GNSS Receiver Fingerprinting Based on Time Skew of Embedded CSAC Clock.

GNSS spoofing has become a significant security vulnerability threatening remote sensing systems. Hardware fingerprint-based GNSS receiver identification is one of the solutions to address this security issue. However, existing research has not provided a solution for distinguishing GNSS receivers of the same specification. This paper first theoretically proves that the CSACs (Chip-Scale Atomic Clocks) used in GNSS receivers have unique hardware noise and then proposes a fingerprinting scheme based on this hardware noise. Experiments based on the neural network method demonstrate that this fingerprint achieved an identification accuracy of 94.60% for commercial GNSS receivers of the same specification and performed excellently in anomaly detection, confirming the robustness of the fingerprinting method. This method shows a new real-time GNSS security monitoring method based on CSACs and can be easily used with any commercial GNSS receivers.

5G Key Technologies for Helicopter Aviation Medical Rescue.

Rapid global population growth and urbanization have heightened the demand for emergency medical rescue, with helicopter medical rescue emerging as an effective solution. The advent of 5G communication technology, characterized by large bandwidth, low latency, and high reliability, offers substantial promise in enhancing the efficiency and quality of helicopter rescue operations. However, the full integration of 5G technology into helicopter emergency medical services is still in its nascent stages and requires further development. In this viewpoint, we present our experience from the Shenzhen University General Hospital of the application of 5G low-altitude network communication technology, body area network disease sensing technology, and 5G air-ground collaborative rapid diagnosis and treatment technology in aeromedical rescue. We consider that the 5G air-to-ground collaborative rapid diagnosis and treatment technology enables high-quality remote consultation, enhancing emergency medical rescue and providing strong support for future rescue operations.

Towards Design Automation of Microfluidic Mixers: Leveraging Reinforcement Learning and Artificial Neural Networks.

Microfluidic mixers, a pivotal application of microfluidic technology, are primarily utilized for the rapid amalgamation of diverse samples within microscale devices. Given the intricacy of their design processes and the substantial expertise required from designers, the intelligent automation of microfluidic mixer design has garnered significant attention. This paper discusses an approach that integrates artificial neural networks (ANNs) with reinforcement learning techniques to automate the dimensional parameter design of microfluidic mixers. In this study, we selected two typical microfluidic mixer structures for testing and trained two neural network models, both highly precise and cost-efficient, as alternatives to traditional, time-consuming finite-element simulations using up to 10,000 sets of COMSOL simulation data. By defining effective state evaluation functions for the reinforcement learning agents, we utilized the trained agents to successfully validate the automated design of dimensional parameters for these mixer structures. The tests demonstrated that the first mixer model could be automatically optimized in just 0.129 s, and the second in 0.169 s, significantly reducing the time compared to manual design. The simulation results validated the potential of reinforcement learning techniques in the automated design of microfluidic mixers, offering a new solution in this field.

Stabilizing Zn2SiO4 Anode by a Lithium Polyacrylate Binder for Highly Reversible Lithium-Ion Storage.

Binders are crucial for maintaining the mechanical stability of the electrodes. However, traditional binders fail to adequately buffer the volume expansion of Zn2SiO4 anode, causing electrode contact failure and considerable capacity loss during cycling. In this study, we propose a simple and effective solution to address these challenges through a combined strategy of hollow structure design and the introduction of an aqueous lithium poly(acrylic acid) (LiPAA) binder. Hollow structures can shorten ion-transfer distance and accommodate volume change outside. The excellent adhesion of the LiPAA binder created a secure connection between the active Zn2SiO4 particles, conductive additives, and the current collector, which enhanced the mechanical stability and integrity of the electrode. As a result of these positive factors, a Zn2SiO4 electrode using LiPAA as a binder can deliver an excellent capacity of 499 mAh g-1 at a high current density of 5 A g-1 and a long life span of 1000 cycles at 1 A g-1 with a capacity retention of 98%, which significantly outperforms other binders. As demonstrated by ex situ X-ray diffraction and ex situ X-ray absorption spectroscopy, the storage of lithium ions in Zn2SiO4 follows a dual conversion-alloying mechanism, using Zn as the redox center. In this process, Zn is first reduced to metallic Zn and then forms a LiZn alloy upon lithium-ion insertion. This work shows that LiPAA offers a promising approach to improve the cycling longevity of conversion and alloying anodes in Li-ion batteries.

Experimental and Numerical Study of a Trapezoidal Rib and Fan Groove Microchannel Heat Sink.

A novel microchannel heat sink (TFMCHS) with trapezoidal ribs and fan grooves was proposed, and the microchannel was manufactured using selective laser melting technology. Firstly, the temperature and pressure drop at different power levels were measured through experiments and then combined with numerical simulation to explore the complex flow characteristics within TFMCHSs and evaluate the comprehensive performance of microchannel heat sinks based on the thermal enhancement coefficient. The results show that, compared with rectangular microchannel heat sinks (RMCHSs), the average and maximum temperatures of TFMCHSs are significantly reduced, and the temperature distribution is more uniform. This is mainly caused by the periodic interruption and redevelopment of the velocity boundary layer and thermal boundary layer caused by ribs and grooves. And as the heating power increases, the TFMCHS has better heat dissipation performance. When P=33 W and the inlet flow rate is 32.5 mL/min, the thermal enhancement factor reaches 1.26.

Machine learning-enhanced predictive modeling for arbitrary deterministic lateral displacement design and test.

The separation of biological particles like cells and macromolecules from liquid samples is vital in clinical medicine, supporting liquid biopsies and diagnostics. Deterministic Lateral Displacement (DLD) is prominent for sorting particles in microfluidics by size. However, the design, fabrication, and testing of DLDs are complex and time-consuming. Researchers typically rely on finite element analysis to predict particle trajectories, which are crucial in evaluating the performance of DLD. Traditional particle trajectory predictions through finite element analysis often inaccurately reflect experimental results due to manufacturing and experimental variabilities. To address this issue, we introduced a machine learning-enhanced approach, combining past experimental data and advanced modeling techniques. Our method, using a dataset of 132 experiments from 40 DLD chips and integrating finite element simulation with a microfluidic-optimized particle simulation algorithm (MOPSA) and a Random Forest model, improves trajectory prediction and critical size determination without physical tests. This enhanced accuracy in simulation across various DLD chips speeds up development. Our model, validated against three DLD chip designs, showed a high correlation between predicted and experimental particle trajectories, streamlining chip development for clinical applications.

Van der Waals Transition Metal Carbo-Chalcogenides: Theoretical Screening and Charge Storage.

High-rate lithium/sodium ion batteries or capacitors are the most promising functional units to achieve fast energy storage that highly depends on charge host materials. Host materials with lamellar structures are a good choice for hybrid charge storage hosts (capacitor or redox type). Emerging layered transition metal carbo-chalcogenides (TMCC) with homogeneous sulfur termination are especially attractive for charge storage. Using density functional theory calculations, six of 30 potential TMCC are screened to be stable, metallic, anisotropic in electronic conduction and mechanical properties due to the lamellar structures. Raman, infrared active modes and frequencies of the six TMCC are well assigned. Interlayer coupling, especially binding energies predict that the bulk layered materials can be easily exfoliated into 2D monolayers. Moreover, Ti2S2C, Zr2S2C are identified as the most gifted Li+/Na+ anode materials with relatively high capacities, moderate volume expansion, relatively low Li+/Na+ migration barriers for batteries or ion-hybrid capacitors. This work provides a foundation for rational materials design, synthesis, and identification of the emerging 2D family of TMCC.

A universal inverse design methodology for microfluidic mixers.

The intelligent design of microfluidic mixers encompasses both the automation of predicting fluid performance and the structural design of mixers. This article delves into the technical trajectory of computer-aided design for micromixers, leveraging artificial intelligence algorithms. We propose an automated micromixer design methodology rooted in cost-effective artificial neural network (ANN) models paired with inverse design algorithms. Initially, we introduce two inverse design methods for micromixers: one that combines ANN with multi-objective genetic algorithms, and another that fuses ANN with particle swarm optimization algorithms. Subsequently, using two benchmark micromixers as case studies, we demonstrate the automatic derivation of micromixer structural parameters. Finally, we automatically design and optimize 50 sets of micromixer structures using the proposed algorithms. The design accuracy is further enhanced by analyzing the inverse design algorithm from a statistical standpoint.

Development and validation of machine learning models to predict frailty risk for elderly.

AIMS: Early identification and intervention of the frailty of the elderly will help lighten the burden of social medical care and improve the quality of life of the elderly. Therefore, we used machine learning (ML) algorithm to develop models to predict frailty risk in the elderly. DESIGN: A prospective cohort study. METHODS: We collected data on 6997 elderly people from Chinese Longitudinal Healthy Longevity Study wave 6-7 surveys (2011-2012, 2014). After the baseline survey in 1998 (wave 1), the project conducted follow-up surveys (wave 2-8) in 2000-2018. The osteoporotic fractures index was used to assess frailty. Four ML algorithms (random forest [RF], support vector machine, XGBoost and logistic regression [LR]) were used to develop models to identify the risk factors of frailty and predict the risk of frailty. Different ML models were used for the prediction of frailty risk in the elderly and frailty risk was trained on a cohort of 4385 elderly people with frailty (split into a training cohort [75%] and internal validation cohort [25%]). The best-performing model for each study outcome was tested in an external validation cohort of 6997 elderly people with frailty pooled from the surveys (wave 6-7). Model performance was assessed by receiver operating curve and F2-score. RESULTS: Among the four ML models, the F2-score values were similar (0.91 vs. 0.91 vs. 0.88 vs. 0.90), and the area under the curve (AUC) values of RF model was the highest (0.75), followed by LR model (0.74). In the final two models, the AUC values of RF and LR model were similar (0.77 vs. 0.76) and their accuracy was identical (87.4% vs. 87.4%). CONCLUSION: Our study developed a preliminary prediction model based on two different ML approaches to help predict frailty risk in the elderly. IMPACT: The presented models from this study can be used to inform healthcare providers to predict the frailty probability among older adults and maybe help guide the development of effective frailty risk management interventions. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Detecting frailty at an early stage and implementing timely targeted interventions may help to improve the allocation of health care resources and to reduce frailty-related burden. Identifying risk factors for frailty could be beneficial to provide tailored and personalized care intervention for older adults to more accurately prevent or improve their frail conditions so as to improve their quality of life. REPORTING METHOD: The study has adhered to STROBE guidelines. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Early delivery of human umbilical cord mesenchymal stem cells improves healing in a rat model of Achilles tendinopathy.

Objective: This study aimed to explore the efficacy and optimal delivery time of human umbilical cord mesenchymal stem cells (hUC-MSCs) in treating collagenase-induced Achilles tendinopathy. Methods: Achilles tendinopathy in rats at early or advanced stages was induced by injecting collagenase I into bilateral Achilles tendons. A total of 28 injured rats were injected with a hUC-MSC solution or normal saline into bilateral tendons twice and sampled after 4 weeks for histological staining, gene expression analysis, transmission electron microscope assay and biomechanical testing analysis. Results: The results revealed better histological performance and a larger collagen fiber diameter in the MSC group. mRNA expression of TNF-α, IL-1ß and MMP-3 was lower after MSC transplantation. Early MSC delivery promoted collagen I and TIMP-3 synthesis, and strengthened tendon toughness. Conclusion: hUC-MSCs demonstrated a therapeutic effect in treating collagenase-induced Achilles tendinopathy, particularly in the early stage of tendinopathy.

Automated design of a 3D passive microfluidic particle sorter.

Microfluidic chips that can sort mixtures of cells and other particles have important applications in research and healthcare. However, designing a sorter chip for a given application is a slow and difficult process, especially when we extend the design space from 2D into a 3D scenario. Compared to the 2D scenario, we need to explore more geometries to derive the appropriate design due to the extra dimension. To evaluate sorting performance, the simulation of the particle trajectory is needed. The 3D scenario brings particle trajectory simulation more challenges of runtime and collision handling with irregular obstacle shapes. In this paper, we propose a framework to design a 3D microfluidic particle sorter for a given application with an efficient 3D particle trajectory simulator. The efficient simulator enables us to simulate more samples to ensure the robustness of the sorting performance. Our experimental result shows that the sorter designed by our framework successfully separates the particles with the targeted size.

Dual-Mode Conversion of Photodetector and Neuromorphic Vision Sensor via Bias Voltage Regulation on a Single Device.

Simultaneous implementation of photodetector and neuromorphic vision sensor (NVS) on a single device faces a great challenge, due to the inherent speed discrepancy in their photoresponse characteristics. In this work, a trench-bridged GaN/Ga2 O3 /GaN back-to-back double heterojunction array device is fabricated to enable the advanced functionalities of both devices on a single device. Interestingly, the device shows fast photoresponse and persistent photoconductivity behavior at low and high voltages, respectively, through the modulation of oxygen vacancy ionization and de-ionization processes in Ga2 O3 . Consequently, the role of the optoelectronic device can be altered between the photodetector and NVS by simply adjusting the magnitude of bias voltage. As a photodetector, the device is able to realize fast optical imaging and optical communication functions. On the other hand, the device exhibits outstanding image sensing, image memory, and neuromorphic visual pre-processing as an NVS. The utilization of NVS for image pre-processing leads to a noticeable enhancement in both recognition accuracy and efficiency. The results presented in this work not only offer a new avenue to obtain complex functionality on a single optoelectronic device but also provide opportunities to implement advanced robotic vision systems and neuromorphic computing.

Binary code similarity analysis based on naming function and common vector space.

Binary code similarity analysis is widely used in the field of vulnerability search where source code may not be available to detect whether two binary functions are similar or not. Based on deep learning and natural processing techniques, several approaches have been proposed to perform cross-platform binary code similarity analysis using control flow graphs. However, existing schemes suffer from the shortcomings of large differences in instruction syntaxes across different target platforms, inability to align control flow graph nodes, and less introduction of high-level semantics of stability, which pose challenges for identifying similar computations between binary functions of different platforms generated from the same source code. We argue that extracting stable, platform-independent semantics can improve model accuracy, and a cross-platform binary function similarity comparison model N_Match is proposed. The model elevates different platform instructions to the same semantic space to shield their underlying platform instruction differences, uses graph embedding technology to learn the stability semantics of neighbors, extracts high-level knowledge of naming function to alleviate the differences brought about by cross-platform and cross-optimization levels, and combines the stable graph structure as well as the stable, platform-independent API knowledge of naming function to represent the final semantics of functions. The experimental results show that the model accuracy of N_Match outperforms the baseline model in terms of cross-platform, cross-optimization level, and industrial scenarios. In the vulnerability search experiment, N_Match significantly improves hit@N, the mAP exceeds the current graph embedding model by 66%. In addition, we also give several interesting observations from the experiments. The code and model are publicly available at https://www.github.com/CSecurityZhongYuan/Binary-Name_Match .

Mechanical and Microcrack Evolution Characteristics of Roof Rock of Coal Seam with Different Angle of Defects Based on Particle Flow Code.

The creation of the natural ceiling rock of the coal seam is rife with fractures, holes, and other flaws. The angle of the defects has a significant influence on the mechanical characteristics and crack evolution of coal seam roof rock. Multi-scale numerical simulation software PFC2D gets adapted to realize the crack propagation and coalescence process in the roof rock of a coal seam with different angles of defects under uniaxial compression. The effect of flaw angles on the micro and macro mechanical characteristics of rock is also discovered. The results show that: (1) the defect angle has influence on the stress-strain, elastic modulus, peak strength, peak strain, acoustic emission (AE) and strain energy of roof rock of coal seam. When the defect angles are different, the starting position of the roof rock in a coal seam fracture is different. The quantity of microcracks firstly reduces with an increase in defect angles before gradually increasing. At the same fault angle, the cracks are mostly tensile ones and only a few shear ones. (2) When the defect angle is less than 90°, tensile and shear fractures are mostly localized at the defect's two tips and propagate along the loading direction. When the defect angle is 90°, the tensile and shear cracks are not concentrated at the tip of the defect. (3) As the defect angles increase, the elastic strain energy rises initially and then falls, and the dissipated energy and total input energy both increase continuously. The elastic strain energy is greatest at the highest strength. The study provides a certain reference for the use of various analysis methods in practical engineering to evaluate the safety and stability of rock samples with pre-existing defects.

Surgical methods influence on the risk of anastomotic fistula after pancreaticoduodenectomy: a systematic review and network meta-analysis.

BACKGROUND: Pancreaticoduodenectomy is the first choice surgical intervention for the radical treatment of pancreatic tumors. However, an anastomotic fistula is a common complication after pancreaticoduodenectomy with a high mortality rate. With the development of minimally invasive surgery, open pancreaticoduodenectomy (OPD), laparoscopic pancreaticoduodenectomy (LPD), and robotic pancreaticoduodenectomy (RPD) are gaining interest. But the impact of these surgical methods on the risk of anastomosis has not been confirmed. Therefore, we aimed to integrate relevant clinical studies and explore the effects of these three surgical methods on the occurrence of anastomotic fistula after pancreaticoduodenectomy. METHODS: A systematic literature search was conducted for studies reporting the RPD, LPD, and OPD. Network meta-analysis of postoperative anastomotic fistula (Pancreatic fistula, biliary leakage, gastrointestinal fistula) was performed. RESULTS: Sixty-five studies including 10,026 patients were included in the network meta-analysis. The rank of risk probability of pancreatic fistula for RPD (0.00) was better than LPD (0.37) and OPD (0.62). Thus, the analysis suggests the rank of risk of the postoperative pancreatic fistula for RPD, LPD, and OPD. The rank of risk probability for biliary leakage was similar for RPD (0.15) and LPD (0.15), and both were better than OPD (0.68). CONCLUSIONS: This network meta-analysis provided ranking for three different types of pancreaticoduodenectomy. The RPD and LPD can effectively improve the quality of surgery and are safe as well as feasible for OPD.

Blockade of dual immune checkpoint inhibitory signals with a CD47/PD-L1 bispecific antibody for cancer treatment.

Background: Even though PD-1/PD-L1 is an identified key "don't find me" signal to active adaptive immune system for cancer treatment, the overall response rate (ORR) for all cancer patients is still limited. Other effective therapeutic modalities to bridge the innate and adaptive immunity to improve ORR are urgently needed. Recently, CD47/SIRPα interaction is confirmed as a critical "don't eat me" signal to active innate immunity. However, the red blood cell (RBC) toxicity is the big concern for the development of CD47-based anti-cancer therapeutics. Methods: Here, we report the development of a CD47/PD-L1 bispecific antibody 6MW3211 to block both PD-1/PD-L1 and CD47/SIRPα signals, and studied the effects of 6MW3211 on anti-tumor immune functions in vitro and in vivo. The pharmacokinetic and toxicity profiles of 6MW3211 were evaluated in GLP non-human primate (NHP) studies. Results: The dual immune checkpoint inhibitory signaling blocker 6MW3211 shows high binding affinity to PD-L1 and low binding affinity to CD47. This inequivalent binding affinity design makes 6MW3211 preferentially bound to PD-L1 on tumor cells followed by disrupting the interaction of CD47/SIRPα. Complex structure determination and flow cytometry assay demonstrated that 6MW3211 has no binding to either human or rhesus monkey RBCs. 6MW3211 effectively blocked both PD-1/DP-L1 and CD47/SIRPα signaling and promoted macrophage phagocytosis of tumor cells. Potent therapeutic efficacies of 6MW3211 in three different mouse models were further observed. Moreover, 6MW3211 was demonstrated to have a fairly good safety profile in a GLP NHP study. In addition, multiplex fluorescent immunohistochemistry (mIHC) staining shows that PD-L1 and CD47 co-express on several different types of human tumor tissues. Conclusions: These results support the development of 6MW3211 for the treatment of PD-L1 and CD47 double positive cancers.

Comparison of Leukocyte-Rich and Leukocyte-Poor Platelet-Rich Plasma on Pressure Ulcer in a Rat Model.

Pressure ulcer (PU) is a common type of chronic wound that is difficult to treat. Platelet-rich plasma (PRP) is rich in cytokines and growth factors, and it can be divided into two categories according to its leukocyte content: leukocyte-poor PRP (P-PRP) and leukocyte-rich PRP (L-PRP). PRP has been applied in a variety of wound treatments, due to its strong ability to promote repair. This study aims to investigate the therapeutic effects of PRP on PU and elucidate the role of leukocytes in the treatment process. Sprague-Dawley rats were used to establish PU models of ischemia-reperfusion injury by applying magnets externally. L-PRP, P-PRP, and saline were injected into the dermal wounds. Wound healing analysis and sampling were performed on days 3, 7, 11, and 15 after treatment. Histological examinations, real-time PCR, immunohistochemical examinations, and biomechanical assay were carried out on the wound samples. The PRP groups exhibited greater wound inflammatory response than the control group in the early stage but the response reduced rapidly as the wound healed. On days 7, 11, and 15, the PRP groups also yielded better wound healing rates and histological outcomes than the control group, with superior biomechanical properties observed on day 15. Among both PRP groups, the L-PRP group attained a higher wound healing rate than the P-PRP group on day 7, with greater significant early inflammatory responses, and more prominent angiogenesis. Therefore, PRP is proven to accelerate the healing of PU, with L-PRP being more effective in regulating inflammation and promoting angiogenesis than P-PRP.

Photoredox/Nickel Dual-Catalyzed Stereoselective Synthesis of Distal Cyano-Substituted Enamides.

Herein, the efficient photoredox/nickel dual-catalyzed cyanoalkylation reaction of enamides is illustrated. A wide scope of enamides and cycloketone oxime esters was well-tolerated, affording the synthetically versatile and geometrically defined ß-cyanoalkylated enamide scaffolds. The synthetic practicality of this protocol was revealed by gram-scale reactions, further transformations of enamides, and late-stage modifications of biologically active molecules.

The Effect of Geometric Parameters on Flow and Heat Transfer Characteristics of a Double-Layer Microchannel Heat Sink for High-Power Diode Laser.

The effect of the geometric parameters on the flow and heat transfer characteristics of a double-layer U-shape microchannel heat sink (DL-MCHS) for a high-power diode laser was investigated in this work. FLUENT 19.2 based on the finite volume method was employed to analyze the flow and heat transfer performance of DL-MCHS. A single variable approach was used to fully research the impact of different parameters (the number of channels, the channel cross-sectional shape, and the aspect ratio) on the temperature distribution, pressure drop, and thermal resistance of the DL-MCHS. The rectangular DL-MCHS heat transfer performance and pressure drop significantly increased with the rise in the channel's aspect ratio due to there being a larger wet perimeter and convective heat transfer area. By comparing the thermal resistance of the DL-MCHS at the same power consumption, it was found that the rectangular DL-MCHS with an aspect ratio in the range of 5.1180-6.389 had the best overall performance. With the same cross-sectional area and hydraulic diameter (AC = 0.36 mm, Dh = 0.417 mm), the thermal resistance of the trapezoidal microchannel heat sink was 32.14% and 42.42% lower than that of the triangular and rectangular ones, respectively, under the condition that the pumping power (Wpp) was 0.2 W. Additionally, the thermal resistance was reduced with the increment of the number of channels inside the DL-MCHS, but this would induce an increased pressure drop. Thus, the channel number has an optimal range, which is between 50 and 80 for the heat sinks in this study. Our study served as a simulation foundation for the semiconductor laser double-layer U-shaped MCHS optimization method using geometric parameters.

ANN-Based Instantaneous Simulation of Particle Trajectories in Microfluidics.

Microfluidics has shown great potential in cell analysis, where the flowing path in the microfluidic device is important for the final study results. However, the design process is time-consuming and labor-intensive. Therefore, we proposed an ANN method with three dense layers to analyze particle trajectories at the critical intersections and then put them together with the particle trajectories in straight channels. The results showed that the ANN prediction results are highly consistent with COMSOL simulation results, indicating the applicability of the proposed ANN method. In addition, this method not only shortened the simulation time but also lowered the computational expense, providing a useful tool for researchers who want to receive instant simulation results of particle trajectories.