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.

Based on the analysis of the spatial distribution of uranium grade in 348 boreholes of a uranium deposit in Xinjiang, the enrichment and spatial variation of uranium ore in two stopes of the deposit are discussed by using multifractal theory. The distribution characteristics of uranium ore of the two stopes are studied by multifractal parameters: the scaling exponent of mass , the scaling exponent of each sub-set and its corresponding fractal dimension , the fractal dimension D0 and information dimension D1. The differences of uranium distribution in two stopes can be well quantified by multifractal spectrum and multifractal parameters( , , ).10m×10m is defined as a fence unit, and the window sizes are respectively set, the singularity exponent of the two stopes are calculated by this element concentration-area(C-A) method. The results show that the multifractal theory and model can organically combine spatial structure information, scale change information and anisotropy information to obtain low-grade and weak mineral resources information, and can effectively distinguish complex and superimposed anomalies. This will provide a basis for local concentration and spatial variation rules of uranium distribution and the design of the parameters of leaching uranium mining well site.

Polymeric composites such as Poly-ether-ether-ketone (PEEK)/carbon fiber (CF) have been widely utilized due to outstanding performances such as high specific strength and specific modulus. The PEEK/CF components via powder-bed fusion additive manufacturing usually show brittle fracture behaviors induced by their poor interfacial affinity and inner voids. These defects are strongly associated with powder packing quality upon deposition. The particle dynamic model has been widely employed to study the interactions of particle motions. Powder property, bulk material property, and interfacial features of composite powders are key factors in the particle dynamic model. In this work, an efficient and systematic material evaluation is developed for composite powders to investigate their deposition mechanism. The discrete element method is utilized to simulate the dynamic behaviors of PEEK/CF composite powders. The powder properties, bulk material properties, and interfacial features of powders are calibrated and justified by experimental measurement, numerical simulation, and design of experiments. The particle dynamic model can well explain the powder flow behaviors and interactions. It reveals that the addition of short CF particles can assist the flow of PEEK powders and improve the packing quality of the composite powders.

Background: Atherosclerosis (AS) is a chronic progressive disease caused by lipometabolic disorder. However, the pathological characteristics and mechanism of AS have not been fully clarified. By high fat and cholesterol diet induction, Tibetan minipig could be developed to the AS model animal which had a very similar pathogenesis to human AS. Methods: Here, we built the AS model of Tibetan minipigs and identified the differential abundance metabolites in the development of AS based on untargeted metabolomics. Results: We found that the sphingolipid metabolism and glucose oxidation were enriched in the AS group and the phenylalanine metabolism was reduced in the AS group. Moreover, in the development of AS, gluconolactone was enriched in the late stage of AS whereas biopterin was enriched in the early stage of AS. Conclusion: Gluconolactone and biopterin could be potential biomarkers for indicating the development of AS. Our research provides novel clues for the metabolic mechanism of AS from the perspective of metabolomics.

In the paper, we develop some numerical schemes including finite difference scheme and finite volume scheme for the fractional Laplacian operator, and apply the resulted numerical schemes to solve some fractional diffusion equations. First, the fractional Laplacian operator can be characterized as the weak singular integral by a integral operator with zero boundary condition. Second, because of the solution of fractional diffusion equation is usually singular near the boundary, we apply finite difference scheme to discrete fractional Laplacian operator and fractional diffusion equation by fractional interpolation function. Moreover, it is find that the differential matrix of the above scheme is symmetric matrix, and strictly row-wise diagonally dominant in special fractional interpolation function. Third, we show finite volume scheme to discrete fractional diffusion equation by fractional interpolation function, and analyze the properties of differential matrix. Finally, the numerical experiments are given, and verify the correctness of theoretical results and the efficiency of the schemes.

In this study, we explore the potential of using a straightforward neural network inspired by the retina model to efficiently restore low-light images. The retina model imitates the neurophysiological principles and dynamics of various optical neurons. Our proposed neural network model reduces the computational overhead compared to traditional signal-processing models while achieving results similar to complex deep learning models from a subjective perceptual perspective. By directly simulating retinal neuron functionalities with neural networks, we not only avoid manual parameter optimization but also lay the groundwork for constructing artificial versions of specific neurobiological organizations.

An analytical method was established by HPLC-DAD-ELSD with C18-NH2 column tandem for the simultaneous determination of hydrophobic components atractylenolide I, II, III, atractylone and hydrophilic compounds glucose, fructose and sucrose in the dried rhizome of Atractylodes macrocephala Koidz (a natural raw material for health foods, Bai-Zhu aka. in Chinese). The method combines different separation capabilities of reversed-phase liquid chromatography and hydrophilic interaction liquid chromatography, can provide a new choice for the simultaneous determination of hydrophilic and hydrophobic compounds in traditional Chinese medicines and health foods. It provided a reference method for the quality control of Bai-Zhu. The results showed that the linear correlation coefficients of the established column tandem chromatographic method were all greater than 0.9990, the relative standard deviation was 0.1%-2.8%, and the average recovery was 96.7%-103.1%. The contents of atractylenolide I, II, III, atractylone, fructose, glucose, and sucrose in 17 batches of Baizhu were 172.3-759.8 μg/g, 201.4-612.8 μg/g, 160.3-534.2 μg/g, 541.4-8723.1 μg/g, 6.9-89.7 mg/g, 0.7-7.9 mg/g, and 1.2-21.0 mg/g, respectively.

In this paper, a new method by combining cold drawing and martensitic transformation was proposed to produce ultrahigh-strength steel wire, and the effect of austenitizing heating rate on microstructure and mechanical properties was studied. Compared with Muffle furnace heating, salt bath heating has a much higher austenitizing heating rate, which is beneficial to obtain ultrafine-grained martensite for steel wire with a larger section. By salt bath hardening and low-temperature tempering, an ultrahigh strong (strength of 2.48 GPa) and tough (elongation of 6.0%) steel wire was produced. Moreover, to explore whether is it possible to replace long-time low-temperature tempering with short-time tempering at elevated temperatures to improve the tempering efficiency, the effect of tempering time on the evolution of microstructure and mechanical properties of the steel wires tempered at 350 °C was systematically studied. The results demonstrate that tempering at 350 °C for 30 s can endow the steel wire with comparable mechanical properties to that of the one tempered at 250 °C for 2 h. With tempering time prolonging, the strength decreases gradually with the precipitated phase transition from χ-Fe5C2 carbide into θ-Fe3C carbide and coarsening, which plays a major role in the strength decrease.

Brain tumors are abnormal cells growth in the brain tissues that can be cancerous, or not. In any case, they could be a very aggressive disease that should be detected as early as possible. Usually, magnetic resonance imaging (MRI) is the main tool commonly adopted by neurologists and radiologists to identify and classify any possible anomalies present in the brain anatomy. In the present work, an automatic unsupervised method called Z2-$\gamma$, based on the use of adaptive finite-elements and suitable pre-processing and post-processing techniques, is introduced. In particular, the proposed methodology is able to automatically classify whether a given MR image represents a healthy or a diseased brain and in this latter case, is able to locate the tumor area.

(1) Background: Investigating the characteristics of photosynthetic physiological changes of leaves in Mangifera indica L. cv. 'GuIfei' under enhanced UV-B radiation, natural light exposed trees were regarded as control, and 96 kJ·m-2·d-1enhanced UV-B radiation was artificially simulated in the field; (2) Methods: The changes of fruit maturity and fruit quality, leaf net photosynthetic rate (Pn), photosynthetic pigments contents, photochemical reaction, activities of photosynthetic enzymes and their genes expressions were determined; (3) Results: Compared with CK, the percentage of mature fruits of the treatment was significantly increased, and fruit quality was better. The net photosynthetic rate (Pn), the contents of photosynthetic pigment, Hill reaction activity and photochemical quenching coefficient (qP) of the treatment leaves showed a significantly higher trend than CK. The activities of Rubisco and RCA, and the expression of Rubisco genes rbcL and rbcS were significantly increased; (4) Conclusions: 96 kJ·m-2·d-1 enhanced UV-B radiation treatment improved Rubisco activity through increasing the expression of Rubisco genes rbcL and rbcS, thereby enhancing the CO2-fixing capacity and dark reaction capacity of leaves. Based on this, it raised the net photosynthetic rate of leaves, which promoted the early maturity of 'Guifei' mango by the fast accumulating photosynthetic products.

On the basis of the force and safety factor analysis of traditional retaining wall, a new analytical method of force and displacement of slope is suggested, the numerical theoretical solution of the stress distribution of the sliding body can be obtained under the conditions, which the stresses distribution are satisfied with the differential equilibrium equations, the boundary conditions, the compatibility equation and the macroscopic equilibrium equations. The interface stresses between the sliding body and the retaining wall are continuous, the theoretical solution of retaining wall stress distributions can be gotten, while the stress differential equilibrium equation, the compatibility equation, macroscopic force and moment balance equations are satisfied. The strain and displacement solutions can be obtained by using Duncan Chang and Hooke constitutive equations for the slope and the retaining wall respectively. The transfer station of landfill in the Guandukou Town of Badong County is taken as an example, the results of the sliding body and retaining wall analysis show: The stress and strain solutions of a slope and a retaining wall can be obtained by the proposed method, the forces between the retaining wall and the sliding body include shear stress and normal stress, and the stress distributions change with the deformation of the sliding body, the shape and material of retaining wall can be optimized according to the calculation results，it is feasible for the proposed analysis method of slope with retaining wall design to be run many years.

Infrared dim and small target detection is a key technology of various detection tasks. However, due to the lack of shape, texture, and other information, it is a challenging task to detect dim and small targets. Recently, since many traditional algorithms ingore global information of infrared images, they generate some false alarms in complicated environments. To address this problem, in this paper, a coarse-to-fine deep learning-based method is proposed to detect dim and small targets. Firstly, a coarse-to-fine detection framework integrating deep learning and background prediction is applied for detecting targets. The framework contains of coarse detection module and fine detection module. In coarse detection stage, Region Proposal Network (RPN) is employed to generate masks in target candidate regions. Then, for further optimize the result, inpainting is utilized to predict the background by the global semnantic of images. In this paper, inpainting algorithm with a mask aware dynamic filtering module is incorporated into the fine detection stage to estimate background in candidate target areas. Finally, compared with existing algorithms, the experimental results indicate that the proposed framework have effective detection capability and robustness for complex surroundings.

More than 110 countries including 500 cities worldwide have set the goal of reaching carbon neutrality. Heating contributes to most of the residential energy consumption and carbon emissions. The green energy transition of fossil-based heating systems is needed to reach the emission goals. However, the heating systems vary in energy source, heating technology, equipment location, and these complexities make it challenging for households to compare heating systems and make decisions. Hence, a decision support tool that provides a generalized ranking of individual heating alternatives is proposed for households as decision-makers to identify the optimal choice. This paper presents an analysis of 13 heating alternatives and 19 quantitative criteria in technological, environmental, and financial aspects, combines ideal solution based Multi-Criteria Decision Making with 6 weighting methods and 4 normalization methods, and introduces ensemble learning with a fuzzy membership function derived from Cauchy distribution to finalize the final ranking. The robustness of the proposed method is verified by 3 sensitive analyses from different aspects. Air to water heat pump, solar heating and direct district heating are the top three rankings in the final result under Danish national average data. A framework is designed to guide the decision-makers apply this ranking guideline with their practical feasible situations.

In this paper, the experimental study of heating in 2 h lunch mode of air-conditioned bed system is carried out. The results show that the power consumption of heat conduction heating is only 0.34 kW·h, and the average heat dissipation is 81.3 W, while the power consumption of convection heating is 1.43 kW·h, with an average heat dissipation of 748.7 W. Regardless of power consumption or heat dissipation, convection heating is significantly higher than heat conduction heating, resulting in the room air temperature of the former increased from 12.3 °C to 17.3 °C, while the latter only increased from 14.4 °C to 15.2 °C. The study found that the water temperature of heat conduction heating can meet the thermal comfort needs of the human body in the range of 38~40 °C, while convection heating requires a higher temperature range, In contrast, the grade of hot water required for heat conduction heating is lower. Through the experiment, it is also found that the temperature of convection heating rises fast, but it is easy to appear dry feeling after a long time, while heat conductive heating has a slow temperature rise. there is a cool feeling within 20 min after the heating starts, and then the thermal comfort is better. The air conditioning system in this paper is used for the heating experiment in the winter lunch break mode, compared with convection heating, the overall thermal comfort of heat conduction heating is better and more energy efficient. it is suggested to adopt heat conduction heating mode in the winter heating of this system.

In the present work report, the MgAl₂O₄and the Ag/MgAl₂O₄ samples were successfully synthesized by the modified hydrothermal and the isovolumetric impregnation methods, respectively. The structural properties of the prepared samples were systematically characterized by XRD, SEM, TEM, DRS, XPS and et al techniques. The photocatalytic degradation of methylene blue by the Ag/MgAl₂O₄ and MgAl₂O₄ samples was comparatively studied under UV lamp irradiation. The results revealed that the prepared Ag/MgAl₂O₄ (pH=6) samples were the most active among the samples in photocatalytic of methylene blue. Under UV lamp irradiation, the Ag/MgAl₂O₄ (pH=6) photodegradation of methylene blue reached to 89.6% within 120 min. And the Ag/MgAl₂O₄ (pH=6) complex photocatalysts displayed a high photochemical stability under repeated irradiation. Repeated irradiate the Ag/MgAl₂O₄ (pH=6) compound, which indicated it had a high photochemical stability.

The diverse physical appearances and wide density range of polymer bead foams offer immense potential across various applications and future advancements. The multi-scale and multi-level structural features of bead foams involve many fundamental scientific topics. This review presents a comprehensive overview of recent progress in bead foams preparation and molding techniques. Initially, a comparative analysis is conducted among bead foam characteristics of distinct polymers, based on their unique properties. Subsequently, a summary and comparison of molding processes employed for fabricating bead foam parts are provided. Beyond traditional methods like steam-chest molding (SCM) and adhesive-assisted molding (AAM), emerging techniques like in-mold foaming and molding (IMFM) and microwave selective sintering (MSS) are highlighted. Lastly, the bonding mechanisms behind the diverse molding methods are discussed.

The physical properties of coal reservoirs are the main restrictions to exploration and development of CBM. The study of the physical characteristics of coal reservoirs and their controlling factors is of great significance to the safe and efficient development and utilization of CBM resources in the mining area. The Pingdingshan No. 10 coal mine was chosen for this study because of its better gas production effect at the test wells. The reservoir properties of the main coal seams of the No. 10 coal mine were tested and analyzed to comprehensively evaluate the reservoir properties. The results indicate that (1) The gas content and methane purity of the No. 4 coal seam are significantly higher than the No. 2 seam, and therefore they have better development potential than the No. 2 seam; (2) The average adsorption time of the No. 2 coal seam is less than that of the No. 4 seam, making it easier to reach the peak production capacity of CBM wells in the short term, but is not conducive to long-term stable production of CBM. The Langmuir volume of the No. 2 coal seam samples was significantly greater than for the No. 4 coal seam; however, the No. 4 coal seam contains about three times the amount of gas in the No. 2 seam; (3) All three coal seams have high porosity, which is favorable for large amounts of CBM adsorption and storage. Micropores predominate, transitional pores are less frequent, and a few mesopores occur; macropores are the least common. Samples from the No. 4 seam contained the highest proportion of micropores; (4) Organic pores were common in the all coal samples, with pore diameters not more than 30 µm, mainly concentrated between 50.5 and 1000 nm. Microfractures with aperture less than 70 nm were relatively frequent, mainly in the 50–65 nm range. A large number of the nanoscale microfractures were curved or jagged; (5) Fractures in the No. 2 and No. 4 coal seam samples with widths of 50 nm to 20 µm were more developed, and many were filled with kaolinite, quartz and other minerals; (6) The samples contained mostly layered silicate minerals (kaolinite), with hard granular minerals (quartz) next, and a very small quantity of iron-type minerals such as siderite and pyrite in dendritic form. The results of this study can provide a reference basis for the large-scale development and utilization of subsequent CBM wells.

ozone-based chemiluminescence detection (CLD) has been widely applied for determining nitric oxide (NO) and its derived species in many different fields, such as environmental monitoring and biomedical research. In humans CLD was applied to determine exhaled NO and NO metabolites in biological samples. The main advantages of CLD are high sensitivity and selectivity for quantitative analysis in a wide dynamic range. Combining CLD with analytical separation techniques like gas or liquid chromatography allows the analytes to be quantified with less disturbance from matrix components or impurities. Sampling techniques like microdialysis and flow injection analysis may be coupled to CLD with the possibility of real-time monitoring of NO. However, details and precautions in experimental practice need to be addressed and clarified to avoid wrongly estimating. Therefore, using CLD as a detection tool requires a deep understanding of sample preparation procedure and chemical reactions used for liberating NO from its derived species. In this review, we discuss the advantages and pitfalls of CLD for determining NO species, the different applications and combinations with other analytical techniques, and provide general practical notes for sample preparation. These guidelines should help other researchers understand CLD data and select the best procedure for detecting NO species.

In order to study the adsorption process and adsorption characteristics of shale oil at the macro scale, the isothermal adsorption experiments of illite and kerogen on heptadecane (oil phase) solution were carried out by infrared spectrophotometry and gas chromatography-mass spectrometry. The reaction mechanism of alkane solution concentration and temperature in the adsorption process was investigated. The adsorption characteristics of heptadecane on the surface and pores of illite and kerogen were elucidated by adsorption isotherm model and adsorption thermodynamic model. The results show that: The concentration of alkane solution contributes to the adsorption of illite and kerogen. As the concentration of the solution continues to increase, the adsorption rate gradually slows down. However, the temperature hardly affects the adsorption rate. At 25℃, 50℃ and 60℃, the adsorption process of illite on alkane solution is more consistent with Langmuir model and Freundlich model, respectively. The difference is that Langmuir model is suitable for describing the whole adsorption process of kerogen on alkane solution. 3) The adsorption process of illite and kerogen to heptadecane solution is an endothermic reaction. Heating helps to accelerate the migration rate of alkanes, which is conducive to the adsorption reaction. The research results clarify the adsorption characteristics of shale oil heavy components from the macro level, and fill the research gap in the application of solid-liquid isothermal adsorption physical experiments to the adsorption and occurrence of shale oil.

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.

African swine fever virus (ASFV), a highly contagious pathogen characterized by a complex structure and a variety of immunosuppression proteins, causes hemorrhagic, acute, and aggressive infectious disease that severely injure the pork product and industry. However, there is no effective vaccine or treatment. The main reasons are not only the complex mechanisms that lead to immunosuppression, but also the unknown functions of various proteins. This review summarizes the interaction between ASFV and host immune system, along with the involvement of virulence related genes and proteins, as well as the corresponding molecular mechanism of immunosuppression of ASFV, encompassing pathways such as cGAS-STING, NF-κB, JAK/STAT, apoptosis, and other modulation. The aim is to summarize the dynamic process during the ASFV infection and entry into host cell, provide a rational insight into development of vaccine and a better clear knowledge of how ASFV impacts host.

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

The current study aimed to investigate the antitumor effects and potent mechanism of cytokine-induced killer (CIK) cells combined with irreversible electroporation (IRE) via an in vitro function assay in an in vivo Panc02 cell–bearing mouse model. We established an in vitro culture assay for CIK cells and determined the proportions of different peripheral lymphocytes. The antitumor effect of the combination of IRE and CIK cells in a Panc02 pancreatic-cancer (PC) subcutaneous-xenograft model was investigated; tumor size and mouse survival rates were recorded. We used flow cytometry (FCM) to analyze the proportion of intratumoral lymphocytes, the expression of chemokine receptors, and the proliferative activity of CIK cells. The proportion of cells that were positive for clusters of differentiation 3 and 8 (CD3+CD8+) and the proportion of CD3+CD56+ cells were both significantly increased after 21 days of in vitro culture. Combined IRE/CIK cell treatment significantly inhibited tumor growth and increased the survival rate of Panc02 cell–bearing mice. Furthermore, infiltration of lymphocytes into tumor tissue was significantly increased by this combination therapy compared with the untreated group or monotherapy group. In addition, IRE significantly enhanced the expression of chemokine receptors elicited by, and the proliferative activity of, CIK cells. In conclusion, IRE combined with CIK cells showed superior antitumor efficacy in a PC subcutaneous-xenograft model, which we attributed to the promotion of lymphocytic infiltration, as well as to upregulation of chemokine receptor expression and of regulators of CIK cell proliferation.

The prediction of drug-target interactions plays a fundamental role in facilitating drug discovery, where the goal is to find prospective drug candidates. With the increase in the number of drug-protein interactions, machine learning techniques, especially deep learning methods, have become applicable for drug-target interaction discovery because they significantly reduce the required experimental workload. In this paper, we present a spontaneous formulation of the drug-target interaction prediction problem as an instance of multi-instance learning. We address the problem in three stages, first organizing given drug and target sequences into instances via a private-public mechanism, then identifying the predicted scores of all instances in the same bag, and finally combining all the predicted scores as the output prediction. A comprehensive evaluation demonstrates that the proposed method outperforms other state-of-the-art methods on three benchmark datasets.

Water pollution, intensified by the release of hard-to-degrade pollutants, poses severe threats to ecosystems, human health, and economic development. The existing advanced oxidation processes often involve high operational costs and may result in secondary pollution, highlighting the necessity for innovative and more sustainable solutions. To address these challenges, our study introduces a cost-effective and eco-friendly corona discharge barrier plasma discharger for wastewater treatment. Through the central composite design/response surface methodology, a high decolorization rate of 98% of methylene blue (MB) within 10 minutes was achieved by optimizing parameters such as pH and voltage. Furthermore, the mechanisms of the generation of reactive oxygen species via this device was discussed in detail and the degradation pathways of MB were described. Moreover, this device is also very energy-efficient, with a low energy density and electrical energy per order of 0.15 watt/mL and 5.79 kWh/m³/order, respectively. In conclusion, the plasma discharger developed in this study provides a cost-effective and environmentally sustainable solution for dye wastewater treatment. This research contributes significantly to the advancement of sustainable dye wastewater management practices, offering an innovative method that meets both environmental and economic objectives.

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.

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.

Promoting green development has become an important way to optimize the ecological and economic structure and promote sustainable development. The Yangtze River Economic Belt is an important economic corridor in China, and it remains to be examined whether the implementation of environmental regulations has enhanced the green total factor productivity (GTFP) of cities. This paper calculates the green total factor productivity of 107 cities within the Yangtze River Economic Belt by using the super-efficient SBM model and the GML index with Chinese city panel data from 2007-2019 and measures the intensity of environmental regulations through textual analysis. The bidirectional fixed-effects model constructed in this paper suggests an inverted U-shaped relationship between environmental regulation and GTFP, which is confirmed by a series of robustness tests. Green technology innovation and advanced industrial structure play a mechanism role in this. In regions with lower levels of green technology and industrial structure, the inverted U-shaped relationship between environmental regulation and green total factor productivity is more significant. In addition, the phenomenon of GTFP rising and then falling because of environmental regulation is more obvious in cities in the middle and upper reaches of the Yangtze River and outside the urban agglomerations. Therefore, when formulating environmental regulation policies, it is necessary to balance the relationship between economic development and environmental protection and harmonize the environment and economic development of the entire Yangtze River Basin.

Abstract: The promotion and development of healthy cities are vital for enhancing human habitats and fostering sustainable economic growth. Based on the core database of Web of Science and the knowledge graph software, this paper makes a quantitative analysis of the literature related to attention recovery abroad. It is found that in recent years, the research on attention recovery has developed rapidly, the number of related literature has been increasing, and the research content presents the characteristics of interdisciplinary integration. By further analyzing the characteristics of research literature, research context, and knowledge basis, this paper summarizes the empirical research based on the existing quantitative analysis, reviews the research field based on the mechanism of attention recovery, and analyzes the development process and trend based on the research basis of attention recovery. Due to the change of modern life style, human health problems are becoming more and more prominent. Attention restoration design provides a new research idea and method to balance the relationship between humans and the urban environment.

In this study, the impact of the addition of high-aspect-ratio nano-ettringite to photocurable epoxy acrylate resin was explored, with a specific focus on the changes observed in viscosity, shrinkage, tensile strength, and elongation at the break of composites formed. Herein, the nano-ettringite samples were modified using KH-570. The 3 wt% or 6 wt% KH-570-modified and unmodified nano-ettringite were dispersed into the resin by ultrasonic treatment in conjunction with mechanical stirring. The grafting effects of nano-ettringite onto KH-570 were analyzed through scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric (TG) analysis, and Fourier transform infrared spectroscopy (FTIR). The results demonstrate that increased dosages of unmodified and modified nano-ettringite led to increased viscosity of the composite while reducing shrinkage. At the same dosage, the photocurable resin with modified nano-ettringite demonstrated a lower shrinkage and a higher tensile strength. Notably, when the amount of modified nano-ettringite was 3 wt%, the highest tensile strength of the composite was 64.61 MPa, presenting a 72.57% increase compared to the blank sample. Furthermore, the incorporation of modified nano-ettringite as a filler provides a new perspective for improving the performance of photocurable epoxy acrylate resin composites.

Endophytic bacteria, especially those participate in nitrogen fixation, play critical roles in supplying essential nutrients for legume plant growth. Compared to the numerous bacterial microbiome reports targeting legume roots and nodules, little is known about embryonic bacteria that facilitate plant nutrient utilization after seed germination. Here, we collected and investigated endophytic bacterial microbiomes in edible pea (Pisum sativum) embryos using five representative cultivars and a pea sprout control. Twenty-six nitrogen-fixing bacteria (NFBs) were isolated from pea embryos, with three strains found in fresh pea (FGP) and snow pea (SP) exhibiting the strongest nitrogenase activity. Some NFB isolates are also potassium-solubilizing bacteria (KSBs) or phosphorus-solubilizing bacteria (PSBs) utilizing inorganic and/or organic phosphorus. All 26 NFBs showed variable levels of indole-3-acetic acid (IAA) secretion. The nutrient-solubilizing NFBs identified in our research are valuable resources for biofertilizer development. At the microbiome level, high-throughput 16S ribosomal RNA (rRNA) sequencing of 40 bacterial collections from pea embryos revealed similar community structures and species abundances and diversities in most samples. Our embryo-derived endophytic bacterial pool provides a microbiome platform for seed dormancy and germination research of edible peas.

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.

In response to the fairness issue arising from the unequal delay of vehicles in different phases at intersections, considering the actual situation of small and variable delays for vehicles in low-saturation intersection phases, this paper proposes the concept of "sacrificing efficiency for fairness." Firstly, the universality of unfair delay phenomena at intersection phases is explained, especially at low-saturation intersections where the fluctuation in phase delays is 1.87 times higher than at other intersections. Then, a fairness evaluation index is constructed using information entropy, and the feasibility of the proposed approach is demonstrated. Subsequently, a signal optimization model that balances efficiency and fairness is proposed. Finally, the proposed model is validated through case studies, showing that it not only simultaneously considers efficiency and fairness but also has minimal impact on efficiency. Moreover, the changes to timing schemes in the efficiency model are much smaller compared to the model that only considers fairness. Sensitivity analysis reveals that the model performs better under low-saturation intersection conditions.

Applied talent is not only a builder of human civilization but also a promoter of economic development. They not only need systematic and solid engineering theoretical knowledge but also rich engineering practical experience. Applied undergraduate colleges are the cradle for cultivating applied talent and shoulder the important mission of cultivating various types of applied talent for society. With the rapid development of higher education, the number of applied undergraduate institutions has sharply increased, but the quality of applied talent cultivated by them has shown a significant decline, which greatly restricts the pace of industrial development in the world. This article proposes a new concept of promoting the "three certificates" (graduation certificate, degree certificate, and skill certificate) education model in applied undergraduate colleges, which is of great significance for the country to cultivate composite talents with the ability of "product design, production management, and product processing" at the same time.

Current sampling methods for detecting the presence of the invasive freshwater hydrozoan Craspedacusta sowerbii rely mainly on visual confirmation of the medusa stage. Confirming the presence of the polyp stage is equally important to observing medusae since typical late summer/early fall occurrences or observations of medusae are sporadic though becoming more frequent. The polyp stage is important as it is the organism’s primary stage and is present throughout the year depending on water temperatures. Therefore, sampling methods for the polyp stage are commonly the collection of substrates such as rocks, plants or pieces of wood in a given body of water and can be cumbersome to examine. Based on preliminary culturing of the polyp stage on glass and plastic microscope slides in the lab, we designed a novel sampling methodology based on submerging 4 substrate types (small glass petri dishes, Hester-Dendy plates, plastic and glass microscope slides) to confirm the presence of C. sowerbii polyps in the field. We tested this method in various lakes in the Illinois-Indiana region (USA). The sampling method we designed was effective in that C. sowerbii polyps (and sometimes colonies) were found on both plastic and glass slides. While this method can be sufficient for detection of the polyp stage, it also shows significant potential for improvement; we highlight water flow and competition and predation from other organisms as significant factors influencing the collection of C. sowerbii polyps for future methodologies.

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.

This paper proposes a new battery balancing control strategy based on state of charge (SOC) for addressing the problem of cell-to-cell differences in lithium-ion battery systems. The strategy utilizes a reconfigurable converter system to transfer energy from high-SOC battery modules to low-SOC battery modules and allows balancing the battery modules while they are powering the load. A MATLAB/Simulink simulation model with five batteries was built to validate the effectiveness of the proposed balancing strategy under unloaded and loaded conditions. The simulation results demonstrate that the proposed strategy achieves more efficient and accurate battery module balancing compared to the previous balancing modes, thus increasing the overall lifespan and safety of the battery pack.

Radio-dynamic therapy (RDT), as an emerging cancer treatment method, has attracted attention due to its remarkable therapeutic efficacy using low-dose, high-energy radiation (such as X-rays), and has shown significant potential in cancer treatment. The RDT system typically consists of scintillators and photosensitizers. Scintillators absorb X-rays and convert them to visible light, activating nearby photosensitizers to generate cytotoxic reactive oxygen species (ROS). Challenges faced by the two-component strategy, including low loading capacity and inefficient energy transfer, hinder its final effectiveness. In addition, the tumor microenvironment (TME) with hypoxia and immunosuppression limits the efficacy of RDTs. Recent advances introduce one-component RDT systems based on nanomaterials with high-Z metal elements, which effectively inhibit deep-seated tumors. These novel RDT systems exhibit immune enhancement and immune memory, potentially eliminating both primary and metastatic tumors. This review comprehensively analyzes recent advances in the rational construction of RDTs, exploring their mechanisms and its application in the treatment of deep-seated tumors. Aimed at providing a practical resource for oncology researchers and practitioners, the review offers new perspectives for potential future directions in RDT research.

The ultrasonic vibration assisted pressing process can improve the fluidity, the uneven distribution of density and particle size of WC-Co powder. However, the microscopic mechanism of ultrasonic vibration on the powder remains unclear. In this paper, WC and Co particles are simulated by three-dimensional spherical models of two particle sizes with the aide of the secondary development of Python. At the same time, the forming process of powder at mesoscale is simulated in the virtue of the finite element analysis software ABAQUS. The influence of vibration amplitude on the fluidity, the filling density and the stress distribution of WC-Co powder is investigated when the ultrasonic vibration is applied to the conventional pressing process. From simulation results, the ultrasonic vibration amplitude has great influences on the density of the compact. With the increase of the ultrasonic amplitude, the compact density also increases gradually. At the initial stage of the compaction, the particles move violently under the action of ultrasonic vibration, the fluidity is obviously enhanced, the arch bridge effect between particles is destroyed, the fine particles quickly fill the pores between the large particles, which increase the density, and the residual stress in the billet decreases after the compaction. From the experimental results, the size distribution of the billet is more uniform, the elastic aftereffect is reduced, the dimensional instability is improved, and the density curves obtained by experiment and simulation are within a reasonable error range.

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.

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.

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.

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.

In this paper, we study the global existence and exponential decay for a dynamic contact problem between a Timoshenko beam with second sound and two rigid obstacles, of which the heat flux is given by Cattaneo's law instead of the usual Fourier's law. The main difficulties arise from the irregular boundary terms, from the low regularity of the weak solution and from the weaker dissipative effects of heat conduction induced by Cattaneo's law. By considering related penalized problems, proving some a priori estimates and passing to the limit, we prove the global existence of the solutions. By considering the approximate framework, constructing some new functionals and applying the perturbed energy method, we obtain the exponential decay result for the approximate solution, and then prove the exponential decay rate to the original problem by utilizing the weak lower semicontinuity arguments.

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.

This article systematically studied the A-optimal designs of a high-order mixture central polynomial model, which is an advancement of the research results for the A-optimal design of the most recent third-order mixture central polynomial model and a more accurate response surface model. This article is based on the simplex center design, obtaining measurement values at various design points, and verifying them through the A-optimal equivalence theorem. We also compare the A-efficiency of designs through examples, which can be applied to the A-optimal precise design of the fourth-order mixture model.

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.

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.

To enhance the corrosion resistance of molybdenum metal in molten zinc, this study successfully fabricated TiB2 coatings on molybdenum substrates using the molten salt electrophoretic deposition technique and investigated their corrosion resistance in molten zinc. Initially, TiB2 nanoparticles with a size ranging from 50 to 150 nm were synthesized using the borothermal reduction method in NaF-AlF3 molten salts at 1238 K. Subsequently, electrophoretic deposition experiment was conducted by applying a cell voltage of 1.2 V (corresponding to an electric field of 0.6V/cm) for 1 h in the molten salts containing TiB2 nanoparticles, resulting in a uniform, continuous, and dense TiB2 coating with a thickness of 35 μm on the molybdenum substrate. Moreover, the corrosion resistance of the TiB2 coated molybdenum metal to molten zinc was tested through continuous immersion. After 120 h of immersion, the TiB2 coating showed no signs of cracking or peeling off, successfully protecting the molybdenum metal substrate from corrosion by molten zinc. The results confirm that the molten salt electrophoretic deposition technique can be used to prepare TiB2 coatings with good resistance to molten zinc corrosion on molybdenum metal.

The characteristics of gap plasmon formed by nanoparticle-on-mirror (NPOM) structure composed of metal nanoparticles (MNP) and metal thin films have aroused the interest of various optoelectronic devices. The resonant spectrum obtained by the internal coupling effect of gap can be flexibly controlled by the polarization of incident light and the thickness of the dielectric layer between MNP and metal thin films. We have theoretically studied the resonance spectra of polarization-dependent gold ellipsoidal nanoparticles (GENP) and gold thin films in the gap region of NPOM structures. GENP and gold thin films are separated by a dielectric layer with a refractive index of 1.36. We observe that the intensity of the local electric field resonance peak in the gap region is inversely proportional to the polarization angle. Similarly, the intensity of the local electric field resonance peak in the gap region is inversely proportional to the thickness of the dielectric layer. We have obtained more than 2200 V/m local electric field intensity (dielectric layer thickness 0.3nm). Finally, the resonant peak wavelength of the electric field in the gap region of the NPOM structure is also controlled by the polarization angle of the incident light and the thickness of the dielectric layer.

Sracocystis cruzi in cattle (Bos taurus) and Sarcocystis poephagicanis in yaks (Bos grunniens) are morphologically indistinguishable. However, the relationship of the two parasites is still un-clear. Here, muscular tissues of the two domestic animals collected from abattoirs in China were examined for sarcocysts of S. cruzi and S. poephagicanis. The sarcocysts isolated from the samples were processed for light microscopy (LM), transmission electron microscopy (TEM), and DNA analysis. Sarcocysts of S. cruzi and S. poephagicanis were found in 405 of 950 (42.6%) cattle and 304 of 320 (95.0%) yaks, respectively. By LM and TEM, the sarcocysts of the two parasites showed similar morphologically characteristics. The thin-walled sarcocysts had hair-like protrusions on the surface. Ultrastructures exhibited the primary cyst wall contained irregularly folded, hirsute or bone-like protrusions. Four genetic markers, 18S rDNA, 28S rDNA, mitochondrial cox1, and apicoplast rpl6 of the two parasites were sequenced and analyzed. The sequences of the four loci presented an interspecific identity of 97.9−98.6%, 97.2−98.1%, 89.5−90.4%, and 96.9−97.2%, respec-tively. Phylogenetic analysis using 28S rDNA and cox1 sequences indicated that both species were placed into a group encompassing Sarcocystis spp. in ruminants with canid as known or putative definitive hosts. Sarcocystis cruzi and S. poephagicanis should represent separated species, and cox1 and rpl6 was suitable for distinguishing between them.

Age-related Macular Degeneration (AMD) is the major cause of elders’ vision loss, early screening and treatment are the most efficient way to reduce the rate of blindness. AI-based methods based on ophthalmic images play great potential for AMD diagnosis. However, low levels of accuracy, robustness, and explainability are challenges for AI approaches applied in clinics. Thus, this study proposed a multi-type of data source fusion method and a multi-model fusion approach for AMD detection. Typical unsupervised (Hierarchical Clustering and K-Means), typical supervised (SVM, VGG-16, and ResNet) methods, and proposed methods (multi-source data fusion-based method and multi-model fusion-based approach) are compared based on Optical Coherence Tomography (OCT), Fundus Autofluorescence (FAF), regular color fundus photography (CFP) and Ultra-Wide field Fundus (UWF) images. Data preprocessing and enhancements of each type of data are discussed. A feature extraction based on unsupervised ML models, feature combination and normalization, and multi-layer perception (MLP) algorithm are involved in the proposed multi-source data fusion-based method. Supervised ML and DL models and a voting mechanism are involved in the multi-model fusion-based approach. Findings show that the proposed methods present a high performance of accuracy and robustness. A real-world UWF database is involved from Shenzhen Aier Hospital. Practical and theoretical contributions are delivered. A reference value for medical diagnosis based on multiple digital images is contributed.

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.

Smart City has emerged as the mainstream paradigm for urban governance innovation, sustainable development, and strategy upgrades, which is drawing attention from scholars worldwide. However, current frameworks for Smart City assessment remain incomplete and simplistic. In is paper, 30 national or provincial capitals in China were selected and we designed a tri-dimensional SPI model—Social, Physical, and Information Space—for smart city spatial development assessment. Utilizing methods such as entropy weighting, coupled coordination degree models, and the Dagum Gini coefficient, this study assesses the spatial development and coupled coordination of 30 cities from 2011 to 2019. Finally, by means of BP neural networks, the study examines the contribution of each indicator to the spatial coupled coordination. The results indicated that with a narrowing disparity in development speeds among different regions, the spatial coupled coordination development level of smart capitals in China has steadily increased, presenting a pattern of staggered distribution. Moreover, the IS subsystem plays the most significant role in coupled coordination. The significance of this research lies in its tri-dimensional spatial perspective of the spatial development and coupled coordination differences of the Smart City, providing evidence-based support for the regional layout and optimization in China.

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.

Light is dispersive in gravitational field. It results in that, under the condition of strong gravitational field, the observed numbers, size, distance and spectrogram of a celestial object is different from the real ones. Therefore, the observed jets of the Sgr A* and M87 need be understood further with the orbit of the stars around them. And, the observed orbits of the S-stars around the Sgr A* is different from the real ones. It implies that the observed optical images of the distant celestial body with strong gravitational field need be further understood with other observations and physics laws to accurately and precisely know the real physical configuration of it.

Chemical flooding methods are used to recover the remaining oil after water flooding, and surfactant-polymer (SP) flooding is an important chemical flooding technology. It is of great significance to design and screen high-performance oil displacement agents in order to overcome the increasingly challenging high temperature (>70 ℃) and high salinity (>10,000 mg/L) reservoirs. Based on the characteristics of high temperature (70 ℃) and high salinity (151,000 mg/L) in the Qinghai Gasi reservoir, the performance of the SP flooding system was studied. This study considered interfacial tension, emulsification, viscosity, compatibility, and oil displacement efficiency. The selected formula demonstrated high resistance to both salinity (151,000 mg/L) and temperature (70 ℃), characterized by a low oil-water interfacial tension (10-2 mN/m) and high viscosity (18.13 mPa.s). After water flooding achieved an oil recovery rate of 64.64%, the subsequent replacement rate of crude oil increased by 26.95%, confirming that SP flooding can be used as a technology for enhancing oil recovery in high-temperature, high-salinity reservoirs represented by Qinghai Gasi reservoirs.

The sexual reproduction of ascomycetes is controlled by the mating-type(MAT) locus. (Pseudo)homothallic reproduction has been hypothesized based on genetic data from Hirsutella sinensis (Genotype #1 of Ophiocordyceps sinensis). In this study, the differential occurrence of the mating-type genes in the MAT1-1 and MAT1-2 idiomorphs, which constitute the genetic control of O. sinensis reproduction, was analyzed in 237 strains and 5 genomes of H. sinensis. The mating-type genes were differentially transcribed in H. sinensis Strain L0106, and the MAT1-2-1 transcript in H. sinensis Strain 1229 was alternatively spliced, with an unspliced intron I containing stop codons; these findings constitute O. sinensis reproduction controls at the transcriptional and coupled transcriptional-translational levels. The occurrence of the mating-type genes and a-/α-pheromone receptor genes differed among the 5 H. sinensis genomes. The differential occurrence and transcription of these genes are inconsistent with self-fertilization under (pseudo)homothallism but suggest self-sterility of H. sinensis, which therefore needs a mating partner to achieve sexual outcrossing under heterothallism or hybridization. Consistent occurrence and transcription of the mating-type genes of both the MAT1-1 and MAT1-2 idiomorphs have been reported in natural and cultivated Cordyceps sinensis, but these findings are insufficient to determine the reproduction mode(s) because of the co-occurrence of >90 fungal species and multiple genomically independent genotypes of O. sinensis. The mutant MAT1-1-1 and α-pheromone receptor transcripts in natural C. sinensis result in the N-terminus or middle-truncated proteins, with changes in hydrophobicity and secondary structures of the proteins, suggesting heterogeneous fungal source(s) of the proteins and hybridization reproduction.

Selenium nanoparticles (SeNPs) can mitigate cadmium (Cd2+) toxicity in crop cultivation and aquaculture. This study investigates the impact of SeNPs on Cd-induced toxicity in Chlamydomonas reinhardtii, a model organism, aiming to mitigate Cd damage in crops. SeNPs were synthesized using sodium carboxymethyl cellulose (CMC) as a dispersant and vitamin C to reduce H2SeO3. Characterization by transmission electron microscopy (TEM) and nanoparticle granulator revealed uniform, stable spherical SeNPs. Cd2+ and SeNPs of varying concentrations were introduced into culture media of wild type (CC125mt+) and cell wall defect type (CC400mt+) Chlamydomonas reinhardtii. Growth curves, photosynthetic pigment content, enzyme activities (T-SOD and CAT), MDA content, and ROS levels were assessed. Fluorescence induction kinetics, QA oxidation kinetics, and Cd2+ content in cells were measured to evaluate SeNPs' effect on Cd toxicity. Results showed SeNPs exacerbated Cd2+ toxicity in CC125mt+ by inhibiting growth and increasing catalase activity, indicating enhanced oxidative damage. SeNPs intensified PSII damage by Cd2+ and increased intracellular Cd2+ content, possibly exacerbating toxicity. SeNPs had no effect on Cd2+ toxicity in CC400mt+. Overall, SeNPs exacerbated Cd2+ toxicity in wild type CC125mt+ but not in cell wall defective CC400mt+. These findings caution against indiscriminate use of nano-selenium to mitigate Cd damage, stressing the importance of careful target selection, dosage, and range considerations.

Estimating the ocean’s subsurface thermohaline information from satellite measurements is essential for understanding ocean dynamics and El Niño phenomenon. This paper proposes an improved double-output Residual Neural Network (DO-ResNet) model to concurrently estimate the ocean subsurface temperature (OST) and ocean subsurface salinity (OSS) in the tropical Western Pacific using multi-source remote sensing data, including sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly(SSHA), sea surface wind (SSW), and geographical information (including longitude (LON) and latitude (LAT)). In the model experiment, Argo data were used to train and validate the model, and the root mean square error (RMSE), normalized root mean square error (NRMSE) and coefficient of determination (R²) were employed to evaluate the model's performance. The results showed that the sea surface parameters selected in this study have a positive effect on the estimation process, and the average RMSE and R² values for estimating OST (OSS) by the proposed model are 0.34 °C (0.05 psu) and 0.91 (0.95), respectively. Under the data conditions considered in this study, DO-ResNet demonstrates superior performance relative to the extreme gradient boosting model, random forest model, and artificial neural network model. Additionally, this study evaluates the model’s accuracy by comparing its estimations of OST and OSS across different depths with Argo-derived data, demonstrating the model's ability to effectively capture the most spatial features, and by comparing NRMSE across different depths and seasons, the model displays strong seasonal adaptability. In conclusion, this research introduces a novel artificial intelligence technique for estimating OST and OSS in the tropical Western Pacific Ocean.

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.

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small targets in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small target objects. Finally, we propose the Smooth Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model's focus on the attributes of detection boxes and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach's efficacy, we conduct comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the [email protected] metric, and 72.03%, 65.37%, and 38.91% in the more stringent [email protected]:0.95 metric, respectively.

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.

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 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.

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.

Results showed that mulberry leaves could significantly reduce backfat thickness (P<0.05) and increase intramuscular fat (IMF) content (P<0.05). Lipidomics analysis showed that mulberry leaves improved lipid profile composition and increased the proportion of triglycerides (TG). Interestingly, IMF content was positively correlated with acyl C18:2 and negatively correlated with C18:1 of differential TGs. In addition, cecal microbiological analysis showed that mulberry leaves could increase the abundance of bacteria such as UCG−005, Muribaculaceae_norank, Prevotellaceae_NK3B31_group, Limosilactobacillus, and so on. At the same time improve the L-tyrosine-baton rouge ester, Oleic acid methyl ester, 21-deoxycortisol, N-acetyldihydrosphingosine, Mulberrin relative abundance. In this study, we found that mulberry leaves could extremely significantly increasing the mRNA relative expression of lipoprotein lipase (LPL), fatty acid-binding protein 4 (FABP4) and peroxisome proliferators-activated receptor γ (PPARγ) in muscle (P＜0.01), significantly increasing mRNA relative expression of diacylglycerol acyltransferase 1 (DGAT1) (P＜0.05) and significantly decreasing the mRNA relative expression of acetyl CoA carboxylase (ACC) (P＜0.05), and aignificantly upregulated hormone-sensitive triglyceride lipase (HSL), peroxisome proliferators-activated receptor α (PPARα) mRNA relative expression (P＜0.05) in backfat. In addition mulberry leaves could increased serum leptin and adiponectin (P＜0.01). Ultimately lead to up-regulation of IMF and down-regulation of backfat.

The Mara River basin (MRB) has a world-famous ecosystem, but the vegetation has been damaged due to economic development in recent years, and there is little known about the area that will experience severe vegetation damage in the future. Based on the vegetation vulnerability system, principal component analysis, and three CMIP6 scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), vegetation vulnerability was calculated in the base period (2010-2019), near-term period (2020-2059) and long-term period (2060-2099) in the MRB. The spatial cluster of vegetation vulnerability was revealed by spatial correlation analysis, and the transition of vegetation vulnerability of different periods was analyzed by stochastic matrix. The results showed that in all periods, the vulnerability showed a high-high cluster in the east, and a low-low cluster midstream and downstream. From the stochastic matrix, the area of high vulnerability increased the least under the SSP1-2.6, while it increased the most under the SSP5-8.5. The vegetation vulnerability upstream increased the most from the base period to the near-term period and long-term period in the MRB. By comparing the vegetation vulnerability under different scenarios, and pointing out the areas with the highest vulnerability increase, this study can better provide comprehensive decision-making for vegetation protection in the MRB.

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with various sensors are expensive and not suitable for mountainous roads due to poor conditions. To address these challenges, this study proposes a customized Unmanned Aerial Vehicles (UAV) inspection system designed for automatic crack detection. The system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method is proposed to enhance the autonomous navigation of UAV flights by generating path planning in mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the YOLOv8 algorithm is introduced to conduct autonomous crack detection from UAV imagery in the offboard module. To validate our UAV inspection system's performance, we conducted several experiments to assess its accuracy, robustness, and efficiency. The experimental results demonstrate that our UAS inspection system accurately estimates the flight trajectory along the mountainous road for autonomous navigation. Furthermore, the experimental detection results also demonstrate that the YOLOv8 algorithm achieves higher recognition accuracy compared to the YOLOv5, and YOLOv7-tiny algorithms, exceeding 3.1%,and 6.8% of mAP, respectively. In summary, the UAV inspection system proposed in this study can provide a useful tool and technological guidance for the regular inspection of mountainous roads.

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.

In this paper, we define the cohomology of a modified $\lambda$-differential left-symmetric algebra with coefficients in a suitable representation. We also introduce the notion of modified $\lambda$-differential left-symmetric 2-algebra. We classify linear deformations and abelian extensions of modified $\lambda$-differential left-symmetric algebras using the second cohomology group and classify skeletal modified $\lambda$-differential left-symmetric 2-algebra using the third cohomology group as our propose cohomology applications. Moreover, we prove that strict modified $\lambda$-differential left-symmetric 2-algebras are equivalent to crossed modules of modified $\lambda$-differential left-symmetric algebras.

Semenov-Tian-Shansky has introduced the modified classical Yang-Baxter equation, which is called the modified $r$-matrix. Relevant studies have been extensive in recent times. This paper is devoted to study cohomology theory of modified Rota-Baxter pre-Lie algebras and its applications. First we introduce the concept and representations of modified Rota-Baxter pre-Lie algebras. We then develop the cohomology of modified Rota-Baxter pre-Lie algebras with coefficients in a suitable representation. As applications, we consider the infinitesimal deformations, abelian extensions and skeletal modified Rota-Baxter pre-Lie 2-algebra in terms of lower degree cohomology groups.

This paper investigates the energy dissipation level of a nonlinear vehicle suspension system. The suspension system is modelled as a mass-spring-damper system, which is designed using the output frequency response function (OFRF) method. This method enables the derivation of an explicit relationship, between the system output spectrum (or Performance metric) and the system nonlinear parameters of interest. The OFRF polynomial, of the energy dissipation level, was derived in terms of the nonlinear parameters of interest. The results obtained show that the OFRF model, of the energy dissipation level, could be used to estimate the amount of heat dissipated, for a set of design parameters. This has potentials of providing some insights, in the design of vibration-based energy harvesters, as some of the wasted heat energy could be converted to electrical energy.

The development of solid-state forms of drugs is an effective mean of solubility enhancement. In this study, two polymorphs named form C, form D, and two co-crystals of sorafenib (SF) with oxalic acid (SF-OA) and malonic acid (SF-MA), by evaporation and slurry method, have been developed and characterized. The single-crystal X-ray diffraction results showed that the structure of form C was similar to of form I. It had similar cellular parameters, but different arrangement of stacking due to π-π conjugation. The drug molecules in SF-OA underwent torsional folding through four hydrogen bonds of intermolecular and intramolecular. The solubility performance of solid-state forms improved significantly. Compared with form I, form C and form D were 1.59, 1.49 times at pH 1.2 HCl, 1.59, 1.87 fold at pH 6.8 PBS. In particular, the SF-OA and SF-MA increased the apparent solubility of SF at pH 1.2 by 8.3 and 6.4 times, at pH 6.8 by 4.0 and 4.5 times respectively. The dissolution mechanisms were analyzed. New solid-state forms showed strong stability at storage conditions of 40 °C/75% RH and 60 °C/0% RH. Notably, the crystal of SF preferred to transform in mixed solvents, and the formation of co-crystals confirmed the ∆pKa rule.

Genomics involving tens of thousands of genes is a complex system determining phenotype. An interesting and vital issue is that how to integrate highly sparse genetic genomics data with a mass of minor effects into prediction model for improving prediction power. We find that deep learning method can work well to extract features by transforming highly sparse dichotomous data to lower dimensional continuous data in a non-linear way. This idea may provide benefits in risk prediction based on genome-wide data associated e.g. integrating most of the information in the genotype data. Hence, we developed a multi-stage strategy to extract information from highly sparse binary genotype data and applied it for risk prediction. Specifically, we first reduced the number of biomarkers via a univariable regression model to a moderate size. Then a trainable auto-encoder was used to extract compact representations from the reduced data. Next, we performed a LASSO problem process over a grid of tuning parameter values to select the optimal combination of extracted features. Finally, we applied such feature combination to two prognostic models, and evaluated predictive effect of the models. The results of simulation studies and real data applying indicated that these highly compressed transformation features could better improve predictive performance and did not easily lead to over-fitting.

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.

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.

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.

Ming-style round-backed armchairs as a classic Chinese chair design are renowned for the blend of ergonomics, practicality, and elegance. Although the Chinese armchair furniture industry has made strides in sustainability, there is still a need for further improvements in material selection, manufacturing processes, and supply chain management to fully support the development of sustainable furniture. Laminated bamboo lumber as new sustainable materials are becoming a popular option for furniture designers in the era of "sustainable design". This research aims to investigate the feasibility and application of laminated bamboo lumber in the design of Chinese armchair furniture and proposes innovative ideas for optimizing the armchair's design. First, an empirical study was conducted to comprehensively understand the classification of Ming-style round-backed armchairs’ structure to develop a 3D model. Secondly, an experimental research method was then employed to explore the parameters of laminated bamboo lumber for future studies. Additionally, a validation experiment was conducted to compare real-world scenarios with simulations using finite element analysis in Ansys. Finally, finite element analysis was used to test the feasibility and application of laminated bamboo lumber in furniture design, focusing on the material’s mechanical properties. Results indicated that laminated bamboo lumber has excellent mechanical properties as furniture design materials, subsequently, using the optimized results, the use of 0.6319 kg of steel was successfully reduced by 23.68% to ensure the armchair’s stability. This provides a reference for the future compter-assist and standardized innovative design of Chinese armchair. By incorporating compter-assist design and lightweight optimization, it is possible to save materials and make more efficient use of resources, thereby contributing to sustainable development goals in the field of furniture design.

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.

The effect of wave forces on the structural safety of fixed oscillating water-column (OWC) devices is critical. In this study, a three-dimensional numerical model of an oscillating water column (OWC) device is constructed using the computational fluid dynamics software OpenFOAM. Subsequently, the hydrodynamic performance of the numerical model is comprehensively validated. Finally, the hydrodynamic performance data are analyzed in detail to obtain meaningful conclusions. The results indicate that the horizontal wave force on the OWC device is approximately 6.6 to 7.9 times greater than the vertical wave force, whereas the lateral wave force is relatively small. Both the horizontal and vertical wave forces decrease as the relative water depth increases under a constant wave period and height. The horizontal wave force increases linearly with the wave height, whereas the vertical wave force increases with the square of the wave height. In addition, the highest dynamic water pressure is observed at the interface between the water surface and device, both within and outside the front wall of the gas chamber. The dynamic water pressure at different locations on the front chamber increases and subsequently decreases as the wave frequency increases.

The influence of growth temperature on the crystalline quality and optical properties of AlGaN at 325 nm, grown using plasma-assisted molecular beam epitaxy (MBE), was investigated. The growth temperature exhibited a significant impact on the growth mode and surface morphology. Specifically, at a growth temperature of 750 degrees Celsius, when appropriate Al and Ga flow rates were employed, the AlGaN layer exhibited exceptional surface morphology and favorable optical properties. The optical properties of the AlGaN material were explored, revealing the presence of two distinct non-radiative recombination centers characterized by different activation energies. The minority carrier lifetimes were measured as 560 ps at 10 K and 64 ps at room temperature. The underlying mechanism was analyzed from the perspective of carrier dynamics, shedding light on the observed phenomena.

Perceived peer support is a critical factor in the academic adjustment of higher vocational college students. While previous studies have explored this relationship, few studies have delved into the underlying mechanisms. This study surveyed 9782 students from 35 higher vocational colleges in 13 provinces and cities in China to explore the relationship and mechanisms between perceived peer support and academic adjustment. Drawing on Conservation of Resources theory and Social Support Resource theory, the findings revealed that perceived peer support not only directly predicted the academic adjustment of higher vocational college students, but also indirectly through the independent mediating effect of academic hope and professional identity, as well as the chain-mediating effect of academic hope to professional identity. Additionally, students from vocational high schools showed a more significant increase in academic hope due to perceived peer support compared to those from general high schools, highlighting the moderating effect of educational background. The study provided a new theoretical perspective for understanding the complex mechanisms of academic adjustment in students and offers valuable guidance for educational practice, emphasizing the importance of fostering perceived peer support, academic hope, and professional identity in higher vocational education.

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.

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 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.

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.

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.

This study explores fault-tolerant consensus in leader-following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler-Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

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.

As split learning (SL) becomes popular for addressing data privacy and resource limitation issues in edge model training process, it is crucial to pre-validate whether the extensive use of wireless and computing resources in SL can achieve the expected training latency and convergence. While digital twin network (DTN) can potentially estimate network strategy performance in pre-validation environments, they are still in their infancy for SL tasks, facing challenges like unknown non-i.i.d. data distributions, inaccurate channel states, and misreport resource availability across devices. To address these challenges, this paper proposes a TransNeural algorithm for DTN pre-validation environment to estimate SL latency and convergence. First, the TransNeural algorithm integrates transformers to efficiently model data similarities between different devices, considering different data distributions and device participate sequence greatly influence SL training convergence. Second, it leverages neural network to automatically establish the complex relationships between SL latency and convergence with data distributions, wireless and computing resources, dataset sizes, and training iterations. Deviations in user reports are also accounted for in the estimation process. Simulations show that the TransNeural algorithm improves latency estimation accuracy by 13.44% and convergence estimation accuracy by 116.74% compared to traditional equation-based methods.

Multi-rotor drones, a kind of unmanned equipment which is widely used in military, commercial consumption and other fields, has been developed very rapidly in recent years. However, their short flight time has hindered the expansion of their application range. This can be addressed by utilizing hydrogen fuel cells, which exhibit high energy density, strong adaptability to ambient temperature, and no pollution emissions, as the power source. Accordingly, the application of hydrogen fuel cells as the power source in multi-rotor drones is a promising technology that has attracted significant research attention. This paper summarizes the development process of hydrogen fuel cell mul-ti-rotor drones and analyzes the key obstacles that need to be addressed for the further development of hydrogen fuel cell multi-rotor drones, including structural lightweight, hydrogen storage methods, energy management strategies. Additionally, prospects for the future development of hydrogen fuel cell multi-rotor drones are presented.

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.

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.