Search

Ammonium dinitramide (ADN), as a novel and environmentally friendly oxidizer, has strong hygroscopicity when exposed to high-humidity air, which seriously hinders its application in solid propellants. Modification of oxidizers by cocrystallization is an effective strategy to improve the hygroscopicity of energetic components. In this paper, the theoretical simulation of ADN/CL-20 cocrystals was developed via a directional hydrogen bonding design to establish a cocrystal with improved hygroscopicity. Intermolecular interaction analyses reveal that hydrogen bonds and van der Waals interactions synergistically lead to the formation of cocrystals. The ADN/CL-20 cocrystal was prepared experimentally by the spray drying self-assembly technique, and the corresponding thermal analysis and sensitivity properties were conducted to illustrate the thermal stability and high safety. Furthermore, the critical relative humidity (CRH) measurement was carried out to evaluate the hygroscopicity of the cocrystal, exhibiting a certain degree of antihygroscopic effect with a CRH of 65%. The hydrogen bonds formed between ADN and CL-20 saturate the ammonium ions of ADN, further preventing ADN from absorbing water molecules in the air. The ADN/CL-20 cocrystal has high specific impulse characteristics (Isp: 272.6 s). Accordingly, this work clearly demonstrates that the ADN/CL-20 cocrystal is expected to be used in a solid propellant to make up for the deficiency of the ADN oxidizer.

Artificial intelligence (AI) and machine learning (ML) have shown tremendous potential in reshaping the landscape of wireless communications and are, therefore, widely expected to be an indispensable part of the next-generation wireless network. This article presents an overview of how AI/ML and wireless communications interact synergistically to improve system performance and provides useful tips and tricks on realizing such performance gains when training AI/ML models. In particular, we discuss in detail the use of AI/ML to revolutionize key physical layer and lower medium access control (MAC) layer functionalities in traditional wireless communication systems. In addition, we provide a comprehensive overview of the AI/ML-enabled semantic communication systems, including key techniques from data generation to transmission. We also investigate the role of AI/ML as an optimization tool to facilitate the design of efficient resource allocation algorithms in wireless communication networks at both bit and semantic levels. Finally, we analyze major challenges and roadblocks in applying AI/ML in practical wireless system design and share our thoughts and insights on potential solutions.

Low-dimensional ferroelectric tunnel junctions are appealing for the realization of nanoscale nonvolatile memory devices due to their inherent advantages of device miniaturization. Those based on current mechanisms have limitations, including low tunneling electroresistance (TER) effects and complex heterostructures. Here, we introduce an entirely new TER mechanism to construct a nanotube ferroelectric tunnel junction with ferroelectric nanotubes as the tunneling region. When rolling a ferroelectric monolayer into a nanotube, due to the coexistence of its intrinsic ferroelectric polarization with the flexoelectric polarization induced by bending, a metal–insulator transition occurs depending on the radiative polarization states. For the pristine monolayer, its out-of-plane polarization is tunable by an in-plane electric field, and the conducting states of the ferroelectric nanotube can thus be tuned between metallic and insulating states viaaxial electric means. Using α-In2Se3as an example, our first-principles density functional theory calculations and nonequilibrium Green's function formalism confirm the feasibility of the TER mechanism and indicate an ultrahigh TER ratio that exceeds 9.9 × 1010% of the proposed nanotube ferroelectric tunnel junctions. Our findings provide a promising approach based on simple homogeneous structures for high density ferroelectric microelectric devices with excellent ON/OFF performance.

During the continuous casting process of low carbon steel, the solidified hook formed in the mold has great effects on the surface quality of the cast slab. Some factory experiments have been conducted to investigate the microscopic characteristics and reveal the influence of process parameters on solidified hooks. The depth of the hooks showed a positive correlation with the deflection angle, length, and oscillation mark (OM) depth, which indicates that the OM depth can serve as an approximate indicator for evaluating the depth of the solidified hooks. On the wide and narrow faces of the cast slab, the depth of the solidified hooks and the temperature distribution in the mold show opposite trends, with lower depths of solidified hooks at positions with higher temperatures. In addition, the influence of process parameters on solidified hooks was analyzed. With the increase in superheat, not only the depth of solidified hooks gradually decreases, but also the ratio of depression-typed marks increases. Increasing casting speed and decreasing immersion depth of the submerged entry nozzle will both lead to a decrease in the depth of the solidified hook.

This study examined the potential modification of hypertension on associations of high-density lipoprotein cholesterol (HDL-C), especially at a very high level, and varied sizes of HDL particle (HDL-P) with the risk of mortality.Very high HDL-C levels were associated with increased risk of mortality in individuals with hypertension, but not in those without hypertension.In individuals with hypertension, the increased risk at a high HDL-C level was attributed to a larger size of HDL-P, which was directly associated with mortality risk. An inverse association with mortality was observed for a smaller size of HDL-P.

We report herein a Pd(II)-catalyzed oxidative coupling reaction of dibenzosiloles with aryl diazoacetates viaa metal carbene migratory insertion process. In this reaction, carbenic carbon formally displaces silicon atoms through continuous cleavage of two C(sp2)–Si bonds and successive formation of two C(sp2)–C(sp3) bonds on a carbenic carbon. Density functional theory (DFT) studies reveal that the reaction proceeds through a Pd(II)/Pd(IV) catalytic cycle, in which the two C–Si bonds are cleaved by two successive oxidative addition/reductive elimination processes rather than traditional transmetalation steps. Additive o-chloranil was necessary for this reaction, which significantly facilitated the reaction by acting as both a ligand and an oxidant, and it played different roles in different reaction stages.

The object detectors can precisely detect the camouflaged object beyond human perception. The investigations reveal that the CNNs-based (Convolution Neural Networks) detectors are vulnerable to adversarial attacks. Some works can fool detectors by crafting the adversarial camouflage attached to the object, leading to wrong prediction. It is hard for military operations to utilize the existing adversarial camouflage due to its conspicuous appearance. Motivated by this, this paper proposes the Dual Attribute Adversarial Camouflage (DAAC) for evading the detection by both detectors and humans. Generating DAAC includes two steps: (1) Extracting features from a specific type of scene to generate individual soldier digital camouflage; (2) Attaching the adversarial patch with scene features constraint to the individual soldier digital camouflage to generate the adversarial attribute of DAAC. The visual effects of the individual soldier digital camouflage and the adversarial patch will be improved after integrating with the scene features. Experiment results show that objects camouflaged by DAAC are well integrated with background and achieve visual concealment while remaining effective in fooling object detectors, thus evading the detections by both detectors and humans in the digital domain. This work can serve as the reference for crafting the adversarial camouflage in the physical world.

Information theory has driven the information and communication technology industry for over 70 years. Great successes have been achieved in both academia and industry. In theory, polar codes and spatially coupled low-density parity-check (LDPC) codes have achieved the theoretical bound. In practice, capacity-approaching coding schemes such as turbo, polar, and LDPC codes are adopted by global wireless standards and implemented with reasonable complexity. However, this by no means suggests a halt in future information theoretic research. For channel coding, coding gain has been the main key performance indicator (KPI). From the practical viewpoint, there is a long list of unfulfilled target KPIs that deserves rigorous and deeper understanding. The inability to fulfil these target KPIs will become the major limitations of future communication systems such as 6G and beyond. Moreover, a diverse set of new 6G services will require new capabilities beyond data transmissions. New opportunities will be created for information theory and channel coding. Above all, we hope that the readers of this survey find the discussion of motivational background and preliminary results from an industry perspective helpful.

Semantic signal processing and communications are poised to play a central part in developing the next generation of sensor devices and networks. A crucial component of a semantic system is the extraction of semantic signals from the raw input signals, which has become increasingly tractable with the recent advances in machine learning (ML) and artificial intelligence (AI) techniques. The accurate extraction of semantic signals using the aforementioned ML and AI methods, and the detection of semantic innovation for scheduling transmission and/or storage events are critical tasks for reliable semantic signal processing and communications. In this work, we propose a reliable semantic information extraction framework based on our previous work on semantic signal representations in a hierarchical graph-based structure. The proposed framework includes a time integration method to increase fidelity of ML outputs in a class-aware manner, a graph-edit-distance based metric to detect innovation events at the graph-level and filter out sporadic errors, and a Hidden Markov Model (HMM) to produce smooth and reliable graph signals. The proposed methods within the framework are demonstrated individually and collectively through simulations and case studies based on real-world computer vision examples.

A distributed lossy compression network with $L$ encoders and a decoder is considered. Each encoder observes a source and sends a compressed version to the decoder. The decoder produces a joint reconstruction of target signals with the mean squared error distortion below a given threshold. It is assumed that the observed sources can be expressed as the sum of target signals and corruptive noises which are independently generated from two symmetric multivariate Gaussian distributions. The minimum compression rate of this network versus the distortion threshold is referred to as the rate-distortion function, for which an explicit lower bound is established by solving a minimization problem. Our lower bound matches the well-known Berger-Tung upper bound for some values of the distortion threshold. The asymptotic gap between the upper and lower bounds is characterized in the large $L$ limit.

Epitaxial SrTiO3(STO) on Si is nowadays the benchmark initial platform for the further addition of functional oxides on Si. Starting the growth of STO on a Sr-passivated Si(001) substrate with 1/2 monolayer (ML) Sr coverage and a (1 × 2) reconstructed Si surface with rows of Si dimers, the final STO/Sr/Si stack exhibits a 1 ML Sr buffer layer and a (1 × 1) Si interface without a dimer. Using first-principles density functional theory calculations, we investigate how the interface evolves from 1/2 to 1 ML Sr coverage, concluding that the latter is indeed most stable and that the reconstruction of the interface takes place during the early stage of the layer-by-layer deposition. Going further, we argue that this (1 × 1) Si interface is non-oxidized. We determine its electronic band alignment and assess its potential interest as a photocathode for water reduction.

Supplemental Digital Content is Available in the Text.Inositol 1, 4, 5-trisphosphate (IP3) signaling-mediated calcium release drives the contraction of vascular smooth muscles and hence regulates blood vessel volume and blood pressure. Melatonin supplementation has been suggested to be beneficial for hypertension. To determine whether the blood pressure–lowering effect of melatonin was accounted for by IP3 signaling, we evaluated the vasoconstriction response and IP3 signaling in isolated mouse thoracic aortic rings during melatonin incubation. C57BL/6 mice were given intraperitoneal injections daily with melatonin, and the systolic blood pressure and contractility of aortic rings from melatonin-treated mice were decreased, and the contraction suppression effect of melatonin was attributed to the impaired expression of contractile proteins in vascular smooth muscle cells rather than IP3 signaling. Our results further showed that melatonin increased the expression of γ-secretase, which could cleave and release the notch intracellular domain, and the notch intracellular domain prevented the transcription of contractile genes by interfering with the interaction between serum response factor and myocardin, the master regulator of contractile protein. In this article, we report a novel mechanism by which melatonin regulates smooth muscle contractility that does not depend on IP3 signaling.

Exploring novel hypergolic fuels for modern space propulsion is highly desired. However, the analysis and understanding of the structure and hypergolic performance at the molecular level are still insufficient. To understand the factors that dictate hypergolicity, we conducted a comparative study on a series of metal–organic frameworks (MOFs) characterized by the same topology but with varied ligand structures. The ignition delay (ID) time trend was found to be imidazole < triazole < tetrazole, and the rapid ID time was 8 ms. By combining experimental studies and density functional theory (DFT) calculations, we found that propargyl and cyanoborohydride groups that functioned as dual hypergolic triggers contributed to the hypergolicity, and a distinct electronic structure was detrimental to ID time. The structure–performance relationships presented herein can potentially provide some fundamental insights into the field of developing high-performance hypergolic fuels.

Soil organic carbon (SOC) is the most important indicators of soil quality and health. Identifying the spatial distribution of SOC and its influencing factors in cropland is crucial to understand the terrestrial carbon cycle and optimize agronomic management. Yunnan Province, characterized by mountainous topography and varied elevation, is one of the highest SOC regions in China. Yet its SOC stock of cropland and influencing factors has not been fully studied due to the lack of adequate soil investigation. In this study, the digital mapping of SOC at 1 km resolution and the estimation of total SOC stock in cropland of Yunnan Province was undertaken using 8 637 topsoil (0–20 cm) samples and a series of spatial data through Random Forest (RF) model. It was showed that across the cropland of Yunnan Province, the mean SOC density and total stock were 4.84 kg m–2and 337.5 Mt, respectively. The spatial distribution indicated that relatively high SOC density regions resided in the northwest and northeast parts of Yunnan Province. Elevation (19.5%), temperature (17.3%), rainfall (14.5%), and Topographic wetness index (9.9%) were the most important factors which controlled spatial variability of SOC density. Agronomic practices (e.g., crop straw treatments, fertilizer management) should be optimized for the sustainable development of crop production with high SOC sequestration capacity in Yunnan Province.

We report a general protocol for the convergent synthesis of 1,4-dicarbonyl Z-alkenes form alkynes using α-diazo sulfonium triflate and water. The C═O, C═C, and C–H bonds are formed under mild conditions with a wide range of functional groups tolerated. The reaction exhibits excellent Z-selectivity and complete regioselectivity. The resulting 1,4-dicarbonyl Z-alkenes can smoothly undergo follow-up conversion to a variety of heteroaromatic scaffolds. Moreover, the reaction also provides a facile access to the corresponding deuterated Z-alkenes and deuterated heteroarenes with a high level of deuterium incorporation (90–97% D-inc.) by directly using D2O, thus rendering the method highly valuable. The comprehensive mechanistic studies indicate that a free carbyne radical intermediate is formed via the photocatalytic single electron transfer process, and KH2PO4plays a crucial role in significant improvements on yield and selectivity based on density-functional theory calculations, providing a new direction for radical coupling reactions of diazo compounds.

In recent years, artificial intelligence (AI) techniques, especially machine learning (ML), have been successfully applied in various areas, leading to a widespread belief that AI will collectively play an important role in future wireless communications. To accomplish the aspiration, we present nine challenges to be addressed by the interdisciplinary areas of AI/ML and wireless communications, with particular focus on the sixth generation (6G) wireless networks. Specifically, this article classifies the nine challenges into computation in AI, distributed neural networks and learning, and semantic communications.

Machine learning is a promising approach to explore the vast amount of wireless data with artificial intelligence (AI) to accomplish a wide variety of large-scale, computation or communication-oriented tasks in next-generation wireless networks, from intelligent computing to environment sensing and intelligent communication. Traditional approaches rely on each individual task having a specific AI model, which results in high hardware (HW)/software (SW) overheads and prevents the deep exploration of the inherent correlation within data and among tasks. The rapidly emerging big AI model or foundation model (FM) has received a lot of attention recently, which aims at building a unified machine learning system based on a generic class of AI models capable of accomplishing multiple natively interrelated tasks.

An azacycle-directed intermolecular aromatic C(sp2)–H functionalization of Cr(0) Fischer carbene complexes under catalyst-free conditions is reported. Arenes with pyridines, pyrimidine, and pyrazole as directing groups reacted with chromium(0) carbene complexes in good yields and excellent regioselectivities. According to the mechanistic studies based on experiments and computations with density functional theory (DFT), the reaction is proposed to follow an unconventional mechanism via oxidative hydrogen migration and reductive elimination, which is different from the classical electrophilic substitution mechanism.

Previous meta-analysis studies have indicated inverse associations between some carotenoids and risks of metabolic syndrome, cardiovascular disease, cancer, and all-cause mortality. However, the results for associations between carotenoids and type 2 diabetes (T2D) remain inconsistent and no systematic assessment has been done on this topic. We conducted a systematic review and meta-analysis to examine the associations of dietary intakes and circulating concentrations of carotenoids with risk of T2D. We searched PubMed and Ovid Embase from database inception to July 2020. Prospective observational studies of carotenoids and T2D risk were included. Random-effects models were used to summarize the RRs and 95% CIs. Thirteen publications were included. Dietary intake of β-carotene was inversely associated with the risk of T2D, and the pooled RR comparing the highest with the lowest categories was 0.78 (95% CI: 0.70, 0.87; I2= 13.7%; n= 6); inverse associations were also found for total carotenoids (n= 2), α-carotene (n= 4), and lutein/zeaxanthin (n= 4), with pooled RRs ranging from 0.80 to 0.91, whereas no significant associations were observed for β-cryptoxanthin and lycopene. Circulating concentration of β-carotene was associated with a lower risk of T2D, and the pooled RR comparing extreme categories was 0.60 (95% CI: 0.46, 0.78; I2= 56.2%; n= 7); inverse associations were also found for total carotenoids (n= 3), lycopene (n= 4), and lutein (n= 2), with pooled RRs ranging from 0.63 to 0.85, whereas no significant association was found for circulating concentrations of α-carotene and zeaxanthin when comparing extreme categories. Dose-response analysis indicated that nonlinear relations were observed for circulating concentrations of α-carotene, β-carotene, lutein, and total carotenoids (all P-nonlinearity < 0.05), but not for other carotenoids or dietary exposures. In conclusion, higher dietary intakes and circulating concentrations of total carotenoids, especially β-carotene, were associated with a lower risk of T2D. More studies are needed to confirm the causality and explore the role of foods rich in carotenoids in prevention of T2D.This systematic review was registered at www.crd.york.ac.uk/prosperoas CRD42020196616.

Previous meta-analysis studies have indicated inverse associations between some carotenoids and risks of metabolic syndrome, cardiovascular disease, cancer, and all-cause mortality. However, the results for associations between carotenoids and type 2 diabetes (T2D) remain inconsistent and no systematic assessment has been done on this topic. We conducted a systematic review and meta-analysis to examine the associations of dietary intakes and circulating concentrations of carotenoids with risk of T2D. We searched PubMed and Ovid Embase from database inception to July 2020. Prospective observational studies of carotenoids and T2D risk were included. Random-effects models were used to summarize the RRs and 95% CIs. Thirteen publications were included. Dietary intake of β-carotene was inversely associated with the risk of T2D, and the pooled RR comparing the highest with the lowest categories was 0.78 (95% CI: 0.70, 0.87; I2 = 13.7%; n = 6); inverse associations were also found for total carotenoids (n = 2), α-carotene (n = 4), and lutein/zeaxanthin (n = 4), with pooled RRs ranging from 0.80 to 0.91, whereas no significant associations were observed for β-cryptoxanthin and lycopene. Circulating concentration of β-carotene was associated with a lower risk of T2D, and the pooled RR comparing extreme categories was 0.60 (95% CI: 0.46, 0.78; I2 = 56.2%; n = 7); inverse associations were also found for total carotenoids (n = 3), lycopene (n = 4), and lutein (n = 2), with pooled RRs ranging from 0.63 to 0.85, whereas no significant association was found for circulating concentrations of α-carotene and zeaxanthin when comparing extreme categories. Dose-response analysis indicated that nonlinear relations were observed for circulating concentrations of α-carotene, β-carotene, lutein, and total carotenoids (all P-nonlinearity < 0.05), but not for other carotenoids or dietary exposures. In conclusion, higher dietary intakes and circulating concentrations of total carotenoids, especially β-carotene, were associated with a lower risk of T2D. More studies are needed to confirm the causality and explore the role of foods rich in carotenoids in prevention of T2D.

The determination of the local cooling rate has a great significance in optimizing the parameters of electroslag remelting (ESR) and improving the quality of the ingots. An innovative method was proposed for calibrating the local cooling rate of M42 high-speed steel (HSS) in the ESR process. After resolidification at different cooling rates under high-temperature laser confocal microscopy, the carbide network spacing of the specimen was observed using a scanning electron microscope. A functional relationship between the cooling rate and average carbide network spacing was established. The average local cooling rate of the solidification process of the M42 HSS ingot was calibrated. The results show that the higher the cooling rate, the smaller the network spacing of the carbides. For the steel ingot with a diameter of 360 mm, the average local cooling rate was 0.562 °C/s at the surface, 0.057 °C/s at the position of 0.25D(where Dis the diameter of the ingot), and 0.046 °C/s at the center of the ingot.

Using a gaze‐contingent moving‐window paradigm, we investigated whether/how deafness affects perceptual processing in Chinese reading. Besides the manipulation of window size, word length of sentences used in the experiment was also manipulated to check whether deafness enhanced the word length effect on perceptual span. Significant interactions of window constraints and deafness and a three‐way interaction were observed on reading rate. Smaller effects of window constraints for deaf Chinese readers and nonreliable three‐way interactions were observed on forward saccade length. This suggests that deaf Chinese readers exhibit a larger perceptual span, and word length affected the span from which information was acquired for comprehension whereas both deafness and word length might have little impact on the span from which information is acquired for oculomotor targeting during natural reading of Chinese.

AbstractA natural fermentation with multispecies is an important technique to produce traditional vinegars. However, the flavor-producing core bacteria closely related to each phase are still poorly understood. The phase characteristics of Sichuan bran vinegar fermentation were investigated in this study. The dynamic changes of physicochemical factors, flavor substances, and bacterial communities in the fermentation process of Sichuan bran vinegar were analyzed. Via a partial least squares regression analysis (PLSR) model and network analysis, the phases of solid-state fermentation were defined, and the core species and corresponding flavor compounds closely related to each fermentation phase were noted. To find the physicochemical factors that have a large influence on the growth of core bacteria at each phase, the phased effects of different factors on the key flavor compounds and bacteria were analyzed. The results showed that temperature was the main influencing factor, promoting the metabolism profile of nonvolatile flavor-producing bacteria in the fourth phase, such as Pseudomonasand Helicobacter.This study provides theoretical support for the establishment of a controllable fermentation system.

•High-throughput first principles calculations for 109 carbon allotropes were performed.•These carbon allotropes are all potential superhard material.•Seven direct band gap semiconductor carbon allotropes were revealed.

Large language models, particularly integrated with image-to-text approaches, show potential for improving thyroid nodule diagnosis; ChatGPT 4.0 is optimal for consistency and accuracy compared with Bard and ChatGPT 3.5.

In this study, the self-assembly structures of 2-methylbenzimidazole (MBI) molecules on Cu(100) and Cu(111) surfaces were examined using scanning tunneling microscopy (STM), atomic force microscopy (AFM), and density functional theory (DFT) calculations. It was found that MBI molecules form hydrogen-bonded structures in one-dimensional (1D) or two-dimensional (2D) configurations on both Cu(100) and Cu(111) surfaces. On the Cu(100) surface, the hydrogen-bonded lines are aligned along two equivalent Cu ⟨11̅0⟩ directions, matching the lattice of the underlying surface. This alignment agrees with calculation results, which shows MBI molecules preferentially adsorbing on hollow sites with their molecular backbones perpendicular to these lines. The molecular lines spread out across the surface area, and the distance between them gradually decreases as coverage increases, indicating repulsive interactions between the lines. Conversely, on the Cu(111) surface, the 1D molecular chains are curly, with the molecular backbones oriented in various directions relative to the substrate, indicating a weaker interaction between the molecules and the substrate compared to that on the Cu(100) surface. With further increases in coverage on both surfaces, densely packed 2D molecular structures emerge, albeit with different lattice parameters. The 2D structures on Cu(100) adjust to match the substrate, whereas those on Cu(111) may assume various orientations. The distance between molecules is greater on Cu(111) than on Cu(100), suggesting a restriction of hydrogen bonding structures on the Cu(100) surface. Additionally, a noticeable height difference among the molecules in the 2D structures was observed, possibly due to a slight tilting of the molecular backbone when more molecules are integrated into the lattice. These findings demonstrate how the assembly structures of molecules depend on the interaction between molecule–molecule and molecule–substrate interactions.