A novel fixed-time stability lemma and its application in the stability analysis of BAM neural networks.

In this paper, we put forward an interesting fixed-time (FXT) stability lemma, which is based on a whole new judging condition, and the minimum upper bound for the stability start time is obtained. In the new FXT stability lemma, the mathematical relation between the upper bound of the stability start time and the system parameters is very simple, and the judgment condition only involves two system parameters. To indicate the usability of the new FXT stability lemma, we utilize it to study the FXT stability of a bidirectional associative memory neural network (BAMNN) with bounded perturbations via sliding mode control.

STMS-YOLOv5: A Lightweight Algorithm for Gear Surface Defect Detection.

Most deep-learning-based object detection algorithms exhibit low speeds and accuracy in gear surface defect detection due to their high computational costs and complex structures. To solve this problem, a lightweight model for gear surface defect detection, namely STMS-YOLOv5, is proposed in this paper. Firstly, the ShuffleNetv2 module is employed as the backbone to reduce the giga floating-point operations per second and the number of parameters.

Wind Speed Prediction Based on Error Compensation.

Wind speed prediction is very important in the field of wind power generation technology. It is helpful for increasing the quantity and quality of generated wind power from wind farms. By using univariate wind speed time series, this paper proposes a hybrid wind speed prediction model based on Autoregressive Moving Average-Support Vector Regression (ARMA-SVR) and error compensation.

Selection of HBV key reactivation factors based on maximum information coefficient combined with cosine similarity.

Background: Hepatitis B Virus (HBV) reactivation is the most common complication for patients with primary liver cancer (PLC) after radiotherapy. How to reduce the reactivation of HBV has been a hot topic in the study of postoperative radiotherapy for liver cancer.

Objective: To find out the inducement of HBV reactivation, a feature selection algorithm (MIC-CS) using maximum information coefficient (MIC) combined with cosine similarity (CS) was proposed to screen the risk factors that may affect HBV reactivation.

Multi-view subspace clustering via adaptive graph learning and late fusion alignment.

Multi-view subspace clustering has attracted great attention due to its ability to explore data structure by utilizing complementary information from different views. Most of existing methods learn a sample representation coefficient matrix or an affinity graph for each single view, then the final clustering result is obtained from the spectral embedding of a consensus graph using certain traditional clustering techniques, such as k-means. However, clustering performance will be degenerated if the early fusion of partitions cannot fully exploit relationships between all samples.

Shape and Composition Evolution in an Alloy Core-Shell Nanowire Heterostructure Induced by Adatom Diffusion.

A core-shell nanowire heterostructure is an important building block for nanowire-based optoelectronic devices. In this paper, the shape and composition evolution induced by adatom diffusion is investigated by constructing a growth model for alloy core-shell nanowire heterostructures, taking diffusion, adsorption, desorption and incorporation of adatoms into consideration. With moving boundaries accounting for sidewall growth, the transient diffusion equations are numerically solved by the finite element method.

A survey of epidemic management data models.

The COVID-19 epidemic has demonstrated the important role that data plays in the response to and management of public health emergencies. It has also heightened awareness of the role that ontologies play in the design of semantically precise data models that improve data interoperability among stakeholders. This paper surveys vocabularies and ontologies relevant to the task of achieving epidemic-related data interoperability.

Design of electromagnetic metasurface using two dimensional crystal nets.

Metasurfaces are of great interest as they exhibit unique electromagnetic properties. Currently, metasurface design focuses on generating new meta-atoms and their combinations. Here a topological database, reticular chemistry structure resource (RCSR), is introduced to bring a new dimension and more possibilities for metasurface design.

Excitation-Wavelength-Dependent Charge-Carrier Lifetime in Hematite: An Insight from Nonadiabatic Molecular Dynamics.

Experiments have reported that the photoexcited carrier lifetime in α-FeO has a significant excitation-wavelength dependence but leave the physical mechanism unresolved. In this work, we rationalize the puzzling excitation-wavelength dependence of the photoexcited carrier dynamics in FeO by performing nonadiabatic molecular dynamics simulation based on the strongly constrained and appropriately normed functional, which accurately describes the electronic structure of FeO. Photogenerated electrons with lower-energy excitation relax fast in the t conduction band within about 100 fs, while the photogenerated electrons with higher-energy excitation undergo first a slower interband relaxation from the e lower state to the t upper state on a time scale of 135 ps, followed by the much faster t intraband relaxation.

A Three-Dimensional ResNet and Transformer-Based Approach to Anomaly Detection in Multivariate Temporal-Spatial Data.

Anomaly detection in multivariate time series is an important problem with applications in several domains. However, the key limitation of the approaches that have been proposed so far lies in the lack of a highly parallel model that can fuse temporal and spatial features. In this paper, we propose TDRT, a three-dimensional ResNet and transformer-based anomaly detection method.

Improved Defect Tolerance and Charge Carrier Lifetime in Tin-Lead Mixed Perovskites: Ab Initio Quantum Dynamics.

Simulations by nonadiabatic (NA) molecular dynamics demonstrate that mixing tin with lead in CHNHPbI can passivate the midgap state created by an interstitial iodine (I) via the imposed compressive strain and upshifted valence band maximum, reduce NA coupling by decreasing electron-hole wave functions overlap, and shortens pure-dephasing time by introducing high-frequency phonon modes. Thus, the charge carrier lifetime extends to 3.6 ns due to the significantly reduced nonradiative electron-hole recombination, which is an order of magnitude longer than the I-containing CHNHPbI, over 2.

Optimal Security Protection Strategy Selection Model Based on Q-Learning Particle Swarm Optimization.

With the rapid development of Industrial Internet of Things technology, the industrial control system (ICS) faces more and more security threats, which may lead to serious risks and extensive damage. Naturally, it is particularly important to construct efficient, robust, and low-cost protection strategies for ICS. However, how to construct an objective function of optimal security protection strategy considering both the security risk and protection cost, and to find the optimal solution, are all significant challenges.

Striking impacts of biomass burning on PM concentrations in Northeast China through the emission inventory improvement.

Biomass burning exerts substantial influences on air quality and climate, which in turn to further aggravate air quality. The biomass burning emissions in particular of the agricultural burning may suffer large uncertainties which limits the understanding of their impact on air quality. Based on an improved emission inventory of the Visible Infrared Imaging Radiometer Suite (VIIRS) relative to commonly used Global Fire Emissions Database (GFED), we thoroughly evaluate the impact of biomass burning on air quality and climate during the episodes of November 2017 in Northeast China which is rich in agriculture burning.

Coupled spreading between information and epidemics on multiplex networks with simplicial complexes.

The way of information diffusion among individuals can be quite complicated, and it is not only limited to one type of communication, but also impacted by multiple channels. Meanwhile, it is easier for an agent to accept an idea once the proportion of their friends who take it goes beyond a specific threshold. Furthermore, in social networks, some higher-order structures, such as simplicial complexes and hypergraph, can describe more abundant and realistic phenomena.

Construction of Structured Random Measurement Matrices in Semi-Tensor Product Compressed Sensing Based on Combinatorial Designs.

A random matrix needs large storage space and is difficult to be implemented in hardware, and a deterministic matrix has large reconstruction error. Aiming at these shortcomings, the objective of this paper is to find an effective method to balance these performances. Combining the advantages of the incidence matrix of combinatorial designs and a random matrix, this paper constructs a structured random matrix by the embedding operation of two seed matrices in which one is the incidence matrix of combinatorial designs, and the other is obtained by Gram-Schmidt orthonormalization of the random matrix.

Enabling Secure Data sharing with data deduplication and sensitive information hiding in cloud-assisted Electronic Medical Systems.

Data sharing is very important for medical researchers to do research on certain diseases in cloud-assisted electronic medical systems. Nonetheless, there are large amounts of duplicate data in shared electronic medical records, which incurs redundant storage. In addition, data sharing of electronic medical records might expose the sensitive information of patients.

A Defect Detection Model for Industrial Products Based on Attention and Knowledge Distillation.

Industrial quality detection is one of the important fields in machine vision. Big data analysis, the Internet of Things, edge computing, and other technologies are widely used in industrial quality detection. Studying an industrial detection algorithm that can be organically combined with the Internet of Things and edge computing is imminent.

WSNEAP: An Efficient Authentication Protocol for IIoT-Oriented Wireless Sensor Networks.

With the development of the Industrial Internet of Things (IIoT), industrial wireless sensors need to upload the collected private data to the cloud servers, resulting in a large amount of private data being exposed on the Internet. Private data are vulnerable to hacking. Many complex wireless-sensor-authentication protocols have been proposed.

A supramolecular self-assembled nanomaterial for synergistic therapy of immunosuppressive tumor.

Triple negative breast cancer (TNBC) is an immunosuppressive "cold" tumor that lacks immune cell infiltration and activation, resulting in a poor response to immune checkpoint blockade (ICB) therapies. In addition, TNBC is poorly responsive to targeted therapies due to the absence of efficient molecular targets. A strategy that can block molecular signal transduction, stimulate immunogenicity, and activate the immune response is a promising approach to achieve ideal clinical benefit.

Highly bright aggregation-induced emission nanodots for precise photoacoustic/NIR-II fluorescence imaging-guided resection of neuroendocrine neoplasms and sentinel lymph nodes.

Owing to the complementary high spatial resolution and signal-to-noise ratio, the dual-modality imaging technique of photoacoustic imaging (PAI)/the second near-infrared window fluorescence imaging (NIR-II FI) holds great promise for the detection of tumor tissues and sentinel lymph nodes (SLNs). Herein, by developing two aggregation-induced emission luminogens (AIEgens) (C-NTBD and O-NTBD) with benzodithiadiazole as the electron acceptor, PAI/NIR-II FI was applied to the diagnosis and treatment of neuroendocrine neoplasms (NENs) and their SLNs for the first time. Both AIEgens exhibited good PAI properties as well as intense NIR-II fluorescence emission.

Intelligent Detection Method of Gearbox Based on Adaptive Hierarchical Clustering and Subset.

Deep learning uses mechanical time-frequency signals to train deep neural networks, which realizes automatic feature extraction and intelligent diagnosis of fault features and gets rid of the dependence on a large number of signal processing technology and experience. Aiming at the problem of misclassification of similar samples, a fault diagnosis algorithm based on adaptive hierarchical clustering and subset (AHC-SFD) is proposed to extract features and applied to gearbox fault diagnosis. Firstly, the adaptive hierarchical clustering algorithm is used to analyze the characteristics of different data, and then the data set is clustered into multiple feature groups; finally, according to the feature group, the SubCNN model is established for multiscale feature extraction, so as to carry out fault diagnosis.

A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs.

Body to body networks (BBNs) are a kind of large-scaled sensor network that are composed of several wireless body area networks (WBANs) in the distributed structure, and in recent decades, BBNs have played a key role in medical, aerospace, and military applications. Compared with the traditional WBANs, BBNs have larger scales and longer transmission distances. The sensors within BBNs not only transmit the data they collect, but also forward the data sent by other nodes as relay nodes.

An Activatable Near-Infrared Afterglow Theranostic Prodrug with Self-Sustainable Magnification Effect of Immunogenic Cell Death.

Herein, we report an activatable near-infrared (NIR) afterglow theranostic prodrug that circumvents high background noise interference caused by external light excitation. The prodrug can release hydroxycamptothecin (HCPT) in response to the high intratumoral peroxynitrite level associated with immunogenic cell death (ICD), and synchronously activate afterglow signal to monitor the drug release process and cold-to-hot tumor transformation. The prodrug itself is an ICD inducer achieved by photodynamic therapy (PDT).

Path Planning and Trajectory Tracking for Automatic Guided Vehicles.

Automated guided vehicle technology has become a hot area of scientific research due to its increasing use in manufacturing and logistics. Its main features are programming and control, remote computer eye tracking, command receiving and execution, autonomous route planning, and autonomous driving execution of tasks, with the advantages of high intelligence and flexibility. In this work, a simple vehicle model is used to study the route planning and tracking control of automatic guided vehicles.