Dynamic SLAM algorithm adopt with eliminating mismatched point chains in grid motion statistics.

Feature matching is an essential part in areas such as target tracking, and three-dimensional reconstruction. In case of rotational motion in the image, the rotating exercise core 8 statistical motion support volume is applied, resulting in low matching accuracy and long time to eliminate mismatching. A principal component analysis method is proposed to calculate rotation angle, feature points are changed in the grid and its neighborhood grid, which sets Gaussian threshold according to Euclid distance between neighborhood feature point and the matching feature point.

Soft water exposure increases the effect of genetic susceptibility on the risk of rheumatoid arthritis.

Background: Rheumatoid arthritis (RA) is influenced by both genetic and environmental factors. The mineral content of domestic water plays an essential role in human health. However, the relationship between water mineral content, genetic predisposition, and RA risk remains unclear.

Combining UDT with XGBoost to identify the geographical origin of black beans by near-infrared spectroscopy.

This study aims to rapidly and non-destructively identify the geographical origin of black beans () using a portable near-infrared (NIR) spectrometer, addressing the challenge of distinguishing black beans due to significant regional variations in quality. A total of 400 black bean samples were collected from five regions in China. To improve classification accuracy, a novel model combining uncorrelated discriminant transform (UDT) with extreme gradient boosting (XGBoost) was proposed for feature extraction and classification.

Minimum rational entropy fault-tolerant control for nonlinear stochastic distribution control systems with quantized signals.

The fault-tolerant control (FTC) issue for quantized nonlinear stochastic distribution control (SDC) systems in the presence of both actuator and sensor faults is addressed. A two-step fuzzy modeling approach is employed to systematically construct the static and dynamic models of the system, which establishes a foundational framework for subsequent fault diagnosis (FD) and FTC. Building upon the model, an adaptive augmented observer is designed to estimate actuator and sensor faults simultaneously, even under the influence of quantization effects.

Diagnosis-informed neuro-subtyping reveals subgroups of autism spectrum disorder with reliable and distinct functional connectivity profiles.

Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by heterogeneous symptoms and neurobiological features, which hinders the identification of reliable biomarkers. Until recently, ASD neuro-subtyping has emerged to detect neural features in each subgroup.

Methods: We implemented neuro-subtyping of ASD using a semi-supervised clustering method, HeterogeneitY through DiscRiminative Analysis (HYDRA), guided by the labeling information of ASD/controls, together with a multi-scale dimension reduction method of high-dimensional input features.

Self-healing, degradable, and implantable tissue-like sensor for monitoring of abnormal bladder activity.

Real-time monitoring of bladder activity is essential for assessing and diagnosing lower urinary tract dysfunction. However, traditional clinical monitoring methods that rely on repeatedly invasive catheters can cause considerable physical and psychological discomfort for patients. In this study, we present a tissue-like implantable sensor that enables real-time bladder activity monitoring while eliminating the need for repeated invasive procedures.

Discovery of Novel c‑MET Inhibitors for Hepatocellular Carcinoma Using an Integrated Virtual Screening Approach.

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality worldwide, with the efficacy of current targeted therapies limited by drug resistance and adverse effects. The receptor tyrosine kinase c-MET has been identified as a promising target for HCC therapy due to its involvement in tumor progression, metastasis, and poor prognosis. However, no c-MET inhibitors have been approved for HCC treatment.

Novel quantitative method of reactive losses of ambient VOCs coupling chemical transport model simulation with receptor measurement.

Traditional photochemical age-based parameterization method (PAPM) exhibits substantial uncertainties in accurately quantifying the reactive losses of ambient volatile organic compounds (VOCs). PAPM primarily accounts for chemical reactions with hydroxyl radicals (•OH) and encounters inherent challenges in reliably estimating the photochemical age of VOCs. Thus, a novel quantitative method was developed for estimating VOC reactive losses by coupling chemical transport model (CTM) simulation with field measurement.

NIRS-based fresh grape ripeness prediction with SPA-LASSO spectral feature selection.

A rapid and non-destructive maturity evaluation model based on Near-Infrared Spectroscopy (NIRS) is proposed for monitoring quality parameter changes during the ripening process of fresh grapes and determining the optimal harvest period. Initially, physicochemical parameter variations of Cabernet Sauvignon grapes across twelve growth stages were studied to support predictions. Subsequently, SPA-LASSO was used to select feature wavelengths from five preprocessed full spectra, and Partial Least Squares Regression (PLSR) was employed to establish models predicting Soluble Solid Content (SSC) and Total Acid (TA) levels.

Multi-Scale Attention Fusion Gesture-Recognition Algorithm Based on Strain Sensors.

Surface electromyography (sEMG) signals are commonly employed for dynamic-gesture recognition. However, their robustness is often compromised by individual variability and sensor placement inconsistencies, limiting their reliability in complex and unconstrained scenarios. In contrast, strain-gauge signals offer enhanced environmental adaptability by stably capturing joint deformation processes.

Research on Soft-Sensing Method Based on Adam-FCNN Inversion in Fermentation.

To address the challenges in modeling and optimization caused by nonlinear dynamic coupling and real-time measurement difficulties of key biological parameters in fermentation processes, this study proposes a soft-sensing method based on Adam-Fully Connected Neural Network inverse. Firstly, a non-deterministic mechanism model is constructed to characterize the dynamic coupling relationships among multiple variables in the fermentation process, and the reversibility of the system and the construction method of the inverse extended model are analyzed. Further, by leveraging the nonlinear fitting capabilities of the Fully Connected Neural Network to identify the inverse extended model, an adaptive learning rate optimization algorithm is introduced to dynamically adjust the learning rate of the Fully Connected Neural Network, thereby enhancing the convergence and robustness of the nonlinear system.

Deep Learning-Enhanced Spectroscopic Technologies for Food Quality Assessment: Convergence and Emerging Frontiers.

Nowadays, the development of the food industry and economic recovery have driven escalating consumer demands for high-quality, nutritious, and safe food products, and spectroscopic technologies are increasingly prominent as essential tools for food quality inspection. Concurrently, the rapid rise of artificial intelligence (AI) has created new opportunities for food quality detection. As a critical branch of AI, deep learning synergizes with spectroscopic technologies to enhance spectral data processing accuracy, enable real-time decision making, and address challenges from complex matrices and spectral noise.

Terahertz Spectroscopy for Food Quality Assessment: A Comprehensive Review.

Terahertz spectroscopy (0.1~10 THz), as a new type of non-destructive testing method with both microwave and infrared characteristics, has shown remarkable potential in the field of food quality testing in recent years. Its unique penetration, high sensitivity, and low photon energy characteristics, combined with chemometrics and machine learning methods, provide an efficient solution for the qualitative and quantitative analysis of complex food ingredients.

Neural Correlates of Flight Acceleration in Pigeons: Gamma-Band Activity and Local Functional Network Dynamics in the AId Region.

Flight behavior in pigeons is governed by intricate neural mechanisms that regulate movement patterns and flight dynamics. Among various kinematic parameters, flight acceleration provides critical information for the brain to modulate movement intensity, speed, and direction. However, the neural representation mechanisms underlying flight acceleration remain insufficiently understood.

An optimization method for hemi-diaphragm measurement of dynamic chest X-ray radiography during respiration based on graphics and diaphragm motion consistency criterion.

Introduction: Existing technologies are at risk of abnormal hemi-diaphragm measurement due to their abnormal morphology caused by lung field deformation during quiet breathing (free respiration or respiratory) interventions in dynamic chest radiography (DCR). To address this issue, an optimization method for hemi-diaphragm measurement is proposed, utilizing graphics and the consistency criterion for diaphragm motion.

Methods: First, Initial hemi-diaphragms are detected based on lung field mask edges of dynamic chest X-ray images abstracted from the DCR at respiratory interventions controlled by the radiologist's instructions.

Leveraging data science to understand and address multimorbidity in sub-Saharan Africa: the MADIVA protocol.

Introduction: Multimorbidity (MM), defined as two or more chronic diseases in an individual, is linked to adverse outcomes. MM is increasing in sub-Saharan Africa due to rapidly advancing epidemiological and social transitions. The Research Hub (MADIVA) aims to address MM by developing data science solutions informed by stakeholder engagement.

Oxytocin Intervention Mitigates Pathological and Behavioral Impairments in APP/PS1 Mice Subjected to Early Social Isolation.

Background: Neuropsychiatric symptoms, such as anxiety and depression, are prevalent during the prodromal phase of Alzheimer's disease (AD). Social isolation (SI) has been implicated as a potential exacerbating factor for emotional disturbances in AD pathogenesis. Despite the well-established role of oxytocin (OXT) in regulating social behavior and mental health, its function and mechanisms in alleviating AD-related psychiatric symptoms remain poorly understood.

Semantic segmentation in adverse scenes with fewer labeled images.

Images captured in adverse scenes suffer from degradation, blurring, and other issues, which are challenging to high-level vision tasks such as semantic segmentation. Labeling plenty of images in adverse scenes for the semantic segmentation task is time-consuming and user-unfriendly. In this study, we explore degraded image semantic segmentation by the semi-supervised paradigm to alleviate the demand for labels.

Pathway-Specific Polygenic Risk Scores for Blood Pressure Traits in a West African Cohort.

Introduction: Genome-wide polygenic risk scores (PRS) are useful for stratifying individuals' risk for polygenic diseases such as hypertension. However, a downside of genome-wide PRS is the lack of information about the distribution of risk burden across biologic pathways. We used pathway-specific PRS to investigate these effects within common anti-hypertensive therapy-target pathways on disease risk in a cohort of West Africans.

Dynamically tunable strong coupling insulator-metal phase transition in VO coupled to mid-infrared SrTiO phonons.

Dynamically tunable strong light-matter coupling in the mid-infrared (MIR) regime holds significant promise for next-generation photonic and thermal devices, especially in the long-wave infrared (LWIR) range of 19-23 μm, where vibrational and thermal signatures are most prominent. In this work, we demonstrate strong coupling between the phase-transition-tunable Fabry-Pérot (FP) cavity modes of vanadium dioxide (VO) and the phonon polaritons (PhPs) of strontium titanate (SrTiO), a polar dielectric with prominent optical phonon resonances in the LWIR region. The insulator-to-metal transition (IMT) of VO enables dynamic tuning of the coupling strength and mode hybridization.

Data-based decentralized control of nonlinear-constrained interconnected systems using reinforcement learning.

This article designs a data-based decentralized controller for mismatched interconnected nonlinear systems having asymmetric input constraints. Initially, it is proved that the decentralized controller for original interconnected systems is composed by the solutions of a set of unconstrained-input optimal control problems of auxiliary subsystems with the preassigned cost functions. Then, in order to solve the Hamilton-Jacobi-Bellman equations arising from these optimal control problems, a data-based policy iteration (PI) algorithm in the reinforcement learning framework is introduced.

Effects of dendritic properties on the correlations in ionic channels emerging from firing rate homeostasis: a two-compartment modeling study.

Unlabelled: Homeostatic regulation of firing rate is an important feature of neural excitability, which is achieved through feedback control of diverse ionic channel expression levels. The output firing rate is controlled by the active currents and passive properties of the dendrites. The objective of this study is to determine how dendritic properties affect the homeostatic regulation of somatic firing rate.

Few-shot network intrusion detection method based on multi-domain fusion and cross-attention.

Deep learning methods have achieved remarkable progress in network intrusion detection. However, their performance often deteriorates significantly in real-world scenarios characterized by limited attack samples and substantial domain shifts. To address this challenge, we propose a novel few-shot intrusion detection method that integrates multi-domain feature fusion with a bidirectional cross-attention mechanism.

Multimodal fusion based few-shot network intrusion detection system.

As network environments become increasingly complex and new attack methods emerge more frequently, the diversity of network attacks continues to grow. Particularly with new or rare attacks, gathering a large number of labeled samples is extremely difficult, resulting in limited training data. Existing few-shot learning methods, while reducing reliance on large datasets, mostly handle single-modality data and fail to fully exploit complementary information across different modalities, limiting detection performance.

Self-adaptive evolutionary neural networks for high-precision short-term electric load forecasting.

Reliable short-term electric load forecasting (STLF) is essential for enhancing grid stability, optimizing energy distribution, and minimizing operational costs in modern power systems. However, existing forecasting models, including statistical approaches and deep learning architectures such as multi-layer perceptron (MLP), struggle to capture complex nonlinear load variations while maintaining computational efficiency. To overcome these limitations, a self-adaptive Kolmogorov-Arnold network (SADE-KAN), an optimized forecasting framework that combines the power of Kolmogorov-Arnold networks (KAN) with self-adaptive differential evolution (SADE) is introduced to enhance both predictive accuracy and computational efficiency.