A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose current psychiatric diagnostic process is subjective and behavior-based. In contrast, functional magnetic resonance imaging (fMRI) can objectively measure brain activity and is useful for identifying brain disorders. However, the ASD diagnostic models employed to date have not reached satisfactory levels of accuracy.

A transfer learning nomogram for predicting prostate cancer and benign conditions on MRI.

Background: Deep learning has been used to detect or characterize prostate cancer (PCa) on medical images. The present study was designed to develop an integrated transfer learning nomogram (TLN) for the prediction of PCa and benign conditions (BCs) on magnetic resonance imaging (MRI).

Methods: In this retrospective study, a total of 709 patients with pathologically confirmed PCa and BCs from two institutions were included and divided into training (n = 309), internal validation (n = 200), and external validation (n = 200) cohorts.

Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding.

Introduction: The time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses significant challenges for motor imagery decoding. Sparse regularization is an effective method for addressing this issue.

Differentiating adrenal metastases from benign lesions with multiphase CT imaging: Deep learning could play an active role in assisting radiologists.

Objectives: To develop and externally validate multiphase CT-based deep learning (DL) models for differentiating adrenal metastases from benign lesions.

Materials And Methods: This retrospective two-center study included 1146 adrenal lesions from 1059 patients who underwent multiphase CT scanning between January 2008 and March 2021. The study encompassed 564 surgically confirmed adenomas, along with 135 benign lesions and 447 metastases confirmed by observation.

Study of Aerogel-Modified Recycled Polyurethane Nanocomposites.

In this study, a liquid regenerated polyether polyol was obtained after the degradation of waste PU foam by the two-component decrosslinker agents ethylene glycol and ethanolamine. The regenerated polyol-based polyurethane foam was modified by adding different ratios of SiO aerogel through the self-preparation of silica aerogel (SiO aerogel) to prepare aerogel/regenerated polyurethane foam nanocomposites of SiO aerogel-modified regenerated polyurethane composites. A series of analytical tests on self-prepared silica aerogel and aerogel-modified recycled polyurethane foam composites were performed.

Design and Processing of Gas Turbine Blades Based on Additive Manufacturing Technology.

Aiming at the problems of the complex shape, difficult three-dimensional (3D) digital modeling and high manufacturing quality requirements of gas turbine blades (GTB), a method of fitting the blade profile line based on a cubic uniform B-spline interpolation function was proposed. Firstly, surface modeling technology was used to complete the fitting of the blade profile of the GTB, and the 3D model of the GTB was synthesized. Secondly, the processing parameters of the additive manufacturing were set, and the GTB model was printed by fused deposition technology.

FOESO-Net: A specific neural network for fast sensorless robot manipulator torque estimation.

Contact torque sensing allows robot manipulators to cooperate with humans and detect accidental collisions in real time to ensure safety. Most sensorless torque estimation schemes, which are based on linear observer approaches, cannot compromise between non-negligible noise and high observation bandwidth. Therefore, fast time-varying nonlinear torque observation cannot be satisfied.

A Low-Cost Multibeam Switching Antenna Using Reconfigurable Hybrid Metasurface for Beamforming Applications.

In this paper, we proposed a multibeam switching antenna based on a low-cost reconfigurable hybrid metasurface applied for beamforming systems. The antenna consists of two parts: a microstrip feed antenna and a transmission hybrid metasurface. The latter is composed of three types of units with different amplitude and phase responses to electromagnetic waves so as to control the beams of the feed antenna.

Research on stress curve clustering algorithm of Fiber Bragg grating sensor.

The global stress distribution and state parameter analysis of the building's main structure is an urgent problem to be solved in the online state assessment technology of building structure health. In this paper, a stress curve clustering algorithm of fiber Bragg grating stress sensor based on density clustering algorithm is proposed. To solve the problem of large dimension and sparse sample space of sensor stress curve, the distance between samples is measured based on improved cosine similarity.

A deep-learning model using enhanced chest CT images to predict PD-L1 expression in non-small-cell lung cancer patients.

Aim: To develop a deep-learning model using contrast-enhanced chest computed tomography (CT) images to predict programmed death-ligand 1 (PD-L1) expression in patients with non-small-cell lung cancer (NSCLC).

Materials And Methods: Preoperative enhanced chest CT images and immunohistochemistry results for PD-L1 expression (<1% and ≥1% were defined as negative and positive, respectively) were collected retrospectively from 125 NSCLC patients to train and validate a deep-learning radiomics model (DLRM) for the prediction of PD-L1 expression in tumours. The DLRM was developed by combining the deep-learning signature (DLS) obtained from a convolutional neural network and clinicopathological factors.

Design of Tunable Broadband Graphene-Based Metasurface with Amplitude-Phase Modulation.

Due to the growing scarcity of spectrum resources in the low-frequency band, the requirement of beam-reconfigurable antennas in the millimeter wave band is urgent. In this paper, a W-band graphene-based metasurface working in a broad bandwidth is proposed with reflective amplitude coding. Here, graphene sheets play a dual role in radiating and regulating electromagnetic waves.

FOSS-Based Method for Thin-Walled Structure Deformation Perception and Shape Reconstruction.

To improve the accuracy of deformation perception and shape reconstruction of flexible thin-walled structures, this paper proposes a method based on the combination of FOSS (fiber optic sensor system) and machine learning. In this method, the sample collection of strain measurement and deformation change at each measuring point of the flexible thin-walled structure was completed by ANSYS finite element analysis. The outliers were removed by the OCSVM (one-class support vector machine) model, and the unique mapping relationship between the strain value and the deformation variables (three directions of x-, y-, and z-axis) at each point was completed by a neural-network model.

Quantum tailoring for polarization-discriminating BiS nanowire photodetectors and their multiplexing optical communication and imaging applications.

In this study, cost-efficient atmospheric pressure chemical vapor deposition has been successfully developed to produce well-aligned high-quality monocrystalline BiS nanowires. By virtue of surface strain-induced energy band reconstruction, the BiS photodetectors demonstrate a broadband photoresponse across 370.6 to 1310 nm.

The Aging Behavior and Life Prediction of CFRP Rods under a Hygrothermal Environment.

Carbon fiber-reinforced polymer (CFRP) composites have been widely used in civil engineering structures due to their excellent mechanical and durability properties. The harsh service environment of civil engineering leads to significant degradation of the thermal and mechanical performances of CFRP, which then reduces its service reliability, service safety, and life. Research on the durability of CFRP is urgently needed to understand the long-term performance degradation mechanism.

Study on Efficient Degradation of Waste PU Foam.

In this paper, the high-efficiency degradation and alcoholysis recovery of waste polyurethane foam were realized using a combination of a high-efficiency alkali metal catalyst (CsOH) and two-component mixed alcoholysis agents (glycerol and butanediol) in different proportions, using recycled polyether polyol and one-step foaming to prepare regenerated thermosetting polyurethane hard foam. The foaming agent and catalyst were adjusted experimentally to prepare regenerated polyurethane foam, and a series of tests were conducted on the viscosity, GPC, hydroxyl value, infrared spectrum, foaming time, apparent density, compressive strength, and other properties of the degradation products of the regenerated thermosetting polyurethane rigid foam. The resulting data were analyzed, and the following conclusions were drawn: The optimal conditions of alcoholysis were obtained when the mass ratio of glycerol to butanediol was 3:2, the amount of cesium hydroxide was 0.

Analysis of the Influencing Factors of the Efficient Degradation of Waste Polyurethane and Its Scheme Optimization.

This work proposes an efficient catalytic recovery and utilization method for waste polyurethane foam. This method uses ethylene glycol (EG) and propylene glycol (PPG) as two-component alcohololytic agents for the alcoholysis of waste polyurethane foams. For the preparation of recycled polyethers, the conditions of different catalytic degradation systems were catalyzed by duplex metal catalysts (DMC) and alkali metal catalysts, and a synergy with both was also used.

The Long-Term Interfacial Evolution and Prediction of Carbon- and Glass-Fiber-Reinforced Epoxy Hybrid Rods under a Hygrothermal Environment.

In order to promote the engineering applications of carbon- and glass-fiber-reinforced epoxy hybrid rods, it is necessary to fully understand its long-term hygrothermal durability. In the present study, the water absorption behaviors of a hybrid rod in a water immersion environment are studied experimentally, the degradation rules of the mechanical properties are obtained, and establishing a life prediction model is attempted. The water absorption of the hybrid rod confirms to the classical Fick's diffusion model, and the water absorption concentration is determined by radial position, immersion temperature, and immersion time.

Motor imagery electroencephalogram classification algorithm based on joint features in the spatial and frequency domains and instance transfer.

Introduction: Motor imagery electroencephalography (MI-EEG) has significant application value in the field of rehabilitation, and is a research hotspot in the brain-computer interface (BCI) field. Due to the small training sample size of MI-EEG of a single subject and the large individual differences among different subjects, existing classification models have low accuracy and poor generalization ability in MI classification tasks.

Methods: To solve this problem, this paper proposes a electroencephalography (EEG) joint feature classification algorithm based on instance transfer and ensemble learning.

Predation preference for extracellular polysaccharides by paramecia and rotifers may have accelerated the decline of membrane biofilm hydraulic resistance.

The hydraulic resistance of biofilm layer on membranes impacts the filtration resistance significantly. The effect of predation by two model microfauna (i.e.

Plant growth-promoting rhizobacteria are important contributors to rice yield in karst soils.

Unlabelled: The difficulty of releasing nutrients from soils in karst areas limits the yield of local crops and leads to poverty. In this study, two strains of plant growth-promoting rhizobacteria (PGPR) were isolated from the rhizosphere soil of typical plants in karst areas, which were both identified as sp. and named GS1 and N1.

Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.

Objectives: Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs).

Methods: Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage.

Outstanding Electrochemical Performance of Ni-Rich Concentration-Gradient Cathode Material LiNiCoMnO for Lithium-Ion Batteries.

The full-concentrationgradient LiNiCoMnO (CG-LNCM), consisting of core Ni-rich LiNiCoO, transition zone LiNiCoMnO and outmost shell LiNiCoMnO was prepared by a facile co-precipitation method and high-temperature calcination. CG-LNCM was then investigated with an X-ray diffractometer, ascanning electron microscope, a transmission electron microscope, and electrochemical measurements. The results demonstrate that CG-LNCM has a lower cation mixing of Li and Ni and larger Li diffusion coefficients than concentration-constant LiNiCoMnO (CC-LNCM).

Biobased Epoxy Composites Reinforced with Acetylated Corn Straw.

Corn straw/epoxy resin composites (CS/ECs) and maleic anhydride acetylated CS/ECs (MA-CS/ECs) were prepared through dry mixing and high-temperature curing. Corn straw is a kind of abundant, eco-friendly, and low-cost biomass material. Unmodified and modified corn straws were characterized using Fourier transform infrared spectroscopy, scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS).