Qualitative Analysis of a Nonautonomous Delayed Stochastic Predator-Prey Model with Beddington-DeAngelis Functional Response.

Predator-prey interactions constitute a fundamental dynamic governing population regulation, community structure, and ecosystem stability [...

Cascaded metasurface for polarization-dependent varifocal vortex beam manipulation.

Vortex beams, characterized by orbital angular momentum (OAM), hold significant potential in optical communications, quantum information processing, and optical manipulation. However, existing metasurface designs are largely confined to single-degree-of-freedom control, such as static OAM generation or fixed focal points, which limiting their ability to integrate polarization multiplexing with dynamic focal tuning. To address this challenge, we propose a tunable multifunctional cascaded metasurface that synergizes polarization-sensitive phase engineering with interlayer rotational coupling, overcoming conventional device limitations.

Prediction of cancer drug response based on heterogeneous graph neural networks and multi-omics data.

Precision prediction of cancer drug response remains a critical challenge in personalized medicine. With ongoing advancements in related research, substantial amounts of cell line omics data and drug feature information have been accumulated, offering valuable resources for investigating cancer drug responses. However, effectively integrating these multi-omics features and constructing accurate and interpretable network-based prediction models remain challenging.

EMFE-YOLO: A Lightweight Small Object Detection Model for UAVs.

Small object detection in Unmanned Aerial Vehicles' (UAVs) aerial images faces challenges such as low detection accuracy and complex backgrounds. Meanwhile, it is difficult to deploy the object detection models with large parameters on resource-constrained UAVs. Therefore, a lightweight small object detection model EMFE-YOLO is proposed based on efficient multi-scale feature enhancement by improving YOLOv8s.

The study of JAZF1-mediated apoptosis of decidual stromal cells by activating the NF-κB signaling pathway in spontaneous preterm birth.

Purpose: Juxtaposed with another zinc finger gene 1 (JAZF1) is known to be involved in various biological processes, including gluconeogenesis, insulin sensitivity, cell differentiation, lipid metabolism, and inflammation. However, its role in spontaneous preterm birth (SPTB) remains unclear.

Patients And Methods: We investigated the expression of JAZF1 mRNA using quantitative reverse transcription-PCR (qRT-PCR) in the decidual tissue of patients with SPTB compared to non-spontaneous preterm birth(non-SPTB).

The relationship between cloud distribution characteristics and meteorological elements over China.

The meteorological elements in the cloud background field influence and change with the cloud macro and micro characteristics, so it is of great significance to study the relationship between them. Using CALIOP Level 2 VFM products and ERA5 reanalysis data, this paper studies and analyzes the relationship between the distribution characteristics of different types and phases clouds and meteorological elements over China through statistical methods. The results show that: In the two-dimensional probability density function distribution of cloud occurrence probability and relative humidity-temperature, there are two significant high value regions, which can be divided into tropical clouds and temperate clouds.

Hybrid Algorithms Based on Two Evolutionary Computations for Image Classification.

Convolutional neural networks (CNNs) and their improved models (like DenseNet-121) have achieved significant results in image classification tasks. However, the performance of these models is still constrained by issues such as hyperparameter optimization and gradient vanishing and exploding. Owing to their unique exploration and exploitation capabilities, evolutionary algorithms offer new avenues for addressing these problems.

Dynamics, chemistry, and modeling studies in the aviation and aerospace transition zone.

In this review, the concept of the aviation and aerospace transition zone (AATZ), that is, the area between the highest altitude of aviation vehicles and the lowest altitude of space vehicles, is introduced. It is 50-150 km away from the Earth's surface, and the coupling between the AATZ and its upper and lower atmosphere causes complex and unique physical and chemical processes. As a transition zone between space weather and Earth weather, the AATZ has great scientific research value and broad application prospects.

Pinpointing drivers and health effects of exceptional summertime ozone pollution in the Sichuan basin of China in 2022.

Tropospheric ozone (O) is a ubiquitous pollutant that is detrimental to human health and ecosystems. The Sichuan Basin (SCB), one of the most populous city clusters in China, has experienced more-intense O pollution episodes and longer O season with more-frequent stagnant conditions over the past decade. In 2022, the prolonged O season featured extremely high levels of O and region-wide O events were observed, posing significant threats to public health.

Choroid plexus enlargement correlated with motor dysfunction in spinocerebellar ataxia type 3.

Objective: To investigate Choroid plexus (CP) structural changes using multimodal MRI, and assess the correlation between CP damage and motor dysfunction in SCA3 patients.

Methods: We prospectively recruited 64 SCA3 patients and 50 age- and sex-matched healthy controls (HCs). CP volume (CPV) and other brain regions were measured using FreeSurfer 7.

Design and Fabrication of Antireflective Coatings with Enhanced Transmittance and High Laser-Induced Damage Threshold for ZnGeP Mid-Infrared Crystals.

Zinc germanium phosphide (ZnGeP, ZGP), a key mid-infrared nonlinear crystal for optical parametric oscillation (OPO) applications, suffers from limited transmittance (∼60%) at pump (2090 nm) and generated (3600-4800 nm) wavelengths, hindering high-energy laser applications. This study overcomes this limitation by designing and experimentally validating highly effective antireflective coatings (ARCs) using TaO/SiO multilayers. An optimized Air|(1.

An Ultra-Precision Smoothing Polishing Model for Optical Surface Fabrication with Morphology Gradient Awareness.

To improve the surface morphology quality of ultra-precision optical components, particularly in the suppression of mid-spatial frequency (MSF) errors, this paper proposes a morphology gradient-aware spatiotemporal coupled smoothing model based on convolutional material removal. By introducing the Laplacian curvature into the surface evolution framework, a curvature-sensitive "peak-priority" mechanism is established to dynamically guide the local dwell time. A nonlinear spatiotemporal coupling equation is constructed, in which the dwell time is adaptively modulated by surface gradient magnitude, local curvature, and periodic fluctuation terms.

Pharmaceuticals in drinking water in suburban communities in Chengdu, China: potential risks on the human health.

Water supplies have come under serious environmental stress in suburban communities in China due to developmental activities. A comprehensive study was carried out to investigate the occurrence of pharmaceutical compounds in county-level drinking water sources within Chengdu City. The solid-phase extraction combined with ultra-high performance liquid chromatography/tandem mass spectrometry was employed to analyze the residues of pharmaceuticals in the water sources for water supply in Chengdu.

Sharpbelly Fish Optimization Algorithm: A Bio-Inspired Metaheuristic for Complex Engineering.

This paper introduces a novel bio-inspired metaheuristic algorithm, named the sharpbelly fish optimizer (SFO), inspired by the collective ecological behaviors of the sharpbelly fish. The algorithm integrates four biologically motivated strategies-(1) fitness-driven fast swimming, (2) convergence-guided gathering, (3) stagnation-triggered dispersal, and (4) disturbance-induced escape-which synergistically enhance the balance between global exploration and local exploitation. To assess its performance, the proposed SFO is evaluated on the CEC2022 benchmark suite under various dimensions.

Non-decomposition method for event-triggered finite-time synchronization control of complex-valued memristive neural networks.

This paper investigates the finite-time synchronization of complex-valued memristive neural networks (CVMNNs) with time-varying delays using an event-triggered control approach. The analysis is conducted in a holistic manner, utilizing the one-norm and sign functions of complex numbers, thereby eliminating the need for decomposition. To alleviate communication pressure, an event-triggered controller is introduced, accompanied by specific conditions and criteria to guarantee synchronization within a finite time frame.

A multimodal functional structure-based graph neural network for fatigue detection.

Fatigue detection remains a critical research focus in the field. Recent studies have attempted to enhance detection performance through multimodal information fusion, yet they largely overlook the impact of functional connectivity among multimodal signals. To address this limitation, we propose a novel multimodal fatigue classification framework integrating electroencephalogram (EEG) and electrocardiogram (ECG) signals.

Adar contributes to genome integrity by regulating R-loop homeostasis in Drosophila.

Background: Adenosine deaminase acting on RNA (Adar) is a critical enzyme involved in post-transcriptional epigenetic regulation through adenosine-to-inosine (A-to-I) RNA editing. However, the biological role and regulatory mechanisms of Adar remain largely unknown.

Results: Using Drosophila as a model, we found that loss of Adar leads to spontaneous genome instability characterized by DNA damage and mitotic defects.

IncorporationNet: a novel bimodal EEG-EOG vigilance estimation method via time-frequency-space feature fusion network.

The assessment of driver vigilance is critical for promoting road safety, as it evaluates a driver's ability to sustain appropriate levels of attention and reaction capabilities. Electroencephalogram (EEG) and electrooculogram (EOG) signals have proven effective in this context. We propose a bimodal time-frequency-space feature fusion framework aimed at enhancing the integration of EEG and EOG features to improve the predictive accuracy of vigilance estimation.

Doping Tuned the Carrier Dynamics in Li-Doped BiSe Crystals Revealed by Femtosecond Transient Optical Spectroscopy.

Topological insulators (TIs) can be widely applied in the fields of ultrafast optical and spintronic devices owing to the existence of topologically protected gapless Dirac surface states. However, the study of ultrafast dynamics of carriers in TIs remains elusive. In this work, the carrier dynamics of Li-doped BiSe single crystals were investigated by femtosecond (fs) transient optical spectroscopy (ΔR/R(t) signals).

Development and Efficacy Assessment of an Angle Sensor-Integrated Upper Limb Exoskeleton System for Autonomous Rehabilitation Training.

In this study, we propose a rehabilitation training system that incorporates active and passive rehabilitation modes to enhance the convenience, efficacy, and safety of rehabilitation training for patients with upper limb hemiplegia. This system facilitates elbow flexion and extension as well as wrist and palm flexion and extension. The experimental results demonstrate that the exoskeleton robot on the affected limb exhibits a rapid response and maintains a highly synchronized movement with the unaffected upper limb equipped with an angle sensor, preserving stability and coordination throughout the movement process without significant delay affecting the overall motion.

Transformer attention-based neural network for cognitive score estimation from sMRI data.

Accurately predicting cognitive scores based on structural MRI holds significant clinical value for understanding the pathological stages of dementia and forecasting Alzheimer's disease (AD). Some existing deep learning methods often depend on anatomical priors, overlooking individual-specific structural differences during AD progression. To address these limitations, this work proposes a deep neural network that incorporates Transformer attention to jointly predict multiple cognitive scores, including ADAS, CDRSB, and MMSE.

TDNN achitecture with efficient channel attention and improved residual blocks for accurate speaker recognition.

In recent years, with the advancement of deep learning, Convolutional Neural Networks (CNNs) have been widely applied in speaker recognition, making CNN-based speaker embedding learning the predominant method for speaker verification. Time Delay Neural Networks (TDNN) have achieved notable progress in speaker embedding tasks. However, TDNN often struggles with accurately modeling multi-scale features when processing complex audio data, which can result in reduced speaker recognition accuracy.

Accurate robot calibration via cascaded adaptive momentum LM and B-spline interpolated PSO.

To improve robotic positioning accuracy and enhance overall manufacturing precision, this paper proposes an adaptive momentum Levenberg-Marquardt cascaded B-spline interpolation particle swarm optimization (AMLM-BIPSO) algorithm for calibrating robotic geometric errors. Initially, a momentum term is incorporated into the traditional Levenberg-Marquardt algorithm to suppress overshooting and oscillation, thereby improving the preliminary estimation of geometric parameters. Subsequently, inspired by the concept of Knowledge-based artificial Neural Networks, B-spline interpolation is embedded into the standard particle swarm optimization framework to refine the final calibration.

Multi-scheme cross-level attention embedded U-shape transformer for MRI semantic segmentation.

Accurate MRI image segmentation is crucial for disease diagnosis, but current Transformer-based methods face two key challenges: limited capability to capture detailed information, leading to blurred boundaries and false localization, and the lack of MRI-specific embedding paradigms for attention modules, which limits their potential and representation capability. To address these challenges, this paper proposes a multi-scheme cross-level attention embedded U-shape Transformer (MSCL-SwinUNet). This model integrates cross-level spatial-wise attention (SW-Attention) to transfer detailed information from encoder to decoder, cross-stage channel-wise attention (CW-Attention) to filter out redundant features and enhance task-related channels, and multi-stage scale-wise attention (ScaleW-Attention) to adaptively process multi-scale features.

STVMamba: precipitation nowcasting with spatiotemporal prediction model.

A lightweight rainfall nowcasting model is required by Sichuan provincial meteorological bureaus. Deep learning methods such as recurrent, convolutional, and Transformer models have been applied to precipitation prediction. However, recurrent models struggle with suboptimal parallel computational efficiency and error accumulation, convolutional models face challenges in capturing long-range dependencies, and Transformer models are limited by their quadratic time complexity.