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Direction of arrival (DOA) estimation is widely used in many applications. Traditional DOA estimation generally adopts the second-order statistics and the uniform linear array (ULA) structure. However, the second-order statistics and the uniform array structure restrict the number of DOAs that can be accurately estimated. In addition, the performance of the second-order statistics-based methods is sensitive to noise. As the wireless environment is becoming increasingly complex with the advent of the 5G era, the propagation channel can be composed of numerous paths. When the number of paths exceeds the size of the antenna array, DOA estimation becomes an under-determined problem, which the second-order statistics-based traditional methods fail to deal with. In order to address the under-determined DOA estimation problem, this paper proposes a method based on higher-order statistics and non-uniform array structure. Higher-order statistics-based methods can not only expand the array aperture, but also suppress the additive Gaussian noise. However, if combined with a uniform array structure, the degrees of freedom of the expanded array is limited. Therefore, we further adopt a non-uniform array structure and optimize its structure. Consequently, the proposed method is capable of achieving $M^{2}$ -level DOA estimation with an M-element array, while simultaneously providing good robustness to noise. For instance, using the proposed method, the DOAs of 20 incoming wave directions can be accurately estimated with a 6-antenna non-uniform array when the signal-to-noise ratio is as low as 0 dB.

Reconfigurable intelligent surface (RIS) has garnered escalating attention in recent years. However, existing works overlook the phase delivery and typically analyze the coverage performance of the RIS under a given location of the RIS, which may limit the coverage improvement of the RIS-aided systems. In this letter, our aim is to investigate how far the coverage distance is by maximizing the sum of the coverage radius for RIS-aided systems with phase delivery. The resulting optimization problem can be solved by reformulating it as a convex problem via variable transformation and block coordinate descent. To get more insight into the coverage range, we obtain the closed-form solutions of the coverage radii and investigate number configuration for the case of deterministic coverage. Furthermore, we relax the deterministic coverage to the probabilistic coverage and analyze how many reflecting elements are needed to achieve a coverage probability close to 1. The results can provide design guidelines for RIS deployment/location and user distribution areas. Numerical simulations demonstrate the benefit of the proposed algorithm compared with existing ones in terms of the coverage radius.

Modern real-time systems often involve numerous computational tasks characterized by intricate dependency relationships. Within these systems, data propagate through cause–effect chains from one task to another, making it imperative to minimize end-to-end latency to ensure system safety and reliability. In this article, we introduce innovative nonpreemptive scheduling techniques designed to reduce the worst-case end-to-end latency and/or time disparity for task sets modeled with directed acyclic graphs (DAGs). This is challenging because of the noncontinuous and nonconvex characteristics of the objective functions, hindering the direct application of standard optimization frameworks. Customized optimization frameworks aiming at achieving optimal solutions may suffer from scalability issues, while general heuristic algorithms often lack theoretical performance guarantees. To address this challenge, we incorporate the “1-opt” concept from the optimization literature (Essentially, 1-opt means that the quality of a solution cannot be improved if only one single variable can be changed) into the design of our algorithm. We propose a novel optimization algorithm that effectively balances the tradeoff between theoretical guarantees and algorithm scalability. By demonstrating its theoretical performance guarantees, we establish that the algorithm produces 1-opt solutions while maintaining polynomial run-time complexity. Through extensive large-scale experiments, we demonstrate that our algorithm can effectively reduce the latency metrics by 20% to 40%, compared to state-of-the-art methods.

ABSTRACTTo explore the correlation between pure tone audiometry at all frequencies and distortion product otoacoustic emission (DPOAE) of hidden hearing loss (HHL). The workers exposed to noise from a factory in Zhangjiakou (noise exposure group, n = 73) and normal young and middle-aged people recruited by society (control group, n = 77) from August 2021 to April 2022 were study subjects, and all of them underwent audiometry in clinic. Compared with the control group, the noise exposure group had significantly higher threshold by extended high-frequency audiometry at all frequencies and higher signal-to-noise ratio threshold (SNR50) (all P < 0.001), and higher amplitude ratio of SP to AP of the waveform induced by short sound at 96, 90, 80 and 70 dB nHL and lower SNR at 6 kHz and above (all P < 0.05).         The pure tone audiometry was correlated with DPOAE test results at the frequencies of 6, 8, 9 and 10 kHz (P < 0.001). The results of speech audiometry in noise were related to the average SNR of DPOAE test at each frequency (P = 0.026,r = −0.265). The ratio of SP to AP in electrocochleogram recorded at the intensity of 96 dB nHL was correlated with the average SNR at 6 kHz and above in DPOAE test (P = 0.018,r = −0.461), and with the average auditory threshold at each frequency in extended high-frequency audiometry (P = 0.032, r = 0.421). DPOAE has certain value in detecting HHL.

The development of the magnetic manipulating system is essential for applications of magnetically actuated miniature robots in biomedical practice, such as targeted therapy and precise surgery. However, the workspaces of existing magnetic manipulating systems for miniature robots are mostly insufficient to manipulate miniature robots inside human bodies. The present study proposes an innovative electromagnets-based manipulating system, TrinityMag, which can produce dynamic three-dimensional (3D) magnetic fields in a human-scale spherical workspace with a 2.6 m diameter. The magnetic field of a single electromagnet is simulated, and a new calibration technic is designed based on deep learning networks. Then, the arrangement of three electromagnets is optimized to produce maximal 3D arbitrary magnetic fields with limited currents. Moreover, a target-tracking algorithm is developed so that the TrinityMag can track the miniature robot in real time. Finally, the TrinityMag is validated in experiments to manipulate a soft millirobot to move in human-scale tortuous tracks with two types of locomotions. The maximum speed of the soft millirobot reaches 11.05 body length/s. Our work contributes to a significant increment in the workspace of the electromagnets-based manipulating system for miniature robots. We further expect that the TrinityMag could push the applications of miniature robots from laboratory to clinical practice.

K-Means algorithm is a popular clustering method. However, it has two limitations: 1) it gets stuck easily in spurious local minima, and 2) the number of clusters $k$k has to be given a priori. To solve these two issues, a multi-prototypes convex merging based K-Means clustering algorithm (MCKM) is presented. First, based on the structure of the spurious local minima of the K-Means problem, a multi-prototypes sampling (MPS) is designed to select the appropriate number of multi-prototypes for data with arbitrary shapes. Then, a merging technique, called convex merging (CM), merges the multi-prototypes to get a better local minima without $k$k being given a priori. Specifically, CM can obtain the optimal merging and estimate the correct $k$k. By integrating these two techniques with K-Means algorithm, the proposed MCKM is an efficient and explainable clustering algorithm for escaping the undesirable local minima of K-Means problem without given $k$k first. Two theoretical proofs are given to guarantee that the cost of MCKM (MPS+CM) can achieve a constant factor approximation to the optimal cost of the K-Means problem. Experimental results performed on synthetic and real-world data sets have verified the effectiveness of the proposed algorithm.

FeCoNiMnCr porous high-entropy alloys were prepared using the activation reaction sintering method with Fe, Co, Ni, Mn, and Cr as raw materials. The oxidation behaviors of FeCoNiMnCrxporous high-entropy alloys with different Cr contents, such as pore structure, the surface oxide film phase composition, structure, and morphology, were characterized by X-ray diffraction (XRD), electron probe micro analysis (EPMA), energy dispersive X-ray spectroscopy (EDS), and pore tester after high-temperature oxidation at 800–1000 °C. The results indicate that the initial porous high-entropy alloy comprises a single FCC solid solution phase. With the increase in Cr content, the average pore diameter, average porosity, and air permeability of the porous high-entropy alloy also increase. The oxidation kinetics of the porous high-entropy alloy after exposure to temperatures between 800 °C and 1000 °C follows a single-stage parabolic evolution law. At the same oxidation temperature, With the increase of Cr content, the oxidation gains gradually increased, at the same Cr content at different temperatures, the antioxidant performance of 1000 °C was the weakest, followed by 800 °C, and 900 °C showed excellent antioxidant performance. The oxidation products are mainly Mn2O3, Mn3O4, (Mn, Cr)3O4, Cr2O3and Fe2O3.

The aim of this study was to investigate the value of genetic testing using exome sequencing (ES) in oligohydramnios pregnancies with or without other structural abnormalities.

Dysregulated metabolism, cell death, and inflammation contribute to the development of metabolic dysfunction-associated steatohepatitis (MASH). Pyroptosis, a recently identified form of programmed cell death, is closely linked to inflammation. However, the precise role of pyroptosis, particularly gasdermin-E (GSDME), in MASH development remains unknown. In this study, we observed GSDME cleavage and GSDME-associated interleukin-1β (IL-1β)/IL-18 induction in liver tissues of MASH patients and MASH mouse models induced by a choline-deficient high-fat diet (CDHFD) or a high-fat/high-cholesterol diet (HFHC). Compared with wild-type mice, global GSDME knockout mice exhibited reduced liver steatosis, steatohepatitis, fibrosis, endoplasmic reticulum stress, lipotoxicity and mitochondrial dysfunction in CDHFD- or HFHC-induced MASH models. Moreover, GSDME knockout resulted in increased energy expenditure, inhibited intestinal nutrient absorption, and reduced body weight. In the mice with GSDME deficiency, reintroduction of GSDME in myeloid cells—rather than hepatocytes—mimicked the MASH pathologies and metabolic dysfunctions, as well as the changes in the formation of neutrophil extracellular traps and hepatic macrophage/monocyte subclusters. These subclusters included shifts in Tim4+or CD163+resident Kupffer cells, Ly6Chipro-inflammatory monocytes, and Ly6CloCCR2loCX3CR1hipatrolling monocytes. Integrated analyses of RNA sequencing and quantitative proteomics revealed a significant GSDME-dependent reduction in citrullination at the arginine-114 (R114) site of dynamin-related protein 1 (Drp1) during MASH. Mutation of Drp1 at R114 reduced its stability, impaired its ability to redistribute to mitochondria and regulate mitophagy, and ultimately promoted its degradation under MASH stress. GSDME deficiency reversed the de-citrullination of Drp1R114, preserved Drp1 stability, and enhanced mitochondrial function. Our study highlights the role of GSDME in promoting MASH through regulating pyroptosis, Drp1 citrullination-dependent mitochondrial function, and energy balance in the intestine and liver, and suggests that GSDME may be a potential therapeutic target for managing MASH.

We provide a semi-empirical model for straightforward and rapid evaluation of the effective index, confinement loss and birefringence of mainstream birefringent anti-resonant hollow-core fibers (Bi-ARFs). It is based on the observation that the polarization-dependent performance of Bi-ARF shows a weak reliance on the order or type of anti-resonant band. The comparison with the simulation results validates the effectiveness of this model. Furthermore, the model provides an optimal bi-thickness selection strategy to maximize birefringence while minimizing loss penalty. We believe that this model offers physical insight of the birefringence mechanism in ARFs and can accelerate the development of high-performance BI-ARFs for polarization-sensitive applications.

Metal–organic frameworks (MOF) are a class of crystalline, porous materials possessing well-defined channels that have widespread applications across the sustainable landscape. Analogous to zeolites, these materials are well-suited for adsorption processes targeting environmental contaminants. Herein, a zirconium MOF, UiO-66, was functionalized with ferrocene for the selective removal of oxyanion contaminants, specifically NO3–, SO42–, and PO43–. Electrochemical oxidation of the embedded ferrocene pendants induces preferential adsorption of these oxyanions, even in the presence of Cl–in a 10-fold excess. Anion selectivity strongly favoring PO43–(Soxy/comp= 3.80) was observed following an adsorption trend of PO43–> SO42–> NO3–> (10-fold)Cl–in multi-ion solution mixtures. The underlying mechanisms responsible for ion selectivity were elucidated by performing ex situX-ray photoelectron spectroscopy (XPS) on the heterogeneous electrode surface postadsorption and by calculating the electronic structure of various adsorption configurations. It was eventually shown that oxyanion selectivity stemmed from strong ion association with a positively charged pore interior due to the spatial distribution of charge by oxygen constituents. While ferrocenium provided the impetus for ion migration–diffusion, it was the formation of stable complexes with zirconium nodes that ultimately contributed to selective adsorption of oxyanions.

Methane emissions from paddy fields can increase under future warming scenarios. Nevertheless, a comprehensive comparison of the temperature sensitivity of methane-related microbial processes remains elusive. Here, we revealed that the temperature sensitivity of methane production (activation energy (Ea) = 0.94 eV; 95% confidence interval (CI), 0.78–1.10 eV) and aerobic (Ea= 0.49 eV; 95% CI, 0.34–0.65 eV) and anaerobic (Ea= 0.46 eV; 95% CI, 0.30–0.62 eV) methane oxidation exhibited notable spatial heterogeneity across 12 Chinese paddy fields spanning 35° longitude and 18° latitude. In addition, the Eavalues of aerobic and anaerobic methane oxidation were significantly positively and negatively correlated to the latitude, respectively, while there was no significant correlation between the Eaof methane production and the latitude. Overall, there were no soil factors that had a significant effect on the Eaof methane production. The Eaof aerobic methane oxidation was primarily influenced by the contents of ammonium and clay, whereas the Eaof anaerobic methane oxidation was mainly influenced by the conductivity. Despite the variation, the overall temperature sensitivity of methane production was significantly higher than that of oxidation at a continental scale; therefore, an increase in the emission of methane from paddy fields will be predicted under future warming. Taken together, our study revealed the characteristics of temperature sensitivity of methane production and aerobic and anaerobic methane oxidation simultaneously in Chinese paddy fields, highlighting the potential roles of soil factors in influencing temperature sensitivity.

Objective: The prognosis of glioblastoma is poor, and therapy-resistance is largely attributed to intratumor hypoxia. Hyperbaric oxygen (HBO) effectively alleviates hypoxia. However, the sole role of HBO in glioblastoma remains controversial. We previously reported that HBO can promote apoptosis, shorten protrusions, and delay growth of glioblastoma, but the molecular mechanism is unclear. We aimed to test candidate genes in HBO-exposed glioblastoma cells and to analyze their correlation with the survival of glioblastoma patients. Methods: Glioblastoma cell lines exposed to repetitive HBO or normobaric air (NBA) were collected for RNA isolation and microarray data analysis. GO analysis, KEGG pathway analysis and survival analysis of the differentially expressed genes (DEGs) were performed. Results: HBO not only inhibited hypoxia-inducing genes including CA9, FGF11, PPFIA4, TCAF2 and SLC2A12, but also regulated vascularization by downregulating the expression of COL1A1, COL8A1, COL12A1, RHOJ and FILIP1L, ultimately attenuated hypoxic microenvironment of glioblastoma. HBO attenuated inflammatory microenvironment by reducing the expression of NLRP2, CARD8, MYD88 and CD180. HBO prevented metastasis by downregulating the expression of NTM, CXCL12, CXCL13, CXCR4, CXCR5, CDC42, IGFBP3, IGFBP5, GPC6, MMP19, ADAMTS1, EFEMP1, PTBP3, NF1 and PDCD1. HBO upregulated the expression of BAK1, PPIF, DDIT3, TP53I11 and FAS, whereas downregulated the expression of MDM4 and SIVA1, thus promoting apoptosis. HBO upregulated the expression of CDC25A, MCM2, PCNA, RFC33, DSCC1 and CDC14A, whereas downregulated the expression of ASNS, CDK6, CDKN1B, PTBP3 and MAD2L1, thus inhibiting cell cycle progression. Among these DEGs, 17 indicator-genes of HBO prolonging survival were detected. Conclusions: HBO is beneficial for glioblastoma. Glioblastoma patients with these predictive indicators may prolong survival with HBO therapy. These potential therapeutic targets especially COL1A1, ADAMTS1 and PTBP3 deserve further validation.

Aerial intelligent reflection surface offers opportunities to increase the data freshness in challenging environments. In this paper, we investigate the integration of unmanned aerial vehicles (UAVs) and intelligent reflection surface (IRS) to assist the transmission from sensor nodes (SNs) to a base station (BS). Our goal is to minimize the Age of Synchronization (AoS) by optimizing the UAV trajectory, SNs schedule, and IRS phase-shift. An actor-critic optimization algorithm is developed to solve the mixed-integer non-convex optimization problem. Numerical results demonstrate the algorithm's superior performance in reducing the AoS compared to existing methods.

Combing unmanned aerial vehicles (UAVs) with nonorthogonal multiple access (NOMA) in ultra-reliable and low latency communication (uRLLC) is deemed as a new service category of 5G wireless networks. The openness nature of wireless environment makes security of information transmission unprecedentedly importance. Traditional cryptography methods cannot cater to uRLLC due to high complex design and processing delay. As a result, physical layer security (PLS) has been designed to secure information transmission by exploiting intrinsic property of wireless channel. However, many prior works ignored the role of UAV in study of PLS in uRLLC, and mobility of UAV deserves further investigation. This paper studies security and reliability performances of UAV-based uRLLC by using NOMA, where two kinds of users with different requirements are served, i.e., users with privacy and low latency requirement (PLR), and users with reliability and low latency requirement (RLR). With the tools of stochastic geometry, we formulate the secrecy rate, a popular metric in study of PLS, by establishing the closed-form expressions for static and dynamic UAV. Key system parameters (e.g., transmit power allocation) are optimized for secrecy rate maximization. Simulations evaluate theoretical analysis and give a significant insight into a positive secrecy rate for the network designer.

Cognitive radio has been suggested as a solution to address the shortage of accessible spectrum caused by the significant demand for wideband services and the fragmentation of spectrum resources. Nevertheless, the sensing performance is rather inadequate owing to the diminished sensing signal-to-noise ratio, especially in complex environments with severe channel fading. Fortunately, applying reconfigurable intelligent surfaces (RIS) for spectrum sensing can efficiently address the aforementioned problems. However, the passive RIS may experience the “double fading” effect, seriously limiting the effectiveness of passive RIS-aided spectrum sensing. Thus, a crucial challenge is how to fully exploit the potential advantages of the RIS and further improve the sensing performance. In this paper, we utilize the passive and active RIS to further enhance detection probability and subsequently develop two different problems for both the passive and active RIS to achieve the detection probability maximization. Considering the complexity of the above problems, we design a one-stage optimization algorithm featuring inner approximation and a two-stage optimization algorithm that employs the bisection method to derive corresponding solutions, and further establish the upper bound and lower bound of the detection probability by employing the Rayleigh quotient. Moreover, we separately explore how many reflecting elements are needed for passive RIS and active RIS and investigate the detection performance comparison of the two types (passive and active) of RIS. Simulation results show that the proposed algorithms outperform existing algorithms under the same parameter configuration, and achieve a detection probability close to 1 with even fewer reflecting elements or antennas than existing schemes.

Endocytosis and recycling control the uptake and retrieval of various materials, including membrane proteins and lipids, in all eukaryotic cells. These processes are crucial for cell growth, organization, function and environmental communication. However, the mechanisms underlying efficient, fast endocytic recycling remain poorly understood. Here, by utilizing a biosensor and imaging-based screening, we uncover a recycling mechanism that couples endocytosis and fast recycling, which we name the clathrin-associated fast endosomal recycling pathway (CARP). Clathrin-associated tubulovesicular carriers containing clathrin, AP1, Arf1, Rab1 and Rab11, while lacking the multimeric retrieval complexes, are generated at subdomains of early endosomes and then transported along actin to cell surfaces. Unexpectedly, the clathrin-associated recycling carriers undergo partial fusion with the plasma membrane. Subsequently, they are released from the membrane by dynamin and re-enter cells. Multiple receptors utilize and modulate CARP for fast recycling following endocytosis. Thus, CARP represents a previously unrecognized endocytic recycling mechanism with kiss-and-run membrane fusion.

A large amount of waste contact is produced in the preparation of organosilicon monomer. The current researches are mainly focused on copper extraction, with little attention paid to silicon. Moreover, the large accumulation of decoppered residue from organosilicon waste causes environmental pollution and resource waste. In this study, a novel beneficiation-metallurgy combination process was developed for the extraction of silicon from decoppered residue, which was defined as high gradient magnetic separation followed by roasting and acid leaching. The results showed that high gradient magnetic separation was effective in separating silicon from metallic impurities. Under the optimum conditions, the removal efficiencies of metallic impurities are more than 80 %. Fe content in the non-magnetic product can be further reduced to 0.82 % by cleaning. The non-magnetic products obtained by a roughing-cleaning process are subjected to roasting and acid leaching experiments. Under the optimum conditions, the removal efficiency of C and O can reach 94.26 % and 96.32 %, respectively. The final silicon powder product meets the standard of "YS/T1109–2016", which can be reused in the production of methylchlorosilane. This process is suitable for large-scale treatment of decoppered residue and is simple enough to help solve the problem of large-scale output of organosilicon waste.

Quality management is a constant and significant concern in enterprises. Effective determination of correct solutions for comprehensive problems helps avoid increased backtesting costs. This study proposes an intelligent quality control method for manufacturing processes based on a human–cyber–physical (HCP) knowledge graph, which is a systematic method that encompasses the following elements: data management and classification based on HCP ternary data, HCP ontology construction, knowledge extraction for constructing an HCP knowledge graph, and comprehensive application of quality control based on HCP knowledge. The proposed method implements case retrieval, automatic analysis, and assisted decision making based on an HCP knowledge graph, enabling quality monitoring, inspection, diagnosis, and maintenance strategies for quality control. In practical applications, the proposed modular and hierarchical HCP ontology exhibits significant superiority in terms of shareability and reusability of the acquired knowledge. Moreover, the HCP knowledge graph deeply integrates the provided HCP data and effectively supports comprehensive decision making. The proposed method was implemented in cases involving an automotive production line and a gear manufacturing process, and the effectiveness of the method was verified by the application system deployed. Furthermore, the proposed method can be extended to other manufacturing process quality control tasks.

This paper presents a reaction kinetics model for analyzing the coal tar hydrodeoxygenation process from the perspectives of deoxygenation, hydrogenation, and cracking. The model is established based on the data from 24 sets of coal tar hydrotreatment experiments performed at different temperatures, pressures, and liquid hourly space velocity (LHSV) in a fixed-bed reactor. In the established model, the reaction kinetics equations, mass transport equations, and property calculation equations are coupled and solved simultaneously. This model can predict the distribution of H2partial pressure, H2O partial pressure, and the concentrations of oxygen-containing compounds, aromatics, and cycloalkanes along the reactor axis, under different conditions (temperature, pressure, and space velocity). The validation experiments show that the model's prediction error for hydrodeoxygenation reaction product concentration is no more than 1.1%, for hydrogenation reaction product concentration is within 6.5%, and for cracking reaction product concentration is within 8.5%.

Selective hydrogenation of acetylene (SHA) reactions is usually performed in fixed-bed reactors (FBR). The traditional SHA kinetics, when coupled with a three-dimensional computational fluid dynamics (CFD) model, requires improvement to accurately predict SHA reaction outcomes. The SHA microdynamics, when integrated with a three-dimensional CFD model, have not been comprehensively examined. In this paper, a mathematical model based on CFD was developed to simulate the reactive flow behavior of SHA in FBR using a novel OleMax100 catalyst. In this study, a 3D catalyst bed numerical simulation of FBR with coupled microdynamics description of conjugate heat transfer and surface catalytic reaction was carried out, and the accuracy of CFD at different Reynolds numbers (Re) was verified by experimental results, which showed a high degree of agreement between the model and experimental results. The effect of the ratio of tube to pellet diameter (D/d=N) on the fluid flow characteristics and SHA reaction in the bed was investigated, and the catalyst shape was considered. The simulation results show that an increase in N significantly improves the homogeneity of the flow field and the heat and mass transfer between the phases in the FBR, which leads to an increase in the conversion efficiency of acetylene. When N was increased from 4.00 to 6.67, the conversion was enhanced by 6.7 %. Increasing the Re value affects the reactivity, and the SHA reaction exists in a reaction zone where excessive residence time exacerbates the decrease in selectivity. The adopted and novel catalyst microkinetic models can provide some theoretical guidance for the optimal design of the SHA reaction and process improvement in FBR.

Pyridines and cyclopropanes are important structural units in chemistry. Herein, we introduce a photoredox-catalyzed approach for the ring opening and 1,3-oxypyridylation of aryl cyclopropanes using 4-cyanopyridines and carboxylic acids. This sequential process involves single-electron oxidation of the aryl cyclopropane, leading to nucleophilic ring opening and radical pyridylation at the benzylic position. The redox-neutral reaction exhibits high regioselectivity under mild reaction conditions, offering a broad substrate scope and wide applicability.

Tantalum (Ta) emerges as a promising element for advanced computed tomography (CT) imaging probes owing to its high X-ray attenuation coefficient and excellent biocompatibility. Nevertheless, the synthesis of renally clear Ta-based imaging probes through simple methods remains a significant challenge. Herein, we introduce a simple and gram-scale approach for the synthesis of renal-clearable Ta nanodots with high water solubility for CT imaging in vivo. The Ta nanodots, coordination polymers, are fabricated via coordination reactions involving Ta(OH)5, citric acid (CA), and hydrogen peroxide. The Ta nanodots exhibit an ultrasmall hydrodynamic diameter (2.8 nm), high water solubility (1.88 g/mL, 688 mg Ta/mL), superior X-ray absorption capacity, gram-scale production capability (>10 g in lab synthesis), renal-clearable ability, and good biocompatibility. The Ta nanodots possess superior CT imaging efficacy across diverse tube voltages, enabling highly sensitive gastrointestinal CT imaging, renal CT imaging, and CT angiography (CTA). Moreover, Ta nanodots maintain robust CT imaging capabilities even at high X-ray energies, and Ta nanodots-based spectral CT achieves metallic artifacts-minimized CTA. The proposed Ta nanodots present substantial potential as a potent CT imaging probe for diagnosing various diseases.

When X-rays and gamma rays irradiate a vacuum Compton detector (VCD), photoelectrons and Compton electrons are generated on the cavity surface, resulting in a strong internal electromagnetic pulse (IEMP) in the cavity. To quantify the characteristics of the IEMP interference on VCD response, an electromagnetic interference-coupling model for a VCD was established with computer simulation technology particle studio (CST PS). The accuracy of the model was verified under pulsed gamma conditions. The characteristics of electromagnetic interference were studied by simulation and experimental methods under a 50 ps pulsewidth electron accelerator. The results are in good agreement in time and frequency. The effects of absorbing materials to strengthen anti-electromagnetic interference of the VCD were simulated using the model. The possible sources of interference signals were discussed as well. The coupling model presented in this article provides an efficient and accurate tool for the anti-electromagnetic interference design of VCDs under pulsed X-rays, gamma ray, and electron radiation.

Mobile edge computing (MEC) has been regarded as a promising technique to support latency-sensitivity and computation-intensive serves. However, the low offloading rate caused by the random channel fading characteristic becomes a major bottleneck in restricting the performance of the MEC. Fortunately, reconfigurable intelligent surface (RIS) can alleviate this problem since it can boost both the spectrum- and energy- efficiency. Different from the existing works adopting either fully active or fully passive RIS, we propose a novel hybrid RIS in which reflecting units can flexibly switch between active and passive modes. To achieve a tradeoff between the latency and energy consumption, an optimization problem is formulated by minimizing the total cost via jointly optimizing the transmission time, the transmit power, the receive beamforming vector, the phase-shift matrix, the mode-switching factor, the amplification factor, the offloading ratio factor, and the computation ability of the user, where the constraints of the maximum energy of users, the maximum power of the RIS, the minimum computation tasks, the transmission time, the offloading ratio factor, the mode-switching factor, the unit moduli of passive units, and the computation ability are taken into account. Considering the complexity of the aforementioned problem, we develop an alternating optimization-based iterative algorithm by combining the successive convex approximation method, the variable substitution, and the singular value decomposition to obtain sub-optimal solutions. Furthermore, in order to gain more insight into the problem, we consider two special cases involving a latency minimization problem and an energy consumption minimization problem, and respectively analyze the tradeoff between the number of active and passive units. Simulation results verify that the proposed algorithm can achieve flexible mode switching and significantly outperforms existing algorithms.

The increasing privacy concerns associated with cloud-assisted image retrieval have captured the attention of researchers. However, a significant number of current research endeavors encounter limitations, including suboptimal accuracy, inefficient retrieval, and a lack of effective result verification mechanisms. To address these limitations, we propose an adaptive verifiable privacy-preserving medical image retrieval (AVPMIR) scheme in the outsourced cloud. Specifically, we utilize the convolutional neural network (CNN) ResNet50 model to extract the feature of each medical image within the dataset of the medical institution, aiming to enhance retrieval accuracy. To enhance retrieval efficiency, we build an encryption searchable index based on a mini-batch $k$k-means clustering algorithm. Furthermore, we present an index merging method in which multi-data owners build a different index tree according to different standards. To check the correctness of the returned results from the cloud server, we construct an adaptive verification framework for the obtained results based on chameleon hash and BLS signature. To provide strong security for the medical image datasets, we design an improved logistic chaotic mapping algorithm. The security analysis demonstrates that AVPMIR can defend various threat models. The experiment analysis further indicates that the AVPMIR can improve retrieval efficiency and demonstrate its practicability.

The latent world model, which efficiently represents high-dimensional observations within a latent space, has shown promise in reinforcement learning-based policies for visual control tasks. Due to a lack of clear environmental context comprehension, its applicability in a variety of contexts with unknown dynamics is constrained. We propose a prototypical context- aware dynamics (ProtoCAD) model to address this issue. This model captures local dynamics using temporally consistent latent contexts and aids generalization in visual control tasks. By grouping prototypes over historical experiences, ProtoCAD collects useful contextual information that improves model-based reinforcement learning dynamics generalization in two ways. First, to guarantee the consistency of prototype assignments for various temporal segments of the same latent trajectory, a temporally consistent prototypes regularizer is used. Then, a context representation is devised to combine the aggregated prototype with the projection embedding of latent states. According to extensive trials, ProtoCAD outperforms competing approaches in terms of dynamics generalization for visual robotic control and autonomous driving applications.

Direct pore-scale and volume-averaging numerical simulations are two methods for investigating the performance of porous volumetric solar receivers. To clarify the difference in the prediction of heat transfer processes, a direct comparison between these two methods was conducted at both steady state and transient state. The numerical models were established based on X-ray computed tomography scans and a local thermal non-equilibrium model, respectively. The empirical parameters, which are indispensable to the volume-averaging simulation, were determined by Monte Carlo ray tracing and direct pore-scale numerical simulations. The predicted outlet air temperature of the receiver by the volume-averaging simulation method corresponded satisfactorily to that in the direct pore-scale simulation. The largest discrepancies were observed when the receiver’s working temperature was elevated, with differences of 5.5% and 3.68% for the steady state and transient state simulations, respectively. However, the volume-averaging method is incapable of capturing the local temperature information of the air and porous skeleton. It underestimates the inlet temperature of the receiver, leading to an overestimation of the receiver’s thermal efficiency, with the largest difference being 6.51%. The comparison results show that the volume-averaging model is a good approximation to the pore-scale model when the empirical parameters are carefully selected.

Morphologies of the porous materials influence the processes of solar radiation transport, flow, and thermal behaviors within volumetric solar receivers. A comprehensive comparative study is conducted by applying pore scale numerical simulations on volumetric solar receivers featuring various morphologies, including Kelvin, Weaire-Phelan, and foam configurations. The idealized unit cell and X-ray computed tomography scan approaches are employed to reconstruct pore scale porous models. Monte Carlo ray tracing and pore scale numerical simulations are implemented to elucidate the radiative, flow, and thermal behaviors of distinct receivers exposed to varying thermal boundary conditions and real irradiation situations. The findings demonstrate that the foam structure exhibits greater solar radiation absorptivity, while Kelvin and Weaire-Phelan structures enhance the penetration depth under non-perpendicular solar irradiation. In comparison with Kelvin and Weaire-Phelan configurations, the foam structure presents efficient convective heat transfer, with the Weaire-Phelan structure showing pronounced thermal non-equilibrium phenomena. The variance in convective heat transfer coefficient between Kelvin and Weaire-Phelan configurations is approximately 8.4%. The foam structure exhibits higher thermal efficiency and flow resistance under non-perpendicular irradiation compared to Kelvin and Weaire-Phelan structures, attributed to its smaller pore size and intricate flow channels. An increase of 1.3% in thermal efficiency is observed with a substantial rise in pressure drop of 32.2%.

Nano zero-valent iron (nZVI) and sulfidation-modified nZVI (S-nZVI) were synthesized and used to remove hexavalent chromium (Cr(VI)) in wastewater. Characterization of the products showed that the sulfidation process significantly changed the morphology of nZVI, with enhanced crystallinity. The effects of S/Fe ratio, pH value, and reaction temperature on Cr(VI) removal were studied. A S/Fe ratio of 0.5 was the most appropriate parameter, with a removal efficiency of 98% in the condition of pH = 2. The effects of anions (Cl−, NO3−, CO32−, and SO42−) and cations (Ca2+and Mg2+) on the Cr(VI) removal efficiency were investigated, and the relevant mechanisms were discussed. The Cr(VI) removal efficiency of S-nZVI was significantly greater than that of nZVI, mainly owing to the existence of FeS layers that could protect Fe0cores and prompt electron transfer. The aging and cycling experiments showed that S-nZVI could maintain its reactivity when facing the corrosion of water, and showed cycling stability. Thus, S-nZVI is an effective and feasible agent for the remediation of Cr(VI)-contained wastewater.