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In this work, we use a Bayesian optimization (BO) algorithm to sample the space of covalent organic framework (COF) components aimed at the design of COFs with a high hole conductivity. COFs are crystalline, often porous coordination polymers, where organic molecular units—called building blocks (BBs)—are connected by covalent bonds. Even though we limit ourselves here to a space of three-fold symmetric BBs forming two-dimensional COF sheets, their design space is still much too large to be sampled by traditional means through evaluating the properties of each element in this space from first principles. In order to ensure valid BBs, we use a molecular generation algorithm that, by construction, leads to rigid three-fold symmetric molecules. The BO approach then trains two distinct surrogate models for two conductivity properties, level alignment vs a reference electrode and reorganization free energy, which are combined in a fitness function as the objective that evaluates BBs' conductivities. These continuously improving surrogates allow the prediction of a material's properties at a low computational cost. It thus allows us to select promising candidates which, together with candidates that are very different from the molecules already sampled, form the updated training sets of the surrogate models. In the course of 20 such training steps, we find a number of promising candidates, some being only variations on already known motifs and others being completely novel. Finally, we subject the six best such candidates to a computational reverse synthesis analysis to gauge their real-world synthesizability. [ABSTRACT FROM AUTHOR]

Recently, the world's first plasma-propelled drone was successfully flown, demonstrating that plasma propulsion technology is suitable for drone flight. The research on plasma propulsion drones has sparked a surge of interest. This study utilized a proxy model and the NSGA-II multi-objective genetic algorithm to optimize the geometric parameters based on staggered thrusters that affect the performance of electroaerodynamics (EAD) thrusters used for solid-state plasma aircraft. This can help address key issues, such as the thrust density and the thrust-to-power ratio of solid-state plasma aircraft, promoting the widespread application of plasma propulsion drones. An appropriate sample set was established using Latin hypercube sampling, and the thrust and current data were collected using a customized experimental setup. The proxy model employed a genetically optimized Bayesian regularization backpropagation neural network, which was trained to predict the effects of variations in the geometric parameters of the electrode assembly on the performance parameters of the plasma aircraft. Based on this information, the maximum achievable value for a given performance parameter and its corresponding geometric parameters were determined, showing a significant increase compared to the sample data. Finally, the optimal parameter combination was determined by using the NSGA-II multi-objective genetic algorithm and the Analytic Hierarchy Process. These findings can serve as a basis for future researchers in the design of EAD thrusters, helping them produce plasma propulsion drones that better meet specific requirements. [ABSTRACT FROM AUTHOR]

The fractional quantum anomalous Hall effect (FQAHE), the analogue of the fractional quantum Hall effect1 at zero magnetic field, is predicted to exist in topological flat bands under spontaneous time-reversal-symmetry breaking2–6. The demonstration of FQAHE could lead to non-Abelian anyons that form the basis of topological quantum computation7–9. So far, FQAHE has been observed only in twisted MoTe2 at a moiré filling factor v > 1/2 (refs. 10–13). Graphene-based moiré superlattices are believed to host FQAHE with the potential advantage of superior material quality and higher electron mobility. Here we report the observation of integer and fractional QAH effects in a rhombohedral pentalayer graphene–hBN moiré superlattice. At zero magnetic field, we observed plateaus of quantized Hall resistance R x y = h v e 2 at v = 1, 2/3, 3/5, 4/7, 4/9, 3/7 and 2/5 of the moiré superlattice, respectively, accompanied by clear dips in the longitudinal resistance Rxx. Rxy equals 2 h e 2 at v = 1/2 and varies linearly with v, similar to the composite Fermi liquid in the half-filled lowest Landau level at high magnetic fields14–16. By tuning the gate-displacement field D and v, we observed phase transitions from composite Fermi liquid and FQAH states to other correlated electron states. Our system provides an ideal platform for exploring charge fractionalization and (non-Abelian) anyonic braiding at zero magnetic field7–9,17–19, especially considering a lateral junction between FQAHE and superconducting regions in the same device20–22.Integer and fractional quantum anomalous Hall effects in a rhombohedral pentalayer graphene–hBN moiré superlattice are observed, providing an ideal platform for exploring charge fractionalization and (non-Abelian) anyonic braiding at zero magnetic field. [ABSTRACT FROM AUTHOR]

We compare algorithms to sample initial positions and momenta of a molecular system for classical trajectory simulations. We aim at reproducing the phase space quantum distribution for a vibrational eigenstate, as in Wigner theory. Moreover, we address the issue of controlling the total energy and the energy partition among the vibrational modes. In fact, Wigner's energy distributions are very broad, quite at variance with quantum eigenenergies. Many molecular processes depend sharply on the available energy, so a better energy definition is important. Two approaches are introduced and tested: the first consists in constraining the total energy of each trajectory to equal the quantum eigenenergy. The second approach modifies the phase space distribution so as to reduce the deviation of the single mode energies from the correct quantum values. A combination of the two approaches is also presented. [ABSTRACT FROM AUTHOR]

Underwater sensing is important for healthcare and sports management monitoring in extreme water environments, where traditional strain sensors often fail. Therefore, we fabricated a superhydrophobic nanocoating wearable textile composite (SNWTC) strain sensor with exceptional liquid repellency, excellent strain sensing performance, and sensing stability in water environments. The conductive pathways in terms of direct overlapping and indirect contact of fluorinated MWCNTs in micro-/nanofiber-coupled network structures endowed the SNWTC surface with excellent conductivity. Also, the superhydrophobicity of the SNWTC was maintained after a series of mechanical and corrosion damage tests due to the synergistic enhancement between epoxy–silicone oligomers/polydimethylsiloxane and textile fibers. Importantly, the SNWTC strain sensor simultaneously achieved a wide sensing range (over 100% strain), superior sensitivity (a gauge factor of 949.5), low detection limit (strain ∼0.2%), and fast response time (∼100 ms) in air. Moreover, it could also efficiently monitor full-scale human motions in air and underwater with equally excellent sensitivity. Overall, the proposed textile composite strain sensor looks promising for divers' extraordinary ocean exploration. [ABSTRACT FROM AUTHOR]

The study of interface spin effects in spintronic multilayer films requires distinguishing the effects generated by different interfaces. However, testing in atmospheric conditions requires a capping layer to protect the films, which introduces new interfaces and limits the study of interface spin-dependent effects. To address this challenge, we have developed an integrated ultra-high vacuum cluster system that includes magnetron sputtering equipment, ion irradiation equipment, and time-resolved magneto-optical Kerr effect (TR-MOKE) equipment. Our sputtering system integrates 12 cathodes in a single chamber, allowing the co-sputtering of four targets. The ultimate vacuum can reach 1 × 10−10 mbar, and the deposition resolution of 0.1 nm can be achieved. Ion irradiation equipment can ionize to produce He+, and by screening and accelerating the implantation of He+ into multilayer films, ion scanning is realized, and up to 30 keV energy can be applied to the films. The TR-MOKE equipment can detect ultra-fast magnetic dynamics processes in vacuum conditions, and its external magnetic field can be rotated 360°. Our vacuum cluster system connects the three subsystems, allowing in situ film deposition, regulation, and characterization. By accurately detecting the effects of different layers, the system can distinguish the interface effects of multilayers. Experimental results demonstrate that the three subsystems can work independently or coordinate to observe the interface effects of multilayers. [ABSTRACT FROM AUTHOR]

In this study, we conducted a meta-analysis to estimate the relationship between the consumption of dairy products and the risk of prostate cancer. We searched PubMed, Embase and Cochrane databases for relevant articles and identified a total of thirty-three cohort studies between 1989 and 2020. The qualities of included studies were assessed using Newcastle–Ottawa scale. Pooled adjusted relative risks (RR) with 95 % CI were calculated. We performed subgroup analyses stratified by dairy type, prostate cancer type, follow-up years, treatment era, collection times, adjustment for confounders and geographic location. In the subgroup analysis stratified by prostate cancer type, the pooled RR were 0·98 (95 % CI 0·94, 1·03) in the advanced group, 1·10 (95 % CI 0·98, 1·24) in the non-advanced group and 0·92 (95 % CI 0·84, 1·00) in the fatal group. In the dose–response analysis, a positive association for the risk of prostate cancer was observed for total dairy products 400 g/d (RR: 1·02; 95 % CI 1·00, 1·03), total milk 200 g/d (RR: 1·02; 95 % CI 1·01, 1·03), cheese 40 g/d (RR: 1·01; 95 % CI 1·00, 1·03) and butter 50 g/d (RR: 1·03; 95 % CI 1·01, 1·05). A decreased risk was observed for the intake of whole milk 100 g/d (RR: 0·97; 95 % CI 0·96, 0·99). Our meta-analysis suggests that high intakes of dairy products may be associated with an increased risk of prostate cancer; however, since many of the studies were affected by prostate-specific antigen (PSA) screening bias, additional studies with an adjustment of PSA screening are needed. [ABSTRACT FROM AUTHOR]

Wearable strain sensors with water repellency can enable the monitoring of human health and body motions when in contact with water; however, realization of such sensors with both high sensitivity and dynamic wetting behaviors simultaneously in air and underwater environment is a challenging task. Herein, a superhydrophobic, breathable, and wearable microfiber composite (SBWMC) strain sensor is fabricated using a stearic acid functionalized multi‐walled carbon nanotubes/TiO2 modified textile. The resultant network of hierarchical micro/nano structure endows the SBWMC surface with excellent conductivity and water damage prevention, and it maintains superhydrophobicity after mechanical abrasion, illumination, and water soaking. More importantly, the SBWMC strain sensor simultaneously achieves the wide sensing ranges (strain over 100%) and superior sensitivity (gauge factor of 461.5) in air, and it can efficiently monitor real‐time bending of human joints in air. Compared to traditional sensors, this SBWMC strain sensor also exhibits superior sensitivity in underwater environment, even comparable to that in air. Undoubtedly, such smart sensor offers broad application prospects in next generation of wearable electronic devices in extremely humid environments. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) clustering is a challenging task due to the complex spatial-spectral structure and high-dimensional property in HSI data. In this letter, a novel clustering information-constrained 3D U-Net subspace clustering network is proposed for HSI clustering. Considering the spatial-spectral information, the proposed network takes the 3D pixel cubes around the pixels as the input. Based on the 3D pixel cubes, a 3D U-Net subspace clustering network is introduced to extract spatial-spectral features from 3D pixel cubes and learn self-representation subspace property among pixels. In order to learn features more suitable for clustering, a clustering information constraint is introduced to explore useful information gain in the existing clustering result. Experiments conducted on three public HSI datasets illustrate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

Perpendicular magnetic anisotropy-based magnetic tunnel junctions (p-MTJs) with low Gilbert damping constant (α) are of particular interest for fast and low-power consumption magnetic random-access memory (MRAM). However, obtaining a faster switching speed and lower power consumption is still a big challenge. Herein, we report a Mo-based perpendicular double free layer structure with a low Gilbert damping constant of 0.02 relative to W-based films, as measured by time-resolved magneto-optical Kerr effect equipment. To show the influence of different film structures on the Gilbert damping constant, we measured the Mo-based single free layer. Thereafter, we deposited the full stacks with the Mo-based double free layer and obtained a high tunneling magnetoresistance of 136.3% and high thermal stability. The results of high-resolution transmission electron microscopy (HR-TEM) and energy-dispersive X-ray spectroscopy (EDS) showed that the Mo-based films had better crystallinity, sharper interfaces, and weaker diffusion than the W-based films and thus produced a weaker external contribution of the Gilbert damping constant. As a result of the weak spin-orbit coupling in the Mo-based structure, the intrinsic contribution of the Gilbert damping constant was also weak, thereby leading to the small Gilbert damping constant of the Mo-based stacks. In addition, the macro-spin simulation results demonstrated that the magnetization switching by the spin transfer torque of the Mo-based MTJs was faster than that of the W-based MTJs. These findings help to understand the mechanism behind the good performance of Mo-based p-MTJ films and show the great promise of these structures in low-power consumption MRAM or other spintronic devices. [ABSTRACT FROM AUTHOR]

Background: There are few studies on immunoglobulin A nephropathy (IgAN) at high altitude. This study aimed to analyze the clinical and pathological characteristics of IgAN between Tibet and Beijing, which provided a basis for improving diagnosis and treatment in Tibet.Method: The clinical and pathological data of 80 patients from the People's Hospital of Tibet Autonomous Region (Tibetan group) and 991 patients from Peking University First Hospital (Beijing group) with IgAN proven by renal biopsy were compared retrospectively between January 2016 and July 2020. The kidney biopsy tissue was sent to the Department of Nephrology, Peking University First Hospital for pathological evaluation.Results: The proteinuria (2.9 [2.0, 4.9] vs. 1.1 [0.5, 2.4] g/day, P < 0.001) in the Tibetan group was significantly higher than that in the Beijing group. The serum albumin (30.4 ± 7.7 vs. 38.2 ± 5.5 g/L, P < 0.001) was significantly lower in the Tibetan group. The eGFR (77.7 ± 37.8 vs. 62.1 ± 33.6 ml/min/1.73 m2, P = 0.001) was higher in the Tibetan group. The percentage of patients with nephrotic syndrome in the Tibetan group was significantly higher than that in the Beijing group (33.8% vs. 4.7%, P < 0.001).Conclusion: There are differences in the clinical and pathological characteristics of IgAN between plateau and plain regions. [ABSTRACT FROM AUTHOR]

Disruption of the blood-brain barrier (BBB) causes or contributes to neuronal dysfunction and several central nervous system (CNS) disorders. Wnt/β-catenin signaling is essential for maintaining the integrity of the adult BBB in physiological and pathological conditions, including stroke. However, how the impairment of the endothelial Wnt/β-catenin signaling results in BBB breakdown remains unclear. Furthermore, the individual contributions of different BBB permeability-inducing mechanisms, including intercellular junction damage, endothelial transcytosis, and fenestration, remains unexplored. Here, we induced β-catenin endothelial-specific conditional knockout (ECKO) in adult mice and determined its impact on BBB permeability and the underlying mechanism. β-catenin ECKO reduced the levels of active β-catenin and the mRNA levels of Wnt target genes in mice, indicating downregulation of endothelial Wnt/β-catenin signaling. β-catenin ECKO mice displayed severe and widespread leakage of plasma IgG and albumin into the cerebral cortex, which was absent in wild-type controls. Mechanistically, both the paracellular and transcellular transport routes were disrupted in β-catenin ECKO mice. First, β-catenin ECKO reduced the tight junction protein levels and disrupted the intercellular junction ultrastructure in the brain endothelium. Second, β-catenin ECKO substantially increased the number of endothelial vesicles and caveolae-mediated transcytosis through downregulating Mfsd2a and upregulating caveolin-1 expression. Interestingly, fenestration and upregulated expression of the fenestration marker Plvap were not observed in β-catenin ECKO mice. Overall, our study reveals that endothelial Wnt/β-catenin signaling maintains adult BBB integrity via regulating the paracellular as well as transcellular permeability. These findings may have broad applications in understanding and treatment of CNS disorders involving BBB disruption. [ABSTRACT FROM AUTHOR]

In recent years, human motion recognition, as an important application of the intelligent perception of the Internet of Things, has received extensive attention. Many applications benefit from motion recognition, such as motion monitoring, elderly fall detection, and somatosensory games. Several existing RF-based motion recognition systems are susceptible to multipath effects in complex environments, resulting in lower recognition accuracy and difficulty in extending to other scenarios. To address this challenge, we propose RF-Motion, a device-free commercial off-the-shelf (COTS) RFID-based human motion recognition system that can detect human motion in complex multipath environments such as indoor environments. And when the environment changes, RF-Motion still has high recognition accuracy, even without retraining. In addition, we use data slicing to solve the problem of discontinuity in the time domain of RFID communication and then use the synthetic aperture (SAR) algorithm to obtain the fingerprint feature matrix corresponding to each motion. Finally, the dynamic time warping (DTW) algorithm is used to match the prior motion fingerprint database to complete the motion recognition. Experiments show that RF-Motion can achieve up to 90% accuracy for human motion recognition in an indoor environment, and when the environment changes, it can still reach a minimum accuracy of 87%. [ABSTRACT FROM AUTHOR]

In the direction of arrival (DOA) estimation problem, when a low-bit analog to digital converter (ADC) is used, the estimation performance severely deteriorates. In this paper, the DOA estimation problem is considered in a low-cost direction finding system with low-bit ADC. To eliminate quantization noise, we propose a novel network ADCnet, which is a composition of fully connected layers and exponential linear unit (ELU) layers, and the input signals are the received signals using low-bit ADC. After the ADCnet, an AtomicNet is also proposed to estimate the DOA from the denoised signals, where atomic vectors are corresponding to the steer vectors. A loss function considering both the reconstruction performance and the sparsity is proposed in the AtomicNet. Different from the exiting atomic norm-based methods, the proposed method can avoid an optimization problem and estimate the DOA with lower computational complexity. Simulation results show that the proposed method outperforms the existing methods in the DOA estimation performance using low-bit ADC. [ABSTRACT FROM AUTHOR]

Edge computing (EC) has recently emerged as a promising paradigm that supports resource-hungry Internet of Things (IoT) applications with low latency services at the network edge. However, the limited capacity of computing resources at the edge server poses great challenges for scheduling application tasks. In this paper, a task scheduling problem is studied in the EC scenario, and multiple tasks are scheduled to virtual machines (VMs) configured at the edge server by maximizing the long-term task satisfaction degree (LTSD). The problem is formulated as a Markov decision process (MDP) for which the state, action, state transition, and reward are designed. We leverage deep reinforcement learning (DRL) to solve both time scheduling (i.e., the task execution order) and resource allocation (i.e., which VM the task is assigned to), considering the diversity of the tasks and the heterogeneity of available resources. A policy-based REINFORCE algorithm is proposed for the task scheduling problem, and a fully-connected neural network (FCN) is utilized to extract the features. Simulation results show that the proposed DRL-based task scheduling algorithm outperforms the existing methods in the literature in terms of the average task satisfaction degree and success ratio. [ABSTRACT FROM AUTHOR]