Identification of Maize Kernel Varieties Using LF-NMR Combined with Image Data: An Explainable Approach Based on Machine Learning.

The precise identification of maize kernel varieties is essential for germplasm resource management, genetic diversity conservation, and the optimization of agricultural production. To address the need for rapid and non-destructive variety identification, this study developed a novel interpretable machine learning approach that integrates low-field nuclear magnetic resonance (LF-NMR) with morphological image features through an optimized support vector machine (SVM) framework. First, LF-NMR signals were obtained from eleven maize kernel varieties, and ten key features were extracted from the transverse relaxation decay curves.

Improved XLNet modeling for Chinese named entity recognition of edible fungus.

Introduction: The diversity of edible fungus species and the extent of mycological knowledge pose significant challenges to the research, cultivation, and popularization of edible fungus. To tackle this challenge, there is an urgent need for a rapid and accurate method of acquiring relevant information. The emergence of question and answer (Q&A) systems has the potential to solve this problem.

Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming.

Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD.

An end-to-end seed vigor prediction model for imbalanced samples using hyperspectral image.

Hyperspectral imaging is a key technology for non-destructive detection of seed vigor presently due to its capability to capture variations of optical properties in seeds. As the seed vigor data depends on the actual germination rate, it inevitably results in an imbalance between positive and negative samples. Additionally, hyperspectral image (HSI) suffers from feature redundancy and collinearity due to its inclusion of hundreds of wavelengths.

Research on weed identification in soybean fields based on the lightweight segmentation model DCSAnet.

Weeds can compete with crops for sunlight, water, space and various nutrients, which can affect the growth of crops.In recent years, people have started to use self-driving agricultural equipment, robots, etc. for weeding work and use of drones for weed identification and spraying of weeds with herbicides, and the effectiveness of these mobile weeding devices is largely limited by the superiority of weed detection capability.

Multi-threshold Image Segmentation based on an improved Salp Swarm Algorithm: Case study of breast cancer pathology images.

Breast cancer poses a significant risk to women's health, and it is essential to provide proper diagnostic support. Medical image processing technology is a key component of all supporting diagnostic techniques, with Image Segmentation (IS) being one of its primary steps. Among various methods, Multilevel Image Segmentation (MIS) is considered one of the most effective and straightforward approaches.

Exploring a novel HE image segmentation technique for glioblastoma: A hybrid slime mould and differential evolution approach.

Glioblastoma is a primary brain tumor with high incidence and mortality rates, posing a significant threat to human health. It is crucial to provide necessary diagnostic assistance for its management. Among them, Multi-threshold Image Segmentation (MIS) is considered the most efficient and intuitive method in image processing.

A classification method for soybean leaf diseases based on an improved ConvNeXt model.

Deep learning technologies have enabled the development of a variety of deep learning models that can be used to detect plant leaf diseases. However, their use in the identification of soybean leaf diseases is currently limited and mostly based on machine learning methods. In this investigation an enhanced deep learning network model was developed to recognize soybean leaf diseases more accurately.

An accelerated sine mapping whale optimizer for feature selection.

An improved whale optimization algorithm (SWEWOA) is presented for global optimization issues. Firstly, the sine mapping initialization strategy (SS) is used to generate the population. Secondly, the escape energy (EE) is introduced to balance the exploration and exploitation of WOA.

A new population initialization of metaheuristic algorithms based on hybrid fuzzy rough set for high-dimensional gene data feature selection.

In the realm of modern medicine and biology, vast amounts of genetic data with high complexity are available. However, dealing with such high-dimensional data poses challenges due to increased processing complexity and size. Identifying critical genes to reduce data dimensionality is essential.

An intent classification method for questions in "Treatise on Febrile diseases" based on TinyBERT-CNN fusion model.

"Treatise on Febrile Diseases" is an important classic book in the academic history of Chinese material medica. Based on the knowledge map of traditional Chinese medicine established by the study of "Treatise on Febrile Diseases", a question-answering system of traditional Chinese medicine was established to help people better understand and use traditional Chinese medicine. Intention classification is the basis of the question-answering system of traditional Chinese medicine, but as far as we know, there is no research on question intention classification based on "Treatise on Febrile Diseases".

A novel method for Pu-erh tea face traceability identification based on improved MobileNetV3 and triplet loss.

Ensuring the traceability of Pu-erh tea products is crucial in the production and sale of tea, as it is a key means to ensure their quality and safety. The common approach used in traceability systems is the utilization of bound Quick Response (QR) codes or Near Field Communication (NFC) chips to track every link in the supply chain. However, counterfeiting risks still persist, as QR codes or NFC chips can be copied and inexpensive products can be fitted into the original packaging.

A new approach to simultaneously reducing, nitrogen doping and noble metal coating of graphene oxide via active-screen plasma.

Graphene is widely used for various applications, especially after nitrogen doping and incorporation with metal nanoparticles. Herein, a simultaneous approach to reducing, nitrogen doping and noble metals coating of graphene oxide (GO) is reported using an advanced active-screen plasma (ASP) technique. With a noble metal plate added as an extra lid of active screen cage, the corresponding noble metal, mainly or fully in pure metal state, depending on the noble metal type, as well as a minority of Fe and Cr, is deposited on GO with simultaneous reduction and nitrogen doping.

Research on Field Soybean Weed Identification Based on an Improved UNet Model Combined With a Channel Attention Mechanism.

Aiming at the problem that it is difficult to identify two types of weeds, grass weeds and broadleaf weeds, in complex field environments, this paper proposes a semantic segmentation method with an improved UNet structure and an embedded channel attention mechanism SE module. First, to eliminate the semantic gap between low-dimensional semantic features and high-dimensional semantic features, the UNet model structure is modified according to the characteristics of different types of weeds, and the feature maps after the first five down sampling tasks are restored to the same original image through the deconvolution layer. Hence, the final feature map used for prediction is obtained by the fusion of the upsampling feature map and the feature maps containing more low-dimensional semantic information in the first five layers.

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu.

Maize is a major global food crop and as one of the most productive grain crops, it can be eaten; it is also a good feed for the development of animal husbandry and essential raw material for light industry, chemical industry, medicine, and health. Diseases are the main factor limiting the high and stable yield of maize. Scientific and practical identification is a vital link to reduce the damage of diseases and accurate segmentation of disease spots is one of the fundamental techniques for disease identification.

Quality Assessment of Protein Docking Models Based on Graph Neural Network.

Protein docking provides a structural basis for the design of drugs and vaccines. Among the processes of protein docking, quality assessment (QA) is utilized to pick near-native models from numerous protein docking candidate conformations, and it directly determines the final docking results. Although extensive efforts have been made to improve QA accuracy, it is still the bottleneck of current protein docking systems.

LDkit: a parallel computing toolkit for linkage disequilibrium analysis.

Background: Linkage disequilibrium (LD) analysis is broadly utilized in genetics to understand the evolutionary and demographic history and helps geneticists identify genes associated with interested inherited traits, such as diseases. There are some tools for linkage disequilibrium analysis either in a local or online way; however, there has been no such tool supporting both graphical user interface (GUI) and parallel computing.

Results: We developed a GUI software called LDkit for LD analysis, which supports parallel computing.

MMAP: a cloud computing platform for mining the maximum accuracy of predicting phenotypes from genotypes.

Unlabelled: Accurately predicting phenotypes from genotypes holds great promise to improve health management in humans and animals, and breeding efficiency in animals and plants. Although many prediction methods have been developed, the optimal method differs across datasets due to multiple factors, including species, environments, populations and traits of interest. Studies have demonstrated that the number of genes underlying a trait and its heritability are the two key factors that determine which method fits the trait the best.

Synthesis and in-vitro antibacterial properties of a functionally graded Ag impregnated composite surface.

Silver is considered promising in medical devices to prevent infection due to its excellent properties of broad antibacterial spectrum and persistent antibacterial activity. Herein, silver impregnated functionally graded composite surfaces have been developed by a novel duplex plasma deposition technique, which combines the double glow sputtering process and active screen plasma nitriding process. The composite surfaces include a surface antibacterial layer and a bottom supporting layer, which are deposited simultaneously.

SiRNA silencing efficacy prediction based on a deep architecture.

Background: Small interfering RNA (siRNA) can be used to post-transcriptional gene regulation by knocking down targeted genes. In functional genomics, biomedical research and cancer therapeutics, siRNA design is a critical research topic. Various computational algorithms have been developed to select the most effective siRNA, whereas the efficacy prediction accuracy is not so satisfactory.

Comparison of sludge treatment by O3 and O3/H2O2.

This work focuses on the comparison of sludge decomposition caused by ozone (O3) alone and by ozone/hydrogen peroxide (O3/H2O2). The content of carbonaceous organic materials, nitrogenous compounds and phosphoric substances in sludge supernatant were measured. The release of soluble chemical oxygen demand, total nitrogen (TN) and total phosphorus (TP) caused by O3/H2O2 treatment were more than by O3 alone.

In situ observation and measurement of composites subjected to extremely high temperature.

In this work, we develop an instrument to study the ablation and oxidation process of materials such as C/SiC (carbon fiber reinforced silicon carbide composites) and ultra-high temperature ceramic in extremely high temperature environment. The instrument is integrated with high speed cameras with filtering lens, infrared thermometers and water vapor generator for image capture, temperature measurement, and humid atmosphere, respectively. The ablation process and thermal shock as well as the temperature on both sides of the specimen can be in situ monitored.