Ping Guo

In-Gel Near-Field Electrospinning (IG-NFES) integrates near-field electrospinning with embedded three-dimensional printing to achieve high-resolution fiber fabrication within a support gel matrix. We present a multiphysics simulation framework to study jet initiation in IG-NFES. Our framework explicitly incorporates gel drag effects and provides systematic parametric analysis quantifying how material property ratios govern jet stability, enabling predictive material selection and process optimization beyond trial-and-error approaches. We systematically investigated the influence of viscosity, density, and dielectric permittivity ratio on the jet initiation behaviors. Our results reveal that viscosity ratio (M) and dielectric permittivity ratio (Q) are the most critical parameters affecting jet formation and stability. A viscosity ratio of 2.5 promotes stable jet elongation, while significantly lower or higher values lead to premature breakup or jet suppression. As Q increases from 3 to 15, jet behavior transitions from no initiation, to droplet breakup, and finally to stable jet formation due to enhanced electric field focusing on the ink. We also analyzed the roles of two key dimensionless numbers: the electric capillary number (CaE) and the Weber number (We). The transition from droplet formation to continuous jetting is governed by CaE, with stable fiber formation observed within the range of 6–12. Similarly, stable jetting occurs when We lies between 10−5 and 10−3. The model predictions were validated with experiments, confirming the identified regime transitions. These findings offer quantitative insights for optimizing material selection and process parameters in IG-NFES, paving the way for advanced applications in robotics, soft electronics, and biomedical devices.

Structural coloration, which generates color through physical structures, has broad applications but often faces challenges in achieving high color performance while being scalable for mass production. In this study, a novel single-layer wavelength-selective reflector capable of producing a broad color gamut with local angle independence is introduced, fabricated using a modulation-assisted ultra-precision machining process. The structure features multi-level submicron steps that sequentially filter light of different wavelengths, overcoming the limitations of conventional periodic structures that suffer from narrow color gamut and angle-dependent color shifts. This approach integrates a purely mechanical machining process, shaping reflectors in a single step using a precisely controlled modulation trajectory. This enables efficient, scalable fabrication while preserving the desired structural characteristics at the nanoscale. Color performance comparable to the sRGB space is demonstrated and full-color images are successfully rendered on metallic surfaces. The proposed reflector design provides a new pathway to scalable production of vibrant, angle-independent structural colors with potential applications in anti-counterfeiting, data storage, and high-quality coloration.

We introduce a novel single-energy structure decomposition (SESD) method using X-ray grating interferometry for quantifying the microstructure of unknown materials. The approach exploits the nonlinear correlation between linear attenuation and diffusion coefficients, specifically demonstrating that the linear diffusion coefficient (LDC) inversely correlates with the diameter of microspheres when their size significantly exceeds the system's autocorrelation length (ACL). We derive an approximation where the LDC of large microspheres is a function of the microsphere diameter and the ACL. By using this relationship, we present a method to express the LDC of unknown microspheres as a linear combination of two reference microspheres. The SESD method integrates dark-field and absorption signals, and provides accurate microsphere size identification. Experimental results validate the effectiveness of SESD, with potential applications in pulmonary disease diagnostics, particularly for imaging pulmonary microstructures.

X-ray grating-based multi-characteristic imaging has garnered significant attention in medical imaging and non-destructive inspection in recent years. However, conventional Talbot-Lau interferometry suffers from low photon utilization efficiency and high system costs. In this context, dual-phase grating interferometry has emerged as a promising alternative, offering higher photon efficiency, shorter acquisition times, and reduced system cost. Despite these advantages, recent studies have revealed a notable issue with dual-phase grating systems: the fringe visibility exhibits significant non-uniformity, characterized by reduced visibility in the center and increased visibility at the edges. This phenomenon severely limits the effective imaging field of view and thus hampers the broader application of this technique. To address these issues, we analyze the underlying physical mechanism responsible for the non-uniform visibility distribution in dual-phase grating systems. Based on this insight, we propose a design method that flattens the visibility profile and enhances fringe visibility, effectively expanding the imaging field of view and improving system performance. Our findings aim to provide guidance for the optimization of dual-phase grating systems and contribute to their broader adoption in practical imaging applications.

Conventional X-ray imaging lacks sensitivity to powdered substances composed of light materials, making the detection of drugs and explosives challenging in security inspection. Grating-based Xray dark-field imaging leverages the small-angle scattering caused by the micro-structures within the objects, thereby enabling the detection of such powders. In the fringe scanning protocol, the data is acquired during object translation, making it suitable for the scenarios of baggage scanner. However, the attenuation and dark-field extraction of the object requires the retrieval of background signal, which typically requires either a stepping motor for the gratings or highresolution detectors. This increases hardware complexity and cost, limiting its broader application in security inspection. To overcome this limitation, we proposed a Hilbert-transform-based method for background signal retrieval. This approach extracts fringe visibility by demodulating the Moiré fringe pattern, aligning with the cost-sensitive requirements of security inspection. We validated the proposed method on a dark-field baggage scanner prototype, successfully retrieving the darkfield signals of typical baggage items.

Scene text detection is crucial for applications like document analysis, autonomous driving, and instant translation. Despite progress in deep learning, existing methods struggle with geometric diversity and multi-scale text due to ineffective feature fusion and context modeling. Traditional approaches often naively combine high-level and low-level features, losing fine details in small text and overemphasizing high-level semantics, reducing detection accuracy. To mitigate these constraints, DPMNet is presented, a novel network with two key modules: The Frequency-Aware Dual-Path Modulation (FADPM) module, which adaptively refines features by enhancing high-frequency details while optimizing low-frequency semantics. The Multibranch Adaptive Fusion (MAF) module leverages multi-scale dilated convolutions and spatial weight allocation to improve context integration. We evaluated the proposed methods TotalText, CTW1500 and ICDAR19 ArT. Extensive experiments have demonstrated the state-of-the-art performance and our method’s exceptional robustness.

Leadless pacemakers have emerged as a mainstream clinical solution, and their communication capabilities, crucial for reliable pacing and device monitoring, continue to evolve. This review systematically examines the fundamental principles of leadless pacemaker communication systems, current design requirements, existing challenges, and future development trends. We outline the bidirectional communication mechanism between leadless pacemakers and external programmers through wireless technologies, focusing on radio-frequency field communication coupled with load modulation techniques to optimize energy efficiency and transmission reliability. Additionally, we analyze the role of artificial intelligence in adaptive communication protocols and explore the clini cal potential of remote monitoring and control systems. This comprehensive analysis aims to serve as a reference for the development of communication architectures for leadless pacemakers.

Large language models (LLMs) have become integral to a wide range of applications worldwide, driving an unprecedented global demand for effective multilingual capabilities. Central to achieving robust multilingual performance is the strategic allocation of language proportions within training corpora. However, determining optimal language ratios is highly challenging due to intricate cross-lingual interactions and sensitivity to dataset scale. This paper introduces Climb (Cross-Lingual Interaction-aware Multilingual Balancing), a novel framework designed to systematically optimize multilingual data allocation. At its core, Climb introduces a cross-lingual interaction-aware language ratio, explicitly quantifying each language's effective allocation by capturing inter-language dependencies. Leveraging this ratio, Climb proposes a principled two-step optimization procedure--first equalizing marginal benefits across languages, then maximizing the magnitude of the resulting language allocation vectors--significantly simplifying the inherently complex multilingual optimization problem. Extensive experiments confirm that Climb can accurately measure cross-lingual interactions across various multilingual settings. LLMs trained with Climb-derived proportions consistently achieve state-of-the-art multilingual performance, even achieving competitive performance with open-sourced LLMs trained with more tokens.

Directed energy deposition (DED), a metal additive manufacturing process, is highly susceptible to process-induced defects such as geometric deviations, lack of fusion, and poor surface finish. This work presents a build-height-synchronized fringe projection system for in-situ, layer-wise surface reconstruction of laser-DED components, achieving a reconstruction accuracy of ${\pm}$46 $μ$m. From the reconstructed 3D morphology, two complementary geometry-based point cloud metrics are introduced: local point density, which highlights poor surface finish, and normal-change rate, which identifies lack-of-fusion features. These methods enable automated, annotation-free identification of common deposition anomalies directly from reconstructed surfaces, without the need for manual labeling. By directly linking geometric deviation to defect formation, the approach enables precise anomaly localization and advances the feasibility of closed-loop process control. This work establishes fringe projection as a practical tool for micrometer-scale monitoring in DED, bridging the gap between process signatures and part geometry for certifiable additive manufacturing.

This study systematically investigates the deformation behavior of novel low-cost Ti-6Al-4V-0.4Fe-0.6Mo alloy with varying microstructures under dynamic impact loading. The microstructures with equiaxed and bimodal were fabricated by controlling the temperatures (880°C, 900°C and 920°C) and cooling rates (air cooling, AC and furnace cooling, FC). Charpy impact testing results show that the 920FC sample, featuring large equiaxed α phases (average size: 8.2 μm), α lamellae (thickness: 1.5 μm), and retained β-phases, exhibits the highest impact toughness of ∼62.5 J/cm 2 . Compared with 880FC (∼45.3 J/cm 2 ), 900FC (∼42.1 J/cm 2 ) and Ti-6Al-4V alloys with equiaxed microstructures (∼50 J/cm 2 ), the impact toughness is improved by 38%, 48% and 25%, respectively. Moreover, the impact toughness of the 920AC sample (∼47.6 J/cm 2 ) is 20% higher than that of 880AC (∼39.5 J/cm 2 ) and 900AC samples (∼39.5 J/cm 2 ). Microstructural analyses results show that both 920AC and 920FC samples exhibit low α phase aspect ratios, high α phase spheroidization proportion, and large α grain sizes. The 920FC sample has a percentage of twin boundaries as high as 58.7%, which is the core mechanism for its superior impact toughness over the 920AC sample. Interestingly, we found that twinning nucleation in both α and secondary α phases (α s ) is induced by stacking faults and the abundant stacking faults form martensite 9R phase. The coarsening and growth of twins are affected by dislocation slip. In addition, the calculated results based on the Yu Rui-huang electron theory show that Fe and Mo weaken the inhibitory effect of V on dislocation slip and provide a low-energy path for twin boundary migration.

RATIONALE: The coexistence of Guillain-Barré syndrome (GBS) and minimal change disease (MCD) is extremely rare. GBS is an autoimmune-mediated peripheral neuropathy that can occasionally be associated with renal complications such as nephrotic syndrome (NS). This case discusses a patient diagnosed with both GBS and MCD, as well as Sjögren syndrome kidney injury, focusing on the potential pathogenesis of these conditions and the role of autoantibodies in their development and treatment outcomes. PATIENT CONCERNS: A young female patient presented with progressively worsening muscle weakness, sensory abnormalities, and edema. Further investigations revealed NS, characterized by proteinuria and hypoalbuminemia. While the neurological symptoms improved initially, the renal manifestations persisted, raising concerns about ongoing kidney damage. In addition, the patient was also found to have Sjögren syndrome kidney injury, along with positive perinuclear antineutrophil cytoplasmic antibody and antinuclear antibody, suggesting an autoimmune-mediated process contributing to the co-occurrence of these conditions. DIAGNOSES: GBS was diagnosed based on characteristic ascending paralysis and demyelination, as evidenced by nerve conduction studies. The diagnosis of MCD was supported by the patient's clinical presentation of NS and kidney biopsy findings. The presence of clinical features such as dry mouth and dry eyes, coupled with positive anti-SSA/Ro52 and anti-Sjögren syndrome antigen B antibodies, pointed to Sjögren syndrome. Kidney biopsy results strongly suggested that kidney damage was likely due to Sjögren syndrome. INTERVENTIONS: The patient was started on immunosuppressive therapy, including prednisone and cyclophosphamide, to address both the autoimmune neuropathy and renal issues. In addition, intravenous immunoglobulin was administered to treat the GBS. Supportive therapies, such as diuretics and albumin infusions, were used to manage edema and protein loss associated with NS. OUTCOMES: The patient showed significant improvement in neurological symptoms, including enhanced muscle strength and reduced sensory deficits. Proteinuria decreased, and renal function gradually stabilized. LESSONS: This case illustrates the rare coexistence of GBS, MCD, and Sjögren syndrome kidney injury in a single patient. Autoimmune markers played a pivotal role in the pathogenesis of these diseases. Immunosuppressive therapy and intravenous immunoglobulin were essential in treating both neurological and renal complications. Further research is needed to deepen our understanding of the overlap of autoimmune diseases and to optimize treatment strategies for such complex cases.

Research on high-performance Ti alloys incorporating oxygen (O) has remained a laboratory procedure and is hindered by the unresolved issue of O segregation-driven failure. Here, we demonstrate that O can tailor a nanoscale local range order O (LRO-O) structure between the oxide and random interstitials in Ti alloy. We introduce 0.36 wt% O into metastable Ti-5Al-5Mo-5V-3Cr alloy using a short-term powder metallurgy approach to produces large-scale materials. The LRO-O structure in designed alloy prevents crack initiation by promoting the active nucleation of <c>-type dislocations and altering the slip modes during tensile and fatigue failure. The alloy has high strength (1.7 GPa), elongation (7.9%), and fatigue strength (1058.3 MPa), which outperforms many high-strength, high-O Ti alloys. Our findings provide a scalable, practical route to superior mechanical properties for Ti alloys without costly alloying elements. The authors demonstrate how interstitial oxygen can be used to tailor nanoscale structures in a Ti alloy, using a powder metallurgy technique, to prevent crack initiation and enhance strength.

Abstract The trade‐off between strength and ductility remains a significant challenge in titanium alloy development. Microstructural engineering is a cost‐effective alternative to balance strength and ductility. In this study, three microstructures were produced in low‐cost titanium alloys via solution treatment, and the alloys with α, α′‐martensite, and β phases exhibited an optimal strength–ductility combination. The hierarchical phase structure, α′‐martensite reorientation, multi‐oriented nanotwins, and dislocation interactions collectively contributed to its high yield strength (~ 1015 MPa), ultimate tensile strength (~ 1307 MPa), and satisfactory ductility (~ 13%). Importantly, this study reveals a new pathway for nanotwin formation in α′ phases and provides key insights for optimizing the mechanical properties of low‐cost titanium alloys.

Data quality is a critical driver of large language model performance, yet existing model-based selection methods focus almost exclusively on English. We introduce MuRating, a scalable framework that transfers high-quality English data-quality signals into a single rater for 17 target languages. MuRating aggregates multiple English "raters" via pairwise comparisons to learn unified document-quality scores,then projects these judgments through translation to train a multilingual evaluator on monolingual, cross-lingual, and parallel text pairs. Applied to web data, MuRating selects balanced subsets of English and multilingual content to pretrain a 1.2 B-parameter LLaMA model. Compared to strong baselines, including QuRater, AskLLM, DCLM and so on, our approach boosts average accuracy on both English benchmarks and multilingual evaluations, with especially large gains on knowledge-intensive tasks. We further analyze translation fidelity, selection biases, and underrepresentation of narrative material, outlining directions for future work.

Eleven new macrolide sesquiterpene pyridine alkaloids (1-8 and 10-12) and twelve known congeners (9 and 13-23) were isolated from Tripterygium regelii roots. Their structures were identified through NMR, HRESIMS, and X-ray crystallography. Additionally, their anti-inflammatory activity was evaluated using a dual luciferase screening system based on aryl hydrocarbon receptor (AHR) activation, as well as a lipopolysaccharide (LPS)-induced macrophage model. Compounds 7 and 13 were found to significantly activate AHR, inhibit nitric oxide production, suppress the JNK and NF-κB/NLRP3 signaling pathways, and reduce inflammation-related proteins expression, including IL-6 and COX 2. This study not only expands the chemical variety of macrolide sesquiterpene pyridine alkaloids but also suggests that compounds 7 and 13 could be potential candidates for inflammation-related disease treatment.

The burgeoning use of titanium alloys in unmanned underwater vehicles has sparked a surge in demand for low-cost titanium alloys that maintain superior mechanical properties. Despite this, a comprehensive understanding of the deformation mechanisms in low-cost titanium alloys with exceptional impact toughness remains elusive. Therefore, the impact toughness and deformation mechanisms of a novel low-cost Ti-6Al-4V-1.5Mo-1.0Fe alloy with two types of bimodal microstructures (BM1 and BM2) and lamellar microstructures (LM1 and LM2) were studied. The BM2 sample demonstrated an impressive balance of mechanical properties, with a yield strength of 1150 MPa, a tensile strength of 1245 MPa, an elongation of 11%, and an impact energy of 42.59 J. The load‒displacement curves indicated that the energy associated with crack initiation accounted for as much as 85% in the BM samples. By integrating nanoindentation and back stress tests, the deformation behavior near the crack path and the mechanisms of crack initiation were examined. The results revealed that crack initiation was intricately linked to the plastic deformation of the microstructures in the vicinity of the notch tips. The activation of {1 2} < 011> tensile twins in αp, along with the pronounced kink deformation of βt, enhanced the degree of plastic deformation, thereby increasing the crack initiation energy. The proportion of crack propagation energy reached 39% for the LM2 sample. The crack propagation mechanisms were further analyzed via SEM and EBSD. The analysis indicated that the crack propagation energy was synergistically influenced by the deformation in the plastic zone near the crack path and the length of the crack path. The pronounced kink plastic deformation and the convoluted crack path observed in the LM2 sample could alleviate the interface stress concentration, thus improving the energy dissipation during crack propagation.

The resistance to near-threshold fatigue crack growth and its correlation with the microstructure of the Ti-5Al-3Mo-3V-2Zr-2Cr-1Nb-1Fe alloy were investigated. K-decreasing fatigue crack propagation rate tests were conducted on compact tension samples (ASTM standard) with a stress ratio R of 0.1 and a frequency of 15 HZ in a laboratory atmosphere. At a similar strength level of 1200 MPa, the sample with a fine basket-weave microstructure (F-BW) displayed the slowest near-threshold fatigue crack propagation rate compared with the samples with equiaxed (EM) and basket-weave (BW) microstructures. The fatigue threshold value (ΔKth) was 4.4 MPa·m1/2 for F-BW, 3.6 for BW, and 3.2 for EM. The fracture surfaces and crack profiles were observed by scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD) to elucidate the mechanism of fatigue crack propagation in the near-threshold regime. The results revealed that the near-threshold crack growth in the three samples was primarily transgranular. The crack always propagated parallel to the crystal plane, with a high Schmid factor. In addition, the near-threshold fatigue crack growth behavior was synergistically affected by the crack tip plastic zone and crack bifurcation. The increased fatigue crack propagation resistance in F-BW was attributed to the better stress/strain compatibility and greater number of interface obstacles in the crack tip plastic zone.

• The TC4 scrap was used to reduce costs. • The high grain boundary density caused excellent corrosion resistance. • The hierarchical phase structure diminishes pitting tendency. • The valence electron theoretical model explains pitting at the α/β interface. Recently, there has been a growing anticipation for developing titanium alloys with low cost and excellent properties. Working towards this goal, a hierarchical phase structure comprising micron-scale α phase and micron-scale and nano-scale α′ phases in Ti-Al-V-Fe-Mo alloys was obtained via vacuum melting, forging, and solution treatment. The high-density hierarchical α′ martensite in Ti-Al-V-Fe-Mo alloys exhibits the high grain boundary density and the weak segregation of Fe and Mo. It promotes the formation of passivation films with low defect density and high film resistance on the alloy surface and the homogenous distribution of solutes within the matrix. This homogeneity significantly diminishes the tendency for pitting corrosion and enhances the overall corrosion resistance of the alloys. In addition, the analysed results of the valence electron theory model show the high electron density difference at α (0001) //β (110) is the primary reason for pitting in the interface of α/β. And the low strongest-bond energy of the α phase leads to its preferential corroded. The calculated results provide theoretical support for the experimental phenomena.

The acquisition of geometric 3-D information is crucial for ensuring quality standards and monitoring procedures in various manufacturing applications. Photometric stereo is an established technique in computer vision to recover 3-D surfaces of objects. However, existing photometric stereo methods mainly focus on normal estimation of objects, without considering the depth estimation. On the other hand, current methods tend to prioritize accuracy while overlooking the confidence of predictions, which holds valuable information within the industry. In this article, we propose a deep learning-based photometric stereo system, consisting of hardware implementation, dataset generation, and algorithm design, to reconstruct 3-D information of physical objects, represented by normal and depth maps. In terms of the proposed algorithm, a coarse-to-fine network is introduced to improve the performance by exploiting the relationship between initial normal and depth predictions. Furthermore, the pixel-wise confidence associated with predictions is also estimated without requiring the ground truth, making a contribution to enhancing both performance and practicality. The experimental results on our synthetic dataset and real samples demonstrate the effectiveness of the proposed method on both normal/depth and confidence estimation.

Manufacturing workers face prolonged strenuous physical activities, impacting both financial aspects and their health due to work-related fatigue. Continuously monitoring physical fatigue and providing meaningful feedback is crucial to mitigating human and monetary losses in manufacturing workplaces. This study introduces a novel application of multimodal wearable sensors and machine learning techniques to quantify physical fatigue and tackle the challenges of real-time monitoring on the factory floor. Unlike past studies that view fatigue as a dichotomous variable, our central formulation revolves around the ability to predict multilevel fatigue, providing a more nuanced understanding of the subject's physical state. Our multimodal sensing framework is designed for continuous monitoring of vital signs, including heart rate, heart rate variability, skin temperature, and more, as well as locomotive signs by employing inertial motion units strategically placed at six locations on the upper body. This comprehensive sensor placement allows us to capture detailed data from both the torso and arms, surpassing the capabilities of single-point data collection methods. We developed an innovative asymmetric loss function for our machine learning model, which enhances prediction accuracy for numerical fatigue levels and supports real-time inference. We collected data on 43 subjects following an authentic manufacturing protocol and logged their self-reported fatigue. Based on the analysis, we provide insights into our multilevel fatigue monitoring system and discuss results from an in-the-wild evaluation of actual operators on the factory floor. This study demonstrates our system's practical applicability and contributes a valuable open-access database for future research.

An English wheel is an exceedingly adaptable instrument in traditional metalworking. It is a manual manufacturing technique, enabling skilled craftsmen and blacksmiths to shape complex compound curves in sheet metal panels. Accurate measurements and precise adjustments are essential when operating an English wheel to ensure that the metal is shaped with the desired curvature. An automated method to form English wheeled panels through robot forming has recently been proposed. For such a method to be successful, accurate tracking of sheet information including positions, orientations, and deformation is important for error compensation and the design of the subsequent tool paths. In this study, a Vicon motion capture system is employed to monitor the position and shape of the sheet metal during the English wheeling process. The initial experimental results demonstrate the potential of such an in-process metrology system, along with possible avenues for future work.

Abstract Directed Energy Deposition (DED) is one of the main additive manufacturing (AM) families, enabling the fabrication of multi-material parts with high material addition rates. However, the incremental nature of DED fabrication makes it prone to local defect formation due to process condition fluctuations. Known for its rapid and precise 3D surface measurement capabilities, digital fringe projection (DFP) was previously demonstrated in process monitoring for powder bed AM. This study brings DFP to the DED process through development of a custom motor stage system and validates its effectiveness in assessing surface topography and build height measurement. Measurements were taken on both correctly deposited builds and builds with off-nominal deposition conditions, where the system was able to detect pitting as small as 0.425 mm in the lateral size and 0.154 mm in depth in the case of reduced laser energy. This work paves the way for future machine learning-enabled interlayer defect identification, classification, and healing via altering subsequent processing settings.

INTRODUCTION: This study aims to assess the performance of the platelet count estimation using artificial intelligence technology on the MC-80 digital morphology analyzer. METHODS: Digital morphology analyzer uses two different computational principles for platelet count estimation: based on PLT/RBC ratio (PLT-M1) and estimate factor (PLT-M2). 977 samples with various platelet counts (low, median, and high) were collected. Out of these, 271 samples were immunoassayed using CD61 and CD41 antibodies. The platelet counts obtained from the hematology analyzer (PLT-I and PLT-O), digital morphology analyzer (PLT-M1 and PLT-M2), and flow cytometry (PLT-IRM) were compared. RESULTS: There was no significant deviation observed before and after verification for both PLT-M1 and PLT-M2 across the analysis range (average bias: -0.845/-0.682, 95% limit of agreement (LOA): -28.675-26.985/-29.420-28.056). When platelet alarms appeared, PLT-M1/PLT-M2 showed the strongest correlation with PLT-IRM than PLT-I with PLT-IRM (r: 0.9814/0.9796 > 0.9601). The correlation between PLT-M1/PLT-M2 and PLT-IRM was strong for samples with interference, such as large platelets or RBC fragments, but relatively weak in small RBCs. The deviation between PLT-M1 and PLT-M2 is related to the number of RBCs. Compared with PLT-I, PLT-M1/PLT-M2 showed higher accuracy for platelet transfusion decisions, especially for samples with low-value PLT. CONCLUSION: The novel platelet count estimation on the MC-80 digital morphology analyzer provides high accuracy, especially the reviewed result, which can effectively confirm suspicious platelet count.

The automotive Synthetic Aperture Radar (SAR) can obtain two-dimensional (2-D) high-resolution images compared with millimeter-wave (MMW) radar which the azimuth resolution is limited by the antenna aperture in the automatic driving system. However, the existence of acceleration causes the vehicle’s motion trajectory is no longer a straight trajectory, so the traditional range model and SAR imaging algorithm are not suitable. To solve these issues, this paper first proposes an equivalent range model to describe the curved trajectory for the little turn case. Then an improved Range Migration Algorithm (RMA) is proposed based on the range model, which combines the azimuth preprocessing with RMA. The azimuth time preprocessing employs a resampling method to eliminate the effect of acceleration. Simulation and experiment results verify the effectiveness of the proposed method.

This work presents a new technique to generate uniform and micron-sized metal powders for additive manufacturing. By collecting discrete chips resulting from ultrasonic vibration machining, we demonstrate the feasibility of all solid-state production consistent powders with tight dimensional tolerance, the ability to control powder geometry, and good efficiency. The technique offers a new route for sustainable and low-cost manufacturing of high-quality metal powders. The powder generation mechanism is analyzed with a special tool path design to ensure consistent dimensions over multiple cuts. An analytical model to predict the dimensions of produced powders under different cutting parameters is introduced. Aluminum and brass powders of different dimensions are produced, and the overall shear ratio that governs the deformation during the machining process is calibrated with the experimental results. The morphology consistency of produced powders is investigated over multiple hours of production, illuminating the role of tool wear on final powder shape. A high-efficiency powder collection system and a scalable solution for parallel production are proposed for the introduced technique. Additive manufacturing experiments (laser powder bed fusion) are conducted using produced A356 aluminum powders, demonstrating the printability of produced powders in additive manufacturing. The microhardness of the printed parts for five different process parameters is measured to be 45% higher than the raw material on average.

With the continuous development and improvement of human factors lighting research, while satisfying the functional lighting of indoor spaces, people are pursuing more breakthroughs and innovations in light quality and light health. The evaluation of the quality of the lighting environment gradually expands from the traditional work surface illuminance to the spatial brightness, so as to improve the observer's visual performance and visual perception. Therefore, this research proposed an evaluation method of spatial brightness for classroom lighting environments. Combined with the observer's field of view, the evaluation indicators of spatial brightness are proposed: the spatial brightness index BSI and the spatial brightness contrast BSC. Use BSI and BSC to measure the brightness and the chiaroscuro of the lighting environment respectively. This research can supplement the current lighting environment evaluation system, and has certain reference value to further improve the current test standards and improve the quality of the lighting environment.

Freeform Fresnel optics represent an emerging category of modern optics that reproduces powerful optical functionalities while maintaining an ultra-compact volume. The existing ultra-precision machining (UPM) technique faces technical challenges in meeting the fabrication requirements for freeform Fresnel optics because of the absence of appropriate geometry definition and corresponding tool path planning strategy to overcome the extreme asymmetry and discontinuity. This study proposes a new scheme for ultra-precision machining using four axes (X, Y, Z, C) to fabricate freeform Fresnel optics, including a general geometry description for freeform Fresnel optics, the quasi-spiral tool path generation strategy to overcome the lack of rotary symmetry, and the adaptive tool pose manipulation method for avoiding tool interference. In addition, the tool edge compensation and the adaptive timestep determination are also introduced to enhance the performance and efficiency of the proposed scheme. The machining of two exemplary freeform Fresnel lenses is successfully demonstrated. Overall, this study introduces a comprehensive routine for the fabrication of freeform Fresnel optics and proposes the adaptive tool pose manipulation scheme, which has the potential for broader applications in the ultra-precision machining of complex or discontinuous surfaces.