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• Achieving structural modulation of nickel hydroxide modified graphene composites through the secondary reaction mechanism between ammonium ions and nickel ions. • Applying analytical methods like TEM and XPS to elucidate the reaction process and identify the suitable amounts of reactants. • Achieving full coverage of the Ku-band with maximum effective absorption bandwidth through the design of dielectric parameters. • Combining electromagnetic parameter analysis and simulation calculations to elucidate the multiple loss mechanisms in the electromagnetic dissipation process. Carbon-based materials exhibit excellent dielectric absorption properties, among which graphene has received particular attention in research of electromagnetic wave absorbing materials because of its high electrical conductivity and unique large-area, thin-layer two-dimensional structural features. However, the electromagnetic absorption performance of the material is hindered from further improvement due to its single component composition. It is influenced by the conductive network of graphene, making it challenging to achieve a balance in impedance matching and electromagnetic loss, thereby restricting its broader application. To address these challenges, we developed a series of nickel hydroxide-modified graphene composites. Through a structural composite design, we optimized overall impedance matching, introduced diverse loss mechanisms to enhance electromagnetic loss performance, and utilized a secondary reaction control method to precisely regulate the deposition of nickel hydroxide on the graphene surface, thereby achieving regulate of the composite material's electromagnetic parameters within a defined range. Under low sample filling ratios and a thin sample thickness of 1.8 mm, the effective absorption bandwidth reaches 6.5 GHz, demonstrating excellent electromagnetic absorption performance. This study provides a controllable design approach for modulating material electromagnetic parameters by influencing the reaction process. It also offers a design method for composites with an outstanding electromagnetic loss mechanism. [ABSTRACT FROM AUTHOR]

Awake prone positioning (APP) can reportedly reduce the need for intubation and help improve prognosis of patients with acute hypoxemic respiratory failure (AHRF) infected with COVID-19. However, its physiological mechanism remains unclear. In this study, we evaluated the effect of APP on lung ventilation in patients with moderate-to-severe AHRF to better understand the effects on ventilation distribution and to prevent intubation in non-intubated patients.

AimTo evaluate the short-term effects of different sunlight exposure on fundus blood flow perfusion (BFP) after near work.MethodsIn this parallel randomised controlled trial, 81 students aged 7–15 with spherical equivalent refraction between −2.00 and +3.00 diopters were randomly assigned to either a low-illuminance (4k lux) group (N=40) or high-illuminance (10k lux) (N=41). Following 1 hour indoor reading, participants had sunlight exposure matching their group’s intensity for 15 minutes. BFPs in the superficial retina, deep retina and choroid were measured at four time points: pre-reading, post-reading, 5th-minute and 15th-minute sunlight exposure.ResultsWithin the initial 5 minutes of sunlight exposure, the 10k lux group showed a tendency for decreased BFP, particularly in the choroid (superficial retina: −0.2, 95% CI −0.9 to 0.5; deep retina: −0.1, 95% CI −0.6 to 0.4; choroid: −0.4, 95% CI −0.8 to 0.0), while the 4k lux group exhibited an increase (superficial retina: 0.7, 95% CI 0.1 to 1.3; deep retina: 0.3, 95% CI −0.2 to 0.8; choroid: 0.1, 95% CI −0.2 to 0.5). From 5 to 15 minutes, BFP decreased in both groups. At the 5th-minute mark, the 10k lux group exhibited a greater decrease in choroid (10k −0.4 vs 4k 0.1, p=0.051). No significant difference was observed after 15 minutes of exposure.ConclusionHigher illuminance sunlight exposure can restore fundus BFP more rapidly than lower; however, duration remains pivotal. To prevent myopia, continuous sunlight exposure for over 15 minutes is recommended to aid in reinstating the fundus BFP increased by near work.Trial registration numberNCT05594732.

Promoting tumor cell apoptosis through the catalytic regulation of reactive oxygen species (ROS) is an ideal therapeutic option for cancer. However, a stable and controllable exogenous source of ROS is still lacking. Efficient and controllable electrocatalysis has shown tremendous potential for cancer treatment, but its key challenge lies in achieving precise, efficient, and controllable electrocatalytic ROS production at the tumor site. This study describes an electrocatalytic treatment technique for hepatocellular carcinoma (HCC) based on traditional Chinese acupuncture. By attaching a biocompatible electrocatalyst NiO-P700with optimal atomic spacing to the surface of silver acupuncture needles, a high-concentration ROS microenvironment was generated around tumor cells via ORRs when the needles were electrified. This induction led to the accumulation of inflammatory factors (IL-1β, IL-6, and TNF-α) and macrophage infiltration, accelerating tumor cell apoptosis and necrosis. Both in vitro and in vivo experiments demonstrated that the rate of ROS production can be rapidly controlled by adjusting voltage and current. Importantly, the high concentration of ROS can be safely and effectively confined to the lesion site without affecting the entire body. Our study attempted to integrate electrocatalysis and acupuncture in HCC treatment, successfully regulating NiO-P atomic spacing and enhancing ORR performance, thereby presenting a safe and reliable perspective for HCC therapy.

Sensing and communication sharing the same hardware and frequency band has become a key technology for future 5G/6G systems to achieve high-performance integrated sensing and communication (ISAC), which has attracted a great deal of attention in both academia and industry. In this letter, a device-free ISAC system based on distributed unmanned aerial vehicles (UAVs) is designed to realize the sensing of vehicles on urban roads and transmit information to base stations through wireless communication. In order to realize autonomous decision-making of UAVs and solve the problem of stochastic optimization and multi-index tradeoffs caused by a time-varying environment, we propose an algorithm based on multi-agent reinforcement learning (MARL). The simulation results verify that the proposed scheme can significantly improve the sensing performance of the UAVs and ensure communication quality with base stations. Besides, we realize the sensing fairness for users, the flight safety of UAVs, and can save energy through hyperparameter tuning.

The rapid response and high energy efficiency of the unmanned aerial vehicle (UAV) are crucial prerequisites for enabling time-sensitive and long-endurance target tracking missions, such as search and rescue, area reconnaissance, and convoy monitoring. However, existing research in target tracking primarily focuses on enhancing tracking accuracy, which struggles to adapt to tasks considering strict time constraints and energy consumption. To address these issues, this paper introduces a model-based reinforcement learning tracking strategy (MRLTS) for the UAV to minimize control costs and achieve user-specified tracking performance, including a two-stage design. In the first stage, a steady-state robust tracking controller is developed based on available model knowledge that forces the UAV to asymptotically approximate a predefined observation path in spite of uncertainties. In the second stage, an intelligent component based on the soft actor-critic (SAC) algorithm is customized to empower the UAV to strike a trade-off between prescribed tracking performance and energy consumption, wherein a skilled barrier function is constructed to interpret specified time constraints. The proposed paradigm can provide a higher sampling efficiency than SAC-based strategy. Simulation results demonstrate that our strategy outperforms benchmarks and results in a 46.3% cost-effectiveness improvement at least.

Surrounding perceptions are quintessential for safe driving for connected and autonomous vehicles (CAVs), where the Bird's Eye View has been employed to accurately capture spatial relationships among vehicles. However, severe inherent limitations of BEV, like blind spots, have been identified. Collaborative perception has emerged as an effective solution to overcoming these limitations through data fusion from multiple views of surrounding vehicles. While most existing collaborative perception strategies adopt a fully connected graph predicated on fairness in transmissions, they often neglect the varying importance of individual vehicles due to channel variations and perception redundancy. To address these challenges, we propose a novel Priority-Aware Collaborative Perception (PACP) framework to employ a BEV-match mechanism to determine the priority levels based on the correlation between nearby CAVs and the ego vehicle for perception. By leveraging submodular optimization, we find near-optimal transmission rates, link connectivity, and compression metrics. Moreover, we deploy a deep learning-based adaptive autoencoder to modulate the image reconstruction quality under dynamic channel conditions. Finally, we conduct extensive studies and demonstrate that our scheme significantly outperforms the state-of-the-art schemes by 8.27% and 13.60%, respectively, in terms of utility and precision of the Intersection over Union.

We revisit the problem of statistical sequence matching between two databases of sequences initiated by Unnikrishnan, (2015) and derive theoretical performance guarantees for the generalized likelihood ratio test (GLRT). We first consider the case where the number of matched pairs of sequences between the databases is known. In this case, the task is to accurately find the matched pairs of sequences among all possible matches between the sequences in the two databases. We analyze the performance of the GLRT by Unnikrishnan and explicitly characterize the tradeoff between the mismatch and false reject probabilities under each hypothesis in both large and small deviations regimes. Furthermore, we demonstrate the optimality of Unnikrishnan’s GLRT test under the generalized Neyman-Person criterion for both regimes and illustrate our theoretical results via numerical examples. Subsequently, we generalize our achievability analyses to the case where the number of matched pairs is unknown, and an additional error probability needs to be considered. When one of the two databases contains a single sequence, the problem of statistical sequence matching specializes to the problem of multiple classification introduced by Gutman, (1989). For this special case, our result for the small deviations regime strengthens previous result of Zhou et al., (2020) by removing unnecessary conditions on the generating distributions.

Ant colony optimization (ACO) algorithm is widely used in the instant delivery order scheduling because of its distributed computing capability. However, the order delivery efficiency decreases when different logistics statuses are faced. In order to improve the performance of ACO, an adaptive ACO algorithm based on real-time logistics features (AACO-RTLFs) is proposed. First, features are extracted from the event dimension, spatial dimension, and time dimension of the instant delivery to describe the real-time logistics status. Five key factors are further selected from the above three features to assist in problem modeling and ACO designing. Second, an adaptive instant delivery model is built considering the customer’s acceptable delivery time. The acceptable time is calculated by emergency order mark and weather conditions in the event dimension feature. Third, an adaptive ACO algorithm is proposed to obtain the instant delivery order schedules. The parameters of the probability equation in ACO are adjusted according to the extracted key factors. Finally, the Gurobi solver in Python is used to perform numerical experiments on the classical datasets to verify the effectiveness of the instant delivery model. The proposed AACO-RTLF algorithm shows its advantages in instant delivery order scheduling when compared to the other state-of-the-art algorithms.

Spur gears endure contact fatigue loads that can lead to surface damage on the gear teeth. Contact fatigue typically arises from dedendum during engineering and experiments, primarily caused by maximum sub-surface shear stress. This study presents a contact model incorporating profile-shift to examine sub-surface shear stress in relation to the occurrence of tooth root pits. Accurate calculations are performed to determine the distribution of sub-surface stress and meshing duration, considering various modification coefficients. The geometric cause affecting the shear stress near the tooth root is found. A deviation coefficient from base circle is proposed to analyze the influence of modification coefficient and pressure angle, which can be used in design reference. The investigation comprehensively explores the effects of profile-shift and pressure angle of spur gears on stress distribution.

To determine the significance of polydactyly identified on prenatal ultrasonography and provide a detailed analysis of characteristics and perinatal outcomes of fetal polydactyly. This is a retrospective cohort study of pregnancies with a postnatal diagnosis of fetal polydactyly between January 2016 and December 2023. The population was divided into 2 groups at postnatal diagnosis: the isolated polydactyly group and the nonisolated polydactyly group. Clinical data, prenatal ultrasonography, related genetic results, and postnatal outcomes were obtained. Our study cohort comprised 328 fetuses with polydactyly. The overall detection rate of polydactyly by prenatal ultrasound was 19.2%, and the first detection rate in the first‐, second‐, and third‐trimester were 0.9%, 14.6%, and 3.7%, respectively. Preaxial polydactyly (PPD) of hand was the most common type and the most common type of foot polydactyly was postaxial polydactyly (PAP) both in the isolated group and in the nonisolated group; the central polydactyly is rare. Syndactyly was the most common abnormality complicated with polydactyly. Between the nonpolydactyly group, the isolatedpolydactyly group and the nonisolated polydactyly group, there was a significant difference in perinatal outcome (P< .001). The second trimester is the best gestational age for prenatal ultrasound detection of polydactyly. Polydactyly of hand was more likely PPD, while polydactyly of foot was more likely PAP. When polydactyly is detected by routine prenatal ultrasound, detailed ultrasound examination and prenatal counseling should be performed to determine the possibility of an underlying genetic syndrome.

The fast advancement of 5G mobile communications, artificial intelligence and Big Data technology has driven the development of the Internet of Things (IoT), while at the same time putting great pressure on existing spectrum resources. Spectrum prediction, as a technology to improve spectrum utilization, faces problems such as poor modeling and ineffective use of historical spectrum sensing data. In addition, the different transmission environments and geographical locations of IoT devices lead to complex and variable wireless mobile channels, which are susceptible to interference from noise and external environments. They lead to inaccurate spectrum sensing results, which can affect the accuracy of spectrum prediction. Therefore, employing long short term memory (LSTM) and bidirectional LSTM (Bi-LSTM) neural networks proposes a dual branch neural network mobile spectrum prediction model (DB-LSTM). The historical sensing data under variable channels are fully utilized to extract forward and backward temporal feature information to overcome the effect of variable environment. First, spectrum sensing using a residual network (ResNet) module and convolutional neural network (CNN) is proposed to extract intrinsic features of signal data and improve the accuracy of spectrum sensing results. This data is employed to train the DB-LSTM model and make for spectrum prediction. The M/G/1 queuing-based model under N-Nakagami channel is used to simulate and generate the signal data set under 2FSK and QPSK modulation methods. The proposed DB-LSTM method is evaluated from three perspectives of accuracy, Error and mean square error (MSE). The results obtained show that compared to the Temporal Convolutional Networks (TCN), Transformer, and Bidirectional Encoder Representations from Transformer (BERT) models, the spectrum prediction accuracy is improved by 2%, 10%, and 1% respectively, the Error is reduced by 29%, 53%, and 4%, respectively, and the MSE is reduced by 23%, 65%, and 4%, respectively.

Coal, a carbon-rich mineral with plentiful reserves, serves not only as a fuel but also as a raw material, presenting lower pollution emissions in the latter use. From a materials chemistry standpoint, coal is a viable raw material for graphene production. This study develops a promising and sustainable method to convert coal into graphene, leveraging its unique macromolecular aromatic structure and high carbon content. The investigation includes an analysis of the lateral size, morphology, and chemical composition of coal-derived graphene using techniques such as X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy, and optical microscopy. Results confirm that coal can effectively replace natural graphite flakes in graphene production, with the derived graphene featuring three to six exfoliated layers and an oxygen content below 5.5%. While the graphene from coal shares a similar morphology to that derived from graphite, it exhibits more structural defects. Interestingly, the macroscopic size of the coal does not influence the microscopic composition and structure of the graphene. However, the thermal reduction method for oxidized graphene proves more effective at repairing structural defects than chemical reduction. Employing coal-derived graphene as a supercapacitor electrode demonstrates excellent cycling stability and ultra-high capacitance storage capacity. The H-CG-325 shows the highest discharge area-specific capacitance across various current densities. At an increased current density of 10 A/g, the H-CG-325 maintains 80.6% of its initial capacitance of 79 F/g observed at 1 A/g. Electrochemical tests reveal that coal-based graphene holds significant potential as a supercapacitor material, indicating promising applications in energy storage and conversion.A promising and sustainable method to convert coal into graphene is developed, leveraging coal’s unique macromolecular aromatic structure and high carbon content. The investigation includes an analysis of the lateral size, morphology, and chemical composition of coal-derived graphene.Graphical Abstract

In this study, bismuthene was intercalated between bilayer Ti2CTx to induce significant modifications in its electronic and phonon structures, thereby enhancing its thermoelectric properties. First-principles calculations reveal that the insertion of bismuthene transforms the Ti2CO2system from a semiconductor into a metal and optimizes the thermoelectric properties of bilayer Ti2CO2by enhancing its power factor and reducing its lattice thermal conductivity. Under the first-principles calculation parameters used in this study, the ZT of the Ti2CO2system increased from 0.12 to 0.55. Conversely, for metallic bilayer MXenes, the introduction of bismuthene led to a substantial decrease in ZT (from 0.53 to 0.11 in the Ti2C system and from 0.07 to 0.05 in the Ti2CCl2system). This study investigates the physical mechanisms underlying the enhancement of thermoelectric properties from both electronic and phononic perspectives and provides theoretical insights into the development and application of MXene-based thermoelectric materials.

Unmanned aerial vehicles (UAVs) associated with federated learning (FL) have been deemed as a prospective framework by utilizing private data generated in the edge devices. However, despite various privacy-preserving and cryptography technologies adopted at the data level, FL still faces a range of security threats to raw data considering the broadcast nature of wireless channel. In this paper, to facilitate the communication-efficiency and privacy-preservation capability, we propose a UAV-enhanced covert federated learning architecture over mmWave massive multiple input multiple output (MIMO) channel, where we harness the covert communication technique in FL in order to avoid eavesdropping of illegal wardens. To achieve a trade-off between the security performance and training cost, we formulate a joint UAV’s trajectory, transmitting power, analog beamforming as well as the required accuracy of FL optimization problem. Furthermore, we propose the multi-agent deep deterministic policy gradient (MADDPG) algorithm to solve the above-mentioned problem. Numerous simulations have been performed to demonstrate both the effectiveness and convergence of the proposed algorithm.

Structural dependency, as widely existed in complex engineering equipment, refers to the structural intervention between components so that replacing a component requires the removal of others on its disassembly path. Naturally, it is cost-efficient to cluster maintenance jobs to share disassembly time and reduce system downtime. However, maintenance management by particularly considering the disassembly structure is rarely reported in the literature. To address such deficiency, we propose an innovative dependency-specific maintenance policy, which realizes the global union of “static” scheduled block maintenance (SBM) and “dynamic” opportunistic maintenance (OM). SBM coordinates preventive maintenance jobs in conjunction, which forms the basic policy framework. OM decides which components are opportunistically replaced in case of failure, which fine-tunes the framework to further exploit the dependency. Motivated by the fractal nature of disassembly structure, we develop a dynamic-programming-based optimization approach, which enables: 1) the joint optimization of model parameters in a sequential manner, and 2) an efficient optimization applicable to large-scale equipment. We demonstrate the model through a case study in the maintenance management of high-speed train bogies. The results show that the proposed policy significantly promotes system availability by coordinating replacement intervals within the same disassembly subtree, and effectively reducing downtime by integrating SBM with OM.

In order to meet the demand for odor detection in the herbal medicine, a multisensor detection system consisting of metal-oxide sensors (MOSs) is designed. With the consideration of possible sensor drift existing in the herbal medicine datasets, a subspace projection method considering multiple constraints including the preservation of discriminative manifold is considered. In addition, discriminant extreme learning machine based on a Chebyshev polynomial weighting (DELM-CPW) is proposed to overcome the negative impact on the calibration recognition result due to the unbalanced number of samples of different classes in the source domain data. Moreover, the odor data of four classes of herbal medicine are collected by our system. They are analyzed with various machine learning algorithms, and the proposed algorithm obtains an accuracy of 0.8393 and an $F1$ score of 0.8313, which are higher than other classical algorithms, it can validate the effectiveness of the designed system and show the potential of the system to be applied in the intelligent identification of herbal medicine.

Nuclear energy is essential for the future development of countries. However, both structural and functional components of nuclear power equipment are facing severe challenges of nuclear irradiation damage after experiencing irradiation growth and irradiation creep. How to avoid irradiation damage to nuclear power equipment has become a hotspot in international research and development of surface protection technology. Deposition of protective coatings on the underlying object surface or in bulk materials has been considered as a near-term solution to enhanc functional components. Different substrate materials are selected according to other service conditions within the reactor. Suitable material selection combined with relevant optimization can significantly increase the service life of materials. This review summarizes recent research on several categories of anti-irradiation coatings prepared by physical vapor deposition technology for current industrial applications. These includes metallic, ceramic, composite and high entropy alloy coatings. The review endeavors to impart a thorough understanding of the properties of these selected anti-irradiation coatings, from the fundamental aspects of their substrate materials to their practical applications across diverse settings. It explores not only the current research progress but also the potential avenues for future advancements. Additionally, the intricate relationships between coating formulations, their resistance to irradiation, and their ultimate performance in various environments are illuminated in this paper.

Ochratoxin A (OTA) is a prevalent mycotoxin found in feed that causes significant kidney injury in animals. Further investigation was needed to devise strategies for treating OTA-induced kidney damage through the gut–kidney axis. Evidence indicates the crucial role of intestinal microbiota in kidney damage development. Inulin, a dietary fiber, protects kidneys by modulating intestinal microbiota and promoting short-chain fatty acid (SCFA) production. However, its precise mechanism in OTA-induced kidney damage remained unclear. In this study, chickens were orally administered OTA and inulin for 2 weeks to investigate inulin’s effects on OTA-induced kidney damage and underlying mechanisms. The alteration of intestinal microbiota, SCFAs contents, and SCFA receptors was further analyzed. Results demonstrated that inulin supplementation influenced intestinal microbiota, increased SCFAs production, and mitigated OTA-induced kidney damage in chickens. The importance of microbiota in mediating inulin’s renal protection was further confirmed by antibiotic and fecal microbiota transplantation experiments. Additionally, inulin exhibited antioxidant and anti-inflammatory properties, alleviating NLRP3 inflammasome activation and pyroptosis. In summary, inulin protected chickens from OTA-induced kidney damage, which might provide a potential strategy to mitigate the harmful effects of mycotoxins through prebiotics and safeguard renal health.

Automatic modulation recognition (AMR) technology enables receivers to automatically recognize the modulation type of the received signal for correct demodulation of the received data, but there are still many shortcomings to be addressed. To achieve accurate and efficient AMR, this paper proposes a data augmentation method for AMR, which can increase the amount of data by seven times and solve the problem of a small sample size more effectively than the existing methods. In addition, this paper proposes a concept of microscale, rationalizes the underwater acoustic signal into time series, and proposes a temporal feature extractor named One2Three block, which can extract temporal features of signals from three microscales. Finally, a spatial feature extractor named the Dual-Stream squeeze-and-excitation (SE) block is designed to abstract and synthesize more advanced spatial features for AMR. The recognition accuracy of the proposed method is verified with eight commonly used modulation modes in underwater acoustic communications on the datasets collected in the South China Sea and the Yellow Sea. The results show that the proposed method can achieve a recognition accuracy of 99% with a lower time and space complexity, and has high robustness to noisy data.

Metal–organic frameworks (MOFs) are one of the most promising hydrogen-storing materials due to their rich specific surface area, adjustable topological and pore structures, and modified functional groups. In this work, we developed automatically parallel computational workflows for high-throughput screening of ∼11,600 MOFs from the CoRE database and discovered 69 top-performing MOF candidates with work capacity greater than 1.00 wt % at 298.5 K and a pressure swing between 100 and 0.1 bar, which is at least twice that of MOF-5. In particular, ZITRUP, OQFAJ01, WANHOL, and VATYIZ showed excellent hydrogen storage performance of 4.48, 3.16, 2.19, and 2.16 wt %. We specifically analyzed the relationship between pore-limiting diameter, largest cavity diameter, void fraction, open metal sites, metal elements or nonmetallic atomic elements, and deliverable capacity and found that not only geometrical and physical features of crystalline but also chemical properties of adsorbate sites determined the H2storage capacity of MOFs at room temperature. It is highlighted that we first proposed the modified crystal graph convolutional neural networks by incorporating the obtained geometrical and physical features into the convolutional high-dimensional feature vectors of period crystal structures for predicting H2storage performance, which can improve the prediction accuracy of the neural network from the former mean absolute error (MAE) of 0.064 wt % to the current MAE of 0.047 wt % and shorten the consuming time to about 10–4times of high-throughput computational screening. This work opens a new avenue toward high-throughput screening of MOFs for H2adsorption capacity, which can be extended for the screening and discovery of other functional materials.

It is one of the concerns of EAP to bridge the disparity of discursive practice between novice and expert writers, but the comparison is largely made across a broad knowledge field and little is known about the likely divergence within a disciplinary divide. In this study, we explore the epistemic positioning in research writing by PhD students and expert writers across applied and pure science disciplines. By focusing on hedges and boosters as its main devices, we examine their forms and functions in the academic texts. Results show that PhD students in applied disciplines use significantly fewer epistemic devices than experts but no significant difference was found in pure sciences. Apart from the differences in linguistic choices, student writers show a preference for authorial positioning at the outset of academic texts while professionals tend to comment on research findings and establish a persuasive interpretation of their value. Additionally, novice writers are inclined to hedge on numerical information (about) but expert writers hedge on claims (likely). We discuss the results from the perspectives of disciplinary epistemology and writer identity. Pedagogical implications are raised on the teaching of epistemic positioning and the enculturation of disciplinary stance in academic writing classrooms.

Immunoassays serve as powerful diagnostic tools for early disease screening, process monitoring, and precision treatment. However, the current methods are limited by high costs, prolonged processing times (>2 h), and operational complexities that hinder their widespread application in point-of-care testing. Here, we propose a novel centrifugo-pneumatic reciprocating flowing coupled with spatial confinement strategy, termed PRCM, for ultrafast multiplexed immunoassay of pathogens on a centrifugal microfluidic platform. Each chip consists of four replicated units; each unit allows simultaneous detection of three targets, thereby facilitating high-throughput parallel analysis of multiple targets. The PRCM platform enables sequential execution of critical steps such as solution mixing, reaction, and drainage by coordinating inherent parameters, including motor rotation speed, rotation direction, and acceleration/deceleration. By integrating centrifugal-mediated pneumatic reciprocating flow with spatial confinement strategies, we significantly reduce the duration of immune binding from 30 to 5 min, enabling completion of the entire testing process within 20 min. As proof of concept, we conducted a simultaneous comparative test on- and off-the-microfluidics using 12 negative and positive clinical samples. The outcomes yielded 100% accuracy in detecting the presence or absence of the SARS-CoV-2 virus, thus highlighting the potential of our PRCM system for multiplexed point-of-care immunoassays.

Purpose To evaluate the performance of Pipeline Embolization Device (PED) in complex, wide-necked middle cerebral artery (MCA) bifurcation aneurysms.Methods We performed a retrospective review of patients treated with PED for complex, wide-necked MCA bifurcation aneurysms between August 2016 and March 2021. In addition to demographic data, we collected aneurysmal neck width, dome-to-neck ratio, complications, and clinical and angiographic follow-up. The embolization degree of aneurysms was evaluated by O’Kelly-Marotta (OKM) grading scale, and the prognosis was assessed with the modified Rankin Scale (mRS).Results From August 2016 to March 2021, a total of 46 patients with 49 MCA bifurcation aneurysms in our center were enrolled, of whom all received PEDs successfully. The O’Kelly-Marotta (OKM) grading showed that post-procedure 15 patients (32.6%) were grade C, another 8 patients (17.4%) were grade D. Aneurysms with small remnant or complete occlusion were 50%, symptomatic ischemic events occurred in 3 (6.5%), and bleeding events in 1 (2.2%). 41 patients underwent a 6-month angiography follow-up, in which 7 patients (17.1%) remained OKM grade C and 30 patients (73.2%) achieved OKM grade D. Complete occlusion and small remnant aneurysms were up to 90.3%. 40 (97.6%) patients’ mRS scores were 0, and 1 (2.4%) patient was 2. No new bleeding and ischemic events occurred during the 6-month.Conclusions The Pipeline Embolization Device provides a safe and effective treatment alternative for complex, wide-necked MCA aneurysms. A larger number with longer-term follow-up data is needed for further verification.

Objectives: To characterize choroidal vascular changes in children with different refractive status. Methods: A study including 5864 children aged 6–9 years was performed to investigate the choroidal vascular index (CVI) in myopic, emmetropic and hyperopic eyes. Each participant had a comprehensive ocular examination with cycloplegic autorefraction performed, axial length (AL) measured and Swept Source-Optical Coherence Tomography (SS-OCT) scans acquired. Choroidal thickness (ChT) was measured by built-in software, and CVI was calculated using a previously validated self-developed algorithm. Results: The mean ChT and CVI were 275.88 ± 53.34 μm and 34.91 ± 3.83 in the macula region, and 191.96 ± 46.28 μm and 32.35 ± 4.21 in the peripapillary region. CVI was significantly lowest for myopes, followed by emmetropes and hyperopes (P&lt; 0.001). CVI varied between different sectors separated by the Early Treatment of Diabetic Retinopathy Study (ETDRS) grid (P&lt; 0.001). Macular CVI decreased horizontally from nasal to temporal quadrant with lowest in center fovea, and vertically from superior to inferior quadrants. Peripapillary CVI was highest in the nasal and lowest in the inferior sector. Multiple regression showed that spherical equivalent (SE), AL, intraocular pressure (IOP), ChT, age, and gender were significantly related to CVI (P&lt; 0.05). Conclusions: In children, the distribution of CVI in the posterior pole is not uniform. A decreased CVI was observed from hyperopia to myopia and was associated with decreased SE, elongated AL, and choroidal thinning. Further study of changes in CVI during myopia onset and progression is required to better understand the role of the choroidal vasculature in myopia development.

In remote areas, natural disasters owing to extreme weather or other causes can easily spread regionally. Unmanned aerial vehicles (UAVs) are regarded as pioneers for exploration and sensing in post-disaster scenarios due to their characteristics of flexible deployment and low cost. Considering the network disconnection caused by the long distances between the sensing area, UAVs, and the ground station (GS), this article aims to maximize the spatial exploration ratio (SER) while minimizing energy consumption with connectivity maintenance. To ensure that the UAV swarm successfully transmits as much post-disaster data as possible back, we develop a worst-case soft actor critic (WCSAC) based deployment algorithm with role management for UAVs. First, we formulate the three-dimensional (3D) UAV deployment problem as a real-time single-step Markov decision process (CMDP), together with role management that includes role assignment and role switching for each UAV. Next, to avoid unlimited energy consumption for complete coverage of remote mission areas, we separate the reward and cost functions and translate the energy consumption minimization to the residual energy constraints with a conditional value-at-risk (CVaR). Finally, visualizations and numerical results prove that the proposed WCSAC-cntRM can strike a better trade-off among the valid SER, energy consumption, and network connectivity than other benchmarks, especially when the emergency mission is dynamically triggered in the corner area.