Herbicide Screening and Application Method Development for Sustainable Weed Management in L. Fields.

Marigold ( L.), a crop of significant medicinal, ornamental, and economic value, faces severe industrialization challenges due to weed-induced yield losses (up to 60%). This study aims to identify safe and highly efficient herbicides for marigold, assess their effects on dominant weeds and crop safety, and provide a practical basis for large-scale cultivation.

Selenium-carrageenan modulates arsenic bioaccessibility in simulated gastrointestinal bio-fluids: Dual mechanisms of gastric promotion and intestinal inhibition.

Inorganic arsenic (As) exposure via oral ingestion poses significant carcinogenic risks, with bioaccessibility in the gastrointestinal tract critical for risk assessment. Selenium (Se), an essential micronutrient, exhibits paradoxical effects on As toxicity, yet its mechanistic role in modulating As bioavailability during digestion remains poorly understood. This study investigates the dual-phase impact of selenated carrageenan (Se-car), a cost-effective organic Se supplement, on As bioaccessibility using an in vitro simulated digestion model.

Mechanisms and health implications of calcium in reducing arsenic bioaccessibility via in vitro gastrointestinal digestion.

Accidental oral ingestion is an important route of exposure to arsenic (As) containing soil and dust. However, the mechanism by which As bioaccessibility is reduced by in vitro digestion of calcium (Ca) remains unknown. In this study, we investigated the effect of Ca intake on the behaviors of As release from simulated gastrointestinal bio-fluids.

Performance investigation of MVMD-MSI algorithm in frequency recognition for SSVEP-based brain-computer interface and its application in robotic arm control.

This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance the performance both in quality and reliability. The proposed MVMD-MSI algorithm combines the advantages of multivariate variational mode decomposition (MVMD) and multivariate synchronization index (MSI).

Motor intent recognition of multi-feature fusion EEG signals by UMAP algorithm.

The key to the analysis of electroencephalogram (EEG) signals lies in the extraction of effective features from the raw EEG signals, which can then be utilized to augment the classification accuracy of motor imagery (MI) applications in brain-computer interface (BCI). It can be argued that the utilization of features from multiple domains can be a more effective approach to feature extraction for MI pattern classification, as it can provide a more comprehensive set of information that the traditional single feature extraction method may not be able to capture. In this paper, a multi-feature fusion algorithm based on uniform manifold approximate and projection (UMAP) is proposed for motor imagery EEG signals.

A Novel Human Papillomavirus 16 L1 Pentamer-Loaded Hybrid Particles Vaccine System: Influence of Size on Immune Responses.

Cervical cancer remains the second-most prevalent female malignancy around the world, leading to a great majority of cancer-related mortality that occurs mainly in developing countries. Developing an effective and low-cost vaccine against human papillomavirus (HPV) infection, especially in medically underfunded areas, is urgent. Compared with vaccines based on HPV L1 viruslike particles (VLPs) in the market, recombinant HPV L1 pentamer expressed in Escherichia coli represents a promising and potentially cost-effective vaccine for preventing HPV infection.

Learning Consensus Representation for Weak Style Classification.

Style classification (e.g., Baroque and Gothic architecture style) is grabbing increasing attention in many fields such as fashion, architecture, and manga.

Stacked Denoising Tensor Auto-Encoder for Action Recognition With Spatiotemporal Corruptions.

Spatially or temporally corrupted action videos are impractical for recognition via vision or learning models. It usually happens when streaming data are captured from unintended moving cameras, which bring occlusion or camera vibration and accordingly result in arbitrary loss of spatiotemporal information. In reality, it is intractable to deal with both spatial and temporal corruptions at the same time.

Sparse Canonical Temporal Alignment with Deep Tensor Decomposition for Action Recognition.

In this paper we solve three problems in action recognition: sub-action, multi-subject, and multi-modality, by reducing the diversity of intra-class samples. The main stage contains canonical temporal alignment and key frames selection. As we know, temporal alignment aims to reduce the diversity of intra-class samples, however, dense frames may yield misalignment or overlapped alignment and decrease recognition performance.

Low-Rank Tensor Subspace Learning for RGB-D Action Recognition.

RGB-D action data inherently equip with extra depth information to improve performance of action recognition compared with RGB data, and many works represent the RGB-D data as a third-order tensor containing a spatiotemporal structure and find a subspace with lower dimension. However, there are two main challenges of these methods. First, the dimension of subspace is usually fixed manually, which may not describe the samples well in the subspace.