In this paper, we establish the generalized Qi-type inequality involving conformable fractional integrals. The results presented here would provide extensions of those given in earlier works.

Balanced and coordinated economic development across regions is a critical goal of regional economic development and new-type urbanization in China. However, few studies have examined economic growth convergence clubs at the county level. To extend the research on convergence clubs, this research applies a log t convergence test and a dynamic spatial ordered Probit model (DSOP) to endogenously identify economic growth convergence clubs in counties and to examine the influence of initial states and structures on club convergence probability. The study sample covers 2286 counties of China from 1992 to 2010. The results show significant convergence club patterns at the county levels, resulting in the gradual formation of six convergence clubs. The DSOP estimation results show that per capita fixed assets, population density and industrialization have promoted convergence club formation to varying degrees.

In the paper, by the Faά di Bruno formula, several identities for the Bell polynomials of the second kind, and an inversion theorem, the authors simplify coefficients of two families of nonlinear ordinary differential equations for the generating function of the Catalan numbers and discover inverses of fifteen closely related lower triangular integer matrices.

The time’s arrow of macroscopic physical phenomenon is reflected by irreversible physical process, which essentially occurs from small probability state to high probability state. In this paper, simplified models are proposed to understand the macroscopic physical process. In order to describe the information of a physical system, we defined the full self-information as "information height" to describe the complexity or difficulty of a macrostate of physical system. In this way, the direction of macroscopic physical process is from high information height to low information height. We can judge the direction of physical process by the information height. If we want the macroscopic physical process to evolve from the low information height state to the high information height state, the system need to add extra information and corresponding energy to increase the information height.

To weaken the harm of Al-Si coating and increase the strength of welded joint, variable thickness of Ni foil (Ni, an austenitic formation element) was added into the lap laser welding Al-Si coated 22MnB5 hot stamping steel/galvanized steel joints. The joints' weld appearance, microstructure, and mechanical properties were explored. The weld altered from X-shape to Y-shape with the increased thickness of Ni foil. During welding, Al-Si coating was melted and diluted into the welding pool, forming δ-ferrite (a rich-Al phase with low toughness and strength) in FZ and FB. This phase deteriorated the strength of the joints. After adding Ni, the amount and size of the δ-ferrite phase decreased. With a significant thickness of Ni foil, δ-ferrite would disappear. However, a new phase (FM, rich-Ni phase) probably formed except PM(a no or negligible Ni phase). The heat-affected zone (HAZ) on the side of 22MnB5 comprised a coarse martensite zone, refined martensite zone, martensite+ferrite zone, and tempered martensite zone from the FZ to the basic material. HAZ on the side of galvanized steel mainly contained ferrite and pearlite. After adding Ni foil, the strength of the joint was more than that without Ni. The maximum strength of the joint can be up to 679MPa because of the disappearance of δ-ferrite. Meanwhile, the toughness of the joint increased. The fracture mode was from three mixed fractures(cleavage, quasi-cleavage, and dimple) to one fracture(dimple).

Sensory descriptive analysis was used to evaluate the aged Feng-flavored baijiu. The results indicated that there are notable differences in the samples of different ages. The samples with shorter storage times and fresh distilled Baijiu present bran flavor, and fresh green flavor. with the increase of storage time, the fragrance of honey, sweetness, flowers, and aging gradually increases. The samples of Feng-flavored baijiu were dected by Headspace solid-phase microextraction (HS-SPME) combined with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC–TOFMS) , in total, 496 sustances were identified in all samples, mainly containing 14 types of substances, such as esters, aldehydes etc., of which 42 substances were found in Feng-flavored baijiu for the first time. Chemometrics was used to analyze the key differential compounds. 143 differential compounds closely related to aging were preliminarily screened, and principal component analysis revealed that these samples were separated by year. 65 differential compounds were selected out by partial least squares discriminant analysis(PLS-DA). Furthermore, 43 key differential compounds were selected from the aged Feng-flavored baijiu combined with variable importance in projection and Pearson correlation coefficients. Partial least squares regression (PLSR) was used to research the correlation of sensory properties and key differential compounds, and the results indicated that most compounds were closely related to the aged Baijiu . This study afford theoretical basis and reference for flavor research of Feng-flavored baijiu.

In order to improve the charge collection efficiency, we simulated and experimentally tested the doping structure of the electron multiplication layer in EBCMOS. In this paper, we simulate the charge collection efficiency of EBCMOS under different doping methods by modeling the collisional scattering of electrons with solid atoms in semiconductor materials and combining the transport trajectories of electrons in the electron multiplication layer, the simulation results indicate that using a layered doping structure to reduce the thickness of the index heavily doped layer can effectively optimize the electric field distribution in the electron multiplier layer and reduce the recombination rate of electrons. The optimized doping structure has a significant effect on improving charge collection efficiency. Based on the simulation results, doped samples were prepared and tested. The test results showed that the charge collection efficiency obtained under the condition of a P-type silicon substrate thickness of 7μm and an index heavily doped layer thickness of 1μm was 72.65%, reducing the thickness of the index heavily doped layer to 0.1μm, the charge collection efficiency obtained can reach 86.27%, which proves that reducing the thickness of the index heavily doped layer can effectively improve the charge collection efficiency of EBCMOS devices.

Pore fluid pressure is very important to the generation, migration and accumulation of petroleum. The previous studies indicate that the Chezhen sag region is typically characterized by pore fluid overpressure (the difference between pore fluid pressure and hydrostatic pressure). To date, the formation mechanism of pore overpressure and the accumulation regularity of “upper source-lower reservoir” type in this region remains unknown. In order to investigate these problems, a 2-D finite element model is established with regards to the fluid-solid coupling in Chegu 25 block of Chezhen depression, fully considering the existing seismic, logging data and regional tectonic stress environment. Meanwhile, we calculate the abnormal overpressure generated at the source rock during hydrocarbon generation and the process of hydrocarbon migration and accumulation along the faults, analyzing the dynamic conditions of hydrocarbon downward accumulation. The results show that overpressure can accelerate migration of hydrocarbon and improve the efficiency of hydrocarbon accumulation. However, when the overpressure is too large, tensile fractures and shear fractures may occur, resulting in hydrocarbon dissipation, and changing the results of oil and gas accumulation. The overpressure at the source rock is mainly caused by hydrocarbon generation, while the overpressure at the reservoir is primarily created by unbalanced compaction. As the dominant channel of hydrocarbon migration, overpressure will change the direction and path of hydrocarbon migration in the fault. Therefore, the “upper source-lower reservoir” hydrocarbon accumulation model is strongly explained by the high permeability of faults and the presence overpressure. In summary, the simulated overpressure results are in good agreement with the mud weight equivalent overpressure and the drill stem tests (DSTs).

This paper, based on the "Unified Theory of Forces," rigorously proves the formula of de Broglie's wave function. This helps us better understand the following important issues: 1) Theoretically prove and understand the essence of de Broglie's wave function; 2) Prove that when the particle's momentum P is determined, the greater the mass of the particle, the more concentrated its wave function distribution, and the weaker the interference effect; 3) Correct the spatial distribution of the wave function of free particles, proving that the wave function of free particles is strictly constrained within a finite spatial range, which is consistent with the facts we observe.

This paper presents a theory on graph topological structure and graph coloring, proving that for any N-order graph structure (with a topological structure similar to ), the maximum number of colors required for coloring is less than or equal to . The Four Color Theorem is just one special case of this theory, with the maximum structure size for a four-color graph being a 4-order structure graph, hence requiring only a maximum of 4 colors for coloring.

In this paper, the effects on the rheological properties, particle size distribution, and fluorescence properties of red wine were investigated under microwave irradiation, and the mechanism of the effects of microwave irradiation on the sensory properties of red wine was discussed. The results showed that the effect of microwave on the rheological properties in red wine could be fitted by the Power-law model and Carson model, through the analysis of the rheological constants, yield stress, and viscosity coefficient of wine, it was found that microwave treatment could improve leg phenomenon and thickening effect by the change of rheological properties; the particle size distribution in wine indicated that microwave irradiation did change the particle size distribution through friction effect and oxidative polymerization, resulting in the wine’s visual effect and mouthfeel; the wine quality could be improved by enhancing the fluorescence intensity under different microwave conditions, which meant that microwave technology could speed up the formation of fluorescent substances, such as the polymerization of flavan-3-ols. In conclusion, microwave treatment could modify the sensory characteristics of wine, and further provide the theoretical basis for the application of microwave in winemaking.

Steam Generator Level Control System (SGLCS) is one of the key instrumentations and control subsystems to ensure the safe operation of nuclear power plants. It is highly recommended to perform reliability analysis for SGLCS for making better maintenance strategy. SGLCS is a digital control system with complex redundant configuration and failure\self-diagnosis\repair action, traditional fault tree is not applicable due to its static property, Markov model is not a good choice too because of heavy burden of creating state transition graph for even medium-size system. Boolean Logic Driven Markov Process (BDMP) can describe complex failure-repair state transition via ‘trigger link’ and three kinds of leaf nodes, as well as keep compact and easy-read struct like fault tree. So BDMP is adopted to analysis reliability of SGLCS. Graphical BDMP modeling environment KB3 and Monte Carlo simulator YAMS is used for BDMP modeling and quantitative analysis respectively, and unavailability of SGLCS is obtained. The contribution of three parts of SGLCS to the unavailability of the system is also evaluated, and it is found that the water supply flow control part contributes the most to the failure of SGLCS.

Suitable soil structure and nutrient security are important for plant growth and development, characteristics of soil fractal dimension and distribution of physical and chemical properties and their interactions play an important role in studying the stability of soil structure and water and fertilizer cycles. As a sustainable management model, intercropping has positive benefits for erosion control, spatial optimization of resources, as well as improving system productivity. The effects of four intercropping methods on soil fractal dimension and physicochemical properties were investigated by intercropping Salvia miltiorrhiza with forage and S. miltiorrhiza with forest under typical karst rock desertification habitats in Guizhou. The results showed that soil nutrient content of intercropping was significantly higher than that of monoculture, the organic carbon content of soil grown under forest is higher than other treatments, and there was a non-significant change in soil water content of intercropping compared with monoculture. The soil fine-grained matter of intercropping was significantly higher than that of monoculture, while the soil fractal dimension showed a tendency to become larger with the increase of fine-grained matter. The intercropping planting, due to its component types and spatial and temporal configurations, leads to differences in soil water and fertilizer interactions, which can be combined with other ecological restoration measures to optimize the composite model and jointly promote the restoration and development of ecologically fragile areas.

This paper first explains the theory of the origin of force from a new perspective, explaining that the origin of all forces is caused by the "missing of energy in the time dimension" (non-conservation in a certain period of time), and constructs a unified theory of force on this basis. Then, through the unified theory of force, the following phenomena are reinterpreted: (1) The common origin of the inverse square law of gravitation and electromagnetic force; (2) The origin of the repulsive force of electrons and protons at small distances; (3) The mystery of the rotation curve of galaxies and the new explanation of "dark matter"; (4) explain the origin of the magnetic field; (5) Predict that moving magnets have the same radiation effect as charged particles (electron magnetic moments also have radiation effects); (6) Reinterpret Maxwell's displacement current; (7) Reinterpret synchrotron radiation and bremsstrahlung; (8) Explain that the strong and weak forces are short-range forces; (9) Predict the true structure of black holes and the origin of gravitational waves;(10) Possible explanation for the origin of the speed of light.

This paper is another applied article based on the author's "Unified Theory of Force". The main work of this paper is as follows: (1) Reinterpretation of the Lorentz contraction effect and rigorous proof that the Lorentz contraction is a physical phenomenon based on the conservation of energy and momentum under non-completely symmetrical relative motion effects. This paper proves that the Lorentz contraction effect not only occurs in the direction of motion but also simultaneously in any direction. (2) For the first time, we attempted to explain the physical meaning of the Planck energy formula in theory and associate the Planck energy formula with the Lorentz contraction effect, which can help us to have a deeper understanding of the true physical meaning behind the Planck energy formula. At the same time, we also made reasonable theoretical predictions about the basic structure of photons.

There is currently no unified explanation for the origin of Earth's magnetic field, planetary magnetic fields, stellar magnetic fields, and neutron star magnetic fields, and most importantly, none of these theories have been experimentally verified. This paper is based on a new unified theory of mechanics and its derived applications, which may provide a better explanation for the origin of magnetic fields in celestial bodies and can be experimentally tested to better answer the observed phenomena: (1) the greater the mass of the celestial body, the stronger the magnetic field; (2) the faster the rotation speed of the celestial body, the stronger the magnetic field; (3) the higher the density of the celestial body, the stronger the magnetic field produced.

This paper is mainly based on a stricter premise of the twin paradox and the assumption of inertial frame, discusses the properties of time and space under the premise of complete symmetry, and draws an interesting conclusion: the simultaneity of different reference frames is possible realized, and the space is relatively independent. And based on this, the twin paradox, cosmic inflation, ultra-distance action of quantum entanglement, microscopic space motion of particles, measurement problems and other phenomena are tentatively explained from a new angle. This interpretation is exploratory and new. At the same time, the author also proposes an experimental way to test the relative independence of space.At the same time, this paper attempts to strictly prove that Einstein's definition of simultaneity and spatial absoluteness in special relativity may be problematic.

We introduce a full binary directed tree structure to represent the set of natural numbers, further categorizing them into three distinct subsets: pure odd numbers, pure even numbers, and mixed numbers. We adopt a binary string representation for natural numbers and elaborate on the composite methodology encompassing odd- and even-number functions. Our analysis focuses on examining the iteration sequence (or composition) of the Collatz function and its reduced variant, which serves as an analog to the inverse function, to scrutinize the validity of the Collatz conjecture. To substantiate this conjecture, we incorporate binary strings into an algebraic formula that captures the essence of the Collatz sequence. By this means, we transform discrete powers of 2 into continuous counterparts, ultimately culminating in the smallest natural number, 1. Consequently, the sequence generated through infinite iterations of the Collatz function emerges as an eventually periodic sequence, thereby validating an enduring 87-year-old conjecture.

We use binary string to represent a natural number and show the composite procedure of odd-number and even-number functions, thus we propose full binary directed tree to represent the set of natural numbers and give another partition the natural number set to three sets: pure odd, pure even and mixed number. For the Collatz conjecture we make use of the parity of a natural number to analyse the sequence of iteration (or composite) of Collatz function and reduced Collatz function analog to the inverse function. We give tabular and binary strings to the algebra expression to state the sequence of Collatz, this is the key topic to proof the conjecture: the discrete powers of 2 can be changed to ultimately continuous powers of 2, finally get to pure even number and to the smallest number 1. The sequence created by the infinite iterations of the Collatz function becomes the ultimately periodic sequence if any natural number is the beginning value, proving the conjecture that has been held for 87 years.

Natural vegetation restoration has become an effective and rapid way for eco-logical restoration in fragile areas. However, the response of soil microorgan-isms remains unclear. Using high-throughput sequencing methods, we evalu-ated the dynamics of soil bacterial and fungal communities during forest suc-cession (shrubland, secondary forest, and primary forest) in the karst region of Southwest China. Secondary forest had significantly higher bacterial α-diversity than the shrubland and primary forest. Soil bacterial community in the primary forest was close to the shrubland but diverged from the secondary forest, and that the fungal community could be discriminated along forest suc-cession. Also, the microbial co-occurrence network indicated that soil fungi had less but more intense relationships than bacteria in the karst forests. Further-more, soil properties (pH, SOC, TN, moisture, and AK), soil microbial biomass (MBP, and MBN), and plant factors (Shannon index of woody plants) drove the dynamics of the soil bacterial community, while soil properties (i.e., pH) mostly explained the variation of the soil fungal community along karst forest succession. Different responses of soil bacteria and fungi to forest succession in a karst region give hints for ecological restoration along forest succession in the karst region.

For a long time, physicists always directly use mathematical tools to deal with physical problems, and few people pay attention to the difference between mathematical theory and physical theory. Just like the dilemma that physicists once faced when dealing with the problem of blackbody radiation function.By analyzing the difference between the theoretical basis of mathematics and the theoretical basis of physics, this paper draws the following conclusions: (1) The theoretical basis of mathematics and the theoretical basis of physics are different, so when we use mathematical tools for physics research, we need to be very careful. (2) Finiteness and discreteness should be the basis of the whole physical theory; This paper points out that it is not advisable to use infinite " " and infinitesimal "0" without restriction and demonstration in physics, as well as the continuity of functions, which will bring a lot of trouble to physical theory.At the same time, through the analysis of Banach-Tarski paradox and Bertrand paradox, this paper proposes that if we revise the basic assumptions of probability theory: assuming that "points" have quantized sizes, and "lines" also have quantized widths. After the correction, we can not only avoid the troubles caused by Bertrand paradox, but also make probability theory better for practical application.

: During the period of COVID-19, the Chinese government implemented a series of actions to prevent the spread of the virus. It is noticed that these preventive actions have generated positive effects on air pollution in Wuhan and Beijing. And, due to the differences in city functions, geographic locations, meteorological conditions, and preventive action details between the two cities, noticeable difference is observed on how they reduced air pollution. This unfortunate incident has become a social sample for studying the industrial and livelihood activities impact on air pollution, otherwise would be highly impossible. This paper starts from observing data from NASA's real-time fine particulate matter (PM2.5) and NO2, and comparing the air pollution in this special time domain with previous years. It is discovered that both PM2.5 and NO2 have been reduced in Wuhan, while only NO2 has significant reduction in Beijing, during the COVID-19 responses. This suggests that the human activity suppression by the COVID-19 control yields different effects on different pollutants and in different cities. Possible reasons for such effects are analysed by considering the public health control levels as well as the pollutant migration, industrial activities, and the weather conditions.

With the advances in industry and commerce, passengers have become more accepting of environmental sustainability issues; thus, more people now choose to travel by bus. Government administration constitutes an important part of bus transportation services as the government gives the right-of-way to transportation companies allowing them to provide services. When these services are of poor quality, passengers may lodge complaints. The increase in consumer awareness and developments in wireless communication technologies have made it possible for passengers to easily and immediately submit complaints about transportation companies to government institutions, which has brought drastic changes to the supply-demand chain comprised of the public sector, transportation companies, and passengers. This study proposed the use of big data analysis technology including systematized case assignment and data visualization to improve management processes in the public sector and optimize customer complaint services. Taichung City, Taiwan was selected as the research area. There, the customer complaint management process in public sector was improved, effectively solving such issues as station-skipping, allowing the public sector to fully grasp the service level of transportation companies, improving the sustainability of bus operations, and supporting the sustainable development of the public sector-transportation company-passenger supply chain.

This study used Dutch potatoes at the end of dormancy as the material to explore the impact of menthol (0.2 and 0.5 g/kg based on potato mass) treatment on sprouting inhibition and potato quality. The findings revealed that a menthol concentration of 0.5 g/kg effectively inhibited potato tuber sprouting and significantly reduced glucoside alkaloids production. After a storage period of 15 days, the sprouting percentage and glucoside alkaloids content of potatoes treated with 0.5 g/kg menthol were observed to be significantly lower at 4.17% and 68.63 mg/kg, respectively, compared to the control group which exhibited values of 100% and 282.01 mg/kg, respectively (P < 0.05). Throughout the storage period, 0.5 g/kg of menthol promoted respiration, reduced malondialdehyde production in potatoes, inhibited polyphenol oxidase activity, and slowed down tissue browning. Additionally, it mitigated the decline in starch and soluble protein content, inhibiting the accumulation of reducing sugars.

This paper mainly explores the feasibility of using desert sand (DS) and recycled aggregate in cement stabilized base. Recycled coarse aggregate (RCA) and DS serve as the substitutes of natural coarse and fine aggregates, respectively, in cement stabilized base. A four-factor and four-level orthogonal test is designed to analyze the unconfined compressive strength, splitting tensile strength, and compressive resilient modulus. Furthermore, this paper investigates the effects of cement content, fly ash (FA) replacement rate, RCA replacement rate, and DS replacement rate on the road performance of cement stabilized base composing of RCA and DS. The test results reveal that the performance of cement stabilized base with partial RCA instead of natural coarse aggregate (NCA) and partial DS instead of natural fine aggregate satisfy the road use. Meanwhile, DS is beneficial to improve the strength of cement stabilized base. The correlation and microscopic analyses of the test results imply the feasibility of applying DS and recycled aggregate to cement stabilized base. It is hoped that this paper can offer a solid theoretical foundation for promoting the application of DS and recycled aggregate in road engineering.

The Shisa family is a type of single-transmembrane adaptor proteins containing an N-terminal cysteine-rich domain and a proline-rich C-terminal region. Nine Shisa subfamily genes have been proposed in most vertebrates; however, some of them might be species-specific. The number of Shisa genes present in zebrafish remains unclear. The purpose of this study was to investigate the evolutionary relationships among Shisa family genes in zebrafish (TU strain) using phylogenetic and syntenic analyses. The function of shisa-2 was preliminarily examined via CRISPR/Cas13d-mediated knockdown. Following identification in zebrafish,10 Shisa family genes, namely shisa-1, 2, 3, 4, 5, 6, 7, 8, 9a, and 9b, were classified into three main clades and six subclades. Their encoding proteins contained a cysteine-rich N-terminal domain and a proline-rich C-terminal region containing different motifs. A specific syntenic block containing atp8a2 and shisa-2 was observed to be conserved across all species. Furthermore, all these genes were found to be expressed during embryogenesis. shisa-2 was expressed in the presomitic mesoderm, somites, and so on. shisa-2 was identified as a regulator of the expression of the somite formation marker mesp-ab. Overall, our study provides new insights into the evolution of Shisa family genes and the control of shisa-2 over the convergent extension cells of somatic precursors in zebrafish.

This paper is the third application-oriented paper based on the "unified theory of forces" and also addresses the shortcomings of previous research in fully explaining the origin and nature of the weak force. The main conclusions of this paper are as follows: (1) It is argued that neutrinos are generated by the gravitational field, and their generation mechanism is similar to the radiation of photons from electron orbital transitions. Particles undergoing orbital transitions in a gravitational field will emit neutrinos. (2) We rigorously derive the energy formula for neutrinos and demonstrate that the energy formula for neutrinos does not comply with the Planck energy formula. Neutrinos with higher energy have longer wavelengths and are more easily observed in experiments.

At the end of 2019, while the world was being hit by the COVID-19 virus and, consequently, was living a global health crisis, many other pandemics were putting humankind in danger. The role of social media is of paramount importance in these kinds of contexts since they help health systems to cope with emergencies by contributing to conducting some activities such as the identification of public concerns, the detection of infections’ symptoms, and the traceability of the virus diffusion. In this paper, we have analyzed comments on events related to cholera, ebola, HIV/AIDS, influenza, malaria, Spanish influenza, swine flu, tuberculosis, typhus, yellow fever, and zika, collecting 369,472 tweets from the 3rd of March to the 15th of September, 2022. Our analysis has started with the collection of comments composed of unstructured texts on which we have applied natural language processing solutions. Afterward, we have employed topic modelling and sentiment analysis techniques to obtain a collection of people’s concerns and attitudes toward these pandemics. According to our findings, people's discussions were mostly about malaria, influenza, and tuberculosis and the focus was on the diseases themselves. As regards emotions, the most popular were fear, trust, and disgust where trust is mainly regarding HIV/AIDS tweets.

To enhance the safety of power grid operations, this study proposes a high-precision short-term photovoltaic power prediction method that integrates information from surrounding pho-tovoltaic stations and the Conv-LSTM-ATT model. In the deep learning prediction model, not only is numerical weather prediction (NWP) data from the target photovoltaic station used as input features, but also highly correlated features from nearby photovoltaic stations are incor-porated. The research begins by analyzing the correlation between irradiance and power se-quences, along with distance factors, to calculate a composite similarity index between the target and other regional photovoltaic stations. Stations with high similarity indices are then selected as data sources. Subsequently, Bayesian optimization techniques are employed to find the optimal data fusion ratios. Ultimately, using the selected data, power prediction mod-eling is conducted via the Conv-LSTM-ATT deep neural network. Experimental results con-firm the superiority of the proposed model, which demonstrates higher predictive accuracy compared to three other classical models. The data fusion strategy determined by Bayesian optimization significantly enhances prediction accuracy, reducing the root mean square error (RMSE) of the test set by 20.04%, 28.24%, and 30.94% for three weather types, respectively.

The AREB/ABF (ABA response element binding) proteins in plants are vital for plant stress responses, but the understanding of AREB/ABFs in orchid species, an important traditional medicinal and ornamental plants, is limited. Here, twelve AREB/ABF genes were identified within three orchids’ complete genome and classified into three groups (groups I, II, and III) via a phylogenetic analysis, which was further supported by the analysis of their conserved motifs and gene structures. The cis-elements analysis showed that hormone response elements followed by light and stress responses were widely rich in AREB/ABFs. A prediction analysis of orchid ABRE/ABF-mediated regulatory network was further constructed through cis-regulatory elements (CREs) analysis of their promoter regions and revealed that several transcriptional factor (TF) gene family were abundant as the potential regulators for orchid AREB/ABFs. Expression profile analysis based on public transcriptomic data showed that most AREB/ABF genes have distinct tissue-specific expression patterns in orchid plants. Additionally, DcaABI5 as the homolog of ABA INSENSITIVE 5 (ABI5) from Arabidopsis was selected for further analysis. The results showed that Arabidopsis transgenic overexpressing DcaABI5 could rescue the ABA insensitive phenotype in the mutant abi5. Collectively, these findings will provide valuable information on AREB/ABF genes in orchids.

Zinc cobalt oxide-zinc hydroxide (ZnCo2O4-Zn(OH)2) microspheres were successfully fabricated on carbon cloth via a sample hydrothermal method. The surface morphology of these microspheres and their efficacy in degrading methyl violet were further modulated by varying the thermal annealing temperatures. The manipulation of thermal annealing temperatures was pivotal in adjusting the porosity of ZnCo2O4-Zn(OH)2 microspheres to amplify their photocatalytic performance. Various analytical techniques were utilized to evaluate the physical and chemical properties of the ZnCo2O4-Zn(OH)2 microspheres, including field-emission scanning electron microscopy, energy-dispersive spectroscopy, X-ray diffraction, field-emission transmission electron microscopy, X-ray photoelectron spectroscopy, and UV-vis spectroscopy. Compared to untreated ZnCo2O4-Zn(OH)2 microspheres, those subjected to thermal annealing exhibited increased specific surface area and light absorption capacity, rendering them highly effective photocatalysts under UVC light exposure. Subsequent studies have confirmed the superior performance of ZnCo2O4-Zn(OH)2 microspheres as a reusable photocatalyst for degrading methyl violet and tetracycline. Additionally, trapping experiments during the photodegradation process involving ZnCo2O4-Zn(OH)2 microspheres have identified hydroxyl radicals (·OH) and superoxide radicals (·O2–) as the primary reactive species.

The threat of fine particulate matter concentration (PM2.5) is increasing globally, Tackling this issue requires an accurate understanding of its trends and drivers. The article investigates the PM2.5 characteristics of 285 prefecture-level cities in China from 2000-2018 based on multiscale geographically weighted regression(MGWR), and the results show that（1）previous studies based on classical MGWR models may be somewhat unstable, while MGWR can reflect the scale of influence of different variables on the dependent variable, and its regression results are more reliable.（2）PM2.5 is very sensitive to carbon emission(CE) factors, and there is a high degree of spatial heterogeneity, and the influence scale of location is the smallest among all variables, close to the municipal scale.（3）In 2000, the constant term all, IS, OFT, CE, and LT positively affect PM2.5, while GDP (jurisdiction) and UR negatively affect PM2.5; in 2010, the constant term all, GDP (jurisdiction), IS, OFT and LT positively affect PM2.5, while UR and CE negatively affect PM2.5; in 2018 the constant term all, IS, OFT and CE factors positively affect PM2.5, and GDP (jurisdiction), UR and LT negatively affect PM2.5.

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

In order to study the performance of a new type of cement-based grouting material under the coupling effect of freezing-thawing cycle and sulfate attack, experiments were designed and carried out. The damage mechanism of the material under the coupling effect of freezing-thawing and Na2SO4 solution was analyzed by measuring the mass change, relative dynamic elastic modulus, compressive strength loss and microstructure of the new grouting material. With increasing freeze-thaw cycles, mass loss and compres-sive strength loss of the sample in 15% Na2SO4 solution gradually increase, and the relative dynamic elastic modulus gradually decreases. During a 30-cycle freeze-thaw cycle, the mass loss rate, compressive strength loss rate, and relative dynamic elastic modulus of the sample are 4.17%, 24.59%, and 84.3%, respectively, showing good corrosion resistance and frost resistance durability. Microscopic analysis showed that SO42- caused the decomposition of C-S-H gel in the sample and the formation of Gypsum, and the widening of crack width aggravated the internal deterioration, indicating that the material deterioration rate increased under the dual-factor coupling effect.

This study takes a two-echelon agricultural products supply chain (APSC) composed of an agricultural product processor (processor) and an agricultural product supermarket (supermarket) as the research object. In order to ensure the stable supply of high-quality agricultural products, this paper considers both fairness concern and corporate social responsibility (CSR), and establishes four Stackelberg game models with or without considering fairness concern and CSR, and obtains the influence mechanism of fairness concern and CSR on bilateral quality efforts in APSC through comparative analysis and numerical analysis. Research shows that the supermarket’s fairness concern can take the role of "profit distribution mechanism", but it is not conducive to improving bilateral quality efforts resulting in the inability to improve market demand, and it is not conducive to improving the utility of the supply chain system and consumer surplus; both members of APSC sharing CSR can not only improve the quality of agricultural products and promote the expansion of market demand, but also can achieve Pareto improvement of both profit of all members and supply chain system. Besides, The CSR shared by both members has a greater impact on bilateral quality efforts decision than the supermarket’s fairness concern, and thus the positive effect of CSR can overcome the negative effect of supermarket’s fairness concern.

Weak signal spectral detection is a serious problem in many applications, especially the target possesses certain frequency band and under low signal-to-noise ratio (SNR). A kind of novel technique based on pre-whitening scale-transformation symmetric bistable parallel stochastic resonance (SR) is proposed to solve the problem. Firstly, the pre-whitening can ensure the even distribution of the receiving signal to fit the requirement of SR processing. Secondly, the scale- transformation can help to utilize the weak signal improvement property especially under low frequency band more effectively. Thirdly, the symmetric bistable parallel SR can try to reduce the variance of the clutter and additive noise fluently. Fourthly, by subtracting the steady state re-sponse of the selected symmetric bistable SR system from the parallel SR output, the weak signal spectral detection objective can be realized successfully. Simulation results based on the real sea clutter radar data guarantee the effectiveness and applicability of the proposed symmetric bistable PSR processing approach.

Establishment of diagnostic methods with low detection limits plays a critical role in the maintenance of early diagnosis, prevention of serious neurological complications, and control of the spread of ZIKA. In this study, we established the micro-droplet digital polymerase chain reaction (ddPCR) and real-time fluorescent quantification PCR (qPCR) protocols for the detection of Zika virus based on the NS5 gene. For the Zika standard plasmid, the standard curve of R² was 0.999, and the amplification efficiency was 92.203%, as determined by qPCR. Both ddPCR and qPCR were positive for cell culture of Zika nucleic acid.The minimum detection limit of ddPCR is 1–2 times lower than qPCR. Moreover, all tests of Dengue virus (1–4 serotypes) were negative in cell culture. Overall, these results suggested than ddPCR may have a lower limit of detection than qPCR.

Polysaccharide from Ficus carica L. (FCP) exert multiple biological activities, as a biological macromolecule, the available knowledge about the specific structures and mechanisms of biological activity of purified 'Brunswick' fig polysaccharide is currently limited. In the present study, by chemical purification and characteristics identified via specific instruments, and then the impact of FCP on the immunomodulation activity in vitro and in vivo was examined. Structural char-acteristics showed that the molecular weight of the FCP was determined to be 127.5 kDa, the primary monosaccharides present in the FCP were galacturonic acid (GalA), arabinose (Ara), galactose (Gal), rhamnose (Rha), glucose (Glc), and xylose (Xyl) at a ratio of 0.321:0.287:0.269:0.091:0.013:0.011. Based on the investigation of in vitro immunomodulatory ac-tivity, FCP was found to stimulate the production of NO, TNF-α, and IL-6 and increased the pi-nocytic activity of macrophages. Further analysis revealed that FCP activated macrophages by interacting with toll-like receptor 4 (TLR4). Moreover, the in vivo test results indicate that FCP showed significant increase serum pro-inflammatory factors in immunosuppressed mice. Overall, this study suggests that FCP has the potential to be utilized as a novel immunomodulator in the pharmaceutical and functional food industries.

Abstract: Financial institutions are currently undergoing a significant shift from traditional robo-advisors to more advanced generative artificial intelligence (GenAI) technologies. This transformation has motivated us to investigate the factors influencing consumer responses to GenAI-driven financial advice. Despite extensive research on the adoption of robo-advisors, there is a gap in understanding the specific contributors and differences in consumer attitudes and reactions to GenAI-based financial guidance, particularly in the context of GenAI. This study aims to address this gap by analyzing the impact of personalized investment suggestions, human-like empathy, and the continuous improvement of GenAI-provided financial advice on consumers' perceived authenticity, their utilitarian attitude towards the use of GenAI for financial advice, and their reactions to GenAI-generated financial suggestions. A comprehensive research model was developed based on Service-Dominant Logic (SDL) and Artificial Intelligence Device Use Acceptance (AIDUA). The model was subsequently employed in a structural equation modeling (SEM) analysis of survey data from 822 mobile banking users. The findings of this study indicate that personalized investment suggestions, human-like empathy, and the continuous improvement of GenAI's recommendations have a positive influence on consumers' perception of its authenticity. Moreover, we discovered a positive correlation between utilitarian attitudes and perceived authenticity, which ultimately influences consumers' responses to GenAI's financial advisory solutions. This is manifested as either a willingness to engage or resistance to communication. This study contributes to the research on GenAI-powered financial services and underscores the significance of integrating GenAI financial guidance into the routine operations of financial institutions. Our work builds upon previous research on robo-advisors, offering practical insights for financial institutions seeking to leverage GenAI-driven technologies to enhance their services and customer experiences.

A novel nano-laminated GdB2C2 material was successfully synthesized using GdH2, B4C, and C via an in-situ solid state reaction approach for the first time. The formation mechanism of GdB2C2 was revealed based on the microstructure and phase evolution investigation. A purity of 96.4 wt. % GdB2C2 was obtained at a low temperature of 1500 ℃, while a near full pure GdB2C2 can be obtained at a temperature over 1700 ℃. The as-obtained GdB2C2 presented excellent thermal stability at high temperature of 2100 ℃ in Ar atmosphere due to the stable frame work formed by the high covalent four-members and eight-members B-C rings in GdB2C2. The minimum reflection loss value (RLmin) of -47.01 dB with an effective absorption bandwidth (EAB) of 1.76 GHz at a thickness of 3.44 mm was obtained for the GdB2C2 synthesized at 1500 ℃. The possible EMWA mechanism could be ascribed to the nano-laminated structure and appropriate electrical conductivity, which facilitated to the good impedance matching, remarkable conduction loss, interfacial polarization as well as multiple interface reflection and scattering. The as-obtained GdB2C2, with excellent EMWA performance as well as remarkable ultra-high temperature thermal stability, could be a promising candidate for the EMWA materials applications in extreme ultra-high temperatures.

Microcystis aeruginosa is a common cyanobacteria found in water blooms and often causes ecological harm. Antibiotics are also increasingly used for the prevention and treatment of bacterial infections in aquaculture. However, since most antibiotics cannot be fully metabolized, they enter the water environment and cause ecological impacts. In this paper, the effects of the two quinolone antibiotics (enrofloxacin and ciprofloxacin) on the population density and microcystins (MCs) production of Microcystis aeruginosa were studied. It is of great significance for the ecological risk assessment of antibiotics to the water environment. The results showed that the 96h EC50 values of enrofloxacin and ciprofloxacin to Microcystis aeruginosa were 56.10mg/L and 49.80mg/L respectively, and the toxicity of ciprofloxacin to Microcystis aeruginosa was slightly stronger than that of enrofloxacin. With the increase of the two quinolone antibiotic concentration, the growth inhibition rate (IR) increased, but when the concentration reached a certain height, the IR would reach its threshold. Long-term exposure to low concentrations of these two quinolone antibiotics is not only more likely to lead to the outbreak of Microcystis aeruginosa, but also increase its toxin production capacity. The highest contents of MCs in enrofloxacin and ciprofloxacin groups were 95.539 μg/g and 93.720 μg/g, respectively. The MCs value of these three enrofloxacin treatment groups was more than above 51.8 times that of control group (CK) on the 4th day; from 8th day to 14th day, the MCs value of these three enrofloxacin treatment groups was more than above 3.2 times that of CK group. For another ciprofloxacin, the MCs value of the treatment group was more than 64.98 times that of the CK group on the 4th day, and from 8th day to 14th day, the MCs value of the treatment group was more than 2.7 times that of the CK group. These findings provide crucial management rationale.

With the development of organizational biology and forced from the EU Cosmetic Regulation (EU/1223/2009) with the complete ban of animal testing for cosmetic purposes, 3D skin models have been widely developed due to their similar physiological structure and metabolic function to native human tissue. 3D skin models for in vitro cosmetic evaluation are still promising alternative methods for evaluating phototoxicity, corrosivity and irritation. These models have been used for early screening and later validation of toxicity and efficacy evaluation by many well-known cosmetic companies. 3D skin models have become an important tool in cosmetic evaluation research. This article reviews the development of 3D skin models and their application in cosmetic evaluation. An outlook on the current application and future technological development directions in the cosmetic field is also presented.

In the paper, the author introduces the notions "multi-order logarithmic numbers" and "multi-order logarithmic polynomials", establishes an explicit formula, an identity, and two recurrence relations by virtue of the Faa di Bruno formula and two identities of the Bell polynomials of the second kind in terms of the Stirling numbers of the fi rst and second kinds, and constructs some determinantal inequalities, product inequalities, logarithmic convexity for multi-order logarithmic numbers and polynomials by virtue of some properties of completely monotonic functions.

This study explores the impact of artificial intelligence (AI) on financial inclusion satisfaction and recommendation, focusing on ethical dimensions and perceived algorithmic fairness. From the perspectives of organizational justice theory and the heuristic-systematic model, we examine how constructs of algorithm transparency, algorithm accountability, and algorithm legitimacy influence users' perceptions of fairness, and subsequently, their satisfaction with and recommendation of AI-driven financial inclusion. Through a survey-based quantitative analysis, our results indicate that perceived algorithmic fairness acts as a mediating factor between the ethical attributes of AI systems and user satisfaction as well as their recommendation. Findings reveal that higher levels of transparency, accountability, and legitimacy enhance customers' perceptions of fairness, which in turn significantly increases both their satisfaction with financial inclusion services facilitated by AI and their likelihood to recommend them. This research not only contributes to the literature on AI ethics by highlighting the critical role of transparent, accountable, and legitimate AI practices in fostering satisfaction and recommendation among users, but also fills a significant gap in understanding the ethical implications of AI in financial inclusion contexts.

This study presented a novel and sustainable method for producing chitin nanocrystals (PA-ChNCs) using phosphoric acid hydrolysis. The produced PA-ChNCs were used as stabilizers for Pickering emulsions and their physicochemical properties as well as stabilization mechanisms were systematically investigated. The PA-ChNCs exhibited small particle sizes and high surface areas, enabling an effective adsorption at the oil/water interface, which prevented coalescence in Pickering emulsions. The particle size and surface charge PA-ChNCs were strongly pH-dependent, which were critical factors for stabilizing Pickering emulsions. The study showed that emulsions with higher O:W ratios (50%) exhibited larger droplets but greater viscosity, forming a stable gel-like matrix that was more stable than emulsions with other O:W ratios after storage for 30 days. Emulsions with higher PA-ChNCs concentrations (2.0 wt%) and higher pH values (pH 10) showed small droplets and high viscosity, leading to stable emulsions. This research contributes to the development of sustainable and effective emulsifiers for use in the food industry, making utilization of seafood waste in an eco-friendly manner possible. Overall, this study highlights the potential of using newly developed PA-ChNCs as stabilizers for Pickering emulsions in food products.

In the future, the pursuit of high-quality economic development and a focus on ecological environmental protection in China will inevitably result in significant conflicts between land use and ecological land use. The challenge lies in achieving sustainable high-quality development while simultaneously protecting the ecological environment, optimizing the land use structure, and promoting a harmonious relationship between humans and the land. These challenges are faced by all regions. Land use conflicts primarily occur in peri-urban areas characterized by prominent economic development and urban agglomeration. Previous studies have mainly focused on analyzing the effects of land use on habitat quality during intense urbanization. However, it is important to recognize that land pressure encompasses economic, ecological, and social aspects. To gain a comprehensive understanding of the spatial conflict of land use and the impact on habitat quality in Ankang, a city that has been advocating ecological protection for the past two decades, this study aims to objectively analyze the spatial trends in land use changes in such cities. Additionally, it aims to provide insights for the harmonious development of land use in eco-region-oriented cities. Using the SSP-RCP scenarios provided by CMIP6, this paper employs a system analysis method, PLUS model, InVEST model, and land use conflict measurement model to dynamically simulate the future habitat quality and spatial conflict patterns of land use in Ankang City. The study explores the spatial coupling effect of both factors under different scenarios. The results indicate the following:(1) Under different future shared socio-economic path scenarios, land use intensity and land conflict levels follow the order of SSP585 (high forcing scenario), SSP370 (medium to high forcing scenario), SSP245 (medium forcing scenario), and SSP126 (low forcing scenario), with intensity and conflict decreasing accordingly. (2) The overall development trend in Ankang City reveals an intensification of land use conflicts and a decrease in habitat quality. The expansion rate of construction land is increasing and exhibiting aggregation, while agricultural land area is expanding and forest land area is continuously decreasing. (3) Land use intensity exhibits a significant positive correlation with land conflict levels, while land conflict levels demonstrate a significant negative correlation with habitat quality. These findings suggest that land use has had some impact on the ecological environment, with indications of habitat degradation. Even in Ankang, where ecological development is highly valued, the city will gradually face conflicts between ecological protection and economic development in future scenarios. The study highlights that Ankang's future development space will be constrained within the context of environmental protection, leading to greater land use conflicts in urban and surrounding areas. Consequently, the quality of habitats will inevitably decline. Therefore, it is crucial to allocate sufficient space for economic development while simultaneously prioritizing ecological protection. This approach will ensure a healthy economic development trajectory and foster a harmonious relationship between humans and the land.

Cancer is a common illness with a high mortality. Compared to traditional technologies, biomarkers detection, with low cost and simple operation, has higher sensitivity and faster speed in early screening and prognosis of cancer. Therefore, extensive research has been focused on the development of biosensors and the construction of sensing interfaces. Molybdenum disulfide (MoS2) is a promising two-dimensional (2D) nanomaterial, whose unique adjustable bandgap shows excellent electronic and optical properties in the construction of biosensor interfaces. It not only has the advantages of high catalytic activity and low manufacturing costs, but also can further expand the application of hybrid structures by different functionalization, and is widely used in various biosensors fields. Herein, the application of electrochemical and optical sensing platforms based on MoS2 in the detection of cancer biomarkers was comprehensively reviewed. Firstly, the structure and preparation method of MoS2 were introduced, and its applicable characteristics in the field of biosensors were explored. Secondly, we comprehensively reviewed the recent construction and application of sensing platform based on MoS2 in the field of cancer biomarkers detection in both electrochemical and optical aspects. The prime characteristics and application performances of MoS2 and its composites were emphasized. Additionally, we also involved some other types of biosensors based on MoS2. Finally, we summarized the challenges and development prospects of MoS2 in the application of cancer biomarkers detection, and provided some insights for the application potential of this kind of emerging nano-materials in a broader field.

In typical Cobalt-chromium (CoCr) alloys powders, aluminum oxide (Al₂O₃) powder can be added. In the present study, we successfully prepared alloyed samples of various powder ratios by laser cladding, and analyzed their microstructure and carbide structural characteristics, including microhardness, biological properties, morphology (using scanning electron microscopy), and crystal structure (using X-ray powder diffraction). Elemental distribution was also determined by energy-dispersive X-ray spectral analysis. The results showed that Al₂O₃ addition caused the alloy to change from slender columnar crystals to columnar grains similar, to equiaxed grains. In addition, Al₂O₃ agglomeration zones appeared, and carbide structures were altered. The mechanism of the observed performance changes was also analyzed.

Coal is an important resource that is closely related to people’s lives and has an irreplaceable role. However, coal mine safety accidents occur from time to time in the process of working underground. To this end, this paper proposes a coal mine environmental safety early warning model to detect abnormalities and ensure worker safety in a timely manner by assessing the underground climate environment. In this paper, the support vector machine (SVM) parameters are optimized using an improved artificial hummingbird algorithm (IAHA), and its safety level is classified by combining various environmental parameters. To address the problems of insufficient global exploration capability and slow convergence of the artificial hummingbird algorithm during iterations, a strategy incorporating Tent chaos mapping and backward learning is used to initialize the population, a Levy flight strategy is introduced to improve the search capability during the guided foraging phase, and then the simplex method is introduced to replace the worst value before the end of each iteration of the algorithm. The IAHA-SVM safety warning model was established using the improved algorithm to classify and predict the safety of the coal mine environment into four classes. Finally, the performance of the IAHA algorithm and the IAHA-SVM model are simulated separately. The simulation results show that the convergence speed and the search accuracy of the IAHA algorithm are improved, and the performance of the IAHA-SVM model has a significant improvement.

This paper studies an effective finite difference scheme for solving two-dimensional Heston stochastic volatility option pricing model problems. A dynamically balanced up-downwind strategy for approximating the cross-derivative is implemented and analyzed. Semi-discretized and spatially nonuniform platforms are utilized. The numerical method comprised is simple, straightforward with reliable first order overall approximations. The spectral norm is used throughout the investigation and numerical stability is proven. Simulation experiments are given to illustrate our results.

A new quinazolinone alkaloid, named peniquinazolinone A (1), as well as eleven known com-pounds, 2-(2-hydroxy-3-phenylpropionamido)-N-methylbenzamide (2), viridicatin (3), viridicatol (4), (±)-cyclopeptin (5a/5b), dehydrocyclopeptin (6), cyclopenin (7), cyclopenol (8), methyl-indole-3-carboxylate (9), 2,5-dihydroxyphenyl acetate (10), methyl m-hydroxyphenylacetate (11), conidiogenone B (12), was isolated from the endophytic Penicillium sp. HJT-A-6. The chemical structures of all compounds were elucidated by comprehensive spectroscopic analysis, including 1D, 2D NMR, and HRESIMS. The absolute configuration at C-13 of peniquinazolinone A (1) was established by applying the modified Mosher’s method. Compounds 2, 3, and 7 exhibited an op-timal promoting effect on the seed germination of Rhodiola tibetica at a concentration of 0.01 mg/mL, while the optimal concentration for compounds 4 and 9 to promote Rhodiola tibetica seed germination was 0.001 mg/mL. Compound 12 showed optimal seed-germination-promoting activity at a concentration of 0.1 mg/mL. Compared with the positive drug 6-benzyladenine (6-BA), compounds 2, 3, 4, 7, 9, and 12 could extend the seed-germination period of Rhodiola tibetica up to the 11th day.

For personalized recommender systems,matrix factorization and its variants have become mainstream in collaborative filtering.However,the dot product in matrix factorization does not satisfy the triangle inequality and therefore fails to capture fine-grained information. Metric learning-based models have been shown to be better at capturing fine-grained information than matrix factorization. Nevertheless,most of these models only focus on rating data and social information, which are not sufficient for dealing with the challenges of data sparsity. In this paper,we propose a metric learning-based social recommendation model called SRMC.SRMC exploits users' co-occurrence pattern to discover their potentially similar or dissimilar users with symmetric relationships and change their relative positions to achieve better recommendations.Experiments on three public datasets show that our model is more effective than the compared models.

Evidence indicates that the long noncoding RNAs are involved in the metformin-mediated anti-cancer processes. However, the potential effects of the long noncoding RNAs in metformin-mediated anti-tumor processes in esophageal squamous cell carcinomas (ESCC) are still elusive. This study uncovered that metformin decreases the level of long noncoding RNAs CCAT1 and SPRY4-IT1 thereby contributing to the down-regulation of c-Myc and vimentin. Also, the RNA level test of human ESCC tissue confirmed the positive correlation between CCAT1 and c-Myc. These findings demonstrated that metformin facilitated anti-cancer effects by targeting the 2 long noncoding RNAs (CCAT1 and SPRY4-IT1) and their consequential targets c-Myc and vimentin. Therefore, the CCAT1 and SPRY4-IT1 might act as novel molecular targets that mediate the anti-tumor effects in esophageal squamous cell carcinoma. This helps in predicting the treatment response of metformin in patients diagnosed with esophageal squamous cell carcinoma.

Schistosoma infection in snails can be monitored by microscopy or indirectly by sentinel mice. As both these approaches sometimes miss infections, more sensitive tests are needed, particularly in low-level transmission settings. In this study, the loop-mediated isothermal amplification (LAMP) technique, designed to detect a specific 28S ribosomal S. japonicum gene with high sensitivity, was compared to microscopy using snail samples from 51 areas endemic for schistosomiasis in five Chinese provinces. The results were compared with those by polymerase chain reaction (PCR) adding DNA sequencing as a reference when needed. The testing of pooled snail samples showed that a dilution factor of 1/50, i.e., one infected snail plus 49 non-infected ones, would still result in a positive reaction after the recommended number of amplification cycles. Testing a total of 232 pooled samples, emanating from 4,006 snail specimens, with the LAMP assay showed a 6.5% rate of infection, while traditional microscopy found only 0.04% positive samples in the same materials. Parallel PCR analysis confirmed the diagnostic accuracy of the LAMP assay, with DNA sequencing even giving LAMP a slight lead. Microscopy and the LAMP test were carried out at local schistosomiasis-control stations demonstrating that the potential of the latter assay to serve as a point-of-care (POC) test with results available within 60–90 minutes, while the more complicated PCR test had to be carried out at the National Institute of Parasitic Diseases (NIPD) in Shanghai, China. In conclusion, LAMP was found to be clearly superior to microscopy and as good as, or better, than PCR. Application of LAMP testing would be useful for surveillance and risk prediction as it requires less time than other techniques and can be used under field conditions, which improves and accelerates schistosomiasis control.

The massive use of electronic equipment has raised concerns about electromagnetic (EM) pollution, leading to increased attention to EM shielding in both civil and military applications. EM absorption materials play a crucial role in reducing the energy of microwaves projected onto their surface. In this work, we propose a novel OTA (over-the-air) antenna test method to evaluate and analyze the functionality of EM absorption materials. This method offers a new and practical way to assess the effectiveness of these materials. Measured results indicate that the presence of the EM absorption material led to a decrease in antenna gain of approximately 2-3 dB, a reduction in efficiency of more than 10-15%, and a smaller radiation pattern during the actual testing process. These findings highlight the impact of EM absorption materials on antenna performance. Compared to the traditional S-parameter testing method, the gain comparison testing method exhibits the advantages of higher accuracy and simpler operation in the lower frequency experiment.

Buckling defect will appear on the outer surface of the deformed ring during constrained ring rolling (CRR) of aluminum alloy thin wall conical ring with inner high ribs (AATWCRIHR), if the geometrical dimension of the ribs doesn't match the wall thickness. To avoid the buckling defect, a quantitative method for characterizing the degree of the buckling defect is proposed using the area of buckling profile. Then an orthogonal experimental scheme was designed taking the width of the middle rib, thickness of wall, and height of the middle rib as design variables and defining the area of buckling profile as optimization objective. Subsequently, a quadratic polynomial response surface model was established by combining the optimization algorithm with the finite element method (FEM), and the geometrical dimension of the middle ribs of the deformed AATWCRIHR is optimized. Also the optimal parameter combination to minimize the area of buckling profile is obtained and verified using FE simulation. Results show that the AATWCRIHR after optimization doesn’t generate the buckling defect during constrained ring rolling, and it is proved that the quantitative buckling defect representation method and the optimization design method based on the response surface model and the finite element simulation results are feasible for the constrained ring rolling process of the AATWCRIHR.

In the paper, the author finds bounds of the sine and cosine along a circle on the complex plane in terms of two double inequalities for the norms of the sine and cosine along a circle on the complex plane.

In the paper, the author discusses and computes bounds of the sine and cosine along straight lines on the complex plane.

The COVID-19 pandemic resulted in government restrictions that altered the lifestyle of people worldwide. Studying the impact of these restrictions on exercise behaviors will improve our understanding of environmental factors that influence individuals’ PA. We conducted a retrospective analysis using an index of government pandemic stringency developed by Oxford and a wearable device for runners to compare strictness of lockdowns and exercise habits, using digitally-logged PA data from more than 7,000 runners on a global scale. Additionally, time-of-day of PA globally and levels of PA in 14 countries are compared between the pre-pandemic year of 2019 and the first pandemic year of 2020. We found that during the pandemic the time-of-day that people exercised experienced a major shift, with significantly more activities logged during standard working hours on workdays (p<0.001) and fewer during the same time frame on weekends (p<0.001). Of the countries examined, Italy and Spain had among the most strict lockdowns and suffered the largest decreases in activity counts, whereas France experienced a minimal decrease in activity counts despite enacting a similarly strict lockdown. This study suggests that there are several factors affecting PA, including government policy, workplace policy, and cultural norms.

Construction using earth materials demonstrates ecological sustainability using locally sourced natural materials and environmentally friendly demolition methods. In this study, the environmental impact of adding cement to soil materials for rammed-earth farmhouse construction in rural China was investigated and comparatively simulated using the One Click LCA database, focusing on the conflict between sustainability objectives and the practical aspects of cement addition. By analyzing how the addition of cement aligns with local construction practices and by addressing the debate surrounding the inclusion of cement in rammed earth construction, our objective is to provide insights into achieving a balance between the environmental impact and the pragmatic considerations of using cement in earthen building practices. Three local structure scenarios are evaluated via simulations: a cement-stabilized rammed earth wall, a fired brick wall, and a localized reinforced concrete frame structure. The quantitative environmental impacts are assessed, and the qualitative differences in adaptation, economic sustainability, and other factors are examined in the context of present-day development in rural China. The results show that the use of cement-stabilized rammed earth wall-supported structures is associated with higher embodied carbon emissions compared to structures supported by reinforced concrete frames and enclosed by brick walls; however, these emissions are lower than those for brick wall-supported structures, while effectively meeting the structural requirements. In addition, the use of cement-stabilized earth for perimeter walls simplifies material management and disposal throughout the building's life cycle, and the cost-effectiveness of cement has been found to be substantially greater than that of reinforced concrete frames and brick structures, improving economic viability and social acceptability, especially among low-income communities in rural areas.

In the paper, by convolution theorem for the Laplace transforms and analytic techniques, the author finds necessary and sufficient conditions for complete monotonicity, monotonicity, and inequalities of several functions involving polygamma functions. By these results, the author derives a lower bound of a function related to the sectional curvature of the manifold of the beta distributions. Finally, the author poses several guesses and open problems related to monotonicity, complete monotonicity, and inequalities of several functions involving polygamma functions.

Ti and its alloys are the most commonly used materials for biomedical applications. However, bacterial infection after implant placement is still one of the significant rising complications. Therefore, the application of the antimicrobial agents into implant surfaces to prevent implant-associated infection has attracted lots of attention. Scientific papers have shown that inorganic antibacterial metal element (e.g. Ag, Cu, Zn) can be introduced to implant surfaces with the addition of metal nanoparticles or metallic compounds into electrolyte via micro-arc oxidation (MAO) technology. In this review, the effects of the composition and concentration of electrolyte and process parameters (e.g. voltage, current density, oxidation time) on morphological characteristics (e.g. surface morphology, bonding strength), antibacterial ability and biocompatibility of MAO antimicrobial coating were discussed in detail. Anti-infection and osseo-integration can be simultaneously accomplished with the selection of the proper antibacterial elements and operating parameters. Besides, MAO assisted by magnetron sputtering (MS) to endow Ti-based implant materials with superior antibacterial ability and biocompatibility was also discussed. Finally, the development trend of MAO technology in the future was forecasted.

The 3x+1 problem asks the following: Suppose we start with a positive integer, and if it is odd then multiply it by 3 and add 1, and if it is even, divide it by 2. Then repeat this process as long as you can. Do you eventually reach the integer 1, no matter what you started with? Collatz conjecture (or 3n+1 problem) has been explored for about 85 years. In this paper, we convert an integer number from decimal to binary number, and convert the Collatz function to binary function, which is multiplication and division of two binary numbers. Finally the iternation of the Collatz function, eventually reach the integer 1, thus we solve the 3x+1 problem completely.

Precision and accuracy estimation is an important index used to reflect the measurement performance and quality of a measurement system. To reveal the significance and connotations of the precision and accuracy estimation index of a close-range photogrammetry system, several common precision and accuracy estimation methods used in close-range photogrammetry are explained from a theoretical perspective, and the mechanism of the internal coincidence precision estimation and the external coincidence accuracy estimation are deduced, respectively. Through detailed experimental design and testing, the validity and reliability of the proposed precision and accuracy estimation methods are verified, which provides strong evidence for the quality control, optimisation, and evaluation of the measurement results from a close-range photogrammetry system. At the same time, it has significance for the further development of precision and accuracy estimation analysis of close-range photogrammetry systems.

Although the neurofilament light chain (NfL) is used as a biomarker for neurodegenerative decline, its use in surgery- and anaesthesia-induced acute cognitive dysfunction remains controversial. We aimed to synthesise the evidence to explore the potential of NfL as an established biomarker for perioperative neurocognitive disorders (PND). We systematically searched the PubMed, EMBASE, MEDLINE, Cochrane Library electronic databases, and Cochrane Central Registry of Clinical Trials. Our results show that CSF NfL levels positively correlated with blood NfL levels (r = 0.63; 95% confidence interval [CI], 0.42-0.77). The pooled standardised mean difference (SMD) showed a significantly higher preoperative CSF NfL levels (SMD = 0.27; 95% CI, 0.07-0.47) and both pre- and postoperative blood NfL levels (SMD = 0.53, 95% CI, 0.40-0.66 and SMD = 0.58, 95% CI, 0.43-0.73) in the postoperative delirium (POD) group. Postoperative blood NfL levels were significantly elevated in both POD group (SMD = 0.49; 95% CI, 0.34-0.64) and no-POD group (SMD = 0.67; 95% CI, 0.53-0.81). Our meta-analysis indicates that NfL may serve as a potential biomarker for POD, and elevated preoperative blood NfL levels could be a risk factor for POD. Further studies are needed to confirm the association of CSF/blood NfL levels with other PND types.

In recent years, with the development of the Internet of Things and distributed computing, the "server-edge device" architecture has been widely deployed. This study focuses on leveraging autoencoder technology to address the binary classification problem in network intrusion detection, aiming to develop a lightweight model suitable for edge devices. Traditional intrusion detection models face two main challenges when directly ported to edge devices: inadequate computational resources to support large-scale models and the need to improve the accuracy of simpler models. To tackle these issues, this research utilizes Extreme Learning Machine for its efficient training speed and compact model size to implement autoencoders. Two improvements over the latest related work are proposed: First, to improve data purity and ultimately enhance detection performance, this study partitions the data into multiple regions based on the prediction results of these autoencoders. Second, leveraging autoencoder characteristics to investigate further the data within each region. We used the public dataset NSL-KDD to test the behavior of the proposed mechanism. The experimental results show that when dealing with multi-class attacks, the model's performance was significantly improved, the accuracy and the F1-score are improved by 3.5% and 2.9%, respectively maintaining its lightweight nature.

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.

Multi-cameras system is widely applied in 3D computer vision especially when multiple cameras are distributed on both sides of the measured object. The calibration methods of multi-cameras system are critical to the accuracy of vision measurement and the key is to find an appropriate calibration target. In this paper, a high-precision camera calibration method for multi-cameras system based on transparent glass checkerboard and ray tracing is described, which is used to calibrate multiple cameras distributed on both sides of the glass checkerboard. Firstly, the intrinsic parameters of each camera is obtained by Zhang’s calibration method. Then, multiple cameras capture several images from the front and back of the glass checkerboard with different orientations, and all images contain distinct grid corners. As the cameras on one side are not affected by the refraction of glass checkerboard, extrinsic parameters can be directly calculated. However, the cameras on another side are influenced by the refraction of glass checkerboard, and the direct use of projection model will produce calibration error. A multi-cameras calibration method using refractive projection model and ray tracing is developed to eliminate this error. Furthermore, both synthetic and real data are employed to validate the proposed approach. The experimental results of refractive calibration show that the error of the 3D reconstruction is smaller than 0.2 mm, the relative errors of both rotation and translation are less than 0.014%, and the mean and standard deviation of reprojection error of 4-cameras system are 0.00007 and 0.4543 pixel. The proposed method is flexible, high accurate, and simple to carry out.

Europium (Eu)-doped fluorapatite (FA) nanorods has a similar biocompatibility with hydroxyapatite (HA) in terms, attracted much attention as cell imaging biomaterials due to their luminescent property. Here, we will discuss the new feature of europium doped fluorapatite (Eu-FA) nanorods as anticancer drug carrier. Eu-FA nanorods was prepared using a hydrothermal method. The morphology, crystal structure, fluorescence and composition are investigated. The specific crystal structure, enabling an effective loading of drug molecules. Doxorubicin (DOX), used as an anticancer model drug, was shown to be effectively loaded onto the surface of the nanorods. The DOX release was fairly pH-dependent, occurring more rapidly at pH 5.5 than pH 7.4 was observed. The intracellular penetration of the DOX-loaded Eu-FA nanorods (Eu-FA/DOX) can be imaged in situ due to the self-fluorescence property. Treatment of melanoma A375 cells with Eu-FA/DOX elicited a more effective apoptosis rate than direct DOX treatment. Overall, Eu-FA show great promise for tracking and treating tumor, may potentially useful as a multifunctional carrier system to effectively load and sustainably deliver drugs.

The CuCrSn alloy is promising as high-strength and high-conductivity Cu alloy due to its relative low smelting requirement, while thus far reports on the CuCrSn alloy is still quite lacked. In this study, the microstructure and properties of Cu-0.20Cr-0.25Sn (wt%) specimens prepared under different rolling and aging combinations were comprehensively characterized, in order to reveal the effects of cold rolling and aging on the CuCrSn alloy. The results show that increasing the aging temperature from 400 ºC to 450 ºC can obviously accelerates the precipitation, and the cold rolling before aging significantly increases the initial microhardness and promotes the precipitation. Whereas, the deformation hardening will be eliminated during the aging process, making the microhardness even decrease monotonically when the aging temperature and the rolling ratio before aging are high. Performing the cold rolling after the aging can maximize the effects of precipitation strengthening and deformation strengthening, and its adverse impact on the conductivity is not serious. A tensile strength of 506.5 MPa and a conductivity of 70.33% IACS were obtained with such a treatment, only the elongation decreases. Different strength-conductivity combinations of the CuCrSn alloy can be achieved through appropriate combinations of the aging and post aging cold rolling conditions.

The pathogenicity of grapevine geminivirus A (GGVA), a recently identified DNA virus, to grapevine plants remains largely unclear. Here, we report a new GGVA isolate (named GGVAQN) obtained from grapevine ‘Queen Nina’ plants with severe disease symptoms; the infectious clone of GGVAQN (pXT-GGVAQN) was also constructed. Infection assays indicated that Nicotiana benthamiana plants inoculated with GGVAQN display upward leaf curling and chlorotic mottling symptoms. Direct injection of Agrobacterium tumefaciens cells carrying pXT-GGVAQN into roots of in vitro-grown ‘Red Globe’ grape plantlets by syringe led to systemic infection with GGVAQN, and the plants exhibited chlorotic mottling symptoms on their upper leaves, and downward curling, interveinal yellowing, and leaf-margin necrosis symptoms on their lower leaves. Our results provide insights into the pathogenicity of GGVA, and a simple and efficient inoculation method to deliver infectious viral clones to woody perennial plants.

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.

In high-risk railway construction, personal protective equipment monitoring is critical but challenging due to small and frequently obstructed targets. We propose YOLO-EA, an innovative model that enhances safety measure detection by integrating ECA into its backbone's convolutional layers, improving discernment of minuscule objects like hardhats. YOLO-EA further refines target recognition under occlusion by replacing GIoU with EIoU loss. YOLO-EA's effectiveness was empirically substantiated using a dataset derived from real-world railway construction site surveillance footage. It outperforms YOLOv5, achieving 98.9% precision and 94.7% recall, up 2.5% and 0.5% respectively, while maintaining real-time performance at 70.774 fps. This highly efficient and precise YOLO-EA holds great promise for practical application in intricate construction scenarios, enforcing stringent safety compliance during complex railway construction projects.

Proton exchange membrane water electrolyzers (PEMWEs) for water electrolysis have received tremendous attention due to their immediate response, high proton conductivity, low ohmic losses and gas crossover rate. However, design high activity, economical and long-term durable electrocatalysts in an acidic environment is still the bottleneck to realize the large-scale commercialization of PEMWEs. Iridium-based materials represent one of the most promising classes of oxygen evolution reaction (OER) catalysts due to their intrinsic stability in acid media over ruthenium-based counterparts. However, only a few innovative approaches have been developed to synthesizing iridium-based catalysts (IBCs) in the past decade, mainly due to achieving high activity may deteriorate the stability of IBCs. Accordingly, various engineering strategies of optimizing IBCs have been proposed to address this issue, including doping engineering, morphology engineering, crystal phase engineering and support engineering. Herein, a critical overview focusing on various synthesis and modulation strategies of IBCs is presented, based on an in-depth understanding of the relationship between electronic structures, charge redistribution and activity as well as stability of the electrocatalysts. In addition, the unprecedented achievements in PEMWEs are summarized. The reaction mechanisms and future perspectives are critically discussed to inspire more rational design of IBCs toward practical applications.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.

To promote low-carbon and green travel, this study proposes a tripartite evolutionary game model among new energy vehicle manufacturers, consumers, and government agencies. The game strategy combinations of each party and the stability conditions of the equilibrium point in the evolutionary game system are analyzed, and the validity of the conclusions is verified through simulation results. The study shows a strong correlation between the three parties in the evolutionary game system. From the perspective of car manufacturers, the probability of producing new energy vehicles is positively proportional to the subsidy received from new energy policy, the additional revenue gained from the management of the carbon trading scheme, and the fine received for producing conventional fuel vehicles. However, from the consumer perspective, the likelihood that consumers purchase new energy vehicles is positively related to the utility value to consumers and the coverage of public charging points in the city, and inversely related to the sales price of new energy vehicles. From the government perspective, a combination of direct subsidy policies and indirect support strategies should be considered to stimulate the rapid development of the new energy vehicle industry.

Nowadays, brain signal processing is performed rapidly in various brain-computer interface (BCI) applications. Most researchers focus on developing new methods for the future or improving the basic implemented models to identify the optimum standalone feature set. Our research focuses on four ideas. One of them introduces future communication models, and the others are for improving old models or methods. These are: 1) new communication imagery model instead of speech imager using the mental task: Due to speech imagery is very difficult, and it is impossible to imagine sound for all of the characters in all of the languages. Our research introduces a new mental task model for all languages that call Lip-sync imagery. This model can use for all characters in all languages. This paper implemented two lip-sync for two sounds, characters or letters. 2) New combination Signals: Selecting an inopportune frequency domain can lead to inefficient feature extraction. Therefore, domain selection is so important for processing. This combination of limited frequency ranges proposes a preliminary for creating Fragmentary Continuous frequency. For the first model, two s intervals of 4 Hz as filter banks were examined and tested. The primary purpose is to identify the combination of filter banks with 4Hz (scale of each filter bank) from the 4Hz to 40Hz frequency domain as new combination signals (8Hz) to obtain well and efficient features using increasing distinctive patterns and decreasing similar patterns of brain activities.3) new supplement bond graph classifier for SVM classifier: When SVM linear uses in very noisy, the performance is decreased. But we introduce a new bond graph linear classifier to supplement SVM linear in noisy data. 4) a deep formula recognition model: it converts the data of the first layer into a formula model (formula extraction model). The main goal is to reduce the noise in the subsequent layers for the coefficients of the formulas. The output of the last layer is the coefficients selected by different functions in different layers. Finally, the classifier extracts the root interval of the formulas, and the diagnosis does based on the root interval. For all of the ideas achieved the results of implementing methods. The results are between 55% to 98%. Less result is 55% for the deep detection formula, and the highest result is 98% for new combination signals.

A millimeter-wave microstrip leaky-wave antenna array based on the substrate-integrated waveguide (SIW) and the complementary split-ring resonator (CSRR) is proposed in this paper. The slow wave characteristics of the SIW are enhanced by etching two rows of symmetrical CSRR-type slots on the surface of the SIW layer. The magnetic coupling between the CSRR-SIW layer and equally spaced circular patches array on the upper layer realizes the radiation of electromagnetic energy. In the 23~30 GHz (relative bandwidth 26.4%) operating frequency band, the horizontal polarization radiation beam scanning from -62° to -2° is realized, with a peak gain of 9.6~11.7 dBi, and the cross-polarization ratio is more than 43.8 dB.

Strip steel surface defect recognition research has important research significance in industrial production. Aiming at the problems of defect feature extraction difficulty, slow detection speed and insufficient dataset, YOLOv5 is improved on the basis of YOLOv5, and YOLO-LFPD (Lightweight fine particle detection) model is proposed. By introducing the RepVGG (Re-param VGG) module, the robustness of the model to the surface defect features of strip steel is enhanced, and the expressive ability of the model is improved. FasterNet is used to replace the backbone network, which ensures the accuracy and accelerates the inference speed, making the model more suitable for real-time monitoring. The use of pruning, GA genetic algorithm with OTA loss function method further reduces the model size while better learning the strip steel defect feature information, thus improving the generalisation ability and accuracy of the model. The experimental results show that the introduction of the RepVGG module and the use of FasterNet can well improve the model performance, with a reduction of 48% in the number of parameters, a reduction of 13% in the number of GFLOPs, an inference time of 77% of the original, and an optimal accuracy compared with the network models in recent years. The experimental results on the NEU-DET dataset show that the accuracy of YOLO-LFPD is improved by 3% to 81.2%, which is better than other models, and provides new ideas and references for the lightweight strip steel surface defect detection scenarios and application deployment.

In order to overcome the limitation of the traditional adaptive Unscented Kalman Filtering (UKF) algorithm in noise covariance estimation for statement and measurement, we propose a hybrid adaptive UKF algorithm based on combining Maximum a posteriori (MAP) criterion and Maximum likelihood (ML) criterion, in this paper. First, to prevent the actual noise covariance deviating from the true value which can lead to the state estimation error and arouse the filtering divergence, a real-time covariance matrices estimation algorithm based on hybrid MAP and ML is proposed for obtaining the statement and measurement noises covariance, respectively; and then, a balance equation the two kinds of covariance matrix is structured in this proposed to minimize the statement estimation error. Compared with the UFK based MAP and based ML, the proposed algorithm provides better convergence and stability.

With the rapid development of society and the deteriorating natural environment, there has been an increase in public events. This study aimed to explore how sadness and fear in the context of public emergency influence moral judgments. This research first induced feelings of sadness and fear by videos about public events and music, and then used moral scenarios from the CNI model to assess participants’ moral thinking. In Study 1, participants were divided into sadness group and neutral group, while in Study 2, participants were divided into fear group and neutral group. During the experiment, participants were exposed to different videos related to public events to induce the corresponding emotions, and emotional music was continuously played throughout the entire experiment. Participants were then asked to answer questions regarding moral judgments. The results showed that based on CNI model, sadness induced in the context of public events significantly increased C parameter, without affecting N or I parameter. Fear increased I parameter, without affecting C or I parameter. That is, sadness and fear induced in the context of public events can influence moral judgments. Specifically, sadness increases individuals’ sensitivity to consequence, and fear increases the general preference for inaction in moral judgments.

Sulfate attack is one of the main factors affecting the durability of concrete structures. In recent years, multi-walled carbon nanotubes (MWCNTs) have attracted the attention of scholars for their excellent mechanical properties and durability performance. In this paper, the influence of sulfate attack and dry-wet cycles on the performance of multi-walled carbon nanotube-lithium slag concrete (MWCNTs-LSC) with varied MWCNTs content (0wt%, 0.05wt%, 0.10wt% and 0.15wt%) and varied water-cement ratios (0.35, 0.40 and 0.45) were investigated. Besides, scanning electron microscopy (SEM) and X-ray computed tomography (CT) tests were conducted to analyze the microstructure and pore structure of the concrete. The results show that concrete incorporated with MWCNTs could effectively mitigate sulfate attack The resistance to sulfate attack of concrete is negatively related to the water-cement ratio when the dry-wet cycle is fixed. The MWCNTs-LSC showed the best compressive strength at the water-cement ratio of 0.35 and 0.10 wt% MWCNTs. The SEM test results showed that the MWCNTs both filled the pores and cracks within the specimen and formed bridges between the cracks, enhancing the resistance to sulfate attack. The CT test results also showed that the addition of MWCNTs could reduce the porosity of concrete, refine the pore size and inhibit the generation and development of cracks, thus optimizing the internal structure of concrete and improving its resistance to sulfate attack.

Ferrocenium (Fc+) inherits a number of molecular/electronic properties from the neutral counterparts’ ferrocene (Fc) including the high symmetry. Both Fc+ and Fc prefer the eclipsed structure (D5h) over the staggered structure (D5d) by an energy of 0.36 kcal·mol-1. The present study using the recently developed excess orbital energy spectrum (EOES) shows that the open shell Fc+ cation exhibits similar conformer dependent configurational changes to the neutral Fc conformer pair. A further energy decomposition analysis (EDA) discloses that the reasons for the preferred structures are different between Fc+ and Fc. The dominant differentiating energy between the Fc+ conformers is the electrostatic energy (EEstat), whereas in neutral Fc, it is the quantum mechanical Pauli repulsive energy (EPauli). Within the D5h conformer of Fc+, the EOES reveals that the -electrons of Fc+ experience more substantial conformer dependent energy changes than the -electrons (assumed the hole is in a β orbital).

In the paper, the authors survey integral representations (including the Lévy--Khintchine representations) and applications of some bivariate means (including the logarithmic mean, the identric mean, Stolarsky's mean, the harmonic mean, the (weighted) geometric means and their reciprocals, and the Toader--Qi mean) and the multivariate (weighted) geometric means and their reciprocals, derive integral representations of bivariate complex geometric mean and its reciprocal, and apply these newly-derived integral representations to establish integral representations of Heronian mean of power 2 and its reciprocal.

In this perspective article, we ponder topographical enhancements of artificial neural networks. In our recent paper in JMLR, we proved a quasi-equivalence between the network width and depth and also discussed the power of intra-links, which can be viewed as network height. In 1982, Hopfield published a network to model human associative memory, which contains many loops for dynamic evolution toward fixed points. Based on noising-denoising loops, diffusion models are recently developed to enable Bayesian modeling and inference with big data. Furthermore, we envision development of multi-AI-agent systems through “netware” engineering as a quantum leap of software engineering for emergent behaviors and autonomous AI at individual and population levels. We believe that the novel use of links and loops in space and time via multi-scale coupling would catalyze the next-generation neural networks.

A delayed crosslinked polymer gel was developed for indepth water control in mature oilfields. The thermal gelation behavior of nonionic polyacrylamide (NPAM) and PEI was investigated, and sodium citrate (NaCit) was selected as a new retarder to prolong the gelation time. The gelation performance of NPAM/PEI gel system can be adjusted by the variation of NPAM or PEI concentration, and a quadratic model was developed by statistical analysis, which predict the gelation time of NPAM/PEI gel system. The obtained model showed high significance and good reliability as suggested by the F-ratio of 175.16 and high adjusted R-square value (0.9732). The decrease of the initial pH value of gelling solution leads to the weaker gel viscosity and longer gelation time due to the protonation of amine groups on the PEI chains. Increasing temperature resulted in higher gel viscosity but shorter gelation time. The addition of NaCit showed a good delayed gelation effect on the NPAM/PEI gel system, and the gel system in the presence of NaCit exhibited a good compatibility with injected and formation water. A dense three-dimensional structure was observed in matured NPAM/PEI/NaCit gel, and it could keep stable below 150℃. The gel system could effectively reduce the permeability (>95%) and restricted the flow of water after matured in natural cores.

Pathogens, especially invasive species, have caused significant global ecological, economic, and social losses in forests. Plant disease research has traditionally focused on direct interactions between plants and pathogens in an appropriate environment. However, recent research indicates that the microbiome can interact with the plant host and pathogens to modulate plant resistance or pathogen pathogenicity, thereby altering the outcome of plant–pathogen interactions. Thus, this presents new opportunities for studying the microbial management of forest diseases. Compared to parallel studies on human and crop microbiomes, research into the forest tree microbiome and its critical role in forest disease progression has lagged. The rapid development of microbiome sequencing and analysis technologies has resulted in the rapid accumulation of a large body of evidence regarding the association between forest microbiomes and diseases. These data will aid the development of innovative, effective, and environmentally sustainable methods for the microbial management of forest diseases. Herein, we summarize the most recent findings on the dynamic structure and composition of forest tree microbiomes in belowground and aboveground plant tissues (i.e., rhizosphere, endosphere, and phyllosphere), as well as their pleiotropic impact on plant immunity and pathogen pathogenicity, highlighting representative examples of biological control agents used to modulate relevant tree microbiomes. Lastly, we discuss the potential application of forest tree microbiomes in disease control as well as their future prospects and challenges.

The high-speed railway operation environment monitoring network system has a wide variety of sensors and inconsistent business requirements, which have certain requirements for data transmission delay and network life. Most of the previous research focuses only on prolonging network lifetime or reducing data transmission delays when designing or optimizing routing protocols, without co-designing the two. In addition, due to the harsh operating environment of high-speed railways, when the network changes dynamically, the traditional routing algorithm will generate unnecessary redesigns and lead to high overhead. Based on the actual needs of high-speed railway operation environment monitoring, this paper proposes a novel Double Q-values adaptive model combined with the existing reinforcement learning method, which considers the energy balance of the network and real-time data transmission, and constructs energy saving and delay. The two-dimensional reward avoids the extra overhead of maintaining a global routing table while capturing network dynamics. In addition, the adaptive weight coefficient is used to ensure the adaptability of the model to each business of the high-speed railway operation environment monitoring system. Finally, simulations and performance evaluations are carried out and compared with previous studies.Finally, simulations and performance evaluations are carried out and compared with previous studies. The results show that the proposed routing algorithm successfully extends the network’s life cycle and gets good real-time data performance. It also saves energy and has fewer delays than the other three routing protocols in different situations.

In the paper, the authors survey integral representations of the Catalan numbers and the Catalan–Qi function, discuss equivalent relations between these integral representations, supply alternative and new proofs of several integral representations, collect applications of some integral representations, and present sums of several power series whose coefficients involve the Catalan numbers.

This paper presents a study to accurately evaluate defects in reinforced concrete decks using ultrasonic pulse-echo signals. Two validation specimens were designed and tested where reinforced concrete deck slab specimens included planned voids and defects. A commercial Ultrasonic Pulse-Echo (UPE) device obtains 2-D images of the void/defect locations of the reinforced concrete deck. The UPE is based on the ultrasonic shear-wave test method using dry-point-contact transmitting and receiving transducers and employing a synthetic aperture focusing technique. The authors analyzed the recorded UPE A-scan data to enhance the accuracy of estimating the defects' locations using the synthetic aperture focusing technique imaging method. The analysis benefitted from an advanced denoising approach and defect echo peak extraction based on empirical modal decomposition, Hurst exponent characterization, and Hilbert envelope estimation. While output from conventional UPC devices can only provide qualitative results, the new method provides quantitative information on the anomalies inside the reinforced concrete deck. The developed approach accurately assists with assessing the location and depth of the voids/defects in the reinforced concrete deck slabs.

The undoped and tungsten (W)-doped vanadium dioxide (VO₂) thin films were prepared by electron beam evaporation associated with ion-beam assisted deposition (IAD). The influence of different W-doped contents (3%-5%) on the optical, electrical, structural, and thermos-mechanical properties of VO₂ thin films was investigated experimentally. Spectral transmittance results showed that with the increase of W-doped contents, the transmittance in the visible light range (400-750 nm) decreases from 60.2% to 53.9%, and the transmittance in the infrared wavelength range (2.5 μm to 5.5 μm) drops from 55.8% to 15.4%. As the W-doped content increases, the residual stress in the VO₂ thin film decreases from -0.276 GPa to -0.238 GPa, but the surface roughness increases. For temperature-dependent spectroscopic measurements, heating the VO2 thin films from 30°C to 100°C showed the most significant change in transmittance for the 5% W-doped VO₂ thin film. When the heating temperature exceeds 55°C, the optical transmittance drops significantly, and the visible light transmittance drops by about 11%. Finally, X-ray Diffraction (XRD) and scanning electron microscope (SEM) were used to evaluate the microstructure characteristics of VO₂ thin films.

A mild, efficient, and photocatalytic synthesis of 6-phosphorylated phenanthridines via tandem radical addition/cyclization/aromatization of 2-isocyanobiphenyls with diarylphosphine oxides is reported. The method features operational simplicity, metal-free conditions, using low-cost rose Bengal as catalyst and sustainable air as terminal oxidant at room temperature, and providing the desired products in moderate to good yields.

This paper is concerned with developing an efficient numerical algorithm for fast implementation of the sparse grid method for computing the $d$-dimensional integral of a given function. The new algorithm, called the MDI-SG ({\em multilevel dimension iteration sparse grid}) method, implements the sparse grid method based on a dimension iteration/reduction procedure, it does not need to store the integration points, nor does it compute the function values independently at each integration point, instead, it reuses the computation for function evaluations as much as possible by performing the function evaluations at all integration points in a cluster and iteratively along coordinate directions. It is shown numerically that the computational complexity (in terms of CPU time) of the proposed MDI-SG method is of polynomial order $O(Nd^3 )$ or better, compared to the exponential order $O(N(\log N)^{d-1})$ for the standard sparse grid method, where $N$ denotes the maximum number of integration points in each coordinate direction. As a result, the proposed MDI-SG method effectively circumvents the curse of dimensionality suffered by the standard sparse grid method for high-dimensional numerical integration.

In the paper, the authors find two explicit formulas and recover a recursive formula for the generalized Motzkin numbers. Consequently, the authors deduce two explicit formulas and a recursive formula for the Motzkin numbers, the Catalan numbers, and the restricted hexagonal numbers respectively.

The era of Internet of Things (IoT) has begun to evolve and with this the devices around us are getting more and more connected. Vehicular Ad-hoc NETworks (VANETs) is one of the applications of IoT. VANET allow vehicles within these networks to communicate effectively with each another. VANETs can provide an extensive range of applications that support and enhance passenger safety and comfort. It is important that VANETs are applied within a safe and reliable network topology; however, the challenging nature of reaching reliable and trustworthy agreement in such distributed systems is one of the most important issues in designing a fault-tolerant system. Therefore, protocols are required so that systems can still be correctly executed, reaching agreement on the same values in a distributed system, even if certain components in the system fail. In this study, the agreement problem is revisited in a VANET with multiple damages. The proposed protocol allows all fault-free nodes (vehicles) to reach agreement with minimal rounds of message exchanges, and tolerates the maximal number of allowable faulty components in the VANET.

Infectious disease like COVID-19 will have a great impact on human society. An effective prevention and control measure is to quickly isolate the source and reduce the population’s exposure to the infection. Therefore, it is very important to quickly trace and identify the virus carrier and its close contacts in order to effectively suppressing transmission. This article provides a method, system and storage medium for locating the source of infection based on big data. We adopt the method of combining Bluetooth and GPS positioning to track the action trajectory of each individual. The method stores the close contact and location information locally and update on the cloud platform for an appropriate period of time. The method and system can provide personal risk levels and regional risk levels through related algorithms. When entering a high-risk area or if the personal risk level within the Bluetooth interconnection range is greater than the set threshold, an alarm function is provided. We can also generate reports on the spread of the epidemic over a period of time. As a result, the system can quickly locate the source of infection and close contacts, providing a reference for quick and highly efficient epidemic prevention and control.

In this paper, we presented a method of retrieving sea surface wind speed from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide mode, which have been extensively acquired over the Arctic for sea ice monitoring. In contrast to the conventional algorithm, i.e., using a geophysical model function (GMF) to retrieve sea surface wind by spaceborne SAR, we introduced an alternative method based on physical model guided neural network. Parameters of SAR normalized radar cross section, incidence angle, and wind direction are used as the inputs of the backward propagation (BP) neural network, and the output is the sea surface wind speed. The network is developed based on more than 11,000 HH-polarized EW images acquired in the marginal ice zone (MIZ) of the Arctic and their collocations with scatterometer measurements. Verification of the neural network based on the testing dataset yields a bias of 0.23 m/s and a root mean square error (RMSE) of 1.25 m/s compared to the scatterometer wind speed. Further comparison of the SAR retrieved sea surface wind speed with independent buoy measurements shows a bias and RMSE of 0.12 m/s and 1.42 m/s, respectively. We also analyzed the uncertainty of retrieval when the wind direction data of a reanalysis model are used as inputs to the neural network. By combining the detected sea ice cover information based on the EW data, one can expect to derive simultaneously sea ice and marine-meteorological parameters by spaceborne SAR in a high spatial resolution in the Arctic.

Sparse reward long horizon task is a major challenge for deep reinforcement learning algorithm. One of the key barriers is data-inefficiency. Even in the simulation environment, it usually takes weeks to training the agent. In this study, a data-efficiency training framework is proposed, where a curriculum learning is design for the agent in the simulation scenario. Different distributions of the initial state are set for the agent to get more informative reward during the whole training process. A fine-tuning of the parameters in the output layer of the neural network for value function is conduct to bridge the gap between sim-to-real. An experiment of UAV maneuver control is conducted in the proposed training framework to verify the method more efficient. We demonstrate that data-efficiency is different for the same data in different training stages.

Recent years have witnessed a boom of fashion bloggings sharing information about clothing and cosmetics on diverse social media platforms. Constant exposure to fashion-related digital information heavily impacts the cognition and behaviour of Chinese youth. Compared to the substantial studies on the impact of social media, scarce research has been conducted on how youth’s cognitive processing of fashion-related digital information interacts with motivational factors to determine the subsequent behaviour. This study made an initial attempt to address this issue by exploring the successive associations between clothing motivation (amotivation, controlled, and autonomous motivation), online critical thinking (for information credibility, objectivity, and relevance), and the subsequent behavioural intentions. A total of 1997 Chinese youths with diverse educational backgrounds voluntarily participated in the study. Results confirmed the direct links between clothing motivation and behavioural intentions, but these links were mediated by different online critical thinking skills. This study provides new insights for both practitioners and scholars in the fields of education, psychology, social media, and marketing.

In order to study the effect of explosion load on the flexural performance of RC beams, two RC beams of the same size were carried out explosion test and bearing capacity test successively. Piezoelectric intelligent aggregate was embedded in the beams in advance to monitor the damage state of the beams, which realized the whole process of damage monitoring of the concrete inside the structure from explosion damage to compression damage.Based on wavelet packet analysis and residual bearing capacity damage evaluation criteria, the beam damage was quantitatively evaluated, and the bending bearing capacity of RC beam after explosion damage was compared and analyzed.The results show that the change of concrete strain gauge signal, the phenomenon of crack development and the increase of internal crack development index indicate that the RC beam is damaged by explosion load.After explosion damage, the damage index of the beam increases and the residual bending capacity decreases, which proves that the piezoelectric intelligent aggregate sensor can effectively monitor the bending capacity change of the component after explosion damage.