Optical biosensor arrays based on nanozymes for environmental monitoring and food safety detection: principles, design, and applications.

Typical biosensing platforms are based on the "lock-and-key" approach, providing high specificity and sensitivity for environmental and food safety monitoring. However, they are limited in their ability to detect multiple analytes simultaneously. With the use of pattern identification methods, biosensor arrays can detect faint fluctuations caused by multiple analytes with similar properties in complex systems.

Proactive personality and its impact on online learning engagement through positive emotions and learning motivation.

The rapid change in information technology has made online or blended learning a new norm in higher education. However, the problem of low student engagement in online learning is becoming more prominent. This study explores how college students' proactive personality affects their online learning engagement through the mediating effects of positive emotions and learning motivation.

Simulation Study and Optimization Strategies for Vacuum Infusion of GFRP Hoses Based on Resin Time-Viscosity Variables.

During the infusion process of a glass-fiber-reinforced thermosetting composite hose, the viscosity of its resin matrix undergoes temporal variations. Consequently, if the impact of resin viscosity changes over time on the internal resin fluidity is not considered during the infusion process, this may result in the incomplete impregnation of the hose, characterized by the presence of numerous voids. This phenomenon adversely affects the quality of the pipe's curing and forming process.

DyLFG: A Dynamic Network Learning Framework Based on Geometry.

Dynamic network representation learning has recently attracted increasing attention because real-world networks evolve over time, that is nodes and edges join or leave the networks over time. Different from static networks, the representation learning of dynamic networks should not only consider how to capture the structural information of network snapshots, but also consider how to capture the temporal dynamic information of network structure evolution from the network snapshot sequence. From the existing work on dynamic network representation, there are two main problems: (1) A significant number of methods target dynamic networks, which only allow nodes to increase over time, not decrease, which reduces the applicability of such methods to real-world networks.

Surface defect detection of steel based on improved YOLOv5 algorithm.

To address the challenge of achieving a balance between efficiency and performance in steel surface defect detection, this paper presents a novel algorithm that enhances the YOLOv5 defect detection model. The enhancement process begins by employing the K-means++ algorithm to fine-tune the location of the prior anchor boxes, improving the matching process. Subsequently, the loss function is transitioned from generalized intersection over union (GIOU) to efficient intersection over union (EIOU) to mitigate the former's degeneration issues.

Quantitative analysis of Cd based on the stress effect of minerals in rice by laser-induced breakdown spectroscopy.

Cadmium (Cd) is a highly toxic heavy metal that can accumulate in the food chain, posing a significant threat to human health. One of the key food sources through which Cd is often observed is rice. Therefore, determining heavy metals in rice is essential to assess the risk status of rice.

Research on heart rate extraction method based on mobile phone video.

As an important indicator of human health, heart rate is related to the diagnosis of cardiovascular diseases. In recent years, extracting the heart rate from the mobile phone image has become a research hotspot. However, the illumination intensity of the background, frame rate of the video, and resolution of the image influence heart rate detection accuracy.

The Intricacies of Sprott-B System with Fractional-Order Derivatives: Dynamical Analysis, Synchronization, and Circuit Implementation.

Studying simple chaotic systems with fractional-order derivatives improves modeling accuracy, increases complexity, and enhances control capabilities and robustness against noise. This paper investigates the dynamics of the simple Sprott-B chaotic system using fractional-order derivatives. This study involves a comprehensive dynamical analysis conducted through bifurcation diagrams, revealing the presence of coexisting attractors.

Research on the Heating Process of CFRP Circular Tubes Based on Electromagnetic Induction Heating Method.

Based on the electromagnetic induction heating method, heating and curing of Carbon Fiber Reinforced Polymer (CFRP) have the advantages of high energy utilization and no pollution. However, in the heating process, both the material weaving structure and mold material can affect the temperature field. Therefore, in this study, an electromagnetic heating finite element analysis model for CFRP circular tubes was established based on the equivalent electromagnetic thermal characteristics of CFRP.

EnRDeA U-Net Deep Learning of Semantic Segmentation on Intricate Noise Roads.

Road segmentation is beneficial to build a vision-controllable mission-oriented self-driving bot, e.g., the Self-Driving Sweeping Bot, or SDSB, for working in restricted areas.

Unrevealing the Hidden Effects of Job Insecurity: A Moderated-Mediation Model of Moral Disengagement and Moral Identity.

Moral disengagement is an intensely negative reaction that triggers unethical behavior in the workplace. By integrating the conservation of resources and moral disengagement theories, the current research examined how moral disengagement can explain the mechanism through which job insecurity results in adverse consequences. Furthermore, moral identity was theorized to moderate the hypothesized relationships.

Classification and Optimization of Basketball Players' Training Effect Based on Particle Swarm Optimization.

Since the professionalization of basketball in China, the number of teenagers participating in basketball training has gradually increased, which has promoted the improvement of basketball level in China. Teenagers 'love' for basketball further promotes the improvement of basketball level in China. However, the reality of basketball in China still lags far behind that of developed basketball countries, in which backward training is an important aspect.

Nondestructive detection of total soluble solids in grapes using VMD-RC and hyperspectral imaging.

Total soluble solids (TSS) are one of the most essential attributes determining the quality and price of fruit. This study aimed to use hyperspectral imaging (HSI) and wavelength selection for nondestructive detection of TSS in grape. A novel method involving variational mode decomposition and regression coefficients (VMD-RC) was proposed to select feature wavelengths.

Vibration responses characteristics of a Ginkgo biloba tree excited under harmonic excitation.

The most effective method of the fruit harvesting is the mechanical harvest. The frequency spectrum of different testing positions on a Ginkgo biloba tree under the impact excitation was tested in the laboratory. The acceleration responses under the harmonic excitation were measured at the frequency of the peak and trough points in the frequency spectrum curves.

Research on nondestructive identification of grape varieties based on EEMD-DWT and hyperspectral image.

Grape varieties are directly related to the quality and sales price of table grapes and consumed products (raisin, wine, grape juice, etc.). To satisfy the identification requirements of rapid, accurate, and nondestructive detection, an improved denoising algorithm based on ensemble empirical mode decomposition (EEMD) and discrete wavelet transform (DWT) is proposed to couple with the hyperspectral image (HSI) of grape varieties in this study.

Decreased effective connectivity between insula and anterior cingulate cortex during a working memory task after prolonged sleep deprivation.

Total sleep deprivation (TSD) causes a decline in almost all cognitive domains, especially working memory. However, we do not have a clear understanding of the degree working memory is impaired under prolonged TSD, nor do we know the underlying neurophysiological mechanism. In this study, we recorded EEG data from 64 subjects while they performed a working memory task during resting wakefulness, after 24 h TSD, and after 30 h TSD.

Deeply Recursive Low- and High-Frequency Fusing Networks for Single Image Super-Resolution.

With the development of researches on single image super-resolution (SISR) based on convolutional neural networks (CNN), the quality of recovered images has been remarkably promoted. Since then, many deep learning-based models have been proposed, which have outperformed the traditional SISR algorithms. According to the results of extensive experiments, the feature representations of the model can be enhanced by increasing the depth and width of the network, which can ultimately improve the image reconstruction quality.

Fracmemristor chaotic oscillator with multistable and antimonotonicity properties.

Memristor is a non-linear circuit element in which voltage-current relationship is determined by the previous values of the voltage and current, generally the history of the circuit. The nonlinearity in this component can be considered as a fractional-order form, which yields a fractional memristor (fracmemristor). In this paper, a fractional-order memristor in a chaotic oscillator is applied, while the other electronic elements are of integer order.

A facile synthesis of two ionized fluorescent carbon dots and selective detection toward Fe and Cu.

In this study, a facile synthesis of two ionized carbon dots (CDs-2 and CDs-3) is reported, in which different ionic pairs are formed at the surface of the carbon core. In contrast to CDs-3, the accumulation of carbon core can be clearly observed in the TEM image of CDs-2. This is due to the linkage of the dibromine alkyl group.

pH-sensitive drug delivery based on chitosan wrapped graphene quantum dots with enhanced fluorescent stability.

Graphene quantum dots (GQDs) were prepared by the pyrolysis of citric acid (CA), which were used for the loading of hydrophilic cytarabine (Cyt), an anti-cancer drug, and then wrapped with chitosan (CS) gels for the encapsulation of the loaded Cyt. The fluorescent stability of GQDs was significantly enhanced in the presence of CS, which might be attributed to the inhibited agglomeration of GQDs by the CS gels. In addition, the burst release of Cyt from the developed carrier was also effectively relieved by the CS coating.

Recent progress of enantioseparation under scale production (2014-2019).

Very different from enantiorecognition at an analytical level, preparative enantioseparation at scale production has to confront many problems. In recent years, various separation techniques have been developed with much progress including high-performance liquid chromatography, liquid-liquid extraction, membrane separation, enantioselective solid-phase absorption, and enantioselective precipitation. Among them, in order to achieve high enantiomerical purity of the enantiomers after separation, two approaches are commonly carried out.

Universal Target Learning: An Efficient and Effective Technique for Semi-Naive Bayesian Learning.

To mitigate the negative effect of classification bias caused by overfitting, semi-naive Bayesian techniques seek to mine the implicit dependency relationships in unlabeled testing instances. By redefining some criteria from information theory, Target Learning (TL) proposes to build for each unlabeled testing instance P the Bayesian Network Classifier BNC P , which is independent and complementary to BNC T learned from training data T . In this paper, we extend TL to Universal Target Learning (UTL) to identify redundant correlations between attribute values and maximize the bits encoded in the Bayesian network in terms of log likelihood.

Discriminatory Target Learning: Mining Significant Dependence Relationships from Labeled and Unlabeled Data.

Machine learning techniques have shown superior predictive power, among which Bayesian network classifiers (BNCs) have remained of great interest due to its capacity to demonstrate complex dependence relationships. Most traditional BNCs tend to build only one model to fit training instances by analyzing independence between attributes using conditional mutual information. However, for different class labels, the conditional dependence relationships may be different rather than invariant when attributes take different values, which may result in classification bias.

Improvement of Laser Transmission Welding of Glass with Titanium Alloy by Laser Surface Treatment.

Laser surface treatment of the titanium alloy was locally oxidized on the metal surface to improve the joint strength of laser transmission welding of high borosilicate glass with titanium alloy. The results find that the welding strength was increased 5 times. The welding mechanism was investigated by the morphology of the welded parts, the tensile-fracture failure mode, the diffusion of the interface elements, and the surface free energy.

A facile avenue to prepare chiral graphene sheets as electrode modification for electrochemical enantiorecognition.

Electrochemical enantiorecognition has attracted much interest due to its advantages of low cost and simple equipment. In this work, a new chiral ionic liquid is prepared and applied as an electrolyte for the one-step synthesis of functional graphene sheets (GO-(S,S)-CIL). More importantly, an electrode is modified by the material for the construction of an electrochemical sensor (GO-(S,S)-CIL-GCE).