Construction and evaluation of China older-adult care service smart supply chain system.

Introduction: As the aging of the population continues to deepen, the pressure on social pensions is gradually increasing, and the issue of assistance has become a problem that must be solved. With the development of science and technology, people's living standard is constantly improving. The older-adult care services expected by the older-adult are wider than meeting the basic daily needs of individuals.

CFM-YOLOv5:CFPNet moudle and muti-target prediction head incorporating YOLOv5 for metal surface defect detection.

Aiming at the problem of low efficiency of manual detection in the field of metal surface defect detection, a deep learning defect detection method based on improved YOLOv5 algorithm is proposed. Firstly, in the feature enhancement part, we replace the multi-head self-attention module of the standard transformer encoder with the EVC module to improve the feature extraction ability. Second, in the prediction part, adding a small target detection head can reduce the negative impact of drastic object scale changes and improve the accuracy and stability of detection.

Prioritized experience replay in path planning via multi-dimensional transition priority fusion.

Introduction: Deep deterministic policy gradient (DDPG)-based path planning algorithms for intelligent robots struggle to discern the value of experience transitions during training due to their reliance on a random experience replay. This can lead to inappropriate sampling of experience transitions and overemphasis on edge experience transitions. As a result, the algorithm's convergence becomes slower, and the success rate of path planning diminishes.

Predicting and factor analysis of rider injury severity in two-wheeled motorcycle and vehicle crash accidents based on an interpretable machine learning framework.

Objective: As one of the vulnerable road users in accidents, how to improve the two-wheeled motorcyclist's driving safety and reduce accident injury is a public health issue. Accurate identification of the factors influencing the severity of accidents is an important prerequisite for mitigating injury from crashes.

Methods: Based on a vehicle and a two-wheeled motorcycle crash accident data from the China in-depth accident study database (CIDAS), this study uses the performance evaluation indicators of accuracy, precision, recall, F1-score, AUC, and the ROC curve.

MOF-Enhanced Chiral ECL Recognition System: Dual-Function in Phenylalanine Enantiomer Detection and Coreaction Acceleration.

The accurate discernment and separation of chiral isomers with high precision remain a significant challenge in various industries and biological fields. In this investigation, an electrochemiluminescent (ECL) chiral recognition platform was devised to ascertain the presence of phenylalanine (Phe). Notably, a homochiral [Ni(l-asp)(bipy)] (Ni-LAB) was established as a dual-function coreactant accelerator and chiral recognition substrate.

A novel MCDM approach for design concept evaluation based on interval-valued picture fuzzy sets.

The assessment of design concepts presents an efficient and effective strategy for businesses to strengthen their competitive edge and introduce market-worthy products. The widely accepted viewpoint acknowledges this as a intricate multi-criteria decision-making (MCDM) approach, involving a multitude of evaluative criteria and a significant amount of data that is frequently ambiguously defined and subjectively influenced. In order to tackle the problems of uncertainty and fuzziness in design concept evaluation, our research creatively combines interval-valued picture fuzzy set (IVPFS) with an MCDM process of design concept evaluation.

The spatial and temporal disaggregation models of high-accuracy vehicle emission inventory.

A high-accuracy gridding vehicle emission inventory is not only the foundation for developing refined emission control strategies but a necessary input to air quality model as well. An accurate approach to the spatiotemporal disaggregation is the key step to improving the accuracy of gridding emission inventories. The existing spatial disaggregation method considers relatively fewer impact factors, lacking adequate correlation analysis among impact factors.

Heterogeneous context interaction network for vehicle re-identification.

Capturing global and subtle discriminative information using attention mechanisms is essential to address the challenge of inter-class high similarity for vehicle re-identification (Re-ID) task. Mixing self-information of nodes or modeling context based on pairwise dependencies between nodes are the core ideas of current advanced attention mechanisms. This paper aims to explore how to utilize both dependency context and self-context in an efficient way to facilitate attention to learn more effectively.

A Hybrid Localization Algorithm for an Adaptive Strategy-Based Distance Vector-Hop and Improved Sparrow Search for Wireless Sensor Networks.

Wireless sensor networks (WSNs) are applied in many fields, among which node localization is one of the most important parts. The Distance Vector-Hop (DV-Hop) algorithm is the most widely used range-free localization algorithm, but its localization accuracy is not high enough. In this paper, to solve this problem, a hybrid localization algorithm for an adaptive strategy-based distance vector-hop and improved sparrow search is proposed (HADSS).

Molecular insights and optimization strategies for the competitive binding of engineered ACE2 proteins: a multiple replica molecular dynamics study.

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) continues to spread globally, and rapid viral evolution and the emergence of new variants pose challenges to pandemic control. During infection, the spike protein of SARS-CoV-2 interacts with the human ACE2 protein its receptor binding domain (RBD), and it is known that engineered forms of ACE2 can compete with wild-type (WT) ACE2 for binding to inhibit infection. Here, we conducted multiple replica molecular dynamics (MRMD) simulations to study the mechanisms of the engineered ACE2 variants 3N39 and 3N94 and provide directions for optimization.

SERS Tracking Oxidative Stress on a Metalloporphyrin Framework by Vitamin C.

Accurate control of charge transfer is crucial to investigate the catalytic reaction mechanism of the biological oxidation process that biomedicine participates in. Herein, we have established an assembly model of metalloporphyrin framework (MPF) nanosheets as the active centers of biological enzymes. The introduction of Vitamin C (VC) into the MPF system can precisely modulate its content of charges.

Facile Preparation of Polysiloxane-Modified Asphalt Binder Exhibiting Enhanced Performance.

The development of polymer-modified asphalt (asphalt = asphalt binder) is significant because the polymer modifier can improve the performance of asphalt mixture and meet the requirements of the modern asphalt pavement. Herein, we present a novel polysiloxane-modified asphalt with enhanced performance, formed by simply mixing hydroxy-terminated polysiloxane (HO-PDMS) into base asphalt at 140 °C. The interaction mechanism of HO-PDMS in base asphalt was characterized by FT-IR, GPC, and DSC.

Exploring conformation changes of Janus kinase 2 pseudokinase mediated by mutations through Gaussian accelerated molecular dynamics and principal component analysis.

The pseudokinase domain (JH2) of the protein tyrosine kinase (Janus kinase 2, JAK2) regulates the activity of a tyrosine kinase domain (JH1) in JAK2, which is further affected by mutations in the JH2. In this work, Gaussian accelerated molecular dynamics (GaMD) simulations followed by construction of free energy landscapes (FELs) and principal component analysis (PCA) were performed to study effect of two mutations V617F and V617F/E596A on the conformations of the ATP-bound JH2. The dynamic analyses reveal that mutations affect the structural flexibility and correlated motions of the JH2, meanwhile also change the dynamics behavior of the P-loop and αC-helix of the JH2.

Building a prediction model of college students' sports behavior based on machine learning method: combining the characteristics of sports learning interest and sports autonomy.

College students' sports behavior is affected by many factors, and sports learning interest and sports autonomy support are potential psychological characteristic factors, which have important influence value on college students' sports behavior. Machine learning methods are widely used to construct prediction models and show high efficiency. In order to understand the impact of sports learning interest and sports autonomy support on college students' sports behavior (physical exercise level), the research decided to use the relevant methods of machine learning to build a prediction model, so as to find the internal relationship between them.

Water-soluble fluorine-functionalized chitooligosaccharide derivatives: Synthesis, characterization and antimicrobial activity.

In this work, a series of water-soluble fluorine-functionalized chitooligosaccharide derivatives were synthesized by conjugating nicotinic acid to chitooligosaccharide via nicotinylation reaction, followed by nucleophilic reaction with ethyl bromide, benzyl bromide and fluorobenzyl bromides. Synthesized derivatives were identified structurally by Fourier Transform Infrared Spectroscopy and Nuclear Magnetic Resonance. In addition, the antibacterial activities of chitooligosaccharide derivatives against several disease-causing bacteria were assessed by the broth dilution method and Kirby-Bauer method, the mycelium growth rate method was used to assessing the antifungal properties of samples against three plant-threatening fungi.

Preparation and application of an imidazolium-based poly (ionic liquid) functionalized silica sorbent for solid-phase extraction of parabens from food samples.

In this work, an imidazolium-based poly (ionic liquid) (poly(1-octyl-3-vinyl- imidazolium naphthalene sulfonate)) functionalized silica (poly(CVImNapSO) @SiO) was successfully prepared for the determination of parabens in food samples. The prepared poly(CVImNapSO)@SiO was characterized by Fourier transform infrared spectrometry (FT-IR), X-ray photoelectron spectrogram (XPS) and Scanning electron microscopy (SEM). The simulation calculation results indicated that the suitable binding energies were between the polymeric ionic liquids and parabens, and the main interactions for extraction were hydrogen bonding, electrostatic and π-π stacking interactions.

EFC-YOLO: An Efficient Surface-Defect-Detection Algorithm for Steel Strips.

The pursuit of higher recognition accuracy and speed with smaller model sizes has been a major research topic in the detection of surface defects in steel. In this paper, we propose an improved high-speed and high-precision Efficient Fusion Coordination network (EFC-YOLO) without increasing the model's size. Since modifications to enhance feature extraction in shallow networks tend to affect the speed of model inference, in order to simultaneously ensure the accuracy and speed of detection, we add the improved Fusion-Faster module to the backbone network of YOLOv7.

Dual CNN cross-teaching semi-supervised segmentation network with multi-kernels and global contrastive loss in ACDC.

The cross-teaching based on Convolutional Neural Network (CNN) and Transformer has been successful in semi-supervised learning; however, the information interaction between local and global relations ignores the semantic features of the medium scale, and at the same time, the information in the process of feature coding is not fully utilized. To solve these problems, we proposed a new semi-supervised segmentation network. Based on the principle of complementary modeling information of different kernel convolutions, we design a dual CNN cross-supervised network with different kernel sizes under cross-teaching.

Instance Segmentation and Berry Counting of Table Grape before Thinning Based on AS-SwinT.

Berry thinning is one of the most important tasks in the management of high-quality table grapes. Farmers often thin the berries per cluster to a standard number by counting. With an aging population, it is hard to find adequate skilled farmers to work during thinning season.

MLACNN: an attention mechanism-based CNN architecture for predicting genome-wide DNA methylation.

Methylation is an important epigenetic regulation of methylation genes that plays a crucial role in regulating biological processes. While traditional methods for detecting methylation in biological experiments are constantly improving, the development of artificial intelligence has led to the emergence of deep learning and machine learning methods as a new trend. However, traditional machine learning-based methods rely heavily on manual feature extraction, and most deep learning methods for studying methylation extract fewer features due to their simple network structures.

A Generalized Hamilton Robust Control Scheme of Trajectory Tracking for Intelligent Vehicles.

To ensure the accuracy and stability of intelligent-vehicle-trajectory tracking, a robust trajectory-tracking control strategy based on generalized Hamilton theory is proposed. Firstly, a dynamic Hamilton dissipative controller (DHDC) and trajectory-tracking Hamilton dissipative controller (TTHDC) were designed based on the established vehicle-dynamics control system and trajectory-tracking control system using the orthogonal decomposition method and control-switching method. Next, the feedback-dissipative Hamilton realizations of the two systems were obtained separately to ensure the convergence of the system.

KRP-DS: A Knowledge Graph-Based Dialogue System with Inference-Aided Prediction.

With the popularity of ChatGPT, there has been increasing attention towards dialogue systems. Researchers are dedicated to designing a knowledgeable model that can engage in conversations like humans. Traditional seq2seq dialogue models often suffer from limited performance and the issue of generating safe responses.

Convolutional neural network based on photoplethysmography signals for sleep apnea syndrome detection.

Introduction: The current method of monitoring sleep disorders is complex, time-consuming, and uncomfortable, although it can provide scientifc guidance to ensure worldwide sleep quality. This study aims to seek a comfortable and convenient method for identifying sleep apnea syndrome.

Methods: In this work, a one-dimensional convolutional neural network model was established.