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Noninvasive optical measurements of the shockwave propagation velocity using multiple pulse illumination allow deducing the shockwave pressure amplitude through Hugoniot relations and an appropriate equation of state of the medium. This technique is particularly useful for spatially resolved measurements near the shockwave emission site. Due to diffraction, however, a shockwave front can significantly change its morphology, rendering precise velocity measurements non-trivial. As solution we propose a wave front evolution (WaFE) velocimetry technique, which applies Huygens principle. We take a shadowgraph of the wave front at subsequent times as initial condition for the acoustic Helmholtz equation and numerically propagate the fronts in time. From the instance of time, when two subsequently taken wave front shadows numerically interfere and form one sharp wave front, the local shock velocity is obtained and the local shock pressure amplitude measured. With artificial test images, it is shown that this technique has excellent sub-pixel accuracy, robustness to noise, and can work with low contrast images and even overlapping and interfering wave fronts. The software is made available freely and can be applied to general shock front velocity measurements. We apply WaFE to determine the anisotropic shockwave emission from an elongated laser-induced plasma in water from shadowgraphs of the shockwave front imaged four times onto the same camera frame using multiple pulse illumination at a repetition rate of 60 MHz. The direction dependence of attenuation of the shockwave pressure amplitude is measured at distances of 50–300 μm to the plasma. [ABSTRACT FROM AUTHOR]

This study presents the development of a novel methodology, image-processing-based ultrasonic velocimetry (IPUV). For evaluating flow velocity, we present a new perspective utilizing image processing for spatiotemporal echo images instead of conventional ultrasonic analysis methods such as pulse compression and quadrature detection. In addition to traditional tracer particle size O (100 μ m) , IPUV has a high applicability for measuring the velocity of microparticle dispersion of the particle size O (1 μ m) and dilute concentration O (0.001 wt. %). Velocimetry utilizing microparticles has significant merit: Velocity measurements can be conducted under conditions with less influence on the flow and higher traceability to fluid flow than the size conventionally used as a tracer particle. The accuracy and effectiveness of velocity analysis based on the IPUV principle are verified numerically and experimentally. In the experiments, IPUV was applied to Couette flow and stirring flow. In the former, the validity of the velocity profiling by IPUV was confirmed compared to the theoretical profiles of Couette flow. In the latter, IPUV measured velocity profiles in the stirring flows with different dispersions [spherical particle O (100 μ m) and mica O (1 μ m) ]. Differences in the ability of dispersed particles to follow turbulent fluctuations in stirring water flow appeared in the frequency spectrum of IPUV velocity profiles. [ABSTRACT FROM AUTHOR]

The lack of rapid, effective, and quantitative testing methods in the initial stages of optical processing has been identified as a significant challenge. In response, an innovative approach has been developed to enhance the Foucault knife-edge test method, integrating digital image processing and spatial geometry algorithms. This has resulted in the creation of a convenient and rapid optical testing method, which has been successfully implemented in practice. The method requires the collection of only two knife-edge shadow grams, which are then subjected to calculation. This enables the theoretical results of the knife-edge shadow gram at the focal point, and the reference results of the wavefront surface processing error of the mirror to be measured can be obtained. The method can be well applied to the testing work of optical workshops and provides a guiding solution with better performance for optical testing than the traditional knife-edge testing method. [ABSTRACT FROM AUTHOR]

There is a currently a lack of large-area plasma sterilization devices that can intelligently identify the shape of a wound for automatic sterilization. For this reason, in this work, a plasma sterilization device with wound-edge recognition was developed using a field-programmable gate array (FPGA) and a high-performance image-processing platform to realize intelligent and precise sterilization of wounds. SOLIDWORKS was used to design the mechanical structure of the device, and it was manufactured using 3D printing. The device used an improvement of the traditional Sobel detection algorithm, which extends the detection of edges in only the x and y directions to eight directions (0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°), completing the wound-edge detection by adaptive thresholding. The device can be controlled according to different shapes of sterilization area to adjust the positioning of a single plasma-jet tube in the horizontal plane for two-dimensional movement; the distance between the plasma-jet tube and the surface of the object to be sterilized can be also adjusted in the vertical direction. In this way, motors are used to move the plasma jet and achieve automatic, efficient, and accurate plasma sterilization. It was found that a good sterilization effect could be achieved at both the culture-medium level and the biological-tissue level. The ideal sterilization parameters at the culture-medium level were a speed of 2 mm/s and a flow rate of 0.6 slm, while at the biological-tissue level, these values were 1 mm/s and 0.6 slm, respectively. ARTICLE HIGHLIGHTS: • Image processing combining eight-direction Sobel edge detection and a median-filtering adaptive threshold was used for wound-edge recognition. • A device with x, y, and z degrees of freedom was designed and 3D printed. • An FPGA-based sterilization platform combining edge-recognition detection, a three-degree-of-freedom kinematic device, and a plasma injector was built to precisely sterilize wounds. [ABSTRACT FROM AUTHOR]

Atomically thin two-dimensional (2D) materials exhibit extraordinary optical, electrical, and mechanical properties. Many functional nanostructures and devices of exceptional performance based on 2D materials have been demonstrated. However, the processing of 2D materials remains challenging due to inadequacies that are mainly driven by high fabrication cost, complex steps, and inefficient impurity control. On the other hand, laser-aided processing techniques offer versatility, nanoscale precision, and high throughput. Numerous efforts have showcased the implementation of laser processing and functionalization of 2D materials to control their physical properties and optimize device functionality. In this Perspective, we summarize research progress on laser-enabled thinning, patterning, doping, and functionalization of 2D materials. Continuing advances in optical processing techniques are anticipated to further accelerate the deployment of 2D materials and devices in many fields, including photonics, optoelectronics, and sensor applications. [ABSTRACT FROM AUTHOR]

Mode decomposition (MD) provides profound evidence to reveal the internal modal transmission mechanism. However, the indelible noise has always been the main stubborn hindrance in practical MD. In the complex superposition case with a large number of modes, the traditional MD is not capable enough to distinguish the real modal intensity and the annoying noise, sustaining an unacceptable accuracy and fluctuation. This paper proposes a radial intensity-orientated MD (RIO-MD) method with reliable noise elimination. Our approach focuses on the inherent modal radial features in Polar coordinates, getting rid of the traditional two-dimensional image processing in Cartesian ones. The RIO-MD introduces the inherent radial intensity relationship into MD for better extracting mode coefficients. Based on the expectable real radial modal intensity, the RIO-MD enables to recognize and extraction of the three kinds of stubborn noise, including interference pattern noise, device noise, and random noise. The RIO-MD works well in mode decomposition case. The values of correlation coefficients (C) between the experimental and reconstructed image are higher than 93%. The mean square error (MSE) is lower than 3 × 10−3. Both the C and MSE keep stable, with the standard deviation 30 times lower than the other widely used methods, demonstrating the high-robustness of the RIO-MD. Due to the reliable noise recognition, the RIO-MD shows great possibility in mode number expansion. [ABSTRACT FROM AUTHOR]

Simultaneous capture of various light information, including polarization and edge information of the objects, has consistently been a fundamental concern within the field of target recognition. However, these tasks are typically accompanied by bulky optical components and active illumination methods, which significantly restricts their use in compact and lightweight applications. Here, we demonstrate a metalens-assisted imaging system that can simultaneously achieve polarization imaging and optoelectronic edge detection in a single shot with low consumption. The dielectric metalens is designed to achieve polarization imaging by dispersing the input polarized light into two orthogonal components, resulting in optoelectronic isotropic edge detection of two-dimensional images after digital post-processing. Compared with the algorithmic methods using a convolution kernel, the proposed system has a much lower computational complexity. The work presented in this study demonstrates the potential applications in machine vision and paves the way for the development of compact target recognition and real-time image processing systems. [ABSTRACT FROM AUTHOR]

In this study, a new method of combining lance–liquid flow characteristics and mixing uniformity is proposed to evaluate the stirring characteristics in the bottom blowing copper molten pool. A fluid simulation model of a bottom blowing molten pool was established, water was used to simulate the melt environment, and an experimental platform was set up for verification. The effects of swirl, multi-channel, and straight pipe spray on the lance–liquid stirring characteristics of the bottom-blown copper molten pool are compared through quantifying the flow characteristics and mixing uniformity. In addition, digital image processing technologies, such as image entropy variance and eddy current map entropy increase, are introduced. Through numerical simulation research, it is found that the transverse velocity of the swirl spray lance is the largest, which makes the rise time of the bubble increase to the greatest extent. Compared with the straight pipe spray, the swirl spray reduces the liquid splash height by 0.054 m, and the degree of vortex flow is higher. The lance phase stability is increased by 37.87%, and the maximum turbulent kinetic energy can be increased by 8.73%. The spray effect of the multi-channel spray is between the two. It is shown that the swirling spray lance can improve the stability of gas in the molten pool, enhance the uniformity of gas–liquid mixing, and improve the operation cycle and the smelting efficiency of the molten pool. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) reconstruction has received extensive attention in recent years and is of great significance to various application fields. We propose a simple method of 3D object reconstruction based on spin–orbit interactions occurring from light reflection at the air–glass interface. By rotating the object, edge images of the object in various orientations are obtained; we overlap these edge images to ultimately reconstruct the 3D object. This 3D image detection based on spin–orbit interaction of light provides a low energy consumption, low cost, and more algorithmic method compared to traditional 3D image processing. This can be used in fields such as medicine, autonomous vehicles, planetary observation, and other areas. [ABSTRACT FROM AUTHOR]

In the production process of high-end PP-R pipes, mixing different colored raw material particles can result in uneven color in the final product, affecting its appearance quality. in addition, color mixing can reduce the physical properties of the pipes, impacting their durability and safety. To address this issue, we propose a visual, non-destructive inspection solution based on image processing technology. The solution aims to enhance detection efficiency and accuracy by reducing background interference and enabling adaptive adjustments in various environments. Initially, the K-Means image segmentation algorithm is employed to eliminate complex background factors from the original image, significantly improving image segmentation accuracy. Subsequently, the Gaussian mixture model algorithm is utilized to automatically extract the color threshold of the foreground image after background removal, facilitating adaptive algorithm adjustments. Finally, the mean value algorithm is introduced to swiftly and accurately identify plastic particles of different colors using the automatically obtained color thresholds. Experimental results demonstrate that this method can quickly and accurately identify different color particles and effectively support the rejection of impurity particles. Through this approach, the algorithm achieves an average detection accuracy of 99.3%. [ABSTRACT FROM AUTHOR]

The dissolution process of CO2 bubbles adhered to a flat plate in a rectangular channel at different flow velocities is studied experimentally and theoretically. In the experiments, the CO2 bubble is manually introduced by means of a needle connected to a micro-syringe in the rectangular channel filled with ultra-pure degassed water. The rectangular channel comprises a transparent 3D printed cavity and a replaceable plate. The water flow velocity in the rectangular channel can be precisely controlled using a liquid flow controller. The CO2 bubble is adhered to the flat plate, which is replaceable and has different wetting properties. The dissolution process of the bubble is visualized using a high-speed camera at different flow velocities. The changing bubble radius over time is calculated using an image processing program. A mathematical dissolution model is developed to estimate the dissolution durations of the CO2 bubble adhered to the flat plate, which includes the effect of contact angle and water flow velocity by using Sherwood number. With appropriate constants, the dissolution model exhibits good agreement with the experimental results. It is found that both the contact angle and water flow velocity play important roles in the bubble dissolution rate. An increase in water flow velocity can help improve the bubble dissolution rate. With a decrease in contact angle, the bubble dissolution rate becomes faster. The dissolution model is used to predict the maximum allowable bubble size at different scanning speeds in immersion lithography. [ABSTRACT FROM AUTHOR]

Acoustic measurements using sine sweeps are prone to background noise and non-stationary disturbances. Repeated measurements can be averaged to improve the resulting signal-to-noise ratio. However, averaging leads to poor rejection of non-stationary high-energy disturbances and, in the case of a time-variant environment, causes attenuation at high frequencies. This paper proposes a robust method to combine repeated sweep measurements using across-measurement median filtering in the time-frequency domain. The method, called Mosaic, successfully rejects non-stationary noise, suppresses background noise, and is more robust toward time variation than averaging. The proposed method allows high-quality measurement of impulse responses in a noisy environment. [ABSTRACT FROM AUTHOR]

This research proposes a novel dynamic image watermarking technique with features inspired by quantum computing principles. This method encodes binary values into qubits and embeds a watermark into an original image. The watermarking process is achieved by utilizing quantum circuits to manipulate the qubits representing the pixel values of the original and watermark images. To extract the watermark, encode each pixel value into a qubit, combine them using quantum operations, and then measure the resultant quantum state. This technique ensures the integrity and authenticity of the image by embedding a watermark that can be extracted with high fidelity. Simulation results show that our technique successfully embeds watermarks while maintaining picture quality. Moreover, this method exhibits robustness against common image processing attacks, highlighting its potential for secure image verification applications. [ABSTRACT FROM AUTHOR]

We determined the probabilities of finding diffusing Si atoms in faulted and unfaulted half-unit cells on the Si(111)-(7 × 7) surface. An adsorbed Si atom on the surface at room temperature moved in the half-unit cells. The atom was adsorbed via atom manipulation and located via under-sampled scanning tunneling microscopy. Images of the half-unit cells with the diffusing Si atom and images of the clean surface were superimposed via image processing to calculate differences used to determine the Si atom positions at room temperature. There were different probabilities for Si atom diffusion in faulted and unfaulted half-unit cells. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop software for reading and managing sunshine duration recorded by Campbell–Stokes heliographs. The software employs digital image processing techniques, such as mean filtering, thresholding, and opening, to interpret digitized images of record cards containing sunshine duration measurements. The software's validation was conducted by comparing global solar irradiation measurements with estimates of global solar irradiation generated from sunshine duration obtained automatically by the software and manually by a meteorological observer. Estimates generated by the automated method showed better performance (mean bias error: 0.084, relative mean bias error: 0.500, root mean square error: 2.045, relative root mean square error: 12.109, and correlation coefficient: 0.954). Additionally, the automated method was significantly faster, taking an average of 41.825 s less than the manual method to perform the readings. This suggests that the software can be used to automate, standardize, and speed up reading sunshine duration data. Furthermore, the use of the software enabled the creation of a daily sunshine duration database, common in meteorological stations, and an hourly database, which is a novelty due to the complexity of the manual reading method. [ABSTRACT FROM AUTHOR]

The reduction of carbon emissions has become a critical global issue, and the use of monoethanolamine (MEA) solution for CO2 absorption is prevalent in industry. To elucidate the mass transfer mechanisms in reactive multiphase flow, we employed high-speed photography and digital image processing to examine the dynamics and mass transfer behavior of CO2 bubbles in a Hele-Shaw cell. The results indicate that as the MEA solution concentration increases, oscillations during bubble ascent diminish, and the terminal velocity decreases. Based on changes in the mass transfer coefficient, the reaction process can be segmented into a phase of intensified mass transfer, marked by a rapid decrease in bubble equivalent diameter, and a phase of deteriorating mass transfer, where the diameter stabilizes. Additionally, we introduced a dimensionless mathematical model for the Sherwood number based on experimental findings, and its reliability was confirmed. [ABSTRACT FROM AUTHOR]

The stayed-cable is an important component of cable-stayed bridges, with cable force being a focal point during construction and bridge operation. The advancement of camera and image processing technology has facilitated the integration of computer vision technology in structural inspection and monitoring. This paper focuses on enhancing cable force measurement methods and addressing the limitations of traditional testing techniques by conducting experimental research on cable force estimation using video recording. The proposed approach involves capturing video footage of the target on the cable with a smartphone. Subsequently, a combination of techniques such as the background subtraction method, image morphology processing, and Hough transform image processing technology are employed to detect the precise center coordinates and ultimately obtain the accurate displacement–time curve of the cable's vibration. In addition, the graphic Circularity Coefficient (CC) has been introduced to assess its effectiveness in post-motion-blur image processing for circular targets. The fundamental frequency of the cable is determined by the fast Fourier transformation, and the relationship between the cable force and the fundamental frequency is used to estimate the cable force. The experimental results are compared with data from accelerometers and force gauges, demonstrating that the frequency measurement error is below 1.2% and the cable force test error is less than 3%. In the process of acquiring the cable's fundamental frequency, the test directly employs the pixel as the displacement unit, eliminating the need for image calibration. The innovative use of the CC in processing motion-blurred targets ensured accurate recognition of target coordinates. The experimental findings highlight the method's simplicity, speed, and accuracy. [ABSTRACT FROM AUTHOR]

Currently, most buildings are constructed using prefabricated concrete slabs supported by a steel skeleton that is generally tied and welded manually. However, if the overall size of the skeleton is incorrect and this error is not noticed before the concrete is poured, then a huge waste is incurred by having to scrap all the prefabricated slabs. Therefore, we propose an automatic system for measuring the frame size needed for prefabricated slabs made of reinforced concrete using LabVIEW and the NI Vision library of machine-vision functions to perform image processing and machine-vision detection. A charge-coupled device camera obtained an overall image, and then, LabVIEW processed the image and extracted its features. The system compared the measured size with the design size and issued a warning if the two differ by more than 0.5 cm. From experimental comparison and verification, the maximum measurement error between the test results and the actual size was 2.7% and the root mean square error was 0.66%. This meets the requirements of actual site construction and provides a scientific reference for realizing the automatic detection of the skeleton size for reinforced-concrete precast slabs. [ABSTRACT FROM AUTHOR]

The accurate and rapid detection and recognition of jet features are key to dynamic monitoring and online control of the electrospinning process. In this study, a real-time recognition system based on OpenCV was introduced into a coaxial laser-assisted electrospinning system to solve the difficulties of accurate jet recognition and to promote an image processing algorithm response. The jet images with laser assistance were more clearly visible than those without laser assistance, and a significant contrast in grayscale levels existed in the jet image to help distinguish jet features. Subsequently, separate algorithms were designed for the jet visible length calculation, and the recognized visible length of the jet and algorithm running speed were compared. The average visible length of the jet with laser assistance was 11.49 mm, which increased by 1.59 mm compared to that without laser assistance. In addition, the running time of the algorithm with laser assistance was 24.89 ms, reduced by 14.84 ms compared to that without laser assistance, indicating the effectiveness of laser assistance to promote the accuracy and running speed of the jet image recognition process. Additionally, real-time detection of the jet angles was achieved to identify instances of excessive deflection during the electrospinning process. Overall, this study has significant potential to promote the dynamic monitoring of an electrospinning jet. [ABSTRACT FROM AUTHOR]

Ascertaining the collective viability of cells in different cell culture conditions has typically relied on averaging colorimetric indicators and is often reported out in simple binary readouts. Recent research has combined viability assessment techniques with image-based deep-learning models to automate the characterization of cellular properties. However, further development of viability measurements to assess the continuity of possible cellular states and responses to perturbation across cell culture conditions is needed. In this work, we demonstrate an image processing algorithm for quantifying features associated with cellular viability in 3D cultures without the need for assay-based indicators. We show that our algorithm performs similarly to a pair of human experts in whole-well images over a range of days and culture matrix compositions. To demonstrate potential utility, we perform a longitudinal study investigating the impact of a known therapeutic on pancreatic cancer spheroids. Using images taken with a high content imaging system, the algorithm successfully tracks viability at the individual spheroid and whole-well level. The method we propose reduces analysis time by 97% in comparison with the experts. Because the method is independent of the microscope or imaging system used, this approach lays the foundation for accelerating progress in and for improving the robustness and reproducibility of 3D culture analysis across biological and clinical research. [ABSTRACT FROM AUTHOR]

This paper delves into the dynamics of a sphere entering flowing water at varying impact velocities and flow speeds. Using a high-speed photography system and image processing, we track the cavity evolution and trajectory. Flowing water is observed to tilt the cavity and postpone its detachment from the free surface. Beyond surface sealing, we identify a flowing-induced pinch-off phenomenon during water entry, marking a transition in closure regimes. This transition establishes a threshold impacting cavity tilt angle and pull-away length. By mapping the phase diagram of flow Reynolds number (Rew) against impact Froude number (Fr), we classify partial surface seal, pinch-off, and surface seal into distinct regimes. The Fr1/3 law effectively predicts the rising trend of cavity depth (H) and pinch-off depth (Hp) in flowing water. However, the Hp/H ratio differs from that reported in existing literature. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation, game playing, and robotics. However, there exist fundamental differences between ANNs' operating mechanisms and those of the biological brain, particularly concerning learning processes. This paper presents a comprehensive review of current brain-inspired learning representations in artificial neural networks. We investigate the integration of more biologically plausible mechanisms, such as synaptic plasticity, to improve these networks' capabilities. Moreover, we delve into the potential advantages and challenges accompanying this approach. In this review, we pinpoint promising avenues for future research in this rapidly advancing field, which could bring us closer to understanding the essence of intelligence. [ABSTRACT FROM AUTHOR]

Establishing quantitative parameters for differentiating between healthy and diseased cartilage tissues by examining collagen fibril degradation patterns facilitates the understanding of tissue characteristics during disease progression. These findings could also complement existing clinical methods used to diagnose cartilage-related diseases. In this study, cartilage samples from normal, osteoarthritis (OA), and rheumatoid arthritis (RA) tissues were prepared and analyzed using polarization-resolved second harmonic generation (P-SHG) imaging and quantitative image texture analysis. The enhanced molecular contrast obtained from this approach is expected to aid in distinguishing between healthy and diseased cartilage tissues. P-SHG image analysis revealed distinct parameters in the cartilage samples, reflecting variations in collagen fibril arrangement and organization across different pathological states. Normal tissues exhibited distinct χ33/χ31 values compared with those of OA and RA, indicating collagen type transition and cartilage erosion with chondrocyte swelling, respectively. Compared with those of normal tissues, OA samples demonstrated a higher degree of linear polarization, suggesting increased tissue birefringence due to the deposition of type-I collagen in the extracellular matrix. The distribution of the planar orientation of collagen fibrils revealed a more directional orientation in the OA samples, associated with increased type-I collagen, while the RA samples exhibited a heterogeneous molecular orientation. This study revealed that the imaging technique, the quantitative analysis of the images, and the derived parameters presented in this study could be used as a reference for disease diagnostics, providing a clear understanding of collagen fibril degradation in cartilage. [ABSTRACT FROM AUTHOR]

A field-programmable photonic gate array is an integrated optical chip that combines electrical control and optical processing, enabling real-time reconfiguration of the optical path through software programming. While most current optical processors rely on Mach–Zehnder interferometer (MZI)-based architectures, those based on micro-disk resonators (MDRs) offer unique characteristics, including high integration and wavelength correlation, providing new ideas for programmable photonic chip architectures. In this paper, a scalable asymmetric MZI-assisted field-programmable micro-ring array (AMZI-FPRA) processor is proposed with a cell area of only 85 × 42 µm2. This design not only has high wavelength selectivity but also possesses dual adjustable wavelengths and coupling coefficients compared with traditional MDRs. By extending the cell into a 2 × 2 AMZI-FPRA using a two-dimensional square mesh approach, it is experimentally demonstrated that different optical path topologies can be realized with a compact footprint, including bandpass bandstop filtering, optical temporal differentiation, microwave delay, wavelength-division multiplexing/demultiplexing, and optical add-drop multiplexing. Increasing the array scale will enable more versatile and high-performance microwave photonic signal processing tasks. The scheme will be a promising candidate at the present time for reconfigurable programmable photonic signal processors due to its wide reconfigurability, on-chip integration, complementary metal–oxide–semiconductor-compatibility, and low power consumption. [ABSTRACT FROM AUTHOR]

Hypersonic stage separation is a significant process, probably involving complex aerodynamic interaction, which determines the survival of two-stage-to-orbit (TSTO) vehicles. The longitudinal stage separation (LSS) scheme is proposed to improve the safety of the parallel-staged TSTO vehicle, where a small interstage gap may result in weak interference. Therefore, an experimental and numerical study of LSS for the parallel-staged TSTO vehicle at Mach 7 with different angles of attack (AoA) is carried out. The dynamic interaction, including variations in the shock structure, wall pressure distribution, and unsteady aerodynamics, is investigated by testing and numerical simulation. The LSS experiments for the TSTO vehicle were performed using a high-speed pneumatic ejection launch system in the JF-12 shock tunnel, and the method was developed using high-speed visualization and image processing techniques to capture the separating trajectory. The numerical simulations were carried out using the overset grid method and solving the Navier–Stokes equations coupled with the rigid body dynamics equations to obtain the laminar flows over the TSTO vehicle during LSS. The qualitative and quantitative comparison of the test and numerical results showed good agreement in terms of aerodynamic performance, flowfield pattern, wall pressure, and separation trajectory. They show that the small interstage gap of the LSS leads to weak type I and VI shock–shock interactions, with short-duration weak shock reflection at a higher AoA. Furthermore, no shock reflection or interstage gap is observed at lower AoA. Moreover, no stage recontact is observed, and the safety and feasibility of LSS for parallel-staged TSTO vehicles are demonstrated. [ABSTRACT FROM AUTHOR]

Slinger atomizers, known as one type of rotary atomizers, have been widely applied in various small gas turbine engines. The fuel can be well atomized by taking advantage of the high rotational speed of the turbine shaft. The geometric characteristics of the injection orifice play an important role in determining the atomization performance of the slingers. The breakup regimes and the droplet size of the slinger atomizers with slot-shaped orifices have rarely reported in the past. Herein, three types of slinger atomizers with different orifice shapes and orifice diameters are tested at rotational speeds of 8000–20 000 rpm and liquid feed rates of 4 up to 20 g/s. High-speed shadowgraph imaging, high-speed digital imaging, and planar Mie technologies are applied to provide the spray breakup process, liquid film injection features, and droplet distribution, respectively. Spray visualizations show that the orifice diameters strongly affect the breakup modes, whereas the orifice shapes have a slight effect. The variation regarding droplet sizing under different heights from the slinger plane is analyzed. The uniformity of the droplet distribution in slot-shaped slinger atomizers is better than that in round-shaped slinger atomizers. Moreover, the smaller orifice diameter results in a small Sauter mean diameter (SMD) for the slinger atomizers with slot-shaped orifices. Finally, a mathematical expression is obtained to predict non-dimensional droplet size (SMD/d) for different slinger atomizers. The present results appear to be the first systematic investigation of the spray characteristics in slinger atomizers with slot-shaped orifices. [ABSTRACT FROM AUTHOR]

Digital Signal Processing (DSP) finds a wide range of applications in various fields, including telecommunications, audio and video processing, biomedical engineering, radar systems, and image processing. Previous DSP designs faced limitations in available processing power and computational resources. Insufficient processing power could result in slower execution times, an inability to handle complex algorithms, or limited capacity to process high-speed or large-scale signals. As the demand for minimal power consumption in DSP circuits continues to grow, reversible logic and quantum-dot cellular automata (QCA) have emerged as promising technologies due to their inherent ability to reduce energy loss. Within this landscape, the arithmetic and logic unit (ALU) plays a vital role in complex circuitry, serving as a key component in digital signal processing applications. However, challenges persist, including high quantum cost and the need to limit the number of cells in the ALU design. To address these challenges, our research aims to develop an efficient ALU by integrating reversible logic and QCA technology. Our focus will be on generating essential components, such as Feynman gates, Fredkin gates, and full adder circuits, which serve as foundational building blocks for reversible logic and QCA designs. These components will be combined to construct a comprehensive ALU capable of performing 20 different operations. Our implementation efforts will be centered around QCADesigner, with a specific emphasis on digital signal processing systems that prioritize energy efficiency and optimal utilization of occupied areas. [ABSTRACT FROM AUTHOR]

Detecting diseases is a vital and crucial step in maintaining healthy, high-yielding plants. The challenge of manually identifying infections is arduous as well. The proposed work is to diagnose plant leaf diseases and discuss their origins and remedies. Image processing is used to discover the infected leaf and provide remedial measures through a mobile robot application. The use of machine learning techniques allows for the detection of leaf diseases using the support vector machine model, the K nearest neighbor model, and the Naïve Bayes classification to categorize the sample leaves. In this paper, the Momordica charantia leaf and the common four diseases dataset are developed, and a classification model is developed to identify and categorize leaf curl, downy mildew, powdery mildew, and angular leaf spot. Based on the disease classification, appropriate chemical pesticides are sprayed by controlling the servo actuated valve in the proposed agriculture robot, which is controlled and validated. The result reveals that the proposed approach has an average accuracy of 82% in identifying the disease type that remains more prevalent in Momordica charantia leaves than other compared classification algorithms. [ABSTRACT FROM AUTHOR]

Denoising algorithms for obtaining the effective data of depth images affected by random noise mainly focus on the processing of gray images. These algorithms are not distinct from traditional image-processing methods, and there is no way to evaluate the effectiveness of denoising after the point cloud transformation of denoised depth images. In this paper, the principle of fractional-order integral denoising is studied in detail and inverse distance weighted interpolation is introduced into a denoising model, which is based on the G–L (Grünwald–Letnikov) fractional-order integral to construct a fractional-order integral with an inverse distance weighted denoising model. The model is used to solve the blurring problem caused by sharp changes at the edge and achieves an excellent denoising effect. By using the optimized fractional-order integral denoising operator to construct a denoising model for depth images, the results of the experiments demonstrate that the fractional-order integral of the best denoising effect achieved by the model is −0.6 ≤ ν ≤ −0.4, and the peak signal-to-noise ratio is improved from +6 to +13 dB. In the same condition, median denoising has a distortion of −30 to −15 dB. The depth image that has been denoised is converted into an image of point clouds, and subjective evaluation indicates that the noise is effectively removed. On the whole, the results demonstrate that the fractional-order integral denoising operator with inverse distance weight shows the high efficiency and the outstanding effect in removing noise from depth images while maintaining the image related to the edge and texture information. [ABSTRACT FROM AUTHOR]

This study delves into the phenomenon of air cavity formation resulting from sphere-liquid impacts, a subject of enduring scientific interest with broad interdisciplinary applications. The actual cavity and air bubble volumes following cavity rupture are for the first time measured and analyzed, in correlation with the thinning of the air filament. Utilizing direct visualization and an original image processing technique, we introduce a method for these precise measurements. We have explored the dynamics of this phenomenon across various fluids, including sunflower oil, glycerin, and polyethylene oxide (PEO) viscoelastic solutions. Impact velocity and fluid properties dictate not only the topology of the air cavity but also the volume dynamics and the evolution of the air thread near cavity rupture. Experiments with PEO solutions show that increasing polymer concentration influences elasticity, resulting in narrower cavity interfaces and reduced air volume attached to the sphere after cavity rupture. The asymmetry of the air cavity is diminished with increasing viscosity and is amplified in the presence of fluid elasticity. Insights into cavity shapes generated by the impact of a spherical body in a liquid, and correlating cavity dynamics with fluid properties, contribute to advancing knowledge in various fields, from solid–fluid interaction and rheology to biomechanics and biology. [ABSTRACT FROM AUTHOR]

In conditions of low visibility, the scattering effect of atmospheric particles can result in images captured by cameras exhibiting a number of problems, including reduced contrast, color distortion, and blurred details. These issues can have a significant impact on the clarity and recognition of the images. In response to these issues, this study combines atmospheric scattering models with wavelet transform, introduces non-blind deconvolution algorithms and, finally, designs a special gamma correction function to ultimately develop a new foggy image restoration model. The relevant outcomes showed that when the dehazing model processed most image samples, its edge preservation was mainly concentrated in a relatively high range, namely [0.80, 0.96]. From the perspective of the structural similarity index, the discrete wavelet transform achieved the highest value of 0.8264. In addition, the peak signal-to-noise ratio of the improved gamma correction function was 18.346. The proposed algorithm for restoring foggy images has the advantages of high clarity, strong color restoration ability, and fast restoration speed, providing more reliable and efficient image data support for applications in related fields. [ABSTRACT FROM AUTHOR]

The characteristics of karst reservoirs are extremely varied and anisotropic, exhibiting notable differences in porosity, permeability, and corresponding fluid flow pathways. Fractured karst petroleum reservoirs, such as distinct caverns and fractures, are an example of a typical discrete media type. The traditional reservoir modeling approach and discrete fracture-like local refinement models are unsuitable for field application of fractured karst oil reservoirs due to the needs of high fidelity geological description and huge computing efforts. Based directly on the spatial characteristics of seismic surveys, a numerical simulation model in three dimensions, akin to a node-like network, is presented here for cracked karst oil reserves. First, the watershed image processing technique and the automatic connection identification procedure are used to extract the three-dimensional node-network model. After that, automatic differentiation is used to build the numerical finite volume scheme, and the proper gradient-based adjoint approach is used to conduct the related historical matching rapidly. After validation by a synthetic model in a commercial simulator, this proposed three-dimensional network numerical model is used for a field reservoir block of deep formation in the Tarim basin to demonstrate its computational efficiency and viability for enormously comparable karst oil reservoirs. [ABSTRACT FROM AUTHOR]

In this study, the effect of aspect ratio of elliptical cross section for a pressure swirl nozzle injecting into stagnant air was investigated experimentally. Three elliptical nozzles with aspect ratios of 1, 1.5, and 2 were manufactured. Shadowgraphy technique was utilized to visualize flow characteristics of the pressure swirl injectors. An in-house image processing code was used to obtain cone angle, breakup length, lateral spread, drop size distribution, and Sauter mean diameter (SMD). It was found that discharge coefficient increased by increasing the elliptical cross section's aspect ratio. By increasing the aspect ratio from 1, the issuing jet's cone angle became different from different viewing angles and was out of symmetry. The cone angle from front view was always larger than from side view. The asymmetry on the elliptical surface caused the thickness of the liquid film at the edges to be higher than in the middle. This asymmetry was amplified as the aspect ratio was increased resulting in the edges having a longer breakup length than in the middle. It was found that the lateral spread decreased and the interaction of the air core with the liquid was reduced with increasing aspect ratio, resulting in an increase in breakup length and drop sizes. Benjamin's model for discharge coefficient was modified, and the influence of aspect ratio was added to the model. Models were developed based on Rizk and Lefebvre and Sivakumar's relations for spray cone angle and breakup length, respectively. Also, a correlation based on Wang and Lefebvre's model was presented for SMD. These developed models were compared with our measurements and other published data with good agreements. [ABSTRACT FROM AUTHOR]

A deep learning super-resolution scheme is proposed to reconstruct a coarse, turbulent temperature field into a detailed, continuous field. The fluid mechanics application here refers to an airflow ventilation process in an indoor setting. Large eddy simulations are performed from a dense simulation grid and provide temperature data in two-dimensional images. The images are fed to a deep learning flow reconstruction model after being scaled down to 100 times. Training and testing are performed on these images, and the model learns to map such highly coarse fields to their high-resolution counterparts. This computational, super-resolution approach mimics the process of employing sparse sensor measurements and trying to upscale to a dense field. Notably, the model achieves high performance when the input images are scaled down by 5 – 20 times their original dimension, acceptable performance when 30, and poor performance at higher scales. The peak signal-to-noise ratio, the structure similarity index, and the relative error between the original and the reconstructed output are given and compared to common image processing techniques, such as linear and bicubic interpolation. The proposed super-resolution pipeline suggests a high-performance platform that calculates spatial temperature values from sparse measurements and can bypass the installation of a wide sensor array, making it a cost-effective solution for relevant applications. [ABSTRACT FROM AUTHOR]

Measurement of the combustion temperature field is an extremely important issue in industrial production. Temperature is one of the key parameters in combustion studies. With the temperature field distribution of the combustion field obtained, heat transfer, heat convection, and heat radiation can be calculated directly and efficiently. Traditional background oriented Schlieren (BOS) is an effective method for non-axisymmetric temperature field measurements, but it requires simultaneous Schlieren imaging at multiple angles for tomographic reconstruction, which will greatly limit its application. In this paper, the compressive sensing algorithm is introduced into the temperature field reconstruction, which establishes the system of equations between the deflection angle and the refractive index gradient. Then, the reconstruction of the non-axisymmetric temperature field is realized by solving the underdetermined system of equations by the method of solving the sparse solution through the compressive sensing. First, light offsets across the non-axisymmetric temperature field are measured by the under-angled BOS system and image processing method. Second, the spatial refractive index field is reconstructed by the compressive sensing BOS method proposed in this paper. Finally, the spatial temperature field is obtained. The experimental results show that by comparing the iRadon reconstruction algorithm and the compressive sensing reconstruction algorithm, the temperature field reconstructed by the compressive sensing under the condition of the under-angled sampling of projection data had a higher accuracy than that reconstructed by the tomographic reconstruction algorithm under the same condition. The average error of the temperature field was reduced from 34.6 to 29.7 K under the same measurement conditions; however, the accuracy is better maintained by using the compressive sensing algorithm under the condition of undersampling projection. [ABSTRACT FROM AUTHOR]

Jet condensation is a complicated two-phase flow phenomenon that occurs in pipe flow, especially in cryogenic fluids. In this study, the condensation flow dynamics of a stream of nitrogen (N2) injected into a subcooled liquid nitrogen (LN2) flow was photographically investigated using a high-speed camera. The pulsating pressure was measured at different observation points using dynamic pressure transducers. Three typical condensation flow patterns were observed and measured under different nitrogen volume fluxes: suck-back flow, transition flow, and oscillation flow. Suck-back flow occurs at a smaller N2 volume flux, where the LN2 is periodically sucked back into the gas chamber. Oscillation flow is encountered at larger N2 volume flux, along with intense pressure fluctuations. Using fast Fourier transform analysis, the frequency of these pressure oscillations was found to be in the range 16–18 Hz with a maximum amplitude of about 10 kPa. An image-processing method was developed to track the fluid motion in the dynamic photographs recorded by the high-speed camera according to the local changes of pixels. The results show the presence of a periodic shrinking of the vapor-plume trail with the same frequency as the pressure oscillation. The mechanism for the low-frequency oscillation flow is, therefore, identified as periodic interfacial condensation. The findings of this study provide strong evidence for the origin of the low-frequency pressure oscillations observed in cryogenic liquid rocket engines. This information can be used to improve the design and operation of such propulsion systems. [ABSTRACT FROM AUTHOR]

This work aimed to apply a simple method to evaluate the effect of anticancer drugs' treatment on in vitro models of cancer cells by laser biospeckle pattern measurements. Thus, the study is concerned with the detection of reduced dosage required for targeting tumors by drug combination encapsulated in liposomal nanoparticles using biospeckle measurements. The curcumin drug encapsulated in liposomal nanoparticles was applied to target the MCF-7 breast cancer cells and HepG2 liver cancer cells on slides as biospecimens with different concentrations for both cancer cell models, separately. For each cancer cell model under treatment, a simple way is to access this information, as the surfaces of biosample were illuminated by a coherent laser beam of green wavelengths of 532.0 nm with output power < 50 mW. The random interference effects were produced, which are known as biospeckle patterns. These speckle patterns were imaged for biosamples of the cancer cells under test treatment for each liposomal drug concentration, digitally. Then, laser biospeckle contrast measurements were used along with an image processing program to detect the change in speckle patterns due to the change in cancer cell size, cancer cell density, and bioavailability, which correlated to the various liposomal curcumin concentrations. First, the obtained results emphasized that the polydispersity index has an acceptable value lower than 0.7 for the effective clinical applications of the used nanocarrier liposome formulations presented in this study. Second, the study pointed to the laser biospeckle technique as a useful method to evaluate intensity and contrast measurements, which correspond to a safe use of the liposomal curcumin drug at a suitable concentration and an acceptable cytotoxicity. [ABSTRACT FROM AUTHOR]

Accurately predicting the fluids holds immense significance in exploration work, assisting in the identification of exploration targets, estimation of reserve potential, and evaluation of reservoirs. In our research, we employed an innovative approach by using the gram angle field (GAF) to transform logging parameters. By adeptly capturing time series information and converting one-dimensional data into two-dimensional matrix representations, GAF takes into account not only the values at each time point but also their relative position and order. This method effectively preserves the temporal evolution characteristics of the original data. The resulting Gram Angle Field matrix can be viewed as a two-dimensional image, facilitating visualization and analysis through image processing techniques. Additionally, we introduced the dynamic graph convolutional network (DGCN) to segment the transformed images. The DGCN structure, employed for feature learning, can extract more comprehensive and representative feature representations from the logging data. Since logging data demonstrate a time series relationship, indicating a temporal correlation between logging curves at different depths, DGCN utilizes dynamic graph structures to capture and comprehend this time series information. This capability enables DGCN to model the evolution process of well log data effectively. DGCN assigns varying weights to nodes and edges at each time step, updating the current node representation with information from neighboring nodes. This localized approach enables DGCN to meticulously focus on significant features at each time step, facilitating the identification of potential patterns and trends in the logging data. Our research not only paves the way for advancements in the field but also provides valuable insights for geologists and professionals engaged in oil and gas exploration. [ABSTRACT FROM AUTHOR]

The human tongue exhibits an orchestrated arrangement of internal muscles, working in sequential order to execute tongue movements. Understanding the muscle coordination patterns involved in tongue protrusive motion is crucial for advancing knowledge of tongue structure and function. To achieve this, this work focuses on five muscles known to contribute to protrusive motion. Tagged and diffusion MRI data are collected for analysis of muscle fiber geometry and motion patterns. Lagrangian strain measurements are derived, and Granger causal analysis is carried out to assess predictive information among the muscles. Experimental results suggest sequential muscle coordination of protrusive motion among distinct muscle groups. [ABSTRACT FROM AUTHOR]

In this paper, an active ultrasound-based touchscreen technology is presented for real-time monitoring of multiple contacts on a glass panel of 20 × 19 cm. Both fundamental Lamb wave modes are generated by sending a linear chirp between 50 and 100 kHz to a single piezoelectric ceramic lead zirconate titanate. Measurement is performed using four piezoelectric ceramic lead zirconate titanate elements, and real-time localization and detection are performed on a system on module i.MX 8 M Nano. Results show that Lamb waves can be used for real-time multi-touch detection and localization on both sides of the panel. Moreover, the prototype developed allows for relative pressure measurement. [ABSTRACT FROM AUTHOR]

Recent advances in artificial intelligence (AI) and computing technologies are currently disrupting the modeling and design paradigms in photonics. In this work, we present our perspective on the utilization of current AI models for photonic device modeling and design. Initially, through the physics-informed neural networks (PINNs) framework, we embark on the task of modal analysis, offering a unique neural networks-based solver and utilizing it to predict propagating modes and their corresponding effective indices for slab waveguides. We compare our model's predictions against theoretical benchmarks and a finite differences solver. Evidently, using 349 analysis points, the PINN approach had a relative percentage error of 0.69272% compared to the finite differences method, which had a percentage error of 1.28142% with respect to the analytical solution, indicating that the PINN approach was more accurate in conducting modal analysis. Our model's continuity over the entire solution domain enhances its performance and flexibility while requiring no training data due to its guidance by Maxwell's equations, setting it apart from most AI approaches. Our model design also flexibly enables simultaneous prediction of multiple modes over any specified intervals of effective indices. In addition, we present a novel reinforcement learning (RL)-based paradigm, employing an actor–critic model for inverse design. We utilize this paradigm to optimize the transmittance of a grating coupler by manipulating the device geometry. Comparing the obtained design to that obtained using the Particle Swarm Optimization (PSO) algorithm, our RL-based approach effectively produced a significant enhancement of 34% in 14 iterations only over the initial design compared to the PSO, which prematurely scored 27% enhancement in 30 iterations, proving that our model navigates the design space more efficiently, achieving a better design than PSO and resulting in a superior design. Based on these approaches, we discuss the future of AI in photonics in forward modeling and inverse design and the untapped potential in bringing these worlds together. [ABSTRACT FROM AUTHOR]

In order to reveal the modulation of particles on turbulent coherent structures and particle concentration variations induced by turbulence, particle image velocimetry experiments were performed in a dilute liquid–solid two-phase turbulent boundary layer. Large-scale coherent structures, including streaks and hairpin vortices, were extracted in the liquid–solid two-phase turbulent boundary layer by using multiple methods, such as proper orthogonal decomposition and finite-time Lyapunov exponent. The results showed that the addition of particles thickened the buffer layer of the average velocity profile, weakened the streamwise turbulence intensity at y+ > 10, strengthened the wall-normal turbulence in the near wall region (y+ < 52), and weakened the Reynolds stress in the logarithmic law region. A more thorough study of the turbulent structure revealed that the particles caused the mean spacing of the near-wall streak structure to increase, hairpin vortex migration to slow down, the uplift of hairpin vortex to be suppressed, and the overall size of the hairpin vortex packet to be compressed. This demonstrated that particles could modulate turbulence by inhibiting the development of hairpin vortices. [ABSTRACT FROM AUTHOR]

Monopole time reversal is an advanced noise source localization technique, but its sidelobe level in localizing sound sources is greatly affected by noise, leading to a decrease in the accuracy of sound source localization under a low signal-to-noise ratio. To address this issue, this paper proposes a time-domain monopole time reversal technique that excludes autocorrelation to reduce the impact of noise on sound source localization results. Numerical simulation and experimental results show that, compared to conventional monopole time reversal techniques, the proposed method can effectively reduce the sidelobes in sound source localization results and achieve accurate sound source localization under −20 dB low signal-to-noise ratio conditions. [ABSTRACT FROM AUTHOR]

Wafer inspection is crucial for semiconductor manufacturing, as it identifies defects in wafers before manufacturing. Wafer defect detection avoids wasting time and production capacity, boosts productivity, and assures production quality. In this paper, we propose an ensemble learning-based method for wafer defect detection that fuses the classification results of four models, namely, ResNet, ResNeSt, ResNeSt + CBAM, and ResNeSt + Self-attention. During the integration phase, we employ a hybrid strategy that combines weighted averaging and voting to determine weight coefficients. Our analysis shows that the model's performance surpasses that of the arithmetic mean model within an interval of 0.8–1, according to our mathematical derivations. Furthermore, results demonstrate and substantiate that optimal performance is attained by setting the weighting value to 1. We experimentally validated the effectiveness of the proposed method on the WM-811k industrial dataset. In the experiments, the ensemble learning based method achieves an accuracy of 99.70%, which outperforms the individual model. Our approach outperforms the traditional arithmetic mean model by combining the strengths of all prediction models to improve prediction accuracy. Experimental results demonstrate that the proposed method has the potential to be an ideal option for wafer defect detection. [ABSTRACT FROM AUTHOR]

Gastrointestinal disorders are a class of prevalent disorders in the world. Capsule endoscopy is considered an effective diagnostic modality for diagnosing such gastrointestinal disorders, especially in small intestinal regions. The aim of this work is to leverage the potential of deep convolutional neural networks for automated classification of gastrointestinal abnormalities from capsule endoscopy images. This method developed a deep learning architecture, GastroNetV1, an automated classifier, to detect abnormalities in capsule endoscopy images. The gastrointestinal abnormalities considered are ulcerative colitis, polyps, and esophagitis. The curated dataset consists of 6000 images with "ground truth" labeling. The input image is automatically classified as ulcerative colitis, a polyp, esophagitis, or a normal condition by a web-based application designed with the trained algorithm. The classifier produced 99.2% validation accuracy, 99.3% specificity, 99.3% sensitivity, and 0.991 AUC. These results exceed that of the state-of-the-art systems. Hence, the GastroNetV1 could be used to identify the different gastrointestinal abnormalities in the capsule endoscopy images, which will, in turn, improve healthcare quality. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of bubble growth and collapse during gas–liquid two-phase transient flow is crucial for minimizing pipeline wear and maintaining the normal operation of the pipeline system. This paper focuses on obtaining the bubble trajectory, movement velocity, and volume change of gas bubbles. The bubble images during the transient flow are first denoised and enhanced, then the moving target detection algorithm and watershed segmentation algorithm are used to segment the bubbles, and the contour reconstruction of the bubbles is performed using fitting to extract the relevant parameters. The movement trajectories of the bubbles are then traced by analyzing the trajectories and velocities of growth and collapse of the first bubbles during the transient flow, specifically when the flow rate of the hydraulic pipeline is 0.75 m/s. Finally, the changes in gas content during the transient flow in the pipeline under different initial flow rates are compared between the algorithm proposed here and the simulation results. [ABSTRACT FROM AUTHOR]

Scalable electronic brain implants with long-term stability and low biological perturbation are crucial technologies for high-quality brain–machine interfaces that can seamlessly access delicate and hard-to-reach regions of the brain. Here, we created "NeuroRoots," a biomimetic multi-channel implant with similar dimensions (7 μm wide and 1.5 μm thick), mechanical compliance, and spatial distribution as axons in the brain. Unlike planar shank implants, these devices consist of a number of individual electrode "roots," each tendril independent from the other. A simple microscale delivery approach based on commercially available apparatus minimally perturbs existing neural architectures during surgery. NeuroRoots enables high density single unit recording from the cerebellum in vitro and in vivo. NeuroRoots also reliably recorded action potentials in various brain regions for at least 7 weeks during behavioral experiments in freely-moving rats, without adjustment of electrode position. This minimally invasive axon-like implant design is an important step toward improving the integration and stability of brain–machine interfacing. [ABSTRACT FROM AUTHOR]

Determining the atomic-level structure of a protein has been a decades-long challenge. However, recent advances in transformers and related neural network architectures have enabled researchers to significantly improve solutions to this problem. These methods use large datasets of sequence information and corresponding known protein template structures, if available. Yet, such methods only focus on sequence information. Other available prior knowledge could also be utilized, such as constructs derived from x-ray crystallography experiments and the known structures of the most common conformations of amino acid residues, which we refer to as partial structures. To the best of our knowledge, we propose the first transformer-based model that directly utilizes experimental protein crystallographic data and partial structure information to calculate electron density maps of proteins. In particular, we use Patterson maps, which can be directly obtained from x-ray crystallography experimental data, thus bypassing the well-known crystallographic phase problem. We demonstrate that our method, CrysFormer, achieves precise predictions on two synthetic datasets of peptide fragments in crystalline forms, one with two residues per unit cell and the other with fifteen. These predictions can then be used to generate accurate atomic models using established crystallographic refinement programs. [ABSTRACT FROM AUTHOR]

Photonic neuromorphic computing has emerged as a promising avenue toward building a high-speed, low-latency, and energy-efficient non-von-Neumann computing system. Photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. Linear weighting and nonlinear spiking activation are two fundamental functions of a SNN. However, the nonlinear computation of PSNN remains a significant challenge. Therefore, this perspective focuses on the nonlinear computation of photonic spiking neurons, including numerical simulation, device fabrication, and experimental demonstration. Different photonic spiking neurons are considered, such as vertical-cavity surface-emitting lasers, distributed feedback (DFB) lasers, Fabry–Pérot (FP) lasers, or semiconductor lasers embedded with saturable absorbers (SAs) (e.g., FP-SA and DFB-SA). PSNN architectures, including fully connected and convolutional structures, are developed, and supervised and unsupervised learning algorithms that take into account optical constraints are introduced to accomplish specific applications. This work covers devices, architectures, learning algorithms, and applications for photonic and optoelectronic neuromorphic computing and provides our perspective on the challenges and prospects of photonic neuromorphic computing based on semiconductor lasers. [ABSTRACT FROM AUTHOR]

Parametric x-ray radiation (PXR) is a prospective mechanism for producing directional, tunable, and quasi-coherent x-rays in laboratory-scale dimensions, yet it is limited by heat dissipation and self-absorption. Resolving these limits, we show the PXR source flux is suitable for medical imaging and x-ray spectroscopy. We discuss the experimental feasibility of these findings for a compact commercial PXR source. [ABSTRACT FROM AUTHOR]