MXene-based SERS spectroscopic analysis of exosomes for lung cancer differential diagnosis with deep learning.

Lung cancer with heterogeneity has a high mortality rate due to its late-stage detection and chemotherapy resistance. Liquid biopsy that discriminates tumor-related biomarkers in body fluids has emerged as an attractive technique for early-stage and accurate diagnosis. Exosomes, carrying membrane and cytosolic information from original tumor cells, impart themselves endogeneity and heterogeneity, which offer extensive and unique advantages in the field of liquid biopsy for cancer differential diagnosis.

Dimensional engineering of interlayer for efficient large-area perovskite solar cells with high stability under ISOS-L-3 aging test.

The utilization of low-dimensional perovskites (LDPs) as interlayers on three-dimensional (3D) perovskites has been regarded as an efficient strategy to enhance the performance of perovskite solar cells. Yet, the formation mechanism of LDPs and their impacts on the device performance remain elusive. Herein, we use dimensional engineering to facilitate the controllable growth of 1D and 2D structures on 3D perovskites.

A hybrid dual-branch model with recurrence plots and transposed transformer for stock trend prediction.

Stock trend prediction is a significant challenge due to the inherent uncertainty and complexity of stock market time series. In this study, we introduce an innovative dual-branch network model designed to effectively address this challenge. The first branch constructs recurrence plots (RPs) to capture the nonlinear relationships between time points from historical closing price sequences and computes the corresponding recurrence quantifification analysis measures.

Addressing scalability and managing sparsity and dropout events in single-cell representation identification with ZIGACL.

Despite significant advancements in single-cell representation learning, scalability and managing sparsity and dropout events continue to challenge the field as scRNA-seq datasets expand. While current computational tools struggle to maintain both efficiency and accuracy, the accurate connection of these dropout events to specific biological functions usually requires additional, complex experiments, often hampered by potential inaccuracies in cell-type annotation. To tackle these challenges, the Zero-Inflated Graph Attention Collaborative Learning (ZIGACL) method has been developed.

Development of a Virtual Robot Rehabilitation Training System for Children with Cerebral Palsy: An Observational Study.

This paper presents the development of a robotic system for the rehabilitation and quality of life improvement of children with cerebral palsy (CP). The system consists of four modules and is based on a virtual humanoid robot that is meant to motivate and encourage children in their rehabilitation programs. The efficiency of the developed system was tested on two children with CP.

Joint Fault Diagnosis of IGBT and Current Sensor in LLC Resonant Converter Module Based on Reduced Order Interval Sliding Mode Observer.

LLC resonant converters have emerged as essential components in DC charging station modules, thanks to their outstanding performance attributes such as high power density, efficiency, and compact size. The stability of these converters is crucial for vehicle endurance and passenger experience, making reliability a top priority. However, malfunctions in the switching transistor or current sensor can hinder the converter's ability to maintain a resonant state and stable output voltage, leading to a notable reduction in system efficiency and output capability.

Protocol Reverse Analysis of Ethernet for Control Automation Technology Based on Sequence Alignment and Pearson Correlation Coefficient.

This study presents a novel algorithm for protocol reverse analysis of EtherCAT. The algorithm combines sequence alignment and the Pearson correlation coefficient. We utilize value distribution statistics and the bit flip rate algorithm to effectively partition the protocol fields.

Double layers optimal scheduling of distribution networks and photovoltaic charging and storage station cluster based on leader follower game theory.

The paper addresses the economic operation optimization problem of photovoltaic charging-swapping-storage integrated stations (PCSSIS) in high-penetration distribution networks. It proposes a dual-layer optimization scheduling model for PCSSIS clusters and distribution network systems. Firstly, a master-slave game model is constructed.

A bi-level optimization strategy of electricity-hydrogen-carbon integrated energy system considering photovoltaic and wind power uncertainty and demand response.

To address the power supply-demand imbalance caused by the uncertainty in wind turbine and photovoltaic power generation in the regional integrated energy system, this study proposes a bi-level optimization strategy that considers the uncertainties in photovoltaic and wind turbine power generation as well as demand response. The upper-level model analyzes these uncertainties by modeling short-term and long-term output errors using robust optimization theory, applies an improved stepwise carbon trading model to control carbon emissions, and finally constructs an electricity-hydrogen-carbon cooperative scheduling optimization model to reduce wind and carbon emissions. The lower-level model incentivizes users to participate in integrated demand response through dynamic energy pricing, thereby reducing the annual consumption cost of load aggregator.

The application of multi-scale simulation in advanced electronic packaging.

Electronic packaging is an essential branch of electronic engineering that aims to protect electronic, microelectronic, and nanoelectronic systems from environmental conditions. The design of electronic packaging is highly complex and requires the consideration of multi-physics phenomena, such as thermal transport, electromagnetic fields, and mechanical stress. This review presents a comprehensive overview of the multiphysics coupling of electric, magnetic, thermal, mechanical, and fluid fields, which are crucial for assessing the performance and reliability of electronic devices.

Adaptive discrete-time neural prescribed performance control: A safe control approach.

Most existing results on prescribed performance control (PPC), subject to input saturation and initial condition limitations, focus on continuous-time nonlinear systems. This article, as regards discrete-time nonlinear systems, is dedicated to constructing a novel adaptive switching control strategy to circumvent the singular problem when the PPC undergoes input saturation, while the initial conditions of the system can be released under the framework of PPC. The main design steps and characteristics include: (1) By devising a new discrete-time global finite-time performance function (DTGFTPF), the constructed performance boundary is shown to survive insensitive to arbitrary initial values, which present in the system; (2) A discrete-time adaptive finite-time prescribed performance controller (DTAFPPC) and a discrete-time adaptive backstepping controller (DTABC) are constructed, simultaneously.

Modeling and Simulation of Eddy Current Dissipation Magnetic Acceleration Noise of Space Inertial Sensors.

The magnetic acceleration noise (MAN) that stems from the eddy current dissipation of a test mass (TM) serves as an important source of noise for space inertial sensors. Given the problem that the eddy current dissipation magnetic acceleration noise (ECDMAN) of a cubic TM defies analytical solutions, an analytical model of ECDMAN for a spherical TM, which has the same volume as the cubic TM, is systematically derived on the basis of the principles of electromagnetism and the fluctuation-dissipation theorem, and this model can be used as an approximate analytical model for the evaluation of this noise term. Based on the approximate analytical model, with the TM of the LISA Pathfinder (LPF) as the research object, this paper obtains a modification coefficient using the approach of combining the analytical method with the finite element method (FEM), and establishes a semi-analytical model of ECDMAN for the cubic TM.

First Principles Study of p-Type Transition and Enhanced Optoelectronic Properties of g-ZnO Based on Diverse Doping Strategies.

By utilizing first principles calculations, p-type transition in graphene-like zinc oxide (g-ZnO) through elemental doping was achieved, and the influence of different doping strategies on the electronic structure, energy band structure, and optoelectronic properties of g-ZnO was investigated. This research study delves into the effects of strategies such as single-acceptor doping, double-acceptor co-doping, and donor-acceptor co-doping on the properties of g-ZnO. This study found that single-acceptor doping with Li and Ag elements can form shallow acceptor levels, thereby facilitating p-type conductivity.

Multi-kernel feature extraction with dynamic fusion and downsampled residual feature embedding for predicting rice RNA N6-methyladenine sites.

RNA N$^{6}$-methyladenosine (m$^{6}$A) is a critical epigenetic modification closely related to rice growth, development, and stress response. m$^{6}$A accurate identification, directly related to precision rice breeding and improvement, is fundamental to revealing phenotype regulatory and molecular mechanisms. Faced on rice m$^{6}$A variable-length sequence, to input into the model, the maximum length padding and label encoding usually adapt to obtain the max-length padded sequence for prediction.

Design of trot gait parameters planning system for parallel quadruped robot based on virtual model controller and fuzzy neural network.

The capability to achieve fast motion in varying road conditions is a crucial research aspect in the dynamic control of quadruped robot. In this study, a gait parameters planning system for quadruped robot based on virtual model controller (VMC) and fuzzy neural network controller (FNNC) is proposed. According to the expert knowledge, the FNNC is designed to help optimize the parameters in the central pattern generator and virtual model controller (CPG-VMC).

Prediction of constitutive model for basalt fiber reinforced concrete based on PSO-KNN.

In order to find a simple method to study the effect of basalt fibers on the mechanical properties of concrete when incorporated into concrete, machine learning is introduced in this work on an experimental basis. The basalt fiber-reinforced concrete (BFRC) specimens were fabricated through independent processing, and the compression tests under different stress states were conducted on the BFRC specimens with different fiber compositions using the MTS816 rock testing system. After obtaining the experimental dataset with the four influencing factors of fiber volume fraction, fiber length, circumferential pressure and strain as input variables and stress as output variable, the BFRC prediction model was established based on extreme gradient boosting, support vector machine, K-nearest neighbor, and Particle Swarm Optimization K-Nearest Neighbor (PSO-KNN) algorithms; Then the predicted fitting results of the training set and test set are analyzed according to the relevant evaluation indexes, and the data indexes indicate that the PSO-KNN model has the best prediction performance, and the PSO-KNN model is used to predict the stress-strain fitting curves of BFRC, and finally the parameter contribution is analyzed based on the information of the curves.

Boosting Alkaline Hydrogen Evolution Reaction by Modulating D-Band Center in Bimetallic Sulfide NiS-FeS Heterointerfaces.

Hydrogen evolution reaction (HER) in alkaline electrolytes is considered to be the most promising industry-scale hydrogen (H) production method but is limited to the lack of low-cost, efficient, and stable HER catalysts. Here, a universal and scalable electrodeposition-sulfidization modulation strategy is developed to directly grow the NiS-FeS heterojunction nanoarray on the commercial Ni foam (NiS-FeS@NF). The as-prepared NiS-FeS@NF catalyst only requires a low overpotential of 71 and 270 mV to reach the current density of 10 and 500 mA cm with a long-lasting lifetime of over 200 h.

Narrowband emission and enhanced stability in top-emitting OLEDs with dual resonant cavities.

Capping layers (CPLs) are commonly employed in top-emitting organic light-emitting diodes (TEOLEDs) due to their ability to optimize color purity, enhance external light out-coupling efficiency, and improve device stability. However, the mismatch in refractive index between CPLs and thin film encapsulation (TFE) often induces light trapping. This study introduces a novel approach by combining a low refractive index material, lithium fluoride (LiF), with the traditional TFE material, silicon nitride (SiN), to form a combined CPL (LiF/SiN), resulting in improved light outcoupling and light reflection properties.

A visually-induced optogenetically-engineered system enables autonomous glucose homeostasis in mice.

With the global population increasing and the demographic shifting toward an aging society, the number of patients diagnosed with conditions such as peripheral neuropathies resulting from diabetes is expected to rise significantly. This growing health burden has emphasized the need for innovative solutions, such as brain-computer interfaces. brain-computer interfaces, a multidisciplinary field that integrates neuroscience, engineering, and computer science, enable direct communication between the human brain and external devices.

A blockchain consensus mechanism for real-time regulation of renewable energy power systems.

With the ongoing development of renewable energy sources, information technologies and physical energy systems are further integrated, which leads to challenges in ensuring the secure and stable operation of renewable energy power systems in the face of potential cyber threats. The strengths of blockchain in cybersecurity make it a promising solution to these challenges. However, existing blockchains are not well-suited for control tasks due to their low real-time performance.

Study of repulsive permanent magnetic levitation mechanism and its dynamic characteristics.

Permanent magnet magnetic levitation (PMFL) system has the characteristics of zero-power levitation, strong load-carrying capacity and self-stabilization, so it has obvious advantages in the application of rail transportation and heavy-duty transmission and other fields. However, due to the lack of active control of electromagnetism and the existence of multi-point coupling, it is easily affected by external factors, and its dynamic characteristics and its complexity. This paper aims to reveal the levitation mechanism of permanent magnet magnetic levitation system and the coupling motion law of bogie by combining theoretical analysis and experimental verification.

Static pinning synchronization control of self-triggered coupling dynamical networks.

In this paper, a new static pinning intermittent control based on resource awareness triggering is proposed. A multi-layer control technique is used to synchronize the coupled neural network. First, a hierarchical network structure including pinned and interaction layers is induced using each pinning strategy.

Active disturbance rejection control with adaptive RBF neural network for a permanent magnet spherical motor.

In response to the issues of low tracking accuracy and poor robustness in the trajectory tracking control of a permanent magnet spherical motor (PMSpM), an active disturbance rejection control (ADRC) scheme combining neural networks is put forward in this research. The unknown total disturbance is approximated by employing a radial basis function (RBF) neural network, with weights updated by an adaptive law and compensated for through the nonlinear feedback loop. This approach addresses the problem of performance degradation of the extended state observer under severe total disturbance, thereby ensuring accurate tracking of the PMSpM.

Selective Photocatalytic Conversion of CO to Ethanol via Unsaturated Cu-O Domains.

Enhancing the selectivity of photocatalytic CO reduction to valuable multicarbon (C) products remains a significant challenge in green synthetic chemistry. Here, we present a dual-center strategy for metal oxides that boosts the photochemical conversion of CO to ethanol by regulating the coordination number of metal and oxygen sites. Notably, CuO catalysts rich in low-coordinated Cu-O domains have achieved nearly perfect ethanol selectivity (96.