Search

High Modulus Asphalt Mixtures (HMAM) have gained widespread utilization in asphalt pavement construction. It is great critical to study the fatigue characteristics of HMAM. At present, various fatigue test methods are employed in indoor testing to assess HMAM under different mechanical states. Therefore, there is a critical problem that the value of fatigue parameters is not unique in the structural design of HMAM. In this study, three fatigue testing methods were initially used to address the above issues: direct tensile testing, indirect tensile testing, and uniaxial compression testing. These tests help to determine the strength yield surface states at different loading rates. And to reveal the effect of loading rate on the mechanical behavior of HMAM. The results show a power function fit and a good correlation between strength and loading rate. The relationship models of strength with speed for indirect tensile, direct tensile and uniaxial compression tests are SI=3.22(v)0.14, SD=3.22(v)0.15 and SU=3.22(v)0.12, respectively.The higher the loading rate for the three fatigue test results, the greater the expansion of the strength yield surface.The fatigue life (Nf) of HMAM underwent testing, yielding its fatigue effective stress ratio. The relationship between the two was and then established. It is found that the point (1, 1) appears on the backextension line, thus effectively complementing the conventional S-N equation. In addition, the fatigue equations for various fatigue test methods were unified based on effective stress strength ratio and the fatigue life. This paper introduces a novel method for the antifatigue design of HMAM.

Abstract The collective light-matter interaction of chiral supramolecular aggregates or molecular ensembles with confined light fields remains a mystery beyond the current theoretical description. Here, we programmably and accurately build models of chiral plasmonic complexes, aiming to uncover the entangled effects of excitonic correlations, intra- and intermolecular charge transfer, and localized surface plasmon resonances. The intricate interplay of multiple chirality origins has proven to be strongly dependent on the site-specificity of chiral molecules on plasmonic nanoparticle surfaces spanning the nanometer to sub-nanometer scale. This dependence is manifested as a distinct circular dichroism response that varies in spectral asymmetry/splitting, signal intensity, and internal ratio of intensity. The inhomogeneity of the surface-localized plasmonic field is revealed to affect excitonic and charge-transfer mixed intermolecular couplings, which are inherent to chirality generation and amplification. Our findings contribute to the development of hybrid classical-quantum theoretical frameworks and the harnessing of spin-charge transport for emergent applications.

Low-dimensional nanomaterials have garnered significant interest for their unique electronic and optical properties, which are essential for advancing next-generation optoelectronic devices. Among these, tellurium suboxide (TeOx)-based nanowires (NWs), with their quasi-one-dimensional (1D) structure, offer distinct advantages in terms of charge transport and light absorption. In this study, we present a comprehensive investigation into the controlled synthesis, structural properties, and optoelectronic performance of TeOx nanowires. Nanowires were synthesized via chemical vapor deposition process and exhibited a high aspect ratio with excellent structural quality, confirmed through Raman spectroscopy, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The TeOx nanowires demonstrated high crystallinity, smooth surface morphology, and consistent growth across the substrate, making them suitable for scalable device fabrication. The optoelectronic characterization of a fabricated photodetector, based on a single TeOx nanowire, revealed remarkable photoresponsivity and stability across a broad range of light intensities. These findings position TeOx nanowires as promising candidates for future optoelectronic devices such as photodetectors and optical sensors.

Abstract Background Underlying liver disease is correlated with hepatocellular carcinoma (HCC) development in patients with hepatitis B virus (HBV) infection. However, the impact of hepatic inflammation and fibrosis on the patients’ prognoses remains unclear. Methods The clinicopathological data of 638 HBV-infected patients with early-stage HCC between 2017 and 2019 were prospectively collected. Hepatic inflammation and fibrosis were evaluated by experienced pathologists using the Scheuer score system. Survival analysis was analyzed using the Kaplan–Meier analysis. Results Application of the Scheuer scoring system revealed that 50 (7.9%), 274 (42.9%), and 314 (49.2%) patients had minor, intermediate, and severe hepatic inflammation, respectively, and 125 (15.6%), 150 (23.5%), and 363 (56.9%) patients had minor fibrosis, advanced fibrosis, and cirrhosis, respectively. Patients with severe hepatitis tended to have a higher rate of HBeAg positivity, higher HBV-DNA load, elevated alanine aminotransferase (ALT) levels, and a lower proportion of capsule invasion (all Pp

Abstract To explore the differences and relationships between the available SARS-CoV-2 strains and predict the potential evolutionary direction of these strains, we employ the hierarchical clustering analysis to investigate the evolutionary relationships between the SARS-CoV-2 strains utilizing the genomic sequences collected in China till January 7, 2023. We encode the sequences of the existing SARS-CoV-2 strains into numerical data through k-mer algorithm, then propose four methods to select the representative sample from each type of strains to comprise the dataset for clustering analysis. Three hierarchical clustering algorithms named Ward-Euclidean, Ward-Jaccard, and Average-Euclidean are introduced through combing the Euclidean and Jaccard distance with the Ward and Average linkage clustering algorithms embedded in the OriginPro software. Experimental results reveal that BF.28, BE.1.1.1, BA.5.3, and BA.5.6.4 strains exhibit distinct characteristics which are not observed in other types of SARS-CoV-2 strains, suggesting their being the majority potential sources which the future SARS-CoV-2 strains’ evolution from. Moreover, BA.2.75, CH.1.1, BA.2, BA.5.1.3, BF.7, and B.1.1.214 strains demonstrate enhanced abilities in terms of immune evasion, transmissibility, and pathogenicity. Hence, closely monitoring the evolutionary trends of these strains is crucial to mitigate their impact on public health and society as far as possible.

To investigate the differences in seismic performance of buckling-restrained braces (BRBs) with significantly different lengths and to explore the influence of length on the energy dissipation efficiency of BRBs within the same structure, this study designed and fabricated two BRBs with lengths of 8.5 m and 3 m based on an actual engineering project. Low-cycle reciprocating load tests were conducted to compare the performance of the two BRBs in terms of hysteretic energy dissipation capacity, tension–compression bearing capacity imbalance coefficient, cumulative plastic deformation capacity, and low-cycle fatigue life. Additionally, the energy dissipation and damping efficiency of BRBs of different lengths within the same structure was analyzed. The results indicate that under cyclic loading based on design displacement, the 8.5 m BRB exhibits a greater equivalent viscous damping ratio, cumulative hysteretic energy dissipation, and cumulative plastic deformation, leading to more efficient energy dissipation and damping effects. The length of the brace is a significant factor affecting the imbalance coefficient of tension–compression bearing capacity, with longer braces resulting in a larger imbalance coefficient. The 3 m BRB shows less deviation from the mean values of various fatigue parameters, indicating more stable low-cycle fatigue performance. Within the same structure, shorter BRBs with larger design displacements achieve higher energy dissipation efficiency, allowing for more effective utilization of their energy dissipation capacity. This study’s conclusions provide valuable references for designers in the rational selection of BRBs of different lengths in actual engineering projects and offer preliminary insights into the energy dissipation efficiency of BRBs of varying lengths within a structure.

Background To enhance perioperative outcomes, a perioperative registry that integrates high-quality real-world data throughout the perioperative period is essential. Singapore General Hospital established the Perioperative and Anesthesia Subject Area Registry (PASAR) to unify data from the preoperative, intraoperative, and postoperative stages. This study presents the methodology employed to create this database. Methods Since 2016, data from surgical patients have been collected from the hospital electronic medical record systems, de-identified, and stored securely in compliance with privacy and data protection laws. As a representative sample, data from initiation in 2016 to December 2022 were collected. Results As of December 2022, PASAR data comprise 26 tables, encompassing 153,312 patient admissions and 168,977 operation sessions. For this period, the median age of the patients was 60.0 years, sex distribution was balanced, and the majority were Chinese. Hypertension and cardiovascular comorbidities were also prevalent. Information including operation type and time, intensive care unit (ICU) length of stay, and 30-day and 1-year mortality rates were collected. Emergency surgeries resulted in longer ICU stays, but shorter operation times than elective surgeries. Conclusions The PASAR provides a comprehensive and automated approach to gathering high-quality perioperative patient data.

Abstract Damage to lamellar keratinocytes, an essential cellular component of the epidermal layer of hoof tissue, can have a detrimental effect on hoof health and the overall production value of dairy cows. We isolated and cultured cow lamellar keratinocytes using the Dispase II and collagenase methods. We purified them by differential digestion and differential velocity adherent methods at each passaging and identified them by keratin 14 immunofluorescence. We established an in vitro model of inflammation in laminar keratinocytes using LPS and investigated whether chicoric acid protects against inflammatory responses by inhibiting the activation of the TLR4/MAPK/NF-κB signaling pathway. The results showed that cow lamellar keratinocytes were successfully isolated and cultured by Dispase II combined with the collagenase method. In the in vitro inflammation model established by LPS, the Chicoric acid decreased the concentration of inflammatory mediators (TNF-α, IL-1β, and IL-6), down-regulated the mRNA expression of TLR4 and MyD88 (P

Transition metal dichalcogenides (TMDs), such as tungsten diselenide (WSe2), are expected to be used in next-generation optoelectronic devices due to their unique properties. In this study, we developed a simple method of using ethanol to scroll monolayer WSe2 nanosheets into nanoscrolls. These nanoscrolls have a quasi-one-dimensional structure, which enhances their electronic and optical properties. The characterization confirmed their unique structure, and the photodetectors made of these nanoscrolls have high sensitivity to polarized light, with anisotropy ratios of 1.3 and 1.7 at wavelengths of 638 nm and 808 nm. The enhanced light response is attributed to the large surface area and quantum wire-like behavior of the nanoscrolls, making them suitable for advanced polarization-sensitive devices.

Radar point clouds will experience variations in density, which may cause incorrect alerts during clustering. In turn, it will diminish the precision of the decision-level fusion method. To address this problem, a target detection algorithm based on fusing radar with a camera in the presence of a fluctuating signal intensity is proposed in this paper. It introduces a snow ablation optimizer (SAO) for solving the optimal parameters of the density-based spatial clustering of applications with noise (DBSCAN). Subsequently, the enhanced DBSCAN clusters radar point clouds, and the valid clusters are fused with monocular camera targets. The experimental results indicate that the suggested fusion method can attain a Balance-score ranging from 0.97 to 0.99, performing outstandingly in preventing missed detections and false alarms. Additionally, the fluctuation range of the Balance-score is within 0.02, indicating the algorithm has an excellent robustness.

Currently, the energy dissipation efficiency of intermediate column dampers is extremely low, and traditional lever amplification damping systems occupy a large space in buildings. Aiming at solving these problems, this paper puts forward a new intermediate column–lever negative stiffness viscous damper (CLNVD), which has the characteristics of small impact on building space and significant amplification of the damper displacement. The CLNVD consists of the following four parts: the viscous damper, the negative stiffness device, the lever, and the intermediate column. This paper introduces the displacement amplification coefficient (fd) to assess the CLNVD’s displacement amplification effect and introduces the energy dissipation coefficient (fE) to assess the CLNVD’s energy dissipation effect. The expressions for fd and fE are derived according to the geometric magnification coefficient and effective displacement factor. Moreover, the impacts of multiple factors including the CLNVD’s position, the lever’s amplification coefficient, the bending line stiffness of beam, the negative stiffness, the damping coefficient, the damping index, and the inter-story displacement on the CLNVD’s fd and fE are elaborated. The analysis results reveal the following: when the CLNVD is located in the middle of the span, the fd and fE of the CLNVD will be maximized, and fE will increase first and then decrease as the beam’s bending line stiffness increases. Meanwhile, the amplification capability of the CLNVD increases as the lever’s amplification coefficient χ rises. When the negative stiffness does not exist, there exists an optimum lever’s amplification coefficient χ that maximizes fE. When the combination of damping coefficient c and index α satisfies a specific relationship, fE of the CLNVD reaches its largest value. When the negative stiffness and the loss stiffness of VD are within the region proposed in this paper, the CLNVD will achieve a higher fd and avoid providing significant negative stiffness. Subsequently, this paper proposes an optimization design method of the CLNVD. Finally, the amplification effect of CLNVD as well as the effectiveness of its optimization design method are verified through examples. In the case study, the CLNVD offers a larger damping ratio under the circumstance of fortification earthquakes. Under fortification and rare earthquakes, the inter-story displacement of Scheme 1 has been decreased by half roughly. According to the above-mentioned results, the CLNVD provides a brand-new approach for designers in the seismic design of buildings. Furthermore, this paper will provide beneficial reference for the damping design of other amplification devices equipped with negative stiffness dampers.

With the installation of rubber isolation bearings in the upper and lower ground layers, an isolated step-terrace structure was created. Considering the ultimate bearing capacity of the rubber bearing under tension as the critical condition, a comprehensive framework was established to evaluate the overturning failure mechanisms present in isolated step-terrace structures. The bound of nominal aspect ratio was identified as the principal control index within this framework, which incorporates critical parameters such as height ratio (α), width ratio (β), vertical tensile stiffness to compressive stiffness ratio (γ), seismic coefficient (k), and nominal vertical compressive stress (σ0) to provide a thorough analysis of the structural responses and potential failure scenarios. In order to further investigate this matter, a scaled model of an isolated step-terrace concrete frame structure featuring two dropped layers and a single span within an 8° seismic fortification zone was meticulously crafted at a 1:10 similarity ratio. Subsequently, a series of shaking table tests were conducted to analyze the structural response under seismic excitation. The findings indicate that: utilizing the bound of nominal aspect ratio as a metric to gauge the anti-overturning capacity of isolated step-terrace structures is a justified approach. In instances where the height ratio remains constant, the bound of nominal aspect ratio for both positive and negative overturning trended upward with an increase in the width ratio. Notably, the bound of nominal aspect ratio for positive overturning consistently registered lower values compared to that of the negative overturning, underscoring the heightened susceptibility of step-terrace structures to positive overturning. Moreover, in scenarios characterized by higher height and width ratios, the structural integrity remained unscathed by any overturning effects arising from insufficient tensile strength in rubber bearings. Furthermore, the bound of nominal aspect ratio exhibited an ascending trend as the seismic coefficient, nominal vertical compressive stress, and vertical tensile stiffness to compressive stiffness ratio decreased. The outcomes derived from the shaking table test not only confirm the impressive seismic performance of the structure, but also, by closely examining the instantaneous stress variations within the upper and lower isolation layers of the model, substantiate the existence of a positive overturning hazard in scenarios marked by higher seismic coefficients (k). This observation aligns seamlessly with the theoretical projections, thereby substantiating the efficacy of the structural overturning failure theory through direct empirical verification.

This paper proposes a new toggle-style negative stiffness viscous damper (TNVD), and evaluates the performance of the TNVD with the displacement amplification factor (fd) and the energy dissipation factor (fE). Firstly, the composition and characteristics of the TNVD are introduced. Subsequently, the displacement amplification factor is introduced to evaluate the displacement amplification ability of the TNVD, and it is decomposed into a geometric amplification factor and an effective displacement coefficient. Then, based on the geometric amplification factor and effective displacement coefficient, the correlation between the TNVD’s displacement amplification ability and inter-story deformation is studied, and an improved TNVD is proposed. By the comparison of the finite element calculation results, it is found that the improved TNVD can utilize the assumption of small structural deformation. After that, the impacts of plentiful aspects, such as the length of the lower connecting rod, the horizontal inclination angle of the lower connecting rod, the inter-story deformation limit, the cross-sectional area of the connecting rod, the damping coefficient, and the negative stiffness on the fd and fE of the improved TNVD, are expounded. The research results show that when the length of the TNVD’s lower connecting rod remains unchanged, the fd and fE present a trend of increasing first and then decreasing with the increase in the horizontal inclination angle of the lower connecting rod. When the inter-story deformation is fixed, there exists an optimal lower connecting rod’s length that satisfies a specific relationship to achieve the optimal geometric amplification factor of the TNVD. By adjusting the damping parameters of the TNVD, we can obtain a better effective displacement coefficient greater than 0.95 in the proposed target region. Meanwhile, the fd and fE increase with the decrease in the negative stiffness. An optimization strategy for the improved TNVD has been proposed to ensure that the TNVD has the characteristics of operational safety, ideal displacement amplification capability, and energy dissipation capability. Furthermore, a multi-objective control design method with an additional improved TNVD structure is proposed. The vibration reduction effect of the structure with the improved TNVD and the effectiveness of the optimization strategy are verified through examples.

Generally speaking, the traditional lever amplification damping system is installed between adjacent columns in a building, which occupies a significant amount of space in the building. In contrast to amplification devices in different forms, the damper displacement of the intermediate column damper system is smaller, and the vibration reduction efficiency is lower. In light of these drawbacks, this study proposes a new amplification device for energy dissipation and vibration reduction, which is based on an intermediate column–lever mechanism with a viscous damper (CLVD). Initially, a specific simplified mechanical model of CLVD is derived. Subsequently, an equivalent Kelvin mechanical model of CLVD is derived to intuitively reflect CLVD’s damping and stiffness effect. The damping ratio added by CLVDs to the structure is calculated according to that model; the additional damping ratio and additional stiffness are utilized to calculate the displacement ratio Rd and shear force ratio Rv of the structure with CLVDs to the structure without CLVDs. Rd and Rv are introduced to evaluate the vibration reduction effect of the structure with CLVDs, and the effects of various parameters (such as intermediate column position, beam’s bending line stiffness, lever amplification factor, damping coefficient, and earthquake intensity) on Rd and Rv are analyzed. The results indicate that when the ratio of the distance from the intermediate column to the edge column to the span of the beam is 0.5, CLVD owns the optimal vibration reduction effect. Increasing the beam’s bending line stiffness is beneficial for CLVD to control structural displacement and shear force; when the leverage amplification factor is too large, the CLVD provides the structure with stiffness as the main factor, followed by damping. Additionally, when the ratio of the displacement amplification factor to the geometric amplification factor satisfies fd/γ = 1/21−0.5α, the CLVD has the optimal displacement control effect on the structure. After that, measures are provided to optimize the CLVD in different situations in order to effectively control the inter-story displacement and the story shear force of the structure. Consequently, a nine-story frame is taken as an example to elaborate the application of CLVDs in the design for energy dissipation and vibration reduction. The results reveal that the CLVD scheme adopting the proposed optimization method can effectively enhance the displacement amplification ability of CLVDs, resulting in an additional damping ratio of up to 12%. At the same time, the inter-story displacement was reduced by almost 40% under fortification earthquakes. Through the research in this study, designers can obtain a new choice in structural vibration reduction design.

Decision-level information fusion methods using radar and vision usually suffer from low target matching success rates and imprecise multi-target detection accuracy. Therefore, a robust target detection algorithm based on the fusion of frequency-modulated continuous wave (FMCW) radar and a monocular camera is proposed to address these issues in this paper. Firstly, a lane detection algorithm is used to process the image to obtain lane information. Then, two-dimensional fast Fourier transform (2D-FFT), constant false alarm rate (CFAR), and density-based spatial clustering of applications with noise (DBSCAN) are used to process the radar data. Furthermore, the YOLOv5 algorithm is used to process the image. In addition, the lane lines are utilized to filter out the interference targets from outside lanes. Finally, multi-sensor information fusion is performed for targets in the same lane. Experiments show that the balanced score of the proposed algorithm can reach 0.98, which indicates that it has low false and missed detections. Additionally, the balanced score is almost unchanged in different environments, proving that the algorithm is robust.

AbstractA significant number of ionic rare earth tailings slopes were generated during the process of rare earth mining and the factor of safety of these slopes continued to decrease under the action of rainfall. This study focussed on a specific tailings slope located in Dayu County, Ganzhou City. Through the utilization of MatDEM numerical simulation software, the variations in infiltration distribution and destabilization evolution of the tailings slope between different seasons were comparatively examined. The results indicated that during the rainy season (May to June), increased rainfall led to increased rate of water infiltration, consequently reducing the stability of the slope and exacerbating landslide damage. Furthermore, the study provided analyses of safety distances under different seasons of rainfall, taking into account the geographical location of the tailings slope in Dayu County, Ganzhou City, and the results of the analyses provided theoretical foundation into ensuring the safety of buildings and residents downstream of the slope.

In order to solve the problems of slow real-time and poor detection of small targets that still exist in multi-sensor information fusion technology, a frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar-vision fusion algorithm for target classification and localization is proposed in this paper. Firstly, the signal model of FMCW MIMO radar is established. Then, the target localization is performed using a forward-backward spatial smoothing (SS) based linear prediction-orthogonal propagator method (LP-OPM) to obtain information about the radar target. Next, the YOLOv7 and the visual ranging algorithms are used to identify and localize the target to get information about the camera target. In addition, a Kalman-weighted fusion algorithm is used to fuse the data from the two sensor targets for output. Finally, the performance of the method is proved to be superior by the experimental results.

ABSTRACT: Moore's Law has been the driving force behind the semiconductor industry for several decades, but as silicon-based transistors approach their physical limits, researchers are searching for new materials to sustain this exponential growth. Two-dimensional transition metal dichalcogenides (TMDs), with their atomically thin structure and enticing physical properties, have emerged as the most promising candidates for downsizing and improving device integration. Emboldened by the direction of achieving large-area and high-quality TMDs growth, wafer-scale TMDs growth strategies have been continuously developed, suggesting that TMDs are poised to become a new platform for next-generation electronic devices. In this review, advanced synthesis routes and inherent properties of wafer-scale TMDs were critically assessed. In addition, the performance in electronic devices was also discussed, providing an outlook on the opportunities and challenges that lie ahead in their development.

This study specifically focuses on the damping efficiency of a damped structure with additional viscous dampers. A two-layer steel frame structure with eight sets of viscous dampers is used to conduct a series of seismic simulation shaking table tests, including a non-damped structure without dampers and two damped structures with dampers placed at 1/2 and 1/6 of the beam span, respectively. By conducting these tests, the energy dissipation, force, and displacement of the damper, as well as the parameters of the structure such as floor displacement and acceleration, are obtained. The main damping efficiency indicators of the damped structure are calculated, including the additional damping ratio, inter-story displacement utilization rate, as well as the reduction rate of the vertex displacement and the base shear relative to the non-damped structure. The study shows that the viscous dampers exhibit full hysteresis loops and a strong energy dissipation capacity in the structure. The seismic response of the vertex displacement and base shear in the damped structure is significantly smaller than that in the non-damped structure. Under different seismic levels, including frequent earthquakes, occasional earthquakes, and rare earthquakes, the damping effect of the dampers placed at 1/2 of the beam span is significantly better than that placed at 1/6 of the beam span. For example, the additional damping ratio for the X-direction artificial wave REN is 19% and 11%, 20% and 13%, and 13% and 11%, respectively. The patterns for inter-story displacement utilization ratio, reduction rate of the vertex displacement, and reduction rate of the base shear are similar. The research findings strongly indicate that the damped structure with additional viscous dampers exhibits excellent damping efficiency. In future damping design, designers need to fully consider the placement of viscous dampers within the beam span.

When subjected to seismic activity, tall isolated buildings with a high aspect ratio are susceptible to overturning as a result of the failure of rubber isolation bearings under tension. In order to address this issue, a guided-rail tension device (GR) has been developed to enhance the tensile strength of rubber bearings. Furthermore, a novel guided-rail isolation rubber bearing (GR&RB) has been proposed as a potential solution. Quasi-static tests have been conducted to investigate the mechanical properties of the GR, as well as the GR&LNR600 and LNR600. Additionally, numerical finite element analysis has been employed to study the seismic response of the GR&RB under El Centro seismic action in a high-rise building with an aspect ratio of approximately 4. The experimental results suggest that the inclusion of GRs has a limited effect on the horizontal mechanical attributes of rubber isolation bearings. Nevertheless, it simultaneously enhances their tensile strength, demonstrating a significant augmentation of 4.23 times. Moreover, the mechanical behavior of the GR aligns with the Hook model. The numerical analysis suggests that the utilization of GR&RB can mitigate the tensile stress levels of rubber isolators. Furthermore, it is recommended to augment the elastic tensile stiffness of the GR while reducing the open value to enhance the tensile efficiency, with the potential to attain up to 75% efficiency in tensile performance.

As one of the main approaches to implement phase gradient in metasurfaces, Pancharatnam-Berry (PB) phase originated from the spin–orbit interaction (SOI) of light has attracted enormous interest during the past several decades. Different from classical PB phase in linear optics that equals to twice the rotational angle of anisotropic elements for half wave plate distance and circular polarization, the recently proposed generalized PB phase indicates that phase gradient equals to multiple times the rotation angle could be achieved by high-order symmetry metallic meta atoms. Here, we show that the generalized PB phase can also be implemented in all-dielectric metasurfaces with higher efficiency than their metallic counterparts. Besides, by merging generalized PB phase and propagation phase, several functional devices are designed that the symmetric performance of PB phase can be decoupled. Moreover, a detailed comparison between meta atoms with C2, C3, and C6 rotational symmetries is conducted to illustrate the difference between classical and generalized PB phase. These results may further supplement the concept of generalized PB phase and could find many applications in optics and photonics..

This study investigates the influence of viscous damper parameters on the damping efficiency of frame shear wall structures. Taking a specific frame shear wall structure as the background, a three-dimensional finite element model is established using a nonlinear dynamic time–history analysis method. The damping ratio, reduction in vertex displacement, reduction in base shear, and inter-story drift utilization rate are selected as the damping performance indicators. Firstly, a sensitivity analysis is conducted to study the influence of different viscous damper parameters on these indicators. Then, the relationship models between the viscous damper parameters and the indicators are fitted using the response surface method, and the fitting effect is evaluated through an F-test and determination coefficient R2. Finally, an objective function based on key damping performance indicators is established to solve for the optimal parameters. The results show that the traditional sensitivity analysis method is unable to comprehensively consider the combined effects of different damping efficiency indicators. The response surface method has high fitting accuracy and good predictability and can serve as an optimization model. Considering the stiffness of supporting components matched with the viscous damper parameters, the feasibility of the optimal damping parameters is demonstrated from an engineering application perspective. A simple and easy-to-operate damping design flowchart is proposed, providing important guidance and reference for designers in frame shear wall structure damping design in the future.

In this paper, we introduce a Reduced-Dimension Multiple-Signal Classification (RD-MUSIC) technique via Higher-Order Orthogonal Iteration (HOOI), which facilitates the estimation of the target range and angle for Frequency-Diverse Array Multiple-Input–Multiple-Output (FDA-MIMO) radars in the unfolded coprime array with unfolded coprime frequency offsets (UCA-UCFO) structure. The received signal undergoes tensor decomposition by the HOOI algorithm to get the core and factor matrices, then the 2D spectral function is built. The Lagrange multiplier method is used to obtain a one-dimensional spectral function, reducing complexity for estimating the direction of arrival (DOA). The vector of the transmitter is obtained by the partial derivatives of the Lagrangian function, and its rotational invariance facilitates target range estimation. The method demonstrates improved operation speed and decreased computational complexity with respect to the classic Higher-Order Singular-Value Decomposition (HOSVD) technique, and its effectiveness and superiority are confirmed by numerical simulations.

The synthesis of two-dimensional lateral heterojunctions with nanoscale characteristic width and sharp interfaces remains challenging. The quantum confinement effects are still difficult to create on 2D materials since widths smaller than 5 nm are necessary for quantum confinement effects and quantum well applications. In this study, we demonstrated the growth of a sub-2-nm tungsten sulfide quantum well array in a monolayer of tungsten selenide, driven by the climb of mismatch dislocation in a heterointerface due to the lattice mismatch. Width-controllable 2D quantum well superlattices are theoretically formed by the mismatch dislocation-driven growth mechanism, according to our analysis. Thus, abundant photonic electronic properties can be obtained in 2D quantum well superlattices formed at varied lateral heterointerfaces, which will support the study of topological insulators and superconductors.

Abstract Background To investigate the efficacy and safety of whole brain radiotherapy (WBRT) combined with epidermal growth factor receptor‐tyrosine kinase inhibitor (EGFR‐TKI) versus WBRT only in the treatment of brain metastasis in non‐small cell lung cancer (NSCLC) patients by pooling open published data. Methods Prospective clinical studies relevant to WBRT+EGFR‐TKI versus WBRT only in the treatment of NSCLC brain metastasis were electronically searched in the Pubmed, EMbase, Cochrane, Wangfang, CNKI and Google scholar databases. The treatment response, 1‐year survival and treatment‐associated toxicity were pooled and expressed by odds ratio (OR) under a fixed or random effect model. The publication bias was evaluated by Begg's funnel plot and Egger's line regression test. Results Eighteen prospective clinical studies were included in the study. The combined results indicated that the objective response rate (ORR) in the WBRT+TKI group was superior to WBRT only with a statistical difference (OR = 2.67, 95% CI: 2.10–3.38, p 0.05), diarrhea (OR = 1.31, 95% CI: 0.83–2.07, p > 0.05), fatigue (OR = 1.40, 95% CI: 0.70–2.81, p > 0.05) and myelosuppression (OR = 0.86, 95% CI: 0.56–1.32, p > 0.05) were not statistically different between the two groups. Conclusions Based on the current publications, WBRT+EGFR‐TKI can improve the treatment response and 1‐year survival rate but not increase the toxicity except for rash compared to WBRT alone in the treatment of brain metastasis in NSCLC patients.

Traditional Angle of Arrival (AoA)-based WiFi array indoor localization algorithms do not fuse Channel State Information (CSI) inter-packet data for estimation, which makes WiFi arrays less effective for localization in complex indoor environments. Most algorithms are overburdened leading to inefficient localization. To address these issues, in this article, an indoor positioning algorithm based on Higher-Order Singular Value Decomposition (HOSVD) is proposed. First, the CSI data are reconstructed as a new measurement matrix by borrowing subcarriers, and a third-order tensor is constructed. Next, tensor compression techniques are used to reduce computational complexity and the signal subspace is obtained by HOSVD. Then, the AoA is obtained by the Reduced Dimension Multiple Signal Classification (RD-MUSIC) method. Finally, the coordinates of the target can be obtained by triangulating the AoAs of the three Access Points (APs). According to the simulation experiments, the AoA can be estimated accurately at a low SNR and with low snapshots. In practical experiments, we can successfully estimate the AoA in complex indoor environments with shorter timelines using HOSVD without modifications to commercial hardware and produce a lower AoA error and localization error rates compared to other algorithms. The effectiveness of our proposed algorithm is proven by simulations and practical experiments.

The paper proposes a fast method for the multidimensional parameter estimation of a polarization-sensitive array. Compared with conventional methods (e.g., MUSIC algorithm), the proposed method applies an iterative approach based on Newton’s method to obtain joint estimation results instead of a spectral search and dimension reduction. It also extends the original Newton method to the 4D scale using the Hessian matrix. To reduce the complexity of establishing the aim function, Nystrom’s method is applied to process the covariance matrix. A new threshold is also proposed to select the results, which can accomplish the parameter estimation with a small number of iterations while guaranteeing a high estimation accuracy. Finally, the proposed algorithm is analyzed in detail and the numerical simulations of various algorithms are compared to verify its effectiveness.

Abstract Based on the finite scattering characters of the millimeter-wave multiple-input multiple-output (MIMO) channel, the mmWave channel estimation problem can be considered as a sparse signal recovery problem. However, most traditional channel estimation methods depend on grid search, which may lead to considerable precision loss. To improve the channel estimation accuracy, we propose a high-precision two-stage millimeter-wave MIMO system channel estimation algorithm. Since the traditional expectation–maximization-based sparse Bayesian learning algorithm can be applied to handle this problem, it spends lots of time to calculate the E-step which needs to compute the inversion of a high-dimensional matrix. To avoid the high computation of matrix inversion, we combine damp generalized approximate message passing with the E-step in SBL. We then improve a refined algorithm to handle the dictionary matrix mismatching problem in sparse representation. Numerical simulations show that the estimation time of the proposed algorithm is greatly reduced compared with the traditional SBL algorithm and better estimation performance is obtained at the same time.

In the environment of unknown mutual coupling, many works on direction-of-arrival (DOA) estimation with sensor array are prone to performance degradation or even failure. Moreover, there are few literatures on off-grid direction finding using regularized sparse recovery technology. Therefore, the scenario of off-grid DOA estimation in sensor array with unknown mutual coupling is investigated, and then a reweighted off-grid Sparse Spectrum Fitting (Re-OGSpSF) approach is developed in this article. Inspired by the selection matrix, an undisturbed array output is formed to remove the unknown mutual coupling effect. Subsequently, a refined off-grid SpSF (OGSpSF) recovery model is structured by integrating the off-grid error term obtained from the first-order Taylor approximation of the higher-order term into the underlying on-grid sparse representation model. After that, a novel Re-OGSpSF framework is formulated to recover the sparse vectors, where a weighted matrix is developed by the MUSIC-like spectrum function to enhance the solution’s sparsity. Ultimately, off-grid DOA estimation can be realized with the help of the recovered sparse vectors. Thanks to the off-grid representation and reweighted strategy, the proposed method can effectively and efficiently achieve high-precision continuous DOA estimation, making it favorable for real-time direction finding. The simulation results validate the superiority of the proposed method.

Direction of arrival (DOA) estimation is an important topic in array signal processing. Currently, most research activities are focused on the single signal transmission (SST) type of signals, i.e. only one physical signal is used to carry the information from a transmitter to a receiver with a given polarisation setting. However, to make full use of the degrees of freedom in spatial domain, signals based on the dual signal transmission (DST) model are more and more widely used, i.e., two signals with different polarisations carrying different information are employed for communication between the transmitter and the receiver. But there is rarely any work on DOA estimation of DST signals. Motivated by such a problem, the paper proposes two methods for DOA estimation of signals based on a mixed signal transmission (MST) model, i.e., a mixture of SST and DST signals. The first method provides a two-step solution and estimate the DOA of the SST signals first and then the DST signals second. The second method estimates the DOA of all signals in one step. Moreover, CRB (Cramér-Rao Bound) for the estimation model is derived to evaluate the performance the proposed methods.

Introduction and Objectives: Circular RNA La Ribonucleoprotein 1B (circ-LARP1B) was reported to serve as an oncogene in many types of cancers. Radiotherapy (RT) is an important element of the multimodal treatment concept in malignancies. Here, this work aimed to investigate the role of circ-LARP1B in the tumorigenesis and radiosensitivity of hepatocellular carcinoma (HCC). Patients or Materials and Methods: Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot were used to detect the expression of genes and proteins. In vitro experiments were conducted using cell counting Kit-8 (CCK-8), colony formation, EDU, transwell, and tube formation assays, respectively. Dual-luciferase reporter assay was employed to identify the target relationship between miR-578 and circ-LARP1B or IGF1R (insulin-like growth factor 1 receptor). In vivo assay was performed using murine xenograft model. Results: Circ-LARP1B was highly expressed in HCC tissues and cells, and high expression of circ-LARP1B was closely associated with poor prognosis. Functional experiments demonstrated that circ-LARP1B silencing impaired cell proliferation, invasion, angiogenesis and reduced radioresistance in vitro. Mechanistically, circ-LARP1B could competitively bind with miR-578 to relieve the repression of miR-578 on the expression of its target gene IGF1R. Further rescue assay confirmed that miR-578 inhibition reversed the inhibitory effects of circ-LARP1B knockdown on HCC cell malignant phenotypes and radioresistance. Moreover, miR-578 overexpression restrained tumorigenicity and enhanced radiosensitivity in HCC cells, which were attenuated by IGF1R up-regulation. Besides that, circ-LARP1B knockdown impeded tumor growth and enhanced irradiation sensitivity in HCC in vivo. Conclusions: Circ-LARP1B knockdown restrained HCC tumorigenicity and enhanced radiosensitivity by regulating miR-578/IGF1R axis, providing a new target for the treatment of HCC.

As a promising platform for versatile electromagnetic (EM) manipulations, metasurfaces have drawn wide interest in recent years due to their unique EM properties and small footprints. However, although great efforts have been made to achieve multifunctionalities, the design of tunable metasurfaces with high compactness is still challenging. Here, a simple yet powerful design methodology for single-layered reconfigurable metasurfaces composed of nonvolatile phase-change material Ge2Sb2Se4Te1 (GSST) is proposed with average working amplitudes of 72.6% and 53% at different crystallization levels. The proposed metasurfaces could not only enable independent phase control at different crystallization levels but also introduced another polarization degree of freedom. As a proof of concept, we numerically demonstrate three kinds of metadevices in the infrared region achieving a multi-focus metalens with tunable foci, multistate vortex beam generator with adjustable topological charges and multi-channel meta-hologram with three independent information channels. It is believed that these multifunctional metasurfaces with both tunability and compactness are promising for various applications including information encryption, chiroptical spectroscopy, chiral imaging and wireless communication.

Pinewood nematode (PWN), the causal agent of pine wilt disease (PWD), causes massive global losses of Pinus species each year. Bacteria and fungi existing in symbiosis with PWN are closely linked with the pathogenesis of PWD, but the relationship between PWN pathogenicity and the associated microbiota is still ambiguous. This study explored the relationship between microbes and the pathogenicity of PWN by establishing a PWN-associated microbe library, and used this library to generate five artificial PWN–microbe symbiont (APMS) assemblies with gnotobiotic PWNs. The fungal and bacterial communities of different APMSs (the microbiome) were explored by next-generation sequencing. Furthermore, different APMSs were used to inoculate the same Masson pine (Pinus massoniana) cultivar, and multi-omics (metabolome, phenomics, and transcriptome) data were obtained to represent the pathogenicity of different APMSs at 14 days post-inoculation (dpi). Significant positive correlations were observed between microbiome and transcriptome or metabolome data, but microbiome data were negatively correlated with the reactive oxygen species (ROS) level in the host. Five response genes, four fungal genera, four bacterial genera, and nineteen induced metabolites were positively correlated with the ROS level, while seven induced metabolites were negatively correlated. To further explore the function of PWN-associated microbes, single genera of functional microbes (Mb1–Mb8) were reloaded onto gnotobiotic PWNs and used to inoculate pine tree seedlings. Three of the genera (Cladophialophora, Ochroconis, and Flavobacterium) decreased the ROS level of the host pine trees, while only one genus (Penicillium) significantly increased the ROS level of the host pine tree seedlings. These results demonstrate a clear relationship between associated microbes and the pathogenicity of PWN, and expand the knowledge on the interaction between PWD-induced forest decline and the PWN-associated microbiome.

Since there is a frequency offset between each adjacent antenna of FDA radar, there exists angle-range two-dimensional dependence in the transmitter. For bistatic FDA-multiple input multiple output (MIMO) radar, range-direction of departure (DOD)-direction of arrival (DOA) information is coupled in transmitting the steering vector. How to decouple the three information has become the focus of research. Aiming at the issue of target parameter estimation of bistatic FDA-MIMO radar, a real-valued parameter estimation algorithm based on high-order-singular value decomposition (HOSVD) is developed. Firstly, for decoupling DOD and range in transmitter, it is necessary to divide the transmitter into subarrays. Then, the forward–backward averaging and unitary transformation techniques are utilized to convert complex-valued data into real-valued data. The signal subspace is obtained by HOSVD, and the two-dimensional spatial spectral function is constructed. Secondly, the dimension of spatial spectrum is reduced by the Lagrange algorithm, so that it is only related to DOA, and the DOA estimation is obtained. Then the frequency increment between subarrays is used to decouple the DOD and range information, and eliminate the phase ambiguity at the same time. Finally, the DOD and range estimation automatically matched with DOA estimation are obtained. The proposed algorithm uses the multidimensional structure of high-dimensional data to promote performance. Meanwhile, the proposed real-valued tensor-based method can effectively cut down the computing time. Simulation results verify the high efficiency of the developed method.

Ti2AlNb alloy is a three-phase alloy, which consists of O phase, β phase and α2 phase. Because of the difference in the mechanical characteristics between phases, Ti2AlNb alloy often exhibits deformation heterogeneity. Based on EBSD images of the Ti2AlNb alloy, a crystal plasticity finite element model (CPFEM) was built to study the effects of O phase and β phase (dominant phases) on stress and strain distribution. Four types of fatigue experiments, and the Chaboche model with 1.2%~1.6% total strain range were conducted to verify the CPFEM. The simulation results showed that the phase boundary was the important position of stress concentration. The main reason for the stress concentration was the inconsistency deformation of grains which resulted from the different deformation abilities of the O and β phases.

Subspace methods are widely used in FMCW-MIMO radars for target parameter estimations. However, the performances of the existing algorithms degrade rapidly in non-ideal situations. For example, a small number of snapshots may result in the distortion of the covariance matrix estimation and a low signal-to-noise ratio (SNR) can lead to subspace leakage problems, which affects the parameter estimation accuracy. In this paper, a joint DOA–range estimation algorithm is proposed to solve the above issues. Firstly, the improved unitary root-MUSIC algorithm is applied to reduce the influence of non-ideal terms in building the covariance matrix. Subsequently, the least squares method is employed to process the data and obtain paired range estimation. However, in a small number of snapshots and low SNR scenarios, even if the impact of non-ideal terms is reduced, there will still be cases where the estimators sometimes deviate from the true target. The estimators that deviate greatly from targets are regarded as outliers. Therefore, threshold detection is applied to determine whether outliers exist. After that, a pseudo-noise resampling (PR) technology is proposed to form a new data observation matrix, which further alleviates the error of the estimators. The proposed method overcomes performance degradation in a small number of snapshots or low SNRs simultaneously. Theoretical analyses and simulation results demonstrate the effectiveness and superiority.

Frequency modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radars are widely applied in target localization. However, during the process, the estimation accuracy decreases sharply without considerable signal-to-noise ratio (SNR) and sufficient snapshot number. It is therefore necessary to consider estimation schemes that are valid under low signal-to-noise ratio (SNR) and snapshot. In this paper, a fast target localization framework based on multiple deep neural networks named Multi-DeepNet is proposed. In the scheme, multiple interoperating deep networks are employed to achieve accurate target localization in harsh environments. Firstly, we designed a coarse estimate using deep learning to determine the interval where the angle is located. Then, multiple neural networks are designed to realize accurate estimation. After that, the range estimation is determined. Finally, angles and ranges are matched by comparing the Frobenius norm. Simulations and experiments are conducted to verify the efficiency and accuracy of the proposed framework.

Background To investigate the diagnostic efficacy of long noncoding RNA metastasis‐associated in lung adenocarcinoma transcript l (MALAT1) as a candidate serological biomarker for non‐small cell lung cancer (NSCLC). Methods Diagnostic studies relevant to circulation long noncoding RNA MALAT1 as a candidate serological biomarker for NSCLC were electronically systematically searched in PubMed, EMBASE, EBSCO and CNKI databases. Suitable studies were included in the meta‐analysis by pooling the diagnostic sensitivity, specificity, positive likelihood ratio (+LR), negative likelihood ratio (−LR), diagnostic odds ratio (DOR) and area under the symmetric ROC curve (AUC) through a random or fixed effects model. Deeks' funnel plot was applied for publication bias evaluation. Results Six studies with eight datasets were finally included in the meta‐analysis after a systematic search of the databases was performed. The pooled diagnostic sensitivity, specificity, +LR, −LR and DOR were 0.81 (95% CI:0.78–0.84), 0.67 (95% CI:0.63–0.71), 2.61 (95% CI:1.81–3.71), 0.28 (95% CI:0.19–0.43) and 13.73 (95% CI:6.19–30.44), respectively. The pooled area under the ROC curve (AUC) were 0.8663 and 0.8658, respectively by symmetric and asymmetric methods. Conclusion Based on the results of our study, serum long noncoding RNA MALAT1 is a promising biomarker for NSCLC screening. However, due to its low specificity, MALAT1 positive cases need further validation for NSCLC by other diagnostic methods such as radiology, cytology, etc.

The biosorption and bioaugmentation performances of Mucor circinelloides were investigated under different contact time, initial metal(loid) concentration and species. The microbe-plant interaction appeared synergistic with enhancing plant growth and alleviating oxidative damages induced by lead, cadmium and arsenic. The bioaugmentation with M. circinelloides led to significant immobilization on lead, cadmium and arsenic as indicated by the decreases of metal(loid) transfer and bioavailability in plant-microbe aqueous system. Lead, cadmium and arsenic were mainly allocated on cell wall and a few parts entered into intercellular system, suggesting cell wall adsorption and intracellular bioaccumulation served as the main mechanisms of M. circinelloides. The adsorption kinetics and isotherms on lead, cadmium and arsenic were fitted well with the pseudo-second-order and Langmuir models, with the maximum adsorption capacities of 500, 15.4 and 29.4 mg·g−1 fungal biomass at pH 6.0 and 25 ℃. The optimum initial concentration and contact time were 300–10–20 mg·L−1 and 2 h. This study provides a basis for M. circinelloides as a promising adsorbent and bioaugmented agent for the cleanup of soil/aqueous environment contaminated with lead, cadmium and arsenic.

This paper studies the joint range and angle estimation of monostatic frequency diverse array multiple-input multiple-output (FDA-MIMO) radar and proposes a joint estimation algorithm. First, the transmit direction matrix is converted into real values by unitary transformation, and the Vandermonde-like matrix structure is used to construct an augmented output that doubles the aperture of the receive array. Then the augmented output is combined into a third-order tensor. Next, the factor matrices are initially estimated. Finally, the direction matrices are estimated utilizing parallel factor (PARAFAC) decomposition, and the range and angle are calculated by employing least square fitting. As contrasted with the classic PARAFAC method, the proposed method can estimate more targets and provide better estimation performance, and requires less computational complexity. The availability and excellence of the proposed method are reflected by numerical simulations and complexity analysis.

A growing number of web users around the world have started to post their opinions on social media platforms and offer them for share. Building a highly scalable evolution prediction model by means of evolution trend volatility plays a significant role in the operations of enterprise marketing, public opinion supervision, personalized recommendation, and so forth. However, the historical patterns cannot cover the systematical time-series dynamic and volatility features in the prediction problems of a social network. This paper aims to investigate the popularity prediction problem from a time-series perspective utilizing dynamic linear models. First, the stationary and nonstationary time series of Weibo hot events are detected and transformed into time-dependent variables. Second, a systematic general popularity prediction model N-SEP2M is proposed to recognize and predict the nonstationary event propagation of a hot event on the Weibo social network. Third, the explanatory compensation variable social intensity (SI) is introduced to optimize the model N-SEP2M. Experiments on three Weibo hot events with different subject classifications show that our prediction approach is effective for the propagation of hot events with burst traffic.

Directional modulation (DM) as a physical layer security technique has been studied based on the traditional antenna arrays; however, in most of the designs, only one signal is transmitted at one carrier frequency. In this paper, signal polarization information is exploited, and a new DM scheme is designed, which can transmit a pair of orthogonally polarized signals to the same direction at the same frequency simultaneously, resulting in doubled channel capacity. These two signals can also be considered as one composite signal using a four-dimensional (4-D) modulation scheme across the two polarization diversity channels. Moreover, compressive sensing (CS)-based formulations for designing sparse crossed-dipole arrays in this context are proposed to exploit the degrees of freedom in the spatial domain for further improved performance, as demonstrated by various design examples.

In this paper, a robust DOA estimation scheme based on sparse Bayesian learning (SBL) for non-circular signals in impulse noise and mutual coupling (MC) is proposed. Firstly, the Toeplitz property of the MC matrix is used to eliminate the effect of array MC, and the array aperture is extended by using the properties of the non-circular signal. To eliminate the effect of impulse noise, the outlier part of the impulse noise is reconstructed together with the original signal in the signal matrix, and the DOA coarse estimation is obtained by balancing the accuracy and efficiency of parameter estimation using the alternating SBL update algorithm. Finally, a one-dimensional search is used in the vicinity of the searched spectral peaks to achieve a high-precision DOA estimation. The effectiveness and robustness of the algorithm for dealing with the above errors are demonstrated by extensive simulations.

In this paper, we study the joint range and angle estimation problem based in monostatic frequency diverse-array multiple-input multiple-output (FDA-MIMO) radar, and propose a method for range and angle estimation base on compressed unitary parallel factor (PARAFAC). First, the received complex signal matrix is stacked into a third-order complex signal tensor. Then, we can transform the obtained third-order complex signal tensor into a third-order real-valued signal tensor by employing forward–backward and unitary transformation techniques. Next, a smaller third-order real-valued signal tensor is composed by using compressing the third-order real-valued signal tensor. After that, PARAFAC decomposition is applied to obtain the direction matrix. Lastly, the angle and range are estimated by employing the least square (LS) fitting. The estimation error of the proposed method is about 10% lower than that of the traditional PARAFAC method under the low number of snapshots. When the number of snapshots is high, the performance of the two methods is close. Moreover, the computational complexity of the proposed method is nearly 96% less than those of the traditional PARAFAC methods in the case of low snapshots, while the gap is larger in the case of high snapshots. The superiority and effectiveness of the method are proved by complexity analysis and simulation experiments.

As a new radar system, FDA-MIMO radar has recently developed rapidly, as it has broad prospects in angle-range estimation. Unfortunately, the performance of existing algorithms for FDA-MIMO radar is greatly degrading or even failing under the condition of array gain-phase error. This paper proposes an innovative solution to the joint angle and range estimation of FDA-MIMO radar under the condition of array gain-phase error and an estimation algorithm is developed. Moreover, the corresponding Cramér-Rao bound (CRB) is derived to evaluate the algorithm. The parallel factor (PARAFAC) decomposition technique can be utilized to calculate transmitter and receiver direction matrices. Taking advantage of receiver direction matrix, the angle estimation can be obtained. The range estimation can be estimated by transmitter direction matrix and angle estimation. To eliminate the error accumulation effect of array gain-phase error, the gain error and phase error are obtained separately. In this algorithm, the impact of gain-phase error on parameter estimation is removed and so is the error accumulation effect. Therefore, the proposed algorithm can provide excellent performance of angle-range and gain-phase error estimation. Numerical experiments prove the validity and advantages of the proposed method.

With the realization of global navigation satellite system (GNSS) completion, GNSS reflectometry (GNSS-R) has become increasingly popular due to the advantages of global coverage and the availability of multiple sources in terms of earth remote sensing. This paper analyzes the Beidou navigation satellite system (BDS) signal reflection detection of multiple satellites and multiple moving targets under multiple-input and multiple-output (MIMO) radar systems and proposes a series of methods to suppress multiple Doppler phase influences and improve the range detection property. The simulation results show the restored target peaks, which match the RCS data more accurately, with the GNSS-R Doppler phase influence removed, which proves the proposed method can improve target recognition and detection resolution performance.

Frequency diverse array (FDA) radar has attracted much attention due to the angle and range dependence of the beam pattern. Multiple-input-multiple-output (MIMO) radar has high degrees of freedom (DOF) and spatial resolution. The FDA-MIMO radar, a hybrid of FDA and MIMO radar, can be used for target parameter estimation. This paper investigates a tensor-based reduced-dimension multiple signal classification (MUSIC) method, which is used for target parameter estimation in the FDA-MIMO radar. The existing subspace methods deteriorate quickly in performance with small samples and a low signal-to-noise ratio (SNR). To deal with the deterioration difficulty, the sparse estimation method is then proposed. However, the sparse algorithm has high computation complexity and poor stability, making it difficult to apply in practice. Therefore, we use tensor to capture the multi-dimensional structure of the received signal, which can optimize the effectiveness and stability of parameter estimation, reduce computation complexity and overcome performance degradation in small samples or low SNR simultaneously. In our work, we first obtain the tensor-based subspace by the high-order-singular value decomposition (HOSVD) and establish a two-dimensional spectrum function. Then the Lagrange multiplier method is applied to realize a one-dimensional spectrum function, estimate the direction of arrival (DOA) and reduce computation complexity. The transmitting steering vector is obtained by the partial derivative of the Lagrange function, and automatic pairing of target parameters is then realized. Finally, the range can be obtained by using the least square method to process the phase of transmitting steering vector. Method analysis and simulation results prove the superiority and reliability of the proposed method.

Whole body vibration (WBV) is a non-invasive physical therapy that has recently been included in the hospital's patient rehabilitation training catalog, but its health effects have not been sufficiently studied. In the present study, to examine the possible effects of WBV on immune cell differentiation, the IFN, IL-4,−17, F4/80 and CD3,−4,−8,−11b,−11c,−19 markers were used to characterizing the cells in mouse spleen. The results showed that the CD4 and CD25 positive lymphocytes in the spleen were significantly increased in the WBV group, and the population of Treg cells was enhanced significantly in response to WBV. Since the differentiation in immune cells is usually associated with microbiota, therefore the intestinal flora was characterized in mice and human individuals. The results indicated that WBV significantly reduced the α-diversity of mouse intestinal microbiota. Moreover, the principal coordinate analysis (PCoA) results indicated that the β-diversities of both mice and human fecal microbiota increased after WBV. Analysis of the bacterial composition indicated that the contents of a variety of bacteria changed in mice upon the stimulation of vibration, such as Lactobacillus animalis in mice, and Lactobacillus paraplantarum and Lactobacillus sanfranciscensis in human. The succeeding correlation analysis revealed that some bacteria with significant content variations were correlated to the regulatory T cell differentiation in mice and physical characteristics in human. Our research will provide the basis for future non-invasive treatment of microbial and immune related diseases.

Due to grid division, the existing target localization algorithms based on sparse signal recovery for the frequency diverse array multiple-input multiple-output (FDA-MIMO) radar not only suffer from high computational complexity but also encounter significant estimation performance degradation caused by off-grid gaps. To tackle the aforementioned problems, an effective off-grid Sparse Bayesian Learning (SBL) method is proposed in this paper, which enables the calculation the direction of arrival (DOA) and range estimates. First of all, the angle-dependent component is split by reconstructing the received data and contributes to immediately extract rough DOA estimates with the root SBL algorithm, which, subsequently, are utilized to obtain the paired rough range estimates. Furthermore, a discrete grid is constructed by the rough DOA and range estimates, and the 2D-SBL model is proposed to optimize the rough DOA and range estimates. Moreover, the expectation-maximization (EM) algorithm is utilized to update the grid points iteratively to further eliminate the errors caused by the off-grid model. Finally, theoretical analyses and numerical simulations illustrate the effectiveness and superiority of the proposed method.