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Calorie restriction has been demonstrated to be effective in treating metabolic dysfunction-associated steatotic liver disease (MASLD). However, it has been limited by poor long-term adherence.

Functional gradient materials (FGMs) have garnered increasing interest for their potential in material and structural design. Additive manufacturing, as a kind of free manufacturing and near-net shaping technology with in-situ controllability of materials, is the dominant technology for fabricating FGMs. While numerous studies have been conducted on titanium alloy FGMs, there is still a lack of research on the structure and properties of the material with the number of transitional layers. This study employs double-wire arc additive manufacturing to fabricate FGMs composed of TC4 and TC11 alloys. The study examines the utilization of direct and continuous transition forms in conjunction with stress relieving and solution aging heat treatment processes. The composition, grain morphology, microstructure, and mechanical properties of FGMs are analyzed. Results indicate that in samples employing direct transition, higher heat treatment temperatures and durations lead to a more uniform composition and microstructure. Additionally, the interface morphology becomes increasingly indistinct, and transversal elongation at the interface is enhanced by the strain compensation of the two materials. In continuous transitional samples, an increase in the number of transitional layers results in a more uniform microstructure near the interface, leading to a reduction in interface morphology and an enhancement of mechanical properties. The fracture position tends to shift closer to the TC4 side, with both process forms exhibiting ductile fractures. The plasticity of the TC4 part is superior, and the ductile fracture morphology is more pronounced. This study offers a valuable experimental foundation for investigating the direct and continuous transition of titanium alloy FGMs.

Titanium alloy is widely utilized in diverse industries due to its exceptional specific strength, making it a material with significant potential for advancement. Nevertheless, homogeneous materials are inadequate to meet the demands of various applications. Functional gradient materials (FGMs) have garnered increasing interest for their ability to tailor materials and structures. The continuous transition in FGMs often offers a more uniform and well-connected interface. However, there remains a lack of comprehensive research on the transition interface. In this study, TC4/TC11 double-alloy materials were produced using double-wire additive manufacturing. The wire feeding rates were adjusted to create materials with varying compositions. The grain morphologies, microstructures, and mechanical properties were examined. It was observed that as the TC11 content increased, the grain size decreased, the β content rose, the ultimate tensile strength improved, and the elongation decreased. Additionally, analysis of the fracture morphologies revealed that the dimples became smaller, indicating characteristics of ductile fracture. Following the solid solution aging heat treatment, it is observed that the α phase increases in size, and the mechanical properties are enhanced. These observations indicate that double-wire additive manufacturing can produce diverse interfaces. Furthermore, the heat treatment process has been shown to enhance the material properties, thus establishing an experimental foundation for FGMs.

Ultra high field diffusion magnetic resonance imaging (dMRI) provides diffusion‐weighted (DW) images with a high signal‐to‐noise ratio, but increases inhomogeneity, which affects the accuracy of dMRI metric reconstruction. Current methods for correcting inhomogeneity rarely consider the accuracy of the reconstructed dMRI metrics. Deep learning models for reconstructing metrics from dMRI signals typically assume that DW images have a homogeneous intensity. To address these challenges, we propose a deep learning model capable of directly reconstructing high‐accuracy dMRI metric maps from inhomogeneous DW images. An attention‐based q‐space inhomogeneity‐resistant reconstruction network (qIRR‐Net) is proposed for the voxel‐wise reconstruction of diffusion tensor imaging and diffusion kurtosis imaging metrics. A training procedure based on data augmentation and consistency loss is introduced to ensure that the reconstruction results of qIRR‐Net are not affected by signal inhomogeneity. The 3T and 7T dMRI data from the Human Connectome Project are used for model training, testing, and evaluation. On the 3T dMRI data with simulated inhomogeneity, qIRR‐Net improves the peak signal‐to‐noise ratio by 5.39 and the structural similarity index measure by 0.18 compared with weighted linear least‐squares fitting. On the 7T dMRI data, the metric maps reconstructed by qIRR‐Net not only exhibit clearer tissue structures but also demonstrate greater stability compared with the weighted linear least‐squares results. The proposed qIRR‐Net enables the accurate reconstruction of dMRI metrics from inhomogeneous DW images. This approach could potentially be expanded to obtain multiple artifact‐free metric maps from ultrahigh field dMRI for neuroscience research and neurology applications. An attention‐based reconstruction model and a training framework incorporating data augmentation and consistency loss have been proposed to achieve inhomogeneity‐resistant dMRI metric reconstruction. The proposed method can be trained on 3T dMRI data and directly applied to 7T data without fine‐tuning, eliminating the need for homogeneous ultrahigh field dMRI data. The effectiveness of the model has been validated on 7T datasets and synthetic homogeneous 3T datasets.

Perovskite solar cells (PSCs) have attracted great attention in both academic and industrial sectors in the past years. Studies demonstrated that processing additive engineering was a facile way to improve the crystallinity and minimize the defect of metal halide perovskites (MHPs). In this study, we report efficient and stable PSCs, where the MHPs thin film is processed with KI additives. It is found that the KI processing additives could not only enhance the crystallization and suppress the defects of MHP thin film, but also boost charge transport, suppress non-radiative recombination, and enhance the hydrophobic properties of MHP thin film. As a result, the PSCs based on the MHPs thin film processed with KI additives exhibit more than 10% enhancement in efficiency and dramatically boosted stability compared to that based on pristine MHPs thin film. Our results indicated that the MHPs processed with processing additives are a simple engineering technique to boost the device performance of PSCs.

Many chiral carboxylic acids with α-amino, α-hydroxyl, and α-methyl groups are concurrently present in mammals establishing unique molecular phenotypes and multiple biological functions, especially host-microbiota symbiotic interactions. Their chirality-resolved simultaneous quantification is essential to reveal the biochemical details of physiology and pathophysiology, though challenging with their low abundances in some biological matrices and difficulty in enantiomer resolution. Here, we developed a method of the chirality-resolved metabolomics with sensitivity-enhanced quantitation via probe-promotion (Met-SeqPro) for analyzing these chiral carboxylic acids. We designed and synthesized a hydrazide-based novel chiral probe, (S)-benzoyl-proline-hydrazide (SBPH), to convert carboxylic acids into amide diastereomers to enhance their retention and chiral resolution on common C18columns. Using the d5-SBPH-labeled enantiomers as internal standards, we then developed an optimized ultrahigh-performance liquid chromatography with tandem mass spectrometry (UHPLC-MS/MS) method for simultaneous quantification of 60 enantiomers of 30 chiral carboxylic acids in one run. This enantiomer-resolved method showed excellent sensitivity (LOD < 4 fmol-on-column), linearity (R2> 0.992), precision (CV < 15%), accuracy (|RE| < 20%), and recovery (80−120%) in multiple biological matrices. With the method, we then quantified 60 chiral carboxylic acids in human urine, plasma, feces, and A549 cells to define their metabolomic phenotypes. This provides basic data for human phenomics and a promising tool for investigating the mammal-microbiome symbiotic interactions.

The Gleeble-3500 thermal simulator was utilized to perform warm compression experiments on multi-directional isothermal forged (MDIFed) mixed microstructure Ti–6Al–4V titanium alloy with partial ultra-fine grains at the conditions of deformation temperatures ranging from 550 °C to 700 °C, strain rates ranging from 0.001 s−1to 0.1 s−1, with the maximum deformation amount at 50%. The results show that the true stress-strain curve exhibits typical dynamic recrystallization (DRX) characteristics at different deformation conditions. Based on the Arrhenius Model, the constitutive equation of the MDIFedTi-6Al–4V titanium alloy was successfully constructed. The correlation coefficient (R) and average absolute relative error (AARE) values are 0.98341 and 7.01%, respectively, indicating that the Arrhenius Model can well describe the warm deformation behavior of the alloy. Through activation energy spectrum analysis, the average activation energy is approximately 308 kJ/mol. Further, utilizing the dynamic material model (DMM), power dissipation diagrams, instability diagrams, and processing maps were drawn, and the optimal warm processing ranges were determined to be 630 °C–700 °C, 0.001s−1∼0.002s−1and 650 °C–700 °C, 0.06s−1∼0.1s−1. At the same time, the unstable domains were identified to be concentrated in the range of 550 °C–640 °C, 0.03s−1∼0.1s−1. Through the analysis of local misorientation and Schmidt factor in EBSD technology, The results indicate that the deformation mechanism of the MDIFed Ti–6Al–4V titanium alloys is primarily governed by dislocation slip and DRX. The activation of multiple slip systems contributes to the coordination and homogeneity of deformation, thereby enhancing the overall plastic deformation ability of the MDIFed Ti–6Al–4V titanium alloys.

We report a novel halogen-bond-assisted NHC-catalyzed (dynamic) kinetic resolution strategy for the synthesis of axially chiral heterobiaryls. A class of axially chiral quinolines are prepared efficiently in excellent enantioselectivities (≤98% ee) employing 3–5 mol % NHC catalyst. Mechanistic studies reveal the indispensability of 5-bromo-2-iodobenzaldehyde in this reaction, in which a pivotal halogen bonding interaction plays a crucial role in the process.

Negative ions are crucial in various scientific and technological fields, such as antimatter research, interstellar chemistry, accelerator-based radionuclide dating, fusion injection for controlled nuclear fusion, and neurological disease treatment. Thus, producing a large number of negative ions is highly important. Herein, we present the first theoretical evidence of a large fraction of negative-ion formation during the grazing scattering of carbon atoms from a CsI(100) surface. The conversion efficiency of the negative ions reaches ≥86% within a projectile energy range of Ep∈ [5,10] keV. These remarkable results are attributed to large anion and cation polarizations, which lead to an evidently large Mott–Littleton polarization interaction. This interaction lowers the energy defect of valence band electron capture to below 1.8 eV near the surface anion sites, drastically increasing the electron-capture probability. The destruction of negative ions occurs owing to affinity electron detachment via the Coulomb barrier tunneling to the vacuum level during the interactions with surface anion sites along their trajectories. Our results indicate that this collision system can be used to design next-generation negative carbon ion sources for studying the isotope shift of electron affinity and provide insights into electron correlation effects and molecular-level irradiation pathological studies, especially for promoting nerve tissue repair and regeneration through Ep∈ [5,10] keV negative carbon ion irradiation.

Neurensin-2(Nrsn2) is a neuro-specific gene linked to neurodevelopmental disorders and has recently been reported to function as a bidirectional emotional regulator, highlighting its molecular roles in the nervous system. However, the connections between Nrsn2, brain architecture, and functionality remain to be fully elucidated. Our study utilized 11.7 T multimodal magnetic resonance imaging (MRI) to assess the impact of Nrsn2gene knockout on the brain’s microstructure, regional functional activity, and network connectivity during different developmental phases in mice. We observed significant changes in the functional brain network connectivity of Nrsn2−/−mice without marked differences in brain microstructure or regional activity. These changes were particularly pronounced in sensory-related areas, such as the gustatory and auditory systems, in both juvenile and adult specimens. Previous studies have correlated the enhanced Default Mode Network (DMN) with depression, and Nrsn2knockout has been associated with stress resilience. Our findings further revealed reduced connectivity in various DMN regions in adult Nrsn2−/−mice, suggesting a potential link to increased stress tolerance. Moreover, the sensory system’s critical role in environmental perception implies that alterations in network connectivity due to Nrsn2knockout could affect the processing and integration of external inputs, thereby influencing emotional experiences.

A novel retrodirective cross-eye system based on microwave photonics is proposed. The scheme utilizes a dual-parallel Mach–Zehnder modulator (DPMZM) to modulate the RF signal received at the left (right) end onto the same optical fiber, transmits the optical signal to the right (left) end, and converts the optical signal back into RF signal by a photodetector (PD). By controlling the bias voltage of the main modulator of two DPMZMs, the phase of the transmitted RF signal can be reversed and the amplitude of the transmitted RF signal can be adjusted. The scheme has large working bandwidth and low cost. Only generating two-phase inverted signals is not enough to implement effective cross-eye jamming. It is necessary to consider nonideal conditions and the impact of echoes on jamming in order to obtain practical technical solutions. The scheme exhibits good robustness, as the relative phase of two jamming signals remains insensitive to bias drift. The scheme provides flexibility in controlling the signal amplitude to meet the requirements for angle deception. From the phase-front distortion perspective, an analysis of the impact of cross-eye parameters on the deception angle is conducted. In the experiment, the transfer functions in the 4–27-GHz frequency range are provided. Through hardware-in-loop simulation, the capability of the scheme to effectively deceive the monopulse radar is validated. Discussions and analysis are conducted on the dynamic cross-eye jamming process and the impact of target echoes on the cross-eye jamming performance. Parameter optimization and control strategy for the scheme are investigated with the impact of target echo considered.

The finite sample theory using higher order asymptotics provides better approximations of the bias and mean squared error (MSE) for a class of estimators. Rilston, Srivastava and Ullah (1996) provided the second-order bias results of conditional mean regression. This paper develops new analytical results on the second-order bias up to order ON-1and MSE up to order ON-2of the conditional quantile regression estimators. First, we provide the general results on the second-order bias and MSE of conditional quantile estimators. The second-order bias result enables an improved bias correction and thus to obtain improved quantile estimation. In particular, we show that the second-order bias are much larger towards the tails of the conditional density than near the median, and therefore the benefit of the second order bias correction is greater when we are interested in the deeper tail quantiles, e.g., for the study of financial risk management. The higher order MSE result for the quantile estimation also enables us to better understand the sources of estimation uncertainty. Next, we consider three special cases of the general results, for the unconditional quantile estimation, for the conditional quantile regression with a binary covariate, and for the instrumental variable quantile regression (IVQR). For each of these special cases, we provide the second-order bias and MSE to illustrate their behavior which depends on certain parameters and distributional characteristics. The Monte Carlo simulation indicates that the bias is larger at the extreme low and high tail quantiles, and the second-order bias corrected estimator has better behavior than the uncorrected ones in both conditional and unconditional quantile estimation. The second-order bias corrected estimators are numerically much closer to the true parameters of the data generating processes. As the higher order bias and MSE decrease as the sample size increases or as the regression error variance decreases, the benefits of the finite sample theory are more apparent when there are larger sampling errors in estimation. The empirical application of the theory to the predictive quantile regression model in finance highlights the benefit of the proposed second-order bias reduction.

In this paper, the Hadfield steel matrix composite reinforced with dispersed high chromium cast iron (HCCI) was prepared by using the hot-rolling forming method. The microstructure and mechanical properties of the bimetal composites were analyzed. The experimental results showed that the brittle HCCI was necked and broken into uneven blocks or particles after hot-rolling deformation. The fractured hard HCCI was encased in Hadfield steel to form HCCI-reinforced Hadfield steel composite. Hot rolling results in good metallurgical bonding of the two metals, and Mn, Cr, Fe and Si elements were diffused at the interface. The wear property of the composite was higher than that of Hadfield steel. The hard HCCI played an important role in protecting the Hadfield steel matrix from wearing. The average impact toughness of the composite reached 38.2 J cm−2. The impact property of the composite was between Hadfield steel and HCCI, but significantly higher than that of as-cast iron.

Since the 1950s, numerous soil and water conservation measures have been implemented to control severe soil erosion in the Liuhe River Basin (LRB), China. While these measures have protected the upstream soil and water ecological environment, they have led to a sharp reduction in the downstream flow and the deterioration of the river ecological environment. Therefore, it is important to evaluate the impact of soil and water conservation measures on hydrological processes to assess long-term runoff changes. Using the Soil and Water Assessment Tool (SWAT) models and sensitivity analyses based on the Budyko hypothesis, this study quantitatively evaluated the effects of climate change, direct water withdrawal, and soil and water conservation measures on runoff in the LRB during different periods, including different responses to runoff discharge, hydrological regime, and flood processes. The runoff series were divided into a baseline period (1956–1969) and two altered periods, i.e., period 1 (1970–1999) and period 2 (2000–2020). Human activities were the main cause of the decrease in runoff during the altered periods, contributing 86.03% (−29.61 mm), while the contribution of climate change was only 13.70% (−4.70 mm). The impact of climate change manifests as a decrease in flood volume caused by a reduction in precipitation during the flood season. Analysis of two flood cases indicated a 66.00%–84.00% reduction in basin runoff capacity due to soil and water conservation measures in the upstream area. Soil and water conservation measures reduced the peak flow and total flood volume in the upstream runoff area by 77.98% and 55.16%, respectively, even with nearly double the precipitation. The runoff coefficient in the reservoir area without soil and water conservation measures was 4.0 times that in the conservation area. These results contribute to the re-evaluation of soil and water conservation hydrological effects and provide important guidance for water resource planning and water conservation policy formulation in the LRB.

A robust decoding model that can efficiently deal with the subject and period variation is urgently needed to apply the brain–computer interface (BCI) system. The performance of most electroencephalogram (EEG) decoding models depends on the characteristics of specific subjects and periods, which require calibration and training with annotated data prior to application. However, this situation will become unacceptable as it would be difficult for subjects to collect data for an extended period, especially in the rehabilitation process of disability based on motor imagery (MI). To address this issue, we propose an unsupervised domain adaptation framework called iterative self-training multisubject domain adaptation (ISMDA) that focuses on the offline MI task. First, the feature extractor is purposefully designed to map the EEG to a latent space of discriminative representations. Second, the attention module based on dynamic transfer matches the source domain and target domain samples with a higher coincidence degree in latent space. Then, an independent classifier oriented to the target domain is employed in the first stage of the iterative training process to cluster the samples of the target domain through similarity. Finally, a pseudolabel algorithm based on certainty and confidence is employed in the second stage of the iterative training process to adequately calibrate the error between prediction and empirical probabilities. To evaluate the effectiveness of the model, extensive testing has been performed on three publicly available MI datasets, the BCI IV IIa, the High gamma dataset, and Kwon et al. datasets. The proposed method achieved 69.51%, 82.38%, and 90.98% cross-subject classification accuracy on the three datasets, which outperforms the current state-of-the-art offline algorithms. Meanwhile, all results demonstrated that the proposed method could address the main challenges of the offline MI paradigm.

To improve the load identification capability and high overloading-resistibility of the silicon-based micro-electromechanical system (MEMS) setback safety device, a MEMS setback safety device with curved zig-zag tracks as the primary load identification mechanism is designed in this article. The dynamic response of the device under launch and drop load is obtained by the finite element method (FEM) simulation. The result shows that the setback safety device can effectively identify the launch load and distinguish the service load. Moreover, the strength model of the curved zig-zag tracks in the collision is established by the quasi-static method. The strength model is verified by FEM simulation, and the result is highly consistent with the FEM simulation result. The influence of structure parameters on the strength of zig-zag tracks was analyzed. Moreover, the methods to improve the overloading-resistibility of structures with surface collision are given. The impact experiment platform was established by Marchant Hammer and two types of experiments were conducted: 1) multiple impact with increasing load amplitude on a single device and 2) single high amplitude impact on the device. The result shows that the device has sufficient strength while being able to effectively differentiate between drop loads.

ABSTRACTDefective mitophagy is consistently found in postmortem brain and iPSC-derived neurons from Alzheimer disease (AD) patients. However, there is a lack of extensive examination of mitophagy status in serum or cerebrospinal fluid (CSF), and the clinical potential of mitophagy biomarkers has not been tested. We quantified biomarkers of mitophagy/autophagy and lysosomal degradation (PINK1, BNIP3L and TFEB) in CSF and serum from 246 individuals, covering mild cognitive impairment due to AD (MCI-AD, n = 100), dementia due to AD (AD-dementia, n = 100), and cognitively unimpaired individuals (CU, n = 46), recruited from the Czech Brain Aging Study. Cognitive function and brain atrophy were also assessed. Our data show that serum and CSF PINK1 and serum BNIP3L were higher, and serum TFEB was lower in individuals with AD than in corresponding CU individuals. Additionally, the magnitude of mitophagy impairment correlated with the severity of clinical indicators in AD patients. Specifically, levels of PINK1 positively correlated with phosphorylated (p)-MAPT/tau (181), total (t)-MAPT/tau, NEFL (neurofilament light chain), and NRGN (neurogranin) levels in CSF and negatively with memory, executive function, and language domain. Serum TFEB levels negatively correlated with NEFL and positively with executive function and language. This study reveals mitophagy impairment reflected in biofluid biomarkers of individuals with AD and associated with more advanced AD pathology.Abbreviation:Aβ: amyloid beta; AD: Alzheimer disease; AVs: autophagic vacuoles; BNIP3L: BCL2 interacting protein 3 like; CU: cognitively unimpaired; CSF: cerebrospinal fluid; LAMP1: lysosomal-associated membrane protein 1; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MCI: mild cognitive impairment; NRGN: neurogranin; NEFL: neurofilament light chain; p-MAPT/tau: phosphorylated microtubule associated protein tau; PINK1: PTEN induced kinase 1; t-MAPT/tau: total microtubule associated protein tau; TFEB: transcription factor EB; TMT: Trail Making Test.

Premature peripheral artery disease (PAD), defined by lower extremity revascularization (LER) at age ≤ 50 years, is associated with poor major adverse limb events. The early onset of disease is thought to be influenced by genetic factors that regulate homeostasis of the vascular wall and coagulation. The aim of this study is to investigate the effect of anticoagulation as an adjunct to antiplatelet therapy on the outcomes of LER in patients with premature PAD.

Bacterial infection has always posed a severe threat to public health. Gold nanoparticles (Au NPs) exhibit exceptional biocompatibility and hold immense potential in biomedical applications. However, their antibacterial effectiveness is currently unsatisfactory. Herein, a chiral antibacterial agent with high stability was prepared by the modification of Au NPs with d-cysteine with the assistance of polyethylene glycol (PEG). The as-synthesized d-cysteine/PEG-Au NPs (D/P-Au NPs) exhibited a stronger (99.5–99.9%) and more stable (at least 14 days) antibacterial performance against Gram-negative (Escherichia coliand Listeria monocytogenes) and Gram-positive (Salmonella enteritidisand Staphylococcus aureus) bacteria, compared with other groups. The analysis of the antibacterial mechanism revealed that the D/P-Au NPs mainly affected the assembly of ribosomes, the biosynthesis of amino acids and proteins, as well as the DNA replication and mismatch repair, ultimately leading to bacterial death, which is significantly different from the mechanism of reactive oxygen species-activated metallic antibacterial NPs. In particular, the D/P-Au NPs were shown to effectively accelerate the healing of S. aureus-infected wounds in mice to a rate comparable to or slightly higher than that of vancomycin. This work provides a novel approach to effectively design chiral antibacterial agents for bacterial infection treatment.

Thermal-induced cracks in explosive crystals could hinder heat conduction, generate hot spots, and ultimately induce detonation, posing safety hazards for the storage and transportation of explosives and impacting their performance. The thermotropic phase transition occurring below decomposition and detonation temperature complicate the evolution and mechanism of the cracks during heating for some explosives. In this work, the generation and evolution of cracks induced by the heating at both 180 and 185 °C above the phase transition temperature were investigated for the classical energetic crystal HMX. Three types of cracks, fast cracks, slow cracks, and small cracks,were observed and the corresponding formation mechanisms and distribution characteristics were systematically studied by optical microscopy, scanning electron microscopy, 3D X-ray computed tomography, X-ray diffraction, and Raman spectroscopy. The results indicate that fast cracksform due to thermal expansion, tending to be perpendicular to the lattice b-direction. Slow cracksarise from the (101) (or (1̅10)) plane slipping and exist only in the subsurface. Small cracksoriginate from the β–δ phase transition of HMX, are denser and more disordered, and mainly occur close to the heated surface. These findings provide further insight into the relationship between thermal damage and the heat conduction characteristics of HMX crystals.

Current density imaging is helpful for discovering interesting electronic phenomena and understanding carrier dynamics, and by combining pressure distributions, several pressure-induced novel physics may be comprehended. In this work, noninvasive, high-resolution two-dimensional images of the current density and pressure gradient for graphene ribbon and hBN-graphene-hBN devices are explored using nitrogen-vacancy (NV) centers in diamond under high pressure. The two-dimensional vector current density is reconstructed by the vector magnetic field mapped by the near-surface NV center layer in the diamond. The current density images accurately and clearly reproduce the complicated structure and current flow of graphene under high pressure. Additionally, the spatial distribution of the pressure is simultaneously mapped, rationalizing the nonuniformity of the current density under high pressure. The current method opens a significant new avenue to investigate electronic transport and conductance variations in two-dimensional materials and electrical devices under high pressure as well as for nondestructive evaluation of semiconductor circuits.

This paper is devoted to introducing and investigating the bounded variation capacity and the perimeter in the abstract Wiener space X, thereby discovering some related inequalities. Functions of bounded variation in an abstract Wiener space Xhave been studied by many scholars. As the continuation of this research, we define the corresponding BV capacity capH⁡(⋅){\operatorname{cap}_{H}(\,\cdot\,)}(now called abstract Wiener BV capacity) and investigate its properties. We also investigate some properties of sets of finite γ-perimeter, with γ being a Gaussian measure. Subsequently, the isocapacitary inequality associated with capH⁡(⋅){\operatorname{cap}_{H}(\,\cdot\,)}is presented and we are able to show that it is equivalent to the Gaussian isoperimetric inequality. Finally, we prove that every set of finite γ-perimeter in Xhas mean curvature in L1⁢(X,γ){L^{1}(X,\gamma)}.

Mesoporous GaN-based multiple quantum wells (MP MQWs) with MP GaN-distributed Bragg reflectors (MP GaN DBRs) are obtained viaelectrochemical etching in a HNO3solution. Wafer-scale MP MQWs with MP GaN DBRs have a smooth surface, low root-mean-square roughness (RRMS), and high reflectivity. Then, the p-GaN layer is grown on the MP MQWs with the MP GaN DBR substrate. Compared with as-grown light-emitting devices (AG LEDs), MP LEDs with MP GaN DBRs possess an increased and blue-shifted luminescence emission. The luminescence enhancement results from the reduced density of dislocations of MP MQWs and the highly reflective effect of MP GaN DBRs. Also, the blue shift is due to reduced stress in the MQW layer.

Objective: Electroencephalography (EEG) with high time-resolution allows for recording dynamic cortical activity during walking and provides new insight into the underlying pathophysiology of gait impairments in PD. However, traditional gait-phase-specific EEG analysis only measures the brain activities in the isolated gait phase, but neglects the between-gait-phase interactions as well as the whole-gait-cycle characteristics, and therefore is unable to effectively reflect the abnormal cortical gait control. Methods: In this study, we introduced three whole-gait-cycle measures of intra-stride EEG activity (i.e., mean desynchronization, amplitude of fluctuations, and coupling to the gait phase), and investigated their abnormalities in PD and relationships with gait impairments, which were further compared with the traditional gait-phase-specific measures. Results: Compared with healthy controls, PD patients showed overwhelming stronger desynchronizations (ERD) across the whole gait cycle in θ, α and low-β bands, implying a cortical compensatory strategy in response to the low efficiency of the motor network. Patients also exhibited weaker intra-stride ERD fluctuations in the central area in α and low-β bands, with reduced amplitude and less coupling to the gait phase, which were correlated with gait impairments in walking speed, gait rhythm and walking stability. However, gait-phase-specific EEG measures did not show any significant correlation with gait impairments in PD. Conclusion: Our results demonstrated the efficiency of whole-gait-cycle EEG measures in characterizing the abnormal cortical gait control, and for the first time, associated gait impairments with weak intra-stride electrocortical fluctuations. Significance: The findings may shed light on the development of cortical-targeted interventions for PD.

Electroencephalogram (EEG) has been widely used in driver fatigue recognition due to its high-resolution and noninvasive characteristics in recent years. However, EEG signals have strong variability and significant individual differences, which put forward extremely high requirements on the generalization ability of the model in practical use. To address these issues, a dynamic threshold distribution domain adaptation network (DTDDAN) is proposed to classify the EEG signals for driver fatigue recognition. In the model, the domain discriminator is introduced to alleviate the marginal distribution difference of different domains, and a new loss, Jensen–Shannon loss (JS loss), is applied to relieve the conditional distribution difference of different domains to learn cross-subject invariant deep features of EEG signals. In addition, we propose a dynamic threshold pseudo label strategy to assign pseudo labels to the samples of the target domain in the training process of the model. This strategy can make sure that the high-quality unlabeled data of the target domain contribute to the model training and the model can fully learn the information of the target domain. With extensive experiments on our self-constructed EEG-based fatigue driving data set, competitive performance is achieved for the cross-subject classification task, with average classification sensitivity, specificity, and accuracy of 89.8%, 93.8%, and 92.2%, respectively. And it is shown that our model can effectively extract cross-subject invariant EEG signals deep features by the experimental results.

This article explores the application of the multiagent reinforcement learning (MARL) algorithm in addressing the large-scale traffic signal control (TSC) problem. To address the TSC problem in complex urban traffic networks, most existing algorithms focus on optimizing local traffic flow at each intersection through decentralized training based on either the local observations or messages from its neighboring intersections, which, however, lacks the concept of cooperative learning. To conquer such limitations, a novel distributed critic with decentralized actor (DCDA) framework is proposed, which allows the communication messages and temporal difference (TD) losses to be exchanged among neighboring intersections. Specially, by considering the traffic network as a communication network of agents (more precisely, intersections and lanes are considered as agents and edges, respectively), a distributed global average TD loss estimation algorithm is designed in the distributed critic step to estimate the global average TD loss estimation and enhance collaboration among agents. Moreover, in the decentralized actor step, the policy gradient decomposition method is adopted for each agents to learn its local policy solely based on its local action-value function. By adhering to the DCDA framework, a novel distributed policy gradient decomposition (DPGD) algorithm is further proposed to address the TSC problem. Empirical experiments demonstrate that the efficiency, robustness, and stability of the DPGD algorithm outperform the state-of-the-art MARL algorithms in both the environments of cooperative adaptive cruise control and adaptive traffic signal control.