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Ambient backscatter communication (ABC) is a promising method of reducing energy consumption in wireless communications. Previous works on signal detection for ABC often assume that the ambient source signal is always present during backscattering. However, this may not be the case due to the random traffic of the ambient source and the asynchronous operation between source and tag. In this work, the effect of source signal traffic on the detection performance is studied for ABC systems. Firstly, the performances of the existing detectors are analyzed in the presence of source traffic. Both random arrival and random departure are considered. The exponential and uniform traffic models are used. Their bit error rate expressions are obtained by taking advantage of different approximation methods. Then, new detectors taking into account the random traffic models are derived by weighting the samples exponentially or linearly with their arrival times. Numerical results show that the random source traffic could cause large performance degradation to the existing detectors, leading to error floors at small signal-to-noise ratios (SNRs). In particular, the exponential departure causes the largest performance degradation, followed by the uniform arrival and departure. Numerical results also show that the new detectors could have significant performance gains over the conventional detectors in the presence of source traffic. In some case, the gain could be over 3 dB in SNR, and it increases with the sample size and traffic parameters. However, this gain could become negative for large SNRs and small sample sizes due to the use of heuristic detection thresholds.

The CLLC converter is widely used in applications requiring highly efficient bidirectional power flows. However, multiple resonant components make its modeling and analysis quite challenging, hence, hard to reach a higher operating efficiency. In this article, the method based on the superposition principle is proposed to provide a more accurate analysis for CLLC converters in the above-resonant-frequency-mode (ARFM) and simplified time-domain analysis is employed for below-resonant-frequency mode. First, the principle of the novel method is analyzed within the introduction of the operating modes for CLLC converter. Then, the time-domain expressions regarding the voltage-gain, resonant currents, and resonant voltages are derived by using the proposed method in ARFM. Afterward, the soft-switching conditions are then redefined in a more accurate way. More detailed synchronous-rectification (SR) timings are derived, which is followed by the optimal hardware parameter design. The proposed concept is finally verified through a 1.5-kW resonant CLLC prototype. The experiment has confirmed that the voltage gain and peak values are consistent with the theoretical calculations. And the operating efficiency is improved by 1.8% with the accurate soft-switching and SR compared with the uncontrolled rectification as confirmed by experimental results. Furthermore, the proposed method surpasses the time-domain analysis method to implement SR considering the parasitic capacitance of switches.

Improving the comprehensive performance of low alloyed Mg is a significant challenge for biomedical applications. This paper developed a high-performance Mg–Zn alloy with uniform ultrafine grains and nano-precipitates through a straightforward, high-temperature reciprocating equal channel angle extrusion (ECAP) process and researched the microstructure, mechanical property, degradation behaviour, and biocompatibility of the developed alloy. Results showed that the lean Mg–2Zn alloy successfully refined grain to about 1 μm and produced plenty of nano-particles with uniform distribution, providing high comprehensive mechanical properties (YS: 235 MPa, UTS: 267 MPa, EL: 15.6 %). Additionally, Zn-riched nano-particles in the matrix could decrease the Zn aggregation at the corrosion layer-matrix interface and form a dense oxide film, achieving a low degradation rate (0.13 mm/year in vivo). Finally, this work realizes the low alloy content, low cost, and good properties of one biodegradable Mg alloy, which will benefit the promotion of future clinical applications.

Herein, we develop a novel method using gas chromatography-ion mobility spectrometry (IMS) with an ammonia dopant for the determination of volatile N-nitrosamines in meat products. The IMS system was implemented with Fourier deconvolution multiplexing to simultaneously improve the resolving power and sensitivity. The ammonia dopant mitigated the formation of N-nitrosamine dimer ions, suppressing the ionization of interferents and shifting the reactant peak from hydrated protons to hydrated ammonium ions. The ammonium adduct product ions of N-nitrosamines were confirmed using time-of-flight mass spectrometry. The instrument limits of detection and quantitation for nitrosamines were 0.78–1.79 and 2.60–5.82 ng/mL, respectively, with recoveries between 71.39% and 110.59% using a simple water distillation method. The developed method showed satisfactory performance when applied to the detection of nitrosamines in various meat products.

The resolving power of the drift tube ion mobility spectrometry (IMS) is mainly dependent on the drift length, the drift voltage, the pulse width of an ion gate, and the pressure inside the drift tube. Electrospray ionization (ESI)-IMS is a highly sensitive and reliable technique for the detection and analysis of nonvolatile compounds, and high resolving power is necessary to separate structurally similar compounds. To improve the analytical performance of atmospheric pressure ESI-IMS, the Fourier deconvolution (FD) multiplexing technique is investigated as an effective and convenient means to improve the resolving power as well as the signal-to-noise ratio. By reducing the equivalent ion gate opening width to 5 μs using a typical Tyndall–Powell ion shutter, a high resolving power RPup to 170 can be achieved with a drift length of 12 cm and a drift voltage of 10 kV. Rhodamine 6G (R6G), sodium dodecyl sulfate (SDS), methacycline, oxytetracycline, and ractopamine were evaluated using the FD-ESI-IMS, and mixtures with similar ion mobility can be well separated without prolonging the drift length.

Water evaporation-induced electricity devices (WEDs) have become extremely attractive, converting ambient heat into electricity while being environmentally friendly. However, most current WEDs are costly and cumbersome to fabricate, which greatly limits their commercialization process. Here, we present WEDs based on starch cross-linked with glass fiber filter paper (Starch-GF). A single device produced an open-circuit voltage of 0.3 V, a short-circuit current of 1.2 μA, and a maximum power density of 1.8 mW/m2in a natural environment under 24 h of continuous measurements. Starch-GF devices can drive electronics after charging capacitors and have environmentally friendly properties. This research contributes significantly to the discovery of hydrovoltaic materials and their practical implementation in hydrovoltaic devices.

Solar-driven carbon dioxide (CO2) reduction into C2+products such as ethylene represents an enticing route toward achieving carbon neutrality. However, due to sluggish electron transfer and intricate C–C coupling, it remains challenging to achieve highly efficient and selective ethylene production from CO2and H2O beyond capitalizing on Cu-based catalysts. Herein, we report a judicious design to attain asymmetric C–C coupling through interfacial defect-rendered tandem catalytic centers within a sulfur-vacancy-rich MoSx/Fe2O3photocatalyst sheet, enabling a robust CO2photoreduction to ethylene without the need for copper, noble metals, and sacrificial agents. Specifically, interfacial S vacancies induce adjacent under-coordinated S atoms to form Fe–S bonds as a rapid electron-transfer pathway for yielding a Z-scheme band alignment. Moreover, these S vacancies further modulate the strong coupling interaction to generate a nitrogenase-analogous Mo–Fe heteronuclear unit and induce the upward shift of the d-band center. This bioinspired interface structure effectively suppresses electrostatic repulsion between neighboring *CO and *COH intermediates via d-p hybridization, ultimately facilitating an asymmetric C–C coupling to achieve a remarkable solar-to-chemical efficiency of 0.565% with a superior selectivity of 84.9% for ethylene production. Further strengthened by MoSx/WO3, our design unveils a promising platform for optimizing interfacial electron transfer and offers a new option for C2+synthesis from CO2and H2O using copper-free and noble metal-free catalysts.

Objective: To discuss the clinical efficacy of treating lumbar muscle strain (LMS) with Tuina (Chinese therapeutic massage) plus Chinese medication hot compress. Methods: A total of 147 LMS patients were randomized into a Tuina group, a Chinese medication hot compress group, and a combined group, each consisting of 49 cases. The Tuina group received Tuina treatment; the Chinese medication hot compress group received Chinese medication hot compress treatment; and the combined group received the forementioned two therapies alternately. The three groups of patients were assessed using the visual analog scale (VAS) and Oswestry disability index (ODI) before treatment and after 2 weeks of treatment. A 2-month follow-up was also conducted to observe the relapse rate. Results: The VAS and ODI scores dropped significantly after treatment in all three groups compared with their baseline (P<0.05), and the combined group surpassed the other two groups in comparing the ODI score (P<0.05). The 2-month follow-up showed that the combined group had the lowest relapse rate among the three groups (P<0.05). Conclusion: Compared to each therapy used alone, Tuina plus Chinese medication hot compress can relieve pain, improve daily living function, and reduce the short-term relapse rate better in treating LMS patients.

Towards the sixth-generation (6G) wireless communication, unmanned aerial vehicles (UAVs) have been regarded as an indispensable part due to its flexible deployment, wide coverage, and high mobility. This also creates challenges for channel research. Scatterers are normally present in the structure of clusters during UAV communication, and cluster-based channel modeling is significant. In this paper, the variational Bayesian-Gaussian mixture model (VB-GMM) algorithm is proposed for clustering, which takes into account the time-space properties. Cluster tracking is implemented using the multipath component distance (MCD) algorithm. Intra- and inter-cluster characterization, such as the number of clusters, cluster power distribution, angular/delay offset, and angular/delay spreads, are well studied. Moreover, cluster lifetime and birth-death (B-D) properties are extracted and analyzed. Based on these cluster characteristics acquired by machine learning (ML) method, a novel UAV-to-ground communication channel model is proposed, and a four-state Markov chain is also introduced to portray the evolution of clusters. Simulation results match well with channel measurements, which verifies the practicality of the proposed model. This paper can give theoretical and technical support for the design and evaluation of UAV-to-ground communication systems.

The primary-parallel-secondary-series LCC resonant high voltage (HV) power supply with single-phase ac input has been widely applied for medical X-ray application. It requires extremely low ripple (<0.5%) for hundreds of kilovolts output voltage, which is quite challenging. Large capacitance is often utilized at the dc bus to absorb the second-order ripple power introduced by the single-phase input, which deteriorates the power density of the system. To address this issue, this article proposes an output voltage ripple suppression strategy based on plug-in repetitive control combined with pulse-frequency modulation. It can realize voltage ripple suppression and zero-voltage switching ( of the LCC converter at the same time. The dc-bus capacitance can, therefore, be remarkably reduced. This strategy has the advantages of no additional hardware costs, directly implemented in digital controller, and insensitive to accuracy of system parameters. Experiments are carried out on a practical 120 kV/900 W HV power supply and confirm the effectiveness of the proposed strategy. The peak–peak output voltage ripple is suppressed to only 0.36%, while the DC capacitance is significantly reduced to 1/12. Comparison of X-ray does prove that the image quality of X-ray machine is consequently greatly improved.

Due to the time-varying and non-uniform terrestrial traffic requirements, efficiently utilizing limited satellite resources to match non-uniform traffic demands brings challenges for Low Earth Orbit (LEO) beam-hopping (BH) technology. Existing LEO BH systems typically assume the fixed beam coverage sizes, potentially leading to resource congestion or wastage. In this letter, we present a novel multi-satellite collaborative two-dimensional load-balancing beam-hopping scheme based on dynamic beam coverage (MSDBC-BH), which encompasses both inter-beam load balancing and inter-satellite load balancing. This letter proposes the dynamic beam coverage design to achieve effective inter-beam load balancing. Additionally, a cell-satellite assignment scheme based on a game-matching algorithm is introduced to satisfy inter-satellite load balancing. Simulation results illustrate that the proposed MSDBC-BH effectively addresses the load imbalance among satellites and improves system throughput.

Autonomous driving safety based on LiDAR perception is increasingly becoming a hot spot. Specifically, 3D adversarial examples always make the prediction results of deep neural network models unpredictable, which poses a major security risk to autonomous driving systems. However, the vulnerability of 3D neural network models to adversarial examples is less explored. At present, existing adversarial attack methods often obtain 3D adversarial examples by perturbing the entire point cloud, which requires a large number of perturbed points, i.e. requires a large perturbation budget. In this paper, we propose a Salient Region Adversarial attack method (SR-Adv) to generate adversarial point clouds, by perturbing fewer regions and fewer points. To our knowledge, we are the first to propose region-based attacks for 3D point clouds. First, the proposed SR-Adv employs game theory to extract salient regions of point clouds. This mechanism assigns a value to each region to measure its importance to the 3D neural network model prediction results and realizes the vulnerability analysis of the 3D model. Second, we propose a novel optimization-based gradient attack algorithm to achieve adversarial attacks on salient regions. We evaluate the proposed SR-Adv attack method on the synthetic datasets ModelNet40 and ShapeNetPart as well as the real-world dataset KITTI and NuScenes. Experimental results show that the proposed SR-Adv achieves a state-of-the-art attack success rate and better imperceptibility by perturbing fewer points on 3D point clouds.

Urban events, such as hurricanes, floods, and epidemic outbreaks, usually have a significant impact on crowd behaviors that may dramatically change people’s mobility patterns, interaction manners, and etc. Accurately foreseeing the abnormal crowd behaviors triggered by urban events can help authorities to dynamically schedule urban resources and services, such as providing shuttle buses, temporal shelters, and psychological first aid. However, traditional crowd behavior prediction models tend to learn regular trends and patterns of crowd traffic, and usually fail to predict the abnormal traffic fluctuations triggered by urban events. In this work, we propose a context-aware framework to accurately predict event-triggered abnormal crowd traffic by explicitly modeling event contexts and their impact. We start by training a state-of-the-art spatiotemporal multi-graph model (MG) leveraging a multi-graph convolutional network (MGCN) to model city-wide venues and their connections, as well as gated recurrent unit (GRU) to capture the crowd traffic patterns within venues. In order to model the time-occasional impact of sudden influential event context (e.g., weather conditions), we propose a multi-task fusion diagram to co-train the MG model by learning a temporal impact embedding with an attention mechanism and recurrent neural network to obtain the MG-MT model. In order to model the hyper-space impact of irregularly distributed event context (e.g., epicenters and trajectories), we propose a multi-view fusion diagram to fine-tune the above-mentioned model by learning a spatial impact graph with a stimulus-response mechanism to obtain the spatiotemporal crowd traffic model (STORM). Experiments using real-world urban data collected from Xiamen, China show that our approach improves over the state-of-the-art baseline by more than 6% in predicting crowd traffic and by more than 40% in the abnormal crowd traffic part.

The Himalayas, which host glaciers, modulate the Indian Monsoon and create an arid Tibetan Plateau, play a vital role in distributing freshwater resources to the world’s most populous regions. The Himalayas formed under prolonged crustal thickening and erosion by glaciers and rivers. Chomolungma (8,849 m)—also known as Mount Everest or Sagarmāthā—is higher than surrounding peaks, and GPS measurements suggest a higher uplift rate in recent years than the long-term trend. Here we analyse the potential contribution of a river capture event in the Kosi River drainage basin on the renewed surface uplift of Chomolungma. We numerically reconstruct the capture process using a simple stream power model combined with nonlinear inverse methods constrained by modern river profiles. Our best-fit model suggests the capture event occurred approximately 89 thousand years ago and caused acceleration of downstream incision rates. Flexural models estimate this non-steady erosion triggers isostatic response and surface uplift over a broad geographical area. We suggest that part of Chomolungma’s anomalous elevation (~15–50 m) can be explained as the isostatic response to capture-triggered river incision, highlighting the complex interplay between geological dynamics and the formation of topographic features.

The personalization of politics and elite polarization have attracted considerable scholarly attention. We conducted an online experimental survey on policy proposals and voters’ evaluations of candidates during the 2020 Taiwanese presidential election. We see an elite effect on voters’ policy attitudes when controlling for party-related factors. The effect shows up as a negative cue but not as a positive cue. Moreover, the negative effect varies by candidate. These findings suggest that in policy attitude formation, opposition to a candidate has a stronger effect than liking a candidate. Overall, this study extends our understanding of the elite effect from the perspective of personalized politics with evidence from the Taiwanese electoral context. This information is critical not only for understanding the political consequences of polarization but also for understanding how it relates to opinion formation among citizens.

This paper proposes a novel localization algorithm using the reconfigurable intelligent surface (RIS) received signal, i.e., RIS information. Compared with BS received signal, i.e., BS information, RIS information offers higher dimension and richer feature set, thereby providing an enhanced capacity to distinguish positions of the mobile users (MUs). Additionally, we address a practical scenario where RIS contains some unknown (number and places) faulty elements that cannot receive signals. Initially, we employ transfer learning to design a two-phase transfer learning (TPTL) algorithm, designed for accurate detection of faulty elements. Then our objective is to regain the information lost from the faulty elements and reconstruct the complete high-dimensional RIS information for localization. To this end, we propose a transfer-enhanced dual-stage (TEDS) algorithm. In Stage I, we integrate the CNN and variational autoencoder (VAE) to obtain the RIS information, which in Stage II, is input to the transferred DenseNet 121 to estimate the location of the MU. To gain more insight, we propose an alternative algorithm named transfer-enhanced direct fingerprint (TEDF) algorithm which only requires the BS information. The comparison between TEDS and TEDF reveals the effectiveness of faulty element detection and the benefits of utilizing the high-dimensional RIS information for localization. Besides, our empirical results demonstrate that the performance of the localization algorithm is dominated by the high-dimensional RIS information and is robust to unoptimized phase shifts and signal-to-noise ratio (SNR).

The LLC full-bridge converter is a highly efficient solution for high-power secondary power supply applications. It is known for its low stress operation and reduced electromagnetic radiation. However, it has a relatively narrow optimized switching frequency and is very sensitive to input voltage variations, particularly at low input voltage conditions. Achieving a balance between a wide input voltage range and high efficiency is challenging. This article proposes an input-coupling type LLC converter (IC-LLC) as a solution. The resonant converter shares the input voltage with a pre-stage. Power is transferred through the prestage during half of the switching cycle, while the resonant stage takes charge during the other half cycle. This results in an improvement in the efficiency of the converter. A 1.5-kW IC-LLC converter system was built and tested. Experimental results show that the input coupling mechanism significantly enhances the efficiency of the dual-stage conversion setup, overcoming the operational challenges associated with LLC converters.

Purpose: We introduce a novel approach for bronchoscopic navigation that leverages neural radiance fields (NeRF) to passively locate the endoscope solely from bronchoscopic images. This approach aims to overcome the limitations and challenges of current bronchoscopic navigation tools that rely on external infrastructures or require active adjustment of the bronchoscope. Methods: To address the challenges, we leverage NeRF for bronchoscopic navigation, enabling passive endoscope localization from bronchoscopic images. We develop a two-stage pipeline: offline training using preoperative data and online passive pose estimation during surgery. To enhance performance, we employ Anderson acceleration and incorporate semantic appearance transfer to deal with the sim-to-real gap between training and inference stages. Results: We assessed the viability of our approach by conducting tests on virtual bronchscopic images and a physical phantom against the SLAM-based methods. The average rotation error in our virtual dataset is about 3.18$_{}^{\circ }$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^\circ $$\end{document}and the translation error is around 4.95 mm. On the physical phantom test, the average rotation and translation error are approximately 5.14$_{}^{\circ }$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^\circ $$\end{document}and 13.12 mm. Conclusion: Our NeRF-based bronchoscopic navigation method eliminates reliance on external infrastructures and active adjustments, offering promising advancements in bronchoscopic navigation. Experimental validation on simulation and real-world phantom models demonstrates its efficacy in addressing challenges like low texture and challenging lighting conditions.

Identification of compounds to modulate NADPH metabolism is crucial for understanding complex diseases and developing effective therapies. However, the complex nature of NADPH metabolism poses challenges in achieving this goal. In this study, we proposed a novel strategy named NADPHnet to predict key proteins and drug-target interactions related to NADPH metabolism via network-based methods. Different from traditional approaches only focusing on one single protein, NADPHnet could screen compounds to modulate NADPH metabolism from a comprehensive view. Specifically, NADPHnet identified key proteins involved in regulation of NADPH metabolism using network-based methods, and characterized the impact of natural products on NADPH metabolism using a combined score, NADPH-Score. NADPHnet demonstrated a broader applicability domain and improved accuracy in the external validation set. This approach was further employed along with molecular docking to identify 27 compounds from a natural product library, 6 of which exhibited concentration-dependent changes of cellular NADPH level within 100 μM, with Oxyberberine showing promising effects even at 10 μM. Mechanistic and pathological analyses of Oxyberberine suggest potential novel mechanisms to affect diabetes and cancer. Overall, NADPHnet offers a promising method for prediction of NADPH metabolism modulation and advances drug discovery for complex diseases.

To reveal the combustion and explosion characteristics of RP-3 aviation kerosene, a three-step simplified mechanism involving CO-CO2, H2-H2O equilibrium was constructed. Using a general program based on Cantera and genetic algorithms, the reaction rate parameters were optimized under given conditions, and a relationship between reaction rate parameters and fuel equivalence ratio was developed. The optimized mechanism was compared with multiple flame parameters under multiple operating conditions of RP-3 aviation kerosene. The results showed that the laminar flame speed and adiabatic flame temperature of the three-step simplified mechanism were in good agreement with the experimental data of RP-3 aviation kerosene within the temperature range of 300 ∼ 600 K and pressure range of 1–5 atm, with an error of less than 10 %. The optimized mechanism corrected using a correction function is in good agreement with experimental and numerical results under multiple conditions, verifying the rationality of the simplified program and the accuracy of the three-step simplified mechanism. For practical engineering issues, RP-3 aviation kerosene explosion experiments were conducted, and the pressure and flame data were analyzed and compared. The results indicated that the three-step simplified mechanism had satisfactory calculation time scales and numerical simulation feasibility.

Advancements in radiative cooling technology have shown significant progress in recent years. However, the limited mechanical properties of most radiative coolers greatly hinder their practical applications, particularly in the context of human cooling fabrics. In this study, we present the fabrication of facile and stretchable radiative coolers with exceptional cooling performance by utilizing the design of porous radiative coolers as guidelines for developing promising elastomer coolers. Subsequently, we employ a simple electrospinning method to fabricate these coolers, resulting in impressive solar reflectivity (∼96.1%) and infrared emissivity (over 95%). During the summer, these coolers demonstrate a maximum temperature drop of ∼9.6 °C. Additionally, the developed coolers exhibit superior hydrophobicity and mechanical properties, with a high strain capacity exceeding 700% and a stress tolerance of over 30 MPa, highlighting their potential for application in automobile textiles and cooling fabrics. Furthermore, we evaluate the radiative cooling performance of stretchable coolers using global-scale modeling, revealing their significant cooling potential across various regions worldwide.

Colorless polyimides (CPIs) are widely used as high-performance materials in flexible electronic devices. From a molecular design standpoint, the industry continues to encounter challenges in developing CPIs with desired attributes, including exceptional optical transparency, excellent thermal stability, and enhanced mechanical strength. This study presents and validates a method for controlling 2-substituents, with a specific emphasis on examining how these substituents affect the thermal, mechanical, optical, and dielectric characteristics of CPIs. The presence of two CF3groups on the same side of the diamine structure ensured the transmittance of the film. The charge transfer effect and the molecular distance are dynamically regulated by changing the 2-substituent (–OCH3/–CH3/H/F). The polyimide exhibited a well-maintained equilibrium between transparency and thermal stability, with a T500nmvalue ranging from 86.2 to 89.6% in the visible region, and a glass transition temperature (Tg) ranging from 358.6 to 376.0 °C. Additionally, the 6FDA-2-MTFMB compound, when combined with methyl, excels as a protective layer and base material, exhibiting excellent performance in various aspects. It has been verified as an appropriate option for flexible photodetectors and wearable piezoresistive sensors. In summary, this systematic investigation will provide a comprehensive and demonstrative methodology for developing CPIs that are capable of adapting to flexible electronic devices.

In this study, we proposed a moderate oxidation strategy for accelerating the oxidative dissolution of zerovalent iron (ZVI) using sulfite (S(IV)), thereby improving the removal of As(V) and As(III). Results revealed that, in the presence of 2.0 mM S(IV), both As(V) and As(III) were selectively converted into scorodite at pH03.0–7.0, while As(III) oxidation and As(V) immobilization were impressed over pH08.0–10.0. Batch experiments, radical quenching experiments, and electron spin resonance (ESR) measurements demonstrated that ZVI initially boosted S(IV) activation to generate SO4•–, •OH, and protons, and in turn, ZVI was further oxidized more intensely by these radicals than by oxygen. Concurrently, substantial protons derived from S(IV) oxidation neutralized hydroxyls produced by ZVI oxidation, maintaining an acidic environment conducive to the generation of scorodite rather than iron (hydr)oxides. Characterizations of X-ray diffraction (XRD), Raman, attenuated total reflectance-Fourier transform infrared (ATR-FTIR), X-ray photoelectron spectroscopy (XPS), X-ray absorption fine structure (XAFS), field emission scanning electron microscopy (FESEM), and high-resolution transmission electron microscopy (HRTEM) confirmed that scorodite was formed in situ and then exfoliated from the surface of ZVI, and approximately 75% of ZVI could still be recovered, which contributed to efficient As removal in successive runs and real As-polluted wastewater. The application of S(IV) achieved a balance among ZVI reactivity improvement, As(V)/As(III) removal, and raw material consumption, making it a promising approach for addressing arsenic contamination in wastewater treatment.

As a traditional Chinese herbal medicine, Schisandrae ChinensisFructus (SC) has been used in medicine and food industry due to its health care and therapeutic effects. Over the past 20 years, the use of SC and its active ingredient lignans in the prevention and treatment of liver diseases has been increasing, and their hepatoprotective effects has increased the interest of the public and academia. Therefore, in the present work, we first determined the effectiveness of SC in the treatment of liver diseases such as metabolic associated fatty liver disease, alcoholic liver disease, cholestatic liver disease and acute liver injury. Subsequently, the pharmacological effects and molecular mechanisms of lignans, the active components of SC, for liver disease treatment were comprehensively summarized for the first time. The results showed that the lignans in SC could achieve hepatoprotective effects by regulating lipid metabolism, anti-fibrosis, anti-inflammation, anti-oxidation, anti-tumor and regulating bile acid metabolism. The mechanism mainly involved adenosine 5'-monophosphate-activated protein kinase, endoplasmic reticulum stress, sterol regulatory element binding protein 1c, autophagy, transforming growth factor-β, mitogen-activated protein kinase, microRNA, nuclear factor kappa-B, nuclear factor erythroid-2-related factor 2, heat shock proteins and pregnane X receptor signaling pathways. These results can lay a scientific foundation for the development of hepatoprotective drugs or functional foods from SC/lignans.

Developing Cu single-atom catalysts (SACs) with well-defined active sites is highly desirable for producing CH4in the electrochemical CO2reduction reaction and understanding the structure-property relationship. Herein, a new graphdiyne analogue with uniformly distributed N2-bidentate (note that N2-bidentate site = N^N-bidentate site; N2≠ dinitrogen gas in this work) sites are synthesized. Due to the strong interaction between Cu and the N2-bidentate site, a Cu SAC with isolated undercoordinated Cu-N2sites (Cu1.0/N2-GDY) is obtained, with the Cu loading of 1.0 wt%. Cu1.0/N2-GDY exhibits the highest Faradaic efficiency (FE) of 80.6% for CH4in electrocatalytic reduction of CO2at −0.96 V vs.RHE, and the partial current density of CH4is 160 mA cm−2. The selectivity for CH4is maintained above 70% when the total current density is 100 to 300 mA cm−2. More remarkably, the Cu1.0/N2-GDY achieves a mass activity of 53.2 A/mgCutoward CH4under −1.18 V vs.RHE. In situelectrochemical spectroscopic studies reveal that undercoordinated Cu-N2sites are more favorable in generating key *COOH and *CHO intermediate than Cu nanoparticle counterparts. This work provides an effective pathway to produce SACs with undercoordinated Metal-N2sites toward efficient electrocatalysis.

This study implemented an optimized 3-D power inductor design using the selective laser melting (SLM) method, which was developed for magnetic metal additive manufacturing. Traditional inductors often lack the optimization of the magnetic circuit, volume, and iron core air gap, resulting in larger sizes, easier magnetic saturation, and lower inductance value. In this work, the finite element analysis is employed to simulate and verify the 3-D design power inductance device. The SLM technology was integrated with oxygen control technology to mitigate the eddy current loss of the inductance. This combination allowed for the quick and accurate fabrication of the 3-D power inductance device without the need for mold making. By maintaining the original volume of the inductor and increasing the length of the magnetic circuit and the saturation magnetic flux, the maximum operating current was enhanced by about 20%. The experimental results with the prototype of the 3-D power inductance device are presented to validate the designed performance. Subsequently, these results are compared with the traditionally shaped inductances to assess the effectiveness of the optimized design.

For the development of new energy automobile industry and the trend of automobile lightweight have put forward a huge demand for high-strength lightweight materials, so developing high strength aluminum alloys is particularly momentous. As the most widely used aluminum alloy, 6000 series aluminum alloys (Al–Mg–Si alloys) have the advantages such as better mechanical properties, outstanding welding properties, excellent formability and fine processing capability, and therefore have obtained great attention as the structural materials. In addition, the Al–Mg–Si alloys with low density and corrosion resistance not only reduce the total weight of the car, but also extend the service life when subjected to chemical corrosion, which effectively achieve energy saving and emission reduction. This paper summarizes the research progress of 6000 series aluminum alloys, in particular, and reviews the methods of microalloying and particle strengthening, and their effects on the microstructure and properties of the alloy, thus providing a reference for the subsequent development of high-performance aluminum alloys in automobile and aerospace industries.

Local defects in coatings allow aggressive electrolytes penetrate to the coating/substrate interface, accelerating the corrosion of the exposed Mg substrate and compromising its biocompatibility. In this work, self-healing calcium hydrogen phosphate dihydrate (DCPD) coating incorporated with 3,5-dinitrosalicylic acid (3,5-D) or 5-amino salicylic acid (5-A) is designed on Mg. The results reveal that the self-healing and corrosion resistance performance of DCPD improves after adding 3,5-D/5-A. Namely, incorporating 3,5-D/5-A into DCPD accelerates the self-healing process and repair the coating at the initial stage. The released 3,5-D/5-A additives could adsorb on the exposed Mg surface and combine with the Ca2+ions to precipitate. The interfacial adsorption mechanism of 3,5-D/5-A additives is disclosed by quantum chemical calculation. The simultaneously responsive adsorption and deposition of additives during coating degradation increase the thickness and compactness of the deposition layer at the coating defects, further inhibiting corrosion and stabilizing pH. These findings suggest taking biocompatibility recovery into consideration of preparing self-healing coating for biomedical applications.

Rapid growth in industrial sectors such as automotive and shipbuilding has highlighted the significance of strip casting technology to produce lightweight alloys, particularly aluminum alloys widely used in both industrial production and daily life. Twin-roll casting (TRC), as an economically and environmentally friendly method for slab/strip production and processing, has attracted significant interest from researchers. However, the development of TRC aluminum alloys faces many challenges due to limited understanding of microstructural characteristics and control mechanisms. To further enhance comprehension of TRC aluminum alloys, this article reviews the influencing parameters, control methods, and existing issues related to TRC sub-rapid solidification (SRS) microstructure. It firstly summarizes TRC equipment types and their solidification characteristics, followed by a detailed analysis on key parameters affecting the evolution of TRC microstructure including rolling speed, roll separation force (RSF), and heat transfer coefficient (HTC). Finally, solutions to TRC defects are summarized and evaluated alongside their underlying mechanisms. This article provides a comprehensive review of the characteristics and control mechanisms of TRC microstructure while offering valuable insights for the future production of high-quality TRC aluminum strips.

Mg is a typical biodegradable metal widely used for biomedical applications due to its considerable mechanical properties and bioactivity. Biodegradable polymers have attracted great interest owing to their favorable processability and inclusiveness. However, it is challenging for the degradation rates of Mg or polymers to precisely match tissue repair processes, and the significant changes in local pH during degradation hinder tissue repair. The concept of combining Mg with polymers is proposed to overcome the shortcomings of materials, aiming to meet repair needs from various aspects such as mechanics and biology. Therefore, it is essential to systematically understand the behavior of biodegradable Mg/polymer composite (BMPC) from the design, manufacturing, mechanical properties, degradation, and biological effects. In this review, we elaborate on the design concepts and manufacturing strategies of high-strength BMPC, the “structure-function” relationship between the microstructures and mechanical properties of composites, the variation in the degradation rate due to endogenous and exogenous factors, and the establishment of advanced degradation research platform. Additionally, the interplay among composite components during degradation and the biological function of composites under non-responsive/stimuli-responsive platforms are also discussed. Finally, we hope that this review will benefit future clinical applications of “structure-function” integrated biomaterials.

• By introducing the Lie algebra method, the Lie-algebra-based theory of disclination and dislocation densities has been established, which captures the rotational nature of disclinations. • The disclination-dislocation coupling equation has been rigorously formulated in a Lie algebra-based theoretical framework, which governs the interplay between disclinations and dislocations. • Quasi-in-situ electron backscattered diffraction characterizations and disclination/dislocation density analyses have been performed to evaluate the deformation mechanisms of Mg alloys at different temperatures. • The disclination-dislocation coupling equation has been used to quantify the dynamic recovery and superplastic deformation mechanisms in the metals, which provides a new insight into the microstructure-property relationship of metallic materials. Dislocations and disclinations are fundamental topological defects within crystals, which determine the mechanical properties of metals and alloys. Despite their important roles in multiple physical mechanisms, e.g., dynamic recovery and grain boundary mediated plasticity, the intrinsic coupling and correlation between disclinations and dislocations, and their impacts on the deformation behavior of metallic materials still remain obscure, partially due to the lack of a theoretical tool to capture the rotational nature of disclinations. By using a Lie-algebra-based theoretical framework, we obtain a general equation to quantify the intrinsic coupling of disclinations and dislocations. Through quasi in-situ electron backscatter diffraction characterizations and disclination/dislocation density analyses in Mg alloys, the generation, coevolution and reactions of disclinations and dislocations during dynamic recovery and superplastic deformation have been quantitatively analyzed. It has been demonstrated that the obtained governing equation can capture multiple physical processes associated with mechanical deformation of metals, e.g., grain rotation and grain boundary migration, at both room temperature and high temperature. By establishing the disclination-dislocation coupling equation within a Lie algebra description, our work provides new insights for exploring the coevolution and reaction of disclinations/dislocations, with profound implications for elucidating the microstructure-property relationship and underlying deformation mechanisms in metallic materials. [Display omitted] [ABSTRACT FROM AUTHOR]

Zirconia toughened alumina (ZTA) nanocomposite ceramics are of high interest in the field of biomedicine due to their exceptionally high hardness and wear resistance. Severe conditions of flood grinding of ZTA nanocomposite ceramics lead to numerous defects and environmental pollution. In order to solve the aforementioned bottleneck, the ternary-hybrid nanofluid minimum quantity lubrication (TNMQL) technology incorporating polyethylene glycol (PEG), water and multilayer graphene (MGNs) with excellent heat transfer and friction reduction was first proposed. The influences of flood grinding, PEG MQL (PMQL), PEG-water (10 % vol, 40 % vol, and 70 % vol) MQL (PWMQL) and TNMQL on the removal mechanism and grinding performances of ZTA nanocomposite ceramics were investigated in this paper. The results showed that the cooling effect of TNMQL was not weaker than that of flood grinding. The TNMQL consumes 27.51 % and 39.30 % less energy compared to the PWMQL (70 % vol) and flood grinding. The TNMQL results in the minimum specific grinding force and surface roughness. The specific tangential grinding force Ft', the specific normal grinding force Fn'and the surface roughness Safrom 0.46 N/mm, 5.78 N/mm and 0.244 μm for flood grinding to 0.24 N/mm, 3.72 N/mm and 0.134 μm, respectively. The TNMQL grinding method resulted in the lowest surface damage, with a 62.40 % reduction in the energy proportion of the high-frequency detail signal compared to flood grinding. The material was mainly removed in ductile mode and there were no obvious cracks and material spalling on the subsurface. The TNMQL technology boasts a straightforward preparation process, environmentally friendly properties, and minimal energy usage. This research demonstrated the feasibility of machining ZTA nanocomposite ceramics using TNMQL.

We introduce HiSC4D, a novel Human-centered interaction and 4D Scene Capture method, aimed at accurately and efficiently creating a dynamic digital world, containing large-scale indoor-outdoor scenes, diverse human motions, rich human-human interactions, and human-environment interactions. By utilizing body-mounted IMUs and a head-mounted LiDAR, HiSC4D can capture egocentric human motions in unconstrained space without the need for external devices and pre-built maps. This affords great flexibility and accessibility for human-centered interaction and 4D scene capturing in various environments. Taking into account that IMUs can capture human spatially unrestricted poses but are prone to drifting for long-period using, and while LiDAR is stable for global localization but rough for local positions and orientations, HiSC4D employs a joint optimization method, harmonizing all sensors and utilizing environment cues, yielding promising results for long-term capture in large scenes. To promote research of egocentric human interaction in large scenes and facilitate downstream tasks, we also present a dataset, containing 8 sequences in 4 large scenes (200 to 5,000 $\text{m}^{2}$m2), providing 36 k frames of accurate 4D human motions with SMPL annotations and dynamic scenes, 31k frames of cropped human point clouds, and scene mesh of the environment. A variety of scenarios, such as the basketball gym and commercial street, alongside challenging human motions, such as daily greeting, one-on-one basketball playing, and tour guiding, demonstrate the effectiveness and the generalization ability of HiSC4D. The dataset and code will be publicly available for research purposes.

Effective Service Function Chains (SFCs) mapping and Virtual Network Functions (VNFs) scheduling are crucial to ensure high-quality service provision for Internet of Things (IoT) tasks. Meeting the varying demands of multiple SFCs poses a significant challenge, particularly when working with the limited resources available in edge computing networks. Most existing working focuses on uniformly mapping and scheduling service requests in a batch processing manner within a given time period, without taking the diversity and priority of VNFs into account. When there is a sudden surge in demand, the issues of VNF queueing waiting and resources imbalance become prominent. To address the mentioned issues, this paper proposes a Deep Reinforcement Learning (DRL)-based VNF cooperative scheduling framework with priority-weighted delay. In light of the urgency of VNFs with higher priorities and the limitations of available resources, we begin by modeling an average queuing delay with priority weight based on the shortest remaining time priority technique. We then formulate a mathematical optimization problem to minimize the modeled delay in VNF scheduling process while providing suitable multidimensional resources in the edge network. Finally, a DRL method with experience replay and target Q-network is designed to effectively obtain the optimal solutions of the optimization problem from experience. The experimental results show that our proposed method outperforms its peers in terms of SFC request acceptance, delay, load balance, and resource utilization.

Aviation kerosene acts as the main fuel for civil and military aircrafts. In order to study the explosion fireball distribution and thermal radiation effects of aviation kerosene storage tank, the dynamic explosion test system was established. The development law of explosion fireball and the thermal radiation characteristics in the presence of various oil volumes (empty, bottom, half and full oil) under the dynamic impact of projectile were studied. The dynamic explosion process was divided into four stages: projectile explosion, aviation kerosene jet flow, aviation kerosene deflagration and pool fire. The evaluation model (Y=aXb) for maximum diameter, maximum height, duration of fireball and oil mass was established. The fireball temperature showed a trend of ‘rise-fall-rise-oscillation attenuation’. The maximum fireball temperature and average temperature increased first and then decreased as increasing the oil volumes, with the maximum values at 1534.67 K and 1285.78 K, respectively. Based on the fireball dynamic model, the damage effect of fireball thermal radiation on personnel was analyzed, the safety radius and injury probability were obtained, and the dynamic explosion fireball thermal radiation field was constructed.

Electrocatalytic hydrogen generation driven by renewable energy sources is severely limited by the slow oxygen evolution reaction (OER). Urea-assisted alkaline hydrogen production offers a perspective approach. However, the construction of efficient and robust anode catalysts is still challenging. Herein, an amorphous/crystalline VOx/Ni/Ni3N-heterostructured catalyst grown on carbon cloth was synthesized and used as a bifunctional electrocatalyst for the hydrogen evolution reaction (HER) and urea electrooxidation reaction (UOR). Benefiting from the electronic modification of intercomponents and abundant active sites, VOx/Ni/Ni3N exhibits an excellent electrochemical performance toward the HER and UOR. Theoretical calculations confirmed that the crystalline/amorphous VOx/Ni/Ni3N heterostructure has a suitable water dissociation energy and H* adsorption energy, thereby promoting the HER process. When the UOR and HER are integrated into an electrolytic device, VOx/Ni/Ni3N requires a potential of 1.40 V to achieve a current density of 10 mA cm–2.

Libraries of collision cross-section (CCS) values have the potential to facilitate compound identification in metabolomics. Although computational methods provide an opportunity to increase library size rapidly, accurate prediction of CCS values remains challenging due to the structural diversity of small molecules. Here, we developed a machine learning (ML) model that integrates graph attention networks and multimodal molecular representations to predict CCS values on the basis of chemical class. Our approach, referred to as MGAT-CCS, had superior performance in comparison to other ML models in CCS prediction. MGAT-CCS achieved a median relative error of 0.47%/1.14% (positive/negative mode) and 1.40%/1.63% (positive/negative mode) for lipids and metabolites, respectively. When MGAT-CCS was applied to real-world metabolomics data, it reduced the number of false metabolite candidates by roughly 25% across multiple sample types ranging from plasma and urine to cells. To facilitate its application, we developed a user-friendly stand-alone web server for MGAT-CCS that is freely available at https://mgat-ccs-web.onrender.com. This work represents a step forward in predicting CCS values and can potentially facilitate the identification of small molecules when using ion mobility spectrometry coupled with mass spectrometry.

Surface wrinkling structures based on a bilayer system are widely employed in storing and encrypting specific optical information. However, constructing a stable wrinkling structure with high-level security remains an extensive challenge due to the delamination issue between the skin layer and the substrate. Herein, a double cross-linking strategy is introduced between a hydrogel layer doped with fluorescent molecules and polydimethylsiloxane to establish a stable interfacial wrinkling structure with dual-mode functionality, in which the light reflection of the wrinkles and fluorescence intensity of fluorescent molecules can be simultaneously regulated by the modulus ratio between the two layers. The spontaneous wrinkling structures with a physically unclonable function can enhance the photoluminescence emission intensity of the wrinkling area under ultraviolet radiation. Meanwhile, the skin layer constructed of acrylamide and acrylic acid copolymer protects the interfacial wrinkling patterns from the loss of a detailed structure for authentication due to external damage. The stable interfacial wrinkling structures with fluorescence can find potential applications in the fields of information storage and encryption.

Despite significantly improved clinical outcomes in EGFR-mutant lung adenocarcinoma, all patients develop acquired resistance and malignancy on the treatment of EGFR tyrosine kinase inhibitors (EGFR-TKIs). Understanding the resistance mechanisms is crucial to uncover novel therapeutic targets to improve the efficacy of EGFR-TKI treatment. Here, integrated analysis using RNA-Seq and shRNAs metabolic screening reveals glutathione S-transferase omega 1 (GSTO1) as one of the key metabolic enzymes that is required for EGFR-TKIs resistance in lung adenocarcinoma cells. Aberrant upregulation of GSTO1 confers EGFR-TKIs resistance and tumor metastasis in vitro and in vivo dependent on its active-site cysteine 32 (C32). Pharmacological inhibition or knockdown of GSTO1 restores sensitivity to EGFR-TKIs and synergistically enhances tumoricidal effects. Importantly, nucleophosmin 1 (NPM1) cysteine 104 is deglutathionylated by GSTO1 through its active C32 site, which leads to activation of the AKT/NF-κB signaling pathway. In addition, clinical data illustrates that GSTO1 level is positively correlated with NPM1 level, NF-κB-mediated transcriptions and progression of human lung adenocarcinoma. Overall, our study highlights a novel mechanism of GSTO1 mediating EGFR-TKIs resistance and malignant progression via protein deglutathionylation, and GSTO1/NPM1/AKT/NF-κB axis as a potential therapeutic vulnerability in lung adenocarcinoma.

Micro-electro-mechanical system (MEMS) sensors have been widely used in the automotive field owing to their small size, low cost, and high reliability. Various defects are caused by complicated environments and multistep processes in the manufacturing process of automotive MEMS pressure sensors. These defects are often small in size but can significantly impact the performance of products. The traditional detection method is inefficient and inaccurate. In this study, an improved you only look once v8 (YOLOv8) C2f-RFCBAMConv (CR) network is proposed to detect the defects of the automotive MEMS pressure sensors. The network is an improved version of YOLOv8. The faster version of the CSP bottleneck with two convolutions (C2f) and receptive-field convolutional block attention (RFCBAMConv) modules are applied. The ablation experiments confirm the impact of C2f and RFCBAMConv modules on network performance. The improved YOLOv8-CR network can effectively enhance the accuracy and speed of defect detection of the automotive MEMS pressure sensor. Furthermore, comparisons are made between the improved YOLOv8-CR network and other networks such as YOLOv8-large, YOLOv5, and faster region-based convolutional neural network (Fast-RCNN), demonstrating its effectiveness in identifying five types of defects in the automotive MEMS pressure sensors. The improved YOLOv8-CR exhibits considerably superior defect detection capabilities with a mean average precision (mAP) of 94.98% and a single picture detection time of 42.68 ms on the test set. The improved YOLOv8-CR network is helpful in realizing real-time and accurate online monitoring of product quality in the key process of the automotive MEMS pressure sensors.

This study evaluated the bioequivalence of the newly developed dapoxetine hydrochloride tablet relative to the marketed reference product by comparing their pharmacokinetic profiles under fasted and fed conditions. A total of 60 healthy Chinese male subjects participated in a single‐center, 2‐period, 2‐sequence, randomized, open‐label, self‐crossover study with a washout period of 14 days, 30 in the fasted group and 30 in the fed group. Following a single 30‐mg oral dose of the test or reference dapoxetine formulation, blood samples were collected before dosing to 72 hours after dosing. Liquid chromatography‐tandem mass spectrometry was performed to measure plasma concentration of dapoxetine and determine pharmacokinetic parameters through noncompartmental analysis. The vital signs and adverse events were also monitored during the study. The 90% confidence intervals of the geometric mean ratios for maximum plasma concentration, area under the plasma concentration‐time curve from time 0 to the last concentration time, and area under the plasma concentration‐time curve from time 0 extrapolated to infinity of the 2 dapoxetine formulations completely fell within the regulatory criteria for bioequivalence of 80%‐125%. In addition, both dapoxetine hydrochloride formulations were generally well tolerated. The generic dapoxetine hydrochloride tablet was bioequivalent to the marketed reference product in healthy Chinese men with no discernible safety differences.

The development of sixth-generation (6G) wireless communication systems brings significant challenges in channel modeling. Conducting channel measurements for 6G communications is highly expensive and cannot cover all scenarios and frequency bands. Moreover, existing conventional channel models fail to accurately predict channel characteristics in unknown frequency band. As a result, predictive channel modeling has emerged as a promising solution for addressing these challenges in 6G channel modeling. In this study, we propose a frequency domain predictive channel model that combines an autoencoder with a coupling Convolution Gated Recurrent Unit (Conv-GRU) cells. The proposed model aims to predict channel characteristics in unknown frequency bands. The proposed predictive channel model is validated by using data collected from multiple frequency bands channel measurements. To evaluate its performance, several commonly used prediction networks, i.e., a general LSTM network, a GRU-based predictive network, and a Conv-LSTM-based predictive network, are conducted as benchmarks for comparison. Based on evaluation results, our proposed predictive channel model achieves the highest level of accuracy in predicting channels. Additionally, we provide a performance bound for extrapolation predictability using a Ray Tracing simulator.

Trichogramma, a genus of egg parasitoid wasps, are widely used as biological control agents and serve as model organisms in parasitoid research. Despite their significance, the understanding of RNA interference (RNAi) in Trichogramma remains very limited. We identified RNAi-associated genes by bioinformatic approaches and experimentally assessed the feasibility of gene knockdown via RNAi for gene functional studies, as well as the susceptibility of T. dendrolimi to envionmental RNAi (dsRNAs targeting 12 genes ). We found that Trichogramma genomes contain a complete set of genes in the RNAi pathway and exhibit extensive gene expansion of dsRNase, which may influence RNAi efficiency by degrading dsRNA. We demonstrated successful RNAi through pupal microinjection in T. dendrolimi Matsumura, providing a technical approach for future gene functional studies. In addition, we observed no evidence of susceptibility to environmental RNAi in either T. dendrolimi adults or larvae. This low environmental RNAi sensitivity in Trichogramma could suggest a reduced risk of RNAi-based pest management strategies affecting nontarget Trichogramma populations. Overall, this study presents a technical approach for conducting gene functional studies in Trichogramma and provides a foundation for evaluating the nontarget effects of RNAi-based pest control strategies on Trichogramma.

Continuous rotation of a fragile, photosensitive microrod in a safe, flexible way remains challenging in spite of its importance to microelectro-mechanical systems. We propose a photovoltaic strategy to continuously rotate a fragile, fluorescent microrod on a LiNbO3/Fe (LN/Fe) substrate using a continuous wave visible (473 nm) laser beam with an ultralow power (few tens of μW) and a simple structure (Gaussian profile). This strategy does not require the laser spot to cover the entire microrod nor does it result in a sharp temperature rise on the microrod. Both experiments and simulation reveal that the strongest photovoltaic field generated beside the laser spot firmly traps one corner of the microrod and the axisymmetric photovoltaic field exerts an electrostatic torque on the microrod driving it to rotate continuously around the laser spot. The dependence of the rotation rate on the laser power indicates contributions from both deep and shallow photovoltaic centers. This rotation mode, combined with the transportation mode, enables the controllable movement of an individual microrod along any complex trajectory with any specific orientation. The tuning of the end-emitting spectrum and the photothermal cutting of the fluorescent microrod are also realized by properly configuring the laser illumination. By taking a microrod as the emitter and a polystyrene microsphere as the focusing lens, we demonstrate the photovoltaic assembly of a microscale light-source system with both spectrum and divergence-angle tunabilities, which are realized by adjusting the photoexcitation position along the microrod and the geometry relationship in the system, respectively.

Organophosphorus–fluorine compounds are of significant utility across biology, pharmacy, and chemical synthesis. Here, we introduce a photocatalyzed oxidative-fluorination approach employing SF6as a formal electrophilic fluorinating reagent. It offers an innovative pathway to forge P(O)–F bonds. Notably, sulfur hexafluoride plays a dual role as both the oxidant and the fluorinating reagent under mild conditions in this transformation. Meanwhile, this method contributes to environmental sustainability by consuming a notorious greenhouse gas, underscoring the ecological benefits of our approach.

• The microstructure of Al-Mn-Si alloy with and without refiner was investigated under sub-rapid solidification (cooling rate 102 to 103 K/s). • The grain size is decreased from millimeters to 73 µm in Al-Mn-Si with Al-Ti-B refiner. • The columnar to equiaxed transition occurs under high temperature gradient due to the presence of a large amount of TiB2 nuclei. • The constitutional undercooling still accounts for the largest proportion (83.7 %) in the total undercooling under sub-rapid solidification. • TiB2 particles are more effective under sub-rapid solidification than slow solidification due to high total undercooling. The mechanical properties of as-cast metallic materials depend strongly on the size and shape of grains, which are critical microstructural parameters dictated by the interplay of nucleation and growth of crystalline solids during solidification. In our experiments, the microstructure transition from coarse columnar crystals into fine equiaxed crystals for dilute Al-Mn-Si alloys was achieved by using sub-rapid solidification with the addition of Al-5Ti-1B grain refiner. The average grain size of Al alloy was reduced from a millimeter size to 73 µm. Through temperature gradient calculation, we found that the acquisition of fine equiaxed crystals could be attributed to the existence of a high number density of TiB 2 , acting as effective nucleation sites with an increase in total undercooling. Furthermore, the curvature supercooling, constitutional undercooling, thermal undercooling, and kinetic undercooling during sub-rapid solidification were quantitatively determined for given solidification rates. Our results showed that constitutional undercooling, rather than thermal undercooling, was primarily responsible for the formation of fine equiaxed grains, with the assistance of Al-5Ti-1B grain refiner. This work provides a new insight into the grain refining mechanism under sub-rapid solidification. [Display omitted] [ABSTRACT FROM AUTHOR]

AbstractObjective:Head-up tilt test (HUTT) is an important tool in the diagnosis of pediatric vasovagal syncope. This research will explore the relationship between syncopal symptoms and HUTT modes in pediatric vasovagal syncope.Methods:A retrospective analysis was performed on the clinical data of 2513 children aged 3–18 years, who were diagnosed with vasovagal syncope, from Jan. 2001 to Dec. 2021 due to unexplained syncope or pre-syncope. The average age was 11.76 ± 2.83 years, including 1124 males and 1389 females. The patients were divided into the basic head-up tilt test (BHUT) group (596 patients) and the sublingual nitroglycerine head-up tilt test (SNHUT) group (1917 patients) according to the mode of positive HUTT at the time of confirmed pediatric vasovagal syncope.Results:(1) Baseline characteristics: Age, height, weight, heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and composition ratio of syncope at baseline status were higher in the BHUT group than in the SNHUT group (all P< 0.05). (2) Univariate analysis: Age, height, weight, HR, SBP, DBP, and syncope were potential risk factors for BHUT positive (all P< 0.05). (3) Multivariate analysis: syncope was an independent risk factor for BHUT positive, with a probability increase of 121% compared to pre-syncope (P<0.001).Conclusion:The probability of BHUT positivity was significantly higher than SNHUT in pediatric vasovagal syncope with previous syncopal episodes.