of a sandwich hollow structure embedded with the electrorheological fluid (ERF). ERF is considered to be one of the most important smart fluids and it is suitable to be embedded in a smart structure due to its controllable viscosity property. ERF’s apparent viscosity can be controlled in response to the electric field and the change is reversible in 10 microseconds. Therefore, the physical properties of the ERF-embedded smart structure, such as the stiffness and damping coefficients, can be changed in response to the applied electric field. A mathematical model is difficult to be obtained to describe the exact characteristics of the ERF embedded ATVA because of the nonlinearity of ERF’s viscosity. Therefore, a fuzzy modeling and experimental validations of ERF-based ATVA from stationary random vibrations of thin plates are presented in this study. Because Type-2 fuzzy sets generalize Type-1 fuzzy sets so that more modelling uncertainties can be handled, a semi-active vibration controller is proposed based on Type-2 fuzzy sets. To investigate the different performances by using different types of fuzzy controllers, the experimental measurements employing type-1 fuzzy and interval type-2 fuzzy controllers are implemented by the Compact RIO embedded system. The fuzzy modeling framework and solution methods presented in this work can be used for design, performance analysis, and optimization of ATVA from stationary random vibration of thin plates.

Ocean exploration is crucial for utilizing its extensive resources. Images captured by underwater robots suffer from issues such as color distortion and reduced contrast. To address the issue, we propose an innovative enhancement algorithm that integrates Transformer and Convolutional Neural Network (CNN) in a parallel fusion manner. Firstly, a novel transformer model is intro-duced to capture local features, employing peak-signal-to-noise ratio (PSNR) attention and linear operations. Subsequently, to extract global features, both temporal and frequency domain features are incorporated to construct convolutional neural network. Finally, the Fourier’s high and low-frequency information of the original image are utilized to fuse different features. To demon-strate the algorithm's effectiveness, underwater images with various levels of color distortion are selected for both qualitative and quantitative analyses. The experimental results demonstrate that our approach surpasses other mainstream methods, achieving superior PSNR and structural sim-ilarity index measure (SSIM) metrics and leading to a detection performance improvement of over ten percent.

It is meaningful to monitor and identify the concrete cracks as they will seriously reduce the fatigue life of pavement. In this paper a new method based on distributed long-gauge optic fiber sensing is proposed to implement the crack identification. Firstly, the damage indicator, namely strain curve envelope area ratio (SCEAR), is proposed and calculated with the measured strain in time series when the plane goes across the pavement where the sensors are installed. Then the indicators with different sensors are normalization by the value of the indicator from one referenced sensor where damage normally will not happen. If the crack happens within the sensing gauge of the sensor, the value of damage indicator SCEAR from the sensor will increase. Therefore, the crack will be identified with the change of the damage indicator. The effect of the proposed method is first verified by some simulations of prefabricated pavement as the cracks can be accurately identified by the proposed method. Moreover, the influence from the aircraft, such as taxiing position, aircraft load and aircraft type, is not obvious enough to mislead the identification results. Lastly the static loading tests were implemented with one small-scale model of prefabricated concrete pavement in the lab. From the experimental results the prefabricated cracks inside the pavement slab can be identified accurately with the proposes method as the parameter SCEAR is increased by about 100% at the crack position, while it is only changed by about 3% at the undamaged position.

Channel bends are one of the most important characteristic features of natural streams. These bends often create the conditions for a hyporheic zone, which has been recognized as a critical component of stream ecosystems. The streambed vertical hydraulic conductivity (K_v), vertical hydraulic gradient (VHG) and Darcian flux (DF) in the hyporheic zone were estimated at 61 locations along a channel bend of the Beiluo River during July 2015 and January 2016. All the streambed attributes showed great spatial variability along the channel bend. Both upward fluxes and downward fluxes occurred during the two test periods, most of studied stream sections were controlled by downwelling, indicating stream water discharge into the subsurface. The average downward flux was higher at the downstream side than at the upstream side of the channel bend, especially in July 2015. The distribution of streambed sediment grain size has a significant influence on the variability of K_v; high percentages of silt and clay sediments generally lead to low K_v values. Higher K_v at the depositional left bank at the upstream site shifted toward the erosional right bank at the downstream site, with K_v values positively correlated with the water depth. This study suggested that the variabilities of K_v and VHG were influenced by the stream geomorphology and that the distribution of K_v was inversely related, to a certain extent, to the distribution of VHG across the channel bend. K_v and VHG were found to have opposite effects on the DF, and the close relationship between K_v and DF indicated that the water fluxes were mainly controlled by K_v.

Two interesting compounds such as quaternary ammonium lignin(QAL) and 2-ethylhexyl phosphoric acid mono-2-ethylhexyl ester (P507) were synthesized as ionic liquids(QP-ILs) for the extraction of metal ions. The extraction behavior of Co(II) and Ni(II) from chloride solution was compared between QP-ILs and P507. The results showed that the extraction rates of Co(II) and Ni(II) in the two solutions varied with the extraction conditions, but QP-ILs has stronger extractability for cobalt than P507, while Ni is on the contrary. Under the same conditions, the Co/Ni separation performance(βCo/Ni) in QP-ILs is superior to that in p507. At pH4.0, 55℃, concentration of 20%, extraction time of 10 minutes, the βCo/Ni of QP-ILs reached to 2703, while that of P507 was only 164. In conclusion, QP-ILs was more efficient than P507 for separating Co from mixed solutions.

Lignin is insoluble in water, limiting its use in the synthesis of adhesives. Therefore, in order to increase the amount of lignin raw materials used in the synthesis of adhesives, a kind of aminated lignin compounds was prepared through lignin amination reaction, and conducting structural analysis in this paper. The particle size of the amino lignin is about 100 nm, with the weight average molecular weight of 57,627 g/mol and a water solubility of 0.45g/100ml. The reaction mechanisms were proposed that the phenolic hydroxyl groups of lignin react with ammonia molecules, and thereby successfully introducing amino groups to generate the aminated lignin compounds. Therefore, this article enriches the scientific theory of lignin reactions and provides reference for the widespread application of lignin raw materials in the field of adhesives.

Purpose: To elucidate the mechanism of CXCR4/EAAT1/GS pathway in CXCL12 regulating invasion and migration in malignant pleural mesothelioma (MPM). Methods: Immunohistochemistry for CXCL12, CXCR4, EAAT1 and GS stainings and correlation analysis between them were conducted in MPM and normal tissues. Western blot and real-time PCR were performed to examine the CXCR4, EAAT1 and GS expression in H2052 cells. Wound healing and transwell assay were applied to determine the cell migration and invasion. MTT was utilized to assess cell viability. Results: CXCL12, CXCR4, EAAT1 and GS were highly expressed in MPM tissues and correlated with each other. CXCL12 upregulated both in protein and mRNA levels of CXCR4, EAAT1 and GS in H2052 cells. The EAAT1 and GS expression upregulated or not by CXCL12 were decreased by CXCR4 and EAAT1 knockdown. CXCR4 antagonist AMD3100 and EAAT1 antagonist TFB-TBOA also resulted in the same effects as CXCR4 and EAAT1 knockdown, respectively. CXCL12 promoted cell invasion and migration and increased the Matrix metalloproteinase 9 (MMP9) mRNA level. CXCR4 and EAAT1 knockdown suppressed all these functions. Furthermore, CXCL12 promoted H2052 cells growth in nude mice, both AMD3100 and TFB-TBOA inhibited this promotion. Conclusions: CXCL12 regulated the invasion and migration through CXCR4/EAAT1/GS pathway in H2052 cells.

Mutations, especially those at the protein-protein interaction (PPI) interface, have been associated with various diseases. Meanwhile, though de novo mutations (DNMs) have been proven important in neuropsychiatric disorders, such as developmental delay (DD), the relationship between PPI interface DMNs and DD has not been well studied. Here we curated developmental delay DNM datasets from the PsyMuKB database and showed that DD patients showed a higher rate and deleteriousness in DNM missense on the PPI interface than sibling control. Next, we identified 302 DD-related PsychiPPIs, defined as PPI harboring a statistically significant number of DNM missenses at their interface, and 42 DD candidate genes from PsychiPPI. We then observed that PsychiPPIs preferentially affected hub proteins in the human protein interactome network. When analyzing DD candidate genes using gene ontology and gene spatio-expression, we found that PsychiPPI genes carrying PPI interface mutations, such as FGFR3 and ALOX5, were enriched in development-related pathways and the development of the neocortex, and cerebellar cortex, suggesting their potential involvement in the etiology of DD. Our results demonstrated that DD patients carried an excess burden of PPI-truncating DNM, which could be used to efficiently search for disease-related genes and mutations in large-scale sequencing studies. In conclusion, our comprehensive study indicated the significant role of PPI interface DNMs in developmental delay pathogenicity.

Dynamic thermogravimetric (TG) analysis under nitrogen environment was used to understand the thermal decomposition process of 3D printing filaments made of wood-filled polylactic acid (PLA)/starch blend. The characteristic temperatures and apparent activation energy (AAE) of the filaments with various starch contents were calculated with well-known kinetic models by Friedman, Flynn-Wall-Ozawa, Coats-Redfern and Kissinger. With the increased starch content in the filament, the onset thermal decomposition temperatures of the filaments decreased gradually from 272.4 to 155.1°C. The thermal degradation degree became smaller, and the transitional temperature interval became larger with increased starch proportion. The AAE values of the three types of filaments with different starch ratios varied between 97 kJ/mol and 114 kJ/mol, depending on material composition and method of calculation. The improved understanding of thermal decomposition behavior of PLA-starch-wood composites can help develop more biodegradable PLA/starch-based filaments for 3D printing.

The treatment of large-area bone defects remains a challenge; however, various strategies have been developed to improve the performances of scaffolds in bone tissue engineering. In this study, poly(lactide-co-glycolide)/hydroxyapatite (PLGA/HA) scaffold was coated with Asp-Gly-Glu-Ala (DGEA)-incorporated collagen for the repair of rat skull defect. Our results indicated that the mechanical strength and hydrophilicity of PLGA/HA scaffold were clearly improved and conducive to cell adhesion and proliferation. The collagen-coated scaffold with DGEA significantly promoted the repair of skull defect. These findings indicated that a combination of collagen coating and DGEA improved scaffold properties for bone regeneration, thereby providing a new potential strategy for scaffold design.