The Oil reservoirs with low permeability account for a large proportion in the world, they surpass half of the total number of the oil reservoirs in China, it is important to exploit this kind of reservoir effectively. The exploitive results of these reservoir using normal water flooding methods are very poor because of the thine pores, bad connections and so on. However, these problems can be well solved by applying gas injection techniques. In this paper, the effects on the recovery effects exerted by the injection parameters such as the oxygen content in the injected air, the injection speed etc. were studied by several experiments using independently designed equipments aiming at one low permeable sandstone reservoir in China. Several conclusions were made from the experiment results: the best recovery effects can be achieved by injecting the oxygen reduced air with 8% of oxygen at the speed of 0.3ml/min (7.41×10-4PV/min) using the methods of injecting the air slugs and the foaming agent slugs alternately; the best oxidation time of the oil was 168 hours and the final oil recovery could be enhanced by 26.5% in this method. It was also showed by the experiments that the larger the permeability ratio is, the larger the recovery factor will be increased. At last, several equations about relationships between the EOR and the air injection parameters were revealed according to the results of the experiments which may offer some references to the LTO process mechanisms study in the similar oil reservoir.

The emergence of the COVID-19 pandemic triggered a swift transition to online learning, remote work and an increased demand for proficiency in telehealth and digital health among health professions students as they prepare for their future careers. In responding to the question: “What are the essential constructs and the validity of a digital competence assessment scale for health professions students", this study sought to develop and validate a digital competence assessment scale (DigiCAS-HPS). The present study followed a mixed-methods approach: qualitative methods (phase 1) for generating and refining items in the scale through literature review on digital frameworks, pilot testing and expert review, and quantitative methods (phase 2) including exploratory and confirmatory factor analysis for its validation. The study revealed a two-factor structure, encompassing Digital Communication, Collaboration, and Online Security (Factor 1) and Digital Content Development and Software Mastery (Factor 2), demonstrating strong construct validity and reliability. Regarding differences in digital competence across groups, older and soon-to-graduate students exhibited higher proficiency in digital content development and software mastery. The two critical facets of DigiCAS-HPS scale could tailor training programs and ensure that health professions students are well-prepared for the evolving telehealthcare landscape. This promotes the growth and accessibility of telehealth, which is increasingly vital in the post-COVID-19 new normal era.

This report gives a comprehensive summary of two problems about graph convolutional networks (GCNs): over-smoothing and heterophily challenges, and outlines future directions to explore.

In this work, we study the dynamical behaviors of the electromagnetic fields and material responses in the hyperbolic metamaterial consisting of periodically arranged metallic and dielectric layers. The thickness of each unit cell is assumed to be much smaller than the wavelength of the electromagnetic waves, so the effective medium concept can be applied. When electromagnetic (EM) fields are present, the responses of the medium in the directions parallel to and perpendicular to the layers are like that of Drude and Lorentz media, respectively. We derive the energy density of the EM fields and the power loss in the effective medium based on Poynting theorem and the dynamical equations of the polarization field. We also show that the Lagrangian density of the system can be constructed. The Euler-Lagrangian equations yield the correct dynamical equations of the electromagnetic fields and the polarization field in the medium. The canonical momentum conjugates to every dynamical field can be derived from the Lagrangian density via differentiation or variation with respect to that field. We apply Legendre transformation to this system, and find that the resultant Hamitonian density is identical to the energy density, up to an irrelevant divergence term.

Based on the panel data from 31 provinces in China from 2012 to 2020, this paper uses the TOPSIS method to measure the development level of the digital economy and environmental quality. Then, a coupled coordination degree model is used to analyse the relationship between the digital economy and environmental quality. Kernel density estimation and the Dagum Gini coefficient method were used to reveal the dynamic evolution trend of the coupled coordination level between the digital economy and environmental quality and the regional trends of coupled indicators. Finally, the dynamics of the above relations were explored with an econometric model. The results show that the coupled coordination degree of the digital economy and environmental quality displays obvious regional heterogeneity, and the national coordination level exhibits dynamic convergence characteristics. The heterogeneity of the coupled coordination degree of the two systems in the eastern region has decreased year by year, except in the western region, the central region and the northeast region, where an upwards trend is observed. Overall, the Gini coefficient of the coupled coordination degree between the digital economy and environmental quality displays a certain downwards trend. The level of economic development, technological innovation level, industrial structure, degree of opening to the outside world, degree of environmental regulation, marketization index and financial development level have a positive influence on the coupled coordination of the digital economy and environmental quality. Environmental regulation is the core driver of the coupling of the two systems, and the economic development level, technological innovation level, industrial structure and degree of openness are the other major drivers. Therefore, in the processes of digital economy transformation and environmental governance, all regions should focus on balanced development between regions to optimize the overall efficiency of the two systems, consider external influence, enhance internal capabilities, and promote the coordination and co-development of the two systems.

In this paper, we study the well-posedness and asymptotic behaviour of solutions to a laminated beam in thermoelasticity of type III. We first give the well-posedness of the system by using the semigroup method. Then, we show that the system is exponentially stable under the assumption of equal wave speeds. Furthermore, it is proved that the system is lack of exponential stability for case of nonequal wave speeds. In this regard, a polynomial stability result is proved.

The field of controlled polymerization is growing and evolving at unprecedented rates, facilitating polymer scientists to engineer the structure and property of polymer materials for a variety of applications. However, the lack of degradability, particularly in vinyl polymers, is a general concern for not only environmental sustainability but also biomedical applications. In recent years, there has been a significant effort to develop reversible polymerization approaches in those well-established controlled polymerization systems. Reversible polymerization typically involves two steps including (i) forward polymerization which converts small monomers into macromolecules, and (ii) depolymerization capable of regenerating original monomers. Furthermore, recycled monomers can be repolymerized into new polymers. In this perspective, we highlight recent developments of reversible polymerization in those controlled polymerization systems and offer insight into the promise and utility of reversible polymerization systems. More importantly, the current challenges and future directions to solve those problems are discussed. We hope this perspective can serve as an “initiator” to promote continuing innovations in this fairly new area.

Pedestrian object detection plays a significant role in intelligent systems such as intelligent traffic and monitoring. Traditional machine learning methods on pedestrian detection have shown various drawbacks, e.g., low accuracy, slow speed, etc. The Convolutional Neural Network (CNN) based object detection algorithms have demonstrated remarkable advantages in the field of pedestrian detection. However, the mainstream CNNs still face the problems of slow speed and low detection accuracy, especially on small and occluded targets from aerial perspective. In this paper, we propose Multi-Scale Attention YOLO (MSA-YOLO) detection algorithm to address the above issues. MSA-YOLO includes a Squeeze, Excitation and Cross Stage Partial (SECSP) channel attention module for CNNs to extract richer pedestrian features with a small number of extra parameters. It also contains a multi-scale prediction module to capture the information among different pedestrian scales, which can recognize the small objects with higher accuracy and significantly reduce the missed detection. To sufficiently evaluate our proposed model, we manually collect and annotate a new benchmark dataset, Aerial Pedestrian Dataset, which has much more sample annotations, features, scenes and image view angles than the existing benchmark datasets. In addition, the images in our dataset have higher resolution than most of benchmark pedestrian detection datasets, which can provide more detailed features of pedestrians and thus improve the model performance. When tested on Aerial Pedestrian Dataset, our proposed MSA-YOLO algorithm significantly outperforms the most used baseline models with almost the same model size. This shows the efficiency of our proposed model. (The code and new dataset will be released to the public later.)

The early 2-factor (E2F) family of transcription factors, including E2F1-8, plays a critical role in apoptosis, metabolism, proliferation, and angiogenesis within glioblastoma (GBM). However, the specific functions of E2F transcription factors (E2Fs) and their impact on the malignancy of Bevacizumab (BVZ)-responsive GBM subtypes remain unclear. This study used data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to explore the impact of eight E2F family members on the clinical characteristics of BVZ-responsive GBM subtypes and possible mechanisms of recurrence after BVZ treatment.To predict the possibility of GBM patient survival and progression, we classified and comparedthe expression of E2Fs according to BVZ-responsive subtypes and employed a machine learning method-TreeBagger, one random forest algorithm. Multiple bioinformatics analyses have shown that the significant increased E2F8 post BVZ treatment may enhance the function of angiogenesis and stem cell proliferation, indicating one of the mechanisms of GBM recurrence after treatment. In addition, BVZ treatment in unresponsive GBM patients may potentially worsen disease progression. Our findings suggest that E2F family members play important roles in GBM malignancy and BVZ treatment response, highlighting their potential as prognostic biomarkers and therapeutic targets and recommending precise BVZ treatment for GBM patients.

Agricultural activities are the second largest source of greenhouse gas emissions, and carbon emissions from agricultural land use (CEALU) have become a hot issue across the world. However, few scholars explored the impact of agricultural land policies on carbon emissions, such as the High-standard farmland construction(HSFC) in China.Thus，by relying on provincial panel data for China for the period 2005-2017, the effect of the high-standard basic farmland construction policy on carbon emissions from agricultural land use per unit area and its regional differences were quantitatively analyzed using the difference-in-difference (DID) model. The results showed that: 1) China's CEALU per unit area presented a fluctuating upward trend during the period 2005-2017, from 392.58 kg/ha to 457.72 kg/ha, with an average annual growth rate of 1.31%. 2) The high-standard farmland construction(HSFC) policy produced a significant carbon emission reduction effect in agricultural land use, and reduced the CEALU per unit area by 10.80% on average. With the promotion of this policy, its carbon emission reduction effect in agricultural land use presented an overall increasing trend. 3) The carbon emission reduction effect of the high-standard farmland construction policy in agricultural land use was significant in central China, but non-significant in eastern China and western China.

This study investigates lost foam shell casting coatings, focusing on the effects of mixing time and standing time on the viscosity and rheological properties of the coatings. The results show that the viscosity of the coating decreases with increasing mixing time and eventually stabilizes. Conversely, the viscosity initially increases and then decreases with prolonged standing time. To ensure optimal coating performance, this study determined that the mixing time should be no less than 50 minutes, and the standing time should not exceed 40 hours.

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.

This paper studies the flexural behavior of the bridge deck link slabs made with the Polyvinyl Alcohol-Strain Hardening Cementitious Composites (PVA-SHCC). Tensile and flexural properties of the PVA-SHCC with four volume fractions, i.e., 0%, 1%, 1.5%, and 2%, were evaluated first. Next, using the similarity theory, composite models with a geometric similarity ratio of 1: 5 were designed to represent the bridge deck with the link slabs. The models considered three materials for link slabs, including concrete, cement mortar and self-compacting PVA-SHCC; and two different curing ages at 7 and 56 days. Bending tests were performed to investigate the flexural behavior of the models. Based on the fractal theory, cracking characteristics of the models with different types of link slabs were compared, and the relationship between Df and the flexural behavior of the models were studied. Numerical models were built to correlate with the results from the bending tests. It is illustrated that the flexural behavior of the self-compacting PVA-SHCC link slab is better than that of concrete and cement mortar link slabs, where the crack initiation and propagation can be postponed.

The part transportation efficiency is a main factor of aircraft sortie generation rate. Part transportation is used to transport spare part from base to carrier. Transportation strategy depends on both demand on carrier and inventory in transportation base. The transportation time and stochastic demand will induce fluctuations of cost and inventory. Thus, a Takagi-Sugeno fuzzy system of dynamic part transportation is established considering transportation time and stochastic demand. And a novel Takagi-Sugeno fuzzy robust control is designed for dynamic part transportation, which will keep transportation cost and part inventory stable. First of all, a fuzzy model with stochastic demand and transportation time is proposed. Then, a novel robust control with cross rule groups is conducted according to production and transportation strategy, which will reduce fluctuations induced by strategies switch. Moreover, robust stability is guaranteed and part can be supplied in time under a low cost. Finally, simulation illustrates usefulness and quickness of the novel Takagi-Sugeno fuzzy robust control. Besides, the proposed method will be useful in other transportation electrification systems with delay time and uncertainty.

The moon always use the same side to face toward the earth, but there is a dead angle in the mainstream theory of explaining this phenomenon. That is, it cannot explain why the moon doesn't rotate around the axis which is a straight line to connect the mass centers of the earth and moon. Because the numerous meteorite impact craters on the lunar surface indicate that the moon is completely possible to obtain external momentums and rotate around this axis. This paper proposes a plain explanation, that is, the universal gravitation between the earth and moon as well as the earth's magnetic field have formed a trinity restraint mechanism on the moon. According to this explanation, the moon's rotation can be locked, and the mechanism of lunar libration has been revealed out, which can also confirm mutually with the natural phenomenon that the moon has sought a balance in the swing. In addition, with the help of all kinds of detection data from the Apollo moon landings and other circumlunar spacecraft, as well as the studies and analysis of lunar soil samples, the conclusion is that as far as a whole for the moon, it belongs to paramagnetic substances, and its relative permeability is between 1.008 and 1.03. Although the magnetic flux density of the earth on the lunar orbit has been dropped below 0.0008125 nT or lower due to the impact of the solar wind, but it can be used as a reason to lock the moon without rotating around the axis which is a straight line to connect the mass centers of the earth and moon. If another main reason to cause the existence of this fact cannot be found, even if the magnetic flux density of the geomagnetism in lunar orbit is very small, it also should not be artificially ignored. In this regard, we can artificially change the intensity of the earth's magnetic field, and carefully observe the lunar libration and in the distance between the earth and the moon, to verify the arguments in this paper.

Drones have been used in a variety of scenarios such as atmospheric monitoring, fire rescue, agricultural irrigation, etc., in which accurate environmental perception is of crucial importance for both decision-making and control. Among the drone sensors, the RGB camera is indispensable for capturing rich visual information for vehicle navigation but encounters a grand challenge in high-dynamic-range scenes that occur frequently in real applications. Specifically, the recorded frames suffer from under-exposure and over-exposure simultaneously and degenerate the successive vision tasks. To solve the problem, we take object tracking as an example and leverage the superior response of event cameras over a large intensity range to propose an event-assisted object tracking algorithm that can achieve reliable tracking under large intensity variations. Specifically, we propose to pursue feature matching from dense event signals, and based on which to (i) design a UNet-based image enhancement algorithm to balance the RGB intensity with the help of neighboring frames in the time domain, and then (ii) construct a dual-input tracking model to track the moving objects from intensity balanced RGB video and event sequence. The proposed approach is comprehensively validated in both simulation and real experiments.

In this paper, a range-gated lidar system utilizing an LN crystal as the electro-optical switch and a SCMOS (Scientific Complementary Metal Oxide Semiconductor) imaging device is designed. To achieve range-gated, we utilize two polarizers and a LN (LiNbO3) crystal to form an electro-optical switch. The optical switch is realized by applying a pulse voltage at both ends of the crystal due to the crystal's conoscopic interference effect and electro-optical effect. The advantage of this system is that low-bandwidth detectors such as CMOS and CCD (Charge-coupled Device) can be used to replace conventional high-bandwidth detectors such as ICCD (Intensified Charge Coupled Device), and time it has better imaging performance under specific conditions at the same. However, after using an electro-optical crystal as an optical switch, a new inhomogeneity error will be introduced due to the conscopic interference effect of the electro-optical crystal, resulting in range error of the lidar system. To reduce the influence of inhomogeneity error on the system, this paper analyzes the sources of inhomogeneity error caused by the electro-optical crystal and gives the crystal inhomo-geneity mathematical expression. A compensation method is proposed based on the above inho-mogeneity mathematical expression. An experimental lidar system is constructed in this paper to verify the validity of the compensation method. The experimental results of the range-gated lidar system show that in a specific field of view (2.6mrad), the lidar system has a good imaging per-formance, its ranging standard deviation is 3.86cm and further decreased to 2.86cm after com-pensation, which verifies the accuracy of the compensation method.

Poverty alleviation is a hallmark of post-revolution Chinese policymaking. Since 1978, the Communist Party of China (CPC) has implemented successive waves of poverty alleviation policies whose effects have become the focus of an ever-increasing body of academic literature. This paper reviews this diverse but limited literature that evaluates the impact of the CPC’s poverty reduction programs through four major channels, namely fiscal investment programs, social safety nets, rural governance on the village-, county- and provincial level, and the relocation of rural populations from destitute regions. This paper aims to synthesize results and evaluate whether and how the abovementioned poverty alleviation programs have had distinct positive or negative impacts on regional development outcomes. Furthermore, I highlight contradictions in empirical findings to motivate the discussion about contextual importance when designing and implementing future poverty alleviation programs. Finally, I suggest that an exhaustive and critical appraisal of the empirical strategies used in this literature would further the development and application of more accurate and informative methodologies.

Automobile industry is recognized as one of the most influential sectors shaping the global economies, societies, and individual lifestyles. Therefore, fierce competitions among different companies are continuously undergoing, and innovations deserve special attention to improve the competitiveness. In the past several years, additive manufacturing (AM) is emerging as an innovative technology in the applications of automobile industry with significant advantages over traditional techniques. As a result, increasing attention has been attracted to combining AM technology with the development of automobile industry. Currently, many automobile players are optimizing their industrial layout by incorporating with innovative AM techniques, and meanwhile, many research progresses have been achieved to meet the market demand. This article aims at presenting a timely review to conclude the recent advances in the application of AM techniques in automobile industry, focusing on the available AM techniques, printable materials, and the industry applications. The future research opportunities and challenges are also outlooked. Hopefully, this work can be useful to the related researchers, as well as the game players in the industry of this field.

Drying grains in a fixed bed can be an interesting alternative to prevent undesirable breaks and fractures of solids due to mechanical shocks, something that cannot be avoided when drying in moving beds. A disadvantage of fixed bed drying is that there can be significant gradients in the critical variables if the layer of particulate solids is thick. This work shows that periodically changing the direction of drying air flow is an effective technique to reduce differences within the bed and allow better uniformity of the product. Drying in a thick layer of 10 cm of sorghum grains was carried out at 50 and 60oC for air flow velocity of 2.1 m.s-1. The results showed that despite increasing the total drying time, the technique actually allowed a significant decrease in the temperature gradients and moisture content of the sorghum grains. The change in direction of the air flow caused the drying front to be displaced, sometimes towards the top, sometimes towards the base of the bed. At 50oC, drying lasted a total of 240 min, bringing the sorghum grains to an average overall moisture content of 0.13 on a dry basis.

We designed a bidirectional reflection distribution function (BRDF) measurement system to research the characteristics of space target materials. This system measures the BRDF data of an aluminum plate and gold foil. A hybrid biogeography-based optimization-firefly (BBO-Firefly) algorithm was proposed to optimize the five-parameter BRDF model. To check the performance of the hybrid BBO-Firefly algorithm, we set up a contrast experiment to optimize the BRDF parameters using the BBO algorithm, Firefly algorithm, and hybrid BBO-Firefly algorithm. The experimental results prove that the hybrid BBO-Firefly algorithm surpasses the BBO algorithm and Firefly algorithm in convergence speed and precision in the same situation and has a better optimization effect. Finally, we substitute the optimization results of the hybrid BPO-Firefly algorithm into the BRDF model to analyze the reflection characteristics of an aluminum plate and gold foil.

Co-inhibitory receptors (Co-IRs) are essential in controlling the progression of immunopathology in rheumatoid arthritis (RA) by limiting T cell activation. The objective of this investigation was to determine the phenotypic expression of Co-IRs T cells and to assess the levels of serum soluble PD-1, PDL-2, and Tim3 in Taiwanese RA patients. Co-IRs T cells were immunophenotyped employing multicolor flow cytometry, and ELISA was utilized for measuring soluble PD-1, PDL-2, and Tim3. Correlations have been detected across the percentage of T cells expressing Co-IRs (MFI) and different indicators in the blood, including ESR, high sensitivity CRP (hsCRP), 28 joint disease activity scores (DAS28), and soluble PD-1/PDL-2/Tim3. In RA patients, we recognized elevated levels of PD-1 (CD279), CTLA-4, and TIGIT on CD4+ T cells; TIGIT, HLA-DR, TIM3, and LAG3 on CD8+ T cells; and CD8+CD279+TIM3+, CD8+HLA-DR+CD38+ T cells. The following tests proved to be correlated with hsCRP: CD4/CD279 MFI, CD4/CD279%, CD4/TIM3%, CD8/TIM3%, CD8/TIM3 MFI, CD8/LAG3%, and CD8+HLA-DR+CD38+%. CD8/LAG3 and CD8/TIM3 MFIs are linked to ESR. DAS28-ESR and DAS28-CRP exhibited relationships with CD4/CD127 MFI, CD8/CD279%, and CD8/CD127 MFI, respectively. CD4+CD279+TIM3+% was correlated with DAS28-ESR (p=0.0084, N=46), DAS28-CRP (p=0.007, N=47), and hsCRP (p=0.002, N=56), respectively. In the serum of patients with RA, levels of soluble PD-1, PDL-2, and Tim3 were extremely elevated. CD4+ TIM3+% (p=0.0089, N=46) and CD8+ TIM3+% (p=0.0305, N=46) were correlated with sTIM3 levels; sPD1 levels were correlated with CD4+CD279+% (p<0.0001, N=31) and CD3+CD279+% (p=0.0084, N=30). Co-IRs expressions on CD4+ and CD8+ T cells, as well as soluble PD-1, PDL-2, and Tim3 levels, could function as indicators of disease activity and potentially play crucial roles in the pathogenesis of RA, in accordance to our findings.

We focus on macromolecules which are modelled as sequentially growing dual scale-free networks. The dual networks are built by replacing star-like units of the primal treelike scale-free networks through rings, which are then transformed in a small-world manner up to the complete graphs. In this respect, the parameter γ describing the degree distribution in the primal treelike scale-free networks regulates the size of the dual units. The transition towards the networks of complete graphs is controlled by the probability p of adding link between non-neighbouring nodes of the same initial ring. The relaxation dynamics of the polymer networks is studied in the framework of generalized Gaussian structures by using the full eigenvalue spectrum of the Laplacian matrix. The dynamical quantities on which we focus here are the averaged monomer displacement and the mechanical relaxation moduli. For several intermediate values of the parameter’s set (γ, p) we encounter for these dynamical properties regions of constant in-between slope.

In addition to their importance in statistical thermodynamics, probabilistic entropy measurements are crucial for understanding and analyzing complex systems, with diverse applications in time series and one-dimensional profiles. However, extending these methods to two- and three-dimensional data still requires further development. In this study, we present a new method to classify spatiotemporal processes based on entropy measurements. To test and validate the method, we selected four classes of similar processes related to the evolution of random patterns: dynamic colored noises ((i)~white and (ii)~red); (iii)~weak turbulence from reaction-diffusion; (iv)~hydrodynamic fully developed turbulence, and (v)~plasma turbulence from MHD. Considering seven possible ways to measure entropy from a matrix, we present the method as a parameter space composed of the two best separating measures of the five selected classes. The results highlight better combined performance of Shannon Permutation Entropy ($S^p_H$) and a new approach based on Tsallis Spectral Permutation Entropy ($S^s_q$). Notably, our observations reveal the segregation of reaction terms in this $S^p_H \times S^s_q$ space, a result that identifies specific sectors for each class of dynamic process, and can be used to train machine learning models for automatic classification of complex spatiotemporal patterns.

Guided image filter implicitly balances noise and details based on local variance, achieving noise suppression while preserving prominent edges. However, we have observed limitations in using variance to distinguish between noise and image details, resulting in the suppression of fine structures alongside noise. Furthermore, the goal of enhancing image details, which is crucial in image filtering, has not been adequately addressed in edge-preserving filter design. In this paper, we approach image filtering as a problem of spectral energy reconfiguration, where the gain of each spectral band is adaptively regulated to achieve simultaneous noise suppression and detail enhancement. Instead of relying on variance, we employ a linear noise estimator to obtain the Signal-to-Noise Ratio (SNR) measurement necessary for dynamic gain regulation. Similar to the guided image filter, we determine the filter coefficients by solving the closed-form solution of the optimizer. We demonstrate that the proposed extended guided filter effectively integrates information from multiple spectral bands locally, allowing for the joint enhancement of meaningful details while simultaneously smoothing noise.

Metasurface constitutes one of the most promising technologies for holographic imaging; however, the work for terahertz (THz) metasurface holographic imaging is relatively limited. Here, we propose a THz dielectric geometric-propagation phase metasurface, which can operate dual-mode (reflection and transmission) and reconfigurable multifunctional holographic imaging. The dual-mode is realized by controlling the Fermi energy level (Ef) of the graphene integrated into the metasurface unit, and the reconfigurable three-channel holographic imaging in reflection or transmission mode are achieved by switching the feed polarization among left-handed circular polarization (LCP), right-handed circular polarization (RCP) and linear polarization (LP). The metasurface is designed based on the transmission mode, and a physical model for switching to the reflection mode is established. For the first time to the best of our knowledge, a reflection-transmission dynamic modulation THz holographic imaging metasurface has been developed. The holographic metasurface operates in transmission mode at Ef = 0.1 eV, and in reflection mode at Ef = 0.9 eV. Compared with recently published holographic imaging metasurfaces, the proposed metasurface offers the following advantages: high holographic efficiencies (42.5% to 49%), more holographic imaging channels, dynamic modulation dual-mode operations, and reconfigurability.

The objective of this study is to develop a predictive model for low-carbon bainitic steel utilising the P and λ parameters. The tempering activation energy of the steel was calculated through statistical methods. Kinetic curves for the hardness equivalents and a nomograph for tempering process parameters were plotted. The error analysis indicates that the predicted values based on the λ and P parameters had errors within 15% and 10%, respectively. The model can be effectively utilised to predict mechanical properties and optimise parameters during the tempering process.

Digital images have become an important carrier for people to access information in the information age. However, with the development of the technology, digital images are vulnerable to illegal access and tampering, to the extent that they pose a serious threat to personal privacy, social order and national security. Therefore, image forensic techniques have become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely applied in the field of image forensics and the performance achieved has significantly exceeded the conventional forensic algorithms. This survey compares the state-of-the-art image forensic techniques based on deep learning in recent years. The image forensic techniques are divided into passive and active forensics. In passive forensics, forgery detection techniques are reviewed, and the basic framework, evaluation metrics and commonly used datasets for forgery detection are presented. The performance, advantages and disadvantages of existing methods are also compared and analyzed according to different types of detection. In active forensics, robust image watermarking techniques are overviewed, the evaluation metrics and basic framework of robust watermarking techniques are presented. The technical characteristics and performance of existing methods are analyzed based on the different types of attacks on images. Finally, future research directions and conclusions are given to provide useful suggestions for people in image forensics and related research fields.

Tomato is the highest-value fruit/vegetable crop worldwide. However, the quality and yield of tomatoes are severely affected by late blight. MicroRNA482s (miR482s) are involved in plant immune system. In this study, miR482c was transiently and stably overexpressed in tomatoes in transgenic plants to explore its mechanism in tomato resistance against late blight. Tomato in transgenic plants transiently overexpressed miR482c displayed larger lesion area than the control plants upon infection. Furthermore, compared with the WT tomato plants, the transgenic tomato plants stably overexpressing miR482c displayed decreased expression of target genes accompanied by lower POD, SOD, and PAL activity activities and higher MDA content, thereby leading to a decline in the ROS scavenging ability and aggravating the damage of lipid peroxidation product accumulation on the cell membrane, eventually enhancing plant susceptibility. This finding indicates that miR482c may act as a negative regulator in tomato resistance by regulating NBS-LRR expression levels and ROS levels.

There are unsolved problems related to inflation, gravity, dark matter, dark energy, missing antimatter, and the birth of the universe. Some of them can be better answered by assuming the existence of aether and hypoatoms. Both were created during the inflation in the very early universe. While aether forms vacuum, hypoatoms, composed of both matter and antimatter and believed to be neutrinos, form all observable matter. In vacuum, aether exists between the particle-antiparticle dark matter form and the dark energy form in a dynamic equilibrium: A + A-bar = gamma + gamma. The same reaction stabilizes hypoatoms and generates a 3-dimensional sink flow of aether that causes gravity. Based on the hypoatom structure, the singularity does not exist inside a black hole; the core of the black hole is a hypoatom star or neutrino star. By gaining enough mass, ca. 3 X 10²² M_sun, to exceed neutrino degeneracy pressure, the black hole collapses or annihilates into the singularity, thus turning itself into a white hole or a Big Bang. The universe is anisotropic. Its center, or where the Big Bang happened, is at 0.66^+0.03_-0.01 times the radius of the observable universe at Galactic coordinates (l, b) = (286⁺¹⁰_-10, -43⁺⁷_-6). If we look from the Local Group to the center of the universe, the universe is rotating clockwise.

It is generally believed that the universe emerged from the Big Bang, before which space and time did not exist. However, Penrose proposed his theory of conformal cyclic cosmology, in which an aeon existed preceding this universe. Based on the cosmological principle, the universe is homogeneous and isotropic, expanding without center, but recently more and more deviations have been observed. By studying the fundamental particles and reactions of nature, we initiated a model that the Big Bang is the result of the collapse of a neutrino star within a black hole. We therefore re-analyze the cosmic microwave background (CMB) and find that the Center of the universe where the Big Bang occurred was at 0.66 ^+0.03 _-0.01 times the radius of the surface of last scattering away from the Local Supercluster (LS) and at Galactic coordinates (l, b) = (286^o +/- 10^o, -43^{o +7} _-6). If we look from the LS to the Center, the universe is spinning clockwise. Our findings are supported by Penrose’s conformal cyclic cosmology, low-variance circle sets in the CMB, and many other independent observational evidence of cosmic inhomogeneities, spatial anisotropies, and time variations.

The rapid urbanization has exerted tremendous pressure on natural systems in mountains. As a measure of sustainable use of natural resources, ecological footprint is an important basis for judging whether the development of a country or region is within the biocapacity. Taking Dali Bai Autonomous Prefecture as an example, this study comprehensively analyzes the impact of human activities on mountain resources and environment from the three aspects of urbanization, land use and ecological carrying capacity. The results show that Dali Prefecture with the urbanization rate of 33% is still in the accelerated stage of urbanization. The urban space presents the core-periphery feature, and the central city is the focus of human existence and living activities. The per capita ecological footprint is 1.14 hm2/person higher than the ecological carrying capacity, meaning Dali Prefecture is in an ecological deficit state. This indicates that there is an uncoordinated state between urbanization and environment. Arable land is the main source of per capita ecological footprint in the prefecture. However, the urban expansion overly occupies the arable land in the plain sub-region, leading the arable land to an ecological deficit state. In the future, the development of the mountainous area should focus on the protection of arable land and choose a new sustainable path.

Astrocytes are essential for normal brain development and functioning. They respond to brain injury and disease through a process referred to as reactive astrogliosis, where the reactivity is highly heterogenous and context dependent. Reactive astrocytes are active contributors to brain pathology and can exert beneficial, detrimental, or mixed effects following brain insults. Transforming growth factor-β (TGF-β) has been identified as one of the key factors regulating astrocyte reactivity. Genetic and pharmacological manipulation of TGF-β signaling pathway in animal models of CNS injury and disease alters pathological and functional outcomes. This review aims to provide recent understanding regarding astrocyte reactivity and TGF-β signaling in brain injury, aging, and neurodegeneration. Further, it explores how TGF-β signaling modulates astrocyte reactivity and function.

Thanks to advanced semiconductor microfabrication technology, chip-scale integration and miniaturization of lab-on-a-chip components, silicon-based optical biosensors have made significant progress for the purpose of point-of-care diagnosis. In this review, we provide an overview of the state-of-the-art in evanescent field biosensing technologies including interferometer, microcavity, photonic crystal, and Bragg grating waveguide-based sensors. Their sensing mechanisms and sensor performances, as well as real biomarkers for label-free detection, are exhibited and compared. We also review the development of chip-level integration for lab-on-a-chip photonic sensing platforms, which consist of the optical sensing device, flow delivery system, optical input and readout equipment. At last, some advanced system-level CMOS-chip packaging examples are presented, indicating the commercialization potential for the low cost, high yield, portable biosensing platform leveraging CMOS processes.

The experiments of Project 8 have been excellent, but the expected goals still difficult to achieve. So much so that some of the results at your fingertips were also missing. In view of this, the focus of this article is to clarify several easy confused concepts. Only in this way, we can reasonably explain the experimental data. The main points are as follows: 1. The value c of the light speed in vacuum and a particle with zero static mass, both of which do not exist in the reality. That is to say, the so-called a photon's static mass is equal to zero but has energy, which is a paradox that confuses two different definitional domains. 2. In the reality, photons are high-speed particles generated by electromagnetic radiation. They must have the characteristics of (static) mass, energy and wave, in order to describe the main body to aim at photons from different angles. 3. After any main body comes into being electromagnetic radiation, its static mass will inevitably decrease accordingly. 4. The charge-mass ratio of an electron is a physical constant, which is the ratio of its charge to the amount of matter, and is not affected by relativistic effects and electromagnetic radiation. 5. The uncertainty of moving electrons is caused by random electromagnetic radiations. Finally, it is pointed out that if the cyclotron frequency of a single electron is measured, and at the same time, its de Broglie wavelength or frequency can also be measured, then its static mass can be obtained. Even so, the expected goals are still difficult to achieve, because random electromagnetic radiations are always taking away continually the matter composition of the single electron being measured. However, this was precisely a result of the research obtained by Project 8, and had universality, which should be reflect on.

Gesture recognition technology has been quickly developed in the field of human-computer interaction. The multiple-input multiple-output (MIMO) radar has been widely adopted in gesture recognition because of its notable spatial resolution. In this work, a highly accurate MIMO radar-based hand gesture recognition algorithm with very low complexity is proposed. To make the proposed system applicable in the industry and work well even when the size of the training data is limited, we applied several low-complexity adaptive signal processing methods to extract features and reduce the noise effect. First, spectrum analysis is applied to range-Doppler maps (RDMs) and a cell-averaging constant false alarm rate (CA-CFAR) with mirror filters is applied to improve the robustness to noise. Afterward, the features related to the distance, speed, direction, and the elevation angle of the moving object are determined by the proposed adaptive signal analysis techniques and the random forest is applied for classification. The proposed system has the capability to precisely distinguish and identify eight motions, including waving, moving to the left or right, patting, pushing, pulling, and rotating clockwise or anticlockwise, with an accuracy of 95%. Experiments demonstrate the capability of the proposed hand gesture recognition system to classify different movements precisely.

Mutations of PKD1 coding for polycystin-1 (PC1) account for most of the autosomal dominant polycystic kidney disease (ADPKD). The extracellular region of PC1 contains many evolutionarily conserved domains for ligand interactions. Among these is the leucine-rich repeats (LRR) in the far N-terminus of PC1. Using zebrafish (Danio rerio) as an in vivo model system, we explored the role of LRR in the function of PC1. Zebrafish expresses two human PKD1 paralogs, pkd1a and pkd1b. Knockdown of both genes in zebrafish by morpholino antisense oligonucleotides produced phenotypes of dorsal axis curvature and pronephric cyst formation. We found that overexpression of LRR suppressed both phenotypes in pkd1-morphant zebrafish. Purified recombinant LRR domain inhibited proliferation of HEK cells in culture and interacted with the heterotrimeric basement membrane protein laminin-511 (α5β1γ1) in vitro. Mutations of amino acid residues in LRR structurally predicted to bind laminin-511 disrupted LRR-laminin interaction in vitro and neutralized the ability of LRR to inhibit cell proliferation and cystogenesis. Our data support the hypothesis that the extracellular region of PC1 plays a role in modulating PC1 interaction with the extracellular matrix and contributes to cystogenesis of PC1-deficiency.

Zucchini (Cucurbita pepo L.) is an important crash crop for producing seeds in Inner Mongolia, China. In August 2021, the harvest season of seed-producing zucchini, fruit rot symptom (cv. Jindi No.1) was found in the Wuyuan region. The incidence of infected fruits of zucchini ranged from 10% to 30% and symptom consisted in 4 to 5 cm diameter lesions where the fruit was in contact with the soil. Ten isolations from six symptomatic fruits were preliminary classified as Fusarium species based on its morphological characters in potato dextrose agar. Phylogenetic tree was constructed by concatenating TEF-1α and RPB2 gene and exhibited the isolates clustered together with the Fusarium solani f. sp. cucurbitae sequences. Pathogenicity tests revealed that two isolates caused over 3 cm diameter lesions on the fruits while negative-controls remained asymptomatic. The colonies reisolated from the leasions were morphologically identical to the original isolates, thus fulfilling Koch’s postulates. Results demonstrated that F. solani f. sp. cucurbitae was the causal agent of fruit rot observed in the field. To our knowledge, this is the first report of fruit rot of seed-producing zucchini caused by F. solani f. sp. cucurbitae in Inner Mongolia, China.

Studying hydro-meteorological factors variations and its links to large-scale atmospheric circulation systems can facilitate the understanding of the hydrological processes and sustainable water resources management in the source region of the Yangtze River (SRYR). Currently, researches mostly focused on the temporal and spatial variation characteristics in hydro-meteorological factors; however, researches on the hydro-meteorological variations and its links to large-scale atmospheric circulation systems in the SRYR are scarce. Based on long-term hydro-meteorological and reanalysis data, this research investigated multiscale variations of hydro-meteorological factors and its links to large-scale atmospheric circulation characteristic indices during 1957~2012 in the SRYR. The results showed that the amounts of streamflows and precipitation in the SRYR declined during the 1990s. Since the 2000s, the amounts of streamflows and precipitation had increased significantly climate in the SRYR. The change trends of precipitation and streamflows in the SRYR are synergetic at annual and seasonal scales, and have three significant periods, namely 3~5 years, 15–20 years and 30–40 years. The South Asia monsoon (SAM) plays a relatively more important role in the hydro-meteorological factors changes in the SRYR. The relative contributions of SAM to streamflows and precipitation changes were 83.6% and 78%, respectively. During the driest (wettest) year, the SAM is relatively weak (strong), and brings less (more) southwest airflow into the SRYR, less (more) precipitation and streamflows will be generated in the SRYR.

A sustainable diet which is healthy and environmental friendly is a climate change mitigation option in addition to being a health promoting diet. However, there is a scarcity of information if the Asian diets are sustainable. Therefore, this study aimed to investigate if the diet of the Malaysian population is healthy and sustainable. This is a cross sectional study using dietary data generated from food frequency questionnaires (FFQ). The carbon footprint data were linked with the food items/ food groups in the FFQ. The nutrients of the participants’ diet were computed and the proportions of those who met the recommended nutrients intake were established. Contribution of carbon footprint for different food groups and total carbon footprint for each participant’s diet were computed and expressed as kgCO₂eq. Comparison of carbon footprint from participants’ diets between age, sex and ethnicity were carried out. A total of 4825 participants were included in the analysis. Majority were Malays (66.4 %), females (84.0%), married (80.0%) and in the age groups of 30s to 40s (68.8%). The mean total energy intake was 2485+1000 kcal/day. Only 40 to 60% of all participants achieved the Malaysia Recommended Nutrient Intake (RNI) for calcium and less than half of the female participants who were aged 50 years and below fulfilled the RNI for iron. The most commonly consumed food groups were vegetables (270g/day), wheat, rice, fruits, sugar, seafood, poultry, legumes, snacks, milk and beef (46g/day). Total carbon footprint from the participants’ diets were 2.96 kgCO₂eq/day, with the highest contributions of carbon footprint from rice, vegetables, beef, sugar, other cereals, poultry, seafood, wheat, milk, fruits, legume and snacks. Subgroups such as males, Malays and younger participants were more likely to consume diets with higher carbon footprint, compared to their counterparts. The participants’ diet was low in carbon footprint and environmentally friendly, however the quality of diet may need to be improved. Education measures should be targeted for all population and specifically for the sub-groups that consumed diets with higher carbon footprint.

The first appearance of viruses can date back to the late last century. As an effective form of malware, viruses reside in the permanent storage of target hosts. Before a virus can execute, that must load into memory from the persistent storage included in the associated file. Due to the reliable destructive power of viruses, many mechanisms have been developed to defend computer systems against these hazardous threats. Antivirus software is one of the most famous and popular among these mechanisms. Most antivirus software uses static analysis (signature-based) technology on files stored in permanent storage, such as hard disks or USB flashes, to detect viruses hidden in files. Fileless malware was developed to enhance the survivability of malware by circumventing detection. Fileless malware only exists in the target hosts’ memory, not files. Antivirus software cannot even access the fileless malware code, much less analyze it, since it may be performed in memory directly without needing to load it from a disk. As a result, it is difficult for an antivirus engine to defend a system against fileless malware attacks. This paper proposes a kernel-based solution called Check-on-Execution (CoE) to detect fileless malware on a Linux system. When a program is going to execute a piece of code in a writable and executable memory area of a process, CoE suspends the code execution first. Coe retrieves the code from memory, packs the code with an ELF header to create an ELF file, and uses VirusTotal to check the file to prevent a Linux system from executing fileless malware. Experimental results show that CoE noticeably enhances the ability of a Linux system to defend itself again fileless malware. CoE is also suitable for protecting a system from shell code injection attacks, such as buffer and heap overflow attacks. It is capable of handling even packed malware. But in this paper, we only focus on fileless malware.

As the most active plateau on the Earth, the Qinghai-Tibet Plateau has a complex crust-mantle structure. Knowledge of the distribution of such a structure provides information for understanding the underlying geodynamic processes. We obtains a three-dimensional density model of crustal and upper mantle beneath Qinghai-Tibet plateau and its surrounding areas from the residual geoid anomalies using the Earth Gravitational Model (EGM) 2008. We estimate a refined density model by iterations, using an initial density contrast model. We confirm that the EGM2008 mission products can be used to constrain the crust-mantle density structures. Our major findings are: (1). At 300-400 km depth, high-D anomalies terminate around Jinsha River Suture (JRS) in the central TP, suggesting that the Indian plate has been reached over the Bangong Nujiang Suture (BNS) and almost reach to the JRS. (2). On the eastern TP, low-D anomalies at the depth of 0-300 km together with high-D anomalies at 400-670 km further verified the current eastward subduction of Indian plate. The ongoing subduction provides forces to the occurrences of frequent earthquakes and volcano. (3). At 600 km depth, low-D anomalies inside the TP illustrate the existence of hot weak material beneath there, contributing to the external material inward-thrusting.

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter $\mathsf{\gamma }$—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

Plant height is significantly correlated with grain traits, which is a component of wheat yield. The purpose of this study is to investigate the main QTLs that control plant height and grain-related traits in multiple environments. In this study, we constructed a high-density genetic linkage map using the Wheat50K SNP Array to map quantitative trait loci (QTLs) for these traits in 198 recombinant inbred lines (RILs). The two ends of the chromosome were identified as re-combination-rich areas in all chromosomes except chromosome 1B. The middle area of the chro-mosomes was identified as the recombination-barren area. Both the genetic map and the physical map showed a significant correlation when p=0.001, with a correlation coefficient between 0.63 and 0.99. However, there was almost no recombination between 1RS and 1BS. In terms of plant height, 1RS contributed to the reduction of plant height by 3.43cm. In terms of grain length, 1RS contributed to the elongation of grain by 0.11mm. A total of 43 QTLs were identified, including 8 QTLs for Plant height(PH), 11 QTLs thousand grain weight(TGW), 15 QTLs for grain length(GL),and 9 QTLs for grain width(GW), which explained 1.36%–33.08% of the phenotypic variation. Seven were environment-stable QTLs, including two loci Qph.nwafu-4B and Qph.nwafu-4D that determined plant height. The explanation rates of phenotypic variation were 7.39%-12.26% and 20.11%-27.08%, respectively. One QTL, Qtgw.nwafu-4B, which influenced TGW, showed an explanation rate of 3.43%-6.85% for phenotypic variation, two co-segregating KASP markers were developed, the physical locations corresponding to KASP_AX-109316968 and KASP_AX-109519968 were 25.888344 MB and 25.847691 MB. Another QTL, Qgw.nwafu-4D, which determined grain width, had an explanation rate of 3.43%-6.85%. Three loci that affected the grain length were Qgl.nwafu-5A, Qgl.nwafu-5D.2 and Qgl.nwafu-6B, illustrating the explana-tion rates of phenotypic variation as 6.72%-9.59%, 5.62%-7.75%, and 6.68%-10.73%, respectively. Two QTL clusters were identified on chromosomes 4B and 4D.

Engineering of quantum computers requires reliable characterization of qubits, quantum operations, and even the entire hardware. Quantum tomography is an indispensable framework in quantum characterization, verification, and validation (QCVV), which has been widely accepted by researchers. According to the tomographic target, quantum tomography can be categorized into quantum state tomography (QST), quantum process tomography (QPT), gate set tomography (GST), process tensor tomography (PTT), and instrument set tomography (IST). Standard quantum tomography toolkits generally consist of basic linear inverse methods and statistical maximum likelihood estimation (MLE) based methods. Furthermore, the performance of standard methods, including effectiveness and efficiency, has been further developed by exploiting Bayesian estimation, neural networks, matrix completion techniques, etc. In this review, we introduce the fundamental quantum tomography techniques, including QST, QPT, GST, PTT, and IST. We first introduce the details of basic linear inverse methods. Then, the framework of MLE methods with constraints is summarized. Finally, we briefly introduce recent further research in developing the performance of tomography. This review provides a primary getting-start in developing quantum tomography, which promotes quantum computer development.

This paper proposes a novel hybrid control strategy for quadrotor unmanned aerial vehicles (UAVs) based on bio-inspired neural dynamics. This strategy addresses the velocity jump issue prevalent in traditional backstepping control and the control signal chattering problem common in traditional sliding mode control. The control system comprises an outer-loop bio-inspired backstepping controller and an inner-loop bio-inspired sliding mode controller, ensuring smooth trajectory tracking even in the presence of external disturbances. The stability of the quadrotor UAV control system is rigorously analyzed using Lyapunov stability theory. Extensive comparative simulation experiments are conducted to assess the robustness and effectiveness of the control system. Results demonstrate superior trajectory tracking performance of the proposed control system compared to other control systems for quadrotor UAVs.

The rapid, sensitive, and selective detection of pathogenic bacteria is of utmost importance in ensuring food safety and preventing the spread of infectious diseases. Here, we present a novel, reusable, and cost-effective impedimetric sensor based on a dual bacteria-imprinted polymer (DBIP) for the specific detection of Escherichia coli O157:H7 and Staphylococcus aureus. The DBIP sensor stands out with its remarkably short fabrication time of just 20 minutes, achieved through the efficient electro-polymerization of o-phenylenediamine monomer in the presence of dual bacterial templates, followed by in-situ template removal. The key structural feature of the DBIP sensor lies in the cavity-free imprinting sites, indicative of a thin layer of bacterial surface imprinting. This facilitates rapid rebinding of the target bacteria within a mere 15 minutes, while the sensing interface regenerates in just 10 minutes, enhancing the sensor's overall efficiency. A notable advantage of the DBIP sensor is its exceptional selectivity, capable of distinguishing the target bacteria from closely related bacterial strains, including different serotypes. Moreover, the sensor exhibits high sensitivity, showcasing a low detection limit of approximately 9 CFU mL-1. The sensor's reusability further enhances its cost-effectiveness, reducing the need for frequent sensor replacements. The practicality of the DBIP sensor was demonstrated in the analysis of real apple juice samples, yielding good recoveries. The integration of quick fabrication, high selectivity, rapid response, sensitivity, and reusability makes the DBIP sensor a promising solution for monitoring pathogenic bacteria, playing a crucial role in ensuring food safety and safeguarding public health.

3D object detection is essential for an accurate and reliable autonomous driving system. Currently, the methods used by the state-of-the-art two-stage detectors are not flexible enough and their fea-ture extraction capabilities are very limited to cope effectively with the disorder and irregularity of point clouds. In this paper, we combine the advantages of both PV-RCNN and PAConv (Position Adaptive Convolution) to create a completely new network, FANet, in order to overcome the ir-regularity and disorder of point clouds. The convolution in our network builds convolutional ker-nels from a basic weight matrix, whose combined coefficients are learned adaptively by LearnNet from relative points. This network allows for flexible modeling of complex spatial variations and geometric structures in the 3D point cloud, enabling better extraction of point cloud features and producing high-quality 3D proposal boxes. Compared to other methods, FANet is superior in terms of 3D object detection accuracy. Extensive experiments on the KITTI dataset have shown a signif-icant improvement in our approach.

The urban transportation network design problem involving exclusive bus lanes (XBLs) has been widely discussed and analyzed in recent years. An improved and more flexible transit lane management strategy—intermittent bus lanes (IBLs)—prove to be potentially more efficient and car-friendly than XBLs. The common benefit of XBLs and IBLs arises from the fact that they separate the bus and car traffic and hence can eliminate the impacts of slowly moving buses on the car traffic. This paper proposes a cell transmission model for separate car and bus traffic (CTM-SCB) in a network with some dedicated roadway segments reserved for buses. By encapsulating the CTM-SCB model, an XBL-based network design problem and an IBL-based network design problem are then formulated and solved, respectively, where the former model statically sets bus lanes while the later one allows a dynamic allocation of bus lanes. A synthetic freeway-arterial network and a real-world urban street network (where the latter was extracted from the Harbin South New Industrial City) are used as test networks for evaluating the proposed models and methods. The numerical results show that both XBLs and IBLs enjoy significant operational efficiency benefits compared to the situation of no protected bus lanes. However, we believe that the expected improvement from XBLs to IBLs need further tests and validations.

Resides derived from liquefaction of oil palm trunk in the presence of polyhydric alcohol with different liquefaction temperature and time were characterized to provide a new approach to understand some fundamental aspects of the liquefaction reaction. Higher temperature and longer reaction time resulted in lower residue content, indicating more decomposition of components of oil palm trunk. The amorphous polymer comprised of lignin, hemicellulose, starch, and cellulose with non-crystalline structure are firstly degraded at low liquefaction temperature, followed by the decomposition of crystalline region of cellulose. Although it was relatively difficult to destroy the ordered structure of cellulose, most of them could be liquefied via prolonging reaction time or enhancing reaction temperature. Nevertheless, it was found that re-condensation of liquefied products occurred during the liquefaction process when higher temperature of 180 oC was used after 60 min, leading to the gradual increase of residue content with increase of reaction time.

Tulou dwellings in Southeast China have captivated global interest due to their distinctive appearance, sustainable construction materials and technologies, and their defensive and collective housing functions. Despite several recognized as World Cultural Heritage sites, the vast majority of tulou buildings are undergoing irreversible decline and destruction, necessitating a comprehensive and systematic study. Taking 83 Tulou buildings in Raoping County, Chaozhou City, Guangdong Province as the research object, this study reconstructs the historical scenes and systematically reveals the emergence, popularity, and consolidation process of tulou dwellings as integrated defensive and residential buildings for ordinary people by conducting a comprehensive analysis of historical documents and local chronicles. Based on an extensive field investigation, the study systematically analyzes the geospatial distribution and the spatial characteristics of Raoping tulou and its residential unit. The results demonstrate the adaptability and flexibility of tulou dwellings, showcasing their developmental process and revealing the inclusive nature of these traditional residences, as well as the initiative of those who reside within them. The research findings contribute to a more dynamic, comprehensive, and authentic understanding of tulou and Chinese traditional residences, providing valuable references for the preservation and sustainable development of tulou architectural heritage.

Our research aimed to explore how resolving periodontal inflammation impacts cytokine expression in the colon of aged Wistar rats. Animal studies have been used to investigate the two-way relationship between periodontitis and Inflammatory Bowel Disease (IBD), where chronic inflammation in either the mouth or intestines can negatively affect the other. We allocated sixteen male Wistar rats, aged between 8-11 months, into four groups: (1) Ligature-induced periodontitis (LIP) without the resolution of periodontal inflammation (RPI) (LIP; n=4), (2) LIP + RPI (n=4), (3) LIP + DSS-induced colitis (DIC) without RPI (n=4), and LIP+DIC+RPI (n=5). We performed histopathological and immunological analyses on the periodontal and intestinal tissues and analysed cytokine expressions using a Rat Cytokine 23-Plex Immunoassay. Our findings showed that animals with and without DIC who underwent RPI showed significantly lower levels of IL-2, IL-4, IL-5, IL-10, IL-13, IL-17, IL-18, and TNF-α in the intestine compared to those without treatment. The RPI effectively reduced the number of inflammatory cells in the lamina propria and restored the epithelial barrier in the intestine in animals with DIC. The resolution of periodontal inflammation significantly reduced the levels of pro-inflammatory cytokines and chemokines in the intestine of aged rats with and without DSS-induced colitis.

With the large-scale application of Internet of Things (IoT) in the medical field, eHealthcare has attracted wide attention. With the help of IoT devices, eHealthcare allows doctors to monitor patients’ health conditions and make recommendations accordingly. The addition of cloud computing greatly saves Healthcare Center’s (HC) computing resources. In order to provide privacy and fine-grained access control, Attribute-b ased encryption (ABE) is widely favored. HC usually operates multiple medical institutions in different regions, and each medical institution can be regarded as a attribute authority. Therefor, multi-authority (MA) ABE is usually concerned to encrypt the patient data. However for existing MA-ABE algorithms, the ciphertext is long and the efficiency of encryption and decryption is low. These problems will greatly increase the computing burden of patients and seriously affect the efficiency of data transmission. Hence we propose a decentralized key-policy MA-ABE in eHealthcare. The proposed algorithm can achieve decentralization, that is, it does not need central authority. Meanwhile, compared with existing MA-ABE algorithms, the length of ciphertext generated by our algorithm is shorter, and the efficiency of encryption and decryption is significantly improved. With the help of Java Pairing Based Cryptography, we carried out the experiments which show that our algorithm can effectively reduce the time required for encryption and decryption.

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, extracting the surface area affected by precipitation using a threshold method constrained by confidence intervals derived from microwave remote sensing images; Second, collecting disaster information from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms was proposed (TextCNN-Attention Disaster Classification); Finally, matching traffic road information on social media with basic geographic data, visualizing the classification of traffic road disaster risk levels, and completing the assessment of traffic road disaster levels based on multi-source heterogeneous data. Using the "7.20" rainstorm event in Henan Province as an example, this study categorizes the disaster impact on traffic roads into five levels: particularly severe, severe, moderate, mild, and affected, as interpreted from remote sensing images. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

In this paper, we propose a partial differential equation model based on phase-field for sentiment analysis in the field of hotel comment texts. The comment texts are converted into word vectors using the Word2Vec tool, and then we utilize the multifractal detrended fluctuation analysis (MF-DFA) model to extract the generalized Hurst exponent of the word vector time series to achieve dimensionality reduction of the word vector data. The dimensionality reduced data is represented in a two-dimensional computational domain, and the modified Allen-Cahn (AC) function is used to evolve the phase values of the data to obtain a stable nonlinear boundary, thereby achieving automatic classification of hotel comment texts. The experimental results show that the proposed method can effectively classify positive and negative samples and achieve excellent results in classification indicators. We compared our proposed classifier with traditional machine learning models and the results indicate that our method possesses a better performance.

It's challenging to achieve robust lane detection depending on single frame when considering complicated scenarios. In order to detect more credible lane markings by using sequential frames, a novel approach to fusing vision and Inertial Measurement Unit (IMU) is proposed in this paper. The hough space is employed as the space where lane markings are stored and it's calculated by three steps. Firstly, a basic hough space is extracted by Hough Transform and primary line segments are extracted from it. In order to measure the possibility about line segments belong to lane markings, a CNNs based classifier is introduced to transform the basic hough space into a probabilistic space by using the networks outputs. However, this probabilistic hough space based on single frame is easily disturbed. In the third step, a filtering process is employed to smooth the probabilistic hough space by using sequential information. Pose information provided by IMU is applied to align hough spaces extracted at different times to each other. The final hough space is used to eliminate line segments with low possibility and output those with high confidence as the result. Experiments demonstrate that the proposed approach has achieved a good performance.

Safe administration of highly cytotoxic chemotherapeutic drugs is a challenging problem in cancer treatment due to the adverse side effects and collateral damage to non-tumorigenic cells. To mitigate these problems, new promising approaches, based on the paradigm of controlled targeted drug delivery (TDD), utilizing drug nanocarriers with biorecognition ability to selectively target neoplastic cells, are being considered in cancer therapy. Herein, we report on the design and testing of a nanoparticle-grid based biosensing platform to aid in the development of new targeted drug nanocarriers. The proposed sensor grid consists of superparamagnetic gold-coated core-shell Fe₂Ni@Au nanoparticles, further functionalized with folic acid targeting ligand, model thiolated chemotherapeutic drug doxorubicin (DOX), and a biocompatibility agent, 3,6,-dioxa-octanethiol (DOOT). The employed dual transduction based on electrochemical and enhanced Raman scattering detection have enabled efficient monitoring of the drug loading onto the nanocarriers, attached to the sensor surface, as well as the drug release under simulated intracellular conditions. The grid’s nanoparticles serve here as the model nanocarriers for new TDD systems under design and optimization. The superparamagnetic properties of the Fe₂Ni@Au NPs aid in nanoparticles’ handling and constructing a dense sensor grid with high plasmonic enhancement of the Raman signals due to the minimal interparticle distance.

The BKL singularity inside a rotating black hole would be a neutrino star. Once its mass exceeds the limit that neutrino degeneracy pressure could support (larger than 3×10²² M_⊙), the neutrino star singularity would collapse, raising the temperature to the maximum and dropping the entropy to the minimum to initiate a Big Bang. From the cosmic microwave background, we find where the Big Bang occurred at about 0.66 times the radius of the surface of last scattering away from us and at Galactic coordinates (l, b) ≃ (286°,-43°). As it expands, the clockwise spinning universe has been veering towards the ellipsoidal. These findings are consistent with independent observations of cosmic inhomogeneities, spatial anisotropies, and time variations.

Image segmentation is a new challenge prob- lem in medical application. The use of medical imaging has become an integral part of research, as it allows us to see inside the human body without surgical intervention. Many researcher have studied brain segmentation. One stage method is used to segment the brain tissues. In this paper, we proposed the multi-stage generative ad- versarial network to solve the problem of information loss in the one-stage. We utilize the coarse-to-fine to improve brain segmentation using multi-stage generative adversar- ial networks (GAN). In the first stage, our model generated a coarse outline for (i) background and (ii) brain tissues. Then, in the second stage, the model generated outline for (i) white matter (WM), (ii) gray matter (GM) and (iii) cerebrospinal fluid (CSF). A good result can be achieved by fusing the coarse outline and refine outline. We conclude that our model is more efficient and accu- rate in practice for both infant and adult brain segmenta- tion. Moreover, we observe that multi-stage model is faster than prior models. To be more specific, the main goal of multi-stage model is to see the performance of the model in a few shot learning case where a few labeled data are available. For medical image, this proposed model can work in a wide range of image segmentation where the convolution neural networks and one-stage methods have failed.

Sinojackia Hu. comprises five to eight Chinese endemic species with high ornamental and medicinal value. However, the generic limits, interspecific relationships and evolutionary history of the genus remain unresolved. In this study, we newly sequenced and assembled three plastomes of S. oblongicarpa and compared them with those of the other congeneric species to explore the taxonomic delimitation of the species and the evolutionary history of the genus. Plastomes structure of Sinojackia species were extremely conserved in terms of number of genes, sequence length and GC content. Codon usage patterns revealed that natural selection may be the main factor shaping codon usage bias. Our phylogenetic tree shows that Sinojackia is monophyletic and can be divided into two clades. Sinojackia oblongicarpa as a distinct species is supported for it is distantly related to S. sarcocarpa. The evolutionary analysis of morphological features indicates that woody mesocarp is an ancestral feature, while mesocarp undeveloped, spongy and fleshy are the later derived. Sinojackia originated in Central-Southeast China during the early Miocene. In this period, it experienced elevated diversification and migrated from the Central-Southeast China to Hunan Province and Sichuan Province with the development of the Asian monsoon and East Asian flora. Sinojackia experienced elevated diversification at intraspecies levels that mainly occurred in the Quaternary. Glacial-interglacial interactions with the monsoon climate may provide favorable expansion conditions for Sinojackia on a small-scale.

Titanium alloys are difficult to machine and have poor tribological properties. This paper investigates the lubricating performance of γ-Al2O3/ZnO hybrid nanofluids for Ti-6Al-4V. The pure and hybrid nanofluid are compared, and the effects of γ-Al2O3/ZnO ratios are studied. The results show that γ-Al2O3/ZnO hybrid nanofluids outperform pure nanofluid in terms of lower friction coefficients and better surface quality. Moreover, the hybrid nanofluid with a mass ratio of Al2O3 to ZnO of 2:1 demonstrates the best lubrication performance with a reduced friction coefficient of up to 22.1% compared to the base solution, resulting in improved surface quality. Al2O3 nanoparticles can adhere to the surface of ZnO nanoparticles and work as a coating, which further enhances the lubrication performance of the water-based nanofluid.

Private Set Intersection (PSI) is a cryptographic method in secure multi-party computation that allows entities to identify common elements in their datasets without revealing their private data. Traditional approaches assume similar-sized datasets and equal computational power, overlooking practical imbalances. In real-world applications, dataset sizes and computational capacities often vary, particularly in the Internet of Things and mobile scenarios where device limitations restrict computational types. Traditional PSI protocols are inefficient here, as computational and communication complexities correlate with the size of larger datasets. Thus, adapting PSI protocols to these imbalances is crucial. This paper explores unbalanced PSI scenarios where one party (the receiver) has a relatively small dataset and limited computational power, while the other party (the sender) has a large amount of data and strong computational capabilities. It introduces three innovative solutions for unbalanced PSI: unbalanced PSI protocol based on Cuckoo filters, unbalanced PSI protocol based on single cloud assistance, and unbalanced PSI protocol based on dual cloud assistance, each addressing the shortcomings of the previous solution. These protocols primarily optimize database computational load, storage capacity, protocol execution time, and security. Depending on performance and security needs, different protocols can be employed for applications such as private contact discovery.

Spirit of science is one of the important components of science education theory. It not only directly affects the level of science education, but also indirectly affects the selection, education and evaluation of scientific researchers, and the development of science and technology in a country or region. Although more than a century ago, the researchers began to discuss the topic of spirit of science. However, to date, the research of the definition, attribute, structural model and its characteristics of the spirit of science has not yet made a breakthrough. Based on the detailed literature review, the related theoretical analysis and the research of the structural model of the spirit of science, this paper puts forward the new definition, the attribute and the establishment of the structural model of the spirit of science, and analyzes the match the situation from new structural model of the spirit of science and the scientific nature published by the American Society for the Advancement of Science. The results of this study are of great significance for raising the level of scientific education and cultivating future scientific researchers, enhancing their motivation and skills in innovation in scientific research and promoting the development of future scientific undertakings.

Robotic systems have been widely used in various industries, so the security of communication between robots and their components has become an issue that needs to be focused on. As a framework for developing robotic systems, the security of ROS2 can directly affect the security of the upper-level robotic systems. Therefore, it is a worthwhile research topic to detect and analyze the security of ROS2. In this paper, we adopt a formal approach to analyze the security of the communication mechanism of ROS2. First, we use a state transition system to model the potential vulnerabilities of ROS2 based on the ROS2 communication mechanism and the basic process of penetration testing. Secondly, we introduce the CIA model on the basis of the established vulnerability model and use LTL to define its security properties. Then, we design and implement a vulnerability detection tool for ROS2 applications based on the vulnerability model and security properties. Finally, we experimentally test some ROS2-based applications, and the results show that ROS2 has vulnerabilities without additional protection safeguards.

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

To investigate the role of sulfhydryl groups and disulfide bonds in different protein-stabilized emulsions, N-ethylmaleimide (NEM) was used as sulfhydryl-blocking agent to be added in the emulsion. The addition of NEM to block the sulfhydryl groups resulted in a reduction of the content of disulfide bonds formation, which enabled destruction of the internal structure of the protein molecule, and then decreased the restriction of protein membrane on the oil droplets. Furthermore, with NEM content increasing in the emulsion, a reduction of protein emulsifying activity and emulsion stability also occurred. At the same time, the intermolecular interaction of the protein on the oil droplet interface membrane was destroyed, and the emulsion droplet size increased with the NEM content in the emulsion. Although NEM blocking sulfhydryl groups not to form disulfide bonds has similar effects on three types of protein emulsion, the degree of myofibrillar protein (MP), egg-white protein isolate (EPI), and soybean protein isolate (SPI) as emulsifier had a subtle difference.

A plant’s early response to pathogen stress is a vital indicator of its disease resistance. In order to study the response mechanism of resistant and susceptible flax cultivars to Fusarium oxysporum f. sp. lini (Foln), we applied RNA-sequencing to analyze transcriptomes of flax with Foln 0.5, 2 and 8 hours post inoculation (hpi). We found a significant difference in the number of differential expression genes (DEGs) between resistant and susceptible flax clutivars. The number of DEGs in the Fusarium-resistant cultivar increased dramatically at 2 hpi, and a large number of DEGs participated in the Fusarium-susceptible cultivar response to Foln infection 0.5 hpi. GO enrichment analysis determined that the up-regulated DEGs of both flax cultivars were enriched such as oxidoreductase activity and oxidation-reduction process. At the same time, the genes involved in diterpenoid synthesis were up-regulated in resistant cultivar, while those involved in extracellular region, cell wall and organophosphate ester transport were down-regulated in susceptible cultivar. KEGG enrichment analysis showed the genes encoded WRKY 22 and WRKY33 which involved in MAPK signaling pathway were up-regulated expressed in S-29 and down-regulated expressed in R-7, negatively regulated the disease resistance of flax; The genes encoded Hsp 90 family which in involved in plant pathogen interaction pathway were up-regulated in R-7 and down-regulated in S-29, which positively regulated the disease resistance of flax; The genes encoded MYC2 transcription factor and TIFY proteins which involved in plant hormone signaling pathway were up-regulated in R-7, and regulated the jasmonic acid metabolism of flax and the signal transduction of plant hormones. Meanwhile seven regulatory genes with the most correlation were screened out, Among Lus10025000.g and Lus10026447.g regulated other genes expressed both in plant hormone signal transduction pathway and MAPK signal pathway. In conclusion, these findings will facilitate further studies on the function of these candidate genes in flax of response to Fusarium stress, and the breeding of disease-resistant flax cultivar.

Environmental responsibility and economic benefits have promoted the development of closed-loop supply chain (CLSC), and shortages and channels are considered to be two important issues in CLSC. This paper explores ordering and channel decisions in CLSC with new and differentiated remanufactured products, and considers the price and sales effort-dependent demands, as well as the proportion of emergency orders determined by emergency order costs and backorder losses, and establishes an integrated and decentralized CLSC game models. We introduce a stochastic sales effort, which affects two types of products. Numerical results show that sales effort and the order quantity of new and remanufactured products exhibit concave and convex functions, respectively. Wherein, the upper limit of sales effort has a greater impact on supply chain decisions. High sales efforts can serve as a means of coordinating dispersed supply chains. Moreover, in different cases, decisions of integrated channel are better than those of decentralized channel. Finally, whether the supply chain adopts an emergency order strategy depends on the relative cost of emergency orders and out of stock costs. According to the research in this article, some management insights are also provided.

The dioecious property of sea buckthorn (Hippophae rhamnoides L.) prevents sex recognition via traditional observation at the juvenile stage, thus impeding breeding and economic cropping; RAPD and SCAR markers were used to identify the sexes. A total of 45 random decamer primers were used to screen genomic DNA pools of staminate and pistillate genotypes for genetic polymorphisms. One female sex-linked marker was identified. D15 (5′-CATCCGTGCT-3′) amplified a particular band of 885 bp, which showed polymorphism among staminate and pistillate genotype plants. The SCAR marker Hrcx-15 was obtained by sequencing the fragment. The alleles of 140 pistillate genotypes were examined but not of the 140 staminate genotypes discerned via taxonomy. Staminate and pistillate genotypes of sea buckthorn plants can be distinguished, using Hrcx-15 as a genetic marker for sex identification and for expediting cultivation for commercial applications.

Exosomes are associated with cancer progression, pregnancy, cardiovascular diseases, central nervous system–related diseases, immune responses and viral pathogenicity. However, study on the role of exosomes in the immune response of teleost fish, especially antiviral immunity, is limited. Herein, serum-derived exosomes from mandarin fish were used to investigate antiviral effect for the exosomes of teleost fish. Exosomes were isolated from mandarin fish serum by ultracentrifugation could internalize into Mandarin fish fry (MFF-1) cells and inhibited Infectious spleen and kidney necrosis virus (ISKNV) infection. To further investigated the underlying mechanisms of exosomes in inhibiting ISKNV infection. The protein composition of serum-derived exosomes was by analysis mass spectrometry and found that myxovirus resistance 1 (Mx1) was incorporated in the exosomes. Furthermore, the scMx1 protein was proved transferred to the recipient cells though the exosomes. Our results found that the serum-derived exosomes from mandarin fish could inhibit ISKNV replication and suggested an underlying mechanism of the serum-derived exosomes antivirus is that serum-derived exosomes incorporation of the Mx1 protein into exosomes and delivery into recipient cells. This study provided an evidence for the important antiviral role of exosomes in the immune system of teleost fish.

There always exist steel cuttings, holes and cracks at the interfaces in the explosive composite plate. Multi-pass friction stir processing (M-FSP) is proposed in this research to optimize the interface microstructure and the interface connection for 1060Al/Q235 explosive composite plate. Results show that the microstructures of 1060Al after M-FSP are fine and uniform owing to the strong stirring effect and recrystallization. Micro-defects formed by the explosive welding can be repaired by the M-FSP. However, M-FSP can also form tunnel defects in the aluminum, especially when the passes are one and two. The melting block and the melting lump in the composite plates are easy to become source of crack. The shear strengths and the bending properties for the 1060Al/Q235 explosive composite plate after M-FSP are the best when the passes are three, with the tool rotation speed of 1200rpm and the forward speed of 60mm/min. The optimized interfaces for the explosive composite plate after M-FSP are mainly by the metallurgical bondings, with a certain thickness and are discontinuous. Therefore, the crack extension stress is the largest and the mechanical properties are the best.

Traditional phase change composites usually suffer poor mechanical property and easy collapsing in the phase changing process. Herein, a highly flexible phase change composite is fabricated using thermoplastic elastomer as the basic gel and the expanded graphite/paraffin as the filler. This new phase change composite shows a tensile strength of 2.1 MPa and a breaking elongation of 220%. It has a melting enthalpy of 145.4 J•g^-1 and a thermal conductivity of 2.2 W•m^-1•K^-1 with 70% of expanded graphite/paraffin. The thermoplastic elastomer based phase change composite exhibits great reversible property after 200 heating/cooling cycles. This flexible phase change composite demonstrates good photo-thermal energy charging/discharging property and shows great potential to be applied in the solar thermal energy systems.

By using the new recently proposed extended rational methods, we set some solitary and multi wave solutions of the coupled Higgs system in the present study. Some new families of hyperbolic and trigonometric solutions of the coupled Higgs equations are successfully obtained. Some solutions are real as some are complex valued functions. Particular forms of some solutions derived by choice of some parameters are demonstrated in three dimensional spaces. The complex valued solutions are also depicted by plotting both real and complex component.

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

S-licarbazepine was prepared by asymmetric reduction of oxcarbazepine with interface-assembled carbonyl reductase at the interface of oil/water biphasic system. The carbonyl reductase was conjugated with polystyrene in the surface of toluene/Tris-HCl biphasic system and formed a surfactant-like structure. The conversion and enantiometric excess of S-licarbazepine reached 97.39% and 99.6% when 3.97 mmol/L oxcarbazepine was reduced by interface-assembled carbonyl reductase with 60 g/L ethanol as co-substrate in toluene/Tris-HCl(12.5:10) biphasic system at 30 °C, 180 rpm for 6 h.

The precipitate phases often play an important influence on the corrosion resistance of 2205 Duplex stainless steel (DSS). In the presented paper, the microstructure and corrosion resistance in the hot-rolled and cold-rolled 2205 DSS aging for different time at 850 °C was investigated by XRD, SEM and potentiodynamic polarization. It has been found that the Chi(χ) phase and Sigm(σ) phase were precipitated in turn after aging treatment of hot-rolled and cold-rolled materials, but the precipitate amount in cold-rolled material is much more than that of hot-rolled samples. The corrosion resistance of the solution-annealed cold-rolled material is similar to the hot-rolled material, but the corrosion resistance of cold-rolled material with precipitate is weaker than that of hot-rolled material after aging treatment. Pitting initiates preferentially in the Cr-depleted region from σ phase in aged hot-rolled 2205, and severe selective corrosion occurs on sigma/ferrite interfaces aged for a long aged lime. However, the initiation of pitting corrosion may take place at the phase boundary, defect and martensite in the aged cold-rolled 2205. The σ phase is further selectively dissolved by electrochemical method to investigate the difference of microstructure and corrosion behavior in hot-rolled and cold-rolled 2205 duplex stainless steel.

The purpose of this study was to explore the relationship between teachers' perception of organizational justice and organizational citizenship behavior and to reveal the mediating role of teacher job burnout between teachers' perception of organizational justice and organizational citizenship behavior. This study used a questionnaire to collect data from 857 teachers in 21 secondary schools in Chongqing, China. Through a series of hierarchical regression analyses, mediating effects tests, structural equation modeling tests, and dominance analyses, the findings consistently indicated that procedural justice was a positive predictor of organizational citizenship behavior, while teacher’s job burnout was a negative predictor of organizational citizenship behavior. In addition, the study results also indicated that job burnout had a significant mediating effect on the relationship between teachers' perceptions of organizational justice and organizational citizenship behaviors, particularly with the passion burnout and burnout of professional self-effectiveness.

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

Pediatric brain tumors are the most common solid tumors in children, causing the highest number of cancer-related deaths at young ages. Although existing therapies have improved the 5-year overall survival rate of PBT patients, some high-risk brain tumors continue to resist current treatments. Additionally, the survivors endure devastating long-term side effects, including cognitive defects and sensory and endocrinal dysfunctions. In the past two decades, immunotherapies have evolved significantly, with great success in many cancers. Immunotherapies developed specifically for PBTs also show promising results. Currently, scores of clinical trials are underway to evaluate the safety and feasibility of a wide-range of immunotherapeutics for PBTs, and this review overviews the most recent advances, including the exciting progress made toward treating medulloblastoma, pediatric glioma, and ependymoma. We summarized the cutting-edge immunotherapies under development, including immune checkpoint blockade, chimeric antigen receptor (CAR)-T cell therapy, natural killer (NK)-cell therapy, cancer vaccines, and oncolytic virus therapy. We find that the results from the recently concluded or ongoing clinical trials on safety and feasibility are very encouraging; however, non-responsiveness and tumor recurrence still haunt them. While this review judges the scope of the current immunotherapies, it also lays out strategies to find alternate solutions for persisting issues.

As one of the main projects of facility agriculture promotion, the photovoltaic greenhouse has the problems of photovoltaic power generation competing for light with crop production, strong indoor chiaroscuro, and uneven light distribution. The internal light uniformity is tested by a zigzag greenhouse model to compare the light transmission effects of different light-transmitting materials applied to photovoltaic greenhouses. 20 line\inch 3mm and 30 line\inch 3mm, 40 line\inch 2mm, 25 line\inch 4mm grating plates, 2mm, and 3mm thick ordinary glass were used as light transmitting components, and the light intensity and light uniformity in the greenhouse were the measurement indicators. The results show that the use of grating plates as covering material can improve the light intensity at the intersection of light and dark, but the overall light transmittance is not as good as glass, because it is plastic which is easy to age with low light transmittance. It can also improve the use of land under the shade of photovoltaic modules to provide a better growth environment for crops. Test results show that the use of a 2mm thick 40-line grating plate can maximize the light intensity of the greenhouse, and the peak value can reach 69336 Lx. In sunny weather, the light intensity from point G to point I in the greenhouse is greater than 20,000 Lx, and the light environment in other areas is between 5,000 Lx and 20,000 Lx, which is suitable for planting shade-loving crops.

In the realm of autonomous vehicle environment perception, the primary objective of point cloud target detection is to swiftly and accurately identify three-dimensional objects from point cloud data. To meet this requirement, the prevalent network architecture employed in the industry is the voxel-based PointPillars model. Nonetheless, this model faces challenges in maintaining detection accuracy when objects are obscured or diminutive in size. In response to this issue, we introduce AGPNet, a novel model that seamlessly integrates four key modules: Data Augmentation, Dynamic Graph CNN, Pillar Feature Net, and Detection Head (SSD). The Data Augmentation module enhances the adaptability of point cloud data to complex and ever-changing real-world environments. The Dynamic Graph CNN module endows the network structure with geometric features, which encapsulate not only the point itself but also its adjacent points. The Pillar Feature Net module translates three-dimensional point cloud data into pseudo-image data through the utilization of voxels. Subsequently, the Detection Head (SSD) module leverages this pseudo-image data to conduct target detection of three-dimensional objects. Our experiments, conducted on the KITTI dataset, demonstrate that our proposed method boosts object detection accuracy by 6-7 percentage points compared to the PointPillars model, while maintaining similar detection times.

The work safety standardization of enterprises based on the traditional work safety theory has played a significant role in reducing the number of accidents and improving work safety to some extent in China. However, some problems are coming with the work safety standardization of enterprises developing constantly in China. On the one hand, it is not combined with the actual situation of the enterprise, lacking pertinence and specificity, these defects resulted that it is not integrated with original safety production management system of enterprise and make it difficult to carry out. On the other hand, there is a lack of systematic management methods for the work safety management system of enterprise, most of enterprises only pay attention to the inspection result rather than the process control. This means after the check of the government, many enterprises will relax to carry out the system. This paper puts forward a new method for optimizing the standardization management mode of work safety based on the Viable System Model(VSM), which can solve the defects of work safety standardization of enterprises management system. An optimization model of work safety standardization based on VSM was construct for explaining the process optimization and control of work safety standardization management. It can improve the connectivity between the enterprise and the government. The conclusion of this paper can provide reference for achieving the development and optimization of work safety standardization of enterprises in China.

Chlorhexidine digluconate (CHX) has shown the ability to significantly reduce inflammation and marginal bone loss (MBL) at 1-year follow-up but limited data exists regarding its long-term efficacy in peri implant stability. The objective was to compare the long-term effects of a placebo gel (16 patients in Group A) or a 0.20% CHX gel (15 patients in Group B) used at each previous surgical and prosthetic stage. Follow-ups were conducted in 2022, investigating by biological, radiological, clinical, mechanical evaluation the long-term effects. Data were statistically analyzed. The research achieved a 96.7% implant success rate over five years, but 41.9% of patients didn't attend annual oral hygiene check-ups. The average MBL was 1.04 ± 0.39 mm, with no significant differences between the two groups. Notably, patients who attended regular periodontal check-ups experienced significantly less MBL than those who didn't (p &lt;0.05). At five years, the effects of CHX were reduced, with both groups showing moderate bone loss. The results suggest early disinfection could enhance both short and long-term outcomes. In fact, patient with initial minor MBL preserve this advantage also at 5 years of follow-up. Additionally, the data underscore the importance of annual check-ups in early detection and management of biological and mechanical complications.

With the development of energy internet, integrated energy system can effectively reduce carbon emissions and improve the utilization of renewable energy. In this paper, a low-carbon optimal scheduling model of integrated energy system considering heat loss of heat network pipeline is proposed. Based on the study of concentrating solar power (CSP) plant and heat storage tank (HS), an optimal scheduling model is established, which takes system operation cost, environmental pollution and penalty cost of abandoning wind and solar energy as objectives. Through the analysis of example results, it is proved that the model proposed in this paper can achieve the goal of reliable, low-carbon and economic operation of the system. At the same time, it shows that CSP unit can reduce the operation cost of system and increase energy coupling and utilization.

The risk classification of barrier lake is the key to conducting emergency treatment in a scientific manner. Such difficulties are faced as short time for risk evaluation, complex evaluation indicators, difficulty in obtaining information quickly and quantifying index weights. Based on this, this paper constructs a quantitative risk classification model for barrier lakes based on D-AHP. On the basis of studies on nearly 100 cases of barrier lakes, an 8-factor evaluation index system and quantitative classification are proposed. The methods of rapid calculation of reservoir capacity curve of barrier lake and intelligent identification of particles on the surface of barrier body were developed, which realized the rapid acquisition of 8-factor evaluation index information in emergency environment. The D-AHP method dealt with inconsistent weight assignment to evaluation factors by experts, which helped achieve weight quantification of 8 factors. The risk assessment on15 barrier lakes such as Tangjiashan barrier lake show that the conclusion drawn with risk classification method proposed in this paper is basically consistent with that of the traditional table-lookup method. However, the table-lookup method ignores cumulative loss’s impacts on the risk level of barrier lakes, and considers the extremely severe loss of barrier lakes as a sufficient condition for the evaluation level to be grade I, and thus a deviation in the evaluation. The risk classification method proposed in this paper is more reasonable and reliable.

Soil transverse isotropy results in different stiffness characteristics in the horizontal and vertical directions. However, the effect is usually neglected in the seismic motion analysis. In this study, an equivalent linear anisotropic soil model was established based on the finite element method and investigated the impact of anisotropic parameters on ground motion at the site under various seismic wave inputs. It is found that the anisotropic parameters have a more significant effect for the seismic waves with the dominant frequency closer to the fundamental frequency of the site. As an example, the soil dynamic parameters in Shanghai Yangshan Port were calibrated by a series of bending elements, resonance columns, and cyclic triaxial tests. The influences of anisotropy on the peak ground acceleration (PGA) and response spectrum are studied for Yangshan Port. Addi-tionally, the standard design response spectra considering the soil anisotropy were provided. A comparison reveals that the existing isotropic design response spectrum may lead to dangerous seismic design for the structures at Yangshan port.

Ship pilots and maritime safety administration have an urgent need for more accurate and earlier warnings on strong wind gusts. This study firstly investigates the “Oriental Star” cruise ship cap-sizing event in 2015, one of the deadliest shipwreck events in recent years, and explores all related hydro-meteorological components in a global mesoscale model. It is found that, rather than the missing signal in raw surface wind prediction, the cumulus precipitation variable (CP) increases dramatically during the accident occurrence, which significantly corresponds to the sub-grid strong wind gust. The effective lead-time can be extended from 24hr (deterministic model) to 48hr (en-semble model). This finding is then verified in another two recent deadly cruise boat accidents. The introduction of the new variable is bearing the hope of improvement to the current maritime safeguard system, on predicting sub-grid strong wind gusts for small-size cruise boats in offshore and inland rivers. Finally, an automatic-response system is developed to provide economical con-vection prediction via Inmarsat email communication, aiming to explore operational severe con-vective gust early-warning and appropriate numerical mesoscale model application.

Continuous rigid frame bridges across valleys are often at the risk of rockfalls caused by heavy rainfalls, earthquakes and debris flows in a mountainous country. Hollow thin-walled bridge piers (HTWBP) in valleys are exposed to the threat of the impact of accidental rockfalls. In the current research, ANSYS/LS-DYNA is used to establish a high-precision rockfall-HTWBP model. The rockfall-HTWBP model is verified against a scaled impact test of a previous research. A mesh independence test is also performed to obtained an appropriate mesh size. Based on the rockfall-HTWBP model, the impact force, damage and dynamic response characteristics of HTWBP under the rockfall impact are studied. In addition, a damage assessment criteria is proposed based on the response surface model combined with Central Composite Design method and Box-Behnken Design method. The main conclusions are as follows: 1）The impact force of rockfall has a substantial impulse characteristic, and the duration of the impulse load is approximately 0.01s. 2）The impacted surface of the pier is dominated by the final elliptic damage with the conical and strip damage areas as the symmetry axis. The cross-sectional damage mode is compression failure in the impact area and shear failure at the corner. 3）The maximum displacement occurs in the middle height of the pier. The maximum displacement increases with impact height, impact velocity and rockfall diameter and decreases with the uniaxial compressive strength of the concrete. 4) The initial impact velocity and diameter of the rockfall are the most significant parameters affecting the damage indices. In addition, a damage assessment method with a damage zoning diagram based on the response surface method is established for the fast assessment of the damage level of impacted HTWBP.

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

One of the most basic pieces of information gained from dynamic electromyography is accurately defining muscle action and phase timing within the gait cycle. The human gait relies on selective timing and the intensity of appropriate muscle activations for stability, loading, and progression over the supporting foot during stance, and further to advance the limb in the swing phase. A common clinical practice is utilizing a low-pass filter to denoise integrated electromyogram (EMG) signals and to determine onset and cessation events using a predefined threshold. However, the accuracy of the defining period of significant muscle activations via EMG varies with the temporal shift involved in filtering the signals; thus, the low-pass filtering method with a fixed order and cut-off frequency will introduce a time delay depending on the frequency of the signal. In order to precisely identify muscle activation and to determine the onset and cessation times of the muscles, we have explored here onset and cessation epochs with denoised EMG signals using different filter banks: the wavelet method, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method. In this study, gastrocnemius muscle onset and cessation were determined in sixteen participants within two different age groups and under two different walking conditions. Low-pass filtering of integrated EMG (iEMG) signals resulted in premature onset (28% stance duration) in younger and delayed onset (38% stance duration) in older participants, showing the time-delay problem involved in this filtering method. Comparatively, the wavelet denoising approach detected onset for normal walking events most precisely, whereas the EEMD method showed the smallest onset deviation. In addition, EEMD denoised signals could further detect pre-activation onsets during a fast walking condition. A comprehensive comparison is discussed on denoising EMG signals using EMD, EEMD, and wavelet denoising in order to accurately define an onset of muscle under different walking conditions.

With the changes in the spatial structure of China's economic development, urban clusters have become the primary carriers of China's regional economy and green growth. The article uses annual data from 2010 to 2021 to study the carbon emission characteristics and carbon reduction pathways of 36 cities in the Yangtze River Delta region. Firstly, cities in the Yangtze River Delta region are divided into six carbon emission types based on decoupling elasticity coefficient and carbon intensity indicators. Then, the STIRPAT model is used to regress the panel data of different carbon emission types for 11 years, analyze the driving factors of carbon emissions, and develop differentiated carbon emission reduction paths for cities with six carbon emission types. According to the results, the city of Type I needs to accelerate low-carbon technology innovation, Type II needs to improve energy efficiency and strengthen low-carbon technology research and development, Type V needs to suppress foreign investment in high energy consumption and high emission projects in the local area, Type VI needs to accelerate the process of new urbanization and optimize industrial structure. Types III and IV have not received effective emission reduction paths, and further research is required on other emission reduction policies and measures.

Due to the special characteristics of the shooting distance and angle of remote sensing satellites, the pixel area ratio of ship targets is small and the feature expression is insufficient, which leads to unsatisfactory ship detection performance and even situations such as missed detection and false detection. In this study, we propose an improved-YOLOv5 algorithm. The improvement strategies mainly include: (1) Add the Convolutional Block Attention Module (CBAM) into the Backbone to enhance the extraction of target-adaptive optimal features; (2) Introduce cross-layer connection channel and lightweight GSConv structure into the Neck to achieve higher-level multi-scale feature fusion and reduce the number of model parameters; (3) The Wise-IoU loss function is used to cal-culate the localization loss in the Output, and assign reasonable gradient gains to cope with dif-ferences in image quality. In addition, during the preprocessing stage of experimental data, a me-dian and bilateral filter method is used for noise reduction to reduce interference from ripples and waves and highlight the information of ship features. The experimental results show that Im-proved-YOLOv5 has a significant improvement in recognition accuracy compared to various mainstream target detection algorithms; Compared to the original YOLOv5s, the mean Average Precision (mAP) has improved by 3.2% and the Frames Per Second (FPN) has accelerated by 8.7%.

Yield calculation is an important link in modern precision agriculture, which is an effective means to improve breeding efficiency, and adjust planting and marketing plans. With the continuous progress of artificial intelligence and sensing technology, yield calculation schemes based on image processing technology have many advantages such as high accuracy, low cost, and non-destructive calculation, and have been favored by a large number of researchers. This article reviews the research progress of crop yield calculation based on remote sensing images and visible light images, describes the technical characteristics and applicable objects of different schemes, and focuses on detailed explanations of data acquisition, independent variable screening, algorithm selection and optimization. Common issues are also discussed and summarized. Finally, solutions are proposed for the main problems that have arisen so far, and future research directions are predicted, with the aim of achieving more progress and wider popularization of yield calculation solutions based on image technology.

(1)Background: This report was an effective evaluation of using multimedia educational program for self-care and quality of life in patients with a stoma at postoperative and established a simple threshold for enterostomy self-care skills.(2)Methods:108 patients with enterostomy were randomly assigned to receive either the intervention multimedia education program (including information about preparation for enterostomy care, cleaning method, manual bag replacement steps and precautions). The primary outcome was self-care ability and quality of life (defined as overall enterostomy self-care ability and overall quality of life), which was assessed at 2 weeks after intervention via an interview.(3)Results: Compared with those who in the conventional stoma education program, patients received the multimedia education program significantly improved their overall self-care ability and quality of life in statistically. The threshold of the enterostomy self-care skill scale was 20 points, and its corresponding sensitivity was 77.8%, and the specificity was 75.7%.(4)Conclusions: According to our study, the multimedia education program could enhance self-care ability of home care and quality of life in patients with enterostomy.

Deep learning is improving by leaps and bounds in remote sensing images (RSIs) analysis, pre-trained convolutional neural networks (CNNs) have shown remarkable performance in remote sensing image scene classification (RSISC). Nonetheless, pre-trained CNNs require massive annotated data as samples for data training. When labeled samples are not sufficient, the most common solution is to the pre-trained CNNs using a great deal of natural image dataset (e.g. ImageNet). However, these pre-trained CNNs require a large quantity of labelled data for training, which is often not feasible in RSISC, especially when the target RSIs have different imaging mechanisms from RGB natural images. In this paper, we proposed an improved hybrid classical-quantum transfer learning CNNs composed of classical and quantum elements to classify open-source RSI dataset. The classical part of the model is made up of a ResNet network which extracts useful features from RSI datasets. To further refine the network performance, a tensor quantum circuit is subsequently employed by tuning parameters on near-term quantum processors. We tested our models on open-source RSI dataset. In our comparative study, we have concluded that the hybrid classical-quantum transferring CNN has achieved better performance than other pre-trained CNNs based RSISC methods with small training samples. Moreover, it has been proved that the proposed algorithm improves the classification accuracy while greatly decreasing sum of model parameters and sum of training data.

To explore impact of weight change (WC) on risk of atherosclerosis measured by cIMT, 20,700 participants from the CLSA follow-up were included in analysis. WC was defined as the difference of weight measured at follow-up and baseline, then quartered into four groups (Q1–Q4). cIMT>1.0 mm was defined as high-risk for atherosclerosis. Adjusted odds ratio (OR, [95% CI]) from logistic regression models were used to evaluate the association between WC and risk of atherosclerosis. At follow-up, participants have gained 0.118 kg weight, on average, and 16.4% of them were at high-risk for atherosclerosis. The mean levels of cIMT were comparable between participants from Q1 to Q4. Compared to Q2 (reference), the ORs (95% CI) were 1.00 (0.86, 1.15), 1.19 (1.03,1.38), and 1.25 (1.08,1.45) for Q1, Q3, and Q4, respectively. A similar pattern was observed when analyses conducted by <65 vs. 65+ years separately, but it was weaker for those aged 65+ years. Results from the jointed distribution analyses indicated that weight loss might increase risk for atherosclerosis among participants with obese at baseline, but not for those with cardiovascular events status at baseline. Weight gain, however, would increase risk for atherosclerosis regardless of cardiovascular events status, or overweight/obese at baseline.

In this paper, thin plate theory and moderately thick plate theory are proposed for analyzing the bending problem of rectangular plates with stepped thickness resting on elastic half-space foundation. The ground reaction is considered as an unknown coefficient and the hypothesis of Winkler foundation model and two-parameter ground model is eliminated in this method, so as to obtain the law of internal force distribution of the plate and the distribution law of contact reaction force between the stepped rectangular plate and foundation. The stepped rectangular plate is divided into upper and lower plates, and thin plate theory and moderately thick plate theory are used to obtain the analytical solution. The obtained analytical solutions in this paper are compared with results reported in other publications to verify the accuracy of this method. The effects of the elastic modulus and dimensions of the plate, and theory of plate are also considered. The analytical solutions show that it is feasible to decompose the stepped rectangular plate into two plates for analyzing the bending properties of stepped rectangular plate, and this method provide a reference for the study of multilayer stepped rectangular plates.

An electrocardiograph (ECG) reflects the health of the human heart and is used to help diagnose arrhythmia and myocardial infarction(MI) in clinical practice. Early diagnosis of arrhythmia helps implement preventive measures and plays a crucial role in saving a patient's life. With the increasing demand of clinicians for ECG analysis technology, intelligent detection and diagnosis of ECG signals has become a more efficient means to assist physicians in diagnosing cardiovascular diseases. This paper introduces an ECG diagnosis approach based on an ensemble deep learning combination of CNN(convolutional neural network) and SLAP(stacked-long short term memory architecture for prediction) architecture. ECG data is denoised and further divided into single heartbeats to achieve data standardization and sample diversity. Adam optimizer and BCEwithlogitsloss multi-classification loss function were used to enhance the model effect, and the system achieved the classification effect of 99.3% average accuracy, 99.0% F1-value, and 99.2% sensitivity in MIT-BIH standard database classification. It also shows good generalization ability on the Tianchi data set.

Vitamin E facilitates testicular development and semen quality in farm animal. And most research tends to investigate antioxidant roles of Vitamin E in reproductive health; some research also find potential non-antioxidant roles of Vitamin E. To further uncover the mechanism by which Vitamin E modulates reproductive health, the paper reviews specific function of Vitamin E and candidate genes involved in male reproductive performance promoted by Vitamin E. The review found that cell proliferation (PDPN, BMP, Myc, AMPKa, GSK3β, PPARγ, CDK4, CDK6, Ki67, PCNA, Cyclin A2, Cyclin B1, Cyclin B3, BLM, REL, KLHL25)-, cell apoptosis (BCL-2, Caspase 9, Bax)-, spermatogenesis (CatSper1, CatSper2, NDRG1, CYP26B1, FSCN3, FLNA, SPCS3, YBX3, RARS)-, hormone receptor (PGR, FSHR, AR)-related genes are the underlying key points. And two miRNAs, miR-107 and miR-493-3p, which mediate cell cycle process, are also identified in response to Vitamin E treatment. The paper paves the way for a comprehensive understanding about effect of Vitamin E on testis development and semen quality in domestic animals.

Sunflower is one of the oil crops and its planting areas was listed behind Soybean, peanut and rapeseeds in China. It will encounter different kind of diseases during planting, thus limited the seeds yield. Fusarium species are the major threat to different crops nowadays, it is very important to identify the species caused diseases on different hosts, so that the potential control measures may be explored. In Inner mongolia, Sunflower Disk Rot (SDR) was found in different sunflower planting field, the average infection ratio around 8.50%, typically in some verieties, its infection ratio reach to 11.67%. The diseased receptacle were collected from different locations and Koch’s postulate was conducted. The results showed that Fusarium verticillioides was the main causal agent caused SDR. Fungicide toxicity tests shown that Fludioxonil and Flutolanil were screened out from six fungicides for controlling F. verticillioides, with EC50 values of 0.05 µg/mL (R = 0.9825) and 0.96 µg/mL (R = 0.9964) , respectively. This is the first report of SDR caused by F. verticillioides, and it will alert sunflower reseachers to include SDR in the disease list, so as to control sunflower diseases with integrated management strategy successfully.