Although the Orthotropic steel decks (OSDs) have been widely used in the construction of long-span bridges, there are frequently reported fatigue cracks after years of operation, and the bridge deck overlay also presents severe damage due to OSD crack-induced stiffness reduction. The ultra-high performance concrete (UHPC), recognized as the most innovative cementitious composites and the next generation of high-performance materials, shows high strength, ductility, toughness and well performance on durability. After its first application to the OSD bridge in early 2000s, the orthotropic Steel-UHPC composite deck has been comprehensively studied worldwide. This review will summarize some important studies and findings on behavior and fatigue performance of orthotropic steel-UHPC composite deck. The existing studies and engineering application indicate that such deck system presents good bending behavior and high fatigue performance. The failure mode of shear studs in UHPC layer is dominated by shear fractures. The cracking of UHPC layer shall consider the superposition effect of stress from both the whole bridge structure and local decks. While some reasonable structural details in the traditional OSD may not work for the orthotropic steel-UHPC composite deck. It is recommended to evaluate the stress behavior and structural parameters, as well as fatigue life by conducting the field test under in-service traffic conditions.

In contemporary society, an increasing number of individuals are identified as the "Indoor Generation". Prolonged periods spent indoors can have detrimental effects on our overall well-being. Consequently, the examination of biophilic indoor environments and their impact on occupant well-being and comfort has emerged as a significant area of study. Besides the construction of new biophilic buildings, numerous existing building stocks retain substantial social, economic, and environmental value. The need for stock renovation is considerable, revitalizing existing structures rather than embarking on new constructions. Swift feedback is crucial during the initial stages of renovation design, fostering consensus among all stakeholders. A comprehensive understanding of proposed renovation plans contributes to improved indoor environment design. Initially, generative adversarial networks (GANs) produce interior renovation drawings based on user preferences and designer styles. Subsequently, the implementation of Mixed Reality (MR) and Diminished Reality (DR) provides users with an immersive perspective, transforming 2D drawings into interactive MR experiences, and facilitating the presentation of interior renovation proposals. This paper outlines the development of a real-time system for architectural designers that seamlessly integrates MR, DR, and GANs results, with the aim of enhancing feedback efficiency during the renovation design process, enabling stakeholders to evaluate and understand renovation proposals more comprehensively. Furthermore, we incorporate several full-reference image quality assessment (FR-IQA) methods to evaluate the quality of the GANs-generated images. The evaluation results indicate that the majority of images fall within the moderate range of quality.

In recent years, graph neural networks (GNNs) have become a promising method for analyzing data structured in graph format. By considering connections between entities in a graph, GNNs are able to extract valuable insights. One notable variation of GNN is the graph attention network (GAT), which employs the attention mechanism and has demonstrated promising performance in various applications. However, its ability to incorporate feature information from nodes beyond the immediate neighborhood is limited, leading to degraded performance on heterophilic data. To address this limitation, this thesis proposes a novel attention-based model, namely the Directional Graph Attention Network (DGAT). This model combines the feature-based attention with the global directional information extracted from the graph topology, as inspired by the Directional Graph Network (DGN). A new class of Laplacian matrices is proposed and an existing theoretical result on DGN is extended. This extension bridges a gap in the literature. The experimental results presented in the thesis, based on nine real-world benchmarks and ten synthetic data sets, demonstrate the superiority of the proposed DGAT model compared to the GAT baseline model. Particularly on heterophilic data sets, DGAT showed a notable average increase of approximately 35% in node classification tasks across all heterophilic real-world data sets. In addition, DGAT outperforms GAT by an average margin of around 51% in all ten synthetic data sets with various levels of heterophily

With the development of data science, more and more machine learning technologies have been designed to solve complicated and challenging real-world tasks containing a large volume of data. And many significant real-world datasets contain data in the form of networks or graphs. Graph Neural Networks is one of the powerful machine learning tools, which could provide a perfect solution to processing a large amount of non-Euclidean data. And because most bio information data in bioinformatics is in the non-Euclidean domain, Graph Neural Networks could then directly be applied to solve problems in bioinformatics. Much research has been done in the field of GNN, and there are also some surveys related to GNN and its applications. However, there has been little research focusing on GNN in bioinformatics, and we think in the future we could better utilize GNN in the field of biology, so we would like to write a literature review to help take a comprehensive look at GNN and their applications in the field of bioinformatics. In this paper, we would first introduce SOTA models in Graph Neural Networks, and second, introduce their applications in bio information. And then we would provide future directions of Graph Neural Networks in bioinformatics.

Pre-trained Language Models (PTLM) have remarkable and successful performance in solving lots of NLP tasks nowadays. And previous researchers have created many SOTA models and these models are included in many long surveys(Qiu et al., 2020). So, we would like to conduct a simple and short survey on this topic to help researchers understand the sketch of PTLM more quickly and comprehensively. In this short survey, we would provide a simple but comprehensive review of techniques, benchmarks, and methodologies in PTLM. And we would also introduce the applications evaluation of PTLM in this simple survey.

To solve two key problems in the identification of people who are infected with COVID-19: the first problem is that the identification accuracy is not high enough. The second problem is that present identification method such as nucleic acid testing is expensive in many countries. Methods: So, I decided to design a fast identification method for COVID-19 patients which is based on deep learning. After the model (CoughNet) learns more than 6,000 cough spectrograms of both COVID-19 patients and normal people, the accuracy rate of identification of COVID-19 patients and normal people is higher than 99% in the test set. Structure: This paper is mainly divided into three parts: the first part introduces the main background and research status of the research; The second part introduces the research methods; The third part introduces the specific process of the experiment.

The anomaly detection task is very important in computer science. And there are a lot of anomaly detection methods. Different from some thresholding methods, some unsupervised methods could make us get more accurate and faster result, which is the object of the project. In this paper, I tried to use EWMA and some other methods in two datasets: Webank time consuming indicators dataset and AIOps Challenge dataset. The paper consists nine parts: background of the project, related work, description of algorithms, implementation details, experimental setup and data sets used, experimental results and discussion, future directions, reference and meeting notes.

Enhancing underwater images in epicontinental sea is a challenging problem owing to the influence of ocean currents, the refraction, absorption and scattering of light by suspended particles, and the weak illumination intensity. Recently, different methods have relied on the underwater image formation model and deep learning techniques to restore the underwater image, but they tend to degrade underwater image, interference of background clutter and miss boundary details of blue regions. Improved image fusion and enhancement algorithm based on a priori dark channel is proposed in this paper. Image edge features sharpening and dark detail enhancement by homomorphism filtering in CIELab color space is realized. In RGB color space, the multi-scale retinal with color restoration (MSRCR) algorithm is used to improve color deviation and enhance color saturation, and the contrast-limited adaptive histogram equalization (CLAHE) algorithm is used to de fog and enhance image contrast. Finally, according to the dark channel images of the three processing results, the final enhanced image is obtained by linear fusion of multiple images and multiple channels. Experimental results demonstrate the effectiveness and practicality of the proposed method on various datasets.

The microspheres constituted by proteinoids synthesized from Fox’s simulation experiments. They had peptide bond structure and weak catalysis, as well as proliferated themselves. Such microspheres were believed the models for primitive life. Due to lack of metabolism and self-reproduction, the microspheres could not meet requirements of life. Thus, how microspheres could evolved into primitive life remain unsolved mysteries. The microspheres were supposed a dissipative structure and the processes of absorption and hydrolysis could be balanced to maintain their stability by consuming proteinoid. Proteinoid molecules differed in their life spans, which were mainly determined by their multi-space structures. Consequently, molecule selection and retention could occur spontaneously in microspheres and lead to a more organized and stabilized structure of the whole microsphere with time through dissipative process. More complex chain network of chemical reactions could happen in microspheres because the proteinoid with complex, ordered multi-space structure and relatively high catalytic activity would retain. In such microspheres, nucleotides could produce and further aggregate into RNA. The synthesis of real proteins could take place with RNA as the template catalyzed by proteinoids or RNA inside microspheres. When template-based protein molecules replaced the proteinoid inside the microspheres, a protein-based self-catalyzed network of chemical reactions could take place. It is plausible if Fox’s proteinoids microspheres is to dawn on a dissipative structure, then molecule selection could occur spontaneously by “dissipative” proteinoids, and the microspheres would acquire catalytic activity due to preserved the proteinoid with a large molecular weight and relatively complex and ordered multi-space structure, and relatively high catalytic activity. Thus the microspheres would spontaneously go to self-organizing, and evolve into primitive life.

Linear mixed models are frequently used for analysing heterogeneous data in a broad range of applications. The restricted maximum likelihood method is often preferred to estimate co-variance parameters in such models due to its unbiased estimation of the underlying variance parameters. The restricted log-likelihood function involves log determinants of a complicated co-variance matrix. An efficient statistical estimate of the underlying model parameters and quantifying the accuracy of the estimation requires the first derivatives and the second derivatives of the restricted log-likelihood function, i.e., the observed information. Standard approaches to compute the observed information and its expectation, the Fisher information, is computationally prohibitive for linear mixed models with thousands random and fixed effects. Customized algorithms are of highly demand to keep mixed models analysis scalable for increasing high-throughput heterogeneous data sets. In this paper, we explore how to leverage an averaged information splitting technique and dedicate matrix transform to significantly reduce computations and to accelerate computing. Together with a fill-in reducing multi-frontal sparse direct solver, the averaged information splitting approach improves the performance of the computation process.

In Lubumbashi, the capital of Haut Katanga in the Democratic Republic of the Congo (DR Congo), diesel power plants are a common source of electricity. The need to utilise local renewable energy sources in DR Congo has increased due to the unreliability of the state grid and the rising cost of running Diesel generators. Solar photovoltaic (PV) panels and batteries, in particular, have recently seen significant price drops. Therefore, it is important for operators and suppliers to choose optimal generators together with a renewable energy system to lessen the energy deficit. Diesel generators are still widely used in DRC, but their efficiency pales in contrast to that of more recent power facilities. Consuming fossil fuels results in high expenses for upkeep and operation, in addition to severe environmental damage. This study assessed the feasibility of using local weather and technical data to evaluate the efficiency of a diesel power plant hybridized with a PV system. Hybrid Optimization Model for Electric Renewable (HOMER) simulations suggest that the hybrid system schedule is preferable due to its many economic and environmental advantages for the local community and its inhabitants. The promotion of such a hybrid system, then, may encourage the sustainable economic development of a stable source of electricity for the Congo Region.

Mathematics first is a logical system. But, the Mathematics Crisis showed that we cannot have a logical self-consistent mathematics system. Physics is a kind of applied science. It first need be valid to invent new production and understand new observation. In one theory of physics, the studied object is usually defined as an abstract concept. For example, the Sun is defined as a point particle in the Newtonian theory of gravity. In different theories of physics, the abstract concept for a same object is different. It determines that logical self-consistent is not needed for different theories of physics. Physics only can be developed from observation and experiment. But, in the past a century, physicists’ main attempt is to have a standard model, such as the Grand Unification or the Theory for Everything, from mathematics derivation. Therefore, the current attempt to develop new physics is questioned. In recent, the strong gravitational field was studied. It is found that the fundamental physical constants which are measured in the weak field shall be varied in a super strong field. For the two reasons, modern physics need be advanced greatly.

In recent, Chae found that, at weak gravitational acceleration g_N≲10⁻⁹ms⁻², the observed orbit of the wide binary stars is not accordant with current theory of binary star. Here, we present, this finding should be an observational evidence for that Newtonian theory of orbit perturbation is still valid in galactic scale.

In any region of a space, the gravitational field cannot be eliminated. The speed of light in a vacuum has never been observed and cannot be observed with current technology. Till now, only the speed of light in a gravitational field has been observed. It results in that all the observed speed of light are always being varied by the gravity. Here, it is presented that light could be dispersion in a gravitational field analogous to the dispersion of light in the Newtonian prism experiment. The relativistic mass density on the surface of a neutron star is on the level of 10¹⁷kg/m³ while on the surface of the Earth is only 10^-7kg/m³, the effect of the gravitational field acting on the speed of light by a neutron star is much larger than that by the Earth. Therefore, the dispersion of light in strong gravitational field could be generally observed from the image of a star and it should have been observed through the double-image gravitational lensing and the spectroscopic binary system. And, it should affect the observed numbers, spectrogram, size and distance of the celestial objects.

There are gravitational field and baryonic matter (star, planet, gases etc.) in our world; gravitational field is much more fundamental for that the relativistic mass of the gravitational field of a neutron star is almost 10²¹ times the mass of the neutron star. And, it is astonishing that the relativistic mass density of the gravitational field on the surface of a neutron star can be larger than the mass density of the neutron star. It is well known that, in the weak gravitational field a light line appears a straight line while in the strong field it is a curved line. It is stressed that the result measured in the strong field is more fundamental for that in a galaxy the measurable object is generally with strong field. It results in that the way that the result measured on the Earth with weak field is used as the universal standard need be revised as gravity is studied in the scale of a galaxy. It seems that a new theory of gravity need be developed for the new observations.

It is discovered that the gravitational field on the surface of a neutron star is with a relativistic mass density of 2.65*10¹⁶~5.87*10¹⁸kgm^-3 which can be larger than the mass density of the neutron star (~10¹⁷kgm^-3). And, the total relativistic mass of the gravitational field of the Sun is ~10⁷ times the mass of the Sun. For different stars, the relativistic mass of the gravitational field is larger as the mass density of the star is larger. In the Milky Way, the total relativistic mass of the gravitational fields is much larger than the total mass of the stars, planets and gas. And, the relativistic mass density of the observed strongest magnetic field is 2.17*10⁸kgm^-3. This discovery should be a new frame to understand the fundamental problem of physics.

It is extremely fascinating and astonishing that the gravitational field on the surface of a neutron star is with a relativistic mass density of 2.65*10¹⁶~5.87*10¹⁸kgm^-3 which can be larger than the mass density of the neutron star (~10¹⁷kgm^-3).Therefore, it is the author’s first intuitional imagining that this field could directly convert into mass. In so strong a gravitational field, electron and proton could be produced directly from graviton–photon collision. The gravitational field exists in everywhere in our universe. No vacuum that the region of a space is “empty” does exist. A particle is clearly always being acted on by the gravitational field. The quantum vacuum fluctuation and vacuum polarization need be re-understood with the interaction between photon and gravitational field. Therefore, the gravitational field is naturally one of the foundations of modern physics.

Appling the controlling relative permittivity and permeability in the equations for the gravitational-magnetic-electric field interaction, a very large variation of the gravitational acceleration of the Earth by electric/magnetic field could be arrived at. This conclusion may be supported by some of the experiments for the gravitational effect of superconductivity.

Studying the two famous old problems that why the moon can move around the Sun and why the orbit of the Moon around the Earth cannot be broken off by the Sun under the condition that calculating with F=GMm/R^2, the attractive force of the Sun on the Moon is almost 2.2 times that of the Earth. We found that the planet and moon are unified as one single gravitational unit which results in that the Sun cannot have the force of F=GMm/R^2 on the moon. The moon is moved by the gravitational unit orbiting around the Sun. It could indicate that the gravitational field of the moon is limited inside the unit and the gravitational fields of both the planet and moon is unified as one single field interacting with the Sun. The findings are further clarified by reestablishing Newton’s repulsive gravity.

In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., Ozone, PM_2.5 and Sulfur Dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exists some works applying machine learning to air quality prediction, most of the prior studies are restricted to small scale data and simply train standard regression models (linear or non-linear) to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration based on meteorological data of previous days by formulating the prediction of 24 hours as a multi-task learning problem. It enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other, and compare with several typical regularizations for multi-task learning including standard Frobenius norm regularization, nuclear norm regularization, ℓ_2,1 norm regularization. Our experiments show the proposed formulations and regularization achieve better performance than existing standard regression models and existing regularizations.

A general method is presented that permits enantiomers, originally present in a low enantiomeric excess (%ee1), to accumulate in a high %ee (%ee2) without a chiral source present. An equation was derived that demonstrates the final %ee2 of any unreacted chiral starting material will become higher when that starting material has reacted to generate an additional chiral center, hence forming two sets of diastereomeric enantiomeric products. Importantly, chiral amplification factor (multiples), %ee2/%ee1, depends on the yield of this reaction (p), which exhibits a nonlinear increase in amplification as p approaches 100%. This process is inevitable. It represents a possible route by which small initial %ee1 values of prebiotic molecules could have been raised to high %ee2 contents during the pre-evolution of life period on Earth, without the need for an added chiral source to be present. This predicted behavior was then experimentally verified in Pictet-Spengler reactions of L-tryptophane methyl ester (L-TME) samples present in low enantiomeric excess (%ee1) with biacetyl or oxaldehyde (model reactions) run to various yields. Each reaction generates stereoisomer products, and chiral amplification of the L-TME’s initial %ee1 values to higher %ee2 values occurs in the unreacted (recovered) L-TME. Repeated cycles of these reactions create very high L-TME %ee values. Self-cyclodimerization of low %ee1 L-amino acids with their D-enantiomers to generate L,L-, D,D- and L,D-cyclic products were computationally studied, leading to the same conclusion. Small %ee1 values are enhanced to larger %ee2 in the unreacted starting materials.

In the bar of our Milky Way, there should be another supermassive black holes B which is with the mass almost 10^-2--10^-5 the Sgr A* and with a distance10,000--14,500 lightyears from Sgr A*. The bar is formed by the stellar orbiting around both of the black holes. In the Hill radius of B, a lots of stars orbit around B while another lots of stars orbit Sgr A*. It determines that there is a “x-shaped structure” in the bar and the shape of bar appears a peanut. As the orbits of the stars is out of the Hill radius of B, they are repulsed to a distant place by B to form the arms.

The new observations, including the fastest star, the circular velocity curve of the Milky Way and the fast galaxy bar, should imply that the mass of the center of the Milky Way is almost 10¹¹ times the mass of the Sun.

Currently, the number of lightning casualties and casualty rates have significantly reduced in developed countries, but there has been no significant reduction in developing countries. On the one hand, this is due to the high frequency of lightning; on the other hand, the vulnerability of people in developing countries is also an influencing factor. Through case analysis and summary, this paper expounds on lightning injury’s mechanism and clinical manifestations. It points out that lightning injury is mainly related to heart problems and the impact on the nervous system, rather than burns, which usually cause fewer consequences. Medical treatment needs to pay attention to the order and principles of treatment. Implementing CPR and auxiliary AED is the most effective way to save lives. Attention should be paid to the practical and effective treatment and nursing of lightning stroke sequelae. Finally, combined with the situation of lightning casualties in china, the existing problems are put forward during the process of early treatment, post-hospital treatment, and nursing observation, which provides an objective basis for the cognition of the scientific nature of the lightning injury.

In this study, a three-dimensional bioelectrochemical reactor system (3D-BERs) with granular activated carbon (GAC) epitomizes a novel treatment technology for treating nitrate-polluted water. The conventional denitrification process faces many challenges, including the huge demand for organic carbon, long-term accumulation of intermediate products, and the adaptation period. Results shown that under the optimal conditions of the COD/NO3--N ratio was 1.5, the denitrification efficiency reached 98.62%, when compared to 81.12% at COD/ NO3--N ratio of 3.5, and the initial pH of 7.5 ± 0.5, NO3--N was entirely removed at 2.2 h without accumulation of nitrite. The high initial ratio of NO2--N/NO3--N is mainly to accelerate the denitrification rate by accelerating the reduction of nitrite. Denitrification process followed by zero-order kinetics linear model for at different concentrations of inlet NO3--N, and achieved higher denitrification rate at greater inlet NO3--N concentration. High-throughput sequencing shows that the community structure and relative abundance of bacteria changed significantly, especially at the genes and the phyla level in immobilized GAC particles. Microbial composition enhanced the removal of nitrogen at the inner surface (IS) and bottom surface (BS) of immobilized GAC carriers. Therefore, this system is expected to be a more efficient and useful supplement or a cost-effective alternative compared to the traditional low carbon to nitrogen wastewater treatment system.

Detection of the region of interest (ROI) is a critical step in laryngeal image analysis for the delineation of glottis contour. The process can improve both computational efficiency and accuracy of the image segmentation task, which will facilitate subsequent analysis and characterization of the vocal fold vibration as it correlates with voice quality and pathology. This study aims to develop machine learning based approaches for automatic detection of ROI for glottis image sequences captured by high-speed video-endoscopy (HSV), a clinical laryngeal imaging modality. In particular, we first applied the supporting vector machine (SVM) method using histogram of oriented gradients (HOG) feature descriptor, and second, trained a convolutional neural network (CNN) model for this task. Comparisons are made for both approaches in terms of accuracy of recognition and computation time.

Pre-existing fracture and secondary cracks in rock mass are formed by natural power, such as magma condensed to igneous rocks and tectonic movement. The orientation and inclination of these fractures obey certain laws relating to the stress, temperature, minerals, water and so on. Therefore, cracks react differently under the same external loading on the condition of various inclination, fissure apertures, stiffness and joint roughness. To simulate the crack propagation, experiments on hollow cylinder cut by one oblique interface mimicking single fracture accumulated numerous data discovering the failure criterion in accordance with the Mohr-Coulomb criterion. And theory on the Terzaghi’s effective principle take an essential role in controlling the behavior of triggering fault. This paper introduced a series of oblique plane cutting the cylinder regarded as fractures at different inclination to concentrate on how the fracture characteristics effect the stress and strain distribution inside the specimen, especially, the relationship between displacement and water head. The key point of this numerical simulation is coupling the solid phase and the fluid phase, specifically, the mechanic and seepage field. According to the statics, curves referring to deformation and water head could be described as increasing lines. Besides, simulation on coupling solid phase and fluid phase can supply crucial evaluation on activating existing fault, and thus predicting induced seismicity in reservoirs or estimating damage in shale gas exploration.

To evaluate the efficacy of fish oil for protection against coronary heart disease (CHD), we conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) evaluating the use of fish oil for protection against CHD. We retrieved relevant articles published from January 1966 to January 2020 by searching the PubMed, EMBASE, Cochrane CENTRAL, and Web of Science databases. RCTs of fish oil in preventing CHD were selected. The study quality was evaluated using the Cochrane Risk of Bias tool with RevMan 5.3 software. The first selection involved 350 citations. After screening and evaluation of suitability, 19 RCTs adjusted for clustering were included in the meta-analysis. All selected manuscripts considered that fish oil was effective in preventing CHD, secondary outcome measures included angina, sepsis and death. Compared with the control group, fish oil may confer significant protection against CHD (odds ratio = 0.84; 95% confidence interval: 0.72–0.98). There was no significant difference in the incidence of secondary outcomes between the observation group and the control group (P > 0.05). The above results show that fish oil plays an important role in reducing CHD and cardiovascular events. However, because of the suboptimal quality of the studies included into the meta-analysis, these results do not justify adding fish oils systematically to the heavy pharmaceutical assortment already recommended in CHD patients.

The arguments on the origin and evolution of the evergreen broad-leaved forest in eastern Asia, especially in the evolutionary process, exist generally, even contrary in some cases. The origin and evolution of the flora of eastern Asia, the formation time of Asian monsoon, and the implications from phylogenetic and molecular biogeographic studies in some important taxa, as well as in palaeobotanical evidences, are debatable. Most researches from different disciplines suggested that the monsoon in Miocene is a key to diversification of eastern Asian flora and its evergreen broad-leaved forest. The common view is that the evergreen broad-leaved forest of East Asia is closely related to the monsoon intensity and developments, which are caused by the uplift of Himalaya-Tibet, during or after the mid-Miocene. The analysis on the floristic elements show that the present subtropical evergreen broad-leaved forest in eastern Asia could have an early or ancient tropical origin and a tropical Asian affinity, but their species are dominated by the Chinese endemic or eastern Asian distribution, many of which are the tropical sister ones. The time of Himalayan uplift and intensity of monsoon climate is believed the key for the formation of the evergreen broad-leaved forest in eastern Asia. Combined existing palaeobotanical findings, the uplift of Himalayas and the formation of monsoon climate, as well as floristic elements of the subtropical evergreen broad-leaved forest, we believe that it was evolved from a Asian tropical rain forest after mid-Miocene in southeastern region of eastern Asia, while the ancient subtropical evergreen broad-leaved forests in southwestern region continuously evolved into the present subtropical ones.

Single particle analysis with cryogenic electron microscopy (cryo-EM) can determine high resolution structures of macromolecular assemblies. To reconstruct final three-dimension(3D) density map, a reliable initial model is essential and ab-initio reconstruction usually can avoid model bias. The grand challenge for ab-initio reconstruction is to alignment two-dimension(2D) low signal-to-noise ratio(SNR) of particle images with random and unknown parameters. Here we formulate a fast ab initio reconstruction method(named AlignFM) based on Fourier Mellin transform and particle-swarm intelligence optimization(PSO) algorithm. It enables simultaneous 2D alignment and 3D refinement, which can fast generate a reliable 3D map. After experiments on several real datasets. AlignFM shows high performance on fast ab initio reconstruction with a small amount of data.

Recent trends in assessing teachers' psychological aspects have led to considering emotional intelligence (EI) as one of the main factors determining teachers' job satisfaction. The present study aims to explore the different effects of emotional intelligence on teachers' job satisfaction. This study used the Scoping review method and focused specifically on studies conducted from January 2014 to February 2023, and 21 studies met the inclusion criteria. Overall, the results indicated that emotional intelligence has both direct and indirect positive effects on teachers' job satisfaction. However, the results on the effects of various EI models on teachers' job satisfaction were inconsistent. The findings suggest that more research is needed to investigate the impact of the ability model of EI on teachers’ job satisfaction, as well as longitudinal studies to understand the long-term effects of this relationship. Finally, studies are also needed to determine whether demographic factors influence the effects of EI on teachers’ job satisfaction in different countries and contexts.

The development of mobile internet technology has enabled companies to use social media for E-commerce. Some companies use personified images and languages to communicate with consumers in this context. How does personified communication affect consumer behavior in social commerce? Are consumers willing to accept this new form of communication under social commerce? To answer these questions, this paper explores consumers' willingness to take per-sonified communication in the context of social commerce. It investigates the role of cognitive needs in regulating the internal mechanism and proposes some suggestions for enterprises to improve social media communication. Specifically, this paper presents an improved model based on the TAM model. In our model, perceived interaction is introduced as a new independent variable, and cognitive need is added as a regulatory variable, which is more suitable for social commerce. We conduct a questionnaire survey on the Internet and analyze data using AMOS and SPSS. The results show that perceived usefulness and perceived interaction positively im-pact attitude, which influences consumers' willingness to purchase. Furthermore, the cognitive need as the regulatory variable significantly affects the influencing path from perceived use-fulness to attitude and purchase intention.

This paper presents the results of an experimental and analytical study on the behavior of cold-formed thick-walled steel plates connected by a single bolt under double shear. Experimental studies were conducted on a total of 54 bolted connections fabricated from two thickness of cold-formed thick-walled steel plates with the nominal thickness of 6 mm and 10 mm. The effects of end distance, edge distance and steel plate thickness were investigated on the bolt bearing behavior. Codes of (modified) ANSI/AISC 360-16 and EN1993-1-8 were applied to predict failure modes and bearing resistance of connections and the predictions were compared with that of measurements. A method was proposed to calculate the elongation of bolt hole at ultimate load. The studies show that fractures occurred on shear planes of specimens failed by shear out due to reduced ductility caused by cold forming process. The modified ANSI/AISC gives the most accurate predictions of failure modes and bearing resistance. However, the bearing resistance will be overestimated if the reduction in area of shear planes caused by occurrence of fracture is ignored for specimens with end distance being higher than 2.0d0 and a reduction factor is needed for calculating bearing resistance of connections. The method proposed in our paper can predict the elongation of bolt hole very well.

As traditional supply chains face increasingly severe environmental issues and countries promote green development and sustainable development policy concepts, cultivating green supply chain operation models is gradually being highly valued by current governments and enterprises. Generally speaking, the production of green products incurs higher additional costs and thus their total production costs also increase. In this work, the product green level is related to the random demand. Under the green supply chain buy-back contract with the green product R&D cost sharing between the manufacturer and the retailer, both the product green level and the order quantity need to be decided to maximize the channel profit. In order to coordinate the green supply chain, the manufacturer needs to share both the risk of good salvage and the green product R&D cost with the retailer. We find that both the wholesale price and buy-back price increase in the manufacturer’s proposition of the green product R&D cost, but decrease in emission reduction efficiency coefficient or carbon trading price. In addition, the product green level, the optimal order quantity and the channel profit increase in emission reduction efficiency coefficient, but decrease in the R&D cost coefficient of the product green level. Interestingly, we find that if the carbon trading price is low, the manufacturer will set a low product green level and the product carbon emission trading is a cost for the supply chain. The increment of the carbon trading price leads to a higher cost such that the channel profit is decreased. However, if the carbon trading price is high, the manufacturer will set a high product green level and the product carbon emission trading is a revenue for the supply chain. The increment of the carbon trading price leads to a higher revenue such that the channel profit is increased.

We propose a new ensemble classification algorithm, named Super Random Subspace Ensemble (Super RaSE), to tackle the sparse classification problem. The proposed algorithm is motivated by the Random Subspace Ensemble algorithm (RaSE). The RaSE method was shown to be a flexible framework that can be coupled with any existing base classification. However, the success of RaSE largely depends on the proper choice of the base classifier, which is unfortunately unknown to us. In this work, we show that Super RaSE avoids the need to choose a base classifier by randomly sampling a collection of classifiers together with the subspace. As a result, Super RaSE is more flexible and robust than RaSE. In addition to the vanilla Super RaSE, we also develop the iterative Super RaSE, which adaptively changes the base classifier distribution as well as the subspace distribution. We show the Super RaSE algorithm and its iterative version perform competitively for a wide range of simulated datasets and two real data examples. The new Super RaSE algorithm and its iterative version are implemented in a new version of the R package RaSEn.

Energy efficiency and leakage magnetic field (LMF) are two important issues in electric vehicle inductive chargers. In this work, the maximum achievable coil efficiency and the corresponding LMF strength are formulated as functions of hardware parameters, and figure of merits (FOM) are proposed for assessing the efficiency and LMF performance of the coil assembly pair. The impacts of the coil assemblies’ geometric parameters on both FOMs are examined with the aid of finite element analysis (FEA), and measures to improve the FOMs are extracted from FEA results. A coil assembly pair is manually optimized within given dimensional limits. Compared with the initial design, the optimized one achieves higher efficiency and lower LMF strength while consuming less copper. The performance improvement is verified by FEA results and experimental data measured on an 85 kHz electric vehicle inductive charger prototype. The key measures for coil assembly optimization are summarized.

Circular RNAs (circRNAs), which are a class of non-coding RNA with covalently closed loops, play important roles in epigenetics regulation of gene expression at both the transcriptional and post-transcriptional level. Accumulating evidence demonstrated that numerous circRNAs were abnormally expressed in tumors and their dysregulation was involved in the tumorigenesis and metastasis of cancer. Although the functional mechanisms of many circRNAs have been revealed, why circRNAs are dysregulated in cancer remains elusive. CircRNAs are generated by a “backsplicing” process, which is regulated by different cis-regulatory elements and trans-acting proteins. Therefore, how these cis- and trans-elements change during tumorigenesis and how they regulate the biogenesis of circRNAs in cancer are two questions that interest us. In this review, we summarized the pathways for the biogenesis of circRNAs; and then illustrated why circRNAs dysregulated in cancer by discussing the changes of cis-regulatory elements and trans-acting proteins that related to circRNA splicing and maturation in cancer.

For the problem of poor accuracy of the existing multi-rate simulation methods, this paper proposes a multi rate real-time simulation method based on the Norton equivalent, compared with multi-rate simulation method based on the ideal source equivalent. After the Norton equivalence of the fast subsystem and the slow subsystem, they are obtained simultaneously at the junction nodes. In order to reduce the amount of simulation calculation, the Norton equivalent circuit is obtained by incremental calculation. The data interface between the fast subsystem and the slow subsystem is realized by extrapolation method. For ensuring the real-time performance of the simulation, the method that the slow subsystem calculates ahead of the fast subsystem is given for the slow subsystem with a large amount of calculation. Finally, the AC/DC hybrid power system was simulated on the real-time simulation platform (FRTDS), and the simulation results were compared with the single-rate simulation, which verified the correctness and accuracy of the method.

The basic equation for estimating the aerodynamic power captured by an Anderson Vertical Axis Wind Turbine (AVAWT) is a solution of the Navier-Stokes(N-S) equations for a baroclinic, inviscid flow. In a nutshell, the pressure difference across the AVAWT is derived from Bernoulli’s equation; an upshot of the integration of the N-S momentum equation for a baroclinic inviscid flow, Euler’s momentum equation. The resulting expression for the pressure difference across the AVAWT rotor is plotted as a function of freestream speed. Experimentally determined airstream speeds at the AVAWT inlet and outlet, coupled with corresponding freestream speeds are used in estimating the aerodynamic power captured. The aerodynamic power is subsequently used in calculating the aerodynamic power coefficient of the AVAWT. The actual power coefficient is calculated from the power generated by the AVAWT at various free stream speeds and plotted as a function of the latter. Experimental results show that, at all free stream speeds and tip speed ratios, the aerodynamic power coefficient is higher than the actual power coefficient of the AVAWT. Consequently, the power generated by the AVAWT prototype is lower than the aerodynamic power captured, given the same inflow wind condition.

Prediction of the likely evolution in the traffic scenes is a challenging task because of high uncertainty of sensing technology and dynamic environment. It leads to failure of planning for intelligent agents like autonomous vehicles. In this paper, we propose a fluid-based physical model to present the influence of surrounding object's motion on driving safety. In our pipeline, the input sensor could be LiDAR, camera, or multi-modal data. We use a Kalman filter to estimate the state space of each detected object, and adopt the properties of stable fluid to build a riskmap based on the density field. The noisy state space are then modeled as the boundary conditions in the simulation of advection and diffusion process. We test our approach on the public KITTI dataset and find this model could handle the short-term prediction in case of misdetection and tracking failure caused by object occlusion, which shows promising in collision risk assessment on road scenes.

This paper mainly used database technology, machine learning, thermodynamic calculation, experimental verification, etc., on integrated computational materials engineering. The interaction between different alloying elements and the strengthening effect of precipitated phases are investigated mainly for martensitic ageing steels. Modelling and parameter optimization were performed by machine learning, and the highest prediction accuracy was 98.58%. Investigated the influence of composition fluctuation on performance and correlation tests to analyze the influence of elements from multiple perspectives. Furthermore, screen out the three-component composition process parameters with composition and performance with high contrast. Thermodynamic calculations studied the effect of alloying element content on the nano-precipitation phase, Laves phase, and austenite in the material. The heat treatment process parameters of the new steel grade were also developed based on the phase diagram. A new type of martensitic ageing steel was prepared by selected vacuum arc melting. The sample with the highest overall mechanical properties had a yield strength of 1887 MPa, a tensile strength of 1907 MPa, and a hardness of 58 HRC. The sample with the highest plasticity had an elongation of 7.8%. The machine learning process for the accelerated design of new ultra-high tensile steels was found to be generalizable and reliable.

Alcohol abuse can lead to alcoholic liver disease, becoming a major global burden. Hovenia dulcis fruit peduncle polysaccharides (HDPs) has the potential to alleviate alcoholic liver injury may play essential roles in treating for alcohol-exposed liver disease, however, the hepatoprotective effects and mechanisms remain unclear. In this study, the hepatoprotective effects of HDPs and its potential mechanisms were investigated in alcohol-exposed mice by liver metabolomics and gut microbiome. The results found that HDPs reduced medium-dose alcohol-caused dyslipidemia (significantly elevated T-CHO, TG, LDL-C), elevated liver glycogen levels and inhibited intestinal-hepatic inflammation (significantly decreased IL-4, IFN-γ and TNF-α), consequently reversing hepatic pathological changes. By applying gut microbiome analysis, HDPs showed significant decreases in Proteobacteria while significant increases in Firmicutes at the phylum level, and increased Lactobacillus abundance while decreased Enterobacteria abundance, maintaining the composition of gut microbiota. Further hepatic metabolomics analysis revealed that HDPs had a regulatory effect on hepatic fatty acid metabolism, by increasing the major metabolic pathways including arachidonic acid metabolism and glycerophospholipid metabolism and identified two important metabolites C00157 and C04230 involved in the above metabolism. Overall, HDPs reduced intestinal dysbiosis and hepatic fatty acid metabolism disorders in alcohol-exposed mice, suggesting that HDPs has a beneficial effect on alleviating alcohol-induced hepatic metabolic disorders.

The fast decoding of low-density parity-check (LDPC) is crucial for its future applications. In this paper, we propose to use Early Termination (ET) and Force Convergence (FC) techniques to accelerate the decoding of two new local/global two-phases decoding algorithms to reduce the Globally Coupled (GC) LDPC codes. Specifically, ET is applied in the local decoding phase of two- phases decoding of GC-LDPC codes to skip the local decoding which is highly likely to fail and avoid the unnecessary iterations. FC is applied in global decoding phase where converged variable nodes are identified and removed from Tanner graph and the decoding can be speed up shrinking parity check matrices. Two variants are proposed for the ET operation in local decoding phase. ET-1 merely stops iteration if it meets the stopping criterion while in ET-1, the iteration number saved in local decoding is added up in global decoding to improve the decoding performance. The simulation results show that the ET-1-FC scheme can save up to 42% decoding time complexity in the low SNR region while keeping the error rate performance nearly unchanged, the ET-2-FC can improve the error rate performance by 0.18dB at bit error rate 0.001 or 0.23dB at frame error rate 0.1. In the waterfalling region of BER/FER curves, both schemes can save about 25% decoding time complexity.

Prior research has extensively examined the relationship between religion and accrual-based earnings management. However, there is currently little research in the relationship between religion and real (non-accrual) earnings management, especially in Europe. This paper aims to fill this research gap and examines whether and how the effect of religion could be linked with firms’ real earnings management activities. Four hypotheses are developed and tested with our results providing indications that the degree of overall religiosity is negatively and significantly associated with real earnings management. Furthermore, when investigating the effects of different religions in Europe, Christianity and Islam have the opposite impact on firms’ real earnings management activities. Overall, our paper indicates that in European countries, the religious environment can mitigate firms’ manipulations on earnings.

Indigenous trees have great economic potential and ecological benefits for enhancing environmental prosperity, mostly in forestry and the forest products sector in the developing countries of Sub-Sahara Africa. The baobab (Adansonia Digitata L.) is known as the African green jewel in both fruit production and medicinal benefits also remarkable for so many forest products exported across the world. Research conducted in the different Sub-Saharan African sub-regions has shown this iconic tree with a majestic outlook has a priority tree species for local and foreign use and conservation. However, data on the benefits and conservation of baobab trees in Africa, especially the Sub-Saharan countries is limited. This study aimed to assess the predominant geo-graphical distribution of the tree, the indigenous (cultural, socio-economic, ecological, and medical/health) benefits, and the conservation strategies of the baobab resources in Sub-Saharan Africa. The baobab tree's succulent roots, bulbs, branches, fruit, pods, foliage, and petals are all nourishing. Baobab parts have been used for diverse reasons in Africa, some countries of Asia, and Europe for the past two centuries due to their medicinal well-being properties. In addition, the medicinal applications of the plant parts are discussed. Many authors have highlighted the baobab tree as one of the most important trees to be saved and localized in Africa because of its high indigenous usage and commercial worth. Anthropogenic global warming may induce a drop in baobab species, which could inflict negative impacts on African economies. As a result, it's critical to research the species' likely future distribution and develop conservation policies. Literature was consulted for records and availability of this tree in the Western, Central, Eastern, and Southern African species records and it was also analyzed what percentage of the current environment would be appropriate in the future. Recent studies suggested that farmers and the locals be provided free seeds and seedlings to encourage biological rejuvenation to maximize the plant's potential, people should be informed about the additional uses of baobab that have been discovered. Individuals must also be educated on simple sustainable agroforestry activities that can be performed in plant and forest management.

In a one price economy, the Fundamental Theorem of Asset Pricing (FTAP) establishes that no-arbitrage is equivalent to the existence of an equivalent martingale measure. Such an equivalent measure can be derived as the normal unit vector of the hyperplane that separates the attainable gain subspace and the convex cone representing arbitrage opportunities. However, in two-price financial models (where there is a bid-ask price spread), the set of attainable gains is not a subspace anymore. We use convex optimization, and the conic property of this region to characterize the “No-Arbitrage” principle in financial models with the bid-ask price spread present. This characterization will lead us to the generation of a set of price factor random variables. Under such a set, we can find the lower and upper bounds (supper-hedging and sub-hedging bounds) for the price of any future cash flow. We will show that for any given cash flow, for which the price is outside the above range, we can build a trading strategy that provides one with an arbitrage opportunity. We will generalize this structure to any two-price finite-period financial model.

Nonlinear ultrasonic testing has been accepted as a promising manner for evaluating material integrity in an early stage. Stress fatigue is the main threats to train safety, railways examinations for stress fatigue are more significant and necessary. A series of ultrasonic nonlinear wave experiments are conducted for rail specimens extracted from railhead with different degree of fatigue produced by three-point bent loading condition. The nonlinear parameter is the indicator of nonlinear waves for expressing the degree the fatigue. The experimental results show that the sensitivity of a third harmonic longitudinal wave is higher than second harmonic longitudinal wave testing. As the same time, collinear wave mixing shows strong relative with fatigue damages than a second longitudinal wave NDT method and provides more reliable results than third harmonic longitudinal waves nonlinear testing method.

Since the 1970s, land subsidence has been developing rapidly in the Beijing Plain, the systematic study of its evolution mechanism is of great significance to the sustainable development of the regional economy. First, based on ENVISAT ASAT and RADARSAT2 data, the land subsidence data in Beijing Plain were obtained using permanent interferometer technology. Second, based on the GIS platform and using fishing net tools, vector data of ground settlement with different resolutions were obtained. Through a series of tests, a scale of 960 metres was selected as the research unit, and the subsidence rate of the grid was obtained from 2004 to 2015. Finally, based on the Mann-Kendall mutation test method, a trend analysis of land subsidence changes in various grids was carried out. The results showed that single-year mutation mainly distributed in the middle and lower parts of the Yongding River alluvial fan and the Chaobai River alluvial fan, mainly occurring in 2015, 2005 and 2013, respectively. The upper and middle alluvial fan of the Chaobai River, the vicinity of the emergency water source and the edge velocity of the groundwater funnel have undergone several sudden changes. Combined with hydrogeology, basic geological conditions and the impact of the South-to-North Water transfer project, we analysed the causes of the mutations in the grid. The research results can provide a basis for the study and prevention of land subsidence in this area and help to further explore the trend characteristics of land subsidence in this area.

In this paper, we study the constrained minimization problem for an energy functional which is related to the following Kirchhoff type equation \begin{equation*} -\Big(\eta+b\big(\int_{\R^{3}}|\nabla u|^{2}dx\big)^{s}\Big)\Delta u+V(x)u=\mu u +\lambda|u|^{p}u,\end{equation*} where $b$ is a positive constant, parameters $\eta\geq0, \lambda>0$, exponents $s>0$, $0

It is important for food recognition to separate each ingredient within a food image at the pixel level. In this paper, we propose a new approach to segment ingredients by utilizing a CNN-based Single-Ingredient Classification Model. In detail, we firstly introduce a standardized biological-based hierarchical ingredient structure and construct a single-ingredient image dataset based on this structure. Then, we build a single-ingredient classification model based on a novel convolutional neural network (CNN) architecture that utilizes an attention mechanism. Afterwards, we propose a new framework for segmentation using the above single-ingredient classification model as the backbone. In this framework, two methods are involved in segmenting ingredients in the food images. We introduce five evaluation metrics (IoU, Dice, Purity, Entirety, Loss of GTs) to assess the performance of ingredient segmentation in terms of ingredient classification. Extensive experiments demonstrate the effectiveness of the proposed method, achieving an maximal mIoU of 0.65, mDice of 0.77, mPurity of 0.83, mEntirety of 0.80, and mLoGTs of 0.06 on the FoodSeg103 dataset. The results confirm that our CNN-based architecture achieves higher segmentation performance compared to ResNet18 and EfficientNet-B0 when used as the backbone for ingredient segmentation. We believe that our ingredient segmentation approach lays the foundation for subsequent ingredient recognition.

The influence of polysialic acid (PSA) and sialic acid (SA) on the gut microbial community composition and metabolites in healthy human was investigated using a bionic gastrointestinal reactor. The results indicated that PSA and SA significantly changed the gut microbiota and metabolites in different degrees. PSA can increase the relative abundances of Faecalibacterium and Allisonella, whereas SA can increase those of Bifidobacterium and Megamonas. Both can significantly increase the content of short-chain fatty acids. The results of metabolome analysis showed that PSA can upregulate ergosterol peroxide and gallic acid and downregulate the harmful metabolite N-acetylputrescine. SA can upregulate 4-pyridoxic acid and lipoic acid. PSA and SA affect gut microbiota and metabolites in different ways and have positive effects on human health. These results will provide reference for the further development of PSA- and SA-related functional foods and health products.

Ice covering on overhead transmission lines would cause damage to transmission system and long-term power outage. Among various de-icing devices, modular multilevel converter (MMC) based DC de-icer (MMC-DDI) is recognized as a promising solution due to its excellent technical performance. Its principle feasibility has been well studied, but few literature discuss its economy or hardware optimization, thus the designed MMC-DDI for high voltage transmission lines is usually too large and too expensive for engineering applications. To fill this gap, this paper presents a quantitative analysis on the converter characteristics of MMC-DDI, and calculates the minimal converter rating and its influencing factors. It reals that, for a given de-icing requirement, the converter rating varies greatly with its AC-side voltage. Then an optimization configuration is proposed to reduce the converter rating and improve its economy. The proposed configuration is verified in a MMC-DDI for a 500kV transmission line as a case study. The result shows, in the case of outputting same de-icing characteristics, the optimized converter rating is reduced from 151 MVA to 68 MVA, and total cost of MMC-DDI is reduced by 48%. This analysis and conclusion are conductive to the optimized design of multilevel DC de-icer, then to its engineering application.

The aim of the study was to investigate the association between maternal dietary iron intake during pregnancy and the gut microbiota characteristics of both the mother and neonate in a well-characterized cohort. A total of 95 mother-neonate dyads were included in our study, with basic information collected through questionnaires. A semi-quantitative Food Frequency Questionnaire (FFQ) was used to assess maternal dietary intake during pregnancy, and maternal dietary iron intake was categorized into <20 mg/d and ≥20 mg/d groups. Fecal samples were collected from the mother in the third trimester and the neonate, allowing for assessment of the community profile and diversity of gut microbiota via 16S rRNA amplicon sequencing. Then, a comparison between different maternal dietary iron intake groups was conducted, adjusting for delivery mode (VD, vaginal delivery; CS, cesarean section) and other potential confounding factors. No significant differences in community profile and diversity were observed for the maternal gut microbiota in different dietary iron intake groups. In neonate fecal samples, the Shannon (P = 0.044) and Simpson (P = 0.010) diversity indices of the gut microbiota were higher in the maternal dietary iron intake ≥20 mg/d group, while Simpson diversity presented the same tendency in vaginal delivery (P = 0.041) after stratification. The relative abundance of the core genus Bifidobacterium showed a significant difference between groups (4.69 [1.19–12.77] vs. 13.98 [3.44–27.28]; P = 0.044). The abundance of Lactobacillus was different in the ≥20 mg/d group under both delivery modes (VD: beta = 2.9, w = 4.13; CS: beta = 2.77, w = 3.8). Our findings suggest that adequate dietary iron intake during pregnancy may promote beneficial bacterial colonization and increase the biodiversity of the neonate gut microbiota.

Emerging pollutants as pharmaceuticals have been focusing international attention for few decades. Ciprofloxacin (CIP) is a common drug widely found in effluents from hospitals, industrial and different wastewater treatment plants, as well as rivers. In this work, the lab-scale 3D-BER system was established, and more than 90% of the antibiotic CIP removal from the Low C/N wastewater. Best results were obtained with current intensity, and different C/N ratio significantly improve the removal of CIP and nitrates, when the ideal conditions were; C/N = 1.5-3.5, pH =7.0-7.5, and I = 60 mA. The highest removal efficiency of CIP = 94.20 %, NO3--N= 95.53 % and total nitrogen (TN) = 84.27 %, respectively. In this novel system, the autotrophic-heterotrophic denitrifying bacteria played vital role for the removal of CIP and enhanced denitrification process. Thus, autotrophic denitrifying bacteria uses CO2 and H2 as carbon sources to reduce nitrates to N2. This system has the assortment and prosperous community revealed at the current intensity of 60 mA, and the analysis of bacterial community structure in effluent samples fluctuates under different condition of C/N ratios. According to the results of LC-MS/MS analysis, the intermediate products were proposed after efficient biodegradation of CIP. Microbial community on biodegrading was mostly found at phylum, and class level was dominantly responsible for the NO3--N and biodegradation of CIP. This work can provide some new insights towards the biodegradation of CIP and the efficient removal of nitrates from low C/N wastewater treatment by the novel 3D-BER system.

The research aims to resolve water pollution generated by dye wastewater. In this paper, the aminated sodium lignosulfonate (AELS) was prepared through the Mannich reaction, and the synthesis conditions were optimized using orthogonal experiments to determine the best amination conditions. The successful preparation of AELS was demonstrated through the use of FT-IR, XRD, TG, SEM and XPS characterization techniques. Then, the adsorption capacity of Methyl blue (MB) by AELS was compared in a range of different circumstances, like pH, adsorbent dose, initial concentration, time, and temperature. Additionally, the kinetics, isotherms, and thermodynamic properties of the AELS adsorption for MB were studied and discussed. The results indicated that the fitting results were consistent with the pseudo-second-order kinetic model and Langmuir isotherm model, in accordance with the mechanism of single-molecular-layer chemisorption. And the adsorption of MB by AELS was a spontaneous and endothermic process. Upon completion of five adsorption experiments, the adsorption capacity of MB by AELS was determined to be 107.95 mg g-1, which was 5.3% lower than the first experiment. This research promotes the effective use of natural resources and allows for the sustainable use of lignin and the advancement of new applications for it.

Contrafreeloading (CFL) refers to animals' tendency to prefer obtaining food through effort rather than free access to food. Researchers have proposed various hypotheses to explain this intriguing phenomenon, but few studies have comprehensively analyzed its influencing factors. In this study, we observed the choice of alternative food containers in budgerigars to investigate their CFL tendencies and the effects of pre-training, food deprivation, and effort required on the CFL levels. The results show that, like most captive animals, budgerigars without food deprivation tend to choose challenging food containers. Additionally, the study finds that pre-trained budgerigars prefer to choose highly challenging food containers, while food-deprived budgerigars tend to choose Easy food containers. Moreover, budgerigars showed more selection time proportion on moderate challenging than high challenging food containers. In conclusion, budgerigars exhibit CFL tendencies, and pre-training enhances their CFL levels, while high challenge and deprivation of food decrease their contrafreeloading level. Studying animal contrafreeloading can help understand why animals choose to expend effort to obtain food rather than accessing it for free, and it also has implications for setting feeding environments to enhance the animal welfare of captive and domesticated animals.

Cellulose-based carbon (CBC) has been widely concerned with its porous structure, high specific surface area, and is liable to adsorb gas molecules and macromolecular pollutants. However, the application of CBC in gas sensing has been little studied. In this paper, ZnO/CBC heterojunction was formed by means of simple co-precipitation and high temperature carbonization. As a new ammonia sensor, the prepared ZnO/CBC sensor can detect ammonia compared with that of the previous pure ZnO ammonia sensor can not at room temperature. It has great gas sensing response, stability and selectivity to ammonia concentration at 200 ppm. This study provides a new idea for the design and synthesis of biomass carbon-metal oxide composites.

Photodynamic therapy (PDT) has garnered significant attention in the fields of cancer treatment and drug-resistant bacteria eradication due to its non-invasive nature and spatiotemporal controllability. Iridium complexes have captivated researchers owing to their tunable structure, exceptional optical properties, and substantial Stokes displacement. However, most of these complexes suffer from aggregation-induced quenching, leading to diminished luminous efficiency. In contrast to conventional photosensitizers, photosensitizers exhibiting aggregation-induced luminescence (AIE) properties retain the ability to generate a large number of reactive oxygen species when aggregated. To overcome these limitations, we designed and synthesized a novel iridium complex named Ir-TPA in this study. It incorporates quinoline triphenylamine cyclomethylated ligands that confer AIE characteristics for Ir-TPA. We systematically investigated the photophysical properties, AIE behavior, spectral features, and reactive oxygen generation capacity of Ir-TPA. The results demonstrate that Ir-TPA exhibits excellent optical properties with pronounced AIE phenomenon and robust capability for producing singlet oxygen species. This work not only introduces a new class of metal iridium complex photosensitizer with AIE attributes but also holds promise for achieving remarkable photodynamic therapeutic effects in future cellular experiments and biological studies.

Reinforcement learning is used for air combat problems in recent years, and the idea of curriculum learning is often used for reinforcement learning, but traditional curriculum learning suffers from the problem of plasticity loss in neural networks. Plasticity loss is the difficulty of learning new knowledge after the network has converged. To this end, we propose a motivational curriculum learning distributed proximal policy optimization (MCLDPPO) algorithm, through which agents trained can significantly outperform the predictive game tree and mainstream reinforcement learning methods. The motivational curriculum learning is to help the agent gradually improve their combat ability by observing the agent's unsatisfactory performance and providing corresponding rewards as a guide. Additionally, a complete tactical maneuver is encapsulated based on existing air combat knowledge, and through the flexible use of these maneuvers, some tactics beyond human knowledge can be realized. In addition, we designed an interruption mechanism for the agent to increase the frequency of decision-making when the agent faces an emergency. When the number of threats the agent receives changes, the current action will be interrupted to reacquire observations and make decisions again. Using the interruption mechanism can significantly improve the ability of the agent. To simulate actual air combat better, we built an air combat environment based on digital twin technology to create an air combat engine core such as missiles and radars. Based on this environment, we develop a distributed intelligent training system that employs Redis as the cache and obtains the data generated in the distributed environment through a multi-threaded TCP/IP protocol, which significantly increases the training data throughput and helps the agent accelerates convergence. The experimental results demonstrate that the agent can fully exploit the situational information to make reasonable decisions and affords air combat tactical adaption, verifying the effectiveness of the algorithmic framework proposed in this paper.

Language bias stands as a noteworthy concern in Visual Question Answering (VQA), wherein models tend to rely on spurious correlations between questions and answers for prediction. This prevents the models from effectively generalizing, leading to a decrease in performance. To address this bias, we propose a novel modality fusion collaborative de-biasing algorithm (CoD). In our approach, bias is considered as the model’s neglect of information from a particular modality during prediction. We employ a collaborative training approach to facilitate mutual modeling between different modalities, achieving efficient feature fusion and enabling the model to fully leverage multi-modal knowledge for prediction. Our experiments on various datasets, including VQA-CP v2, VQA v2, and VQA-VS, using different validation strategies, demonstrate the effectiveness of our approach. Notably, employing a basic baseline model resulted in an accuracy of 60.14% on VQA-CP v2.

The digital economy promotes economic development, industrial upgrading, and environmental protection. This article calculates the green total factor productivity (GTFP) based on the SBM-DDF model and uses the entropy method and principal component analysis to calculate the digital economy index. We use panel data from 282 Chinese cities to test the driving effect of the digital economy on green total factor productivity. The study results show that the digital economy significantly increases GTFP, and the heterogeneity of this impact is further explored. We also explored the mechanism of the digital economy in promoting green. The digital economy can indirectly increase industrial production efficiency by promoting innovation in green technologies.

Ambient vibration energy is widely being harnessed as a source of electrical energy to drive low-power devices. The vibration energy harvester (VEH) of interest employs an electromagnetic transduction mechanism, whereby ambient mechanical vibration is converted to electrical energy. The limitations affecting the performance of VEHs, with an electromagnetic transduction structure, include its operational bandwidth as well as the enclosure-size constraint. In this study, an analysis and design of a nonlinear VEH system is conducted, using the Output Frequency Response Function (OFRF) representations of the actual system model. However, the OFRF representations are determined from the Generalised Associated Linear Equation (GALE) decompositions of the system of interest. The effect of both nonlinear damping and stiffness characteristics, to respectively extend the average power and operational bandwidth of the VEH device, is demonstrated.

Generally, stochastic functional differential equations (SFDEs) pose a challenge as they often lack explicit exact solutions. Consequently, it becomes necessary to seek certain favorable conditions under which numerical solutions can converge towards the exact solutions. This article aims to delve into the convergence analysis of solutions for stochastic functional differential equations by employing the framework of G-Brownian motion. To establish the goal, we find a set of useful monotone type conditions and work within the space Cr((−∞,0];Rn). The investigation conducted in this article confirms the mean square boundedness of solutions. Furthermore, this study enables us to compute both LG2 and exponential estimates.

Determining accurate PM2.5 pollution concentrations and understanding their dynamic patterns is crucial for scientifically informed air pollution control strategies. Traditional reliance on linear correlation coefficients for ascertaining PM2.5 related factors only uncovers superficial relationships. Moreover, the invariance of conventional prediction models restricts their accuracy. To enhance the precision of PM2.5 concentration prediction, this study introduces a novel integrated model that leverages feature selection and a clustering algorithm. Comprising three components - feature selection, clustering, and integrated prediction, the model first employs the non-dominated sorting Genetic Algorithm (NSGA-III) to identify the most impactful features affecting PM2.5 concentration within air pollutants and meteorological factors. This step offers more valuable feature data for subsequent modules. The model then adopts a two-layer clustering method (SOM+K-means) to analyze the multifaceted irregularity within the dataset. Finally, the model establishes the Extreme Learning Machine (ELM) weak learner for each classification, integrating multiple weak learners using the Adaboost algorithm to obtain a comprehensive prediction model. Through feature correlation enhancement, data irregularity exploration, and model adaptability improvement, the proposed model significantly enhances the overall prediction performance. Data sourced from 12 Beijing-based monitoring sites in 2016 were utilized for an empirical study, and the model's results compared with five other predictive models. The outcomes demonstrate that the proposed model significantly heightens prediction accuracy, offering useful insights and potential for broadened application to multifactor correlation concentration prediction methodologies for other pollutants.

COVID-19 patients suffer from detrimental effects of cytokine storm and not much success has been achieved to overcome this issue. We sought to test the ability of selenium in reducing the impact of two important cytokine storm players; IL-6 and TNF-a. The effects of four selenium compounds were evaluated on the secretion of these cytokines from THP-1 macrophages in vitro following LPS challenge. Also, potential impact of methylseleninic acid (MSeA) on Nrf2 and IkBa was determined following short treatment of THP-1 macrophages. MSeA was observed to be the most potent selenium form to reduce IL-6 and TNF-a levels among the four selenium compounds tested. In addition, an increase in Nrf2 and decrease in pIkBa in human macrophages was observed following MSeA treatment. Our data indicate that COVID-19 patients might benefit by suppressing their cytokine storm with addition of MSeA to the standard therapy.

These years have witnessed growing research on applying rheology in grinding and flotation treatment of finely disseminated ores. The slurry rheology has long been identified as the comprehensive effect of inter-particle interactions, including their aggregation (coagulation and flocculation) and dispersion states in slurry, which are more impactive under the fine-particle effect. In this regard, rheology has the potential to play a significant role in interpreting the flowing and deforming phenomenon of inter-particle aggregates, particle-bubble aggregates, and flotation froth. Though much attention has been paid to the rheological effect in industrial suspension, this has not been the case for mineral grinding and flotation for fine particles. The influential mechanism of rheology on the sub-processes of mineral processing has not been systemically concluded and revealed thoroughly, without which the underpinning mechanism for enhancing the processing efficiency has been difficult to discover. This paper reviews the current application and importance of rheology in the fine mineral processing, and the potential research direction in the field is proposed.

The heat release response of the flame to acoustic excitation is a critical factor for understanding combustion instability. In the present work, the nonlinear heat release response of a methane-air non-premixed flame to the low frequency acoustic excitations is experimentally investigated. The flame describing function (FDF) was measured based on the overall CH* chemiluminescence intensity and the velocity fluctuations obtained by two-microphone method. The CH* chemiluminescence images and the schlieren images were analyzed to reveal the mechanism of nonlinear response. The excitation frequency ranges from 10Hz to 120Hz. The forced relative velocity fluctuation amplitude ranges from 0.10 to 0.50. The corresponding flame Strouhal number (Stf) ranges from 0.43 to 4.67. The study has shown that the flame length responds more sensitively to changes in excitation amplitude when subjected to relatively high frequency excitations. The normalized flame length (Lf/D) decreases from 3.79 to 2.37 with the increase of excitation amplitude at excitation frequency of 100Hz. The number of oscillation zones along the flame increases with increasing excitation frequency, which is consistent with the increase of the Stf. The low-pass filtering characteristic of FDF is caused by the dispersion of multiple oscillation zones, as well as the cancellation effect of the adjacent oscillation zones under relatively high frequency excitation. The cancellation effect of the positive and negative oscillation zones with various Stf is main mechanism for the local gain peak and valley. When two adjacent oscillation regions have approximate amplitudes, the overall phase-lag becomes more sensitive to changes in excitation frequency and amplitude. This sensitivity leads to nonlinear anomalous changes in the phase-lag near the frequency corresponding to the gain valley. The calculated disturbance convection time is consistent with the measured time delay in the short flame scenario. Future research is necessary to establish whether the observed agreement occurs due to the oscillation zone consistently occurring proximate to the flame's center of mass, coupled with a precise determination of the average convective velocity.

Machine learning (ML) is widely used in the field of crop-growing information identification based on high-resolution remote sensing images. With Baoying County in Jiangsu Province, China, as the study area, this paper used Sentinel-2 images during the winter wheat growth period to construct its spectral, textural, and topographic features during its growth period and proposes a winter wheat-growing area extraction method based on the extreme gradient boosting (XGBoost) algorithm, which was compared with traditional ML algorithms such as the support vector machine (SVM), classification and regression tree (CART), and random forest (RF) algorithms. The results indicated that (1) a winter wheat-growing area identification model based on the XGBoost algorithm was successfully constructed based on Sentinel-2 images, considering 27 spectral, textural, and topographic features; (2) the constructed model could effectively extract winter wheat in the study area with an overall accuracy of 93.43% and only a small error compared with the actual winter wheat-growing area in Baoying County, meeting the accuracy requirement for crop identification in the study area; and (3) the deep learning algorithm XGBoost outperformed the three traditional ML algorithms, among which the RF algorithm was better than the SVM and CART algorithms, both of which had poor identification performance and a large error compared with the actual growing area. This paper provides a scientific basis for the accurate extraction of winter wheat-growing areas and further research on winter wheat growth monitoring and yield estimation.

Drone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images with different degrees of blurring use the same weights, leading to increasing errors in the feature fusion process layer by layer. Based on the physical properties of image blurring, this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network (AMD-GAN), which innovatively applies the degree of image blurring to guide the adjustment of the weights of multi-scale fusion, effectively suppressing the errors in the multi-scale fusion process and enhancing the interpretability of the feature layer. The research work in this paper reveals the necessity and effectiveness of a priori information on image blurring levels in image deblurring tasks. By studying and exploring the image blurring levels, the network model focuses more on the basic physical features of image blurring. Meanwhile, this paper proposes an image blurring degree description model, which can effectively represent the blurring degree of aerial remote sensing images. The comparison experiments show that the algorithm in this paper can effectively recover images with different degrees of blur, obtain high-quality images with clear texture details, outperform the comparison algorithm in both qualitative and quantitative evaluation, and can effectively improve the object detection performance of aerial remote sensing blurred images. Moreover, the average PSNR of this paper's algorithm tested on the publicly available dataset RealBlur-R reached 41.02dB, surpassing the latest SOTA algorithm.

Human activities for exploitation and utilization of coastal zones have transformed coastline morphology and severely changed regional flow fields, underwater topography, and sediment distribution in the sea. In this study, single-beam bathymetry coupled with sediment sampling and analysis was carried out to ascertain submarine topography, geomorphology and sediment distribution patterns and explore sediment provenance in Qinzhou Bay, China. The results show the following. 1) The underwater topography in Qinzhou Bay is complex and variable, with water depths in the range of 0–20 m. It can be divided into four underwater topographic zones: the central (outer Qinzhou Bay), eastern (Sanniang Bay), western (east of Fangcheng Port), and southern (outside of the bay) parts. 2) Based on geomorphological features, the study area comprises four major submarine geomorphological units (i.e., tide-dominated delta, tidal sand ridge group, tidal scour troughs, and underwater slope) and two intertidal geomorphological units (i.e., tidal flat and abrasion platforms). 3) Sandy sediments are widely present in Qinzhou Bay, accounting for 70% of the total sediments. From the mouth of the Maowei Sea to the offshore, the sediments gradually become coarser, shifting from sandy mud to muddy sand, and then to fine sand and medium–coarse sand. The detrital minerals contained in the sediments mainly consist of quartz, feldspar, debris, ilmenite, leucosphenite, and tourmaline, whereas the clay minerals are dominated by kaolinite, followed by illite and smectite. The sediment provenance is mainly terrigenous input from near-source rivers. With sea reclamation and dam construction, outer Qinzhou Bay has experienced enormous morphological variation of its coastline. Together with the channel effect where the velocity of ebb tide is greater than that of flood tide, the underwater topography is characterized by increased scale and height difference of troughs and ridges as well as enhanced offshore deposition.

The escalation of mining activities in the karst regions of Guizhou Province has heightened the occurrence of water-inrush incidents in deep coal mines. This study focused on water-inrush phenomena within the Xinhua mining area of Jinsha County, Guizhou Province, aiming to investigate the sources of these incidents. The findings indicated that the overlying limestone of the Changxing Formation in the coal seam served as a vulnerable aquifer under certain conditions, leading to water inrushes. The analysis of the spatiotemporal distribution patterns of water-inrush incidents at the working face indicated that previous mining operations damaged the shallow Changxing Formation limestone, resulting in the accumulation of goaf water and the formation of numerous mining-induced fractures. These fractures served as rapid conduits for water inrushes from both atmospheric precipitation and underground sources at the deep working face. The examination of surface water and mine water quality demonstrated that both exhibited similar characteristics, predominantly featuring bicarbonate, sulfate, and sodium compositions. Investigation into the relationship between mine water inflow and atmospheric precipitation established that atmospheric precipitation influenced the mine water supply cycle, with a replenishment period of ~10 months during the operational phase of the Jinyuan Coal Mine and about one month post-closure. The fractures induced by mining activities within the Changxing Formation limestone facilitated water flow, with atmospheric precipitation serving as the primary water source for the mine. This study offered a valuable scientific foundation for addressing water-related damage resulting from atmospheric precipitation in mines susceptible to water inrushes under analogous hydrogeological conditions.

With the deterioration of the bridge performance and ever-increasing amount of traffic, the bridge safety is becoming a concern for engineering community. A method that can assess the bridge's condition in real-time is urgently needed. The main factors that hinder the bridge condition assessment are the uncertain operational environments. A new moving principal component analysis (MPCA) based method is developed for structural damage detection of bridges in operational environments in this paper. Two main operational environmental factors: the environmental temperature and traffic loads, are studied in the assessment process to verify the robustness and practicality of the proposed method. The numerical and experimental results show that the proposed method is effective and accurate to assess the bridge condition in operational environments.

Due to the highly nonlinear, multi-stage, and strongly time-varying marine lysozyme fermentation process, it is difficult to assure the stability and prediction accuracy of the traditional single global soft sensor model on a global scale. This study innovatively proposed a soft sensor model based on an improved seagull optimization algorithm (ISOA) combined with Gaussian process regression (GPR) weighted ensemble learning. First, the sample data set is divided into multiple local sample subsets by the improved density peak clustering algorithm (ADPC). Second, the Gaussian process regression model is optimally altered with an improved seagull optimization algorithm for the purpose of establishing the corresponding sub-prediction model. Finally, the prediction model's fusion strategy is ultimately determined depending on the degree of connection between the test samples and a subset of local pieces. Simulation results show that the proposed soft sensor model, which searches GPR based on ISOA and combines various sub-models, can predict the key biochemical parameters of the marine lysozyme fermentation process better with less prediction error under the condition of fewer training data, and it can be expanded to the soft sensor models of general nonlinear systems, according to simulation results.

In this paper, stochastic multi-objective intelligent framework (MOIF) is performed for distribution network reconfiguration to minimize power losses, the number of voltage sags, the system's average RMS fluctuation, the average system interruption frequency (ASIFI), the momentary average interruption frequency (MAIFI), and the system average interruption frequency (SAIFI) considering the network uncertainty. The unscented transformation (UT) approach is applied to model the demand uncertainty due to simplicity to implement and no presumptions to simplify. A human-inspired intelligent method named improved mountaineering team-based optimization (IMTBO) is applied to determine the decision variables defined as the network's optimal configuration. The conventional MTBO is improved using a quasi-opposition-based learning strategy to overcome premature convergence and achieve the optimal solution. The simulation results showed that in single- and double-objective optimization, some objectives are weakened compared to their base value, while the results of the MOIF indicated a fair compromise between different objectives, and all objectives are improved. The results of MOIF based on the IMTBO cleared that the losses are reduced by 30.94%, the voltage sag numbers and average RMS fluctuation are reduced by 33.68% and 33.65%, and also ASIFI, MAIFI, and SAIFI are improved by 6.80%, 44.61%, and 0.73%, respectively. Also, the superior capability of the MOIF based on the IMTBO is proved compared to the conventional MTBO, particle swarm optimization, and artificial electric field algorithm. Moreover, the results of the stochastic MOIF based on the UT showed the power loss increased by 7.62%, voltage sag, and SARFI increased by 5.39% and 5.31%, and ASIFI, MAIFI, and SAIFI weakened by 2.28%, 6.61%, and 1.48%, respectively compared to the deterministic MOIF model.

This paper investigates the problem of cooperative standoff tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) with control input constraints. In order to achieve accurate target tracking in the presence of unknown background wind and target motion, a coordinated standoff target tracking algorithm is proposed. The objective of the research is to steer multiple UAVs to fly a circular orbit around a moving target with prescribed intervehicle angular spacing. To achieve this goal, two control laws are proposed, including relative range regulation and space phase separation. On one hand, a heading rate control law based on Lyapunov guidance vector field is proposed. The convergence analysis show that the UAVs can asymptotically converge to a desired circular orbit around the target, regardless of their initial position and heading. Through rigorous theoretical proof, it is concluded that the command signal of the proposed heading rate controller will not violate the boundary constraint on heading rate. On the other hand, a temporal phase is introduced to represent the phase separation and avoid discontinuity of the wrapped space phase angle. On this basis, a speed controller is developed to achieve equal phase separation. The proposed airspeed controller met the requirement of airspeed constraint. In addition, to improve the robustness of the aircraft during target tracking, an estimator is developed to estimate the composition velocity of the unknown wind and target motion. The proposed estimator uses the offset vector between the UAV’s actual flight path and the desired orbit which is defined by Lyapunov guidance vector field to estimate the composition velocity. The stability of the estimator is proved. Simulations are conducted under different scenarios to demonstrate the effectiveness of the proposed cooperative standoff target tracking algorithm. The simulation results of indicate that, the temporal-phase-based speed controller can achieve fast convergence speed and small phase separation error. Additionally, the composition velocity estimator exhibits fast response speed and high estimation accuracy.

In this paper, we consider the fourth-order parabolic equations with integer and fractional order time derivatives with Neumann boundary conditions. The integer order time derivatives are approximated by backward Euler difference quotients, and the fractional order time derivatives are approximated by L1 interpolation. We propose the block-centered finite difference scheme for fourth-order parabolic equations of integer and fractional order time derivatives. We prove the stability of the block-centered finite difference scheme and the second-order convergence of the discrete L2 norms of the approximate solution and its derivatives of every order. Numerical examples are given to verify the effectiveness of the block-centered finite difference scheme.

A recently identified broiler myopathy known as wooden breast (WB) is predominantly found in the pectoralis major muscle of fast-growing broiler hybrids and is causing significant losses to the poultry industry. The aim of this study was to investigate the effects of WB syndrome on raw meat texture, purge loss and thermal properties of intramuscular connective tissue of pectoralis major muscle in the early post-mortem period. Results showed that WB muscles at 1d postmortem was associated with significantly increased stiffness and significantly increased purge loss compared to normal muscles (NB). However, at 3d postmortem, these parameters did not differ between the two groups. Insoluble and total collagen content was significantly higher in WB muscles compared to NB muscles, and the extractability of intramuscular connective tissue (IMCT) was also higher and remained stable in the early postmortem period. These were coincided with significantly lower protein content in the sarcoplasmic protein fraction and myofibrillar protein fraction of WB muscles compared to NB muscles (P < 0.05). The IMCT of these two groups showed different thermal properties, as the enthalpy of denaturation (ΔH) was significantly lower in WB muscles compared to NB muscles. The WB syndrome had a great effect on the texture and connective tissue properties of the meat compared to normal muscle, with a tendency for having a lower water holding capacity and higher raw meat hardness

According to this study, approximately half of Africa's forests are utilized primarily or partially for the production of wood and non-wood commodities. Aims to evaluate Africa's forestry and forest products, namely Wood Forest Products (WFPs) and Non-wood Forest Products (NWFPs) in the sixteen (16) West African countries. While adhering to the following guidelines: wood extraction and preparation, analyzing wood primarily used as an energy source in Africa, identifying non-wood forest products in Africa, the state of export, trade, and customs procedures in West Africa, and examining the role of forests and forest stakeholders in Africa's low-carbon economy transition. An exploratory literature review of selected wood forest products and non-wood forest products (plants and animals) in West Africa identifying the country, the natural land area with the natural habitat issues of the forest, the species most harvested and traded in the West African sub-region. The study reemphasized some government legislation, policies, and market trade failures and limitations while also stating that trees may help in the low-carbon revolution through interventions aimed at maintaining, improving, and restoring natural capital have demonstrated that high environmental requirements of sustainable forest management (SFM) may be met in both natural and planted forests. The study identified a systematic assessment of the most common forest products (wood and non-wood forest products) considering the available data on the national forest reserves of the selected countries in West Africa. The study also revealed the need for biodiversity conservation of the available forest reserves to help mitigate the impact of global warming targeting the United Nation’s Sustainable Development Goal 13- Climate Action. Which is focused on integrating climate change mitigation, adaptation, impact reduction, and early warning signs into the national policies, improving forest planning and management education, awareness-raising, and institutional capacity within the sub-region.

Although educational timetabling problems have been studied for decades, one type, the STP, has not developed as quickly as the other two types due to its diversity and complexity. Also, most of the STP research has only focused on the educators’ availabilities when studying the educator aspect, and the educators’ preferences and expertise have not been taken into consideration. This paper proposes a conceptual model for the school timetabling problem considering educators’ availabilities, preferences and expertise as a whole, and chooses a common real-world school timetabling scenario to study. A mathematical model is presented. A Virtual search space for dealing with the large search space is introduced, and the artificial bee colony algorithm is adapted and applied to the proposed model. The proposed approach is simulated with a random-generated large dataset. The experimental results demonstrate that the proposed approach is able to solve the STP and handle a large dataset in an ordinary computer hardware environment, which significantly reduces computational costs. Compared to the traditional CP method, the proposed approach is more effective and can provide more satisfactory solutions in considering educators’ availabilities, preferences, and expertise levels.

Research shows that stress, a common problem in dense cities, can be relieved in the natural environment. As great significance has been attached to the urban environment and public health in the society, this paper aims to study the relation and interaction between the perceived sensory dimension of urban park green space, attention restoration and state empathy. Therefore, we conducted an on-site questionnaire survey in four typical parks in Chengdu in terms of age, sex, daily stress, frequency of access in parks and other basic information of the respondents. The main part consists of perceived dimension, state empathy and attention restoration. The software SPSS24.0 is applied to the test of the validity and reliability of the PSD Scale, and then the important correlation between the perceived sensory dimensions in the parks and visitors' attention restoration is analyzed through multiple linear regression. Finally, the moderation effect of state empathy is tested by PROCESS. The findings show that (1) only seven dimensions in the PSD Scale are effective; (2) Serene and Refuge in the perceived sensory dimensions have significant effect on the restorative components of attention. (3) Except the dimensions of Rich in Species and Refuge, empathy enhanced the moderation effect in the interaction between the other five dimensions the Perceived Restorative Scale(PRS), especially in the interaction between the dimension Social and PRS. However, this study needs to be further explored to provide scientific basis and design strategy guidance for the research on the restoration potential of urban park green space in high-density urban areas.

The growing number of the accessed energy-efficient frequency conversion air conditioners is likely to generate a large number of harmonics on the power grid. The following shortage in the reactive power of peak load may trigger voltage collapse. Hence, this conflicts with people’s expectations for a cozy environment. Concerning the problems mentioned above, an active management scheme is put forward to balance the electricity use and the normal operation of air conditioning systems. To be specific, schemes to suppress the low voltage ride through (LVRT) and harmonic are designed firstly. Then to deaden the adverse effects caused by nonlinear group load running on the grid, and to prevent the unexpected accidents engendered from grid malfunction, the dynamic sensing information obtained by an online monitor is analyzed, which can be seen as an actively supervise mechanism. The combined application of active and passive filtering technology is studied as well. Thirdly, the new energy storage is accessed reliably to cope with peak-cutting or grid breaking emergencies, and the fuzzy control algorithm is researched. Finally, system feasibility is verified by functional modules co-operation simulation, and active management target is achieved under scientific and reasonable state-of-charge (SOC) management strategy.

The model proposed in this study was based on the assumption that the biomass attached to the anode served as biocatalysts for MFC exoelectrogenesis, and this catalytic effect was quantified by the exchange current density of anode. By modifying the Freter model and combining it with the Butler-Volmer equation, this model could adequately describe the processes of electricity generation, substrate utilization, and the suspended and attached biomass concentrations, at both batch and continuous operating modes. MFC performance is affected by the operating variables such as initial substrate concentration, external resistor, influent substrate concentration, and dilution rate, and these variables were revealed to have complex interactions by data simulation. The external power generation and energy efficiency were considered as indices for MFC performance. The simulated results explained that an intermediate initial substrate concentration (about 100 mg/L under this reactor configuration) needed to be chosen to achieve maximum overall energy efficiency from substrate in the batch mode. An external resistor with the value about that of the internal resistance boosted the power generation, and a resistor with several times of that of the internal resistance achieved better overall energy efficiency. At continuous mode, dilution rate significantly impacted the steady-state substrate concentration level (thus substrate removal efficiency and rate), and attached biomass could be fully developed when the influent substrate concentration was equal to or higher than 100 mg/L at any dilution rate of the tested range. Overall, this relatively simple model provided a convenient way for evaluating and optimizing the performance of MFC reactors by regulating operating parameters.

Radar transmit signal design is a critical factor for the radar performance. In this paper, we investigate the problem of radar signal waveform design under the small signal power conditions for detecting a doubly spread target, whose impulse response can be modeled as a random process, in a colored noise environment. The doubly spread target spans multiple range bins (range-spread) and its impulse response is time-varying due to fluctuation (hence also Doppler-spread), such that the target impulse response is both time-selective and frequency-selective. Instead of adopting the conventional assumption that the target is wide-sense stationary uncorrelated scattering,we assume that the target impulse response is both wide-sense stationary in range and in time to account for the possible correlation between the impulse responses corresponding to close range intervals. The locally most powerful detector, which is asymptotically optimal for small signal cases, is then derived for detecting such targets. The signal waveform is optimized to maximizing the detection performance of the detector or equivalently maximizing the Kullback-Leibler divergence. Numerical simulations validate the effectiveness of the proposed waveform design for the small signal power conditions and performance of optimum waveform design are shown in comparison to the frequency modulated waveform.

The RPL protocol is a routing protocol for low power and lossy networks. In such a network, energy is a very scarce resource; so many studies are focused on minimizing global energy consumption. End-to-end latency is another important performance indicator of the network, but existing research tends to focus more on energy consumption and ignore the end-to-end delay of data transmission. In this paper, we propose a kind of energy equalization routing protocol to maximize the surviving time of the restricted nodes so that the energy consumed by each node is close to each other. At the same time, a multi-path forwarding route is proposed based on the cache utilization. The data is sent to the sink node through different parent nodes at a certain probability, not only by selecting the preferred parent node, thus avoiding buffer overflow and reducing end-to-end delay. Finally, the two algorithms are combined to accommodate different application scenarios. The experimental results show that the proposed three improved schemes improve the reliability of the routing, extend the lifetime of the network, reduce the end-to-end delay, and reduce the number of DAG reconfiguration.

Oxygen evolution reaction is a momentous part of electrochemical energy storage and conversion devices such as rechargeable metal-air batteries. It is particularly urgent to develop low-cost and efficient electrocatalysts for oxygen evolution reactions. As a potential substitute for noble metal electrocatalysts, transition metal selenides are still challenging in improving the activity of oxygen evolution reaction and the research of reaction intermediates. In this study, a simple one-step solvothermal method was used to prepare a polymetallic compound carbon matrix composite (Co9Se8/Ni3Se4/Fe3O4@C) with a multilayered nanosheets structure. It exhibited good OER activity in an alkaline electrolyte solution with an overpotential of 268 mV at 10 mA/cm2 in an alkaline medium. Besides, this catalyst also showed excellent performance in the 24 hours stability test. The composite presents a multi-layer sheet structure, which effectively improves the contact between the active site and the electrolyte. The selenide formed by Ni and Co has a synergistic effect, Fe3O4 and Co9Se8 form a heterojunction structure, which can effectively improve the reaction activity by initiating the electronic coupling effect through the interface modification. In addition, carbon quantum dots have rich heteroatoms and electron transferability, which improves the electrochemical properties of the composites. This work provides a new strategy for the preparation of highly efficient OER electrocatalysts utilizing the multimetal synergistic effect.

Solar energetic particles (SEPs) are bursts of high-energy particles that originate from the Sun and can last for hours or even days. The aim of this study is to understand how the characteristics of energetic particles was affected by the characteristic parameters of corotating interaction regions (CIRs). In particular, the particle intensity distribution with time and space in different CIRs are studied. The propagation and acceleration of particles are described by the focused transport equation (FTE). We used a three-dimensional magnetohydrodynamic (MHD) model to simulate the background field including CIRs. By changing the inner boundary conditions, we constructed CIRs with different solar wind speeds, angles between the polar axis and rotation axis, and the azimuthal widths of the fast streams. Impulsive particles are injected at the inner boundary. We then study the SEPs in different backgrounds. The results show that the CIR widths are related to the solar wind speed, tilt angles, and the azimuthal widths of the fast stream. The acceleration of particles in the reverse and forward compression regions is mainly influenced by the solar wind speed difference and the slow solar wind speed, respectively. Particles with lower energy (sub-MeV) are more sensitive to the parameters of CIRs. Different CIR parameters also greatly affect the spatio-temporal distribution of particle intensities in different ways. These results indicate that, it is crucial to consider the conditions of large-scale structure in interplanetary space when studying or predicting energetic particles. Additionally, our findings provide empirical guidance for forecasting the characteristics of SEPs.

Age-related declines in vitamin D receptor (VDR) expression have been implicated in the onset of sarcopenia. The increased expression of VDR in muscle is known to increase muscle mass, whereas VDR knockout in the muscle decreases muscle mass in rats. Similar to VDR, the age-related decline in protein deacetylase sirtuin (SIRT) expression is linked to the development of sarcopenia and age-related muscle dysfunction. This study aimed to investigate whether the VDR agonist 1,25-dihydroxyvitamin D3 (1,25VD3) exerts beneficial effects on muscles through interactions with sirtuins and, if so, the underlying molecular mechanisms. 1,25VD3 treatment increases the expression of VDR, SIRT1, and SIRT3 in C2C12 myotubes. Additionally, it stimulates the differentiation of C2C12 myoblasts into myotubes. Furthermore, 1,25VD3 significantly enhanced the expression of key myogenic markers, including myosin heavy chain (MyHC) proteins (MyHC I and II), MyoD, and MyoG, and increased the phosphorylation of AMPK and AKT. Conversely, VDR knockdown in myoblasts induces myotube atrophy by downregulating SIRT1 and SIRT3 expression. Furthermore, 1,25VD3 ameliorated muscle atrophy and apoptosis induced by IFN-γ/TNF-α co-treatment to C2C12 myotube. Moreover, 1,25VD3 downregulated the increased expression of muscle atrophy-associated proteins, including FoxO3a, MAFbx, and MuRF1 in IFN-γ/TNF-α induced atrophy model. Importantly, IFN-γ/TNF-α significantly reduced the mtDNA copy number in C2C12 myotube, whereas the presence of 1,25VD3 effectively prevented this decrease. These results indirectly suggest that 1,25VD3 has the potential to develop as a therapeutic or preventive agent for age-mediated muscle atrophy by enhancing the VDR/SIRT1/SIRT3 axis and inhibiting the FoxO3-mediated atrophy pathway.

With the development of information technology and the wide application of building information modelling technology, the construction industry continues to make digital changes. Managers are trying to apply smart construction site management to promote the upgrading of production modes. We used CiteSpace software to analyze 1707 articles from China National Knowledge Infrastructure, Web of Science and Scopus to understand the current research hotspots of global scholars in this field. Results show that: (1) The number of studies on smart construction sites increases rapidly by year. Researchers from China, America and the UK have the most influential studies. (2) The cooperation between researchers and institutions is not close enough. In addition, the directions of research in the field are still scattered; (3) Chinese scholars are good at building intelligent platforms and evaluating intelligent systems from multiple perspectives. International scholars are willing to pursue technological innovation, allowing the continuous development of intelligent construction technology. (4) The applications in this field have not been popularized despite relatively perfect technology and typical cases. Future scholars should gradually improve the theoretical basis and industry standards of smart construction sites, promote the development of intelligent construction technology and establish the evaluation standards of qualified smart sites. This study will provide scholars in this field with the theoretical basis and research directions for further in-depth research, help construction companies to understand the development status and trend of smart construction sites and accelerate the intelligent transformation of construction companies.

Stemming from the object overlap and undertraining from the few samples, the road dense object detection is confronted with the poor object identification performance and the inability to recognize edge objects. Based on this, one transfer learning-based YOLOv3 approach for identifying dense objects in the road has been proposed. Firstly, Darknet-53 network structure is adopted to obtain pre-trained YOLOv3 model, then the transfer training is introduced as the output layer for the special dataset of 2000 images containing vehicles; in the proposed model, one random function is adapted to intialize and optimize the weights of the transfer training model, which is seperately designed from the pre-trained YOLOv3; and the object detection classifier replaces the fully connected layer, which further improves the detection effect, the reduced size of the network model can further reduce the training and detection time, and can be better applied to actual scenarios. The experimental results demonstrate that the object detection accuracy of the presented approach is 87.75% for the Pascal VOC 2007 dataset, which is superior to the traditional YOLOv3 and the traditional YOLOv2 by 3.05% and 11.15%, respectively. Besides, the test was carried out using UA-DETRAC, a public road vehicle detection dataset, the object detection accuracy of the presented approach reaches 79.23% in detecting images, which is 4.13% better than the traditional YOLOv3, and compared with the existing relatively new object detection algorithm YOLOv5, the detection accuracy is 1.36% better. Moreover, the detection speed of the proposed YOLOv3 method reaches 31.2 Fps/s in detecting images, which is 7.6 Fps/s faster than the traditional YOLOv3, and compared with the existing relatively new object detection algorithm YOLOv5, the speed is 4.3 Fps/s faster; the proposed YOLOv3 performs 79.38Bn of floating point operations per second in detecting video, which obviously surpasses the traditional YOLOv3 and the newer object detection algorithm YOLOv5.

Agricultural mechanization is an important factor to improve the green total factor productivity of planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Based on the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of output oriented distance function to measure the green total factor productivity of China’s planting industry based on net carbon sink, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China’s planting industry. The empirical analysis finds that mechanization can significantly promote the planting green total factor productivity, and this basic conclusion is still robust after using instrumental variables, sub sample regression. Further research found that the path of mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover. The mechanism of operation scale expansion, factor allocation optimization and technical efficiency change is not significant. Given these findings, the paper adds considerable value to the empirical literature and also provides various policy- and practical implications.

In this paper, we analyzed the live fish trajectory recorded from an experiment in an experimental vertical slot fishway. Combined with a numerical simulation, we demonstrated that randomness shown in fish trajectory might not merely be attributed to fish's random choices in its swimming, also could be an adaption consequence to the bulk unsteady turbulent flow structure. Simple superposing the fish trajectory on the time-averaged flow field obtained either by interpolating on discrete point measurements or numerical simulation is not an ideal method for fish movement description in fishway engineering. How to model the fish paths in transient flow and the necessity of simultaneous recording of the flow field and the fish locomotion are challenging topics. The suggested spectrum analysis of the flow field may provide a new general method to reproduce the fish trajectory in a complex turbulent flow.

In this paper, we propose a new mathematical model based on the association between susceptible and recovered individual, where the association between susceptible and recovered individual is disturbed by white noise. This model is based on demographic changes and is used for long term behavior. We study the stability of equilibria of the deterministic model and prove the conditions for the extinction of diseases. Then, we investigate and obtain the critical condition of the stochastic epidemic model for the extinction and the permanence in mean of the disease with the white noise. To verify our results, we present some numerical simulations for real data related to disease.

Tumor invasion, metastasis, and recrudesce remain a considerable challenge in the treatment of gastric cancer (GC). Herein, we first identified that RBFOX3 (RNA binding protein fox-1 homolog 3) was significantly up-regulated in GC tissues and negatively linked to the survival rate of GC patients. RBFOX3 promoted cell division and cell cycle progression in vitro as well as in vivo. Furthermore, RBFOX3 increased cell invasion and migration ability. Interestingly, both the suppression of GC cell multiplication and invasion moderated by the silencing of RBFOX3 was rescued by HTERT up-regulation. Additionally, RBFOX3 augmented the resistance of GC cells to 5-fluorouracil (5-Fu) by repressing RBFOX3. Mechanistically, exogenous up-regulation of RBFOX3 triggered promoter activity and HTERT expression thereby enhancing the division and development of GC cells. Importantly, our findings revealed that RBFOX3 interacted with AP-2β to modulate the HTERT expression as demonstrated by co-immunoprecipitation analysis. In conclusion, our study indicates that high expression of RBFOX3 promotes GC progression and development but predicts worse prognosis by stimulating HTERT signaling. Moreover, the results suggest that the RBFOX3/AP-2β/HTERT pathway is a novel target for the development of therapeutic agents for the prevention and treatment of GC reappearance and metastasis.

The merge areas of freeway work zones include relatively significant safety hazards that have continuously led to urgent safety issues to be solved by the management departments. In order to make up for the cumbersome process of independent identification of rear-end collision and lane change collision on complex road sections, an appropriate identification method of traffic conflicts in the merge area of freeway work zone was explored, this study collected vehicle running tracking data from the merge areas of multiple work zones, using an unmanned aerial vehicle video technique. Based on an inter-frame difference method and the principle of a spatio-temporal context visual tracking algorithm, the vehicles were detected and tracked, and the coordinate data of the vehicles in continuous motion were parsed using MATLAB extension tools. Based on the behavior characteristics of vehicle conflict avoidance, a new identification method for evading severe traffic conflicts is proposed according to the initial velocity, acceleration and accident rate of section traffic. Then, a statistical analysis was performed on the spatial distribution characteristics of the traffic conflicts in typical merge areas. The impacts of the road conditions in work zones, vehicle factors, and traffic flow factors on the traffic conflicts were analyzed. A binomial logistic model was established to identify the main influencing factors. The results show that in the merge area of the freeway work zone, there are serious traffic conflicts between vehicles in the following two situations: (Ⅰ) v∈[7,13.5] m/s and a∈[-3.96,-0.65]m/s2; and (Ⅱ) v∈[13.5,24.3] m/s, and a∈[-3.96,-1.57] m/s2. The probabilities of serious traffic conflicts in the first and last 25 m of the merge area are greater than those in the other sections. The smaller the space between the upstream work zone and the merge area, the greater the probability of serious traffic conflicts between vehicles. When the average vehicle speed is relatively high, the probability of serious conflicts is the highest, i.e., by a multiple of 5.95 from the baseline. Moreover, the probability of serious conflicts between vehicles is higher for larger vehicles, i.e., 4.765 times that for small vehicles. The research results can serve as a reference for freeway management departments to improve the safety levels of merge areas during road work. For example, the probability of serious conflicts can be effectively reduced by setting up reasonable speed limit signs in the work zone, increasing the spacing between work zone and merge area, and appropriately diverting large vehicles.

The key issue in the post-genomic era is how to systematically describe the association between small molecule transcripts or translations inside cells. With the rapid development of high-throughput “omics” technologies, the achieved ability to detect and characterize molecules with other molecule targets opens up the possibility of investigating the relationships between different molecules from a global perspective. In this article, a Molecular Associations Network(MAN) is constructed and comprehensively analyzed by integrating the associations among miRNA, lncRNA, protein, drug, and disease, in which any kind of potential associations can be predicted. More specifically, each node in MAN can be represented as a vector by combining two kinds of information including the attributes of the node itself (e.g. sequences of ncRNAs and proteins, semantics of diseases and molecular fingerprints of drugs) and the manner of the node in the complex network (associations with other nodes). Random Forest classifier is trained to classify and predict new interactions or associations between biomolecules. In the experiment, the proposed method achieves a superb performance with 0.9735 AUC in 5-fold cross-validation, which show that the proposed method can provide new insight for exploration of the molecular mechanisms of disease and valuable clues for disease treatment.

Human activities for exploitation and utilization of coastal zones have transformed coastline morphology and severely changed regional flow fields, underwater topography, and sediment distribution in the sea. In this study, single-beam bathymetry coupled with sediment sampling and analysis was carried out to ascertain submarine topography, geomorphology and sediment distribution patterns and explore sediment provenance in Qinzhou Bay, China. The results show the following. 1) The underwater topography in Qinzhou Bay is complex and variable, with water depths in the range of 0–20 m. It can be divided into four underwater topographic zones: the central (outer Qinzhou Bay), eastern (Sanniang Bay), western (east of Fangcheng Port), and southern (outside of the bay) parts. 2) Based on geomorphological features, the study area comprises four major submarine geomorphological units (i.e., tide-dominated delta, tidal sand ridge group, tidal scour troughs, and underwater slope) and two intertidal geomorphological units (i.e., tidal flat and abrasion platforms). 3) Sandy sediments are widely present in Qinzhou Bay, accounting for 70% of the total sediments. From the mouth of the Maowei Sea to the offshore, the sediments gradually become coarser, shifting from sandy mud to muddy sand, and then to fine sand and medium–coarse sand. The detrital minerals contained in the sediments mainly consist of quartz, feldspar, debris, ilmenite, leucosphenite, and tourmaline, whereas the clay minerals are dominated by kaolinite, followed by illite and smectite. The sediment provenance is mainly terrigenous input from near-source rivers. With sea reclamation and dam construction, outer Qinzhou Bay has experienced enormous morphological variation of its coastline. Together with the channel effect where the velocity of ebb tide is greater than that of flood tide, the underwater topography is characterized by increased scale and height difference of troughs and ridges as well as enhanced offshore deposition.

(1) In order to study the influence of chaotic system on wheat image encryption, a method to find the optimal chaotic system encryption based on wheat image is proposed. (2) 10 different chaotic system schemes are combined to encrypt wheat images by 13 common chaotic maps, respectively. The best chaotic system scheme is obtained by considering the anti-attack capability of these encryption schemes, which analyzes 8 commonly used image encryption performance evaluation indexes. (3) The experimental results show that the new four-dimensional chaotic system has the best encryption effect, which is suitable for wheat image encryption. (4) The proposed scheme of wheat image encryption based on chaotic system provides a reference for other crop image encryption.

This paper investigates the reconfigurable intelligent surface (RIS)-aided uplink multicell massive multiple-input multiple-output (mMIMO) communication system with transceiver hardware impairments (THWIs), and the practical and feasible two-timescale scheme is used to design the phase shifts of RIS. We consider the Rician channel model and use maximal-ratio combining (MRC) technology to process the received signal at BS. The expression of the uplink achievable rate is derived and analyzed. Besides, genetic algorithm (GA) is used to optimize the phase shifts of RIS to maximize the data rate. Finally, the accuracy of the derived results is verified. The simulation results show that appropriately increasing the number of RIS’s reflecting elements can compensate for the performance loss caused by inter-cell interference and THWIs, and reduce the demand for the number of BS antennas, which can significantly reduce the hardware costs at the BS.

We report a simple and high-performance flexible strain sensor based on liquid metal nanoparticles (LMNPs) on polyimide substrate by laser induced deposition. The LMNPs were prepared by ultrasonic method, and then the femtosecond laser direct writing technology was used to induce the assembly and deposition of the LMNPs to form liquid metal microwires. Laser local sintering enhances the connection between particles, and a wearable strain sensor was fabricated with the high sensitivity as high as 76.18, the good linearity (a correlation coefficient of 0.999) in a wide sensing rage, the fast response/recovery time of 159 ms/120 ms. Attributed to these extraordinary strain sensing performances, the sensor can sense various dynamic strains in real time, monitor both subtle physiological activities and large human motions. It can be adhered to human skin, and well reflect the change of facial expression and realize real-time monitoring of facial expression. And the vocal cord vibration can be detected for speech recognition while the sensor attached to the outside of the throat.

Currently, active and intelligent packaging have been developed to solve the spoilage problem for rich protein foods during storage, especially by adding anthocyanin extracts. In such film system, the antioxidant and antibacterial properties were dramatically increased by adding anthocyanins. The physicochemical properties were enhanced through interactions between active groups in anthocyanins and reactive groups in polymer chains. Additionally, active and intelligent film could monitor the spoilage of rich protein foods in response to pH change. Therefore, this film could monitor the sensory acceptance, and extending the shelf life of rich protein foods simultaneously. In this paper, the structure and functional properties of anthocyanins, the composite actions of anthocyanin extracts and biomass materials, and their reinforced properties of active and intelligent film were discussed. Also, the applications of this film in quality maintenance, shelf-life extension and quality monitoring for fresh meat, aquatic product and milk were summarized. The film that achieves high stability and continuous release of anthocyanins on demand will become an underlying trend in packaging applications for rich protein foods.