Advantages of Broad-Spectrum Influenza mRNA Vaccines and Their Impact on Pulmonary Influenza.

Influenza poses a significant global health challenge due to its rapid mutation and antigenic variability, which often leads to seasonal epidemics and frequent outbreaks. Traditional vaccines struggle to offer comprehensive protection because of mismatches with circulating viral strains. The development of a broad-spectrum vaccine is therefore crucial.

Oral Yeast-Cell Microcapsule-Mediated DNA Vaccines Against Induce Effective Intestinal Immunity and Modulate Gut Microbiota.

is a common opportunistic pathogen that causes gastrointestinal diseases in livestock and poultry. Our preliminary research has demonstrated that administering oral yeast-cell microcapsule (YCM)-mediated DNA vaccines can effectively stimulate mucosal immunity, thereby preventing the occurrence of gastrointestinal diseases. In this study, the α-toxin gene was first cloned and the H126G and C-terminal (C247-370) mutations were created.

Protective Efficacy of an Inactivated Recombinant Serotype 4 Fowl Adenovirus Against Duck Adenovirus 3 in Muscovy Duck.

Background: Duck adenovirus 3 (DAdV-3) is an emerging pathogen that has caused severe economic losses to the duck industry in China. Recently, the infection of ducks with serotype 4 fowl adenovirus (FAdV-4) has also been reported in China. Therefore, an efficient bivalent vaccine to control the diseases caused by DAdV-3 and FAdV-4 is extremely urgent.

Structural Design and Electromagnetic Performance Analysis of Octupole Active Radial Magnetic Bearing.

This study addresses the challenges of magnetic circuit coupling and control complexity in active radial magnetic bearings (ARMBs) by systematically investigating the electromagnetic performance of four magnetic pole configurations (NNSS, NSNS, NNNN, and SSSS). Initially, equivalent magnetic circuit modeling and finite element analysis (FEA) were employed to analyze the magnetic circuit coupling phenomena and their effects on the magnetic flux density distribution for each configuration. Subsequently, the air gap flux density and electromagnetic force were quantified under rotor eccentricity caused by unbalanced disturbances, and the dynamic performances of the ARMBs were evaluated for eccentricity along the x-axis and at 45°.

Enhancing Time Series Anomaly Detection: A Knowledge Distillation Approach with Image Transformation.

Anomaly detection is critical in safety-sensitive fields, but faces challenges from scarce abnormal data and costly expert labeling. Time series anomaly detection is relatively challenging due to its reliance on sequential data, which imposes high computational and memory costs. In particular, it is often composed of real-time collected data that tends to be noisy, making preprocessing an essential step.

Free-Space to SMF Integration and Green to C-Band Conversion Based on PPLN.

In this study, we experimentally demonstrate a PPLN-based free-space to SMF (single-mode fiber) conversion system capable of efficient long-wavelength down-conversion from 518 nm, optimized for minimal loss in highly turbid water, to 1540 nm, which is ideal for low-loss transmission in standard SMF. Leveraging the nonlinear optical properties of periodically poled lithium niobate (PPLN), we achieve a wavelength conversion efficiency of 1.6% through difference frequency generation while maintaining a received optical signal-to-noise ratio of 10.

The Design of a Low-Noise CMOS Image Sensor Using a Hybrid Single-Slope Analog-to-Digital Converter.

In this study, we describe a low-noise complementary metal-oxide semiconductor (CMOS) image sensor (CIS) with a 10/11-bit hybrid single-slope analog-to-digital converter (SS-ADC). The proposed hybrid SS-ADC provides a resolution of 11 bits in low-light and 10 bits in high-light. To this end, in the low-light section, the digital-correlated double sampling method using a double data rate structure was used to obtain a noise performance similar to that of the 11-bit SS-ADC under low-light conditions, while maintaining linear in-out characteristics.

CabbageNet: Deep Learning for High-Precision Cabbage Segmentation in Complex Settings for Autonomous Harvesting Robotics.

Reducing damage and missed harvest rates is essential for improving efficiency in unmanned cabbage harvesting. Accurate real-time segmentation of cabbage heads can significantly alleviate these issues and enhance overall harvesting performance. However, the complexity of the growing environment and the morphological variability of field-grown cabbage present major challenges to achieving precise segmentation.

Identification Method for Railway Rail Corrugation Utilizing CEEMDAN-PE-SPWVD.

Rail corrugation intensifies wheel-rail vibrations, often leading to damage in vehicle-track system components within affected sections. This paper proposes a novel method for identifying rail corrugation, which combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), permutation entropy (PE), and Smoothed Pseudo Wigner-Ville Distribution (SPWVD). Initially, vertical acceleration data from the axle box are decomposed using CEEMDAN to extract intrinsic mode functions (IMFs) with distinct frequencies.

Research on the Stress Characteristics of Reuse of Semi-Rigid Base.

Semi-rigid bases are widely used in road construction due to their excellent properties, high rigidity, and frost resistance, and they have been in service for many years. However, as the service life increases, the maintenance demands also grow, with traditional maintenance methods still being the primary approach. Based on a typical case using ground-penetrating radar (GPR) technology, this study explores the issue of cracks in semi-rigid bases and their impact on overlay layers.

Enhanced FFT-Root-MUSIC Algorithm Based on Signal Reconstruction via CEEMD-SVD for Joint Range and Velocity Estimation for FMCW Radar.

Frequency-modulated continuous-wave (FMCW) radar is used to extract range and velocity information from the beat signal. However, the traditional joint range-velocity estimation algorithms often experience significant performances degradation under low signal-to-noise ratio (SNR) conditions. To address this issue, this paper proposes a novel approach utilizing the complementary ensemble empirical mode decomposition (CEEMD) combined with singular value decomposition (SVD) to reconstruct the beat signal prior to applying the FFT-Root-MUSIC algorithm for joint range and velocity estimation.

Semantically-Enhanced Feature Extraction with CLIP and Transformer Networks for Driver Fatigue Detection.

Drowsy driving is a leading cause of commercial vehicle traffic crashes. The trend is to train fatigue detection models using deep neural networks on driver video data, but challenges remain in coarse and incomplete high-level feature extraction and network architecture optimization. This paper pioneers the use of the CLIP (Contrastive Language-Image Pre-training) model for fatigue detection.

LiDAR-Based Negative Obstacle Detection for Unmanned Ground Vehicles in Orchards.

In orchard environments, negative obstacles such as ditches and potholes pose significant safety risks to robots working within them. This paper proposes a negative obstacle detection method based on LiDAR tilt mounting. With the LiDAR tilted at 40°, the blind spot is reduced from 3 m to 0.

Fused Multi-Domains and Adaptive Variational Mode Decomposition ECG Feature Extraction for Lightweight Bio-Inspired Key Generation and Encryption.

Security is one of the increasingly significant issues given advancements in technology that harness data from multiple devices such as the internet of medical devices. While protecting data from unauthorized user access, several techniques are used including fingerprints, passwords, and others. One of the techniques that has attracted much attention is the use of human features, which has proven to be most effective because of the difficulties in impersonating human-related features.

Exploring Technology Acceptance of Healthcare Devices: The Moderating Role of Device Type and Generation.

The increasing adoption of healthcare devices necessitates a deeper understanding of the factors that influence user acceptance in this rapidly evolving area. Therefore, this study examined the factors influencing the technology acceptance of healthcare devices, focusing on radar sensors and wearable devices. A total of 1158 valid responses were used to test hypotheses, mediation, and moderation effects using SmartPLS 4.

Affinity Peptide-Based Circularly Permuted Fluorescent Protein Biosensors for Non-Small Cell Lung Cancer Diagnosis.

Non-small cell lung cancer (NSCLC) is the predominant form of lung cancer and poses a significant public health challenge. Early detection is crucial for improving patient outcomes, with serum biomarkers such as carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCCAg), and cytokeratin fragment 19 (CYFRA 21-1) playing a critical role in early screening and pathological classification of NSCLC. However, due to being mainly based on corresponding antibody binding reactions, existing detection technologies for these serum biomarkers have shortcomings such as complex operations, high false positive rates, and high costs.

Enhanced Localization in Wireless Sensor Networks Using a Bat-Optimized Malicious Anchor Node Prediction Algorithm.

The accuracy of node localization plays a crucial role in the performance and reliability of wireless sensor networks (WSNs), which are widely utilized in fields like security systems and environmental monitoring. The integrity of these networks is often threatened by the presence of malicious nodes that can disrupt the localization process, leading to erroneous positioning and degraded network functionality. To address this challenge, we propose the security-aware localization using bat-optimized malicious anchor prediction (BO-MAP) algorithm.

Robust and Fast Point Cloud Registration for Robot Localization Based on DBSCAN Clustering and Adaptive Segmentation.

This paper proposes a registration approach rooted in point cloud clustering and segmentation, named Clustering and Segmentation Normal Distribution Transform (CSNDT), with the aim of improving the scope and efficiency of point cloud registration. Traditional Normal Distribution Transform (NDT) algorithms face challenges during their initialization phase, leading to the loss of local feature information and erroneous mapping. To address these limitations, this paper proposes a method of adaptive cell partitioning.

Integrating Vision and Olfaction via Multi-Modal LLM for Robotic Odor Source Localization.

Odor source localization (OSL) technology allows autonomous agents like mobile robots to localize a target odor source in an unknown environment. This is achieved by an OSL navigation algorithm that processes an agent's sensor readings to calculate action commands to guide the robot to locate the odor source. Compared to traditional 'olfaction-only' OSL algorithms, our proposed OSL algorithm integrates vision and olfaction sensor modalities to localize odor sources even if olfaction sensing is disrupted by non-unidirectional airflow or vision sensing is impaired by environmental complexities.

Development and Evaluation of Five-in-One Vaccine Microneedle Array Patch for Diphtheria, Tetanus, Pertussis, Hepatitis B, and Type b: Immunological Efficacy and Long-Term Stability.

: The development of a five-in-one vaccine microneedle patch (five-in-one MN patch) aims to address challenges in administering vaccines against Diphtheria (DT), Tetanus (TT), Pertussis (wP), Hepatitis B (HBsAg), and type b (Hib). Combining multiple vaccines into a single patch offers a novel solution to improve vaccine accessibility, stability, and delivery efficiency, particularly in resource-limited settings. : The five-in-one MN patch consists of four distinct microneedle arrays: DT and TT vaccines are coated together on one array, while wP, HepB, and Hib vaccines are coated separately on individual arrays.

Building of CuO@Cu-TA@DSF/DHA Nanoparticle Targets MAPK Pathway to Achieve Synergetic Chemotherapy and Chemodynamic for Pancreatic Cancer Cells.

With the increase of reactive oxygen species (ROS) production, cancer cells can avoid cell death and damage by up-regulating antioxidant programs. Therefore, it will be more effective to induce cell death by using targeted strategies to further improve ROS levels and drugs that inhibit antioxidant programs. Considering that dihydroartemisinin (DHA) can cause oxidative damage to protein, DNA, or lipids by producing excessive ROS, while, disulfiram (DSF) can inhibit glutathione (GSH) levels and achieve the therapeutic effect by inhibiting antioxidant system and amplifying oxidative stress, they were co-loaded onto the copper peroxide nanoparticles (CuO) coated with copper tannic acid (Cu-TA), to build a drug delivery system of CuO@Cu-TA@DSF/DHA nanoparticles (CCTDD NPs).

How to Fabricate Hyaluronic Acid for Ocular Drug Delivery.

This review aims to examine existing research on the development of ocular drug delivery devices utilizing hyaluronic acid (HA). Renowned for its exceptional biocompatibility, viscoelastic properties, and ability to enhance drug bioavailability, HA is a naturally occurring biopolymer. The review discussed specific mechanisms by which HA enhances drug delivery, including prolonging drug residence time on ocular surfaces, facilitating controlled drug release, and improving drug penetration through ocular tissues.

Experimental and Theoretical Design on the Development of Matrix Tablets with Multiple Drug Loadings Aimed at Optimizing Antidiabetic Medication.

Diabetes is a growing global health crisis that requires effective therapeutic strategies to optimize treatment outcomes. This study aims to address this challenge by developing and characterizing extended-release polymeric matrix tablets containing metformin hydrochloride (M-HCl), a first-line treatment for type 2 diabetes, and honokiol (HNK), a bioactive compound with potential therapeutic benefits. The objective is to enhance glycemic control and overall therapeutic outcomes through an innovative dual-drug delivery system.

Innovative Nanomedicine Delivery: Targeting Tumor Microenvironment to Defeat Drug Resistance.

Nanodrug delivery systems have revolutionized tumor therapy like never before. By overcoming the complexity of the tumor microenvironment (TME) and bypassing drug resistance mechanisms, nanotechnology has shown great potential to improve drug efficacy and reduce toxic side effects. This review examines the impact of the TME on drug resistance and recent advances in nanomedicine delivery systems to overcome this challenge.

The Influence of Fresh Latex Coagulation on the Parameter Characteristics of the Yeoh Hyperelastic Constitutive Model for Natural Rubber.

The coagulation of fresh latex is one of the critical processes that impacts rubber quality during natural rubber processing. Constitutive relationships are the basis for the study of the mechanical properties of rubber materials and serve as a prerequisite for material simulation studies. However, studies on the effect of different coagulation methods on natural rubber constitutive relationships have yet to be carried out, and the current models used for natural rubber constitutive relationships need to be improved.