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Abstract Background Broodiness significantly impacts poultry egg production, particularly notable in specific breeds such as the black-boned Silky, characterized by pronounced broodiness. An understanding of the alterations in ovarian signaling is essential for elucidating the mechanisms that influence broodiness. However, comparative research on the characteristics of long non-coding RNAs (lncRNAs) in the ovaries of broody chickens (BC) and high egg-laying chickens (GC) remains scant. In this investigation, we employed RNA sequencing to assess the ovarian transcriptomes, which include both lncRNAs and mRNAs, in eight Taihe Black-Bone Silky Fowls (TBsf), categorized into broody and high egg-laying groups. This study aims to provide a clearer understanding of the genetic underpinnings associated with broodiness and egg production. Results We have identified a total of 16,444 mRNAs and 18,756 lncRNAs, of which 349 mRNAs and 651 lncRNAs exhibited significantly different expression (DE) between the BC and GC groups. Furthermore, we have identified the cis-regulated and trans-regulated target genes of differentially abundant lncRNA transcripts and have constructed an lncRNA-mRNA trans-regulated interaction network linked to ovarian follicle development. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation analyses have revealed that DE mRNAs and the target genes of DE lncRNAs are associated with pathways including neuroactive ligand-receptor interaction, CCR6 chemokine receptor binding, G-protein coupled receptor binding, cytokine-cytokine receptor interaction, and ECM-receptor interaction. Conclusion Our research presents a comprehensive compilation of lncRNAs and mRNAs linked to ovarian development. Additionally, it establishes a predictive interaction network involving differentially abundant lncRNAs and differentially expressed genes (DEGs) within TBsf. This significantly contributes to our understanding of the intricate interactions between lncRNAs and genes governing brooding behavior.

The security of network data transmission has always been a focus of attention. With the rapid development of quantum computers and the rise of intelligent algorithms such as semantic analysis, traditional data encryption methods struggle to ensure the secure transmission of data. The avoidance routing protocol improves transmission security by circumventing malicious nodes and dangerous areas, but it misses the actual optimal path setup, reducing routing opportunities and increasing communication delays, thus failing to meet the application requirements that demand high security and low latency. To address these issues, this paper proposes a multi-path secure transmission method (FCMPR) based on link security assessment and fountain coding. We have designed a link security assessment method based on a random forest traffic detection model, which can obtain the link's confidence level to assess its security. Furthermore, we have proposed a secure multi-path routing transmission mechanism based on fountain code, enabling safe and rapid data transmission without avoiding malicious nodes. Experimental results show that even with a high proportion of malicious nodes in the network, FCMPR can significantly improve the data transmission rate, reduce the transmission delay, and enhance communication quality while ensuring data security.

The exchange of perceptual information between autonomous vehicles could significantly improve driving safety. In general, obtaining more information means driving more safely. However, Frequent information sharing consumes a significant amount of channel bandwidth resources, which will reduce transmission efficiency and increase delay, especially in crowded cities. This paper presents a novel method of motion prediction compensation to solve this problem. Firstly, we propose a distributed fountain coding scheme to improve transmission efficiency and reduce vehicles’ delay in acquiring peripheral information. Secondly, we design a mobile prediction model and information transmission control algorithm to reduce traffic while ensuring information reliability. The simulation results show that the prediction accuracy of this method is above 94 %, the information transmission is reduced by more than 50 %, and the vehicle perception rate is increased by 34 %.

Internal solitary waves (ISWs) resonate with meter-scale surface waves, which causes crests to overturn and waves to break strongly, generating many whitecaps. We analyze the polarized SAR characteristics of ISWs based on nonpolarized scattering theory and polarization decomposition theory. The surface breaking waves induced by ISWs generate short-scale waves carrying Bragg waves on the one hand, which enhances the polarized Bragg scattering (PD) contribution at the decimeter or centimeter scale and generate additional nonpolarized scattering (NP) contribution through specular scattering on the other hand. The copolarized scattering intensity in ISW regions is always higher than that in unperturbed sea surface under both L and C bands, with higher PD and NP contributions. The average PD is 1.3 (1.7) times higher than that of the unperturbed sea surface, and the average NP is approximately 82% (165%) higher than that of the sea surface. The PD and NP values associated with the convergent zones are always larger than those associated with the divergent zones, indicating that breaking waves mainly occur in convergence. The results of $\bm{HA}\alpha$ decomposition show that the scattering mechanism of ISWs is surface scattering with reduced scattering entropy and mean scattering angle. We construct compact polarization based on fully polarized SAR, and extract polarization parameters that indicate increased degree of polarization at ISWs, which supports our inference of enhanced specular scattering.

Erythropoietin-producing hepatocyte receptor A2 (EphA2) is an attractive target for immunotherapy due to its high expression in a variety of solid tumors including prostate cancer. Among various types of immunotherapeutics, chimeric antigen receptor T (CAR-T) cell therapy has made promising progress in hematological and solid tumors. Here, we detected the expression of EphA2 in prostate cancer cells and developed a second-generation CAR targeting EphA2 with CD28 as a co-stimulatory receptor to explore its tumor suppressive potential for prostate cancer in vitro and in vivo. EphA2 was highly expressed on the surface of PC3 and DU145 cells. EphA2 CART cells effectively inhibited prostate cancer growth in an antigen-dependent manner in vitro and in vivo. In addition, tumor cells could stimulate the proliferation of CAR-T cells and the release of cytokine IFN-γ in vitro. These findings shed light on EphA2 as a potential target for prostate cancer, promising EphA2 specific CAR-T cells for the treatment of prostate cancer.

Purpose: As an important reference index for the early diagnosis of non-small cell lung cancer (NSCLC), CYFRA21-1 still lacks the detection of low equipment cost, wide linear range and high sensitivity. Surface-enhanced Raman scattering (SERS) is a vibration spectrum technology based on the surface plasma of precious metal nanoparticles, which has been effectively applied to the detection of tumor markers. The combination of SERS technology and sensors has great potential in the ultrasensitive detection of tumor markers. The purpose of this study was to develop a sensitive method using SERS to quantitatively detect CYFRA21-1 for early diagnosis of NSCLC. Methods: A double-antibody sandwich immunoassay based on SERS was designed and tested to implement the ultrasensitive detection of CYFRA21-1 in the serum of NSCLC patients. Results: Gold @Ag nanorods (Au @Ag NRs) with higher Raman signals were prepared and used as probes, while magnetic graphene oxide was used as a magnetic substrate. The immunized probe, immune substrate and CYFRA21-1 standard substance in the buffer system formed a double-antibody sandwich structure. The standard curve displayed a liner range from 1pg mL−1 to 10 ng mL−1, and the detection limit (LOD) is 0.8943pg mL−1. The Raman intensity exhibited a wide linear relationship with the logarithm of CYFRA21-1 concentration. Conclusion: Our study successfully established a double-antibody sandwich immunoassay based on SERS. This method demonstrated high specificity and sensitivity for detecting CYFRA21-1 protein content in serum. It has the potential to be applied for early detection of lung cancer biomarkers.

The Taihe Black-Bone Silky Fowl (Gallus gallus domesticus Brisson) possesses significant value in terms of consumption, medicinal applications, and ornamental appeal, representing a precious genetic resource and traditional Chinese medicinal material. However, considerable variation exists within populations regarding egg-laying performance. This study integrates a whole-genome selection signal analysis (SSA) with a transcriptome analysis to identify genes associated with egg-laying traits in Taihe Black-Bone Silky Fowls. We identified 31 candidate genes under selection from the high-yield chicken (HC) and low-yield chicken (LC) groups. Additionally, through RNA-seq analysis, 257 common differentially expressed genes (DEGs) were identified from four comparative groups. Two overlapping genes—LPL and SETBP1—were found in both the selected gene and DEG lists. These selected genes and DEGs were enriched in pathways related to ovarian development, including the lysosome pathway, the ECM–receptor interaction pathway, the TGF-beta signaling pathway, the Wnt signaling pathway, the PPAR signaling pathway, and the glycerolipid metabolism pathway. These research findings contribute to the breeding of Taihe Black-Bone Silky Fowls with high egg production traits and provide a theoretical foundation for exploring the regulatory mechanisms of avian reproduction.

Introduction: Long non-coding RNA (lncRNA) refers to a category of non-coding RNA molecules exceeding 200 nucleotides in length, which exerts a regulatory role in the context of ovarian development. There is a paucity of research examining the involvement of lncRNA in the regulation of ovary development in Taihe Black-Bone Chickens. In order to further investigate the egg laying regulation mechanisms of Taihe Black-Bone Chickens at different periods, transcriptome analysis was conducted on the ovarian tissues at different laying periods.Methods: This study randomly selected ovarian tissues from 12 chickens for RNA-seq. Four chickens were selected for each period, including the early laying period (102 days, Pre), the peak laying period (203 days, Peak), and the late laying period (394 days, Late). Based on our previous study of mRNA expression profiles in the same ovarian tissue, we identified three differentially expressed lncRNAs (DE lncRNAs) at different periods and searched for their cis- and trans-target genes to draw an lncRNA-mRNA network.Results and discussion: In three groups of ovarian tissues, we identified 136 DE lncRNAs, with 8 showing specific expression during the early laying period, 10 showing specific expression during the peak laying period, and 4 showing specific expression during the late laying period. The lncRNA-mRNA network revealed 16 pairs of lncRNA-target genes associated with 7 DE lncRNAs, and these 14 target genes were involved in the regulation of reproductive traits. Furthermore, these reproductive-related target genes were primarily associated with signaling pathways related to follicle and ovary development in Taihe Black-Bone Chickens, including cytokine-cytokine receptor interaction, TGF-beta signaling pathway, tyrosine metabolism, ECM-receptor interaction, focal adhesion, neuroactive ligand-receptor interaction, and cell adhesion molecules (CAMs). This study offers valuable insights for a comprehensive understanding of the influence of lncRNAs on poultry reproductive traits.

Integration of multimodal therapies into one nanoplatform holds great promise to overcome the drawbacks of conventional single-modal therapy. Herein, we designed and constructed an ultrafine gold nanoparticle (Au NPs) decorated and methylene blue (MB) embedded core–shell-satellites structured Au@Ag nanorods@SiO2 nanocomposites (CSSNs) using a wet-chemical method. It can be found that excellent photothermal conversion performance and outstanding reactive oxygen species (ROS) production capacity were integrated on this one nonocomposites. In vitro experiments, all cancer cells were killed in nanocomposites with two laser irradiation group, which is better than that of nanocomposites without laser irradiation (12 % cancer cells were killed) and nanocomposites with one laser irradiation (660 nm laser, 84 % cancer cells were killed or 808 nm laser, 95 % cancer cells were killed). It is obviously that the synergistic effect was achieved between chemodynamic therapy (CDT), photodynamic therapy (PDT) and photothermal therapy (PTT). The vivo results further confirmed that the as-prepared nanocomposites can suppress the tumor growth without obvious systemic toxicity. This work will pave a new avenue for the construction of multifunctional nanocomposites and their multimodal therapy application for cancers.

Abstract B7-H3 is an attractive target for immunotherapy because of its high expression across multiple solid tumors, including prostate cancer, and restricted expression in normal tissues. Among various types of tumor immunotherapy, chimeric antigen receptor T (CAR-T) cell therapy has shown remarkable success in hematological tumors. However, the potency of CAR-T cell therapy in solid tumors is still limited. Here, we examined the expression of B7-H3 in prostate cancer tissues and cells and developed a second-generation CAR that specifically targets B7-H3 and CD28 as costimulatory receptor to explore its tumoricidal potential against prostate cancer in vitro and in vivo. The high expression of B7-H3 was detected on both the surface of PC3, DU145 and LNCaP cells and prostate cancer tissues. B7-H3 CAR-T cells efficiently controlled the growth of prostate cancer in an antigen-dependent manner in vitro and in vivo. Moreover, tumor cells could induce the proliferation of CAR-T cells and the release of high levels of cytokines of IFN-γ and TNF-α in vitro. Results demonstrated that B7-H3 is a potential target for prostate cancer therapy that supports the clinical development of B7-H3 specific CAR-T cells for prostate cancer.

With the rapid development of urban transportation and the increase in per capita car ownership, the problem of urban traffic congestion is becoming increasingly prominent. Due to the uneven distribution of crowd in different regions of the city, it is difficult to determine and solve the traffic dynamics congestion. In order to solve the problem that it is difficult to determine the dynamics of traffic congestion areas caused by uneven distribution of vitality in different regions of mountainous cities, a crowded mega mountainous city is selected as research object and it proposes a model to calculate the change characteristics of regional crowd gathering. Baidu Heatmap is used as it could distinguish crowd gathering in certain urban core area. The heat map pictures in dozens of consecutive days is extracted and researchers conducted pixel statistical classification on thermal map images. Based on the pixel data of different levels of the pictures, the calculation model is established and an algorithm based on particle swarm optimization is proposed. The calibration of the relative active population equivalent density is conducted, and the distribution characteristics of crowd gathering in time and space are analyzed. The results show that there are obvious spatiotemporal characteristics for this selected city. In time, holidays have an important impact on crowd gathering. The peak time of crowd gathering on weekdays is different from that on rest days. The research in this paper has a direct practical value for the identification of traffic congestion areas and the corresponding governance measures. The dynamic identification of population gathering areas in mountainous mega cities, demand prediction for various transportation regions, and future population OD(Origin—Destination) planning are of great significance.

Abstract Due to the time-varying and space-varying characteristics of the underwater acoustic channel, the communication process may be seriously disturbed. Thus, the underwater acoustic communication system is facing the challenges of alleviating interference and improving communication quality and communication efficiency through adaptive modulation. In order to select the optimal modulation mode adaptively and maximize the system throughput ensuring that the bit error rate (BER) meets the transmission requirements, this paper introduces deep reinforcement learning (DRL) into orthogonal frequency division multiplexing acoustic communication system. The adaptive modulation is mapped into a Markov decision process with unknown state transition probability. Thereby, the underwater communication channel environment is regarded as the state of DRL, and the modulation mode is regarded as action. The system returns channel state information (CSI) and signal–noise ratio in every time slot through the feedback link. Because the Deep Q-Network optimizes in the changing state space of each time slot, it is suitable for a variety of different CSI. Finally, simulations in different underwater environments (SWellEx-96) show that the proposed adaptive modulation scheme can obtain lower BER and improve the system throughput effectively.

Abstract A disintegrin and metalloprotease-10(ADAM10) promotes the metastasis of prostate cancer (PCa), but the specific mechanism is indistinct. Herein, DU145 cell lines with stable overexpression and knockdown of ADAM10 were constructed. We found that ectopic expression of ADAM10 not only significantly facilitated cell proliferation, migration, invasion, and inhibited apoptosis, but also could specifically hydrolyze ephrin-A5 and release the ephrin-A5 soluble ectodomain into extracellular media in vitro. These effects were reversed by ADAM10 depletion or treatment of GI254023X. Meanwhile, the co-location and physical interaction among EphA3, ephrin-A5, and ADAM10 were observed in PCa cells using immunofluorescence and immunoprecipitation techniques. Interestingly, overexpression of EphA3 exerted opposite effects in DU145 (ephrin-A5 + ) cells and PC-3 (ephrin-A5 ± ) cells. In addition, the pro-tumor function of EphA3 was reversed by the treatment with the exogenous ephrin-A5-Fc, which increased the phosphorylation level of EphA3 in PC-3 (ephrin-A5 ± ) cells. In nude mice, ADAM10 accelerated growth of the primary tumor, decreased the level of ephrin-A5 in the tumor tissue, but increased the level of ephrin-A5 in the peripheral blood, accompanied with an increase in the expression of CD31 and VEGF (vascular endothelial growth factor) in the tissue. What is more, the serum ephrin-A5 content of patients with metastatic PCa was significantly higher than that of the non-metastatic group (P

The use of γ-secretase inhibitors to inhibit the activation of Notch receptors can effectively inhibit the malignant process of tumors. Here, we demonstrate that p53 can modulate the effect of DAPT (a γ-secretase inhibitor) on the activation of small GTPase Rac1, thereby affecting cell migration of non-small-cell lung cancer H1299 and A549 cells. After treatment with 20 μM DAPT, activation of Rac1 was increased in H1299 cells but not in A549 cells. We further found that the migration ability of H1299 cells was increased, whereas that of A549 cells was reduced. The effect of DAPT on H1299 migration was repressed by Rac1-T17N, a dominant inactivated mutant of Rac1. H1299 is a p53-deficient cell line. When p53 protein was overexpressed in H1299 cells with a pEGFP-p53 plasmid, DAPT treatment no longer activated Rac1 and increased migration ability. Moreover, DAPT promoted the migration of H1299 cells by increasing the activity of Rac1 through the non-canonical Notch pathway. Taken together, these results indicate that the expression of p53 protein in lung cancer cells regulates the effect of DAPT on cell migration by modulating the activation of Rac1, suggesting that p53 may affect the therapeutic effects of Notch inhibitors in lung cancer patients.

More convolutional neural network (CNN) methods are widely utilized for ship detection in synthetic aperture radar (SAR) images. Nevertheless, there are still some problems that need to be addressed. First, detection methods may misclassify islands and onshore objects as ships. Second, due to the large scale and low resolution, a majority of ships are regarded as small targets and difficult to detect. Third, due to the different sizes of ships, the hyperparameters of anchor-based methods are difficult to set. Thus, we propose an anchor-free method called improved fully convolutional one-stage object detector (Improved-FCOS) to solve these problems. First, a multilevel feature attention mechanism is proposed to extract effective features and collect global context information. Second, we propose a feature refinement and reuse module with two stages to refine small ship features. Third, a head improvement module is designed to optimize the methods for classification and localization of ship targets. Finally, a modified varifocal loss is adopted to better train the classification branch. We conduct ablation experiments on LS-SSDD-v1.0 and verify the detection performance of the proposed modules. Additionally, we compare the Improved-FCOS with other state-of-the-art methods by experiments on three datasets, including the SAR-Ship-Dataset, HRSID, and LS-SSDD-v1.0. Detection results show that Improved-FCOS obtains the best detection performance on all datasets and indicate that the Improved-FCOS is more accurate and robust.

A graphene quantum dots (GQDs) and manganese dioxide (MnO2) co-decorated, polydopamine (PDA) capped iron oxyhydroxide (FeOOH) nanorod was firstly constructed as a promising platform for magnetic resonance imaging (MRI) guided tumor synergistic therapy. Each component played an irreplaceable role. Tumor microenvironment activated cascade Fenton catalytic activities and MRI capability at different stages were obtained, which can simultaneously realize diagnosis and chemodynamic therapy (CDT) for cancer. In addition, photothermal therapy (PTT) was also achieved owing to the excellent photothermal conversion efficiency of PDA and GQDs, which further improve the Fenton catalytic activities and realized synergistic therapy for cancer. The experimental results showed that only about 30% of cancer cells was killed when CDT or NIR laser alone for cancer inhibition. However, when PTT and CDT were employed simultaneously to treat the cancer cells, the therapeutic efficiency has increased by 300% (90% of cancer cells was killed and about 10% cancer cells survived) due to combinatorial effect of PTT and CDT. Obviously, a more effective treatment of cancer could be realized using MRI guided tumor synergistic therapy with the as-prepared nanocomposites. This study provides a proof of concept of design and construction of complex nanomaterials for cancer diagnosis and treatment.

Multicarrier backscattering has been proposed to improve the communication rate, but the complex circuit structure of multicarrier backscattering devices requires more power consumption, resulting in devices far away from the radio frequency (RF) source without enough power to maintain communication, which greatly reduces the limited communication range in backscattering. To solve this problem, this paper introduces carrier index modulation (IM) into orthogonal frequency division multiplexing (OFDM) backscattering and proposes a dynamic subcarrier activated OFDM-IM uplink communication scheme suitable for passive backscattering devices. When the existing power collection level of the backscatter device is detected, only a subset of carrier modulation is activated using part of the circuit modules to reduce the power threshold required for device activation. The activated subcarriers are mapped by a block-wise combined index using the look-up table method, which can not only transmit information using traditional constellation modulation but also carry additional information through the frequency domain carrier index. Monte Carlo experiments show that this scheme can effectively increase the communication distance and improve the spectral efficiency of low-order modulation backscattering when the power of the transmitting source is limited.

Ovarian cancer is one of the most common gynecological malignancies in women worldwide with a poor survival rate. Cinnamaldehyde (CA), a bioactive substance isolated from cinnamon bark, is a natural drug and has shown that it can inhibit the progression of other tumors. However, the role of CA in ovarian cancer and its mechanism is poorly understood. In this study, wound healing assays, plate cloning, CCK-8, and transwell assays were used to determine cell proliferation and invasion. Western blot and flow cytometry were used to detect apoptosis levels. Western blot and immunofluorescence were used to detect changes in cellular EMT levels. The Western blot was used to detect levels of the PI3K/AKT signaling pathway. In vivo, we established a subcutaneous transplantation tumor model in nude mice to verify the role of CA in the progression and metastasis of ovarian cancer. Our data showed that in vitro CA was able to inhibit the cell viability of ovarian cancer. The results of scratch assay and transwell assay also showed that CA inhibited the proliferation and invasion ability of A2780 and SKOV3 cells. In addition, CA promoted apoptosis by increasing the expression of cleaved-PARP and cleaved-caspase 3 in ovarian cancer cells. Mechanistically, we found that CA inhibited the EGF-induced PI3K/AKT signaling pathway and reduced the phosphorylation levels of mTOR, PI3K, and AKT. The EGF-induced EMT process was also abolished by CA. The EMT process induced by AKT-specific activator SC79 was also suppressed by CA. Furthermore, in in vivo, CA significantly repressed the progression of ovarian cancer as well as liver metastasis. In all, our results suggest that CA inhibits ovarian cancer progression and metastasis in vivo and in vitro and inhibits EGF-induced EMT processes through the PI3K/AKT signaling pathway.

Marine oil spills can cause serious damage to marine ecosystems and biological species, and the pollution is difficult to repair in the short term. Accurate oil type identification and oil thickness quantification are of great significance for marine oil spill emergency response and damage assessment. In recent years, hyperspectral remote sensing technology has become an effective means to monitor marine oil spills. The spectral and spatial features of oil spill images at different levels are different. To accurately identify oil spill types and quantify oil film thickness, and perform better extraction of spectral and spatial features, a multilevel spatial and spectral feature extraction network is proposed in this study. First, the graph convolutional neural network and graph attentional neural network models were used to extract spectral and spatial features in non-Euclidean space, respectively, and then the designed modules based on 2D expansion convolution, depth convolution, and point convolution were applied to extract feature information in Euclidean space; after that, a multilevel feature fusion method was developed to fuse the obtained spatial and spectral features in Euclidean space in a complementary way to obtain multilevel features. Finally, the multilevel features were fused at the feature level to obtain the oil spill information. The experimental results show that compared with CGCNN, SSRN, and A2S2KResNet algorithms, the accuracy of oil type identification and oil film thickness classification of the proposed method in this paper is improved by 12.82%, 0.06%, and 0.08% and 2.23%, 0.69%, and 0.47%, respectively, which proves that the method in this paper can effectively extract oil spill information and identify different oil spill types and different oil film thicknesses.

The received signal strength (RSS) fingerprint-based indoor localization has been considered as a promising solution, due to its relatively high localization accuracy and its ease of use in widespread Wireless Local Area Network (WLAN) infrastructure. A major bottleneck is that the offline fingerprint calibration is time consuming and labor intensive. In this study, inspired by our analysis that multi-density is inherent to the RSS distribution, we present a new radio map construction scheme, called Adaptive Density Graph-based Manifold Alignment (ADG-MA), which can reduce the number of Reference Points (RPs) in offline phase. In particular, it utilizes the density features to capture the exact neighborhood relations of RSS. Furthermore, the approach labels the RSS from user traces to construct the radio map. The extensive experiments demonstrate that the proposed method can construct an accurate radio map at a low deployment cost, as well as achieve a high localization accuracy.

The introduction of the relay in heterogeneous networks (HetNets) can enhance cell-edge signals, but it will also bring coexistence of multiple interference which is composed of co-tier, cross-tier and cross time slot (caused by the half-duplex mode of the relay) interference, resulting in the ineffectiveness of the existing interference alignment (IA) schemes. To solve the above problem, the partial IA scheme, which achieves IA in the case of limited antennas by rationally designing the order and method of eliminating interference, is proposed in this paper. First, we align the interference from other base stations (BSs) into the interference space generated by pico BS to reduce the interference dimension of the relay, and then solve the interference between BSs and users by null space and concatenated precoding schemes. Furthermore, the feasibility condition of antenna configuration is analyzed. The simulation results show that the proposed scheme is superior to the traditional IA scheme in sum rate, and the performance of the proposed scheme is further improved as the relay transmission power increases.

Underwater acoustic target recognition is very complex due to the lack of labeled data sets, the complexity of the marine environment, and the interference of background noise. In order to enhance it, we propose an attention-based residual network recognition method (AResnet). The method can be used to identify ship-radiated noise in different environments. Firstly, a residual network is used to extract the deep abstract features of three-dimensional fusion features, and then a channel attention module is used to enhance different channels. Finally, the features are classified by the joint supervision of cross-entropy and central loss functions. At the same time, for the recognition of ship-radiated noise in other environments, we use the pre-training network AResnet to extract the deep acoustic features and apply the network structure to underwater acoustic target recognition after fine-tuning. The two sets of ship radiation noise datasets are verified, the DeepShip dataset is trained and verified, and the average recognition accuracy is 99%. Then, the trained AResnet structure is fine-tuned and applied to the ShipsEar dataset. The average recognition accuracy is 98%, which is better than the comparison method.

This study revealed changes in the quality, structural and functional collagen properties of cattle rumen smooth muscle (CSM) during F-T cycles. The results showed that thawing loss, pressing loss, β-galactosidase, β-glucuronidase activity, β-sheet content, emulsifying activity index (EAI), emulsion stability index (ESI), surface hydrophobicity, and turbidity of samples were significantly (p < 0.05) increased by 108.12%, 78.33%, 66.57%, 76.60%, 118.63%, 119.57%, 57.37%, 99.14%, and 82.35%, respectively, with increasing F-T cycles. Meanwhile, the shear force, pH, collagen content, α-helix content, thermal denaturation temperature (Tmax), and enthalpy value were significantly (p < 0.05) decreased by 30.88%, 3.19%, 33.23%, 35.92%, 10.34% and 46.51%, respectively. Scanning electron microscopy (SEM) and SDS-PAGE results indicated that F-T cycles induced an increase in disruption of CSM muscle microstructure and degradation of collagen. Thus, repeated F-T cycles promoted collagen degradation and structural disorder in CSM, while reducing the quality of CSM, but improving the functional collagen properties of CSM. These findings provide new data support for the development, processing, and quality control of CSM.

In the past decades, emerging technologies such as unmanned driving and indoor navigation have developed rapidly, and simultaneous localization and mapping has played unparalleled roles as core technologies. However, dynamic objects in complex environments will affect the positioning accuracy. In order to reduce the influence of dynamic objects, this article proposes an improved simultaneous localization and mapping algorithm combined with semantic segmentation model. First, in the pre-processing stage, in order to reduce the influence of dynamic features, fully convolutional network model is used to find the dynamic object, and then the output image is masked and fused to obtain the final image without dynamic object features. Second, in the feature-processing stage, three parts are improved to reduce the computing complexity, which are extracting, matching, and eliminating mismatching feature points. Experiments show that the absolute trajectory accuracy in high dynamic scene is improved by 48.58% on average. Meanwhile, the average processing time is also reduced by 21.84%.

As a new flavor for the successive cancellation decoding of polar codes, bit-flipping technology can be used to improve the frame error rate performance of polar codes at moderate code lengths. In this paper, a generalized segmented scheme of bit-flipping technology is taken into full consideration, in which several segmented decoding patterns are described. First, the basic ones considering single-error-correcting and multiple-error-correcting are described, where a fully protected CRC is used. Second, with a constructed critical set for the segmented scheme, the improved version of the above basic patterns is fully discussed. Unfrozen bits corresponding to the critical set called critical information bits are divided into several parts and protected by cyclic redundancy check codes. Third, for a better performance of the segmented scheme, a segmented strategy indicating the positions of segments is effectively designed by analyzing the effect of positions of cyclic redundancy check and the error probability of each bit. At last, the frame error rate performance and the average computational complexity of the segmented decoding patterns are analyzed at matching average computational complexity or under equivalent frame error rate for different rates of polar codes. By proper design, the simulation results in additive white Gaussian noise channel demonstrated that the segmented decoding patterns can efficiently improve the error-correction performance of the flip-based scheme at different rates of polar codes while keeping a lower complexity.

Internal solitary waves (ISWs) near the Dongsha Atoll in the northern South China Sea (SCS) can be divided into incident, reflected, and refracted waves. Compared with the incident and refracted ISWs, the reflected ISWs are less likely to appear, but their impact on the ecological environment and marine activity should not be underestimated. In this work, field experiments were performed and moderate-resolution imaging spectroradiometer (MODIS) images were collected to analyze the reflected ISWs. Satellite observations showed that they were excited by the collision between the incident ISWs and the Dongsha Atoll and are often in the form of a wave packet composed of 2 to 5 solitons. During propagation, its spatial range gradually increases and interacts with the incoming waves generated by the next tidal cycle until it dissipates at approximately 117.5° E. Eighty percent of the reflected ISWs occur from April to June. The length of the crest line is mainly between 50 and 150 km, and the average propagation speed is approximately 1.57 m/s, which is smaller than that of the incident ISWs. In situ observations showed that the amplitudes of the reflected ISWs were between 10 and 20 m, accounting for only 40% of the incident ISWs. Compared with the incident ISWs, the vertical, velocity, and zonal velocities of the reflected ISWs were all attenuated. The results of the depth-integrated horizontal energy flux calculation showed that the energy of the reflected ISWs was only 61% of that of the incident ISWs. The reflected ISWs accelerated the velocity of the surface flow field during propagation, and the maximum velocity on the sea surface was approximately 0.60 m/s. Strong tides are beneficial for the generation of reflected ISWs near the Dongsha Atoll. Incident and reflected ISWs are all first-mode ISWs.

Jiangsu coastal wetland has the largest area of the invasive plant, Spartina alterniflora (S. alterniflora), in China. S. alterniflora has been present in the wetland for nearly 40 years and poses a substantial threat to the safety of coastal wetland ecosystems. There is an urgent need to control the distribution of S. alterniflora. The biological characteristics of the invasion process of S. alterniflora contribute to its multi-scale distribution. However, the current classification methods do not deal successfully with multi-scale problems, and it is also difficult to perform high-precision land cover classification on multi-temporal remote sensing images. In this study, based on Landsat data from 1990 to 2020, a new deep learning multi-scale residual convolutional neural network (MRCNN) model was developed to identify S. alterniflora. In this method, features at different scales are extracted and concatenated to obtain multi-scale information, and residual connections are introduced to ensure gradient propagation. A multi-year data unified training method was adopted to improve the temporal scalability of the MRCNN. The MRCNN model was able to identify the annual S. alterniflora distribution more accurately, overcame the disadvantage that traditional CNNs can only extract feature information at a single scale, and offered significant advantages in spatial characterization. A thematic map of S. alterniflora distribution was obtained. Since it was introduced in 1982, the distribution of S. alterniflora has expanded to approximately 17,400 ha. In Jiangsu, the expansion process of S. alterniflora over time was divided into three stages: the growth period (1982–1994), the outbreak period (1995–2004), and the plateau period (2005–2020). The spatial expansion direction was mainly parallel and perpendicular to the coastline. The hydrodynamic conditions and tidal flat environment on the coast of Jiangsu Province are suitable for the growth of S. alterniflora. Reclamation of tidal flats is the main factor affecting the expansion of S. alterniflora.

Objectives: This study aims to investigate the diagnostic and prognostic values of EpCAM, TGM2, and HE4 in endometrial cancer (EC).Methods: In this study, 42 patients diagnosed with EC (EC group), 41 patients diagnosed with myoma (benign group), and 43 healthy women (healthy group), who applied to Affiliated Hospital of Xuzhou Medical University between March 2018 - September 2019 were recruited. Serum EpCAM, TGM2, and IL-33 levels were measured by ELISA, while serum HE4 and CA-125 levels were measured by ECLIA. The serum markers listed above were also measured in 12 paired pre- and post-operative EC patients. The diagnostic and prognostic values of serum markers were analyzed.Results: The serum EpCAM, TGM2, HE4, CA-125, and IL-33 levels were significantly higher in the EC group. The sensitivity and specificity of combined detection of EpCAM and HE4 was 92.86 and 69.05%, which were significantly higher than using a single marker or other combinations. Among these markers, serum HE4 levels were significantly higher in patients with myometrial invasion, metastasis, and lymphovascular invasion (p = 0.006, p = 0.0004, p = 0.0004, respectively). And serum TGM2 levels were significantly decreased in post-operative than that of pre-operative EC patients (p < 0.001).Conclusions: The combination of EpCAM and HE4 showed the highest specificity and sensitivity in the diagnosis of EC. HE4 was successful in the detection of high-risk individuals preoperatively. Additionally, TGM2 might be a prognostic factor for EC.

Data dissemination is an essential application in Internet of Things (IoT) network. It is generally relayed hop-by-hop due to the limitation of the communication distance of IoT devices. However, multi-hop transmission increases dissemination time, especially in a low duty-cycle network. Channel contention is another factor result in long delay, because most of IoT devices and Wi-Fi equipment work within the same bandwidth (2.4 GHz). To reduce the transmission delay of data dissemination, our group proposes a method of multi-channel transmission simultaneously (MCTS) using no-feedback fountain code. Instead of terminating sending data by feedback as a traditional fountain code protocol, in the MCTS, the sink node pours a certain amount of packets into the network according to the distance of object nodes to guarantee reliable transmission. By making use of channel encoding, the data to ZigBee nodes can be carried by some subcarriers of Wi-Fi in multiple ZigBee channels simultaneously when Wi-Fi sends itself data. This method technically avoids the delay from channel contention. Compared with the flooding method, the delay of the MCTS sending data from the sink node to all ZigBee nodes is decreased to 1/4.

The Tiangong-2 space laboratory was launched by China on 15 September 2016, carrying the Interferometric Imaging Radar Altimeter (InIRA), the first of the latest generation of imaging altimeters that can perform imaging and acquire elevation information simultaneously. This paper analyzes the feasibility of using InIRA images to obtain two-dimensional characteristics of oceanic internal solitary waves (ISWs) and information about vertical sea surface fluctuations caused by the propagation of ISWs. The results show that InIRA demonstrates a relatively reliable ability to observe ISWs with high resolution and can identify the fine-scale features of ISWs of different forms. Furthermore, InIRA can observe centimeter-level changes in the Sea Surface Height Anomaly (SSHA) caused by ISWs. The geometric relationship between the sensor’s flight direction and the propagation direction of ISWs does not affect its detection effect. However, the swath width of InIRA is too narrow to fully capture ISW information, and the height accuracy of InIRA height product images is not insufficient to detect the height information of small-scale ISWs. These shortcomings need to be considered in the future development of imaging altimeters to increase their potential for detecting mesoscale phenomena in the ocean.

Dental resin composites (DRCs) with diverse fillers added are widely-used restorative materials to repair tooth defects. The addition of fillers brings an improvement in the mechanical properties of DRCs. In the past decade, diverse fillers have emerged. However, the change of emerging fillers mainly focuses on the chemical composition, while the morphologic characteristics changes are often ignored. The fillers with new morphologies not only have the advantages of traditional fillers (particles, fibrous filler, etc.), but also endow some additional functional characteristics (stronger bonding ability to resin matrix, polymerization resistance, and wear resistance, drug release control ability, etc.). Moreover, some new morphologies are closely related to the improvement of traditional fillers, porous filler vs. glass particles, core-sheath fibrous vs. fibrous, etc. Some other new morphology fillers are combinations of traditional fillers, UHA vs. HA particles and fibrous, tetrapod-like whisker vs. whisker and fibrous filler, mesoporous silica vs. porous and silica particles. In this review, we give an overall description and a preliminary summary of the fillers, as well as our perspectives on the future direction of the development of novel fillers for next-generation DRCs.

Because of the specific characteristics, like high vehicular mobility, unstable topology, and interruption of inter-vehicle wireless communication, it is hard to make a perfect decision on packet forwarding in highly dynamic topology VANETs. Especially in a sparse urban environment, the poor connectivity of nodes will mostly cause problems such as data packet loss and routing redundancy. Therefore, how to choose the best relay node becomes a key challenge in the design of fast and reliable routing protocols. This paper presents real-time effective information traffic routing (RTEIT), which provides an optimal route for forwarding the data packets toward their destination when choosing the relay node. RTEIT introduces a new network parameter named effective information traffic which can estimate the connectivity of nodes by the path that has been successfully created. Moreover, for avoiding unexpected communication interruption, we propose a new formula to evaluate the status of the links via considering the speed, direction, and location information. Finally, the node utility, as the criterion of routing decision, is calculated by effective information traffic and link status. We use the simulator of SUMO and NS-3 platform to evaluate RTEIT, and the results are compared with GPSR MOPR, and MM-GPSR. The evaluation results demonstrate that RTEIT outperforms in terms of packet loss rate, end-to-end delay, and network yield.

This paper proposes a low-complexity estimation algorithm for weighted subspace fitting (WSF) based on the Genetic Algorithm (GA) in the problem of narrow-band direction-of-arrival (DOA) finding. Among various solving techniques for DOA, WSF is one of the highest estimation accuracy algorithms. However, its criteria is a multimodal nonlinear multivariate optimization problem. As a result, the computational complexity of WSF is very high, which prevents its application to real systems. The Genetic Algorithm (GA) is considered as an effective algorithm for finding the global solution of WSF. However, conventional GA usually needs a big population size to cover the whole searching space and a large number of generations for convergence, which means that the computational complexity is still high. To reduce the computational complexity of WSF, this paper proposes an improved Genetic algorithm. Firstly a hypothesis technique is used for a rough DOA estimation for WSF. Then, a dynamic initialization space is formed around this value with an empirical function. Within this space, a smaller population size and smaller amount of generations are required. Consequently, the computational complexity is reduced. Simulation results show the efficiency of the proposed algorithm in comparison to many existing algorithms.

Small-scale motion recognition has received wide attention recently with the development of environmental perception technology based on WiFi, and some state-of-the-art techniques have emerged. The wide application of small-scale motion recognition has aroused people’s concern. Handwritten letter is a kind of small scale motion, and the recognition for small-scale motion based on WiFi has two characteristics. Small-scale action has little impact on WiFi signals changes in the environment. The writing trajectories of certain uppercase letters are the same as the writing trajectories of their corresponding lowercase letters, but they are different in size. These characteristics bring challenges to small-scale motion recognition. The system for recognizing small-scale motion in multiple classes with high accuracy urgently needs to be studied. Therefore, we propose MCSM-Wri, a device-free handwritten letter recognition system using WiFi, which leverages channel state information (CSI) values extracted from WiFi packets to recognize handwritten letters, including uppercase letters and lowercase letters. Firstly, we conducted data preproccessing to provide more abundant information for recognition. Secondly, we proposed a ten-layers convolutional neural network (CNN) to solve the problem of the poor recognition due to small impact of small-scale actions on environmental changes, and it also can solve the problem of identifying actions with the same trajectory and different sizes by virtue of its multi-scale characteristics. Finally, we collected 6240 instances for 52 kinds of handwritten letters from 6 volunteers. There are 3120 instances from the lab and 3120 instances are from the utility room. Using 10-fold cross-validation, the accuracy of MCSM-Wri is 95.31%, 96.68%, and 97.70% for the lab, the utility room, and the lab+utility room, respectively. Compared with Wi-Wri and SignFi, we increased the accuracy from 8.96% to 18.13% for recognizing handwritten letters.

The estimation of direction-of-arrival (DOA) of signals is a basic and important problem in sensor array signal processing. To solve this problem, many algorithms have been proposed, among which the Stochastic Maximum Likelihood (SML) is one of the most concerned algorithms because of its high accuracy of DOA. However, the estimation of SML generally involves the multidimensional nonlinear optimization problem. As a result, its computational complexity is rather high. This paper addresses the issue of reducing computational complexity of SML estimation of DOA based on the Alternating Minimization (AM) algorithm. We have the following two contributions. First using transformation of matrix and properties of spatial projection, we propose an efficient AM (EAM) algorithm by dividing the SML criterion into two components. One depends on a single variable parameter while the other does not. Second when the array is a uniform linear array, we get the irreducible form of the EAM criterion (IAM) using polynomial forms. Simulation results show that both EAM and IAM can reduce the computational complexity of SML estimation greatly, while IAM is the best. Another advantage of IAM is that this algorithm can avoid the numerical instability problem which may happen in AM and EAM algorithms when more than one parameter converges to an identical value.

We address the problem of DOA estimation in positioning of nodes in wireless sensor networks. The Stochastic Maximum Likelihood (SML) algorithm is adopted in this paper. The SML algorithm is well-known for its high resolution of DOA estimation. However, its computational complexity is very high because multidimensional nonlinear optimization problem is usually involved. To reduce the computational complexity of SML estimation, we do the following work. (1) We point out the problems of conventional SML criterion and explain why and how these problems happen. (2) A local AM search method is proposed which could be used to find the local solution near/around the initial value. (3) We propose an algorithm which uses the local AM search method together with the estimation of DML or MUSIC as initial value to find the solution of SML. Simulation results are shown to demonstrate the effectiveness and efficiency of the proposed algorithms. In particular, the algorithm which uses the local AM method and estimation of MUSIC as initial value has much higher resolution and comparable computational complexity to MUSIC.

Microinjection is a promising tool for microdroplet generation, while the microinjection for microdroplets generation still remains a challenging issue due to the Laplace pressure at the micropipette opening. Here, we apply a simple and robust substrate-contacting microinjection method to microdroplet generation, presenting a size-adjustable microdroplets generation method based on a critical injection (CI) model. Firstly, the micropipette is adjusted to a preset injection pressure. Secondly, the micropipette is moved down to contact the substrate, then, the Laplace pressure in the droplet is no longer relevant and the liquid flows out in time. The liquid constantly flows out until the micropipette is lifted, ending the substrate-contacting situation, which results in the recovery of the Laplace pressure at the micropipette opening, and the liquid injection is terminated. We carry out five groups of experiments whereupon 1600 images are captured within each group and the microdroplet radius of each image is detected. Then we determine the relationship among microdroplet radius, radius at the micropipette opening, time, and pressure, and, two more experiments are conducted to verify the relationship. To verify the effectiveness of the substrate-contacting method and the relationship, we conducted two experiments with six desired microdroplet radii are set in each experiment, by adjusting the injection time with a given pressure, and adjusting the injection pressure with a given time. Then, six arrays of microdroplets are obtained in each experiment. The results of the experiments show that the standard errors of the microdroplet radii are less than 2% and the experimental errors fall in the range of ±5%. The average operating speed is 20 microdroplets/min and the minimum radius of the microdroplets is 25 μm. This method has a simple experimental setup that enables easy manipulation and lower cost.

It is necessary to improve the energy efficiency of batteries in wireless sensor networks (WSNs). The multiple-input multiple-output (MIMO) technique has become an important means to ameliorate WSNs, and interference management is the core of improving energy efficiency. A promising approach is interference alignment (IA), which effectively reduces the interference and improves the throughput of a system in the MIMO interference channels. However, the IA scheme requires perfect channel state information (CSI) at all transceivers in practice, which results in considerable feedback overhead. Thus, limited IA feedback has attracted much attention. In this paper, we analyze the throughput loss of the K-user MIMO interference channels when each transmitter delivers multiple streams in one slot, and derives the upper-bound of the system interference leakage and throughput loss. Then, to reduce the interference leakage and throughput loss for the MIMO interference alignment with limited feedback, a joint power and feedback bit allocation optimization scheme is proposed. The simulation results show that, compared with the conventional schemes, the presented optimal scheme achieves less residual interference and better performance in the system throughput.

Several broadcast algorithms have been developed in recent years. However, the problem of reducing routing overhead in ad hoc networks is always to be concerned. This paper proposes an improved directional broadcast algorithm based on Brownian motion for AODV protocol (DBB-AODV). We bring Brownian motion into network model and gain the distribution of the nodes which encountered destination before. This algorithm uses encounter records to predict the direction to destination and forwards RREQ packets with reasonable probability according to the distribution just mentioned. Simulation results indicate that DBB-AODV saves up to 20% of average routing overhead compared to the probabilistic protocol and about 80% of the blind flooding.

This paper addresses the issue of reducing the computational complexity of Stochastic Maximum Likelihood (SML) estimation of Direction-of-Arrival (DOA). The SML algorithm is well-known for its high accuracy of DOA estimation in sensor array signal processing. However, its computational complexity is very high because the estimation of SML criteria is a multi-dimensional non-linear optimization problem. As a result, it is hard to apply the SML algorithm to real systems. The Particle Swarm Optimization (PSO) algorithm is considered as a rather efficient method for multi-dimensional non-linear optimization problems in DOA estimation. However, the conventional PSO algorithm suffers two defects, namely, too many particles and too many iteration times. Therefore, the computational complexity of SML estimation using conventional PSO algorithm is still a little high. To overcome these two defects and to reduce computational complexity further, this paper proposes a novel modification of the conventional PSO algorithm for SML estimation and we call it Joint-PSO algorithm. The core idea of the modification lies in that it uses the solution of Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and stochastic Cramer-Rao bound (CRB) to determine a novel initialization space. Since this initialization space is already close to the solution of SML, fewer particles and fewer iteration times are needed. As a result, the computational complexity can be greatly reduced. In simulation, we compare the proposed algorithm with the conventional PSO algorithm, the classic Altering Minimization (AM) algorithm and Genetic algorithm (GA). Simulation results show that our proposed algorithm is one of the most efficient solving algorithms and it shows great potential for the application of SML in real systems.

Cooperative downloading is one of the effective methods to improve the amount of downloaded data in vehicular ad hoc networking (VANET). However, the poor channel quality and short encounter time bring about a high packet loss rate, which decreases transmission efficiency and fails to satisfy the requirement of high quality of service (QoS) for some applications. Digital fountain code (DFC) can be utilized in the field of wireless communication to increase transmission efficiency. For cooperative forwarding, however, processing delay from frequent coding and decoding as well as single feedback mechanism using DFC cannot adapt to the environment of VANET. In this paper, a cooperative downloading method for VANET using concatenated DFC is proposed to solve the problems above. The source vehicle and cooperative vehicles encodes the raw data using hierarchical fountain code before they send to the client directly or indirectly. Although some packets may be lost, the client can recover the raw data, so long as it receives enough encoded packets. The method avoids data retransmission due to packet loss. Furthermore, the concatenated feedback mechanism in the method reduces the transmission delay effectively. Simulation results indicate the benefits of the proposed scheme in terms of increasing amount of downloaded data and data receiving rate.

Ovarian cancer is one of the most common gynecological malignancies In women worldwide with a poor survival rate. Cinnamaldehyde (CA), a bioactive substance isolated from cinnamon bark, is a natural drug and has shown that it can inhibit the progression of other tumors. However, the role of CA in ovarian cancer and its mechanism is poorly understood. In this study, wound healing assays, plate cloning, CCK-8, and transwell assays were used to determine cell proliferation and invasion. Western blot and flow cytometry were used to detect apoptosis levels. Western blot and immunofluorescence were used to detect changes in cellular EMT levels. The Western blot was used to detect levels of the PI3K/AKT signaling pathway. In vivo, we established a subcutaneous transplantation tumor model in nude mice to verify the role of CA in the progression and metastasis of ovarian cancer. Our data showed that in vitro CA was able to inhibit the cell viability of ovarian cancer. The results of scratch assay and transwell assay also showed that CA inhibited the proliferation and invasion ability of A2780 and SKOV3 cells. In addition, CA promoted apoptosis by increasing the expression of cleaved-PARP and cleaved-caspase 3 in ovarian cancer cells. Mechanistically, we found that CA inhibited the EGF-induced PI3K/AKT signaling pathway and reduced the phosphorylation levels of mTOR, PI3K, and AKT. The EGF-induced EMT process was also abolished by CA. The EMT process induced by AKT-specific activator SC79 was also suppressed by CA. Furthermore, in in vivo, CA significantly repressed the progression of ovarian cancer as well as liver metastasis. In all, our results suggest that CA inhibits ovarian cancer progression and metastasis in vivo and in vitro and inhibits EGF-induced EMT processes through the PI3K/AKT signaling pathway. [ABSTRACT FROM AUTHOR]

Mountainous road landscape is the main source of driving information. The characteristics of two-lane mountainous road result in real-time dynamic changes in the driver's vision interesting areas. In order to explore the dynamic gaze characteristics, a driving experiment is conducted, and the gaze data of 10 drivers are collected. Markov chain is used to analyze the change process of the gaze. The results show that: (1) when the current gaze point is in the straight front area, different road landscape has no significant impact on the gaze shift probability; (2) when the current gaze point is in the near left area, next gaze will expand the search scope to obtain much more driving information; (3) when the current gaze point is in the near right area, there is a high probability that the driver's next gaze will return to the front area; (4) when the current gaze point is in the far right area, the gaze will move back and forth between the near right and the far right areas; (5) when the current gaze point is in the far left area, there is a high probability that the gaze will remain in current area; (6) the main source of traffic information obtained by the driver in mountainous road landscape is the straight front area in the vision field, and the gaze point constantly shifts between the far ahead and the near ahead. The research results can provide technical reference for the construction of landscape in mountainous two-lane road. [ABSTRACT FROM AUTHOR]