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Background The association between platelet endothelial cell adhesion molecule‐1 (PECAM‐1) with cerebral small‐vessel disease and cognition in dementia‐free subjects remains uninvestigated. Methods and Results A prospective cohort of dementia‐free subjects was recruited from memory clinics and followed up for 5 years. Annual neurocognitive assessments and twice‐yearly brain magnetic resonance imaging scans were performed. Associations of baseline plasma PECAM‐1 levels with cerebral small‐vessel disease, cognitive decline (Montreal Cognitive Assessment scores and executive function Z scores), and incident dementia were evaluated. Of 213 subjects (aged 70.2±7.7 years, 51.2% men), median PECAM‐1 levels were 0.790 (interquartile range, 0.645–0.955 ng/mL). Compared with the highest tertile, subjects within the lowest PECAM‐1 tertile had greater cross‐sectional white matter hyperintensity volume (β=4.84 [95% CI, 0.67–9.01]; P=0.023), age‐related white matter change scores (β=1.39 [95% CI, 0.12–2.67]; P=0.033), and cerebral microbleeds (Adjusted risk ratio, 2.59 [95% CI, 1.19–5.62]; P=0.016). Of the 204 participants with follow‐up data (median, 60.0 [interquartile range, 60.0–60.0] months), 24 (11.8%) developed incident dementia. Compared with the highest tertile, subjects within the lower tertiles of PECAM‐1 had a higher risk of incident dementia (first tertile: adjusted hazard ratio [AHR], 4.52 [95% CI, 1.35–15.13]; P=0.024; second tertile: AHR, 3.28 [95% CI, 1.02–10.60]; P=0.047). The lowest PECAM‐1 tertile was associated with greater progression of white matter hyperintensity volume (β=4.15 [95% CI, 0.06–8.24]; P=0.047), cerebral microbleeds (incident relative risk [IRR], 2.21 [95% CI, 1.05–4.65]; P=0.036), and decline in executive function (β=−0.45 [95% CI, −0.76 to −0.14]; P=0.004), and Montreal Cognitive Assessment (β=−1.32 [95% CI, −2.30 to −0.35]; P=0.008) scores. Conclusions In dementia‐free subjects, lower circulating PECAM‐1 levels are associated with greater cerebral small‐vessel disease progression and cognitive decline, thus warranting future study as a potential therapeutic target.

Abstract On 24 April 2023, a Coronal Mass Ejection event caused the solar wind to become sub‐Alfvénic, leading to the development of an Alfvén Wing configuration in the Earth's magnetosphere. Alfvén Wings have previously been observed as cavities of low flow around moons in Jupiter's and Saturn's magnetospheres, but the observing spacecraft did not have the ability to directly measure the Alfvén Wings' current structures. Through in situ measurements made by the Magnetospheric Multiscale spacecraft, the 24 April event provides us with the first direct measurements of current structures during an Alfvén Wing configuration. These structures are observed to be significantly more anti‐field‐aligned and electron‐driven than the typical diamagnetic magnetopause current, indicating the disruption caused to the magnetosphere current system by the Alfvén Wing formation. The magnetopause current is then observed to recover more of its typical, perpendicular structure during the magnetosphere's recovery from the Alfvén Wing formation.

The rapid pace of urbanization underscores the importance of waste monitoring and management in urban planning and environmental conservation. Remote sensing technology enables the aerial observation of terrestrial and marine features, with high-resolution images revealing diverse objects. Deep learning techniques have gained prominence for enhancing waste monitoring precision and efficiency. This article surveys deep learning approaches for waste monitoring in remote sensing images, focusing on relevant datasets. It reviews existing remote sensing datasets, including those from uncrewed aerial vehicles and satellites, for monitoring solid waste and marine debris. Nine publicly available datasets are described in detail, highlighting their origins and applications. The monitoring methods include two kinds of methods: 1) semantic segmentation; and 2) object detection. Semantic segmentation focuses on pixel-level classification and boundary delineation, while object detection targets object-level localization and shape. Representative methods within these categories are explored, and benchmark results from recent studies are summarized to evaluate the performance of various techniques. The discussion addresses current limitations and suggests future research directions, aiming to assist researchers and professionals in environmental monitoring.

Pork is a favorite type of meat with a large share in the structure of consumption worlwide, including China and Russia. Pork production in China and Russia has been constantly growing over the last years. This type of meat remains to be in high demand due to its sensory properties despite consumer attitude to pork fat content. This review presents the short history of pig farming in China and Russia, as well as the modern trends in the development of this industry. The data on the pork production and consumption in two countries over the last years are compared. Characteristics that consumers consider important when buying pork and negative factors influencing consumer choice are described. Consumer properties of meat products from pork depend on keeping conditions and feeding of animals. Meat composition and functional properties depend greatly on pig breed. Information about pig breeds that are raised in China (depending on a region) and Russia, as well as indicators of productivity of pigs of certain breeds, are presented.

Under the influence of crosswind, when high-speed trains (HSTs) meet on a bridge, they produce complex vortexes, strong aerodynamic loads, and other aerodynamic effects. The purpose of this paper is to reveal the influences of crosswind and windbreaks on the vortexes generated by HSTs, the pressure distributions on the surfaces of the trains, and the aerodynamic load coefficients of the trains when they meet on a bridge, as well as the influence of the pressure waves generated by the trains on the windbreaks. The three-dimensional incompressible improved delayed detached eddy simulation (IDDES) method based on the SST k-ω turbulence model is used for numerical calculation purposes, and the overset grid method is used to realize the relative motions of the trains. The results show that the windbreaks can reduce the negative pressure (NP) imposed on the train surface and effectively improve the pressure distribution; crosswinds have a significant impact on the vortexes generated by trains, and the vortexes generated by the upstream train affect the stability of the downstream train; windbreaks can reduce the aerodynamic load applied when trains meet and thus improve the safety of the trains; and the head and tail waves generated by trains impose pressure on the windbreaks, which affects the reliability of the windbreaks installations. The simulation results can provide a preliminary reference for future research.

Convolutional neural networks (CNNs) have been successfully applied to motor imagery (MI)-based brain–computer interface (BCI). Nevertheless, single-scale CNN fail to extract abundant information over a wide spectrum from EEG signals, while typical multi-scale CNNs cannot effectively fuse information from different scales with concatenation-based methods. To overcome these challenges, we propose a new scheme equipped with attention-based dual-scale fusion convolutional neural network (ADFCNN), which jointly extracts and fuses EEG spectral and spatial information at different scales. This scheme also provides novel insight through self-attention for effective information fusion from different scales. Specifically, temporal convolutions with two different kernel sizes identify EEG $\boldsymbol{\mu }$ and $\boldsymbol{\beta }$ rhythms, while spatial convolutions at two different scales generate global and detailed spatial information, respectively, and the self-attention mechanism performs feature fusion based on the internal similarity of the concatenated features extracted by the dual-scale CNN. The proposed scheme achieves the superior performance compared with state-of-the-art methods in subject-specific motor imagery recognition on BCI Competition IV dataset 2a, 2b and OpenBMI dataset, with the cross-session average classification accuracies of 79.39% and significant improvements of 9.14% on BCI-IV2a, 87.81% and 7.66% on BCI-IV2b, 65.26% and 7.2% on OpenBMI dataset, and the within-session average classification accuracies of 86.87% and significant improvements of 10.89% on BCI-IV2a, 87.26% and 8.07% on BCI-IV2b, 84.29% and 5.17% on OpenBMI dataset, respectively. What is more, ablation experiments are conducted to investigate the mechanism and demonstrate the effectiveness of the dual-scale joint temporal-spatial CNN and self-attention modules. Visualization is also used to reveal the learning process and feature distribution of the model.

To investigate the thermoplastic deformation behavior of FV520B steel, the Gleeble-3800 thermal simulation test machine is used to perform hot compression test on FV520B steel. Select a deformation temperature of 900 °C - 1 050 °C, set the strain rate to 0,005 s-1 - 5 s-1 . The results indicate that the Arrhenius constitutive model of FV520B steel with strain compensation correlation coefficient value is 0,99601, and the average relative error is 3,061 %, realizing the fitting of flow stress and prediction, verified the feasibility of the model.

The rapid development and widespread application of Unmanned Aerial Vehicles (UAV) have raised significant concerns about safety and privacy, thus requiring powerful anti-UAV systems. This survey provides an overview of anti-UAV detection and tracking methods in recent years. Firstly, we emphasize the key challenges of existing anti-UAV and delve into various detection and tracking methods. It is noteworthy that our study emphasizes the shift toward deep learning to enhance detection accuracy and tracking performance. Secondly, the survey organizes some public datasets, provides effective links, and discusses the characteristics and limitations of each dataset. Next, by analyzing current research trends, we have identified key areas of innovation, including the progress of deep learning techniques in real-time detection and tracking, multi-sensor fusion systems, and the automatic switching mechanisms that adapt to different conditions. Finally, this survey discusses the limitations and future research directions. This paper aims to deepen the understanding of innovations in anti-UAV detection and tracking methods. Hopefully our work can offer a valuable resource for researchers and practitioners involved in anti-UAV research.

Abstract When the solar wind speed falls below the local Alfvén speed, the magnetotail transforms into an Alfvén wing configuration. A Grid Agnostic Magnetohydrodynamics for Extended Research Applications (GAMERA) simulation of Earth's magnetosphere using solar wind parameters from the 24 April 2023 sub‐Alfvénic interval is examined to reveal modifications of Dungey‐type magnetotail reconnection during sustained sub‐Alfvénic solar wind. The simulation shows new magnetospheric flux is generated via reconnection between polar cap field lines from the northern and southern hemisphere, similar to Dungey‐type magnetotail reconnection between lobe field lines mapping to opposite hemispheres. The key feature setting the Alfvén wing reconnection apart from the typical Dungey‐type is that the majority of new magnetospheric flux is added to the polar cap at local times 1–3 (21‐23) in the northern (southern) hemisphere. During most of the sub‐Alfvénic interval, reconnection mapping to midnight in the polar cap generates relatively little new magnetospheric flux.

To evaluate the hygroscopic cloud seeding in reality, this study develops a hybrid microphysics scheme using a Weather Research and Forecasting (WRF) model, WDM6-NCU (WDM6 modified by National Central University), which involves 43 bins of seeded cloud condensation nuclei (CCN) in the WDM6 bulk method scheme. This scheme can describe the size distribution of seeded CCN and explain the process of the CCN imbedding and cloud and raindrop formation in detail. Furthermore, based on the observational CCN size distribution applied in the modelling, a series of tests on cloud seeding were conducted during the seeding periods of 21–22 October 2020 with stratocumulus clouds. The model simulation results reveal that seeding in in-cloud regions with an appropriate CCN size distribution can yield greater rainfall and that spreading the seeding agents over an area of 40–60 km2 is the most efficient strategy to create a sufficient precipitation rate. With regard to the microphysical processes, the main process that causes the enhancement of precipitation is the strengthening of the accretion process of raindrops. In addition, hygroscopic particles larger than 0.4 µm primarily contribute to cloud-seeding effects. The study results could be used as references for model development and warm-cloud-seeding operations.

Abstract Target tracking has a wide range of applications in intelligent transportation, real‐time monitoring, human‐computer interaction and other aspects. However, in the tracking process, the target is prone to deformation, occlusion, loss, scale variation, background clutter, illumination variation, etc., which bring great challenges to realize accurate and real‐time tracking. Tracking based on Siamese networks promotes the application of deep learning in the field of target tracking, ensuring both accuracy and real‐time performance. However, due to its offline training, it is difficult to deal with the fast motion, serious occlusion, loss and deformation of the target during tracking. Therefore, it is very helpful to improve the performance of the Siamese networks by learning new features of the target quickly and updating the target position in time online. The broad learning system (BLS) has a simple network structure, high learning efficiency, and strong feature learning ability. Aiming at the problems of Siamese networks and the characteristics of BLS, a target tracking method based on BLS is proposed. The method combines offline training with fast online learning of new features, which not only adopts the powerful feature representation ability of deep learning, but also skillfully uses the BLS for re‐learning and re‐detection. The broad re‐learning information is used for re‐detection when the target tracking appears serious occlusion and so on, so as to change the selection of the Siamese networks search area, solve the problem that the search range cannot meet the fast motion of the target, and improve the adaptability. Experimental results show that the proposed method achieves good results on three challenging datasets and improves the performance of the basic algorithm in difficult scenarios.

Abstract Promoting cooperation in the network has always been a focus of evolutionary game dynamics research. In this paper, a three-strategy local decision model is designed to promote cooperative behavior between agents. The hide right is introduced to accurately describe the agents which tend to choose conservative strategy to achieve stable payoffs. Agents only consider their own and non-defective neighbors’ payoffs information to make their decisions for next move. According to the adaptive dynamics, the equilibrium states under different conditions have been studied. It is found that the introduction of hide strategy helps to reduce the occurrence of defection. The conclusion of this paper will provide a reference for the cooperation promotion mechanism of selfish agents that are closer to the actual situation.

Abstract To evaluate the impact of elevator travel on intraocular pressure after vitreoretinal surgery with gas tamponade. Patients undergoing pars plana vitreoretinal surgery with and without gas insertion were recruited on post-operative day 1. All intraocular pressures were measured three times by Tono-Pen AVIA (Reichert, USA) on the fourth floor and, after rapid ascent in an elevator, on the 12th floor of the hospital. All patients were observed and asked for any symptoms of pain or nausea for at least 15 min. In this study, 54 patients were recruited. Twenty-seven patients underwent vitreoretinal procedures with gas insertion, while 27 patients without gas insertion acted as controls. The mean age of patients was 60.9 years. The mean changes in intraocular pressure of the patients with gas insertion (+ 1.39 mmHg) were greater than those without gas insertion (− 0.43 mmHg) and statistically significantly different (95% CI 1.17–2.48, P

This study investigates the evolutions of microstructure and performance of TiNbZrTaHf-based alloys from equiatomic high-entropy alloys to Ti-rich dilute alloys. A novel biocompatible alloy, Ti50Nb12.5Zr12.5Ta12.5Hf12.5, was developed with a high yield strength of 839 ± 12 MPa, low modulus of 61.5 GPa, low magnetic susceptibility of ∼1.99 × 10−6 cm3g−1, and unprecedented tensile plasticity of 28.7 ± 2.2%. The study discusses the underlying mechanisms responsible for these superior performances, attributing the exceptional plasticity to the activation of cross-slip dislocations, enhanced dynamic recovery and high resistance to crack propagation. The findings provide new insights into the development of biomedical titanium alloys.

With the rapid increase of economic globalization, the significant expansion of shipping volume has resulted in shipping route congestion, causing the necessity of trajectory prediction for effective service and efficient management. While trajectory prediction can achieve a relatively high level of accuracy, the performance and generalization of prediction models remain critical bottlenecks. Therefore, this article proposes a dual-attention (DA) based end-to-end (E2E) neural network (DAE2ENet) for trajectory prediction. In the E2E structure, long short-term memory (LSTM) units are included for the task of pursuing sequential trajectory data from the encoder layer to the decoder layer. In DA mechanisms, global attention is introduced between the encoder and decoder layers to facilitate interactions between input and output trajectory sequences, and multi-head self-attention is utilized to extract sequential features from the input trajectory. In experiments, we use a ro-ro ship with a fixed navigation route as a case study. Compared with baseline models and benchmark neural networks, DAE2ENet can obtain higher performance on trajectory prediction, and better validation of environmental factors on ship navigation.

Lunar crustal magnetic anomalies (LCMA) are sub-ion-gyroradius structures that have been shown to stand off the solar wind (SW) plasma from the Moon’s surface, forming shock-like discontinuities and reflecting incident SW protons. In this Letter, the results of high-resolution, 2D fully kinetic simulations show a bursty electron-only magnetic reconnection in the SW-LCMA interaction region, characterized by the quasiperiodic formation and ejection of magnetic islands and strong parallel electron flows along the X-point separator lines. The islands are observed to modify the magnetic pressure pileup and Hall electric field above the LCMA, leading to sharp increases in reflected protons that drive electromagnetic fluctuations downstream and short distances upstream in the SW.

This article presents a statistical analysis of overlapping double ion-energy dispersion events in the northern cusp (“double dispersion”). Double dispersion in either cusp is a phenomenon associated with multiple reconnections occurring on the dayside magnetosphere as a result of its constant interaction with the variable solar wind. Using observations from a low Earth orbiting (LEO) Defense Meteorological Satellite Program (DMSP) satellite, we analyze 138 dayside events selected by the automatic algorithm extended from our previous work. We conducted a correlation study between the number of detected double dispersion events and 1) the month of the year to analyze the seasonal response of the cusp, and 2) solar wind interplanetary magnetic field (IMF) components and clock/cone angles to investigate its relationship with magnetic reconnection. We found that dispersion events occur more frequently during the northern summer months (i.e., when the dipole is tilted Sunward) and when the By component of IMF is positive. In addition, we provide a machine-readable list of the events and the code used to automatically detect the events.

Summary: Metabolic syndrome (MetS) as a multifactorial disease is highly prevalent in countries and individuals. Monitoring the conventional risk factors (CRFs) would be a cost-effective strategy to target the increasing prevalence of MetS and the potential of noninvasive CRF for precisely detection of MetS in the early stage remains to be explored. From large-scale multicenter MetS clinical dataset, we discover 15 non-invasive CRFs which have strong relevance with MetS and first propose a broad learning-based approach named Genetic Programming Collaborative-competitive Broad Learning System (GP-CCBLS) with noninvasive CRF for early detection of MetS. The proposed GP-CCBLS model can significantly boost the detection performance and achieve the accuracy of 80.54%. This study supports the potential clinical validity of noninvasive CRF to complement general diagnostic criteria for early detecting the MetS and also illustrates possible strength of broad learning in disease diagnosis comparing with other machine learning approaches.

Abstract How the genetic landscape governs a tumor’s response to immunotherapy remains poorly understood. To assess the immune-modulatory capabilities of 573 genes associated with altered cytotoxicity in human cancers, here we perform CRISPR/Cas9 screens directly in mouse lung cancer models. We recover the known immune evasion factors Stat1 and Serpinb9 and identify the cancer testis antigen Adam2 as an immune modulator, whose expression is induced by KrasG12D and further elevated by immunotherapy. Using loss- and gain-of-function experiments, we show that ADAM2 functions as an oncogene by restraining interferon and TNF cytokine signaling causing reduced presentation of tumor-associated antigens. ADAM2 also restricts expression of the immune checkpoint inhibitors PDL1, LAG3, TIGIT and TIM3 in the tumor microenvironment, which might explain why ex vivo expanded and adoptively transferred cytotoxic T-cells show enhanced cytotoxic efficacy in ADAM2 overexpressing tumors. Together, direct in vivo CRISPR/Cas9 screens can uncover genetic alterations that control responses to immunotherapies.

Abstract Background Cancer is a leading cause of premature death worldwide and incidence is expected to rise in the coming decades. Many cohort studies, measuring lifestyle factors at one time-point, have observed that overall healthy lifestyles were inversely related to cancer incidence. However, there is little knowledge on the impact of lifestyle modification within adulthood. Methods Using the Norwegian Women and Cancer study, two repeated self-reported assessments of lifestyle behaviours were used to calculate healthy lifestyle index scores at each time-point (N = 66 233). The associations between change in healthy lifestyle index score and lifestyle-related cancer incidence, including alcohol-, tobacco-, obesity-, and reproductive-related, and site-specific breast and colorectal cancer incidence were estimated using Cox proportional hazard regression models. To assess nonlinearity in the dose–response relationships, restricted cubic spline models were used. Results Independent of baseline lifestyle, positive lifestyle changes were inversely related to the incidence of overall lifestyle-related cancers, as well as alcohol-related, tobacco-related, obesity-related, and reproductive-related cancers, but not breast and colorectal site-specific cancers. An association between lifestyle worsening and cancer incidence compared to stable lifestyle was observed. Conclusions This study provides evidence that overall lifestyle changes among cancer-free women between the ages of 41 and 76 impact the incidence of many cancer types. Regardless of baseline lifestyle, there was a negative dose–response relationship between magnitude of positive lifestyle change and the incidence of overall lifestyle-related cancers. We observed that underlying this trend was an especially clear association between lifestyle worsening and increased risk compared to stable lifestyle. For adult women, maintaining a stable healthy lifestyle and lifestyle improvement are important for preventing the occurrence of many cancer types.

This paper addresses integral sliding mode output feedback fault-tolerant control (FTC) of unmanned marine vessels (UMVs) with unknown premise variables and actuator faults. Due to the complexity of the marine environment, the presence of uncertainties in the yaw angle renders the premise variables in the Takagi–Sugeno (T–S) fuzzy model of UMVs unknown. Consequently, traditional integral sliding mode techniques become infeasible. To address this issue, a control strategy combining integral sliding mode based on output feedback with a compensator utilizing switching mechanisms is proposed. First, a radial basis function neural network is used to approximate the nonlinear terms in the UMV T–S fuzzy model. In addition, an integral sliding mode surface is constructed based on fault estimation information and membership function estimation. On this basis, an FTC scheme based on integral sliding mode output feedback is developed to ensure that the UMV system is asymptotically stable and satisfies the prescribed H∞ performance index. Finally, simulation results are provided to demonstrate the effectiveness of the presented control strategy.

Fusing structural-functional images of the brain has shown great potential to analyze the deterioration of Alzheimer’s disease (AD). However, it is a big challenge to effectively fuse the correlated and complementary information from multimodal neuroimages. In this work, a novel model termed cross-modal transformer generative adversarial network (CT-GAN) is proposed to effectively fuse the functional and structural information contained in functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). The CT-GAN can learn topological features and generate multimodal connectivity from multimodal imaging data in an efficient end-to-end manner. Moreover, the swapping bi-attention mechanism is designed to gradually align common features and effectively enhance the complementary features between modalities. By analyzing the generated connectivity features, the proposed model can identify AD-related brain connections. Evaluations on the public ADNI dataset show that the proposed CT-GAN can dramatically improve prediction performance and detect AD-related brain regions effectively. The proposed model also provides new insights into detecting AD-related abnormal neural circuits.

Abstract Background Biomedical named entity recognition (BioNER) is a basic and important task for biomedical text mining with the purpose of automatically recognizing and classifying biomedical entities. The performance of BioNER systems directly impacts downstream applications. Recently, deep neural networks, especially pre-trained language models, have made great progress for BioNER. However, because of the lack of high-quality and large-scale annotated data and relevant external knowledge, the capability of the BioNER system remains limited. Results In this paper, we propose a novel fully-shared multi-task learning model based on the pre-trained language model in biomedical domain, namely BioBERT, with a new attention module to integrate the auto-processed syntactic information for the BioNER task. We have conducted numerous experiments on seven benchmark BioNER datasets. The proposed best multi-task model obtains F1 score improvements of 1.03% on BC2GM, 0.91% on NCBI-disease, 0.81% on Linnaeus, 1.26% on JNLPBA, 0.82% on BC5CDR-Chemical, 0.87% on BC5CDR-Disease, and 1.10% on Species-800 compared to the single-task BioBERT model. Conclusion The results demonstrate our model outperforms previous studies on all datasets. Further analysis and case studies are also provided to prove the importance of the proposed attention module and fully-shared multi-task learning method used in our model.

Abstract The near lunar surface contains small‐scale magnetic field structures that provide a natural test bed for observing plasmas with a non‐zero Hall electric field, as well as potentially facilitating electron‐only reconnection. This study presents observational evidence of magnetized electrons as well as demagnetized ions when THEMIS‐ARTEMIS probe B reached an altitude of ∼15 km above the lunar surface. Additionally, observations suggest the presence of a field line topology change and traversal of a closed magnetic field structure containing solar wind electrons, suggestive of magnetic reconnection having occurred at some point between the solar wind interplanetary magnetic field and a lunar crustal magnetic field. Thus, the observations presented here are consistent with previous studies that predict prominent Hall electric fields near lunar crustal magnetic fields and further suggest that the solar wind interplanetary magnetic field may reconnect with lunar crustal magnetic fields, most likely via electron‐only reconnection.

IntroductionAlzheimer's disease (AD) is a chronic neurodegenerative disease of the brain that has attracted wide attention in the world. The diagnosis of Alzheimer's disease is faced with the difficulties of insufficient manpower and great difficulty. With the intervention of artificial intelligence, deep learning methods are widely used to assist clinicians in the early recognition of Alzheimer's disease. And a series of methods based on data input with different dimensions have been proposed. However, traditional deep learning models rely on expensive hardware resources and consume a lot of training time, and may fall into the dilemma of local optima.MethodsIn recent years, broad learning system (BLS) has provided researchers with new research ideas. Based on the three-dimensional residual convolution module and BLS, a novel broad-deep ensemble model based on BLS is proposed for the early detection of Alzheimer's disease. The Alzheimer's Disease Neuroimaging Initiative (ADNI) MRI image dataset is used to train the model and then we compare the performance of proposed model with previous work and clinicians' diagnosis.ResultsThe result of experiments demonstrate that the broad-deep ensemble model is superior to previously proposed related works, including 3D-ResNet and VoxCNN, in accuracy, sensitivity, specificity and F1.DiscussionThe proposed broad-deep ensemble model is effective for early detection of Alzheimer's disease. In addition, the proposed model does not need the pre-training process of its depth module, which greatly reduces the training time and hardware dependence.

This work investigates biodiesel production via transesterification of Mexican palm oil with methanol catalyzed by binary solid base core–shell catalysts with improved catalytic stability. A series of CaO–ZnO mixed solids were prepared using an inexpensive co–precipitation method by varying ZnO content from 5 to 20 mol%. Several factors, such as surface basicity, ZnO content, phase compositions, and thermal treatment of the catalysts, were all proven to be crucial for the production of biodiesel with good quality. Thermal treatment could effectively remove the surface adsorbed water and impurities and improved the catalytic activity. The addition of ZnO to CaO significantly enhanced the catalysts’ stability; however, it led to lower surface basicity and slightly diminished catalytic activity. ZnO doping inhibited the formation of surface Ca(OH)2 and promoted the formation of Ca–Zn–O or CaZn2(OH)6 phase as the core and a surface CaCO3 shell, which effectively decreased Ca2+ leaching by approximately 74% in methanol and 65% in a methanol–glycerol (4:1) mixture. A combined method of separation and purification for obtaining clean biodiesel with high quality was proposed. The biodiesel obtained under the control conditions exhibited properties which satisfied the corresponding standards well.

We report new theories and simulations for electron acceleration in magnetic islands generated by magnetic reconnection in the shock turbulence in a quasi-parallel shock, using a 2 and 1/2 dimensional particle-in-cell simulation. When an island is moving, unmagnetized electrons are accelerated by the Hall electric field pointing toward the island center. In a stationary island, some electrons are energized by “island betatron acceleration” due to the induction electric field when the island core magnetic field changes with time. In the simulation, almost all of the high-energy electrons in the shock transition region that show a power-law distribution are accelerated in ion-skin-depth-scale magnetic flux ropes, and about half of them are accelerated by the Hall electric field and island betatron acceleration. These mechanisms can produce a power-law electron distribution, and also inject electrons into the diffusive shock acceleration. The mechanisms are applicable to quasi-parallel shocks with high Alfvén Mach numbers ( M _A > 10), including planetary bow shocks and shocks in astrophysical objects such as supernova remnants.

Numerous structures conducive to magnetic reconnection are frequently observed in the turbulent regions at quasi-parallel shocks. In this work, we use a particle-in-cell simulation to study 3D magnetic reconnection in shock turbulence. We identify and characterize magnetic null points, and focus on reconnection along the separator between them. We identify a reconnection region with strong parallel current, a finite parallel potential, and counterrotating electron flows. Electrons are shown to be accelerated by the parallel electric field before being scattered at the null.

Abstract Interactions between solar wind ions and neutral hydrogen atoms in Earth's exosphere can lead to the emission of soft X‐rays. Upcoming missions such as SMILE and LEXI aim to use soft X‐ray imaging to study the global structure of the magnetosphere. Although the magnetosheath and dayside magnetopause can often be driven by kinetic physics, it has typically been omitted from fluid simulations used to predict X‐ray emissions. We study the possible results of soft X‐ray imaging using hybrid simulations under quasi‐radial interplanetary magnetic fields, where ion‐ion instabilities drive ultra‐low frequency foreshock waves, leading to turbulence in the magnetosheath, affecting the dynamics of the cusp and magnetopause. We simulate soft X‐ray emission to determine what may be seen by missions such as LEXI, and evaluate the possibility of identifying kinetic structures. While kinetic structures are visible in high‐cadence imaging, current instruments may not have the time resolution to discern kinetic signals.

Alzheimer's disease (AD) is a progressive neurodegenerative disease, and the development of AD is irreversible. However, preventive measures in the presymptomatic stage of AD can effectively slow down deterioration. Fluorodeoxyglucose positron emission tomography (FDG-PET) can detect the metabolism of glucose in patients' brains, which can help to identify changes related to AD before brain damage occurs. Machine learning is useful for early diagnosis of patients with AD using FDG-PET, but it requires a sufficiently large dataset, and it is easy for overfitting to occur in small datasets. Previous studies using machine learning for early diagnosis with FDG-PET have either involved the extraction of elaborately handcrafted features or validation on a small dataset, and few studies have explored the refined classification of early mild cognitive impairment (EMCI) and late mild cognitive impairment (LMCI). This article presents a broad network-based model for early diagnosis of AD (BLADNet) through PET imaging of the brain; this method employs a novel broad neural network to enhance the features of FDG-PET extracted via 2D CNN. BLADNet can search for information over a broad space through the addition of new BLS blocks without retraining of the whole network, thus improving the accuracy of AD classification. Experiments conducted on a dataset containing 2,298 FDG-PET images of 1,045 subjects from the ADNI database demonstrate that our methods are superior to those used in previous studies on early diagnosis of AD with FDG-PET. In particular, our methods achieved state-of-the-art results in EMCI and LMCI classification with FDG-PET.

Terrestrial mud volcanoes (MVs) represent the surface expression of conduits tapping fluid and gas reservoirs in the deep subsurface. Such plumbing channels provide a direct, effective means to extract deep microbial communities fueled by geologically produced gases and fluids. The drivers accounting for the diversity and composition of these MV microbial communities, which are distributed over a wide geographic range, remain elusive. This study characterized the variation in microbial communities in 15 terrestrial MVs across a distance of ∼ 10 000 km on the Eurasian continent to test the validity of distance control and physiochemical factors in explaining biogeographic patterns. Our analyses yielded diverse community compositions with a total of 28 928 amplicon sequence variances (ASVs) taxonomically assigned to 73 phyla. While no true cosmopolitan member was found, ∼ 85 % of ASVs were confined within a single MV. Community variance between MVs appeared to be higher and more stochastically controlled than within MVs, generating a slope of the distance–decay relationship exceeding those for marine seeps and MVs as well as seawater columns. For comparison, physiochemical parameters explained 12 % of community variance, with the chloride concentration being the most influential factor. Overall, the apparent lack of fluid exchange renders terrestrial MVs a patchy habitat, with microbiomes diverging stochastically with distance and consisting of dispersal-limited colonists that are highly adapted to the local environmental context.

With the spread of distributed renewable energy, residents are shifting from being mere consumers to being energy producers and consumers. This role shift poses challenges to the electricity trading mechanism that connects distributed renewable energy sources to the grid. In this paper, a new efficient and secure blockchain-based distributed community energy trading mechanism is proposed, called CE-SDT. Our system is proved to be stable and scalable. It can also help shift loads and power peaks and reduce customer costs by 60%. As a result, our proposed blockchain-based trading mechanism, as compared to the centralized trading mechanism, is applied to microgrids formed by distributed renewable energy sources, not only obtaining greater economic benefits but also reducing the carbon footprint of residents, and furthermore, it promotes low or zero-carbon configurations of the power system, thereby achieving certain environmental benefits.

In-plane polarized ferroelectric thin films typically exhibit complicated multidomain states, not desirable for optoelectronic device performance. Here, the authors combine interfacial symmetry engineering and anisotropic strain to design single-domain in-plane polarized ferroelectric BaTiO3 films.

Abstract In this paper, a variable projection approach based on the BFGS (Broyden–Fletcher–Goldfarb–Shanno) method for image reconstruction problems is proposed, which is an alternative to the common alternating minimisation scheme. The image restoration is expressed as a nonlinear least‐squares problem with reduced parameter space. To improve the efficiency of the algorithm, the BFGS method is proposed to be used to optimise the reduced objective function. The large‐scale problem considered in this paper is projected on to a small Krylov subspace using Lanczos bidiagonalisation. The regularisation parameter is selected by a weighted generalised cross validation criterion. Numerical examples demonstrate the efficiency and effectiveness of the proposed algorithm.

Mn3Sn is an anti-ferromagnetic material which displays a large magneto-optical Kerr effect, despite lacking a ferromagnetic moment. Here, the authors show that likewise, Mn3Sn, also presents a particularly large magneto-optical Voigt signal, with a negligible change in the quench time over a wide temperature range.

With the rapid growth of shipping volumes, ship navigation and path planning have attracted increased attention. To design navigation routes and avoid ship collisions, accurate ship trajectory prediction based on automatic identification system data is required. Therefore, this study developed an encoder–decoder learning model for ship trajectory prediction, to avoid ship collisions. The proposed model includes long short-term memory units and an attention mechanism. Long short-term memory can extract relationships between the historical trajectory of a ship and the current state of encountered ships. Simultaneously, the global attention mechanism in the proposed model can identify interactions between the output and input trajectory sequences, and a multi-head self-attention mechanism in the proposed model is used to learn the feature fusion representation between the input trajectory sequences. Six case studies of trajectory prediction for ship collision avoidance from the Yangtze River of China and the eastern coast of the U.S. were investigated and compared. The results showed that the average mean absolute errors of our model were much lower than those of the classical neural networks and other state-of-the-art models that included attention mechanisms.

Electrochemical impedance spectroscopy (EIS) is a sensitive analytical method for surface and bulk properties. Classical EIS and derived electrochemical capacitance spectroscopy (ECS) with a redox couple are label-free approaches for biosensor development, but doubts arise regarding interpretability when a redox couple is employed (redox EIS) due to interactions between electroactive probes and interfacial charges or forced potential. Here, we demonstrated redoxless ECS for directly determining surfactant adsorption on screen-printed carbon electrodes (SPCEs), validated through a simulation of equivalent circuits and the electrochemistry of electronic dummy cells. Redoxless ECS provides excellent capacitance plot loci for quantifying the interfacial permittivity of di-electric layers on electrode surfaces. Redoxless ECS was compared with redox EIS/ECS, revealing a favorable discrimination of interfacial capacitances under both low and high SDS coverage on SPCEs and demonstrating potential for probeless (reagentless) sensing. Furthermore, the proposed method was applied in an electrolyte without a redox couple and bare electrodes, obtaining a high performance for the adsorption of surfactants Tween-20, Triton-X100, sodium dodecyl sulfate, and tetrapropylammonium bromide. This approach offers a simple and straightforward means for a semi-quantitative evaluation of small molecule interactions with electrode surfaces. Our proposed approach may serve as a starting point for future probeless (reagentless) and label-free biosensors based on electrochemistry, eliminating disturbance with surface charge properties and minimizing forced potential bias by avoiding redox couples. An unambiguous and quantitative determination of physicochemical properties of biochemically recognizable layers will be relevant for biosensor development.

Epilepsy is a chronic brain disease that causes persistent and severe damage to the physical and mental health of patients. Daily effective prediction of epileptic seizures is crucial for epilepsy patients especially those with refractory epilepsy. At present, a large number of deep learning algorithms such as Convolutional Neural Networks and Recurrent Neural Networks have been used to predict epileptic seizures and have obtained better performance than traditional machine learning methods. However, these methods usually transform the Electroencephalogram (EEG) signal into a Euclidean grid structure. The conversion suffers from loss of adjacent spatial information, which results in deep learning models requiring more storage and computational consumption in the process of information fusion after information extraction. This study proposes a general Graph Convolutional Networks (GCN) model architecture for predicting seizures to solve the problem of oversized seizure prediction models based on exploring the graph structure of EEG signals. As a graph classification task, the network architecture includes graph convolution layers that extract node features with one-hop neighbors, pooling layers that summarize abstract node features; and fully connected layers that implement classification, resulting in superior prediction performance and smaller network size. The experiment shows that the model has an average sensitivity of 96.51%, an average AUC of 0.92, and a model size of 15.5 k on 18 patients in the CHB-MIT scalp EEG dataset. Compared with traditional deep learning methods, which require a large number of parameters and computational effort and are demanding in terms of storage space and energy consumption, this method is more suitable for implementation on compact, low-power wearable devices as a standard process for building a generic low-consumption graph network model on similar biomedical signals. Furthermore, the edge features of graphs can be used to make a preliminary determination of locations and types of discharge, making it more clinically interpretable.

In this work, Meisenheimer et al. use careful epitaxial growth of FeGa thin films to achieve a metastable state with remarkably high magetostrictive coefficients. Materials with strong magnetostrictive properties are vital components in magnetoelectric multiferroic heterostructures, with considerable potential for use a variety of technologies.

Abstract The 4d transition metal perovskites Can+1RunO3n+1 have attracted interest for their strongly interacting electronic phases showing pronounced sensitivity to controllable stimuli like strain, temperature, and even electrical current. Through multi-messenger low-temperature nano-imaging, we reveal a spontaneous striped texture of coexisting insulating and metallic domains in single crystals of the bilayer ruthenate Ca3(TixRu1-x)2O7 across its first-order Mott transition at $$T \approx 95$$ T ≈ 95 K. We image on-demand anisotropic nucleation and growth of these domains under in situ applied uniaxial strain rationalized through control of a spontaneous Jahn-Teller distortion. Our scanning nano-susceptibility imaging resolves the detailed susceptibility of coexisting phases to strain and temperature at the transition threshold. Comparing these nano-imaging results to bulk-sensitive elastoresistance measurements, we uncover an emergent “domain susceptibility” sensitive to both the volumetric phase fractions and elasticity of the self-organized domain lattice. Our combined susceptibility probes afford nano-scale insights into strain-mediated control over the insulator-metal transition in 4d transition metal oxides.

Objective This study compared the catechin composition of different tea byproducts and investigated the effects of dietary supplementation with green tea byproducts on the accumulation of abdominal fat, the modulation of lipid metabolism, and the inflammatory response in red feather native chickens. Methods Bioactive compounds were detected, and in vitro anti-obesity capacity analyzed via 3T3-L1 preadipocytes. In animal experiments, 320 one-day-old red feather native chickens were divided into 4 treatment groups: control, basal diet supplemented with 0.5% Jinxuan byproduct (JBP), basal diet supplemented with 1% JBP, or basal diet supplemented with 5×106 colony-forming unit (CFU)/kg Bacillus amyloliquefaciens+5×106 CFU/kg Saccharomyces cerevisiae (BA+SC). Growth performance, serum characteristics, carcass characteristics, and the mRNA expression of selected genes were measured. Results This study compared several cultivars of tea, but Jinxuan showed the highest levels of the anti-obesity compound epigallocatechin gallate. 3T3-L1 preadipocytes treated with Jinxuan extract significantly reduced lipid accumulation. There were no significant differences in growth performance, serum characteristics, or carcass characteristics among the groups. However, in the 0.5% JBP group, mRNA expression of fatty acid synthase (FAS) and acetyl-CoA carboxylase (ACC) were significantly decreased. In the 1% JBP group, FAS, ACC and peroxisome proliferator-activated receptor γ levels were significantly decreased. Moreover, inflammation-related mRNA expression levels were decreased by the addition of JBP. Conclusion JBP contained abundant catechins and related bioactive compounds, which reduced lipid accumulation in 3T3-L1 preadipocytes, however there was no significant reduction in abdominal fat. This may be due to a lack of active anti-obesity compounds or because the major changes in fat metabolism were not in the abdomen. Nonetheless, lipogenesis-related and inflammation-related mRNA expression were reduced in the 1% JBP group. In addition, dietary supplementation with tea byproducts could reduce the massive amount of byproducts created during tea production and modulate lipid metabolism and the inflammatory response in chickens.

The study proposes an o-phthalaldehyde (OPA) sensor for rapid and reliable detection of OPA in healthcare disinfection practices, based on a hydrogel-modified screen-printed carbon electrode strip. The hydrogel film, which contains glycine and N-acetylcysteine, reacts with OPA to produce a reductive isoindole derivative. The derivative is then oxidized for OPA determination using cyclic voltammetry. The proposed sensor achieves an optimal detection time of 20–30 s and requires only a small analyte volume of 5 µL. It exhibits good precision (10%) and sensitivity (3.3 μA/cm2 mM) in a phosphate-buffered solution (pH 7.6), with excellent linearity (R2 > 0.97) and precision (2 mM) and precision around the minimum effective concentration (0.3%). Overall, the proposed sensor offers a promising and practical solution for accurate and reliable OPA detection in clinical disinfection practices.

Clinical manifestations and courses of eye diseases in children are profoundly variable, from minor irritations, pain, infections, inflammations, ocular misalignment, refractive errors and visual impairment, to permanent blindness [...]