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Abstract Left ventricular assist device (LVAD) implantation is an effective alternative treatment to heart transplantation, especially for end‐stage heart failure patients who are ineligible for or unable to await a heart transplant. This report describes a complex and innovative surgery where LVAD implantation was performed alongside multiple concomitant cardiac and aortic procedures. A 62‐year‐old male patient with complicated comorbidities developed acute myocardial infarction and subsequent refractory advanced heart failure. Given his critically ill condition and intractable anatomical malformations, the CH‐VAD left ventricular assist system implantation was performed concomitantly with the Bentall procedure, coronary artery bypass grafting, tricuspid valvuloplasty, and foramen ovale closure. The patient was successfully discharged. This case details the medical decision‐making process and surgical strategy and demonstrates the feasibility of LVAD implantation combined with multiple additional cardiac and aortic procedures in expert cardiac centres. Success relies on experienced cardiac surgeons and a multidisciplinary LVAD Heart Team, ensuring excellence in surgical techniques, preoperative evaluation, post‐operative care, and rehabilitation.

In this paper, we developed a novel numerical method for solving general nonlinear fractional ordinary differential equations (FODEs). First, we transformed the nonlinear FODEs into the equivalent Volterra integral equations. We then developed a time-stepping algorithm for the numerical solution of the Volterra integral equations based on the third-order Taylor expansion for approximating the integrands in the Volterra integral equations on a chosen mesh with the mesh parameter $ h $. This approximation led to implicit nonlinear algebraic equations in the unknowns at each given mesh point, and an iterative algorithm based on Newton's method was developed to solve the resulting implicit equations. A convergence analysis of this numerical scheme showed that the error between the exact solution and numerical solution at each mesh point is $ \mathcal{O}(h^{3}) $, independent of the fractional order. Finally, four numerical examples were solved to verify the theoretical results and demonstrate the effectiveness of the proposed method.

Abstract Lead (Pb2+) toxification is a concerning, unaddressed global public health crisis that leads to 1 million deaths annually. Yet, public policies to address this issue have fallen short. This work harnesses the unique abilities of crown ethers, which selectively bind to specific ions. This study demonstrates the synergistic integration of highly-scalable silicon photonics, with crown ether amine conjugation via Fischer esterification in an environmentally-friendly fashion. This realizes an integrated photonic platform that enables the in-operando, highly-selective and quantitative detection of various ions. The development dispels the existing notion that Fischer esterification is restricted to organic compounds, facilitating the subsequent amine conjugation for various crown ethers. The presented platform is specifically engineered for selective Pb2+ detection, demonstrating a large dynamic detection range, and applicability to field samples. The compatibility of this platform with cost-effective manufacturing indicates the potential for pervasive implementation of the integrated photonic sensor technology to safeguard against societal Pb2+ poisoning.

Abstract The coronavirus disease 2019 (COVID-19) remains serious around the world and causes huge deaths and economic losses. Understanding the transmission dynamics of diseases and providing effective control strategies play important roles in the prevention of epidemic diseases. In this paper, to investigate the effect of delays on the transmission of COVID-19, we propose a delayed SEIR model to describe COVID-19 virus transmission, where two delays indicating the incubation and recovery periods are introduced. For this system, we prove its solutions are nonnegative and ultimately bounded with the nonnegative initial conditions. Furthermore, we calculate the disease-free and endemic equilibrium points and analyze the asymptotical stability and the existence of Hopf bifurcations at these equilibrium points. Then, by taking the weighted sum of the opposite number of recovered individuals at the terminal time, the number of exposed and infected individuals during the time horizon, and the system cost of control measures as the cost function, we present a delay optimal control problem, where two controls represent the social contact and the pharmaceutical intervention. Necessary optimality conditions of this optimal control problem are exploited to characterize the optimal control strategies. Finally, numerical simulations are performed to verify the theoretical analysis of the stability and Hopf bifurcations at the equilibrium points and to illustrate the effectiveness of the obtained optimal strategies in controlling the COVID-19 epidemic.

In this paper, we consider maximum hands-off control problem governed by a nonlinear dynamical system, where the maximum hands-off control constraint is characterized by an $ L^{0} $ norm. For this problem, we first approximate the $ L^{0} $ norm constraint by a $ L^{1} $ norm constraint. Then, the control parameterization together with sequential adaptive switching time optimization technique is proposed to approximate the optimal control problem by a sequence of finite-dimensional optimization problems. Furthermore, a smoothing technique is exploited to approximate the non-smooth maximum operator and an error analysis is investigated for this approximation. The gradients of the cost functional with respect to the decision variables in the approximate problem are derived. On the basis of these results, we develop a gradient-based optimization algorithm to solve the resulting optimization problem. Finally, an example is solved to demonstrate the effectiveness of the proposed algorithm.

Stretchable power sources, especially stretchable lithium-ion batteries (LIBs), have attracted increasing attention due to their enormous prospects for powering flexible/wearable electronics. Despite recent advances, it is still challenging to develop ultra-stretchable LIBs that can withstand large deformation. In particular, stretchable LIBs require an elastic electrolyte as a basic component, while the conductivity of most elastic electrolytes drops sharply during deformation, especially during large deformations. This is why highly stretchable LIBs have not yet been realized until now. As a proof of concept, a super-stretchable LIB with strain up to 1200% is created based on an intrinsically super-stretchable polymer electrolyte as the lithium-ion conductor. The super-stretchable conductive system is constructed by an effective diblock copolymerization strategy via photocuring of vinyl functionalized 2-ureido-4-pyrimidone (VFUpy), an acrylic monomer containing succinonitrile and a lithium salt, achieving high ionic conductivity (3.5 × 10−4 mS cm−1 at room temperature (RT)) and large deformation (the strain can reach 4560%). The acrylic elastomer containing Li-ion conductive domains can strongly increase the compatibility between the neighboring elastic networks, resulting in high ionic conductivity under ultra-large deformation, while VFUpy increases elasticity modulus (over three times) and electrochemical stability (voltage window reaches 5.3 V) of the prepared polymer conductor. At a strain of up to 1200%, the resulting stretchable LIBs are still sufficient to power LEDs. This study sheds light on the design and development of high-performance intrinsically super-stretchable materials for the advancement of highly elastic energy storage devices for powering flexible/wearable electronics that can endure large deformation.

Inspired by the design principle of pearl structure, a bottom-up flake powder self-assembly arrangement strategy, flake powder metallurgy, is used to prepare graphene films (GFs) reinforced CoCrFeNiMn high-entropy alloy (HEA) matrix composites with a pearl laminated structure. Flaky HEA powder was prepared by ball milling method and homogeneously mixed with Ni plated GFs. Vacuum hot-press sintering (VHPS) technique was carried out to solidify the mixed powders to obtain composites with uniform distribution of GFs(Ni) and flaky HEA. The results show that the bottom-up preparation strategy can effectively fabricate bionic laminated HEA matrix composites, and the composites have a distinct pearly laminated structure. The tensile strength of the composites with 5 vol% GFs(Ni) content reached 834.04 MPa, and the elongation reached 26.58 %. The compressive strength in parallel and perpendicular laminar directions reached 2069.66 MPa and 2418.45 MPa at 50 % strain, respectively. The laminated GFs(Ni)/HEA matrix composites possessed excellent strength and maintained good plasticity. In this study, the strengthening and toughening mechanism of the laminated GFs(Ni)/HEA matrix composites is discussed in detail, and the results show that the laminated structure and GFs(Ni) are favorable for the hardening and strengthening of the HEA matrix.

Abstract We present FMNLO, a framework to combine general-purpose Monte Carlo generators and fragmentation functions (FFs). It is based on a hybrid scheme of phase-space slicing method and local subtraction method, and accurate to next-to-leading order (NLO) in QCD. The new framework has been interfaced to MG5_aMC@NLO and made publicly available in this work. We demonstrate its unique ability by giving theoretical predictions of various fragmentation measurements at the LHC, followed by comparison with the data. With the help of interpolation techniques, FMNLO allows for fast calculation of fragmentation processes for a large number of different FFs, which makes it a promising tool for future fits of FFs. As an example, we perform a NLO fit of parton fragmentation functions to unidentified charged hadrons using measurements at the LHC. We find the ATLAS data from inclusive dijet production show a strong constraining power. Notable disparities are found between our gluon FF and that of BKK, DSS and NNFF, indicating the necessities of additional constraints and data for gluon fragmentation function.

This study focuses on the center-staged swirl model combustion chamber, conducting experiments and numerical simulations to investigate the unstable combustion characteristics of diffusion flames under different Reynolds numbers and air–fuel ratios. The results were analyzed using methods such as Empirical Mode Decomposition (EMD), Fast Fourier Transform (FFT), and Proper Orthogonal Decomposition (POD). The research found that the first three intrinsic mode functions (IMFs) of the combustion chamber pressure fluctuation signal (DP) correspond to different physical fluctuation characteristics. Specifically, the 1st IMF represents the fluctuation characteristics of the heat release rate, corresponding to the flame shear region in the heat release rate field; the 2nd IMF represents the fluctuation characteristics of airflow swirl, corresponding to the swirl vortex structure region in the vorticity field; the 3rd IMF represents the flame detachment fluctuation characteristics, corresponding to the flame detachment region in the heat release rate field. Using the same experimental and numerical calculation methods to study another swirl model combustion chamber, the results also showed the aforementioned correspondence, further demonstrating the accuracy of the experimental results and the universality of this conclusion.

Rubberized steel fiber reinforced recycled aggregate concrete (RSRAC) is an environmental-friendly building material produced from waste concrete and tires. However, the evolution of fracture behavior in RSRAC under flexural loads still remains unknown, and the individual as well as the coupling effects of recycled aggregate (RA), crumb rubber, and steel fiber are also unclear. In this paper, four groups of specimens with different contents of RA, crumb rubber, and steel fiber were prepared, and a series of four-point bending tests were conducted. The loading process was monitored by acoustic emission (AE) in real-time, and the fracture behavior was then analyzed through AE signals using multiple statistical methods including Ib-value method, cumulative energy analysis, intensity analysis, K-means clustering and RA-AF analysis. Scanning electron microscope (SEM) was also employed to give explanations in microscale. Results show that the fracture process of RSRAC can be divided into micro-crack stage, main collapse stage, and residual stage. A fluctuation greater than 0.06 in Ib-value could be regarded as a criterion for this division. AE counts, cumulative AE energy as well as cluster analysis are also effective in judging the sub-stages. The dominant failure mode in micro-crack stage and residual stage is tensile crack, and shear cracks mainly occur in the main collapse stage. SEM investigation has revealed that the shear cracks in the main collapse stage mainly come from the shear failure of acicular crystals or micro-chimeras at the interface. For effects of different materials, it is found that rubber can reduce the brittleness and improve the ductility of the concrete, but will lower the strength properties. Steel fiber can increase the brittleness of the fracture behavior and reduce the ductility, but is favorable to strength. While RA can increase the brittleness and is harmful to both ductility and strength. The coexistence of rubber and steel fiber can significantly shorten the residual stage. The work is expected to provide a deeper understanding on the properties of RSRAC.

Target geolocation in long-range oblique photography (LOROP) is a challenging study due to the fact that measurement errors become more evident with increasing shooting distance, significantly affecting the calculation results. This paper introduces a novel high-accuracy target geolocation method based on multi-view observations. Unlike the usual target geolocation methods, which heavily depend on the accuracy of GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System), the proposed method overcomes these limitations and demonstrates an enhanced effectiveness by utilizing multiple aerial images captured at different locations without any additional supplementary information. In order to achieve this goal, camera optimization is performed to minimize the errors measured by GNSS and INS sensors. We first use feature matching between the images to acquire the matched keypoints, which determines the pixel coordinates of the landmarks in different images. A map-building process is then performed to obtain the spatial positions of these landmarks. With the initial guesses of landmarks, bundle adjustment is used to optimize the camera parameters and the spatial positions of the landmarks. After the camera optimization, a geolocation method based on line-of-sight (LOS) is used to calculate the target geolocation based on the optimized camera parameters. The proposed method is validated through simulation and an experiment utilizing unmanned aerial vehicle (UAV) images, demonstrating its efficiency, robustness, and ability to achieve high-accuracy target geolocation.

Most deep learning methods in hyperspectral image (HSI) classification use local learning methods, where overlapping areas between pixels can lead to spatial redundancy and higher computational cost. This paper proposes an efficient global learning (EGL) framework for HSI classification. The EGL framework was composed of universal global random stratification (UGSS) sampling strategy and a classification model BrsNet. The UGSS sampling strategy was used to solve the problem of insufficient gradient variance resulted from limited training samples. To fully extract and explore the most distinguishing feature representation, we used the modified linear bottleneck structure with spectral attention as a part of the BrsNet network to extract spectral spatial information. As a type of spectral attention, the shuffle spectral attention module screened important spectral features from the rich spectral information of HSI to improve the classification accuracy of the model. Meanwhile, we also designed a double branch structure in BrsNet that extracted more abundant spatial information from local and global perspectives to increase the performance of our classification framework. Experiments were conducted on three famous datasets, IP, PU, and SA. Compared with other classification methods, our proposed method produced competitive results in training time, while having a greater advantage in test time.

In order to improve the receiver performance of the sparse code multiple access (SCMA) system based on polar codes, the cyclic redundancy check (CRC) aided joint iterative detection and decoding receiver scheme based on simplify soft cancellation list (SSCANL) decoder (C-JIDD-SSCANL) was proposed.A polar code decoder in the C-JIDD-SSCANL receiver used the SSCANL algorithm.In this algorithm, decoding node deletion technology was used to simplify L times of soft cancellation (SCAN) decoding required by soft cancellation list (SCANL) algorithm by deleting frozen bit nodes, then the computational process of soft information update between nodes was simplified, and the computational complexity of decoding algorithm was reduced.The simulation results show that the SSCANL algorithm can obtain the same performance as the SCANL algorithm, and its computational complexity is reduced compared with the SCANL algorithm.Compared with the joint iterative detection and decoding scheme based on SCAN decoder (JIDD-SCAN) and the CRC aided joint iterative detection and decoding scheme based on SCAN decoder (C-JIDD-SCAN), the performance of C-JIDD-SSCANL receiver scheme based on SSCANL decoder is improved by about 0.65 dB and 0.59 dB respectively when the bit error rate is 10-4.

Abstract Monolithic two-section InGaAs/GaAs double quantum well (DQW) passively mode-locked lasers (MLLs) with asymmetric waveguide, consisting of the layers of p-doped AlGaAs waveguide and no-doped InGaAsP waveguide, emitting at ~ 1.06 μm, with a fundamental repetition rate at ~ 19.56 GHz have been demonstrated. Modal gain characteristics, such as a gain bandwidth and a gain peak wavelength of the MLL, as a function of the saturable absorber (SA) bias voltage (V a ) as well as the injection current of gain section (I g), were investigated by the Hakki-Paoli method. With the increase of V a , the lasing wavelength and net modal gain peak of the MLL both exhibited red-shifts to longer wavelength significantly, while the modal gain bandwidth was narrowed. Both the net modal gain bandwidth and gain peak of the MLL followed a polynomial distribution versus the reverse bias at the absorber section. In addition, for the first time, it was found that V a had an obvious effect on the modal gain characteristics of the MLL.

Non-premixed swirl combustion has been widely used in pieces of industrial combustion equipment such as industrial boilers, furnaces, and certain specific gas turbine combustors. In recent years, the combustion instability of non-premixed swirl flames has begun receiving attention, yet there is still a lack of related research in academia. Therefore, in this study, we conducted experimental research on a swirl stabilized gas flame model combustor and studied the heat release response characteristics of the swirl combustor through the flame transfer function. Firstly, the flame transfer function (FTF) was measured under different inlet velocities and equivalence ratios, and the experimental results showed that the FTF gain curve of the non-premixed swirl flame exhibited a significant “bimodal” shape, with the gain peaks located around 230 Hz and 330 Hz, respectively. Secondly, two oscillation modes of the flame near the two gain peaks were identified (the acoustic induced vortex mode Mv and the thermoacoustic oscillation mode Ma), which have not been reported in previous studies on swirl non-premixed flames. In addition, we comprehensively analyzed the flame pulsation characteristics under the two oscillation modes. Finally, the coupling degrees between velocity fluctuations, fuel pressure fluctuations, and heat release fluctuations were analyzed using the Rayleigh Index (RI), and it was found that in the acoustic-induced vortex mode, a complete feedback loop was not formed between the combustor and the fuel pipeline, which was the main reason for the significant difference in the pressure fluctuation amplitude near 230 Hz and 330 Hz.

The key advantage of silicon photonics comes from its potential for large scale integration, in a low-cost and scalable fashion. This has sustained the growth in the area despite disadvantages such as the lack of a monolithic light source, or the absence of a second order non-linear response (χ(2)). Thus far, the work in the field has focused on reporting individual devices from a single die, with excellent performances. Wafer-level results, an area which has not been addressed sufficiently, is a critical aspect of silicon photonics and will provide the community with information regarding scalability and variation, which will be the key differentiating advantage of silicon photonics over other photonic platforms. In this work, we report the development of a low-loss, high-bandwidth C-band silicon photonic platform on a 200 mm CMOS-compatible process line, demonstrating wafer-level performance in the process. Ultra-low waveguide propagation loss with median values as low as 0.43 dB/cm has been achieved. Silicon Mach-Zehnder and microring modulators with median bandwidth of 38.5 and 43 GHz respectively are presented. Finally, germanium waveguide-integrated photodetectors with median bandwidth of 43 GHz are reported. The results reported in this work are comparable to prior demonstrations concerning individual devices. The baseline designs on this platform presented in this work can be accessed commercially from CompoundTek.

Early warning of natural disasters (earthquakes and landslides) and engineering disasters (mining and tunnel collapse) has always been a complex problem in the field of engineering. In the current paper, the uniaxial compression loading tests of rock specimens are methodically observed by employing strain data acquisition equipment and an infrared thermal imager. By examining the variation pattern of strain field and temperature field data before rock damage, a critical damage identification method in conjunction with the strain and temperature data analysis is proposed. The corresponding time of total failure under the action of uniaxial compression is rationally determined by the strain data variation from increasing to decreasing in a 45-degree direction, and the location of the crack is predicted based on the slope of the tangent line of the strain curve in x- and y-direction at the failure time. Additionally, the slowing down of the temperature growth trend at the monitoring point and the significant variation of the local temperature over a short period in the infrared thermal image could be utilized as precursory characteristics for the rock damage. These research findings could be very beneficial in predicting the early warning of the surrounding rock instability in geotechnical engineering.

Heparanase, a member of the carbohydrate-active enzyme (CAZy) GH79 family, is an endo-β-glucuronidase capable of degrading the carbohydrate moiety of heparan sulphate proteoglycans, thus modulating and facilitating remodeling of the extracellular matrix. Heparanase activity is strongly associated with major human pathological complications, including but not limited to tumour progress, angiogenesis and inflammation, which make heparanase a valuable therapeutic target. Long-due crystallographic structures of human and bacterial heparanases have been recently determined. Though the overall architecture of human heparanase is generally comparable to that of bacterial glucuronidases, remarkable differences exist in their substrate recognition mode. Better understanding of regulatory mechanisms of heparanase in substrate recognition would provide novel insight into the anti-heparanase inhibitor development as well as potential clinical applications.

Intramuscular fat (IMF) contributes importantly to various aspects of meat quality, and genetic regulation is an effective pathway to improve IMF deposition in sheep. In this study, we systematically explored the effect of IMF content on meat quality in Tan sheep and investigated the regulatory mechanism of flavor precursors metabolism and IMF deposition. The results revealed that IMF significantly affected meat color, total muscle fiber numbers, and muscle fiber types in Tan sheep. Widely-targeted metabolomic analysis showed that amino acids were the main differential flavor precursors between lambs with different IMF content. Importantly, the comparison of fatty acid profiles revealed that saturated fatty acids and monounsaturated fatty acids are beneficial for IMF deposition. Furthermore, integrated analysis between metabolome and transcriptome indicated that MME is a key gene resulting in the reduction of amino acids in lambs with high IMF content; and the joint analysis between fatty acid profiles and transcript profiles showed that ADIPOQ, FABP4, PLIN1, PPARGC1A, SLC2A1 accelerated IMF deposition through positive regulation of saturated fatty acids and monounsaturated fatty acids metabolism. These results revealed key changes in meat quality affected by IMF content and the corresponding genetic mechanism, which may provide a new insight for understanding the IMF differential deposition and for improving meat quality in Tan sheep.

An amendment to this paper has been published and can be accessed via the original article.

A monolithic two-section InGaAs/GaAs double quantum wells (DQWs) mode-locked laser (MLL) emitting at 1.06 μm is demonstrated. Stable mode locking operation is achieved up to 80 °C. The fundamental repetition rate is at ~9.51 GHz with a signal-to-noise ratio (SNR) of more than 55 dB, and up to the fourth harmonic at ~38.04 GHz is observed. The characteristic temperature (T0) of the laser and the influences of absorber bias voltage on T0 have been systematically investigated. From our findings, T0 shows a two-segment feature, and is slightly affected by the absorber bias voltage for photon saturation.

The 2 µm waveband is capable of enabling pervasive applications. The demonstration of the hollow-core photonic bandgap fiber and the thulium-doped fiber amplifier has highlighted the fiber propagation and amplification aspects of fiber communications, indicating its potential as an adjunct to present communication infrastructure at the O/C bands. The above is especially imperative given the current concerns with regards to the upper bandwidth limit of the single-mode fiber. Furthermore, the waveband could facilitate many more applications such as LIDAR and free-space communication. However, water absorption (OH-) is high at most of the 2 μm waveband and this will impact the optical insertion loss of applications implemented in the wavelength region. The relative low water absorption region of the waveband falls within 1950 – 2000 nm. As such, the development of a hybrid/heterogeneous III-V/silicon laser source that operates within the region is important for 2 µm silicon photonics. In this work, we demonstrate a III-V/Si hybrid tunable laser operating from 1955 - 1992 nm for the first time. Room temperature continuous wave operation is achieved with a maximum laser output power of 8.1 mW. This wavelength-tunable laser operates specifically within the low water absorption window, indicating good wavelength suitability for applications at the 2 μm waveband.

In this paper, we consider mathematical modelling and parameter identification problem in bioconversion of glycerol to 1,3-propanediol by Klebsiella pneumoniae. In view of the dynamic behavior with memory and heredity and experimental results in batch culture, a two-stage fractional dynamical system with unknown fractional orders and unknown kinetic parameters is proposed to describe the fermentation process. For this system, some important properties of the solution are discussed. Then, taking the weighted least-squares error between the computational values and the experimental data as the performance index, a parameter identification model subject to continuous state inequality constraints is presented. An exact penalty method is introduced to transform the parameter identification problem into the one only with box constraints. On this basis, we develop a parallel Particle Swarm Optimization algorithm to find the optimal fractional orders and kinetic parameters. Finally, numerical results show that the model can reasonably describe the batch fermentation process, as well as the effectiveness of the developed algorithm. Keywords: fractional dynamical system, parameter identification, parallel optimization,

A judgment method of the final setting time of mass concrete based on strain monitoring and the selection criterion of the starting point of effective deformation are proposed, and the safety of the concrete structure is evaluated based on this. Firstly, the strain monitoring theory of mass concrete is analyzed to clarify the temperature effect component and monitoring mechanism of an early strain of concrete. Then, taking the mass concrete plate of an engineering project as the research object combined with the mechanical analysis, the early strain monitoring data are segmented, and the relationship between the strain response and the temperature in different stages is fitted based on robust regression, so as to explore the correlation between strain and temperature in each stage and better explain the setting process of the concrete structure. Finally, taking the final setting time as the starting point of the effective strain, the safety of the concrete structure is further evaluated. The results show that there is a clear linear relationship between the strain response and temperature, and a high negative correlation between strain and temperature after the final setting. The time point when the frequency modulus corresponding to the strain enters a steady change and decreases with the increase of temperature is taken as the final setting time, which conforms to the concrete condensation mechanism and the temperature effect of early strain monitoring.

Long exposure time and wide field can effectively improve the ability of a space surveillance telescope to detect faint space targets. However, complicated situations pose challenges for space target detection. Background star images usually manifest a rotated streak, and target trajectories can be crossed, discontinuous, or nonlinear. This paper presents an accurate and robust space target detection method, namely, spatiotemporal pipeline multistage hypothesis testing (SPMHT), to overcome the issues. Specifically, the method includes the following two stages: First, in the spatiotemporal pipeline filtering step, Spatiotemporal-related Intersection over Union (SrIoU) is used to calculate the IoU score instead of the traditional method. Benefiting from the differences between motion characteristics of targets and stars and the insensitivity of the SrIoU score to the noise, the spatiotemporal pipeline filtering can effectively remove the streak images of background stars and obtain candidate points. Second, a series of candidate points is further organized into a tree structure. We pruned in the tree structure combined with these candidate trajectories by using velocity and direction feature of moving objects. Furthermore, in the search step, fast adaptive sequence region search is used to reduce the computational cost. The experimental results for two datasets, simulated image datasets and real captured image datasets, demonstrate the effectiveness in addressing the difficulties of space target detection in complicated situations.

Concerning the issue that the convolutional neural network (CNN) accelerator design on most FPGA ends fails to effectively use the sparsity and considering both bandwidth and energy consumption,two improved CNN calculation optimization strategies based on linear systolic array architecture are proposed.Firstly,convolution is transformed into matrix multiplication to take advantage of sparsity.Secondly,in order to solve the problem of large I/O demand in traditional parallel matrix multiplier,linear systolic array is used to improve the design.Finally,a CNN acceleration comparative analysis of the advantages and disadvantages between parallel matrix multiplier and two improved linear systolic arrays is presented.Theoretical proof and analysis show that compared with the parallel matrix multiplier,the two improved linear systolic arrays make full use of sparsity,and have the advantages of less energy consumption and less I/O bandwidth occupation.

Object detection is a basic and important task in the field of aerial image processing and has gained much attention in computer vision. However, previous aerial image object-detection approaches have insufficient use of scene semantic information between different regions of large-scale aerial images. In addition, complex background and scale changes make it difficult to improve detection accuracy. To address these issues, we propose a relationship representation network for object detection in aerial images (RelationRS): (1) Firstly, multi-scale features are fused and enhanced by a dual relationship module (DRM) with conditional convolution. The dual relationship module learns the potential relationship between features of different scales and learns the relationship between different scenes from different patches in a same iteration. In addition, the dual relationship module dynamically generates parameters to guide the fusion of multi-scale features. (2) Secondly, the bridging visual representations module (BVR) is introduced into the field of aerial images to improve the object detection effect in images with complex backgrounds. Experiments with a publicly available object detection dataset for aerial images demonstrate that the proposed RelationRS achieves a state-of-the-art detection performance.

High-precision, real-time, and long-range target geo-location is crucial to UAV reconnaissance and target strikes. Traditional geo-location methods are highly dependent on the accuracies of GPS/INS and the target elevation, which restricts the target geo-location accuracy for LRORS. Moreover, due to the limitations of laser range and the common, real time methods of improving the accuracy, such as laser range finders, DEM and geographic reference data are inappropriate for long-range UAVs. To address the above problems, a set of work patterns and a novel geo-location method are proposed in this paper. The proposed method is not restricted by conditions such as the accuracy of GPS/INS, target elevation, and range finding instrumentation. Specifically, three steps are given, to perform as follows: First, calculate the rough geo-location of the target using the traditional method. Then, according to the rough geo-location, reimage the target. Due to errors in GPS/INS and target elevation, there will be a re-projection error between the actual points of the target and the calculated projection ones. Third, a weighted filtering algorithm is proposed to obtain the optimized target geo-location by processing the reprojection error. Repeat the above process until the target geo-location estimation converges on the true value. The geo-location accuracy is improved by the work pattern and the optimization algorithm. The proposed method was verified by simulation and a flight experiment. The results showed that the proposed method can improve the geo-location accuracy by 38.8 times and 22.5 times compared with traditional methods and DEM methods, respectively. The results indicate that our method is efficient and robust, and can achieve high-precision target geo-location, with an easy implementation.

Abstract Background To explore the mechanism of the different clinical efficacies of mesenchymal stem cell transplantation (MSCT) and identify a possible serum biomarker for predicting the therapeutic effect of MSCT in rheumatoid arthritis (RA) patients. Methods A total of 105 patients with persistently active RA and poor responses to traditional medication were randomly divided into MSCT and control groups. Outcomes were evaluated according to the 28-joint Disease Activity Score and Health Assessment Questionnaire, serological indicators, regulatory T cell (Treg) to T helper 17 (Th17) cell ratio, and inflammatory cytokine levels. Twelve weeks after MSCT, the outcomes of the MSCT group were evaluated according to the European League against Rheumatism response criteria. Patients with a good or moderate response were added to the response group, and those with no response were added to the no-response group. Results No serious adverse events were reported for either MSCT subgroup (28 in the response group and 24 in the no-response group). The therapeutic effects lasted for 48 weeks without continuous administration. Notably, a transient increase in serum IFN-γ (>2 pg/ml) levels was observed in the response group, but not in the no-response group. Furthermore, an increase in IL-10 levels and the Treg/Th17 ratio and a reduction in IL-6 levels appeared 2–3 weeks after the transient IFN-γ increase. Conclusions Allogeneic MSCT is safe and feasible, and we propose high serum IFN-γ levels as a potent biomarker for predicting MSCT response. Trial registration chictr.org, ChiCTR-ONC-16008770. Registered 3 July 2016, http://www.chictr.org.cn/showproj.aspx?proj=14820

A mid-infrared (MIR) biochemical sensor based on a one-time Si etching suspended microracetrack resonator with lateral subwavelength-grating metamaterial cladding is theoretically and experimentally demonstrated on a commercial 340-nm-thick-top-silicon silicon-on-insulator platform. The suspended structure offers an increased interaction area between the mode field and the chemicals under investigation, as well as good sensitivity. The one-time Si etching process also eases the fabrication. The suspended waveguide is optimized to obtain a balance between propagation loss and sensitivity. The suspended microracetrack resonator is experimentally measured at 2 μm wavelength with an extinction ratio of 12.1 dB and a full-width at half-maximum of 0.13 nm, which corresponds to a quality factor (Q factor) of 15 300. With the equivalent refractive index method and a specially developed numerical model, the sensing performance based on the waveguide structure has been simulated and analyzed. The simulation results show that the expected sensitivity of fundamental TE mode can achieve 337.5 nm/RIU. This one-time Si etching suspended microracetrack resonator shows great potential for ultrasensitive MIR optical biochemical sensing applications.

Thermally tunable Fano and electromagnetically induced transparency (EIT) resonances are theoretically and experimentally demonstrated based on a Mach-Zehnder interferometer assisted Bragg grating-microring coupled resonant system on silicon-on-insulator (SOI) platform. In this paper, the destructive and constructive coupling between the two resonators, the microring resonator and the Fabry-Perot resonator formed by two Bragg gratings, give rise to the Fano and EIT resonances, respectively. The resonance lineshape can be controlled and converted by tuning the optical path length of the MZI arm. The device performance has been theoretically analyzed by using a specially developed numerical model. The coupled resonant system was designed, fabricated, and characterized on a commercial (SOI) platform. The tuning and conversion of the resonance lineshape by thermo-optical effect have been experimentally observed and verified, with good agreement between the experimental data and the simulations.

InGaAs/GaAs/AlGaAs multiple wavelength quantum well (QW) semiconductor laser diodes (LDs) have been fabricated by impurity-free vacancy disordering (IFVD) QW intermixing (QWI) method. The IFVD-QWI process was carried out by sputtering-depositing SiO2 mask layers on top of the complete InGaAs/GaAs/AlGaAs QW laser structure, emitting at 980 nm wavelength, and followed by a rapid thermal annealing at 880 °C for 60 s. The lasing wavelength of the devices fabricated from the intermixed wafer was blue-shifted with the increase of the mask layer thickness. The maximum emission wavelength blue shift of a processed as-cleaved laser reached 112 nm with the output-power more than 1000 mW. By using such an IFVD-QWI technique, multi-wavelength integrated LDs have also been successfully fabricated from a single chip.

In this paper, top-illuminated InP-based uni-traveling-carrier photodiodes (UTC-PDs) with dipole-doped structure which achieve both high speed and high responsivity simultaneously have been reported. With optimization of device size and layer structure, dipole-doped UTC-PDs have demonstrated a measured optical-to-electrical 3-dB bandwidth of over 40 GHz and a maximum responsivity of 0.6 A/W at optical input power of 100 mW. By using a semi-analytical equivalent circuit model, the 3-dB bandwidth was predicted to be 103 GHz. This high 3-dB bandwidth and responsivity yield a high figure-of-merit value of 61.8 A/W·GHz.

A compact and ultrabroadband polarization beam splitter (PBS) utilizing a hybrid plasmonic Y-branch (HPYB) on a silicon-on-insulator (SOI) platform is proposed and numerically demonstrated. The HPYB consists of a vertical hybrid plasmonic waveguide (HPW) and a horizontal HPW formed by silicon (Si) and silver (Ag) strip waveguides sandwiched with a silicon dioxide (SiO2) layer, in which the vertical and horizontal hybrid plasmonic modes (HPMs) are excited by the input transverse electric (TE) and transverse magnetic (TM) modes, respectively. The HPMs are split into different ports and coupled back to TE and TM modes to implement the polarization splitting function. A simplified and compact HPYB is robust for the HPMs' generation. The structure is wavelength insensitive since the HPMs' excitation is weakly correlated to the optical wavelengths. The simulation results show that the HPYB-based PBS has a compact footprint of $5 \times 1.8\ \mu\text{m}^2$ and an ultralarge working bandwidth of 285 nm, with the polarization crosstalk

Vehicle detection in aerial images plays a significant role in civil and military applications and it faces many challenges including the overhead-view perspective, the highly complex background, and the variants of vehicles. This paper presents a robust vehicle detection scheme to overcome these issues. In the detection stage, we propose a novel algorithm to generate oriented proposals that could enclose the vehicle objects properly as rotated rectangles with orientations. To discriminate the object and background in the proposals, we propose a modified vector of locally aggregated descriptors (VLAD) image representation model with a recently proposed image feature, i.e., local steering kernel (LSK) feature. By applying non-maximum suppression (NMS) after classification, we show that each vehicle object is detected with a single-oriented bounding box. Experiments are conducted on aerial images to compare the proposed method with state-of-art methods and evaluate the impact of the components in the model. The results have proven the robustness of the proposed method under various circumstances and the superior performance over other existing vehicle detection approaches.

A nonlinear dynamical system, in which the feed rates of glycerol and alkali are taken as the control functions, is first proposed to formulate the fed-batch culture of 1,3-propanediol (1,3-PD) production. To maximize the 1,3-PD concentration at the terminal time, a constrained optimal control model is then presented. A solution approach is developed to seek the optimal feed rates based on control vector parametrization method and improved differential evolution algorithm. The proposed methodology yielded an increase by 32.17% of 1,3-PD concentration at the terminal time.