Investigating the mechanism of auxin-mediated fulvic acid-regulated root growth in Oryza sativa through physiological and transcriptomic analyses.

As rice is one of the most crucial staple food sources worldwide, enhancing rice yield is paramount for ensuring global food security. Fulvic acid (FA), serving as a plant growth promoter and organic fertilizer, holds significant practical importance in studying its impact on rice root growth for improving rice yield and quality. This study investigated the effects of different concentrations of FA on the growth of rice seedlings.

A general adaptive unsupervised feature selection with auto-weighting.

Feature selection (FS) is essential in machine learning and data mining as it makes handling high-dimensional data more efficient and reliable. More attention has been paid to unsupervised feature selection (UFS) due to the extra resources required to obtain labels for data in the real world. Most of the existing embedded UFS utilize a sparse projection matrix for FS.

Diffusion probabilistic model for bike-sharing demand recovery with factual knowledge fusion.

The mining of diverse patterns from bike flow has attracted widespread interest from researchers and practitioners. Prior arts concentrate on forecasting the flow evolution from bike demand records. Nevertheless, a tricky reality is the frequent occurrence of missing bike flow, which hinders us from accurately understanding flow patterns.

Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training.

Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available, and instant inference on the target domain is required. Despite many efforts into TTT, there is a confusion over the experimental settings, thus leading to unfair comparisons.

Daily Schedule Recommendation in Urban Life Based on Deep Reinforcement Learning.

In our daily lives, people frequently consider daily schedule to meet their needs, such as going to a barbershop for a haircut, then eating in a restaurant, and finally shopping in a supermarket. Reasonable activity location or point-of-interest (POI) and activity sequencing will help people save a lot of time and get better services. In this article, we propose a reinforcement learning-based deep activity factor balancing model to recommend a reasonable daily schedule according to user's current location and needs.

Stable and efficient perovskite solar cells hydrogen bonding and coordination.

The instability of perovskite solar cells (PSCs) is primarily caused by the unavoidable ion migration in the perovskite layer. Ion migration and accumulation influence the properties of perovskite and functional layers, resulting in severely degraded device performance. Herein, we introduced an n-type, low optical gap-conjugated organic molecule (, COTIC-4F or COTIC-4Cl) to serve as the perovskite photoactive layer in a perovskite precursor solution for broadening the near-infrared spectral response and enhancing the efficiency of PSCs.

Multi-Level Graph Neural Network With Sparsity Pooling for Recognizing Parkinson's Disease.

Parkinson's disease (PD) is a neurodegenerative disease of the brain associated with motor symptoms. With the maturation of machine learning (ML), especially deep learning, ML has been used to assist in the diagnosis of PD. In this paper, we explore graph neural networks (GNNs) to implement PD prediction using MRI data.

Metabolomic and transcriptomic analyses of rice plant interaction with invasive weed .

Introduction: is an annual weed in paddy fields, which can engage in competition with rice, leading to a severe yield reduction. However, theunderlying mechanism governing this interaction remain unknown.

Methods: In this study, we investigated the mutual inhibition between rice and the weed undermono-culture and co-culture conditions.

Hierarchical Active Learning With Qualitative Feedback on Regions.

Learning classification models in practice usually requires numerous labeled data for training. However, instance-based annotation can be inefficient for humans to perform. In this article, we propose and study a new type of human supervision that is fast to perform and useful for model learning.

Micro-Supervised Disturbance Learning: A Perspective of Representation Probability Distribution.

The instability is shown in the existing methods of representation learning based on Euclidean distance under a broad set of conditions. Furthermore, the scarcity and high cost of labels prompt us to explore more expressive representation learning methods which depends on as few labels as possible. To address above issues, the small-perturbation ideology is firstly introduced on the representation learning model based on the representation probability distribution.

Biochar aerogel enhanced remediation performances for heavy oil-contaminated soil through biostimulation strategy.

Petroleum leakages can seriously damage the soil environment and cause a persistent harm to human health, due to the release of heavy oil pollutants with a high viscosity and high molecular weight. In this paper, biochar aerogel materials were successfully prepared under 600, 700 and 800 ℃ (accordingly labeled as 600-aerogel, 700-aerogel and 800-aerogel) with green, sustainable and abundant sisal leaves as raw materials for the remediation of heavy oil-contaminated soil. The remediation performances of biochar aerogel supplement for heavy oil-contaminated soil were investigated, while microbial abundance and community structure were characterized.

Evaluation of key microbial community succession and enzyme activities of nitrogen transformation in pig manure composting process through multi angle analysis.

This experiment aimed to investigate changes in enzyme activity, microbial succession, and nitrogen conversion caused by different initial carbon-to-nitrogen ratios of 25:1, 35:1 and 20:1 (namely CK, T1 and T2) during pig manure composting. The results showed that the lower carbon-to-nitrogen ratio (T2) after composting retained 19.64 g/kg of TN which was more than 16.

Heterogeneous Feature Selection Based on Neighborhood Combination Entropy.

Feature selection aims to remove irrelevant or redundant features and thereby remain relevant or informative features so that it is often preferred for alleviating the dimensionality curse, enhancing learning performance, providing better readability and interpretability, and so on. Data that contain numerical and categorical representations are called heterogeneous data, and they exist widely in many real-world applications. Neighborhood rough set (NRS) can effectively deal with heterogeneous data by using neighborhood binary relation, which has been successfully applied to heterogeneous feature selection.

Physiological and transcriptomic responses of to ciprofloxacin reveal molecular mechanisms for antibiotic removal.

Microalgae-based technology is an effective and environmentally friendly method for antibiotics-contaminated wastewater treatment. To assess the tolerance and removal ability of to ciprofloxacin (CIP), this study comprehensively revealed the responses of to CIP exposure and its degradation processes through physiological and transcriptomic analyses. Although the photosynthetic system was inhibited, the growth of was not negatively affected by CIP.

Large-Scale Meta-Heuristic Feature Selection Based on BPSO Assisted Rough Hypercuboid Approach.

The selection of prominent features for building more compact and efficient models is an important data preprocessing task in the field of data mining. The rough hypercuboid approach is an emerging technique that can be applied to eliminate irrelevant and redundant features, especially for the inexactness problem in approximate numerical classification. By integrating the meta-heuristic-based evolutionary search technique, a novel global search method for numerical feature selection is proposed in this article based on the hybridization of the rough hypercuboid approach and binary particle swarm optimization (BPSO) algorithm, namely RH-BPSO.

Interactive and Complementary Feature Selection via Fuzzy Multigranularity Uncertainty Measures.

Feature selection has been studied by many researchers using information theory to select the most informative features. Up to now, however, little attention has been paid to the interactivity and complementarity between features and their relationships. In addition, most of the approaches do not cope well with fuzzy and uncertain data and are not adaptable to the distribution characteristics of data.

A Robust Breast ultrasound segmentation method under noisy annotations.

Background And Objective: A large-scale training data and accurate annotations are fundamental for current segmentation networks. However, the characteristic artifacts of ultrasound images always make the annotation task complicated, such as attenuation, speckle, shadows and signal dropout. Further complications arise as the contrast between the region of interest and background is often low.

Outlier Detection Based on Fuzzy Rough Granules in Mixed Attribute Data.

Outlier detection is one of the most important research directions in data mining. However, most of the current research focuses on outlier detection for categorical or numerical attribute data. There are few studies on the outlier detection of mixed attribute data.

FPGA-Based High-Throughput CNN Hardware Accelerator With High Computing Resource Utilization Ratio.

The field-programmable gate array (FPGA)-based CNN hardware accelerator adopting single-computing-engine (CE) architecture or multi-CE architecture has attracted great attention in recent years. The actual throughput of the accelerator is also getting higher and higher but is still far below the theoretical throughput due to the inefficient computing resource mapping mechanism and data supply problem, and so on. To solve these problems, a novel composite hardware CNN accelerator architecture is proposed in this article.

A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network.

In mobile wireless sensor network (MWSN), the lifetime of the network largely depends on energy efficient routing protocol. In the literature, cluster leader (CL) is selected based on remaining energy of mobile sensor nodes to enhance sensor network lifetime. In this study, a novel connectivity-based Low-Energy Adaptive Clustering Hierarchy-Mobile Energy Efficient and Connected (LEACH-MEEC) routing protocol was proposed, where CL is selected based on connectivity among neighboring nodes and the remaining energy of mobile sensor nodes.

Flow Stress Prediction and Hot Deformation Mechanisms in Ti-44Al-5Nb-(Mo, V, B) Alloy.

To elucidate the hot deformation characteristics of TiAl alloys, flow stress prediction, microstructural evolution and deformation mechanisms were investigated in Ti-44Al-5Nb-1Mo-2V-0.2B alloy by isothermal compression tests. A constitutive relationship using the Arrhenius model involving strain compensation and back propagation artificial neural network (BP-ANN) model were developed.

Restricted Boltzmann Machines With Gaussian Visible Units Guided by Pairwise Constraints.

Restricted Boltzmann machines (RBMs) and their variants are usually trained by contrastive divergence (CD) learning, but the training procedure is an unsupervised learning approach, without any guidances of the background knowledge. To enhance the expression ability of traditional RBMs, in this paper, we propose pairwise constraints (PCs) RBM with Gaussian visible units (pcGRBM) model, in which the learning procedure is guided by PCs and the process of encoding is conducted under these guidances. The PCs are encoded in hidden layer features of pcGRBM.

Contrast in Haze Removal: Configurable Contrast Enhancement Model Based on Dark Channel Prior.

Conventional haze-removal methods are designed to adjust the contrast and saturation, and in so doing enhance the quality of the reconstructed image. Unfortunately, the removal of haze in this manner can shift the luminance away from its ideal value. In other words, haze removal involves a tradeoff between luminance and contrast.

Local-Density-Based Optimal Granulation and Manifold Information Granule Description.

Constructing information granules (IGs) has been of significant interest to the discipline of granular computing. The principle of justifiable granularity has been proposed to guide the design of IGs, opening an avenue of pursuits of building IGs carried out on a basis of well-defined and intuitively appealing principles. However, how to improve the efficiency and accuracy of the resulting constructs is an open issue.