The 1060 aluminium alloy is widely applied to electronic industry, construction area, aerospace field, traffic engineering, decoration and consumption goods market for its good chemical, physical and mechanical properties. In general, excellent processing property is necessary and important for manufacturing of complicated panels. In this paper, a special 2D ultrasonic vibration incremental forming method is designed to improve its plasticity and mechanical properties. Three kind of processing methods including traditional single point incremental forming, longitudinal ultrasonic vibration incremental forming and 2D ultrasonic vibration incremental forming are conducted for flexible manufacture of conical parts of 1060 aluminium alloy sheet. Then micro-hardness tests, residual stress tests and scanning electron microscopy tests are carried out to probe its revolution of micro-structure and mechanical properties and to analysis the effects of different types of ultrasonic vibration on plasticity and fracture characteristic of 1060 aluminium alloy. It is proved that 2D ultrasonic vibration is facilitated to improvement of plasticity and surface qualities of 1060 aluminium alloy than the other two processing methods. So, the novel 2D ultrasonic vibration incremental forming possesses substantial application values on flexible and rapid manufacturing of complicated thin-walled component of aluminium alloy.

Vibrations of blades and tower have important impact for wind turbine. This paper presents a active controller design to suppress blade edgewise vibrations under aerodynamic load and gravitational load.Treating the sum of aerodynamic load input in edgewise direction and gravitational load as unknown disturbance input,a stochastic disturbance accommodating control(SDAC) approach is proposed to design a controller which it utilizes a minimum-variance unbiased estimator(MVUE) to estimate both state and unknown input. The stability analysis proved that the proposed SDAC is bounded in mean square.In order to verify the performance of the minimum-variance unbiased estimator and the proposed SDAC, numerical simulations using Matlab/Simulink have been carried out for the National Renewable Energy Laboratory 5-MW wind turbine.Under the different circumstance which exists the random process and measure noise and noise free. It is shown that the estimation value by MVUE can tracking the real state and unknown input. The results are also compared to the traditional linear quadratic regulator(LQR) and show that the proposed stochastic disturbance accommodating control scheme can further reduce displacement in edgewise vibrations direction and the control strategy is more effective than the LQR.

Pristimerin (PM) is a naturally occurring quinonemethide triterpenoid compound that isolated from the Celastraceae and Hippocrateaceae families. Its anticancer effects have attracted a great deal of attention, but the mechanisms of action remain obscure. In this study, we screened for the active compounds of Pristimerin using a drug-likeness approach. Potential protein targets of Pristimerin were predicted by PharmMapper and Coremine database. Candidate protein targets were then uploaded to GeneMANIA and GO pathway analysis. Finally, compound-target, target-pathway, and compound-target-pathway networks were constructed using Cytoscape 3.3. The results showed that Pristimerin had good drug ability and identified 13 putative protein targets. Network analysis revealed that these targets are associated with cancer, inflammation and other physiological processes. In summary, Pristimerin is predicted to target a variety of proteins and pathways to form a network that exerts systemic pharmacological effects.

With reference to our 4G model of final unification, strong coupling constant and strong interaction charge of magnitude $e_s\cong 2.95e$, it is possible to understand the nuclear stability range with a simple power law of the form, $A_s\cong \left(Z+2.95\right)^{1.2\pm0.015}$

In order to understand the defense response of Sanchezia nobilis to biotic and abiotic stresses, this study investigated the phenomenon of S. nobilis under biotic of feeding by Achatina fulica, and measured the temporal changes of malondialdehyde content, catalase and peroxidase activities under biotic of feeding by Achatina fulica and abiotic stress of garden pruning conditions on S. nobilis. The results showed that: 1) S. nobilis was seriously damaged by A. fulica and the average hourly food intake of A. fulica is 0.22 ± 0.01 g. 2) Leaf cutting stimulation and snail feeding stress significantly affected the content of malondialdehyde, the activities of catalase and peroxidase in the leaves of S. nobilis. 3) Compared with the control group, the snail feeding and leaf cutting stimulated stress could induce the leaf of S. nobilis to synthesize more malondialdehyde under varying durations, and the activities of catalase and peroxidase were significantly enhanced in a short time, and the induction response of snail feeding biological stress was faster than that of leaf cutting stimulated abiotic stress. 4) After the defense enzyme was strengthened, the activities of catalase and peroxidase in the feeding stress group of snails showed a gradual downward trend, while the activities of catalase and peroxidase in the leaf cutting stimulation stress group showed a first upward and then downward trend, indicating that abiotic stress had more lasting effects on plants than biological stress. It can be seen that biotic stress and abiotic stress significantly affect the defense response of plants, and the defense-related substances malondialdehyde, catalase and peroxidase play an important role in plants' response to biotic and abiotic stress. This study has important production guidance significance for carrying out stress resistance breeding or cultivation management of garden plants.

Feedback trading theory is one of the most primitive theories about financial market. But for a long time，researches and modelings on this topic are rarely seen. The model in this paper shows the effects of sentiment shocks on asset prices in a market characterized by feedback trading in the long run. We find that，generally，feedback trading will lead to cognitive bias effect and trading inducement effect. Cognitive bias effect increases with the feedback trading parameter ( FTP) . In our model，the abnormal volatility of asset prices is captured by cognitive bias effect，sentiment shock effect and trading inducement effect．

There is no doubt that seagrass beds constitute one of the most productive ecosystems in shallow coastal waters. Despite this, picoplankton in seagrass ecosystems has received relatively little attention. The purpose of this study was to compare picoplankton growth and mortality rates between seagrass and unvegetated habitats using chamber incubations. We tested two main hypotheses: (i) incubation with seagrass would result in higher bacterial growth rates due to increased DOM release from seagrass photosynthesis, and (ii) Synechococcus spp. would be lower in the presence of seagrass due to competition for inorganic nutrients. Our results showed bacterial growth rates of 2.31 and 2.44 d–1 without and with seagrass, respectively. In seagrass chambers, bacterial growth was higher than that in non-seagrass chambers, suggesting that organic carbon coming from the seagrass community may support bacterial production. Furthermore, we observed a negative Synechococcus spp. growth rate (-0.9 d–1) in the seagrass treatment, likely reflecting nutrient competition with the seagrass. Small-scale chambers proved to be a useful tool for studying the factors controlling spatial and temporal patterns of picoplankton across different habitats. Furthermore, future studies should examine picoplankton growth over a wider range of spatial scales in seagrass beds and adjacent unvegetated sediment.

Semi-continuous data are very common in the social science and economics. In this paper, a Bayesian variable selection procedure is developed to assess the influence of exogenous factors including observed and unobserved on the semi-continuous data. Our formulation is based on the two-part latent variable model with polytomous response. We consider two schemes for the penalties of regression coefficients and factor loadings: the Bayesian spike and slab bimodal prior and the Bayesian lasso prior. Within the Bayesian framework, we implement Markov Chains Monte Carlo sampling method to conduct posterior inference. To facilitate posterior sampling, we recast the logistic model in part one as the norm-like mixture model. Gibbs sampler is designed to draw observations from the posterior. Our empirical results show that with suitable hyper-parameters, the spike and slab bimodal method slightly outperforms the Bayesian lasso in the current analysis. Finally, a real example related to the China household finance survey is analyzed to illustrate the application of the methodology.. .

In mesoscale eddies, chemical properties and biological composition are different from those in surrounding water, due to their unique physical processes. It is unclear how physical-biological coupling occurs in warm-core eddies, particularly since no study has examined the relationship between bacteria and viruses. The purpose of the present study was to examine the influence of anticyclonic warm eddy on the relationship between bacterial and viral abundances as well as viral activity (viral production) in different depths. In the core of warm eddy, bacterial abundance (0.48 to 2.82 ×105 cells mL−1) fluctuated less than that of the outside eddy (1.12 to 7.03×105 cells mL−1). In particular, there was four-folds higher of viral-bacterial abundance ratio (VBR) estimated within the eddy below DCM than the outside eddy. We suggest that prevailing physical forces with downwelling at the center of the anticyclonic warm eddy, viruses can also be transmitted into the deep ocean directly through adsorption on particulate organic matter during sinking. Overall, our findings provide valuable insights into the interaction between bacterial and viral communities and their ecological mechanism within the warm eddy.

3D printing is a non-traditional additive manufacturing process. It is different from the traditional subtractive manufacturing process. It offers exceptional rapid prototyping capabilities and results that traditional subtractive manufacturing methods cannot attain, especially in applications involving curved or intricately shaped components. Despite its advantages, metal 3D printing will face porosity, warpage, and surface roughness issues. These issues will affect the future practical application of the parts indirectly, for example, the structural strength and the parts assembly ca-pability. Therefore, this study compares the qualities of the warpage, weight, and surface rough-ness after milling and grinding processes under the same materials (316L stainless steel) between general rolled steel and 3D additive steel. Experimental results show that 3D printing parts are approximately 13% to 14% lighter than the general rolled steel. The surface roughness performance of 3D printing steel is better than general rolled steel under the same material after milling or grinding processing. The hardness of the 3D printing steel is better than that of the gen-eral rolled steel. The research verifies that 3D additive manufacturing can use surface processing to optimize surface performance and achieve the functions of lightness and hardness.

Aluminum wire is a common material for wire bonding due to its resistance to oxidation and low price. It does not melt when becoming a free air ball (FAB) during the electronic flame-off (EFO) process with wettability, and is applied by wedge bonding. This study used 20μm Zn-Coated Al-0.5wt.%Si (ZAS) wires to improve the FAB shape after the EFO process, while maintaining stability of the mechanical properties, including the interface bonding strength and hardness. In order to test circuit stability after ball bonding, the current-tensile test was performed. During the experiment, it was found that 80nm ZAS with wire bonding has lower resistance and higher fusing current. For the bias tensile test, the thicker Zn film diffused into the Al-Si matrix easily, after which the strength was reduced. The ball-bond interfaces had no change in their condition before and after the bias. Accordingly, ZAS could be a promising candidate for ball bonding in the future.

The quality of healthcare services is a very important indicator of social welfare in developed and emerging countries. Good quality levels are critical to ensuring patient safety, improving health outcomes, increasing patient satisfaction, optimizing resource utilization, and meeting regulatory requirements. The stable and sustainable operation of medical institutions urgently requires the establishment of a reliable and effective medical quality performance evaluation platform. However, the complexity of the evaluation process, involving numerous evaluators and the consideration of different subjective and objective criteria, poses significant challenges and essentially constitutes a group multi-criteria decision-making problem. This article uses the overall medical environment of the institution, medical service attitude, medical security, administrative service measures, etc. as measurement standards to deal with this challenge. It proposes a medical service quality measurement model suitable for both qualitative and quantitative analysis. In view of the heterogeneity of information, the two-tuple fuzzy language calculation method is introduced to enable evaluators to skillfully manage diverse information and effectively avoid potential information omission problems in the subjective evaluation integration process. The effectiveness and feasibility of the model were demonstrated by applying it to the operational framework of a medical institution in central Taiwan.

Mitochondrial DNA copy number (mtDNAcn) is associated with mitochondrial function contributing various diseases. Understanding of the association between mtDNAcn and infant neurodevelopment, as well as the role of racial disparities, remains unknown. A cohort study was conducted with 55 preterm infants and a single blood sample was collected from each infant during their NICU stay to measure mtDNAcn. NICU Network Neurobehavioral Scale (NNNS) and Bayley Scale of Infant Developmental testing Edition III was assessed during NICU until 2 years of corrected age (CA). Linear regression models were performed to investigate the relationship between the clinical characteristics, neurobehavioral outcomes and mtDNAcn. Majority of our subjects were male, White, non-Hispanic, had C-section, and without preterm premature rupture of membrane (PPROM). Increased mtDNAcn was found associated with younger birth gestational age (GA), nonoptimal neurodevelopment. However, the opposite associations between mtDNAcn and neurodevelopmental outcomes were observed between Black and White infants up to 1 year of CA. Adverse early life experience, together with increased mtDNAcn in White infants, and decreased mtDNAcn in Black infants may be considered as significant positive predictors of poor early life neurodevelopmental outcomes in infants.

In response to increasing urbanization and the need for infrastructure resilient to natural hazards, this study introduces an AI-driven predictive model designed to assess the risk of soil liquefaction. Utilizing advanced ensemble machine learning techniques, the model integrates geotechnical and geographical data to accurately predict the potential for soil liquefaction in urban areas, with a specific focus on Yokohama, Japan. This methodology leverages comprehensive datasets from geological surveys and seismic activity to enhance urban planning and infrastructure development in smart cities. The primary outputs include detailed soil liquefaction risk maps that are essential for effective urban risk management. These maps support urban planners and engineers in making informed decisions, prioritizing safety, and promoting sustainability. The model employs a robust combination of neural networks and gradient boosting decision trees to analyze and predict data points, assessing soil susceptibility to liquefaction during seismic events. Notably, the model achieves high accuracy in predicting soil classifications and N-values, which are critical for evaluating soil liquefaction risk. Validation against an extensive dataset from geotechnical surveys confirms the model’s practical effectiveness. Moreover, the results highlight the transformative potential of AI in enhancing geotechnical risk assessments and improving the resilience of urban areas against natural hazards.

This paper addresses challenges and solutions in urban development and infrastructure resilience, particularly in the context of Japan's rapidly urbanizing landscape. It explores the integration of smart city concepts to combat land subsidence and liquefaction, phenomena highlighted by the 2011 Great East Japan Earthquake. Using advanced technologies such as smart sensing and predictive analytics through kriging and ensemble learning, the study aims to improve the accuracy of geotechnical investigations and urban planning. By analyzing data from 433 geotechnical surveys in Setagaya, Tokyo, it develops predictive models to accurately determine the depth of bearing layers critical to urban infrastructure. The results demonstrate the superiority of ensemble learning in predicting the depth of bearing layers, with significant implications for smart city development and the sustainability of urban environments. This interdisciplinary approach not only seeks to mitigate risks associated with geological disturbances, but also promotes sustainable urban growth that prioritizes safety and prosperity. Through advanced technology and comprehensive research, the study underscores the potential of smart cities to address urban complexities and ensure a resilient, sustainable future.

In recent decades, with the ever-growing scale of video data, near-duplicate videos continue to emerge. Data quality issue caused by near-duplicate videos is becoming more and more prominent, which has affected the application of normal videos. Although current studies on near-duplicate video detection can be helpful to uncover data quality issues for videos, they still lack a process of automatic merging for the video data represented by high-dimensional features, which are difficult to automatically clean the near-duplicate videos to improve data quality for video datasets. At present, there are few studies on near-duplicate video data cleaning. The existing studies have the sensitive problems of video data orderliness and clustering initial center under a condition that prior distribution is unknown, which seriously affect the accuracy of near-duplicate video data cleaning. To address the above issues, an automatic near-duplicate video data cleaning method based on a consistent feature hash ring is proposed in this paper. First, a residual network with convolutional block attention modules, a long short-term memory deep network, and an attention model are integrated to construct an RCLA deep network with the multi-headed attention mechanism to extract spatiotemporal features of video data. Then, a consistent feature hash ring is constructed, which can effectively alleviate the sensitivity of video data orderliness while providing a condition of near-duplicate video merging. To reduce the sensitivity of the initial cluster centers to results of near-duplicate video cleansing, an optimized feature distance-means clustering algorithm is constructed by utilizing a mountain peak function on a consistent feature hash ring, which can implement automatic cleaning of near-duplicate video data. Finally, experiments are conducted based on a commonly used dataset named CC_WEB_VIDEO and a coal mining video dataset. Compared with some existing works, simulation results demonstrate that the performance of the proposed method.

Amid the swift progression of the global automobile industry, the aspiration for lightweight vehicle design has surfaced as a crucial approach to boost fuel efficiency and curtail environmental impact. The objective of this study revolves around achieving holistic optimization of vehicle structure via detailed scrutiny of vehicle process characteristics, amalgamated with a Back Propagation neural network (BPNN) optimized by a genetic algorithm (GA) and a topology optimization (TO) method.The process initiates with an examination of the vehicle's technical characteristics, succeeded by an optimization protocol that couples GA with BPNN. This exhaustive optimization procedure derives the best-fit values of design parameters and identifies the globally optimal lightweight design approach. Subsequently, a comprehensive structural optimization is actualized by modifying the topological distribution of the structure.A simulation experiment of the conceptual car body beam skeleton is conducted as a conclusive step, and the anti-collision capacity of the car body beam skeleton is assessed by measuring deformation intrusion. The findings reveal that in the vehicle topology optimization, a comparative analysis of pre and post-optimization results indicates an improved topological density across diverse sections. The topological density of the vehicle's frontal structure escalates from 0.75 to 0.8. Following optimization, the stiffness assessment records a marginal increase (within the range of 0.02–0.05), implying that the lightweight design does not compromise the general rigidity of the structure.The optimized body structure manifests superior crashworthiness in the crash test: the deformation intrusion diminishes by 27% in front-end collisions. In side impacts, the deformation and intrusion at critical points are reduced by 13.15% to 10% respectively. The comprehensive method proposed herein proves to be an efficient strategy to actualize the lightweight design of vehicles.

Major depressive disorder (MDD) is one of the most prevalent and disabling mental disorders worldwide. Among the symptoms of MDD, sleep disturbance such as insomnia is prominent and the first reason patients may seek professional help. However, the underlying pathophysiology of this comorbidity is still elusive. Recently, genome-wide association studies (GWAS) have begun to unveil the genetic background of several psychiatric disorders, including MDD and insomnia. Identifying the shared genomic risk loci between comorbid psychiatric disorders could be a valuable strategy to understand their comorbidity. This study seeks to identify the shared genes and biological pathways between MDD and insomnia based on their shared genetic variants. First, we performed a meta-analysis based on the GWAS summary statistics of MDD and insomnia obtained from Psychiatric Genomics Consortium and UK Biobank, respectively. Next, we associated shared genetic variants to genes using two gene mapping strategies: (a) positional mapping based on genomic proximity and (b) expression quantitative trait loci (eQTL) mapping based on gene expression linkage across multiple tissues. As a result, a total of 719 shared genes were identified. Over half (51%) of them are protein-coding genes. Functional enrichment analysis shows that the most enriched biological pathways are related to epigenetic modification, sensory perception, and immunologic signatures. We also identified druggable targets using a network approach. Together these results may provide insights into understanding the genetic predisposition and underlying biological pathways of comorbid MDD and insomnia symptoms.

The space gravitational wave detection and drag free control requires the micro-thruster to have very low thrust noise within 0.1mHz~100mHz, which poses a great challenge to the ground thrust test. The evaluation and decoupling of thermal noise are the difficulties in the application of torsion balance for most thrusters dissipate heat in the experiment. The research has adopted a calibration scheme of micro-Newton thrust torsion balance. On the basis of Lisa Pathfinder's former research and using ideas inspired from PID control and multi time scale (MTS) for reference, the paper proposes to expand the state space of temperature to be applied on thrust prediction based on fine tree regression (FTR), to subtract the thermal noise filtered by transfer function fitted with z-domain vector fitting (ZDVF). The results show that the thrust amplitude thrust density in diurnal temperature fluctuation is decoupled from 24μN/Hz1/2 to 4.9μN/Hz1/2 at 0.11mHz. And the 1μN square wave modulations of electrostatic fins (ESF) is extracted from the simultaneously ambiguous background of temperature for PTC's heating and cooling. The FTR method is well demonstrated in thermal noise decoupling and can guide the design of thermal control and be extended to other physical quantities for noise decoupling.

This research aims to explore factors that influence sustainable music learning system from psychological perspective, and innovatively constructs a combination of qualitative and quantitative approaches. For first time, combines nonlinear system and identifies relevant factors affecting music learning by establishing data system for questionnaire answering and a quantitative model for actual academic performance. Existing research mainly looks at several factors that affect fragmented music learning system from perspective of teachers' teaching rules, emphasizing phased teaching state and learning goals of teachers as main body, thus ignoring music learning students, who are a large group of music learning objects. The research fills gap in this type of research object, focusing on sustainable music learning system from emotional perspective and students who occupy a dominant position in music learning, so as to more accurately explore impact of their emotional influencing factors on music learning system. The most prominent innovation of this study lies in construction of stochastic nonlinear system for psychological influencing factors of student music learning and a quantitative model for actual learning effect. Through analysis of nonlinear system, weight values of several negative emotions on music learning system are obtained. The comprehensive impact of emotional factors and practical learning on music learning system can be obtained by weighting them together.

Cytochrome P450 monooxygenases (P450s) are promising versatile oxidative biocatalysts. However, the practical use of P450s in vitro is limited by their dependence on the co-enzyme NAD(P)H and the complex electron transport system. Using H2O2 simplifies the catalytic cycle of P450s; however, most P450s are inactive in the presence of H2O2. By mimicking the molecular structure and catalytic mechanism of natural peroxygenases and peroxidases, an artificial P450 peroxygenase system has been designed with the assistance of a dual-functional small molecule (DFSM). DFSMs, such as N-(ω-imidazolyl fatty acyl)-l-amino acids, use an acyl amino acid as an anchoring group to bind the enzyme, and the imidazolyl group at the other end functions as a general acid-base catalyst in the activation of H2O2. In combination with protein engineering, the DFSM-facilitated P450 peroxygenase system has been used in various oxidation reactions of non-native substrates, such as alkene epoxidation, thioanisole sulfoxidation, and alkanes and aromatic hydroxylation, which showed unique activities, and regio- and enantioselectivities when compared with native P450s. Moreover, the DFSM-facilitated P450 peroxygenase system can switch to the peroxidase mode by mechanism-guided protein engineering. In this short review, the design, mechanism, evolution, application, and perspective of these novel non-natural P450 peroxygenases for the oxidation of non-native substrates are discussed.

This research aims to explore factors that influence sustainable music learning system from psychological perspective, and innovatively constructs a combination of qualitative and quantitative approaches. For first time, combines nonlinear system and identifies relevant factors affecting music learning by establishing data system for questionnaire answering and a quantitative model for actual academic performance. Existing research mainly looks at several factors that affect fragmented music learning system from perspective of teachers' teaching rules, emphasizing phased teaching state and learning goals of teachers as main body, thus ignoring music learning students, who are a large group of music learning objects. The research fills gap in this type of research object, focusing on sustainable music learning system from emotional perspective and students who occupy a dominant position in music learning, so as to more accurately explore impact of their emotional influencing factors on music learning system. The most prominent innovation of this study lies in construction of stochastic nonlinear system for psychological influencing factors of student music learning and a quantitative model for actual learning effect. Through analysis of nonlinear system, weight values of several negative emotions on music learning system are obtained. The comprehensive impact of emotional factors and practical learning on music learning system can be obtained by weighting them together.

Ion current rectification (ICR) is the ratio of ion current by forward bias to by backward bias and is a critical indicator of diode performance. In previous studies, many attempts have been continued to improve the performance of this ICR, but there is the intrinsic problem for geometric changes that induce ionic rectification due to their fabrication problem. Additionally, the high ICR could be achieved in the narrow salt concentration range only. Here, we propose a multi-layered bipolar ionic diode based on an asymmetric nanochannel network membrane (NCNM), which is realized by soft lithography and self-assembly of homogenous-sized nanoparticles. Owing to the freely changeable geometry based on soft lithography, the ICR performance can be explored according to the variation of microchannel shape. Interestingly, very stable ICR performance can be achieved using the multi-layered 3D configuration for the bipolar diode in a broad range of salt concentrations (0.1mM ~ 100 mM). This demonstrates the promising potential of multi-layered NCNM for applications in highly concentrated electrolytes, such as biosensors, desalination, or energy harvesting.

Excessive vibration of temporary grandstand owing to crowd has lateral rhythmic motions can cause human in panic, which attracted increasing attention in recent years. This paper focuses on experiments that a temporary grandstand occupied 20 participants is oscillated by a shaking table with a series of random waves and induced by crowd with rhythmic swaying motions at lateral direction, respectively. And then a series of vibration perception questionnaires about crowd reaction are obtained. Evaluation of annoying level derives in concept of degree of membership and annoying rate method is proposed, then the human health risk of vibration serviceability of temporary grandstand is assessed with acceleration vibration dose value(VDV). From these results it is clear that standing crowd is more tolerant to vibration than seated crowd. The measured vibrations generated by crowd activities on temporary grandstand can cause panic in crowd. New relationship between the annoyance rate and structural acceleration VDV at logarithmic coordinate is proposed. The findings of this study can be utilized to manage the vibration of temporary grandstand and assess the human health risk.

This research aims to explore factors that influence sustainable music education from a psychological perspective, and innovatively constructs a combination of qualitative and quantitative approaches. For first time, combines stochastic nonlinear system of control theory and identifies relevant factors affecting sustainable music learning by establishing a data system for questionnaire answering and a quantitative model for actual academic performance. Existing research mainly looks at several factors that affect fragmented music education from perspective of teachers' teaching rules, emphasizing phased teaching state and learning goals of teachers as main body, thus ignoring sustainable music education and students, who are a large group of music learning objects. The research fills gap in this type of research object, focusing on sustainable music education from an emotional perspective and students who occupy a dominant position in music learning, so as to more accurately explore impact of their emotional influencing factors on sustainable music learning. The most prominent innovation of this study lies in construction of stochastic nonlinear system for psychological influencing factors of sustainable student music learning and a quantitative model for actual learning effect. Through analysis of nonlinear system, weight values of several negative emotions on sustainable music learning are obtained.

The azimuth wheel-rail of large aperture radio telescope is the key component, it not only sup-ports the whole weight of the antenna, but also directly affects the pointing performance of the antenna with its surface accuracy. The whole weight of the radio telescope is hundreds or even thousands of tons, its azimuth frame rollers have great contact stress with the wheel-rail surface, repeated rolling can cause rolling contact fatigue on the wheel-rail surface, resulting in wear, cracks and other damage to the wheel-rail, and even lead to failure or fracture of the wheel-rail in serious cases, so it is very important to monitor the damage of the antenna wheel-rail. In order to visually detect the use of antenna wheel-rail surface, this paper proposed the method using electromagnetic ultrasonic detection to detect the damage of antenna wheel-rail surface for the first time. Based on the electromagnetic ultrasonic nondestructive testing principle, the simplified wheel-rail model containing wear, corrosion and crack damage is simulated. The results show that this method can effectively detect the surface damage of the antenna wheel-rail surface, and it can provide an important reference for the research of wheel-rail damage detection of large radio telescope.

Objective: To use the ancient and modern medical record cloud platform (V2.3.5) to organize and explore the medication patterns of the "Three Causes, Extremity, and One Disease Syndrome Formula" by Chen Wuxuan, and to summarize its therapeutic ideas. Method: Collect and organize the "San Yin Si Tian Fang" in the "San Yin Ji Yi Zheng Fang Lun", and use the modern medical record cloud platform (V2.3.5) to conduct medication frequency statistical analysis, cluster analysis, and complex network analysis. As a result, a total of 16 Chinese herbal medicines were collected in the "Three Causes, Extreme and One Disease Syndrome Formula", involving 75 traditional Chinese medicines. Statistical analysis showed that roasted licorice was the most commonly used, followed by Pinellia ternata, papaya, ginger, and Poria cocos; Medications mainly include warm drugs, sweet and pungent flavors, as well as drugs that promote lung and spleen meridians. Cluster analysis can divide the above-mentioned traditional Chinese medicine into two groups, with specific grouping as follows. Group 1: ginger, white peony, aconite, papaya, and achyranthes bidentata; Group 2: roasted licorice, poria cocos, dried ginger, pinellia ternata, and green peel. Conclusion: The characteristics of the 16 prescriptions are biased towards Xin Gan Wen, emphasizing the regulation of the spleen and stomach, which may be related to the common use of prescription by doctors in the Song Dynasty and the tendency towards warm and dry medication.

Agriculture is an industry that heavily relies on the environment. The increasing frequency of ex-treme disasters attributable to climate change has adversely affected agricultural land and dis-rupted the stability of the supply of agricultural products. Therefore, we implemented a research program focused on current strategies for adapting to agricultural meteorological disasters. The primary goal was to actively promote disaster prevention information and techniques through information platforms and applications and to provide refined forecasting and an early warning system to enhance preparedness for and recovery from agricultural disasters. This initiative can transform passive mitigation into dynamic action by disseminating crucial information to farmers. We integrated meteorological data and crop characteristics to develop disaster prevention strate-gies for the predisaster, during-disaster, and postdisaster stages to empower farmers to implement autonomous disaster prevention, ensuring their profitability and minimizing the impact of disas-ters. This paper presents an agricultural disaster prevention system involving cross-disciplinary collaboration to establish an early warning system for crop disasters. However, the effectiveness of the early warning system relies on the availability of complete meteorological and crop infor-mation, necessitating high-density agricultural meteorological stations, refined weather forecast services, historical crop damage data, and ongoing research and development on disaster preven-tion technology. In the system, a diversified social media interface is used to provide local farmers with weather forecast information. When impending disasters are identified, the platform acti-vates disaster prevention measures to reduce agricultural losses. This article paper introduces Taiwan’s agricultural disaster prevention system as a complete disaster prevention strategy.

Global warming may increase the potential evapotranspiration (Etp), thereby affecting the amount of clean water resources available in a region. In this study, the systematic biases in temperature outputs of 24 global climate models (GCMs) under 3 emission scenarios are corrected by using 8 bias correction methods. The trend of Etp in Yangzi River Delta in the 21st century is projected by using 4 Etp calculation formulas. The uncertainty of the projections is estimated and decomposed by using multi-way analysis of variance. The influence of uncertainty on the projected change signal is quantified by using the signal-to-noise ratio. The results show that, relative to the reference period (1971~2000), the annual mean daily Etp in Yangzi River Delta will increase by 0.14~0.17mm/d during the period 2021~2050 and by 0.21~0.41mm/d during the period 2061~2090, respectively. During 2021~2050, the uncertainty of Etp increase projections is dominantly contributed by the main effects of GCM (63%) and Etp calculation formula (24%). During 2061~2090, it is mainly contributed by the main effect of GCM (36%), followed by the main effects of emission scenario (34%) and Etp calculation formula (18%). The increase projections of Etp are generally reliable and robust during the two projection periods.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

The problems of operating range and costs are the two most critical bottlenecks restricting the extensive application of electric vehicles at home and abroad. There are also some prominent problems in China's electric vehicles, such as slow improvement of electric vehicle's operating range, difficulty in charging, slow charging, low utilization efficiency of charging resources, and high battery cost for electric vehicles, which lead to poor competitiveness of electric vehicles compared with traditional internal combustion engine (I.C.E.) vehicles. This paper analyzes the key factors restricting the development and popularization of electric vehicles in China from the aspects of strategic policy, sales situation and self problems. Through summarizing the experience and lessons of French standardization development strategy and electric vehicle development mode, this paper puts forward the hypothesis leading the development of electric vehicles through standardization to enhance their competitiveness, gives the specific suggestions, and briefly analyzes the feasibility from the aspects of product situation. The research content of this paper provides a certain basis and ideas for the future research work.

The coronavirus disease 2019 (COVID-19) has spread over the world for more than one year. COVID-19 often develops life-threatening hypoxemia. Endothelial injury caused by the viral infection leads to intravascular coagulation and ventilation-perfusion mismatch. However, besides above pathogenic mechanisms, the role of alveolar edema in the disease progression has not been discussed comprehensively. Since the exudation of pulmonary edema fluid was extremely serious in COVID-19 patients, we bring out a hypothesis that severity of alveolar edema may determine the size of poorly-ventilated area and the blood oxygen content. Treatments to pulmonary edema (alcohol-oxygen vapor therapy and fluid management) may be great helpful for reducing occurrence of severe cases. Given that late mechanical ventilation may cause mucus (edema fluid) to be blown to the deep of the small airways, oxygen therapy should be given at the early stages. The optimal time and SpO₂ threshold for oxygen therapy are also discussed.

Air pollutants such as volatile organic compounds (VOCs), nitrogen oxides (NOx), sulfur dioxide (SO2), as well as water pollutants including heavy metal, are harmful to human and environment. Effective control and reduction of their pollution is therefore an important topic for today’s scientists. Fly ash (FA) is a type of industrial waste that can cause multiple environmental problems if discharged into the air. On the other hand, because of its high porosity, large specific surface area, and other unique characteristics, the FA can also be used as a low-cost and high efficient adsorbent with some simple modifications. This paper reviews the effects of FA on treatment of the above air and water pollution based on our research experience over many years, including to the current status of global FA utilization, physicochemical properties, principle of adsorption, and the application direction of FA in the future. It focuses on the use of nanocomposite technology to fabricate functional FA fibrous membranes to adsorb VOCs from air, and treat heavy metal wastewater. This present review first describes the fabrication technology of FA nanocomposites and their mechanism of adsorption VOCs from air. Utilization of nanofiber technology to fabricate multi-functional FA emerging composite materials to mitigate air and water pollution has great potential in the future, especially use of pollutant material to control other pollutants.

This paper aims to address the exponential stability and stabilization problems for a class of delayed nonlinear Markov jump systems under randomly occurring Denial-of-Service (DoS) attack and packet loss. Firstly, the stochastic characteristics of DoS attack and packet loss are depicted by the attack success rate and packet loss rate. Secondly, a Period Observation Window (POW) method and a hybrid-input strategy are proposed to compensate for the impact of DoS attack and packet loss on the system. Thirdly, A Dynamic Event-triggered Mechanism (DETM) is introduced to save more network resources and ensure the security and reliability of systems. Then, by constructing a general common Lyapunov functional and combining with the DETM and other inequality analysis techniques, the less conservative stability and stabilization criteria for the underlying systems are derived. In the end, the effectiveness of our result is verified through a numerical example.

Viruses can induce the fusion of infected and neighboring cells, leading to the formation of syncytia. Cell-cell fusion is mediated by viral fusion proteins on the plasma membrane of infected cells that interact with cellular receptors on neighboring cells. Viruses use this mechanism to spread rapidly to adjacent cells or escape host immunity. For some viruses, syncytium formation is a hallmark of infection and a known pathogenicity factor. For others, the role of syncytium formation in viral dissemination and pathogenicity remains poorly understood. Human cytomegalovirus (HCMV) is an important cause of morbidity and mortality in transplant patients and the leading cause of congenital infections. Clinical HCMV isolates have broad cell tropism but differ in their ability to induce cell-cell fusions, and little is known about the molecular determinants. We developed a system to analyze HCMV glycoprotein B (gB) variants in a defined genetic background. HCMV strains TB40/E and TR were used as vectors to compare the fusogenicity of six gB variants from congenitally infected fetuses with those from three laboratory strains. Five of them conferred the ability to induce fusion of MRC-5 human embryonic lung fibroblasts to one or both backbone strains, as determined by a split GFP-luciferase reporter system. The same gB variants were not sufficient to induce syncytia in infected ARPE-19 epithelial cells, suggesting that additional factors are involved. The system described here allows a systematic comparison of the fusogenicity of viral envelope glycoproteins and may help to clarify whether fusion-promoting variants are associated with increased pathogenicity.

Excretory-secretory products (ESPs) are the main research targets for investigating the hosts and helminths interaction. Parasitic worms can migrate to parasitic sites and avoid the host immune response by secreting this product. Angiostrongylus cantonensisis an important food-borne zoonotic parasite that causes severe neuropathological damage and symptoms, including eosinophilic meningitis or meningoencephalitis in humans. Benzaldehydes are organic compounds composed of a benzene ring and formyl substituents. This compound has anti-inflammatory and antioxidation properties. Previous studies showed that 3-hydroxybenzaldehyde (3-HBA) and 4-hydroxybenzaldehyde (4-HBA) can reduce apoptosis in A. cantonensis ESPs treated astrocytes. These results on the protective effect underlying benzaldehyde have primarily focused on cell survival. The study was designed to investigate the molecular mechanisms of endoplasmic reticulum stress (ER stress) and oxidative stress in astrocytes in A. cantonensis ESPs treated astrocytes and to evaluate the therapeutic consequent of 3-HBA and 4-HBA. First, we initially established the RNA-seq dataset in each group, including Normal, ESPs, ESPs+3-HBA, and ESPs+4-HBA. We also found that benzaldehyde (3-HBA and 4-HBA) can stimulate astrocytes to express ER stress-related molecules after ESP treatment. The level of oxidative stress could also be decreased in astrocytes by elevating antioxidant activity and reducing ROS generation. These results suggested that benzaldehyde may be a potential therapeutic compound for human angiostrongyliasis to support brain cell survival by inducing the expression levels of ER stress- and oxidative stress-related pathway.

Informal caregivers are essential in supporting stroke survivors' recovery, but it is unclear whether community interventions can promote healthy eating behaviors and social participation among stroke survivors and their caregivers. The Train-Your-Brain (TYB) program's impact on adherence to national dietary guidelines and social participation among informal caregivers and stroke survivors were assessed in a quasi-experimental study. The TYB program had nine sessions, with one focusing on healthy lifestyle. Participants were prompted to discuss the topics covered, leading to social participation. Dietary habits were assessed using fruits and vegetables consumption questions, while social participation was measured using the Social Impact Measurement Framework. Mann-Whitney U-test and Wilcoxon Signed Rank Test were utilized to analyze the data. Eighteen informal caregivers and 27 stroke survivors participated in the program. Results showed a significant increase in fruit and vegetable intake and social participation in informal caregivers after the program, but there were no significant changes in stroke survivors. Informal caregivers play a crucial role in stroke survivors' lifestyle habits and in encouraging social connections with other stroke survivors. Thus, healthcare professionals should prioritize caregiver support and education to facilitate long-term dietary and social changes that can positively impact stroke survivor's recovery and quality of life.

Inflammasomes are multiprotein complexes that activate inflammatory responses by inducing pyroptosis and secretion of pro-inflammatory cytokines. Along with many previous studies on inflammatory responses and diseases induced by canonical inflammasomes, an increasing number of studies have demonstrated that non-canonical inflammasomes, such as mouse caspase-11 and human caspase-4 inflammasomes, are emerging key players in inflammatory responses and various diseases. Flavonoids are natural bioactive compounds found in plants, fruits, vegetables, and teas and have pharmacological properties in a wide range of human diseases. Many studies have successfully demonstrated that flavonoids play an anti-inflammatory role and ameliorate many inflammatory diseases by inhibiting canonical inflammasomes. Others have demonstrated the anti-inflammatory roles of flavonoids in inflammatory responses and various diseases, with a new mechanism by which flavonoids inhibit non-canonical inflammasomes. This review discusses recent studies that have investigated the anti-inflammatory roles and pharmacological properties of flavonoids in inflammatory responses and diseases induced by non-canonical inflammasomes and further provides insight into developing flavonoid-based therapeutics as potential nutraceuticals against human inflammatory diseases.

The safe and efficient mining of coal seams with low porosity, low permeability and high inhomogeneity under complex geological conditions is a major challenge, among which the permeability of coal seams is crucial for coal mine gas extraction. The development of coal seam permeability enhancement technology can help coal mines to produce safely and efficiently, and the extracted coal bed methane can be utilized as a green energy. In order to study the effect of freezing and thawing on the evolution of mechanical and permeability properties of coal, triaxial permeability tests were carried out on low-permeability coal under two kinds of surrounding pressures. At the same time, dry, saturated and freeze-thaw coal were set up for comparison, and the effects of water and freeze-thaw were separated from each other. The triaxial mechanics and percolation laws of dry, saturated and freeze-thaw coal rocks were obtained, and the mechanism of the influence of freeze-thaw on the permeability evolution law of coal was revealed. The research results can provide theoretical guidance for the gas extraction technology of low permeability coal seams with increased permeability.

The plant productivity-richness relationship (PPR) is one of the most debated and important issues in ecology. There have been distinct stages in the research of this issue, including the discovery of the different PPR shapes, respective tests of influencing processes, and integrative research with vegetation investigation, manipulation experiments, and theoretical analysis. The debate largely focuses on what the dominant shapes and underlying mechanisms are. Recent integrative research works following analyses of respective processes affecting PPR have found that the humped, asymptotic, positive, negative, and irregular shapes of PPR are linked to each other. One shape of PPR may change into another. The balance between positive and negative processes determines the different shapes of PPR. Plant diversity has a globally positive effect on plant productivity.

In the field of delta-sigma modulators, reducing system power consumption without sacrificing accuracy has become a challenge. The summing circuit, as the main part of the cascade-of-integrator-in-feedforward delta-sigma modulator, consumes a significant amount of power in active summing. On the other hand, passive summing circuits have low power consumption but can cause attenuation in the summing results. This article proposes a low-power multi-bit delta-sigma modulator based on passive and attenuationless summation scheme. The summation circuit achieves multiplication of the voltage signal carried on the summation capacitor through bidirectional sampling technique, compensating for the inherent attenuation caused by passive summation, thus eliminating the need for active OTA to achieve perfect summation. A 2-order delta-sigma modulator based on 4-bit 1-order passive noise-shaping (NS) SAR quantizer is designed to verify the scheme. By combining a passive NS SAR as the 4-bit quantizer and the third-stage integrator of the system, and incorporating an energy-efficient cascode floating inverter amplifier (FIA) structure as the core active OTA, these elements have significantly enhanced the energy-efficiency of the modulator. Simulation results show that at a supply voltage of 1.2 V and a bandwidth of 20 kHz, the SNDR reaches 102.62 dB, power consumption is only 148.32 μW, and Schreier figure-of-merit (FoM) of SNDR is 183.92 dB.

The synthesis 2-tert-butyl-4-methylphenol is of great significance because of its widely application in industry, and the development of highly efficient catalyst is necessary to the alkylation of p-cresol and tert-butyl alcohol. Here, an efficient and mild method was established, caprolactam was chosen as hydrogen-bonding acceptor, p-toluenesulfonic acid was employed as hydrogen-bonding donor, and deep eutectic solvent (DES) was prepared to catalyze the alkylation reaction. The structure of the DES catalyst was characterized by 1H NMR spectra, thermogravimetric analysis and fourier transform infrared spectra (FT-IR). In addition, response surface design based on Box-Behnken method was employed to optimize the alkylation reaction process parameters, the study of reaction kinetics was also carried out subsequently. The recycle performance of the catalyst was evaluated by recovery experiments, and a good result was obtained. Comparing with the literature reported, we here provide a mild method to the synthesis of 2-tert-butyl-4-methylphenol.

Health issues of children with intellectual disabilities (ID) have gained significant attention as a key focus of China health goals. This study aimed to compare the differences in displacement skills and problem behaviors among children with varying degrees and types of ID, while also exploring their relationship. The study included 90 participants who were children with ID and had completed TGMD-2 displacement skills and Strengths and Difficulties Questionnaire (SDQ) assessments. Covariance, partial correlation, and multiple linear regression analyses were conducted to examine the differences and correlations between these factors. The findings revealed that higher severity of intellectual disability was associated with lower scores in locomotor skills and increased manifestation of problem behaviors. Notably, there were significant variations in locomotor skills and problem behaviors among children with different types of intellectual disabilities. There is a positive correlation between locomotor skills and problem behaviors in children with intellectual disabilities, where locomotor skills can predict the occurrence of problem behaviors. In Conclusion, the research confirms that the development of locomotor skills can have a positive impact on the accompanying problem behaviors of children with developmental disabilities.

In autonomous driving systems, high-speed and real-time image processing, along with object recognition, are crucial technologies. This paper builds upon the research achievements in in-dustrial item sorting systems and proposes an object recognition and sorting system for auton-omous driving. In industrial sorting lines, goods sorting robots often need to work at high speeds to efficiently sort large volumes of items. This poses a challenge to the robot's real-time vision and sorting capabilities, making it both practical and economically viable to implement a real-time and low-cost sorting system in a real-world industrial sorting line. Existing sorting systems have limitations such as high cost, high computing resource consumption, and high power consump-tion. These issues lead to the fact that existing sorting systems are typically used only in large industrial plants. In this paper, we design a high-speed, low-cost, low-resource-consumption FPGA (Field-Programmable Gate Array) based item sorting system that achieves similar perfor-mance to current mainstream sorting systems at a lower cost and consumption than existing sorting systems. The recognition part employs a morphological recognition method, which segments the image using a frame difference algorithm and then extracts the color and shape features of the items. To handle sorting, a six-degree-of-freedom robotic arm is introduced in the sorting segment. The improved cubic B-spline interpolation algorithm is employed to plan the motion trajectory and consequently control the robotic arm to execute the corresponding actions. Through a series of experiments, this system achieves an average recognition delay of 25.26ms, ensures smooth operation of the gripping motion trajectory, minimizes resource consumption, and reduces implementation costs.

In this nano era, nanomaterials and nanostructures are popular in developing novel functional materials. However, the combinations of nanoproducts with materials at micro and macro scale can open new routes for conceiving novel materials with an improved or even new functional performances. In this study, a brand-new hybrid, containing both nanofibers and particles having a microsize, was fabricated using a sequential electrohydrodynamic atomization (EHDA) process. Firstly, the microparticles loaded with drug (berberine hydrochloride, BH) molecules in the cellulose acetate (CA) were fabricated using a solution electrospraying process. Later, these microparticles were suspended into a co-dissolved solution containned BH and a hydrophilic polymer (polypyrrolidone, PVP), and were co-electrospun into the nanofiber/microparticle hybrids. The EHDA processes were clearly recorded, and the resultant products showed a typical hybrid topography, with microparticles distributed all over the nanofibers, which was demonstrated by SEM assessments. FTIR and XRD demonstrated that the components within the hybrids were presented in an amorphous state and have fine compatibility with each other. In vitro dissolution tests verified that the hybrids were able to provide the designed dula-step drug release profiles, a combination of the fast release step of BH from the hydrophilic PVP nanofibers through an erosion mechanism and the sustained release step of BH from the insoluble CA microparticles via a typical Fickian diffusion mechanism. The present protocols pave a new way for developing trans-scale functional materials.

Over recent years, the importance of patent literature has become more recognized in the academic setting. In the context of artificial intelligence, deep learning, and data sciences, patents are relevant to not only industry but also academe and other communities. In this article, we focus on deep tomographic imaging and perform a preliminary landscape analysis of the related patent literature. Our search tool is PatSeer . Our patent bibliometric data is summarized in various figures and tables. In particular, we qualitatively analyze key deep tomographic patent literature.

By the study of the pattern book Ying Zao Fa Shi (building regulations of Song Dynasty, 1103 AD), while analyzing the combining and dimensioning rule of timber framework and tile work, a model self-generating program has been compiled for the first time. The operating framework has been firstly defined, while solving the issues of clustering principle, connecting method, output classification, etc. with the detailed description of algorithm theory. Taking the corner bracket set and nine-ridge roof for example, after the compilation and debug by Grasshopper, according to various input parameters, various models have been generated automatically by the plugin, proving the velocity and the veracity of the algorithm.

Influenced by geological, climatic, and natural weathering factors, the Great Wall heritage is sus-ceptible to deformations, posing significant challenges to the comprehensive preservation of the Great Wall sites. To investigate techniques for monitoring deformations in extensive linear cultural heritage sites, such as the Great Wall, this study utilizes the SBAS-InSAR technique for deformation monitoring and analysis. A dataset comprising 161 Sentinel-1A images spanning from March 2017 to January 2022 was chosen for SBAS-InSAR processing, yielding a deformation velocity field. To verify result credibility, a typical mountainous segment spanning approximately 896.53 km within the Great Wall landscape corridor underwent analysis. The findings suggest that around 75.8% of the landscape corridor along the Shanxi section of the Ming Great Wall maintain relative stability, displaying deformation rates varying from -10 to 10 mm/year. The remaining 24.2% of the land-scape corridor experiences notable deformations, including a maximum subsidence rate of 33.1 mm/year and a maximum subsidence of 148.6 mm. This study illustrates the potential utility of the SBAS-InSAR technique for monitoring and evaluating surface deformations in extensive linear cultural heritage sites.

Preeclampsia (PE) continues to be a significant cause of maternal and neonatal morbidity and mortality. Angiotensin II type I receptor autoantibody (AT1-AA) has been reported accompanied by PE women in numerous studies. Therefore, detecting the mechanism of AT1-AA production is the key breakthrough in understanding PE. Deficiency in MFG-E8 has been verified can lead to autoimmune diseases due to dysfunction in macrophage phagocytosis and the production of autoantibodies. In the present study, we examined the expression of MFG-E8 in PE patients. Compared with the control, PE patients had a lower expression of placental MFG-E8 and a higher expression of placental AT1R. Then, we tested the effect of MFG-E8 on AT1-AA production and macrophage phagocytosis of trophoblast apoptosis in PE rats induced by low-dose endotoxin(LPS) and trophoblast model. In vivo, experiments results showed that systolic blood pressure, 24h urinary protein, serum TNF-α,sLFt-1 and AT1-AA levels, and placental trophoblast apoptosis caused by LPS were all significantly alleviated by MFG-E8. Moreover, administrated with MFG-E8 has shown decreased trophoblast apoptosis and AT1R exposure in vitro, and enhanced the phagocytosis of dead trophoblasts by macrophages. That supports the idea that MFG-E8 could be a potential therapeutic target for attenuating AT1-AA and protecting patients from AT1-AA and PE.

TiC particles were prepared from Ti and C powders, and TiC-reinforced Cu-based composites were prepared by powder metallurgy with SPS sintering. The effects of SPS sintering temperature and TiC particle reinforcement phase content on the electrical and mechanical properties of Cu-based composites were investigated. The reinforcement of TiC particles by thermal mismatching at increasing sintering temperature has a significant strengthening effect on the composites, but the high temperature causes softening of the Cu matrix and the growth of grains reduces the properties of the composites. In this study, the effects of porosity and density on the composite properties were not significant.

Shale gas reservoirs (SGR) are important replacements for conventional energy resources and have been widely exploited by hydraulic fracturing technologies. On the one hand, due to the inherent ultra-low permeability and porosity, there is stress sensitivity in the reservoirs generally. On the other hand, hydraulic fractures and the stimulated reservoir volume (SRV) generated by the massive hydraulic fracturing operation have contrast properties with the original reservoirs. These two phenomena bring huge challenges in SGR transient pressure analysis. Although some works in the literatures have been done on the transient pressure analysis of multi-fractured horizontal wells in SGR, unfortunately, none of them has taken the stress sensitivity and spatially varying permeability of SRV zone into consideration simultaneously. To fill this gap, this paper first idealizes the SGR to be four linear composite regions. What’s more, SRV zone is further divided into sub-sections on the basis of non-uniform distribution of proppant within SRV zone which easily yields spatially varying permeability away from the main hydraulic fracture. The stress sensitivity is characterized by the varying permeability depended on the pore pressure. By means of perturbation transformation and Laplace transformation, an analytical multi-linear flow model (MLFM) is obtained and validated by the comparison with the previous model. On the basis of our model, the flow regimes are identified and the sensitivity analysis of critical parameters are conducted to further understand the transient pressure behaviors. The research results provided by this work are of significance for well test interpretation and production performance analysis of SGR.

Limited knowledge is available about the association between autistic spectrum disorder (ASD) and precocious puberty. Our study examined the association between the two medical conditions, and the effect modification by sex and neuropsychiatric comorbidities in a nationwide population. To compare the risk of precocious puberty between ASD and non-ASD cases, we conducted Cox regression analysis, using ASD as exposure and time-to-precocious puberty as outcome. We adjusted for sex, attention-deficit/hyperactivity disorder (ADHD), tics disorder, obsessive–compulsive disorder (OCD), anxiety disorder, intellectual disability, and epilepsy. We performed a moderation analysis to examine the potential moderating effects by sex or comorbidities. Patients with ASD were prone to have precocious puberty, with an adjusted hazard ratio (aHR) of 1.80 (95% CI: 1.61-2.01). For the effect modification, sex, specifically females, moderated the association between ASD and precocious puberty, with relative excess risk due to interaction (RERI) 7.35 (95% CI 4.90-9.80). No significant effect modification was found for any of the comorbidities within the scope of additive effect modification. We found that patients with ASD were prone to precocious puberty, regardless of sex or comorbid neuropsychiatric disorders. Girls with ASD are at particularly higher risk to develop precocious puberty.

We report a self-assembly synthesis of silicon nanoparticles/nitrogen-doped reduced graphene oxide/ carbon nanofiber (Si@N-doped rGO/CNF) composites as potential high-performance anodes for rechargeable lithium-ion batteries (LIB) through the electrostatic attraction between amino and carboxyl groups. Nitrogen atoms generate a large number of vacancies or defects on the graphite plane, providing additional transmission channels for the diffusion of lithium ions, and improving the conductivity of the electrode. Carbon nanofiber (CNF) can help maintain the stability of the electrode structure and prevent silicon nanoparticles from falling off the electrode, prevent silicon nanoparticles from being directly exposed to the electrolyte, and can form a stable solid electrolyte interface (SEI) film. The three-dimensional conductive structure composed of Si, nitrogen atom-doped reduced graphene oxide (N-doped rGO), and CNF can effectively buffer the volume changes of silicon nanoparticles, shorten the transmission distance of lithium ions (Li+) and electrons, and make the electrode have good conductivity and stability in mechanical properties. In addition, compared with the Si@N-doped rGO and Si/rGO/CNF composite electrode, the Si@N-doped rGO/CNF composite electrode shows good cycle performance and rate capability, and its reversible specific capacity can reach 1418.8 mAh/g. The capacity retention rate is 64.7%, and the coulomb efficiency is 95%.

Water quality forecasting is increasingly significant for agricultural management and environmental protection. Enormous amounts of water quality data are increasingly being collected by advanced sensors, which leads to an interest in using data-driven models for predicting trends in water quality. However, the unpredictable background noises introduced during water quality monitoring seriously degrade the performance of those models. Meanwhile, artificial neural networks (ANN) with feed-forward architecture lack the capability of maintaining and utilizing the accumulated temporal information, which leads to biased predictions in processing time series data. Hence, we propose a water quality predictive model based on a combination of Kernal Principal Component Analysis (kPCA) and Recurrent Neural Network (RNN) to forecast the trend of dissolved oxygen. Water quality variables are reconstructed based on kPCA method, which aims to reduce the noise from the raw sensory data and preserve actionable information. With the RNN's recurrent connections, our model can make use of the previous information in predicting the trend in the future. Data collected from Burnett River, Australia was applied to evaluate our kPCA-RNN model. The kPCA-RNN model achieved R2 scores up to 0.908, 0.823 and 0.671 for predicting the concentration of dissolved oxygen in the upcoming 1, 2 and 3 hours, respectively. Compared to current data-driven methods like ANN and SVR, the predictive accuracy of the kPCA-RNN model was at least 8 %, 17 % and 21 % better than the comparative models in these 3 cases. The study demonstrates the effectiveness of the kPAC-RNN modeling technique in predicting water quality variables with noisy sensory data.

The use of additive manufacturing in fabricating composite components has been gaining traction in the past decade. However, some issues with mechanical perfor-mance still need to be resolved. The issue of material porosity remains a pertinent one that needs more understanding to be able to determine viable solutions. Different re-searchers have examined the subject of porosity issues in AM-fabricated CFRP compo-sites. However, more research to quantitatively determine the effects of fabrication temperatures at the micro-scale is still needed. This study employed micro CT scan analysis to quantitatively compare the effects of fabrication temperatures at 230°C, 250°C, 270°C, and 290°C for CF-PA and CF-ABS composites. This followed an SEM evaluation which was used to determine the effects of the temperatures on interlayer and intralayer porosity generation. The porosity volume was related to the mechanical properties results in which it was determined how deposition temperatures affect the porosity volumes. It was also determined that semicrystalline composites are generally more affected by fabrication temperatures than amorphous composites, with the rela-tionship between porosity and mechanical properties also established. The overall po-rosity volume from the interlayer and intralayer voids was also determined, with the interlayer voids found to play a more determinant role in influencing the mechanical properties.

Under the oxidative stress condition, the small RNA (sRNA) Oxys that acts as essential post-transcriptional regulators of gene expression is produced and plays a regulatory function with the assistance of the RNA chaperone Hfq protein. Interestingly, experimental studies found that the N48A mutation of Hfq protein could enhance the binding affinity with OxyS while resulting in defection of gene regulation. But, how the Hfq protein interacts with sRNA Oxys and the origin of the stronger affinity of N48A mutation are both unclear. In this paper, molecular dynamics (MD) simulations were performed on the complex structure of Hfq and OxyS to explore their binding mechanism. The molecular mechanics generalized Born surface area (MM/GBSA) and interaction entropy (IE) method were combined to calculate the binding free energy between Hfq and OxyS sRNA, and the computational result is in excellent correlation with the experimental result. Per-residue decomposition of the binding free energy revealed that the enhanced binding ability of the N48A mutation mainly comes from the increased van der Waals interactions (vdW). This research explores the binding mechanism between Oxys and chaperone protein Hfq, and revealed the origin of the strong binding affinity of N48A mutation. The results provided important insights on the mechanism of gene expression regulation affected by protein mutations.

Wearable technology has been proposed as a potential tool to change the way of human life, such as the smart bracelet and the Google Glass. In the wearable technology, the inertial sensor has great significance in tracking the object movements. The paper focused on detecting the movements of user’s finger based on the inertial sensor to give the control signals. Firstly, the attitude matrix, which represented the transformation relation of carrier coordinate system and the navigation coordinate system, was obtained. Secondly, the attitude matrix was expressed based on the quaternions. Thirdly, the finger gesture was processed by the attitude matrix to get the attitude angle. Finally, the robot was controlled by attitude angle to make the moving action. The experimental results showed the detection of the finger movement is effective.

In coastal areas around the world, seagrass meadows play a crucial ecological and economic role. A large amount of seagrass beds dominates primary production and contributes to the high secondary productivity of this ecosystem. The microbial loop (consuming bacterial biomass by grazers and using seagrass-derived detritus by bacteria) may be an important mechanism for transferring seagrass-derived organic matter to the aquatic food chain. This study aims to improve our understanding of how bacterial growth and mortality (grazing and viral lysis rates) differ in seagrass and adjacent unvegetated meadow habitats. We found that viral lysis and grazing caused similar mortality rates of bacteria in a seagrass environment during the summer. It has been found, however, that bacterial production is controlled by the availability of resources (bottom-up control) and is cycled within the bacteria-virus-DOC loop in adjacent unvegetated waters. Our study region may be affected by this shift in organic matter fate and cycling.

The search of DNA polymorphisms useful for the genetic improvement of dairy farm animals lasts more than 40 years with relevant findings in cattle for milk traits, where the best combination of alleles for dairy processing have been found in casein genes and in the DGAT1. Nowadays, similar results are not reached yet in river buffaloes despite advanced genomic technologies and accurate phenotype records are available. The aim of the present study was to investigate and validate in a larger buffalo population the effect on six milk traits of four single nucleotide polymorphisms (SNP) in the CSN1S1, CSN3, SCD and LPL genes, previously reported to be associated with or affect dairy traits in smaller populations often belonging to one farm. A total of 800 buffaloes were genotyped. Daily milk yield (dMY, kg), protein yield (dPY, kg) and fat yields (dFY, kg), fat and protein contents (dFP, % and dPP, %), somatic cell count (SCC, 103cell/ml) and urea (mg/dl) were individually recorded in each month for the whole lactation from 2010 to 2021. A total of 15,742 individual milk test day records (2,496 lactations) were available on 680 buffalo cows with 3.6±1.7 parity (from 1 to 13) and 6.1±1.2 test day records per lactation on average. Three out four SNP in CSN1S1, CSN3 and LPL were associated with at least one of analyzed traits. In particular, the CSN1S1 (AJ005430:c.578C>T) gave favorable associations with all yield traits (dMY, p=0.022; dPY, p=0.014 ; dFY, p=0.029) and SCS (p=0.032), whereas the CSN3 (HQ677596: c.536C>T) positively associated with SCS (p=0.005) and milk urea (p=0.04). Favorable effect on dMY (p=0.028), dFP (p=0.027) and dPP (p=0.050) were observed for the LPL. Conversely, SCD did not show any association with milk traits. This is the first example of confirmation study carried out in the Mediterranean river buffalo for genes of economic interest in dairy field and it represents a very important indication for the preselection of young bulls destined to breeding programs in the view of a more sustainable dairy production.

This paper proposes an interpretable geometry solution based on the formal language set of text and diagram. Geometry problems are solved by machines, which still poses challenges in natural language processing and computer vision. Significant progress promotes existing methods in the extraction of geometric formal languages. However, the neglect of the graph structure information in the formal language and the lack of further refinement of the extracted language set can lead to the poor effect of the theorem prediction and affect the accuracy in problem solving. In this paper, the formal language graph is constructed by the extracted formal language set and used to theorem prediction by graph convolutional network. So as to better extract the relationship set of diagram elements, an improved diagram parser was proposed. The test results indicate that the improved method has good results in solving the problem with interpretability geometry.

The objective of this study was to investigate the liquefaction resistance of chemically improved sandy soils in a straightforward and accurate manner. Using only the existing experimental databases and artificial intelligence, the goal was to make predictions without conducting physical experiments. Emphasis was placed on the significance of data from 20 loading cycles of cyclic undrained triaxial tests to determine the liquefaction resistance and the contribution of each explanatory variable. Different combinations of explanatory variables were considered. Regarding the predictive model, it was observed that a case with the liquefaction resistance ratio as the dependent variable and other parameters as explanatory variables yielded favorable results. In terms of exploring combinations of explanatory variables, it was found advantageous to include all variables as doing so consistently resulted in a high coefficient of determination. The inclusion of the liquefaction resistance ratio in the training data was found to improve the predictive accuracy. In addition, the results obtained when using a linear model for the prediction suggested the potential to accurately predict the liquefaction resistance using historical data.

This paper considers a two-way relay network, where two source nodes exchange messages through several relays in the presence of an eavesdropper, and the channel state information (CSI) of the eavesdropper is imperfectly known. The amplify-and-forward relay protocol is used and the relay beamforming weights are designed. The model is built up to minimize the total relay transmit power while guaranteeing the quality of service at users and preventing the eavesdropper from decoding the signals. Due to the imperfect CSI, a semi-infinite programming problem is obtained. An algorithm is proposed to solve the problem, and the iterative points are updated through the linesearch technique, where the feasibility are preserved during iterations. The optimality property is analyzed. The obtained subproblems are quadratic constrained quadratic programming problems, either with less than $4$ constraints or with only one variable, which are solved optimally. Simulation results demonstrate the importance of the proposed model, and imply that the proposed algorithm is efficient and converges very fast, where more than 85% of the problems are solved optimally.

To study the feasibility and effect of compounding lignin and its derivatives to improve expansive soil, the physical properties, mechanical properties, and microstructure of the improved soil mixed with calcium lignosulfonate alone, calcium lignosulfonate and lignin fibers were investigated through indoor experiments and electron microscope scanning. Select samples with better performance for water stability tests, and discuss the improvement mechanism of expansive soil by combining macroscopic mechanical changes with microstructural characteristics. The research results show that the addition of calcium lignosulfonate can reduce the water content of the soil and enhance its strength of the soil. The overall performance is the best when the calcium lignosulfonate content is 3%. 3% calcium lignosulfonate compounded with 1.5% lignin fiber has the best all-around performance; the composite improved soil has a better pore-filling effect, the whole is more compact, and the connection between particles significantly enhances the strength, which greatly inhibits the development of cracks good water stability. Lignin and its derivatives compound improved expansive soil have better effect than traditional improvement methods, and have no pollution to the environment, which can provide a particular reference for engineering practice

To study the feasibility and effect of compounding lignin and its derivatives to improve expansive soil, the physical properties, mechanical properties, and microstructure of the improved soil mixed with calcium lignosulfonate alone, calcium lignosulfonate and lignin fibers were investigated through indoor experiments and electron microscope scanning. Select samples with better performance for water stability tests, and discuss the improvement mechanism of expansive soil by combining macroscopic mechanical changes with microstructural characteristics. The research results show that the addition of calcium lignosulfonate can reduce the water content of the soil and enhance its strength of the soil. The overall performance is the best when the calcium lignosulfonate content is 3%. 3% calcium lignosulfonate compounded with 1.5% lignin fiber has the best all-around performance; the composite improved soil has a better pore-filling effect, the whole is more compact, and the connection between particles significantly enhances the strength, which greatly inhibits the development of cracks good water stability. Lignin and its derivatives compound improved expansive soil have better effect than traditional improvement methods, and have no pollution to the environment, which can provide a particular reference for engineering practice.

Three new compounds (1, 11 and 12), together with 32 known ones, were isolated from the root bark of Morus alba L. using various chromatographic methods. The structures of the undescribed compounds were elucidated based on 1D, 2D NMR and HRESIMS data analysis, while the known ones were identified by comparison of their spectroscopic data with those reported in the literature. All the isolates were evaluated for their cytotoxic activities against human gastric cancer HGC27 cells by CCK-8 assay. Among them, compounds 5, 8, 10 and 30 exhibited cytotoxic activities on HGC27 cells with IC50 values of 10.24 ± 0.89 μM, 28.94 ± 0.72 μM, 6.08 ± 0.34 μM and 33.76 ± 2.64 μM, respectively. Furthermore, compound 10 was confirmed to reduce proliferation ability, increase apoptosis rate and inhibit cell migration pathway by annexin V/PI double staining experiment, transwell experiment and western blot analysis.

Frontal Polymerization (FP) is recognized as a polymer synthesis method capable of generating a highly localized and self-propagating exothermic front, considered a more rapid and energy-efficient manufacturing scheme for polymer-based fiber-reinforced composite materials. This study conducts a numerical investigation based on the Finite Element Method into the initiation and propagation of frontal polymerization of acrylamide. Utilizing Differential Scanning Calorimetry (DSC) experiments, the study phenomenologically tests the curing kinetics reactions of acrylamide (AM) in deep eutectic solvent (DES)-based polymerizable systems, thereby establishing a curing kinetics model for the autocatalytic reaction of polyacrylamide. By coupling the heat conduction diffusion equation of the frontal polymerization reaction, the Finite Element Method is employed to solve the reaction-diffusion model, aiming to explore the evolution of temperature and degree of curing during the manufacturing process. The results demonstrate the reliability and accuracy of the nth-order autocatalytic model in the study of curing kinetics for DES-based synthesis, indicating that the front propagation speed of the AM-based FP reaction is influenced by the activation temperature, duration of activation temperature, and initial temperature of the solution. Experimental validation of the polyacrylamide manufacturing through frontal polymerization corroborates the accuracy and reliability of the model predictions, providing a reference for the numerical model study and temperature control in the synthesis of acrylamide composite hydrogels via FP.

The mangrove plant Acanthus ilicifolius and its relative, A. mollis, have been previously proved to possess diverse pharmacological effects. Therefore, evaluating the differentially expressed proteins of these species under tidal flooding stress is essential to fully exploit and benefit from their medicinal values. The roots of A. ilicifolius and A. mollis were exposed to 6 h of flooding stress per day for 10 days. The dry weight, hydrogen peroxide (H2O2) content, anatomical characteristics, carbon and energy levels, and two-dimensional electrophoresis coupled with MALDI-TOF/TOF MS technology were used to reveal the divergent flooding resistant strategies. A. ilicifolius performed better under tidal flooding stress, which was reflected in the integrity of the morphological structure, more efficient use of carbon and energy, and a higher percentage of up-regulated proteins associated with carbon and energy metabolism. A. mollis could not survive in flooding conditions for a long time, as revealed by incomplete cell structures of the roots, less efficient use of carbon and energy, and a higher percentage of down-regulated proteins associated with carbon and energy metabolism. Energy provision and flux balance played a role in the flooding tolerance of A. ilicifolius and A. mollis.

In this paper, we design two inertial-type subgradient extragradient algorithms with line-search process for solving the pseudomonotone variational inequality problems (VIPs) and common fixed-point problem (CFPP) of finite Bregman relatively nonexpansive mapping and a Bregman relatively demicontractive mapping in p-uniformly convex and uniformly smooth Banach spaces, which are more general than Hilbert spaces. Under mild conditions, we derive weak and strong convergence of the suggested algorithms to a common solution of the VIPs and CFPP, respectively. Additionally, an illustrated example is furnished to back up the feasibility and implementability of our proposed methods.

The diffusion coefficient--a measure of dissipation, and the entropy--a measure of fluctuation are found to be intimately correlated in many physical systems. Unlike the fluctuation dissipation theorem in linear response theory, the correlation is often strongly non-linear. To understand this complex dependence, we consider the classical Brownian diffusion in this work. Under certain rational assumption, i.e. in the bi-component fluid mixture, the mass of the Brownian particle $M$ is far greater than that of the bath molecule $m$, we can adopt the weakly couple limit. Only considering the first-order approximation of the mass ratio $m/M$, we obtain a linear motion equation in the reference frame of the observer as a Brownian particle. Based on this equivalent equation, we get the Hamiltonian at equilibrium. Finally, using canonical ensemble method, we define a new entropy that is similar to the Kolmogorov-Sinai entropy. Further, we present an analytic expression of the relationship between the diffusion coefficient $D$ and the entropy $S$ in the thermal equilibrium, that is to say, $D =\frac{\hbar}{eM} \exp{[S/(k_Bd)]}$, where $d$ is the dimension of the space, $k_B$ the Boltzmann constant, $\hbar $ the reduced Planck constant and $e$ the Euler number. This kind of scaling relation has been well-known and well-tested since the similar one for single component is firstly derived by Rosenfeld with the expansion of volume ratio.

Eco-innovation denotes developing green practices using environmentally friendly innovative approaches or technologies. Although, eco-innovation has been broadly applied in different in-dustries, such as low-carbon production and manufacturing, how to implement such innovation in education for sustainable development (ESD) has rarely been studied. Therefore, this research considered a reflective and cooperative learning approach to science, technology, engineering, arts, and math (STEAM) education. A case study examined Wanluan Township of Pingtung County in southern Taiwan. Four departments’ students and teachers at Meiho University were involved in situated learning. Students of hospitality management played farm owners who en-gaged in organic agriculture to produce food and beverages. These agricultural products were farm-to-table, cooked and served for customers in a natural dining setting through the students’ teamwork. Students of tourism, sports & leisure management, and food science & nutrition played tourists in a self-guided travel context, who engaged in acts such as visiting buildings to understand heritage while observing that the houses were still in use a dwelling. This encouraged reflection on the importance of cultural preservation. According to the results from the role-play farm owners and tourists, eco-innovation can represent a sustainable marketing strategy for im-proving the local-community economy and can be implemented in a practical situation in STEAM. The goal of ESD for 2030 — societal transformation — is to foster students’ responsible behavior and attitudes in a personally authentic manner, thereby fostering sustainability learning and understanding.

From the perspective of individual resource and social capital, this paper aims to explain how employees’ political skill affect their job satisfaction and turnover intention, through the mediating role of popularity. Using a sample of 237 dyad surveys from supervisors and employees in the Yangtze River Delta of China, we found that: 1) political skill is positively correlated with job satisfaction and negatively correlated with turnover intention, and that these correlations are partially mediated by individual popularity; 2) positive affect (PA) moderates the relationship between political skill and job satisfaction, and negative affect (NA) moderates the relationship between political skill and turnover intention. The implications of our findings and future research directions are discussed.

Warming-induced drought stress and El Nino associated summer precipitation failure are responsible for increased forest fire intensities of tropical and temperate forests in Asia and Australia. However, both effects are unclear for boreal forests, the largest biome and carbon stock over land. Here we combined fire frequency, burned area and climate data in the Altai boreal forests, the southmost extension of Siberia boreal forest into China, and explored their link with ENSO (El Nino and South Oscillation). Surprisingly, both summer drought severity and fire occurrence have shown significant (P<0.05) teleconnections with La Nina events of the previous year, and therefore provide an important reference for forest fire prediction and prevention in Altai. Despite a significant warming trend, the increased moisture over Altai has largely offset the effect of warming-induced drought stress, and lead to an insignificant fire frequency trend in the last decades, and largely reduced burned area since the 1980s. The reduced burned area could also benefit from the fire suppression efforts and greatly increased investment in fire prevention since 1987.

The physics of Hubble's law, motivated by the anomalous acceleration of Pioneer 10/11, suggests that the Hubble constant corresponds to a gravitational field of the Universe. The Hubble constant inferred from Pioneer 10/11 data is $\sim 69$ km/s/Mpc. The anomalous acceleration of Pioneer 10/11 can be interpreted as an observational evidence for Mach's principle. The redshift is an apparent value. Its intrinsic value has a limit resulting to $v

The microbiota of human breast milk (HBM) contributes to infant gut colonization; however, whether bacterial extracellular vesicles (EVs) are present in HBM or might contribute to this process remains unknown. In the present study, we characterized the HBM microbiota of healthy Korean mothers and measured the key bacteria likely affecting infant gut colonization by analyzing both the microbiota and bacterial EVs. A total of 22 HBM samples were collected from lactating mothers. The DNA of bacteria and bacteria-derived EVs was extracted from each sample. Gene analysis was performed using Illumina MiSeq. Firmicutes accounted for the largest portion among the phyla, followed by Proteobacteria, Bacteroides, and Actinobacteria in both bacteria and bacterial EV samples. At the genus level, Streptococcus (25.1%) and Staphylococcus (10.7%) were predominant in bacterial samples, whereas Bacteroides (9.1%), Acinetobacter (6.9%), and Lactobacillaceae(f) (5.5%) were prevalent in bacterial EV samples. Several genera including Bifidobacterium were significantly positively correlated between the two samples. Our findings reveal the diverse bacterial communities in HBM of healthy lactating mothers and suggest the presence of key bacteria with metabolic activity in HBM and that EVs derived from these bacteria may contribute to the vertical transfer of gut microbiota from mother to infant.

State transition in the multiple-levels system has the great potential applications in the quantum technology. In this article we employ a deterministic approach in complex space to analyze the dynamics of the 1s-2p electron transition in the hydrogen atom. The electron’s spin motion is embodied in the framework of quantum Hamilton mechanics that allows us to examine the transition dynamics more precisely. The transition is driven by an oscillating electric field in the z-direction. The electron’s transition process can be visualized by monitoring its motion in the complex space. The quantum potential and the total energy proposed in this paper provide new indices to observe the dynamic changes of electrons in the transition process.

Purpose:To explore (theoretically) the key parameters and their influence on the time profiles of photosensitizer (riboflavin), free radicals, singlet oxygen, oxygen and the efficacy of corneal collagen crosslinking (CXL)in both type-I and oxygen-mediated type-II mechanisms, specially the role of oxygen and the initiator regeneration. Methodology:Coupled kinetic equations are derived and numerically solved under the quasi-steady state condition for the 2-pathway mechanisms of CXL. The key parameters explored include (bI, V, Q', K, K',Q,P) and their influence on the time profiles of photosensitizer (riboflavin, C), radicals (R), singlet oxygen(S), oxygen (X) and efficacy (E), parameters of (K,K',Q) define the relative strength of type-I and type-II process. The oxygen depletion profile, X(t), and the associated singlet oxygen, S(t), depend on the parameters of V, Q' and the initial value of oxygen. The coupling strength given by (bI) governs almost all profiles, where b is an effective absorption parameter and I is the UV light intensity.Results:Our numerical method for CXL dynamic profiles demonstrated the following important features: (i) Type-I and type-II coexit in CXL, in the presence of oxygen. However, there is no type-II when oxygen is depleted or in a condition without oxygen. (ii) Type-I with bimolecular termination, the radical R(t) is a function of [K'(bIgC)]^0.5, leading to the steady-state efficacy given by a scaling law of 1/(bI)^0.5, in contract to that of type-II which is almost independent to the light intensity. (iii) The depletion rate (2 to 5 minutes) of X(t) is much faster than that of C(t) (10 to 20 minutes), (iv) Thepure type-II profile, has a transition point from straight line to saturating curve and matches the depletion point of singlet oxygen S(t). (v) Improved CXL efficacy of type-I and type-II may be achieved by external supply of photoinitiator (riboflavin) and oxygen, respectively.

This article explored how some state policies on rehabilitation, protection and utilization of mangroves are implemented at two locals; namely, Ca Mau provice and its sub-unit Ngoc Hien District as well as Ho Chi Minh City and its sub-unit Can Gio District and what are considered as emgerging issues in the implementation process. The study employed mixed research methods including document and thematic analysis, in-depth interviews, and group discussion. The findings showed that state policies would be adapted through the ways in which these two locals manage to silmultaneously orient their work toward accommodating the madates placed upon them and cope with the reality of socio-economic difficulties at their levels. The findings also showed some issues that need to consider addressing; namely, locals’ struggles to fill the gaps between written and implemented policies in mangrove rehabilitation, conflict among sectors and stakeholders in mangrove utilization and protection, problems in guaranteeing adequate budget for in-need-locals, inconsistence in policies on "forest: water surface” ratio in mangrove-shrimp farm, and unintended consequences in forest thinning and exploitation regimes. Based on the finding analysis, the articles discussed lessons learned from the policy implementation process on mangrove rehabilitation, management and forest allocation to household in the south of Vietnam.

The article delves into the intricate phase transitions of 1-Octadecanol and n-Nonadecane within a binary system, unveiling dynamic structural changes under varying conditions. Through Fouri-er-transform infrared (FTIR) analysis, specific molecular vibrations were identified, shedding light on the molecular composition and interactions. The study highlights the challenges in detecting subtle phase transitions and emphasizes the individuality of molecular behaviours in closely related compounds. The findings underscore the complexity of phase transitions in binary systems and advocate for a nuanced approach to studying molecular structures and behaviours. Addi-tionally, the research contributes valuable insights into the molecular dynamics of Organic Polymer Functional Adsorption Materials, offering implications for synthetic waxy layers in plant epidermis.

Marine industries are dependent on the ocean and often have a direct impact on the marine environment. Although previous studies have talked about pollution from marine industries, it is not known to what extent and to what extent these behaviors affect the ocean. Therefore, this study examines the impacts of major marine industries on the marine environment, using China as an example. The Ministry of Ecology and Environment of the People's Republic of China categorizes the quality of seawater into Class I, Class II, Class III, Class IV, and Inferior Class IV in descending order according to the quality from good to poor. In this study, water quality of Class I and Class II are defined as good quality seawater, Class III as average quality, Class IV and Class IV as poor quality, and the proportion of the area of seawater with different water quality to the total area of the sea area symbolizes the overall quality of the marine environment. We use panel data from 11 provinces and cities along the coast of mainland China from 2006 to 2020, estimated by the full feasible generalized least squares (FGLS) method, to study the impacts of the development of coastal tourism, marine transportation, marine shipbuilding, marine salt, mariculture, and marine fishing on the marine environment. The empirical results show that, in descending order of the degree of influence, the marine industries that cause a decrease in the area of good quality seawater are coastal tourism, mariculture and shipbuilding, and the marine industries that cause an increase in the area of poor quality seawater are marine transportation and shipbuilding, while the marine salt industry has no obvious influence on the marine environment, and the development of the marine fishing industry indirectly contributes to the improvement of the marine environment.

Multi-Object Tracking (MOT) is a key technology for Unmanned Aerial Vehicles (UAVs). Traditional tracking-by-detection methods firstly employ an object detector to retrieve targets in each image and then track them based on a matching algorithm. Recently, the popular multi-task learning methods has been dominating this area since they can detect targets and extract Re-Identification (Re-ID) features in a computationally efficient way. However, the detection task and the tracking task have conflicting requirements on image features, leading to the poor performance of the joint learning model compared to separate detection and tracking methods. The problem is more severe when it comes to UAV images due to the presence of irregular motion of large number of small targets. In this paper, we propose a balanced Joint Detection and Re-ID learning (JDR) network to address the MOT problem in UAV vision. To better handle the non-uniform motion of objects in UAV videos, the Set-Member Filter is applied which describes object state as a bounded set. An appearance matching cascade is then proposed based on target state set. Furthermore, a Motion-Mutation module is designed to address the challenges posed by the abrupt motion of UAV. Extensive experiments on the VisDrone-MOT2019 dataset demonstrate that our proposed model, termed as SMFMOT, outperforms state-of-the-arts by a large margin and achieves superior performance on the MOT tasks in UAV videos.

Detection of early stage corrosion on slender steel members is crucial for preventing buckling failures of steel structures. An active photoacoustic fiber optic sensors (FOS) system has been reported for early stage steel corrosion detection of steel plates and rebars using surface ultrasonic waves. The objective of this paper is to investigate the surface corrosion/rust detection problem on steel rods using numerically simulated surface ultrasonic waves. The finite element method (FEM) is applied in simulating the propagation of ultrasonic waves on steel rod models. Transmission mode of damage detection is adopted, in which one source (transmitter) and one sensor (receiver) are considered. In this research, radial displacements at the receiver were simulated and analyzed by short-time Fourier transform (STFT) for detecting, locating, and quantifying a surface rust located between the transmitter and the receiver. From our time domain and frequency domain analyses, it is found that the presence, location, and dimensions (length, width, and depth) of surface rust can be estimated by ultrasonic waves propagating through the surface rust.

A new biodegradable paper-based film is a hot research topic in the development of green agriculture. In this paper, a black paper base film coated with cooked tung oil with excellent mechanical properties, hydrophobic surface, high heat transfer and strong weather resistance was prepared by spraying high pigment carbon black solution on the surface of base paper. The results showed that the surface solidified oil film with rough structure was made by the brush coating process of cooked tung oil, and the base film of black paper was given hydrophobic structure, and the contact angle reached 98.9 °. Cooked tung oil permeates into the inside of the paper base, and after curing, it forms a multi-dimensional network film structure. The maximum tensile stress of the black paper base film is about 123% higher than that of the original paper base film. The coloring of carbon black makes the black paper base film have heat conduction effect, and the average heat transfer rate reaches 15.12 °C/s. Cooked tung oil is combined with the paper-based fiber high-toughness layer to form a stable system. The existence of the cured film improves the basic mechanics and hydrophobicity, and the resistance to ultraviolet radiation and hot air is greatly improved. This study provides a feasible scheme for the application of a black paper base film coated with cooked tung oil.

Nicotine exposure may affect NSCLC is associated with lung cancer in humans. Whether nicotine exhibits carcinogenesis promoted activities in tumor growth still unknown. Nicotine is known to have dichotomous effects on cancer biology, acting like a pro- or anti-carcinogenesis agent. There are different functions between adenocarcinoma and squamous NSCLC cancer cells. Excess generation of nicotine may inhibit mitochondrial metabolism, protein modification, and DNA cleavage. Materials and Methods: We used the H520 NSCLC line obtained from human lung epithelial cells to detected nicotine growth and toxicity using MTT assay and western blotting. The concentration of nicotine stimulated cell growth to correspond to low concentration, while high concentration was cytotoxic. Results: According to MTT assay results, at 1.0 μM nicotine has significantly enhanced the H520 cell viability (%). Nicotine induced lung cancer carcinogenesis through mechanisms of α7nAchR, EGFR, HDAC2/4/5, Cyclin D/Cyclin E, Bcl-2, p-Akt, and inflammatory proteins of NF-KappaB and COX2 increases at 1.0 μM. Apoptosis proteins were decreases at 1.0 μM nicotine by p21, p27, c-jun, and p38α using western blotting. Nicotine stimulates tumor growth is mediated through α7nicotinic-acetylcholine receptors (α7nAChR), possibly involving inflammation. On the other hand, at high nicotine concentrations (> 1.0 μM) with consistent cytotoxic effects and appeared to be due to direct cell kill. Nicotine can prevent apoptosis induced by NSCLC. Conclusion: Therefore, the effects on chemotherapeutics by NSCLC malignant cell lines, nicotine in concentrations as low as 1.0 μM decreased. These mechanisms are responsible for the genotoxic effects caused by nicotine. This leads to downstream effects on decreased apoptosis, increased cell proliferation and transformation. The malignant NSCLC cells respond to the treatment with nicotine in lung cancer, the nicotine-mediated induction of growth may provide one of its links to α7nAchR or EGFR.

Our previous studies demonstrated that quercetin (Q) could be ingested and metabolized by macrophages and exerted prophylactic immuno-stimulatory activity and therapeutic anti-inflammatory effects on lipopolysaccharide (LPS)-treated macrophages ex vivo. To further clarify its possible anti-inflammatory mechanism, Q was selected to treat mouse peritoneal macrophages that obtained from female BALB/c mice exposed to LPS i.p. for 12 h. Relative gene expression of pro-/anti-inflammatory (TNF-α/IL-10) cytokines and components of inflammation-related intracellular signaling pathways (TLR2, TLR4, NF-κB, JAK2 and STAT3) was analyzed using two-step reverse transcription (RT) and real-time quantitative polymerase chain reaction (qPCR). STAT3 protein phosphorylation was determined using an in-cell ELISA method. As a result, Q and its metabolite quercetin-3-O-β-D-glucuronide (Q3G) decreased TNF-α gene expression amounts and ratios of pro-/anti-inflammatory (TNF-α/IL-10) cytokine gene expressions, but increased IL-10 gene expression amounts in activated inflammatory macrophages, supporting a substantial anti-inflammatory potential of Q and Q3G treatments. However, Q3G had lower effects than those of Q. Importantly, Q inhibited TLR2 gene expression and phosphorylation of STAT3 protein in the inflamed cells. Our results are the first report to suggest that Q inhibits LPS-induced inflammation ex vivo through suppressing TLR2 gene expression and STAT3 protein phosphorylation in activated inflammatory macrophages. Q has potential to further apply for treating inflammation-associated diseases.

This article provides a thorough overview of biomarkers, pathophysiology, and molecular pathways involved in the transition from acute kidney injury (AKI), acute kidney disease (AKD) to chronic kidney disease (CKD). It categorizes the biomarkers of AKI into stress, damage, and functional markers, highlighting their importance in early detection, prognosis, and clinical application. The pathophysiological mechanisms underlying AKI, AKD, including renal hypoperfusion, sepsis, nephrotoxicity, and immune responses, with the impact on renal injury. In addition, various molecules play pivotal roles in inflammation and hypoxia, triggering maladaptive repair, mitochondrial dysfunction, immune system reactions, and cellular senescence of renal cell. Key signaling pathways, such as Wnt/β-Catenin, TGF-β/SMAD, and Hippo/YAP/TAZ, promote fibrosis and impact renal function. The Renin-Angiotensin-Aldosterone System (RAAS) triggers a cascade leading to renal fibrosis, with aldosterone exacerbating oxidative stress and cellular changes that promote fibrosis. Clinical evidence suggests RAS inhibitors may protect against CKD progression, especially post-AKI, though more extensive trials are needed to confirm their full impact.

Marek’s disease (MD) is an important neoplastic disease caused by serotype 1 Marek's disease virus (MDV-1), which results in severe economic losses worldwide. Despite vaccination practices that have controlled the MD epidemic, current increasing MD-suspected cases indicate the persistent viral infections circulating among vaccinated chicken farms in many countries. However, the lack of available information about phylogeny and molecular characterization of circulating MDV-1 field strains in Taiwan reveals a potential risk in MD outbreaks. This study investigated the genetic characteristics of 18 MDV-1 isolates obtained from 17 vaccinated chicken flocks in Taiwan between 2018 and 2020. Based on the sequences of the meq oncogene, phylogenetic analysis demonstrated that the circulating Taiwanese MDV-1 field strains were predominantly in a single cluster that showed high similarity with strains/isolates from countries of East Asian region. Because the isolated strains were obtained from CVI988/Rispens vaccinated chicken flocks and the molecular characteristics of the Meq oncoprotein showed the features like vvMDV and vv+MDV strains, the circulating Taiwanese MDV-1 field strains may have higher virulence compared with vvMDV pathotype. In conclusion, the presented data demonstrate the circulation of hypervirulent MDV-1 strains in Taiwan and highlight the importance of routine surveillance and precaution strategies in response to the emergence of enhanced virulent MDV-1.

This paper proposes a single-stage asymmetrical half-bridge fly-back (AHBF) converter with resonant mode using dual-mode control. The presented converter has an integrated boost converter and asymmetrical half-bridge fly-back converter and operates in resonant mode. The boost-cell always operates in discontinuous conduction mode (DCM) to achieve high power factor. The presented converter operates simultaneously using a variable-frequency-controller (VFC) and pulse-width-modulation (PWM) controller. Unlike the conventional single-stage design, the intermediate bus voltage of this controller can be regulated depending on the main power switch duty ratio. The asymmetrical half-bridge fly-back converter utilizes a variable switching frequency controller to achieve the output voltage regulation. The asymmetrical half-bridge fly-back converter can achieve zero-voltage-switching (ZVS) operation and significantly reduce the switching losses. Detailed analysis and design of this single-stage asymmetrical half-bridge fly-back converter with resonant mode is described. A wide AC input voltage ranging from 90 to 264 Vrms and output 19 V/ 120 W prototype converter was built to verify the theoretical analysis and performance of the presented converter.

Previous research has paid little attention to the relationship between corporate governance and brand equity in the tourism industry. This study aims to investigate the moderating effect of corporate governance (CG) on the relationship between brand equity (BE) and corporate profit-ability (CP) with ten control variables including five company characteristics and five macroeconomic variables. Financial data is retrieved from the Taiwan Economics Journal Database (TEJ), covering a total of 196 records from 32 listed companies for 16 years. Results indicate that BE has a significantly positive impact on CP in Taiwanese-listed tourism companies, and that CG moderates the relationship between BE and CP. These findings could help management executives enhance profitability by deepening BE and CG in the tourism industry. Managerial implications are also discussed.

The stability of the filling roof as an important bearing unit in the stope of the access stope in the filling mining method is of great significance to guarantee the safe and efficient production of the mine. Arrangement of mining approach in downward cemented filling stope is the key factor af-fecting stability of filling body roof. Based on combination of laboratory test, theoretical analysis and numerical simulation, the influence of different mining approach arrangement on stability of filling body roof is analyzed. The weak filling surface is formed between adjacent mining paths. The mechanical strength of the weak filling surface is significantly reduced by laboratory experiments. The relationship between distribution of the weak filling surface, azimuth angle and stability of filling roof is further studied by numerical simulation. The results show that the stability of the filler roof is the best when the upper and lower stratified mining approach is arranged vertically.

The present study propose an innovative turn-boring operation method and focuses on finding optimal turn-boring process parameters for 15-5PH Stainless steel by considering multiple performance characteristics using Taguchi orthogonal array with the grey relational analysis, the effect of machining variables such as concentration of cutting fluid , temperature of cutting fluid , feed rate, depth of cut and cutting speed are optimized with considerations of multiple performance characteristics namely surface roughness, roundness error and material removal rate, the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) is shown that the most significant factor is cutting speed, followed by feed rate, concentration of cutting fluid, radial depth of cut and temperature of cutting fluid. Finally, confirmation tests were carried out to make a comparison between the experimental results and developed model. Experimental results have shown that machining performance in the turn-boring process can be improved effectively through this approach.

Nanoparticle-assisted polymerase chain reaction (nanoPCR) is a novel method for rapid detection of pathogens. A sensitive and specific multiple nanoPCR assay was developed for simultaneous detection of three subgroups avian leucosis virus (ALV). In this study, three pairs of primers that were designed based on gp85 gene sequences of subgroup A, B and J, optimizations were carried out for better performance of the nanoPCR assay and remarkably improved the detection limit of the assay. The detection limit of the assay was 101copies/μl, which was 100-fold more sensitive than conventional PCR assays. The nanoPCR assay amplified the specific 336 bp, 625 bp and 167 bp fragments of ALV-A, -B and –J respectively and no cross-reactivity were found with the genomic DNA or cDNA of Newcastle disease virus, avian infectious laryngotracheitis virus, Marek's disease virus, infectious bronchitis vaccine, infectious bursal disease virus and avian encephalomyelitis Virus. To evaluate the detection ability of the assay, 186 clinical samples were detected by nanoPCR and the detection results were further confirmed by sequencing. The sequencing results were 100% in coincidence with the nanoPCR detection results, suggesting the possibility of clinical application of the newly developed nanoPCR assay. Taking all the results together, a highly specific and sensitive nanoPCR was successfully developed, which could be useful for rapid diagnosis of ALV in poultry farming.

Hepatitis E virus (HEV) is a common cause of acute hepatitis worldwide, accounting for approximately 25% of deaths among pregnant women. We previously reported that pregnancy serum facilitates HEV replication in vitro. However, the differences in host cells with HEV infection induced by pregnancy serum and fetal bovine serum (FBS) are unclear. In this study, differentially expressed proteins were identified in HEV-infected hepatoma cells (HepG2) supplemented with different sera by using isobaric tags for relative and absolute quantitation. Proteomic analysis indicated that HEV infection significantly induced 1014 differentially expressed proteins in HEV-infected HepG2 cells when supplemented with FBS compared with pregnancy serum. Further validation by Western blot confirmed that filamin A, heat-shock proteins 70 and 90, Cytochrome c, and Thioredoxin were associated with HEV infection. This comparative analysis provides an important basis to further investigate HEV pathogenesis in pregnant women and HEV replication.

Few studies have examined the dietary protein intake and sources in combination with the longitudinal changes in brain structure markers. Our study aimed to examine the association between dietary protein intake and different sources of dietary protein with the longitudinal rate of change in brain structural markers. A total of 2723 and 2679 participants from the UK Biobank were included in the analysis. The relative and absolute amounts of dietary protein intake were calculated through a 24-hour dietary recall questionnaire. The longitudinal change rates of brain structural biomarkers were computed using two waves of brain imaging data. We utilized multiple linear regression to examine the association between dietary protein and different sources and the longitudinal changes in brain structural biomarkers. Restrictive cubic splines were used to explore nonlinear relationships, and stratified and sensitivity analyses were conducted. Increasing the proportion of animal protein in dietary protein intake is associated with a slower reduction in the total hippocampus volume (THV, β: -0.02524, P<0.05), left hippocampus volume (LHV, β: -0.02435, P<0.01) and right hippocampus volume (RHV, β: -0.02544, P<0.05).Higher intake of animal protein relative to plant protein is linked to a lower atrophy rate in THV (β: -0.01249, P<0.05) and LHV (β: -0.01173, P<0.05) and RHV (β: -0.01193, P<0.05). Individuals with higher intake of seafood exhibit lower longitudinal rate of change in HV compared to those with no consume (THV, β: -0.004514; P<0.05; RHV, β: -0.005527, P<0.05). In subgroup and sensitivity analyses, there was not a significantly alteration. A moderate increase in the intake and proportion of animal protein in the diet, especially from seafood, is associated with a lower longitudinal change rate in hippocampus volume.

(1) Background: Ectopic pregnancy (EP) is when fertilized ovum implanted outside of uterine cavity. The incidence was 1.5% to 2.0% of all pregnancies [1, 2]. EP decreases fertility ability [3]. This study focused on the pregnancy rate after EP and various parameters influencing pregnancy. (2) Methods: The data was extracted from National Health Insurance Research Database (NHIRD) during 2000-2013. The study group enrolled women with one EP history. Those who did not have EP was put into the control group. Chi-square/Fisher exact test was performed on category variable and t-test was used on continue variables. (3) Results: The EP group had a higher cumulative rate of conceive, 41.55% than control group, 37.14% and 1.16 times higher risk in developing pregnancy (P<0.001). The conceiving rate was lower than the control group in the first 5 years, but higher than the control group in the 10th to 14th years. (4) Conclusions: The fecundability after EP is lower at the beginning, but regained after long term follow.

The increasing prevalence of obesity and sedentary lifestyles has led to an increased incidence of metabolic syndrome (MetS) worldwide. In Taiwan, middle-aged women are at a greater risk of MetS, type 2 diabetes, and cardiovascular disease than men are because they have more subcutaneous fat and larger waist circumferences compared to men with equal visceral fat levels. This study investigated the effects of calorie restriction supplemented with fish oil (CRF) in middle-aged women with MetS. For 12 weeks, 75 eligible participants were randomly assigned either calorie restriction (CR) or CRF. Both dietary intervention groups were further divided into two age groups: ≤45 and >45 years. The changes in MetS severity, inflammatory status, iron status, and red blood cell fatty acid profile were evaluated. Seventy-one participants completed the trial. Both dietary interventions significantly ameliorated MetS and improved the participants’ inflammatory status. CR significantly increased total iron binding capacity, whereas CRF increased hepcidin levels. Furthermore, CRF significantly increased the n-6/n-3 and arachidonic acid/docosahexaenoic acid ratios. In conclusion, CR and CRF improved the anthropometric and MetS characteristics of early-middle aged women, including body weight, blood glucose levels, triglyceride levels, as well as the scores for the homeostasis model assessment of insulin resistance and quantitative insulin sensitivity cheque index. Dietary intervention was more effective in >45-year-old women than ≤45-year-old women.

Medium-deep Borehole Heat exchanger (MBHE) has been widely paid attention for building heating. In order to avoid the thermal interference of adjacent MBHEs, temperature distribution characteristics of medium-deep rock-soil was investigated in this work. The evolution on maximum rock-soil thermal affected radius (MTAR) in full life cycle was further analyzed. The results showed that the rock-soil thermal affected area (RTAA) continuously expands in both radial and vertical directions during a heating season. Several factors of thermal extraction load, fluid velocity, geothermal gradient and pipe length have main impacts on the RTAA in vertical direction. Rock-soil thermal conductivity affects the RTAA in both radial and vertical directions. In non-heating season, temperature difference of rock-soil in radial direction dominates the temperature recovery in the first year while geothermal flow nearly dominates temperature recovery after 20 years. The thermal affected radius (TAR) in the 30 th year could be determined as an effective MTAR in full life cycle. In order to decrease installed distance of adjacent MBHEs, a new well layout form by deflecting borehole was proposed. The recommended deflection angle is equal to or larger than 4 times TAR angle. This work provides scientific reference for promoting application of multiple MBHE arrays.

Open source software such as OpenDSS has given a lot of help to distribution network researchers and educators. With high penetration of distributed renewable energy resources into distribution network, tradition distribution steady state analysis software such as OpenDSS is faced with difficulty in handling distributed generators. Three-phase distributed generators are often modeled in sequence frame while unbalanced distribution network are usually modeled in phase frame. So a load flow in sequence-phase coupled frame is proposed to handle models described in both frames. Voltage controlled DGs which are difficult to cope with in OpenDSS are handled in proposed program. The steady state analysis platform is programmed with open source Modelica language and the main aim of this paper is to introduce an open source platform for active distribution network steady analysis include load flow and short circuit analysis which can be easily adopted and improved by other educators and researchers.

Cloud-induced atmospheric extinction and occlusion have a major effect on the effectiveness and quality of telescope observations. Real-time cloud cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. At ground-based astronomical telescope sites, cloud cover distribution is currently analyzed using manual observation methods. However, the main disadvantages of manual observation methods are human subjective, heavy workloads and poor real-time performance. Therefore, a real-time automatic cloud images classification method is desperately needed. This paper presents a new cloud identification method, which is named PSO+XGBoost model by combining eXtreme Gradient Boosting (XGBoost) and Particle Swarm Optimization (PSO). The entire cloud image is divided into 37 sub-regions in order to more precisely identify the distribution of the clouds. 19 features are then extracted, including the sky background, star density, lighting conditions, and subregion grayscale values. The experimental results have shown that the overall classification accuracy of 96.91% and our model is able to outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.

Background: Coronavirus disease (COVID-19) related pneumonia is leaded by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which infects host cells through receptors named Angiotensin-converting enzyme 2 (ACE2). Smoking is thought to be related to poor disease prognosis, as a large amount of evidence highlights the negative effects of smoking on lung health and its causal relationship with various respiratory diseases. Methods: We first evaluated the role of ACE2 expression level with tobacco effect. And correlation analyses were used to identify the related genes of ACE2 both in smoker trait and human normal multi-tissues. After intersections, hub genes were later used to further GSEA and co-regulated mechanisms were explored by GeneMANIA. We used UALCAN to perform survival curves of pan-cancers included 33 types of cancers. New clinical model of top co-occurrence cancer type was constructed and validated. Results: Effected by cigarette smoking, the expression level of ACE2 expressed statistically upwards in current smokers compared with never smokers, upwards in groups after acute smoke exposed compared with normal control. But no strong evidence detected in third-hand smoke, as poor amounts of samples, only 4. A little trend of expressing upwards became in groups after third-hand smoke compared with groups after clean air exposing. 4 genes included PIR, ADH7, AKR1C2 and AKR1C3 were identified as ACE2 related genes in smoker trait and human normal multi-tissues. Then, we made the survival curves of pan-cancers in 4 genes. Brain lower grade glioma was the co-occurrence type as both ACE2, PIR and AKR1C3 had the significantly prognostic situation. Later, we made the new clinical prediction model as the C-index was 0.827 and the Area Under Curves (AUCs) of 1-year survival, 2-year survival and 3-year survival were 0.921(95%CI, 0.882-0.961), 0.911(95%CI, 0.860-0.962) and 0.878(95%CI, 0.818-0.939). In inner validation, the AUCs of 1-year survival, 2-year survival and 3-year survival were 0.777(95%CI, 0.640-0.914), 0.916(95%CI, 0.842-0.990) and 0.888(95%CI, 0.768-1.000). In outer validation one, the AUCs of 1-year survival, 2-year survival and 3-year survival were 0.764(95%CI, 0.672-0.856), 0.848(95%CI, 0.783-0.913) and 0.748(95%CI, 0.656-0.841). And in outer validation two the AUCs of 1-year survival, 2-year survival and 3-year survival were 0.740(95%CI, 0.654-0.826), 0.766(95%CI, 0.688-0.843) and 0.794(95%CI, 0.721-0.866). Conclusions: In our study, we compared the ACE2 expression level between smokers and non-smokers crowds and identified associated hub genes. Then we explored the further role of hub genes in tumor fields to identify the correlation and influence between the avoidable host factors such as smoking on COVID-19 contamination and tumor. ACE2 and AKR1C3 are potential prognostic genes for brain lower grade glioma, and we created the web dynamic nomogram and encapsulation app through validations.

Excessive body fat is a major contributor to obesity, which is related to high blood, total cholesterol and triglycerides. Lemons (Citrus limon) are renowned for their antioxidant and lipid-lowering properties. First, Sugar-free fermentation demonstrated better results lower sugar content and was identified as the preferred fermentation method. Next, the effect of two doses of sugar-free fermented lemon peel juice (SF-FLPJ) on obesity was investigated. Sprague Dawley (male rats) were randomly divided into 4 groups and fed a standard feed or high-fat diet (HFD) to induce fatness for eight weeks. The two groups of rats fed an HFD were administered SF-FLPJ at 0.45 or 0.9 g/kg body weight daily for eight weeks. The obese rats administered 0.9 g/kg (body weight/day) of SF-FLPJ exhibited significant reductions in body fat mass and percentage; they also had lower blood cholesterol and triglyceride levels than the other groups. Additionally, the rats displayed diminished liver fat accumulation. The results suggest that SF-FLPJ can be a supplement in managing body fat, contributing positively to addressing obesity within the context of health management, and reduce obesity-associated issues.