Search

1 La narcolepsie est un trouble chronique caracterise par une somnolence diurne excessive (1) La cause est une deficience d'un neuropeptide appele orexine (hypocretine) (1), qui regule l'etat d'eveil. La [...]

Preparing chiral plasmonic nanoparticles (NPs) with strong chiroptical responses is crucial in numerous fields including constructing optical materials, chiral sensing, and chiral-dependent biological processes. However, precise regulation over the chiral optical activity and chiral configuration of plasmonic NPs is still a challenge. In this work, we report Au helicoid NPs with different chiral structures and reversal chirality directed by the oligomeric structure of inducer glutathione (GSH). By precisely controlling the oligomeric structure of GSH and other synthetic parameters, we successfully prepared chiral Au helicoid NPs with a high anisotropy factor of 0.03. The obtained chiral Au NPs demonstrated an excellent performance in discriminating penicillamine (Pen) enantiomers. Our findings provide a construction strategy for chiral Au NPs and contribute insight into the regulation effect of chiral inducers on the growth of chiral metal NPs.

South Korea's golf courses are known for their exclusivity and unique designs. Due to limited space in the golf-crazed city of Seoul, many of the country's top courses are located in distant areas. The country's geography, with its mountainous terrain and limited land availability, has led to the creation of courses that capitalize on the natural beauty of the country. Some of the top-rated courses include Nine Bridges on Jeju Island, South Cape Owners Club on the southeast coast, and Whistling Rock northeast of Seoul. These courses offer breathtaking views and are often associated with wealthy corporations, with initiation fees reaching over $1 million. While public golf options are limited and expensive, South Koreans have found alternative ways to enjoy the sport, such as multilevel driving ranges and simulated golf. [Extracted from the article]

With the advent of the era of data-driven material R&D, more and more countries have begun to build material Big Data sharing platforms to support the design and R&D of new materials. In the application process of material Big Data sharing platforms, storage and retrieval are the basis of resource mining and analysis. However, achieving efficient storage and recovery is not accessible due to the multimodality, isomerization, discrete and other characteristics of material data. At the same time, due to the lack of security mechanisms, how to ensure the integrity and reliability of the original data is also a significant problem faced by researchers. Given these issues, this paper proposes a blockchain-based secure storage and efficient retrieval scheme. Introducing the Improved Merkle Tree (MMT) structure into the block, the transaction data on the chain and the original data in the off-chain cloud are mapped through the material data template. Experimental results show that our proposed MMT structure has no significant impact on the block creation efficiency while improving the retrieval efficiency. At the same time, MMT is superior to state-of-the-art retrieval methods in terms of efficiency, especially regarding range retrieval. The method proposed in this paper is more suitable for the application needs of the material Big Data sharing platform, and the retrieval efficiency has also been significantly improved.

The interface properties in two-dimensional (2D) layered materials and their van der Waals (vdW) homo-/heterostructures are of importance in both uncovering novel physical phenomena and optimizing device performance. Despite considerable research interest and enthusiasm direct toward the interlayer coupling in 2D homo- and heterostructures, there is limited research on the coupling at the 2D layered material-substrate interface. This limitation is due to the challenges in achieving direct detection. Currently, the coupling mechanisms at the 2D layered material-substrate interface is ambiguous, which needs greater attention. In this study, we have systematically investigated the interface coupling between monolayer WS2and its supported substrates using high-temperature and high-vacuum in-situRaman spectroscopy through monitoring the low-frequency Raman mode of monolayer WS2. Our findings reveal that both interfacial spacing and strain can significantly affect the coupling strength between the monolayer WS2and the supported substrate. More notably, we found that the strategic introduction of appropriate interfacial strain can effectively enhance the interface coupling. Consequently, we have succeeded in achieving effective regulation of the sample-substrate coupling viaa convenient way of controlling the cooling process during annealing. Our findings contribute to a deeper understanding of the coupling correlation between 2D layered materials and substrates, which is of great significance for the design and optimization of high-performance devices based on 2D layered semiconductors.

As a significant geometric feature of 3D point clouds, sharp features play an important role in shape analysis, 3D reconstruction, registration, localization, etc. Current sharp feature detection methods are still sensitive to the quality of the input point cloud, and the detection performance is affected by random noisy points and non-uniform densities. In this paper, using the prior knowledge of geometric features, we propose a Multi-scale Laplace Network (MSL-Net), a new deep-learning-based method based on an intrinsic neighbor shape descriptor, to detect sharp features from 3D point clouds. First, we establish a discrete intrinsic neighborhood of the point cloud based on the Laplacian graph, which reduces the error of local implicit surface estimation. Then, we design a new intrinsic shape descriptor based on the intrinsic neighborhood, combined with enhanced normal extraction and cosine-based field estimation function. Finally, we present the backbone of MSL-Net based on the intrinsic shape descriptor. Benefiting from the intrinsic neighborhood and shape descriptor, our MSL-Net has simple architecture and is capable of establishing accurate feature prediction that satisfies the manifold distribution while avoiding complex intrinsic metric calculations. Extensive experimental results demonstrate that with the multi-scale structure, MSL-Net has a strong analytical ability for local perturbations of point clouds. Compared with state-of-the-art methods, our MSL-Net is more robust and accurate.

Tables are a ubiquitous data format for insight communication. However, transforming data into consumable tabular views remains a challenging and time-consuming task. To lower the barrier of such a task, research efforts have been devoted to developing interactive approaches for data transformation, but many approaches still presume that their users have considerable knowledge of various data transformation concepts and functions. In this study, we leverage natural language (NL) as the primary interaction modality to improve the accessibility of average users to performing complex data transformation and facilitate intuitive table generation and editing. Designing an NL-driven data transformation approach introduces two challenges: 1) NL-driven synthesis of interpretable pipelines and 2) incremental refinement of synthesized tables. To address these challenges, we present NL2Rigel, an interactive tool that assists users in synthesizing and improving tables from semi-structured text with NL instructions. Based on a large language model and prompting techniques, NL2Rigel can interpret the given NL instructions into a table synthesis pipeline corresponding to Rigel specifications, a declarative language for tabular data transformation. An intuitive interface is designed to visualize the synthesis pipeline and the generated tables, helping users understand the transformation process and refine the results efficiently with targeted NL instructions. The comprehensiveness of NL2Rigel is demonstrated with an example gallery, and we further confirmed NL2Rigel's usability with a comparative user study by showing that the task completion time with NL2Rigel is significantly shorter than that with the original version of Rigel with comparable completion rates.

This study focused on producing metal matrix composite (MMC) coatings on Ti–6Al–4V alloy through laser surface alloying using a novel combination of Inconel 625 and SiC precursor materials. Various ratios of alloying powders were examined to evaluate surface properties such as microhardness, wear resistance, and friction coefficient, along with analyzing the phase composition and microstructure of the coatings. The in situsynthesized MMC coatings exhibited the presence of α-Ti, NiTi, NiTi2, and TiC phases. Additionally, Ti5Si3and α-Ti/Ti5Si3eutectic structures were observed when the SiC content exceeded 20%. In comparison to the titanium substrate, the MMC coating significantly enhanced microhardness by over threefold and reduced wear by 95%. However, it was crucial to carefully select the appropriate combination of alloying powders to avoid a substantial decrease in friction performance and excessive formation of cracks. Through a comparative analysis of experimental results, the optimal precursor material composition was identified as 85% Inconel 625 and 15% SiC. This study demonstrated the effective utilization of Inconel 625 and SiC alloying materials to enhance the surface properties of titanium alloys, thereby expanding their application in challenging environments.

Parallel buck converters are increasingly being taken to interface pulsed power loads (PPLs) in ships. The voltage of the converters will deteriorate under the periodic power steps of PPL in a very short time. To this end, this article proposes an adaptive damping ratio control (ApDRC) for the parallel converters. The mechanism of voltage regulation is explored, enabling the voltage to be regulated directly and accurately by the damping ratio of the system. Unlike most existing controls, of which performances are parameter-associated, ApDRC is parameter-independent through adaptive damping ratio design, such that good voltage regulation can be always ensured regardless of varying changes in the load. Moreover, the load transient evolution is first investigated, revealing how duty-cycle saturation affects the load transients, and what is the corresponding solution. Experimental results demonstrate that the proposed control can achieve fast voltage regulation while having small static errors and good disturbance rejection.

Infrared invisibility has continued to develop in recent decades, triggered by novel physics, advanced fabrication technology and raw materials. However, the quick response of infrared stealth during transmedia (water–air/water–land) movement, which is important in practical scenarios, has not been investigated. Here, we employ the random thermal effect of the interaction between laser and matter to construct a bionic flexible thin-film superhydrophobic infrared invisibility cloak. The average emissivity of invisibility cloak is 0.224 over 8–14 ${{\mu }}\text{m}$, and specular reflectivity at the incident angle of 10 degree is 1.1% over 2.5–16 ${{\mu }}\text{m}$. These characteristics indicate the integrated stealth functionality against active (laser) and passive (thermal) detection, respectively. The contact angle between the cloak and water is 154°, which also indicates its unique cross-media response capability. We also show that the developed infrared stealth cloak is robust to practical (nonideal) manufacturing conditions. Our design strategy expands the concept of infrared stealth to cross-media applications.

Introduction: Variations in the left atrial appendage (LAA) morphology are associated with different embolic risk in patients with atrial fibrillation (AF). Data are scarce regarding the association between LAA morphology and Embolic stroke of undetermined source (ESUS).Patients and methods: Using cardiac computed tomography (CCT) scans, LAA morphology was categorized as either chicken wing (CW), cactus, windsock, or cauliflower. Furthermore, we examined the presence of large secondary lobes arising from the main lobe, considering their existence as indicative of a complex LAA morphology. LAA morphologies were compared between ESUS (n= 134) and AF patients (n= 120); and between ESUS patients with (n= 24) and without (n= 110) subsequent AF diagnosis during long-term follow-up.Results: ESUS patients had a significantly higher prevalence of cauliflower morphology compared to AF group (52% vs 34%, respectively, p= 0.01); however, no significant difference was found between the groups when categorizing LAA morphology to either CW or non-CW. ESUS patients had significantly higher prevalence of large secondary lobes compared with AF patients (50% vs 29%, respectively, p= 0.001). When comparing ESUS patients with and without AF diagnosis during follow-up (20–48 months of follow-up, median 31 months), there were no significant differences in the prevalence of the “classical” morphologies, but large secondary lobes were significantly more prevalent among those withoutsubsequent AF diagnosis.Conclusion: ESUS patients have a high prevalence of complex LAA morphology, which might be associated with an increased risk for thrombus formation even in the absence of AF.

A two-component gas sensor based on Quartz-Enhanced Photoacoustic Spectroscopy (QEPAS) and Light-Induced Thermoelastic Spectroscopy (LITES) was realized by the time-division multiplexing technique. Two off-beam acoustic microresonator (AMRs) were used in the experiment for twice excitation and enhancement of QEPAS signal. The four fiber collimators were connected with the two off-beam AMRs to be responsible for laser propagation in the two AMRs. After passing through the second AMR, the laser beam was incident to the sidewall of the quartz tuning fork (QTF) by the fiber collimator 5 for the excitation of the LITES signal, which realized the superposition of QEPAS signals and LITES signal on a single QTF. The time-division operation of distributed feedback lasers was realized by optical switches to accomplish the detection of C2H2 and CH4. The experimental results showed that the minimum detection limits of C2H2 and CH4 were 2.06 ppmv and 7.14 ppmv, respectively, which proved that the gas sensor had good sensing performance.

Accurate measurement of the three-dimensional (3D) movement of discrete particles is crucial for comprehending complex granular rheology in silos. In this paper, the acceleration and angular velocity of particles in 3D silos are measured by using a spherical detector based on inertial technology and magnetic positioning technology. The acceleration of particles is the largest in the center of silos, which suggest that the resistance generated by friction and extrusion is the smallest. Surprisingly, the angular velocity distribution follows lognormal function except for particles near the outlet. The correlation between acceleration and angular velocity is opposite in different flow regions. It reveals for the first time that the extent to which the resultant force on the particles affects their rotational motion is related to the flow pattern. These results have practical significance for regulating the granular flow pattern and optimizing the structural design of silos.

Amid the growing concern for health-oriented food choices, salt reduction has received widespread attention, particularly in the exploitation of salt alternatives. Peptides with a saltiness-enhancing effect may provide an alternative method for salt reduction. The objective of this study was to isolate and extract novel peptides with salt-reducing effects by fermenting goose blood using a Lactobacillus plantarumstrain. Five potential target peptides were screened by a virtual database prediction and molecular docking. Sensory evaluation and E-tongue analysis showed that five peptides (NEALQRM, GDAVKNLD, HAYNLRVD, PEMHAAFDK, and AEEKQLITGL) were identified as target peptides. Particularly, the results of E-tongue showed that GDAVKNLD can increase the saltiness intensity (2.87 ± 0.02) in the complex system. The sensory evaluation results also indicated an increase in saltiness intensity (46.67 ± 4.67 mmol/L NaCl) after adding GDAVKNLD. The results of molecular dynamics simulation indicated that five peptides have good ability to bind tightly to TMC4 receptor, thereby stimulating it to exert an active effect. And these peptides interacted with the TMC4 receptor via hydrogen bonding, hydrophobic interactions, and electrostatic interactions. This research lays a theoretical foundation for discovering novel salty/saltiness-enhancing peptides and provides meaningful contributions to efforts in salt reduction.

Zealexin A1 is a nonvolatile sesquiterpene phytoalexin, which not only exhibits extensive antifungal and insecticidal activities but also has the ability to enhance the drought resistance of plants, and thus has potential applications in agricultural and food fields. In this study, the biosynthetic pathway of zealexin A1 was constructed in Saccharomyces cerevisiaefor the first time, and the highest production of zealexin A1 reported to date was achieved. First, through screening of sesquiterpene synthases from various plants, BdMAS11 had a stronger (S)-β-macrocarpene synthesis ability was obtained, and the heterologous synthesis of zealexin A1 was achieved by coexpressing BdMAS11 with cytochrome P450 oxygenase ZmCYP71Z18. Subsequently, after the site-directed mutagenesis of BdMAS11, fusion expression of farnesyl diphosphate synthase ERG20 and BdMAS11, and tailored truncation of BdMAS11 and ZmCYP71Z18, the strain coexpressing the manipulated BdMAS11 and original ZmCYP71Z18 produced 119.31 mg/L of zealexin A1 in shake-flask fermentation. Finally, the production of zealexin A1 reached 1.17 g/L through fed-batch fermentation in a 5 L bioreactor, which was 261.7-fold that of the original strain. This study lays the foundation for the industrial production of zealexin A1 and other terpenoids.

cGAS/STING pathway, which is highly related to tumor hypoxia, is considered as a potential target for remodeling the immunosuppressive microenvironment of solid tumors. Metal ions, such as Mn2+, activate the cGAS/STING pathway, but their efficacy in cancer therapy is limited by insufficient effect on immunogenic tumor cell death of a single ion. Here, we evaluate the association between tumor hypoxia and cGAS/STING inhibition and report a polymetallic-immunotherapy strategy based on large mesoporous trimetal-based nanozyme (AuPdRh) coordinated with Mn2+(Mn2+@AuPdRh) to activate cGAS/STING signaling for robust adaptive antitumor immunity. Specifically, the inherent CAT-like activity of this polymetallic Mn2+@AuPdRh nanozyme decomposes the endogenous H2O2into O2to relieve tumor hypoxia induced suppression of cGAS/STING signaling. Moreover, the Mn2+@AuPdRh nanozyme displays a potent near-infrared-II photothermal effect and strong POD-mimic activity; and the generated hyperthermia and •OH radicals synergistically trigger immunogenic cell death in tumors, releasing abundant dsDNA, while the delivered Mn2+augments the sensitivity of cGAS to dsDNA and activates the cGAS-STING pathway, thereby triggering downstream immunostimulatory signals to kill primary and distant metastatic tumors. Our study demonstrates the potential of metal-based nanozyme for STING-mediated tumor polymetallic-immunotherapy and may inspire the development of more effective strategies for cancer immunotherapy.

Steering selectivity in photocatalytic conversion of CO2, especially toward deep reduction products, is vital to energy and environmental goals yet remains a great challenge. In this work, we demonstrate a facet-dependent photocatalytic selective reduction of CO2to CH4in Cu-doped TiO2catalysts exposed with different facets synthesized by a topological transformation from MIL-125 (Ti) precursors. The optimized round cake-like Cu/TiO2photocatalyst mainly exposed with the (001) facet exhibited a high photocatalytic CO2reduction performance with a CH4yield of 40.36 μmol g–1h–1with a selectivity of 94.1%, which are significantly higher than those of TiO2(001) (4.70 μmol g–1h–1and 52.6%, respectively), Cu/TiO2(001 + 101) (18.95 μmol g–1h–1and 69.6%, respectively), and Cu/TiO2(101) (14.73 μmol g–1h–1and 78.9%, respectively). The results of experimental and theoretical calculations demonstrate that the Cu doping dominating the promoted separation and migration efficiencies of photogenerated charges and the preferential adsorption on (001) facets synergistically contribute to the selective reduction of CO2to CH4. This work highlights the significance of synergy between facet engineering and ion doping in the design of high-performance photocatalysts with respect to selective reduction of CO2to multielectron products.

The incomplete blocking of small-sized polysulfides by pore size and the effect on Li+transport are generally neglected when the size-sieving effect is employed to suppress the shuttling of polysulfides. Herein, ion-selective modified layers with pore sizes equal to, greater than, and less than 0.8 nm, respectively, on the polypropylene separator are fabricated to obtain the preferable pore size for separation of polysulfides and Li+. As a result, the modified layer with a pore size of 0.8 nm can efficiently inhibit the shuttling of polysulfides and simultaneously boost the diffusion of Li+under the double effect of the size advantage and electrostatic shielding. Consequently, the battery using a separator with a modified layer having a pore size of 0.8 nm possesses a lower capacity attenuation of 0.047% after 1000 cycles at 2.0 C. This work serves as a vital guide for suppressing polysulfide shuttle using ion-selective sieving effects for lithium–sulfur batteries.

We present a machine learning (ML) approach to static contact angle measurement, trained on a large data set (>7.2 million) of half drop contours based on solutions to the Young–Laplace equation where the contact angle is known a priori(removing all sources of error from human input). The data set included the effects of surface roughness, gravity, the size of drop relative to the image, and reflections of the drop on the surface. The presented ML model (valid for contact angles >110°), in combination with a new automated image and contour processing approach, is shown to be more accurate than other methods when benchmarked against an experimental data set, with an estimated error of 1°. The ML model is also 2 orders of magnitude faster at predicting contact angles than Young–Laplace fitting (the current best practice approach). The accuracy and speed of the presented approach provides a viable pathway toward robust and reproducible high-throughput contact angle analysis. This approach, Conan-ML, is open-source and provided for the use and development of new approaches to goniometry.

ABSTRACTThis paper proposes a capacity model to address the impact of automated dedicated lanes on the capacity of the signalized intersection. Firstly, the car-following modes in the mixed traffic flow are analyzed, and the influence of the setting of the automated dedicated lanes on the average headway is discussed. Secondly, a new capacity model with automated dedicated lanes is derived based on the classic capacity model. Then, a signalized intersection capacity model considering the automated dedicated lanes is further derived based on the saturation flow rate method. Finally, numerical simulation experiments are designed to discuss the key parameters on the traffic capacity of a signalized intersection. The results show that (i) the automated dedicated lanes are conducive to improving the traffic capacity; (ii) when the penetration rate of CAVs is less than 52%, the traffic capacity of the signalized intersection can be increased by nearly 1.25 times with the automated dedicated lanes. These findings can provide theoretical support for designing and optimizing signalized intersections in high levels (i.e. L3-L5) CAVs environments.

Quantum dots (QDs) exhibit superior brightness and photochemical stability, making them the preferred option for highly sensitive single-molecule detection compared with fluorescent dyes or proteins. Nevertheless, their high surface energy leads to nonspecific adsorption and poor colloidal stability. In the past decades, we have found that QD-based fluorescent nanoparticles (FNs) can not only address these limitations but also enhance detection sensitivity. However, the photoluminescence quantum yield (PLQY) of FNs is significantly lower compared with that of original QDs. It is urgent to develop a strategy to solve the issue, aiming to further enhance detection sensitivity. In this study, we found that the decrease of PLQY of FNs prepared by free radical polymerization was attributed to two factors: (1) generation of defects that can cause nonradiative transitions resulting from QD-ligands desorption and QD-shell oxidation induced by free radicals; (2) self-absorption resulting from aggregation caused by incompatibility of QDs with polymers. Based on these, we proposed a multihierarchical regulation strategy that includes: (1) regulating QD-ligands; (2) precisely controlling free radical concentration; and (3) constructing cross-linked structures of polymer to improve compatibility and to reduce the formation of surface defects. It is crucial to emphasize that the simultaneous coordination of multiple factors is essential. Consequently, a world-record PLQY of 97.6% for FNs was achieved, breaking through the current bottleneck at 65%. The flexible application of this regulatory concept paves the way for the large-scale production of high-brightness QD-polymer complexes, enhancing their potential applications in sensitive biomedical detection.

Botrytis cinereais an important fungal pathogen that causes gray mold disease in plants. Previously, Bacillus velezensisTCS001 live culture presented broad-spectrum antifungal activity against various plant pathogenic fungi and oomycetes, particularly B. cinerea. Here, the bioactivity of lipopeptides produced by TCS001 against B. cinereawas investigated. The IC50values of the crude lipopeptide extract (CLE) from TCS001 to suppress mycelial growth and conidial germination were 14.20 and 49.39 mg/L, respectively. SEM and TEM imaging revealed that CLE caused morphological deformities and ultrastructural changes in the mycelium. Transcriptomic analyses combined with ΔBcpsdmutant construction demonstrated that the CLE could confer antifungal activity via suppressing Bcpsdexpression in the pathogen. In addition, the CLE activated the plant immune system by increasing the content of defense-related enzymes and the expression of marker genes in immunity signaling pathways in cucumber plants. Therefore, TCS001 CLE could be potentially developed into biopesticides for the biocontrol of gray mold disease.

This article studies the impact of strain relief loops on RF-induced heating of active implantable medical devices (AIMDs) during magnetic resonance imaging (MRI) scans at 1.5 T. Two commercially available AIMDs, a 40-cm lead length peripheral nerve stimulator (PNS) and a 60-cm lead length spinal cord stimulator (SCS), were utilized. In the in vitro study, lead-tip heating was calculated using the transfer function (TF) method and compared to direct measurement results for strain relief loops at various locations. In the human model analysis, RF-induced heating along six PNS and two SCS trajectories within the Duke, Ella, and FATS models was evaluated for strain relief loops positioned at various locations along the implantation trajectories. From the in vitro study, we can see up to a 48.3% reduction in the overall lead-tip heating. Inappropriate placement may result in doubled temperature rises. Significant temperature rise variation (up to 9°C) is observed with loop position, loop diameter, number of turns, and patient landmark. Differentiation of cumulative summation analysis is necessary to comprehend the strain relief loop's effect on RF-induced heating. The findings suggest that strategically placing the loop can effectively mitigate RF-induced heating for AIMDs exposed to 1.5-T MR environments.

The demand for high-precision temperature measurement is expanding into the fields of deep low-temperature and strong magnetic field, and CrOxNy thin film sensor has shown good prospects in these fields for its high sensitivities, high thermal cycling stability, and low magnetoresistance. The substrate temperature, as an important parameter in the thin film growth process, plays an important regulatory role in sensor performance, which has not been systematically studied in the preparation process of CrOxNy thin film cryogenic sensor. Here, CrOxNy thin films were deposited on polished and oxidized silicon wafers at different substrate temperatures (100 ° C ~ 400 °C) by dc magnetron reactive sputtering. The crystal structure and composition of the thin film were characterized by X-ray Diffractometer and X-ray Photon-electron Spectroscopy. Then, CrOxNy thin film temperature sensors were fabricated using micro-electromechanical system (MEMS) micromachining process. The performance tests show that the sensor with higher substrate temperature has lower temperature sensitivity, smaller magnetoresistance effect under $0\sim 9$ T magnetic field in $4\sim 300$ K, and relatively smaller temperature measurement deviation in the low temperature region (below ~100 K), whereas the sensor with lower substrate temperature has the opposite trend. CrOxNy thin film sensors prepared at 400 ° C substrate temperature exhibit more accurate deep cryogenic measurement capabilities under strong magnetic field. This study will provide guidance for the preparation of high-precision cryogenic temperature sensors applied to deep low-temperature and strong magnetic field.

As a crucial technology for improving the autonomous sensing capability of optical sensors on unmanned aerial vehicles (UAVs), object detection has emerged as a research focus in the UAV patrol inspection. To address the problem of high computational complexity in detection models carried by UAV, a lightweight network model based on You Only Look Once (YOLO) v7 is designed. Based on this model, a smoke detection algorithm is developed. First, a new multiparameter gated activation function (Better-mish) based on the Mish function is constructed. Second, Conv + batch normalization (BN) + SiLU (CBS) and channel attention (CA) modules are improved based on the Better-mish. The Improved-CA is replaced with the Conv in mix-precision convolution (MPConv) to build CA-MPConv. The improved modules are added to the backbone of the model. Third, the backbone is reconstructed in combination with re-parameterization visual geometry group (RepVGG) module. An improved YOLO v7 lightweight model is proposed, and a smoke detection algorithm based on this model is developed. Finally, several experiments are conducted on Smoke and common object in context (COCO) datasets. The experimental results show three key points as follows: 1) compared with Sigmoid linear unit (SiLU), Mish, and other activation functions, Better-mish can alleviate the gradient disappearance of neural networks in the saturation region; 2) compared with YOLO v7, the improved YOLO v7 model reduces computational complexity by 93.24% and only increases model parameters by less than 2%; and 3) the algorithm can maintain effective recognition of smoke images at low computational complexity. The smoke detection algorithm provides a theoretical basis and an effective algorithm for the early warning of fire hazards in the UAV patrol inspection.

This article explores the influence of patient postures on radiofrequency induced heating in active implantable medical devices (AIMDs) within a 1.5T magnetic resonance imaging system. Employing the transfer function method, the study assesses RF-induced heating for AIMDs in four different postures. Three AIMD types deep brain stimulation (DBS), cardiac rhythm management (CRM), and sacral neuromodulation (SNM)] are studied across three human models (Duke, Ella, and Fats) at four imaging landmarks under two RF exposure levels: B1+ and specific absorption rate (SAR) restrictions. When comparing temperature rises observed in various postures to those obtained from the typical/standard posture, it was noted that temperature rises can vary significantly. Specifically, temperature rises were found to have variations of up to 82% and 96% for prone and supine arm loop postures, respectively, while remaining within 27% for leg and trunk arm loop postures. Across DBS, CRM, and SNM systems, averaged temperature rises can vary up to 96%, 96%, and 89%, respectively. Noteworthy landmark-specific variations are observed, with changes of up to 43%, 33%, 95%, and 96% for head, cardiac, abdomen, and hip scans. The study underscores the significant impact of patient posture on AIMD heating, emphasizing the necessity for evaluations under diverse conditions.

The rapid expansion of the satellite industry has presented numerous opportunities across various sectors and significantly transformed people's daily lives. However, the high energy consumption resulting from frequent task execution poses challenges for satellite management. Energy consumption has become an important factor to be considered in the design of future satellite management systems. The energy-efficient satellite range scheduling problem (EESRSP) aims to optimize task sequencing profits within the satellite management system while simultaneously conserving energy. To address this problem, a mixed-integer scheduling model is constructed, taking into account the energy consumption of ground stations during telemetry, tracking, and command (TT&C) operations. Then, we propose a reinforcement learning-based memetic algorithm (RL-MA) that incorporates a heuristic initialization method (HIM). The HIM enables the algorithm to rapidly generate high-quality initial solutions by leveraging task features associated with EESRSRP. RL-MA employs both population search and local search (LS) techniques to explore the satellite TT&C task plan. RL-MA incorporates two genetic operators, crossover and mutation, into the population-based search. In the LS stage, multiple random and heuristic LS operators are incorporated through an ensemble LS strategy. To improve search performance, Q-learning, a classical class of reinforcement learning (RL) methods tailored to problem characteristics, is utilized for selecting effective operators. RL dynamically adjusts LS operators based on strategy performance. Experimental results demonstrate that the proposed RL-MA can effectively generate sound solutions for EESRSP with varying task scales. Furthermore, the improvement strategies employed in the algorithm are validated to enhance the scheduling performance of RL-MA. This study reveals that integrating RL with an ensemble of LS operators can significantly enhance the algorithm's exploit capability. Moreover, this LS approach applies to solving other types of satellite scheduling problems.

Information and communication technology (ICT) provides employees with convenience in communication. However, it also creates a preoccupation with and urges to respond quickly to work‐related ICT messages during nonworking time, which is defined as workplace telepressure after hours (WTA). Drawing on the job demand–resource model, conservation of resource theory, and workplace anxiety theory, this study explores how and when task interdependence and dispositional workplace anxiety affect WTA and how individuals cope with WTA. A total of 269 full‐time workers from an online survey panel completed questionnaires at three time‐points. We found that both task interdependence and dispositional workplace anxiety are positively related to WTA. The perception of pay‐for‐responsiveness moderates the relationship between task interdependence and WTA, such that the relationship is significant only for employees with a strong perception of pay‐for‐responsiveness. Others' approval contingency of self‐worth moderates the relationship between dispositional workplace anxiety and WTA, and the relationship is significant only for employees with high degrees of others' approval contingency of self‐worth. Finally, WTA arising from external work requirements or the internal pursuit of achieving work goals prompts employees to generate responsiveness coping strategies. Overall, these findings suggest that task interdependence and dispositional workplace anxiety are important factors affecting employees' WTA and highlight the importance of being responsive to WTA.

In this randomized controlled trial of 286 participants with elevated triglycerides, treatment with vupanorsen, an ANGPTL3 inhibitor, lowered remnant cholesterol by up to 59% and VLDL cholesterol by up to 67% over placebo.The effect of the treatment was sustained throughout 24 weeks and consistent across key patient subgroups.ANGPTL3 inhibition may be a promising approach to treat patients with elevated triglycerides.

To evaluate the vascular impedance of the pulmonary arteries in fetuses with tetralogy of Fallot (TOF) by Doppler echocardiography. A total of 42 fetuses with TOF (TOF group) and 84 gestational age‐matched normal fetuses (control group) were prospectively collected from the Second Xiangya Hospital of Central South University from August 2022 to January 2023. The severity of TOF was classified into mild TOF (zscore ≥−2), moderate TOF (−4 < zscore < −2), or severe TOF (zscore ≤−4) according to the zscore value of the pulmonary annulus diameter. The pulsatility index (PI) of the main pulmonary artery (MPA), distal left pulmonary artery (DLPA), and distal right pulmonary artery (DRPA) were measured by pulsed‐wave Doppler. The differences in clinical data and echocardiographic parameters between TOF group, control group, and TOF subgroups were compared. Compared with the control group, MPA‐PI increased significantly, whereas DLPA‐PI and DRPA‐PI decreased in TOF group (all P< .001). There were no significant differences in MPA‐PI and DRPA‐PI among mild TOF, moderate TOF, and severe TOF (all P> .05). However, DLPA‐PI decreased significantly in severe TOF compared with mild TOF (P< .05). Fetuses with TOF presented increased vascular impedance in the pulmonary trunk and decreased impedance in distal pulmonary artery branches. Further large and follow‐up studies are needed to demonstrate the associations between those changed vascular impedances and the development of PA in patients with TOF.

This article proposes a methodology for designing co-polarized in-band full-duplex (IBFD) antenna arrays with high isolation. An equivalent circuit model of IBFD antenna arrays is established, and transmission zeros are introduced by configuring controllable coupling paths between the arrays. Based on the proposed equivalent circuit model, the high-isolation co-polarized IBFD antenna arrays are designed by utilizing a wavefront phase compensation technique and a controllable coupling structure. Two co-polarized IBFD cavity slot antenna arrays based on the TM510 mode cavity are designed for validation, where a transmission zero is obtained in the passband to improve the isolation between the transmitter (Tx) and the receiver (Rx) ports. Compared with the reference antenna array, the isolation of the designed IBFD antenna array is improved by about 22 dB. The theoretical analysis and design process of the IBFD antenna array is given in detail. A second-order co-polarized IBFD antenna array is fabricated and measured, and the measured results are in good agreement with the simulated results.

This article proposes an enhanced approach to sequential convex programming for optimizing onboard trajectories by incorporating nonuniform rational basis splines (NURBS) curves. The contributions of the proposed method are threefold. First, it effectively reduces the number of optimization variables by utilizing NURBS curves as representations for control variables. Second, the convex-hull property of NURBS curves enables the replacement of the control inequality constraints with those defined by NURBS control points, leading to a significant reduction in the overall number of constraints. Finally, the integration of the homotopy approach with NURBS curves offers increased flexibility in shaping the curves and facilitates the solution of optimization problems involving bang–bang optimal control. Numerical demonstrations are provided to illustrate the effectiveness and efficiency of the proposed approach for onboard applications.

3D dense captioning aims to semantically describe each object detected in a 3D scene, which plays a significant role in 3D scene understanding. Previous works lack a complete definition of 3D spatial relationships and the directly integrate visual and language modalities, thus ignoring the discrepancies between the two modalities. To address these issues, we propose a novel complete 3D relationship extraction modality alignment network, which consists of three steps: 3D object detection, complete 3D relationships extraction, and modality alignment caption. To comprehensively capture the 3D spatial relationship features, we define a complete set of 3D spatial relationships, including the local spatial relationship between objects and the global spatial relationship between each object and the entire scene. To this end, we propose a complete 3D relationships extraction module based on message passing and self-attention to mine multi-scale spatial relationship features and inspect the transformation to obtain features in different views. In addition, we propose the modality alignment caption module to fuse multi-scale relationship features and generate descriptions to bridge the semantic gap from the visual space to the language space with the prior information in the word embedding, and help generate improved descriptions for the 3D scene. Extensive experiments demonstrate that the proposed model outperforms the state-of-the-art methods on the ScanRefer and Nr3D datasets.

With the development and depletion of shallow coal resources, deep coal mining has gradually become normalized. In the face of increasingly severe corrosion environment of coal mining, it is urgent to develop new materials with excellent corrosion resistance for key parts of coal mining equipment. In this paper, SiC, Al2O3and SiO2were used as raw materials to prepare three-dimensional network configuration SiC ceramics by organic foam impregnation method. Then the three-dimensional network configuration SiC-Fe composite was prepared by atmospheric pressure casting process. And by electrochemical test, static immersion corrosion test, scanning electron microscope and first-principles simulation calculation, the corrosion resistance of three-dimensional network configuration SiC-Fe composite in harsh mine water environment was characterized, meanwhile the corrosion resistance mechanism was analyzed. The results show that the corrosion potential increases from - 495.756 mV to -379.626 mV, and the corrosion current density decreases from 3.6864 μA/cm2 to 2.1709 μA/cm2when the SiC ceramic reinforcement is added. The inhibition efficiency based on polarization curve measurement is increased by 41.11%. The charge transfer resistance increases from 5417 Ω·cm2to 6437 Ω·cm2, and the capacitance value drops from 159.96 μF·cm−2to 90.12 μF·cm−2. The inhibition efficiency based on electrochemical impedance spectroscopy is increased by 18.83%. First principles calculations show that the addition of SiC reinforcement showed a greater resistance in preventing the migration of corrosive media to the surface of the Fe substrate and can effectively slow down corrosion, which was consistent with the experimental results.

IMPORTANCE: The Sentinel Node Oriented Tailored Approach (SENORITA) randomized clinical trial evaluated quality of life (QoL) and nutritional outcomes between the laparoscopic sentinel node navigation surgery (LSNNS) and laparoscopic standard gastrectomy (LSG). However, there has been no report on the QoL and nutritional outcomes of patients who underwent stomach-preserving surgery among the LSNNS group. OBJECTIVE: To compare long-term QoL and nutritional outcomes between patients who underwent stomach-preserving surgery and those who underwent standard gastrectomy and to identify factors associated with poor QoL outcomes in patients who underwent stomach-preserving surgery. DESIGN, SETTING, AND PARTICIPANTS: This study is a secondary analysis of the SENORITA trial, a randomized clinical trial comparing LSNNS with LSG. Patients from 7 tertiary or general hospitals across the Republic of Korea were enrolled from March 2013 to December 2016, with follow-up through 5 years. Data were analyzed between August and September 2022. Among trial participants, patients who underwent actual laparoscopic standard gastrectomy in the LSG group and those who underwent stomach-preserving surgery in the LSNNS group were included. Patients who did not complete the baseline or any follow-up questionnaire were excluded. INTERVENTION: Stomach-preserving surgery vs standard gastrectomy. MAIN OUTCOMES AND MEASURES: Overall European Organization for Research and Treatment of Cancer QoL Questionnaire Core 30 (EORTC QLQ-C30) and stomach module (STO22) scores, body mass index, hemoglobin, protein, and albumin levels. RESULTS: A total of 194 and 257 patients who underwent stomach-preserving surgery and standard gastrectomy, respectively, were included in this study (mean [SD] age, 55.6 [10.6] years; 249 [55.2%] male). The stomach-preserving group had better QoL scores at 3 months postoperatively in terms of physical function (87.2 vs 83.9), dyspnea (5.9 vs 11.2), appetite loss (13.1 vs 19.4), dysphagia (8.0 vs 12.7), eating restriction (10.9 vs 18.2), anxiety (29.0 vs 35.2), taste change (7.4 vs 13.0), and body image (19.5 vs 27.2). At 1 year postoperatively, the stomach-preserving group had significantly higher body mass index (23.9 vs 22.1, calculated as weight in kilograms divided by height in meters squared) and hemoglobin (14.3 vs 13.3 g/dL), albumin (4.3 vs 4.25 g/dL), and protein (7.3 vs 7.1 g/dL) levels compared to the standard group. Multivariable analyses showed that tumor location (greater curvature, lower third) was favorably associated with global health status (β, 10.5; 95% CI, 3.2 to 17.8), reflux (β, −8.4; 95% CI, −14.7 to −2.1), and eating restriction (β, −5.7; 95% CI, −10.3 to −1.0) at 3 months postoperatively in the stomach-preserving group. Segmental resection was associated with risk of diarrhea (β, 40.6; 95% CI, 3.1 to 78.1) and eating restriction (β, 15.1; 95% CI, 1.1 to 29.1) at 3 years postoperatively. CONCLUSIONS AND RELEVANCE: Stomach-preserving surgery after sentinel node evaluation was associated with better long-term QoL and nutritional outcomes than standard gastrectomy. These findings may help facilitate decision-making regarding treatment for patients with early-stage gastric cancer. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01804998

Domain adaption (DA) is a challenging task that integrates knowledge from source domain (SD) to perform data analysis for target domain. Most of the existing DA approaches only focus on single-source-single-target setting. In contrast, multisource (MS) data collaborative utilization has been extensively used in various applications, while how to integrate DA with MS collaboration still faces great challenges. In this article, we propose a multilevel DA network (MDA-NET) for promoting information collaboration and cross-scene (CS) classification based on hyperspectral image (HSI) and light detection and ranging (LiDAR) data. In this framework, modality-related adapters are built, and then a mutual-aid classifier is used to aggregate all the discriminative information captured from different modalities for boosting CS classification performance. Experimental results on two cross-domain datasets show that the proposed method consistently provides better performance than other state-of-the-art DA approaches.

Ensemble clustering integrates a set of base clustering results to generate a stronger one. Existing methods usually rely on a co-association (CA) matrix that measures how many times two samples are grouped into the same cluster according to the base clusterings to achieve ensemble clustering. However, when the constructed CA matrix is of low quality, the performance will degrade. In this article, we propose a simple, yet effective CA matrix self-enhancement framework that can improve the CA matrix to achieve better clustering performance. Specifically, we first extract the high-confidence (HC) information from the base clusterings to form a sparse HC matrix. By propagating the highly reliable information of the HC matrix to the CA matrix and complementing the HC matrix according to the CA matrix simultaneously, the proposed method generates an enhanced CA matrix for better clustering. Technically, the proposed model is formulated as a symmetric constrained convex optimization problem, which is efficiently solved by an alternating iterative algorithm with convergence and global optimum theoretically guaranteed. Extensive experimental comparisons with 12 state-of-the-art methods on ten benchmark datasets substantiate the effectiveness, flexibility, and efficiency of the proposed model in ensemble clustering. The codes and datasets can be downloaded at https://github.com/Siritao/EC-CMS.

In high sea conditions and complex environments, for the safe landing of unmanned aerial vehicles (UAVs) on unmanned surface vehicles (USVs), it is necessary to accurately measure the distance and speed between UAV and USV simultaneously. In response to this requirement, this article proposes a frequency modulated continuous wave (FMCW) lidar system that uses a visible light wavelength (632 nm), which is low absorption in humid environments, enabling simultaneous measurement of speed and distance. The system directly modulates a 632 nm semiconductor distributed Bragg reflector (DBR) laser with a triangular wave current modulation signal. The modulated laser output features continuous frequency tuning and is divided into two beams. One beam serves as the reference light, while the other beam is used as the detection light and incident on the measurement target. The detection light reflected (scattered) by the target is collected by a photodetector (PD) and mixed with the reference light for coherent heterodyne detection. The beating signal carries information about the distance and velocity of the target. The system has been experimentally verified to simultaneously measure distance within a detection range of 5–350 cm and a velocity range of 0.5–850 cm/s. Moreover, the system’s capability for simultaneous distance and velocity measurement under humid conditions has also been demonstrated. This lidar system would have broad application in the precise measurement of distance and velocity simultaneously in humid environment.

This article presents a comprehensive validation study on the efficacy of the planar-integrated periodic permanent magnetic (PIPPM) focusing system, employing a specially designed and fabricated two-channel beamstick to assess its performance in 1-D electron beam array transport. This study centers on two PIPPM stacks: magnetic strips and magnetic rings. The analysis proves that the two-channel beamstick utilizing the planar magnet configuration with magnetic strips falls short in addressing material nonuniformity, leading to a deficiency in the adjustment mechanism for electron beam transport and an unsatisfactory transmission rate. In light of these challenges, the study proposes an alternative approach utilizing magnetic rings, with a detailed analysis of their magnetic properties. The experimental results demonstrate that the beam transmission rates in the two channels, measured at 116 mm, exceed expectations at 86.5% and 87.4%, respectively. These outcomes substantiate the rationality and effectiveness of the PIPPM design and introduce an innovative solution for advancing research in four-beam integrated traveling wave tubes (TWTs).

Gallium nitride (GaN) high-electron-mobility transistors (HEMTs) are widely applied in the radio frequency (RF) power amplifier of the wireless communication field because of superior features. With the massive applications of RF GaN HEMT, the reliability challenges, accompanied with the electrostatic discharge (ESD) issues, are paying increasing attention. The electrical and small-signal characteristics degradation after the repetitive ESD stresses for the RF GaN HEMT have been investigated in this work. It is found that the Schottky gate of the device is damaged after the ESD stresses. These damages induce the shift of pinchoff voltage and the current reduction. However, the damage mechanisms are different for the following two ESD stress scenarios. The Schottky gate of the device zapped on the gate is damaged by the high current density and that of the device zapped on the drain is damaged by the high electric field. Meanwhile, the discharge current can induce traps in the buffer region, which causes the drain current to decrease further. In addition, the S-parameters are measured before and after the ESD stresses. Based on the small-signal equivalent circuit, the main intrinsic elements of the device, including capacitance, inductance, and resistance, have been extracted and analyzed. The results show that the gate-dependent parameters ${C} _{\text {gs}}$ , ${R} _{\text {gs}}$ , and ${g} _{\text {m}}$ , and the drain-dependent parameters ${C} _{\text {ds}}$ and ${G} _{\text {ds}}$ are changed by the ESD stresses due to the damages in the gate and buffer regions.

Unsupervised person reidentification (re-ID) aims to perform person retrieval in an unsupervised manner across multiple cameras. Most recent works usually adopted contrastive learning with clustering strategy to guide learning process. However, the domain gap between cameras easily causes the high intraclass variance and prohibits an effective contrastive learning. To overcome this problem, we propose dual-graph contrastive learning (Dual-GCL) to fully utilize the structures between the instances as well as the relationship between the instances and the centroids generated by the clustering strategy. First, a dual-graph is constructed by utilizing the affinities of instances in the feature space and the pseudolabel space derived from clustering strategy with the node-to-node contrastive learning. Then, to capture the relations between the dual-graph and the centroids, we perform Dual-GCL at both the node level and the super-node level. The contrastive learning based on the two levels can encode multilevel structural representations between the instances corresponding to the nodes in dual-graph and align the centroids between different cameras in the shared embedding space. Moreover, a camera-aware augmentation scheme that exploits the semantic relationship is proposed to alleviate the domain discrepancy between cameras. Experimental results demonstrate that our approach outperforms the state-of-the-art methods.

Visual simultaneous localization and mapping (SLAM) in dynamic scenes is a prerequisite for robot-related applications. Most of the existing SLAM algorithms mainly focus on dynamic object rejection, which makes part of the valuable information lost and prone to failure in complex environments. This article proposes a semantic visual SLAM system that incorporates rigid object tracking. A robust scene perception frame is designed, which gives autonomous robots the ability to perceive scenes similar to human cognition. Specifically, we propose a two-stage mask revision method to generate fine mask of the object. Based on the revised mask, we propose a semantic and geometric constraint (SAG) strategy, which provides a fast and robust way to perceive dynamic rigid objects. Then, the motion tracking of rigid objects is integrated into the SLAM pipeline, and a novel bundle adjustment is constructed to optimize camera localization and object six-degree of freedom (DoF) poses. Finally, the evaluation of the proposed algorithm is performed on publicly available KITTI dataset, Oxford Multimotion dataset, and real-world scenarios. The proposed algorithm achieves the comprehensive performance of $\text{RPE}_{\text{t}}$ less than 0.07 m per frame and $\text{RPE}_{\text{R}}$ about 0.03${}^{\circ}$ per frame in the KITTI dataset. The experimental results reveal that the proposed algorithm enables accurate localization and robust tracking than state-of-the-art SLAM algorithms in challenging dynamic scenarios.

Artificial neural networks (ANNs) have revolutionized microwave computer-aided design by leveraging machine-learning techniques to tackle complex problems. One critical aspect of this process is the automatic modeling of the neural network structure. The conventional approach, which involves qualitative adjustments to prevent underfitting and overfitting, often leads to inefficiencies when the initial structure significantly deviates from the optimal one. This article presents a groundbreaking approach to address this issue, proposing an automatic modeling algorithm for neural networks. This algorithm employs Bayesian theory to optimize the ANN model structure. Based on the Bayesian theory, the formula for calculating effective parameters in a multihidden layer neural network is derived, allowing the initial structure to adopt any form and permitting efficient, quantitative adjustments. This innovative approach enables the computation of effective parameters under any given structure. A higher maximum number of effective parameters in multihidden layer ANN has been obtained compared to single-hidden layer ANN, thus improving modeling accuracy. Compared with the existing Bayesian-based automated ANN model generation methods, the proposed approach significantly enhances both modeling accuracy and speed. The effectiveness of this method is verified through the application of three microwave components.

Fibrosis is a prevalent pathological condition observed in various organs and tissues. It primarily arises from the excessive and abnormal accumulation of the extracellular matrix, resulting in the structural and functional impairment of tissues and organs, which can culminate in death. Many forms of fibrosis, including liver, cardiac, pulmonary, and renal fibrosis, are considered irreversible. Maternally expressed gene 3 (MEG3) is an imprinted RNA gene. Historically, the downregulation of MEG3 has been linked to tumor pathogenesis. However, recent studies indicate an emerging association of MEG3 with fibrotic diseases. In this review, we delve into the current understanding of MEG3’s role in fibrosis, aiming to shed light on the molecular mechanisms of fibrosis and the potential of MEG3 as a novel therapeutic target.What is already known on this topic – Fibrosis, a condition characterized by excess build-up of the extracellular matrix in various organs, can lead to organ failure and is often irreparable. This includes fibrosis of the liver, heart, lungs, and kidneys. MEG3, an RNA gene, which is known to be downregulated in tumors, has recently been linked to fibrosis.What is already known on this topic – Our review investigates this new connection between MEG3 and fibrosis. We aim to provide insights into the molecular mechanisms of fibrosis and illuminate the potential role of MEG3 as a promising therapeutic target for fibrosis treatments.What is already known on this topic – Our review investigates this new connection between MEG3 and fibrosis. We aim to provide insights into the molecular mechanisms of fibrosis and illuminate the potential role of MEG3 as a promising therapeutic target for fibrosis treatments.