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Metastasis accounts for almost 90% of breast cancer-related fatalities, making it frequent malignancy and the main reason of tumor mortality globally among women. LSD1 is a histone demethylase, which plays an important role in breast cancer. In order to explore the effect of LSD1 on invasion and migration of breast cancer, we treated breast cancer cells with MCF7 and T47D exosomes knocked down by LSD1, and the invasion and migration of breast cancer cells were significantly enhanced. This phenomenon indicates that LSD1 can inhibit the invasion and migration of breast cancer cells. miR-1290 expression was downregulated in LSD1 knockdown MCF7 exosomes. By analyzing the database of miR-1290 target gene NAT1, we verified that miR-1290 could regulate the expression of NAT1. These data provide fresh insights into the biology of breast cancer therapy by demonstrating how the epigenetic factor LSD1 stimulates the breast cancer cells' invasion and migration via controlling exosomal miRNA. [ABSTRACT FROM AUTHOR]

Global climate change has intensified the water cycle, leading to frequent extreme precipitation events, posing a significant threat to railway infrastructure and safety operations. Based on the analysis of past and future precipitation changes in China, this study investigates the impact of climate change on railway safety operations. The study reveals the following findings: (1) Under the influence of the intensified East Asian summer monsoon and the northward shift of the subtropical high during the 2017-2021 compared to the 2012-2016, precipitation has significantly decreased (120 mm) in the regions south of the Yangtze River and South China, while it has increased (60 mm) in the regions from the eastern of Northwest China to the middle and lower reaches of Yangtze River; The operational precipitation risk has decreased for Urumqi, Lanzhou, Qinghai-Tibet Group, Xi'an, and Wuhan railway bureaus (abbreviated as Bureau), while it has increased for Nanchang, Chengdu, Zhengzhou, Shanghai, and Shenyang Bureaus. Particularly noteworthy is that despite a decrease in total annual precipitation for Nanchang bureau (15.3 mm/a), the frequency of intense precipitation events has increased, leading to an increased operational precipitation risk. (2) During the 21[sup st] century, under high (SSP5-8.5), medium (SSP2-4.5), and low (SSP1-2.6) forcing scenarios, all projections indicate that most of the China will experience an increasing trend in precipitation, with significant increases in precipitation observed in the regions south of the Yangtze Rive, South and Southwest China. The higher the greenhouse gas emissions, the more pronounced the increasing trend in precipitation. (3) Compared to the 20[sup th] century, under high (SSP5-8.5), medium (SSP2-4.5), and low (SSP1-2.6) forcing scenarios, all projections indicate that the total annual precipitation hours, railway inspection, speed limit, and closure risk hours have all increased on a national scale during the 21[sup st] century. The operational precipitation risk for railways has also increased. The higher the alert level for railway precipitation (precipitation < inspection < speed limit < closure), the higher the proportion of risk hours compared to the 20th century. By the late 21[sup st] century, the railway inspection, speed limit, and closure risk hours have increased by 175%, 463%, and 647%, respectively, compared to the 20[sup th] century. [ABSTRACT FROM AUTHOR]

Motion object detection (MOD) with freely moving cameras is a challenging task in computer vision. To extract moving objects, most studies have focused on the difference in motion features between foreground and background, which works well for dynamic scenes with relatively regular movements and variations. However, abrupt illumination changes and occlusions often occur in real-world scenes, and the camera may also pan, tilt, rotate, and jitter, etc., resulting in local irregular variations and global discontinuities in motion features. Such complex and changing scenes bring great difficulty in detecting moving objects. To solve this problem, this paper proposes a new MOD method that effectively leverages local and global visual information for foreground/background segmentation. Specifically, on the global side, to support a wider range of camera motion, the relative inter-frame transformations are optimized to absolute transformations referenced to intermediate frames in a global form after enriching the inter-frame matching pairs. The global transformation is fine-tuned using the spatial transformer network (STN). On the local side, to address the problem of dynamic background scenes, foreground object detection is optimized by utilizing the pixel differences between the current frame and the local background model, as well as the consistency of local spatial variations. Then, the spatial information is combined using optical flow segmentation methods, enhancing the precision of the object information. The experimental results show that our method achieves a detection accuracy improvement of over 1.5% compared with the state-of-the-art methods on the datasets of CDNET2014, FBMS-59, and CBD. It demonstrates significant effectiveness in challenging scenarios such as shadows, abrupt changes in illumination, camera jitter, occlusion, and moving backgrounds. [ABSTRACT FROM AUTHOR]

To enhance the microhardness and wear resistance of low-carbon steel, laser cladding was employed to create A100-Y2O3 cladding coatings that remained free of cracks. The phase composition, microstructure, and element distribution of these coatings were examined using XRD and SEM analyses, respectively. The microhardness and wear resistance of the A100-Y2O3 cladding coatings were tested by an HXS-1000 A type digital liquid crystal intelligent microhardness tester and an ML-10 friction and wear tester, respectively. The XRD results show that the addition of Y2O3 did not change the phase composition of the A100-Y2O3 cladding coatings. With the addition of Y2O3, the grains of the A100-Y2O3 cladding coatings are finer compared with those of the A100-0%Y2O3 cladding coating. The upper part of the A100-Y2O3 cladding coatings were composed of fine equiaxed grains. The average microhardness of the A100-0%Y2O3 cladding coatings was 532.489 HV. With the addition of Y2O3, the microhardness of the A100-Y2O3 cladding coatings was obviously improved, and the average microhardness of A100-1.5%Y2O3 coating reached 617.290 HV. The A100-Y2O3 cladding coatings were reduced, and the worn surface became relatively smooth owing to the addition of Y2O3. The addition of Y2O3 significantly improved the wear resistance of the A100-Y2O3 cladding coatings. [ABSTRACT FROM AUTHOR]

To address global challenges, research on the safety of polar navigation is indispensable. However, most of studies focus on traditional surface vessels, with few research studies on submarine. The dynamic response of submarine during surface navigation in floating ice channels under special conditions is studied in this work. Firstly, a model of the submarine incorporating an intact internal frame was established. Subsequently, the FEM-ALE coupled method was employed to simulate the structure-ice interaction, and the obtained results was verified by the Colbourne method. Then, the parametric study (navigation speed, ice thickness, and floating ice size) were analyzed from the perspectives of ice resistance, stress and plastic strain. Finally, an empirical equation suitable for the interaction between submarine and floating ice during surface navigation is improved based on the Colbourne method. [ABSTRACT FROM AUTHOR]

Despite possessing a simple nervous system, the Caenorhabditis elegans exhibits remarkably intelligent behavior. However, the underlying mechanisms involved in sensory processing and decision making, which contribute to locomotion behaviors, remain unclear. In order to investigate the coordinated function of neurons in achieving chemotaxis behavior, we have developed a digital twin of the C. elegans that combines a connectome-based neural network model with a realistic digital worm body. Through training the digital worm using offline chemotaxis behavioral data generated with a PID controller, we have successfully replicated faithful sinusoidal crawling and intelligent chemotaxis behavior, similar to real worms. By ablating individual neurons, we have examined their roles in modulating or contributing to the regulation of behavior. Our findings highlight the critical involvement of 119 neurons in sinusoidal crawling, including B-type, A-type, D-type, and PDB motor neurons, as well as AVB and AVA interneurons, which was experimentally demonstrated. We have also predicted the involvement of DD04 and DD05 neurons and the lack of relevance of DD02 and DD03 neurons in crawling, which have been confirmed through experimentation. Additionally, head motor neurons, sublateral motor neurons, layer 1 interneurons, and layer 1 and layer 5 sensory neurons are expected to play a role in crawling. In summary, we present a novel methodological framework that enables the establishment of an animal model capable of closed-loop control, faithfully replicating realistic animal behavior. This framework holds potential for examining the neural mechanisms of behaviors in other species. [ABSTRACT FROM AUTHOR]

Laser cladding technology is a reliable and efficient surface modification technology, which has been widely used in surface alloying and composite processing of steel materials. Firstly, the characteristics of laser cladding technology were introduced, and the effects of process control and the material system on the geometric shape, size, microstructure, and properties of cladding coating were analyzed by summarizing the research results of laser cladding on steel surfaces. The results show that with the increase of laser power, the dilution rate and width of the cladding coating increase, and the grain becomes coarse. Thus, the wear resistance deteriorates. Compared with alloy cladding coating, composite cladding coating exhibits better wear and corrosion resistance, but the plastic toughness is worse than alloy cladding coating. The research progress of surface alloying and composite processing of steel worldwide was analyzed from various aspects. Current results suggest that laser cladding alloying and compounding can enhance the wear resistance and corrosion resistance of steel materials. Based on the summary of the current research results, the development prospect and planning of laser cladding technology in the field of surface alloying and composite processing of steel are further pointed out. [ABSTRACT FROM AUTHOR]

In this brief, the problem of tracking control for uncertain nonlinear systems with periodic disturbances is studied. Based on the prescribed performance technology, two kinds of error transformation functions different from the traditional backstepping method are introduced, such that the proposed low-computation adaptive control method can ensure the boundedness of the signals of the closed-loop system. Meanwhile, the counter evidence method reveals that the system output can achieve accurate tracking. Compared with existing results, firstly, the complexity explosion problem is avoided without introducing a filter, making our result more applicable and less complex. Secondly, for the time-dependent periodic disturbances considered in the system, a function approximator based on Fourier series expansion can achieve accurate approximation with the least number of Gaussian basis functions. Comparative simulation results of the van der Pol oscillator illustrate the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

The waveguide structure made of lithium niobate crystal (LiNbO3, LN) can further improve the device integration. It has been widely used in electro-optical modulator, frequency conversion, acousto-optic Q-switching and other optoelectronic devices. It has important application prospects in optical fiber communication, photoelectric sensing, lidar, aerospace and other fields. The LN waveguide made by traditional Ti diffusion method has poor resistance to photorefractive damage in short wave applications. The LN waveguide made by annealing proton exchange method only support TM mode (transverse magnetic mode) single polarization transmission, and its application field is limited. In this paper, a new Zn diffusion method is proposed to fabricate magnesium doped LN ridge waveguide. By establishing the diffusion model and simulation of the waveguide, the technological conditions were explored and tested. The lowest transmission loss of LN waveguide is 0.86 dB/cm, and the photorefractive damage threshold can reach 184 kW/cm². It will provide a better preparation way for the research and development of high power lithium niobate waveguide integrated optoelectronic devices. [ABSTRACT FROM AUTHOR]

In this article, a command filtering‐based adaptive event‐triggered neural network control scheme is proposed for a class of uncertain switched nonlinear systems with unknown control coefficient and input saturation. First, radial basis function neural networks are used as function approximators to estimate unknown nonlinear functions. Then, an event‐triggering mechanism based on the tracking error is introduced to avoid the over‐consumption of communication resources. Furthermore, command filters are employed to solve the problem of complexity explosion that exists in conventional backstepping control design, and the error compensation signals are designed to reduce the errors caused by the filters. Considering that the unknown control gain and input saturation exist in many practical applications, a Nussbaum‐type function is thus introduced into the controller design to address these challenging issues. Finally, stability of the closed‐loop system is strictly proven under a standard Lyapunov stability analysis framework. The effectiveness of the proposed control scheme is illustrated by a simulation example. [ABSTRACT FROM AUTHOR]

Thick steel/aluminum/aluminum-alloy composite plates are one of the key materials connecting steel structures and aluminum alloy structures and have been widely used in the shipbuilding industry and other fields. However, steel/aluminum/aluminum-alloy composite plates with a total thickness of more than 10 mm and a steel layer thickness of more than 5 mm are prone to problems such as inconsistent deformation of component metals and low bonding strength during the rolling process, and cannot be continuously prepared. To solve this problem, this article proposes a two-pass different temperature rolling process for thick steel/aluminum/aluminum-alloy composite plates and conducts research on Q235B steel, 1060 aluminum, and 5083 aluminum alloy as component metals. The results show that the process is reliable. It can prepare Q235B/1060/5083 composite plates with a thickness of 15.65 mm without oxygen protection measures. Meanwhile, the interfacial shear and pull-off strength of the composite plates obtained under different experimental conditions in this article are higher than the requirements of the US military standards MIL-J-24445A and Chinese ship standard CB20091-2012. The composite plates show good performance in 90° and 137° bending tests without obvious defects. Under the best condition, a 1.48 μm interlocking diffusion layer is formed at the steel/aluminum interface of the composite plates, the interfacial shear strength exceeds 70 MPa, and the interfacial pull-off strength exceeds 110 MPa. Finally, according to the experimental results, the reasons for the feasibility of the two-pass different temperature rolling of thick steel/aluminum/aluminum-alloy composite plates are given. [ABSTRACT FROM AUTHOR]

This paper considers the issue of adaptive neural decentralised tracking control for a class of output-constraint switched interconnected nonlinear systems with unknown backlash-like hysteresis control input. First, neural networks (NNs) are applied to approximate unknown nonlinear functions, and an NNs switched state observer is designed to estimate unmeasured system states. Then, the dynamic surface control technique is used to avoid the influence of explosion of complexity. In addition, the problem of output constraints is solved by introducing the barrier Lyapunov functions. Based on the Lyapunov stability theory, all signals in the switched closed-loop system can be verified to be uniformly ultimately bounded under the proposed control method. Moreover, the system output can track the target trajectory well within a small bounded error. Finally, a numerical simulation result is given to illustrate the effectiveness of the adaptive decentralised control scheme. [ABSTRACT FROM AUTHOR]

With the increasing number of infrastructures constructed in marine and cold regions, research on and applications of calcium sulphoaluminate (CSA) cement have been flourished, but the hydration process of CSA at low temperature has not been systematically investigated. To characterize the influence of low temperature on the hydration characteristics, freshly mixed CSA mortars were cured at −10, −5, 0, 5, and 20°C, respectively. The hydration process was investigated by electrical resistivity, compressive strength, and microstructure analyses. Results show that the hydration process (especially the induction period) is lengthened by low curing temperature. Both the electrical resistivity and compressive strength increase with an increase in the curing temperature. The compressive strength was reduced at a low curing temperature. Among these five curing temperatures, 5°C is the optimal curing temperature. Low temperatures do not change the kinds of hydrates, but reduce their amount. The scanning electron microscopy results illustrate that fewer hydrates fill the pores in specimens cured at low temperatures, while more hydrates form at higher temperatures. Moreover, low curing temperature contributes to the formation of coarse ettringite crystals. For the cement used at low temperature, the induction period should be reduced by adjusting the calcining process and composition proportion. [ABSTRACT FROM AUTHOR]

Bacterial pathogens are a major cause of infectious diseases in aquatic animals. The abuse of antibiotics in the aquatic industry has led to the proliferation of antibiotic resistance. It is therefore essential to develop more effective and safer strategies to increase the efficacy and extend the life span of the antibiotics used in aquaculture. In this study, we show that six aquaculture bacterial pathogens (i.e., Aeromonas hydrophila , Vibrio alginolyticus , Edwardsiella tarda , Streptococcus iniae , Vibrio harveyi , and Vibrio fluvialis) in the stationary phase can be rapidly killed after immersion in gentamicin- or neomycin-containing, ion-free solutions for a few minutes. Such hypoionic shock treatment enhances the bacterial uptake of gentamicin in an ATP-dependent manner. Importantly, we demonstrate, as a proof of concept, that gentamicin under hypoionic shock conditions can effectively kill A. hydrophila in vivo in a skin infection model of zebrafish (Danio rerio), completely curing the infected fish. Given that pathogenic bacteria generally adhere to the skin surface and gills of aquatic animals, our strategy is of potential significance for bacterial infection control, especially for small-scale economic fish farming and ornamental fish farming. Further, the combined treatment can be completed within 5 min with a relatively small volume of solution, thus minimizing the amount of residual antibiotics in both animals and the environment. [ABSTRACT FROM AUTHOR]

In proof-of-work (PoW)-based blockchain networks, the miners contribute their distributed computation in solving a crypto-puzzle competition to win the reward. To secure stable profits, some miners organize mining pools and share the rewards from the pool in proportion to each miner’s contribution. However, some miners may exhibit malicious behaviors which cause a waste of distributed computation resource, even posing a threat on the efficiency of blockchain networks. In this paper, we propose a new game-theoretic framework to incentivize miners mining honestly and help to bring about a higher total welfare of blockchain networks. We first formulate the mining process as a noncooperative iterated game. We then propose a mechanism in terms of zero-determinant strategies (ZD strategies) to encourage the cooperative mining and improve the efficiency of mining in PoW-based blockchain networks. In addition, we theoretically analyze the maximum system welfare of the target pool through the method of optimization. Numerical illustrations are also presented to support our theoretical results. [ABSTRACT FROM AUTHOR]

With ever increasing people's awareness of low carbon and environmental protection, electric vehicles are gradually gaining wide popularity. However, the driving endurance of the electric vehicle is the biggest shortage that hinders the fully acceptance of this new vehicle technology. To deal with this shortage, this paper proposed a vehicle-to-vehicle (V2V) electricity trading scheme based on Bayesian game pricing in blockchain-enabled Internet of vehicles (BIoV). Specifically, the Bayesian game is adopted for pricing in the distributed BIoV with incomplete information sharing. The optimal pricing under the linear strategic equilibrium has been obtained which maximizes the utilities of both sides of electricity transaction. The transaction volume is determined from the formulated convex problem that maximizes the social welfare. Then, the pricing game is implemented by the dedicated smart contract. Blockchain guarantees its trustworthiness, security, and reliability. Finally, the experimental results show that referring to the benchmark of static game with complete information, the proposed Bayesian game with incomplete information can achieve approximate satisfaction of users. The degree of approximation can reach to 98% when the pricing ranges of buyers and sellers are close. Moreover, the proposed scheme has great advantages over the static game with complete information in terms of communication overhead and timeliness in the decentralized IoVs. [ABSTRACT FROM AUTHOR]

In reality, data objects often belong to several different categories simultaneously, which are semantically correlated to each other. Multi-label learning can handle and extract useful information from such kind of data effectively. Since it has a great variety of potential applications, multi-label learning has attracted widespread attention from many domains. However, two major challenges still remain for multi-label learning: high dimensionality and correlations of data. In this paper, we address the problems by using the technique of partial least squares (PLS) and propose a new multi-label learning method called rPLSML (regularized Partial Least Squares for Multi-label Learning). Specifically, we exploit PLS discriminant analysis to identify a latent and common space from the variable and label spaces of data, and then construct a learning model based on the latent space. To tackle the multi-collinearity problem raised from the high dimensionality, a $$\ell _2$$ -norm penalty is further exerted on the optimization problem. The experimental results on public data sets show that rPLSML has better performance than the state-of-the-art multi-label learning algorithms. [ABSTRACT FROM AUTHOR]

This work summarizes the role and current applied status of formal methods in software reengineering after reviewing their fundamentals. And then it gives a structural analysis from program to the corresponding formal specification in the implementation of software reengineering. Finally, it concludes and highlights the future work. [ABSTRACT FROM AUTHOR]

A distributed sensor is developed for detection of cracks and measurement of crack widths in composite materials. The sensor consists of a number of individual segments on one line with gauge lengths designed according to the structural and materials requirements. Return loss measurements at the interfaces between adjoining segments provide the basis for crack detection and measurement. An optical time domain reflectometer (OTDR) is employed for interrogation of the sensor signal. The quasi-distributed sensors were embedded in reinforced concrete beams in order to evaluate their crack detection capability. Experiments involved both monotonic as well as fatigue loading. The sensors were embedded in the tension zone of the concrete beams in order to facilitate comparison of sensor response with visual crack observations. Experimental results in terms of crack detection capability and resolution are presented and discussed. [ABSTRACT FROM PUBLISHER]

A distributed sensor is developed for detection of cracks and measurement of crack widths in composite materials. The sensor consists of a number of individual segments on one line with gauge lengths designed according to the structural and materials requirements. Return loss measurements at the interfaces between adjoining segments provide the basis for crack detection and measurement. An optical time domain reflectometer (OTDR) is employed for interrogation of the sensor signal. The quasi-distributed sensors were embedded in reinforced concrete beams in order to evaluate their crack detection capability. Experiments involved both monotonic as well as fatigue loading. The sensors were embedded in the tension zone of the concrete beams in order to facilitate comparison of sensor response with visual crack observations. Experimental results in terms of crack detection capability and resolution are presented and discussed. [ABSTRACT FROM PUBLISHER]

Despite the severe social burden caused by Alzheimer's disease (AD), no drug than can change the disease progression has been identified yet. The structural brain network research provides an opportunity to understand physiological deterioration caused by AD and its precursor, mild cognitive impairment (MCI). Recently, persistent homology has been used to study brain network dynamics and characterize the global network organization. However, it is unclear how these parameters reflect changes in structural brain networks of patients with AD or MCI. In this study, our previously proposed persistent features and various traditional graph-theoretical measures are used to quantify the topological property of white matter (WM) network in 150 subjects with diffusion tensor imaging (DTI). We found significant differences in these measures among AD, MCI, and normal controls (NC) under different brain parcellation schemes. The decreased network integration and increased network segregation are presented in AD and MCI. Moreover, the persistent homology-based measures demonstrated stronger statistical capability and robustness than traditional graph-theoretic measures, suggesting that they represent a more sensitive approach to detect altered brain structures and to better understand AD symptomology at the network level. These findings contribute to an increased understanding of structural connectome in AD and provide a novel approach to potentially track the progression of AD. [ABSTRACT FROM AUTHOR]

Recent research of persistent homology in algebraic topology has shown that the altered network organization of human brain provides a promising indicator of many neuropsychiatric disorders and neurodegenerative diseases. However, the current slope-based approach may not accurately characterize changes of persistent features over graph filtration because such curves are not strictly linear. Moreover, our previous integrated persistent feature (IPF) works well on an rs-fMRI cohort while it has not yet been studied on metabolic brain networks. To address these issues, we propose a novel univariate network measurement, kernel-based IPF (KBI), based on the prior IPF, to quantify the difference between IPF curves. In our experiments, we apply the KBI index to study fluorodeoxyglucose positron emission tomography (FDG-PET) imaging data from 140 subjects with Alzheimer's disease (AD), 280 subjects with mild cognitive impairment (MCI), and 280 healthy normal controls (NC). The results show the disruption of network integration in the progress of AD. Compared to previous persistent homology-based measures, as well as other standard graph-based measures that characterize small-world organization and modular structure, our proposed network index KBI possesses more significant group difference and better classification performance, suggesting that it may be used as an effective preclinical AD imaging biomarker. [ABSTRACT FROM AUTHOR]