Perovskite Light-Emitting Devices Based on Solid-State Diffusion In Situ Dynamic Thermal Crystallization.

Due to the excellent photonic and electrical properties of metal halide perovskite materials, perovskite light-emitting devices have the potential to replace OLED devices as the next-generation of commercial light-emitting devices. In this article, we controlled the surface morphology of PbBr using an in situ dynamic thermal crystallization process, which increased the specific surface area of the films and promoted the solid-state diffusion rate. The CsPbBr PeLEDs prepared using this method achieved a maximum current efficiency of 7.

Exploring a novel HE image segmentation technique for glioblastoma: A hybrid slime mould and differential evolution approach.

Glioblastoma is a primary brain tumor with high incidence and mortality rates, posing a significant threat to human health. It is crucial to provide necessary diagnostic assistance for its management. Among them, Multi-threshold Image Segmentation (MIS) is considered the most efficient and intuitive method in image processing.

Multistable ghost attractors in a switching laser system.

This paper studies the effects of a switching parameter on the dynamics of a multistable laser model. The laser model represents multistability in distinct ranges of parameters. We assume that the system's parameter switches periodically between different values.

Antibacterial and antibiofilm activity of stabilized silver (Ag) nanoparticles against drug-resistant clinical pathogens.

Infectious diseases remain among the most pressing concerns for human health. This issue has grown even more complex with the emergence of multidrug-resistant (MDR) bacteria. To address bacterial infections, nanoparticles have emerged as a promising avenue, offering the potential to target bacteria at multiple levels and effectively eliminate them.

Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes.

Adverse pregnancy outcomes, such as low birth weight (LBW) and preterm birth (PTB), can have serious consequences for both the mother and infant. Early prediction of such outcomes is important for their prevention. Previous studies using traditional machine learning (ML) models for predicting PTB and LBW have encountered two important limitations: extreme class imbalance in medical datasets and the inability to account for complex relational structures between entities.

Poly(vinyl alcohol)/chitosan hydrogel incorporating chitooligosaccharide-gentisic acid conjugate with antioxidant and antibacterial properties as a potential wound dressing.

The design and development of wound dressing with antioxidant and antibacterial properties to accelerate wound healing remain challenging. In this study, we synthesize a chitooligosaccharide-gentisic acid (COS-GSA) conjugate using the free-radical grafting method, and fabricate a poly(vinyl alcohol) (PVA)/chitosan (CH)/COS-GSA (PVA/CH/CG) hydrogel using a freeze-thaw method. We characterize the synthesized COS-GSA conjugates using through polyphenol assay, absorbance, and H NMR spectroscopy and evaluate their antioxidant properties.

A classification method for soybean leaf diseases based on an improved ConvNeXt model.

Deep learning technologies have enabled the development of a variety of deep learning models that can be used to detect plant leaf diseases. However, their use in the identification of soybean leaf diseases is currently limited and mostly based on machine learning methods. In this investigation an enhanced deep learning network model was developed to recognize soybean leaf diseases more accurately.

Dynamical analysis of the effects of circadian clock on the neurotransmitter dopamine.

The circadian clock is an autonomous timing system that regulates the physiological and behavioral activities of organisms. Dopamine (DA) is an important neurotransmitter that is associated with many biological activities such as mood and movement. Experimental studies have shown that the circadian clock influences the DA system and disorders in the circadian clock lead to DA-related diseases.

Path planning for mobile robots in complex environments based on improved ant colony algorithm.

Aiming at the problems of the basic ant colony algorithm in path planning, such as long convergence time, poor global path quality and not being suitable for dynamic environments and unknown environments, this paper proposes a path planning method for mobile robots in complex environments based on an improved ant colony (CBIACO) algorithm. First, a new probability transfer function is designed for an ant colony algorithm, the weights of each component in the function are adaptively adjusted to optimize the convergence speed of the algorithm, and the global path is re-optimized by using the detection and optimization mechanism of diagonal obstacles. Second, a new unknown environment path exploration strategy (UPES) is designed to solve the problem of poor path exploration ability of the ant colony algorithm in unknown environment.

High-efficiency all-fluorescent white organic light-emitting diode based on TADF material as a sensitizer.

The use of TADF materials as both sensitizers and emitters is a promising route to achieve high-efficiency all-fluorescent white organic light-emitting diodes (WOLEDs). In this study, the thermally-activated delayed-fluorescent (TADF) material DMAC-TRZ (9,9-dimethyl-9,10-dihydroacridine-2,4,6-triphenyl-1,3,5-triazine) was selected as a sensitizer for the conventional fluorescent emitter DCJTB (4-(dicyanomethylene)-2--butyl-6-(1,1,7,7-tetramethyljulolidyl-9-enyl)-4-pyran), which was co-doped in a wide bandgap host of DPEPO (bis[2-(diphenylphosphino)phenyl]ether oxide) to fabricate WOLEDs. For the emitting layer of DPEPO:DMAC-TRZ:DCJTB, the DPEPO host can dilute the exciton concentration formed on the DMAC-TRZ sensitizer, which benefits the suppression of exciton quenching.

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.

First-principles study of magnetic interactions and excitations in antiferromagnetic van der Waals material MPX(M=Mn, Fe, Co, Ni; X=S, Se).

Transition metal phosphorus trichalcogenides MPX(M = Mn, Fe, Co, Ni; X = S, Se), as layered van der Waals antiferromagnetic (AFM) materials, have emerged as a promising platform for exploring two-dimensional (2D) magnetism. Based on density functional theory, we present a comprehensive investigation of the electronic and magnetic properties of MPX. We calculated the spin exchange interactions as well as magnetocrystalline anisotropy energy.

Resilient and Tough Conductive Polymer Hydrogel for a Low-Hysteresis Strain Sensor.

Conductive polymer hydrogels are vital in strain sensors, yet achieving high resilience and toughness is a challenge. This study employs a prestretch method to engineer a tough conductive polymer hydrogel with sufficient resilience. Initially, a blend film of polyvinylalcohol (PVA) and ethylene-vinyl alcohol copolymer (EVOH) is prepared through solution casting, followed by a swelling process to form a PVA-EVOH hydrogel.

An accelerated sine mapping whale optimizer for feature selection.

An improved whale optimization algorithm (SWEWOA) is presented for global optimization issues. Firstly, the sine mapping initialization strategy (SS) is used to generate the population. Secondly, the escape energy (EE) is introduced to balance the exploration and exploitation of WOA.

A new population initialization of metaheuristic algorithms based on hybrid fuzzy rough set for high-dimensional gene data feature selection.

In the realm of modern medicine and biology, vast amounts of genetic data with high complexity are available. However, dealing with such high-dimensional data poses challenges due to increased processing complexity and size. Identifying critical genes to reduce data dimensionality is essential.

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.

Application of Cartilage Extracellular Matrix to Enhance Therapeutic Efficacy of Methotrexate.

Background: Rheumatoid arthritis (RA) is characterized by chronic inflammation and joint damage. Methotrexate (MTX), a commonly used disease-modifying anti-rheumatic drug (DMARD) used in RA treatment. However, the continued use of DMARDs can cause adverse effects and result in limited therapeutic efficacy.

Underwater Fish Segmentation Algorithm Based on Improved PSPNet Network.

With the sustainable development of intelligent fisheries, accurate underwater fish segmentation is a key step toward intelligently obtaining fish morphology data. However, the blurred, distorted and low-contrast features of fish images in underwater scenes affect the improvement in fish segmentation accuracy. To solve these problems, this paper proposes a method of underwater fish segmentation based on an improved PSPNet network (IST-PSPNet).

Conversion of the bronchial tree into a conforming electrode to ablate the lung nodule in a porcine model.

Background: Radiofrequency ablation (RFA) is one of the treatment options for lung nodules. However, the need for exact delivery of the rigid metal electrode into the center of the target mass often leads to complications or suboptimal results. To overcome these limitations, a concept of conforming electrodes using a flexible material has been tested in this study.

Research on the influence of yogalates comprehensive rehabilitation training on postoperative recovery of breast cancer patients.

Background: In the present study, we analyzed the effects of comprehensive yogalates rehabilitation training on side effects caused by postoperative adjuvant treatment and postoperative physical and mental health in breast cancer patients who had undergone radical mastectomy.The purpose of this investigation was to test the hypothesis that 12 weeks of yogalates training would reduce the peripheral diameter of the upper arm, improve flexibility and posture, and improve sleep quality compared with a non-treated control group.

Methods: 36 women with breast cancer who had undergone radical mastectomy in Shanghai were selected and randomly divided into the experimental group (n=20) and the control group (n=16).

Collaborative Perception-The Missing Piece in Realizing Fully Autonomous Driving.

Environment perception plays a crucial role in enabling collaborative driving automation, which is considered to be the ground-breaking solution to tackling the safety, mobility, and sustainability challenges of contemporary transportation systems. Despite the fact that computer vision for object perception is undergoing an extraordinary evolution, single-vehicle systems' constrained receptive fields and inherent physical occlusion make it difficult for state-of-the-art perception techniques to cope with complex real-world traffic settings. Collaborative perception (CP) based on various geographically separated perception nodes was developed to break the perception bottleneck for driving automation.

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.

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach.

Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.

Fusing Local Shallow Features and Global Deep Features to Identify Beaks.

Cephalopods are an essential component of marine ecosystems, which are of great significance for the development of marine resources, ecological balance, and human food supply. At the same time, the preservation of cephalopod resources and the promotion of sustainable utilization also require attention. Many studies on the classification of cephalopods focus on the analysis of their beaks.

A computational stochastic procedure for solving the epidemic breathing transmission system.

This work provides numerical simulations of the nonlinear breathing transmission epidemic system using the proposed stochastic scale conjugate gradient neural networks (SCGGNNs) procedure. The mathematical model categorizes the breathing transmission epidemic model into four dynamics based on a nonlinear stiff ordinary differential system: susceptible, exposed, infected, and recovered. Three different cases of the model are taken and numerically presented by applying the stochastic SCGGNNs.