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Soybeans are an essential source of food, protein, and oil worldwide, and the nodules on their root systems play a critical role in nitrogen fixation and plant growth. In this study, we tackled the challenge of limited high-resolution image quantities and the constraints on model learning by innovatively employing image segmentation technology for an in-depth analysis of soybean nodule phenomics. Through a meticulously designed segmentation algorithm, we broke down large-resolution images into numerous smaller ones, effectively improving the model's learning efficiency and significantly increasing the available data volume, thus laying a solid foundation for subsequent analysis. In terms of model selection and optimization, after several rounds of comparison and testing, YOLOX was identified as the optimal model, achieving an accuracy of 91.38% on the test set with an R2 of up to 86%, fully demonstrating its efficiency and reliability in nodule counting tasks. Subsequently, we utilized YOLOV5 for instance segmentation, achieving a precision of 93.8% in quickly and accurately extracting key phenotypic indicators such as the area, circumference, length, and width of the nodules, and calculated the statistical properties of these indicators. This provided a wealth of quantitative data for the morphological study of soybean nodules. The research not only enhanced the efficiency and accuracy of obtaining nodule phenotypic data and reduced costs but also provided important scientific evidence for the selection and breeding of soybean materials, highlighting its potential application value in agricultural research and practical production. [ABSTRACT FROM AUTHOR]

A multi-layer stacked Dynamic Random Access Memory (DRAM) platform is introduced to address the memory wall issue. This platform features high-density vertical interconnects established between DRAM units for high-capacity memory and logic units for computation, utilizing Wafer-on-Wafer (WoW) hybrid bonding and mini Through-Silicon Via (TSV) technologies. This 3DIC architecture includes commercial DRAM, logic, and 3DIC manufacturing processes. Their design documents typically come from different foundries, presenting challenges for signal integrity design and analysis. This paper establishes a lumped circuit based on 3DIC physical structure and calculates all values of the lumped elements in the circuit model with the transmission line model. A Cross-Process Signal Integrity Analysis (CPSIA) method is introduced, which integrates three different manufacturing processes by modeling vertical stacking cells and connecting DRAM and logic netlists in one simulation environment. In combination with the dedicated buffer driving method, the CPSIA method is used to analyze 3DIC impacts. Simulation results show that the timing uncertainty introduced by 3DIC crosstalk ranges from 31 ps to 62 ps. This analysis result explains the stable slight variation in the maximum frequency observed in vertically stacked memory arrays from different DRAM layers in the physical testing results, demonstrating the effectiveness of this CPSIA method. [ABSTRACT FROM AUTHOR]

Vernacular dwellings carry both material and spiritual connotations, playing a significant role in preserving and developing regional culture. While current research on vernacular dwellings has primarily focused on the cultural center, there is a critical knowledge gap regarding the "differentiation" of dwellings in border areas. This knowledge gap poses risks to the sustainable development of vernacular dwellings. Therefore, this study employs a space syntax approach to analyze the spatial configuration of the Tibetan vernacular dwellings of the semi-agricultural and semi-pastoral areas in Gannan prefecture, China, which is situated in the Tibetan border area. The findings reveal the following key insights: (i) disparities exist in the spatial organization of "sacred–secular" node spaces and the structural characteristics of buildings, resulting in a misalignment between physical center spaces and psychological center spaces; (ii) variation in cultural intensity contributes to distinct characteristics, i.e., "prototype", "transition", and "mutant", among dwellings in different counties; and (iii) the continuous development and adaptation of dwellings hinge on the homogeneity and differentiation of regional culture. The crux of achieving sustainable dwelling development lies in preserving the diversity of dwellings. This study offers a more comprehensive understanding of the relationship between the spatial configuration of vernacular dwellings in the Tibetan border area and their sociocultural connotations. Consequently, it provides valuable guidance for promoting the sustainable development of these dwellings. [ABSTRACT FROM AUTHOR]

The investigation of plant phenotypes through 3D modeling has emerged as a significant field in the study of automated plant phenotype acquisition. In 3D model construction, conventional image preprocessing methods exhibit low efficiency and inherent inefficiencies, which increases the difficulty of model construction. In order to ensure the accuracy of the 3D model, while reducing the difficulty of image preprocessing and improving the speed of 3D reconstruction, deep learning semantic segmentation technology was used in the present study to preprocess original images of soybean plants. Additionally, control experiments involving soybean plants of different varieties and different growth periods were conducted. Models based on manual image preprocessing and models based on image segmentation were established. Point cloud matching, distance calculation and model matching degree calculation were carried out. In this study, the DeepLabv3+, Unet, PSPnet and HRnet networks were used to conduct semantic segmentation of the original images of soybean plants in the vegetative stage (V), and Unet network exhibited the optimal test effect. The values of mIoU, mPA, mPrecision and mRecall reached 0.9919, 0.9953, 0.9965 and 0.9953. At the same time, by comparing the distance results and matching accuracy results between the models and the reference models, a conclusion could be drawn that semantic segmentation can effectively improve the challenges of image preprocessing and long reconstruction time, greatly improve the robustness of noise input and ensure the accuracy of the model. Semantic segmentation plays a crucial role as a fundamental component in enabling efficient and automated image preprocessing for 3D reconstruction of soybean plants during the vegetative stage. In the future, semantic segmentation will provide a solution for the pre-processing of 3D reconstruction for other crops. [ABSTRACT FROM AUTHOR]

Breast milk is tailored for optimal growth in all infants; however, in some infants, it is related to a unique phenomenon referred to as breast milk jaundice (BMJ). BMJ is a type of prolonged unconjugated hyperbilirubinemia that is often late onset in otherwise healthy-appearing newborns, and its occurrence might be related to breast milk itself. This review aims to systematically evaluate evidence regarding breast milk composition and the development of BMJ in healthy neonates. PubMed, Scopus and Embase were searched up to 13 February 2023 with key search terms, including neonates, hyperbilirubinemia, and breastfeeding. A total of 678 unique studies were identified and 12 were ultimately included in the systematic review with narrative synthesis. These included studies covered both nutritional compositions (e.g., fats and proteins) and bioactive factors (e.g., enzymes and growth factors) of breast milk and formally assessed the difference in the concentration (or presence) of various endogenous components of breast milk collected from mothers of BMJ infants and healthy infants. The results were inconsistent and inconclusive for most of the substances of interest, and there was only a single study available (e.g., total energy and mineral content, bile salts and cytokines); conflicting or even contradictory results arose when there were two or more studies on the subject matter (e.g., fats and free fatty acids contents and epidermal growth factor). The etiology of BMJ is likely multifactorial, and no single constituent of breast milk could explain all the BMJ cases observed. Further well-designed studies are warranted to investigate the complex interaction between maternal physiology, the breast milk system and infant physiology before this field could be progressed to uncover the etiology of BMJ. [ABSTRACT FROM AUTHOR]

We investigate the stability of traveling front solutions in the neural field model. This model has been studied intensively regarding propagating patterns with saturating Heaviside gain for neuron firing activity. Previous work has shown the existence of traveling fronts in the neural field model in a more complex setting, using a nonsaturating piecewise linear gain. We aimed to study the stability of traveling fronts in the neural field model utilizing the Evans function. We attained the Evans function of traveling fronts using an integration of analytical derivations and a computational approach for the neural field model, with previously uninvestigated piecewise linear gain. Using this approach, we are able to identify both stable and unstable traveling fronts in the neural field model. [ABSTRACT FROM AUTHOR]

Extremely low frequency electromagnetic field (ELF-EMF) exists widely in public and occupational environments. However, its potential adverse effects and the underlying mechanism on nervous system, especially behavior are still poorly understood. In this study, zebrafish embryos (including a transfected synapsin IIa (syn2a) overexpression plasmid) at 3 h post-fertilization (hpf) were exposed to a 50-Hz magnetic field (MF) with a series of intensities (100, 200, 400 and 800 μT, respectively) for 1 h or 24 h every day for 5 days. Results showed that, although MF exposure did not affect the basic development parameters including hatching rate, mortality and malformation rate, yet MF at 200 μT could significantly induce spontaneous movement (SM) hypoactivity in zebrafish larvae. Histological examination presented morphological abnormalities of the brain such as condensed cell nucleus and cytoplasm, increased intercellular space. Moreover, exposure to MF at 200 μT inhibited syn2a transcription and expression, and increased reactive oxygen species (ROS) level as well. Overexpression of syn2a could effectively rescue MF-induced SM hypoactivity in zebrafish. Pretreatment with N-acetyl-L-cysteine (NAC) could not only recover syn2a protein expression which was weakened by MF exposure, but also abolish MF-induced SM hypoactivity. However, syn2a overexpression did not affect MF-increased ROS. Taken together, the findings suggested that exposure to a 50-Hz MF inhibited spontaneous movement of zebrafish larvae via ROS-mediated syn2a expression in a nonlinear manner. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS)—through a surgically implanted electrode to the subthalamic nucleus (STN)—has become a widely used therapeutic option for the treatment of Parkinson's disease and other neurological disorders. The standard conventional high-frequency stimulation (HF) that is currently used has several drawbacks. To overcome the limitations of HF, researchers have been developing closed-loop and demand-controlled, adaptive stimulation protocols wherein the amount of current that is delivered is turned on and off in real-time in accordance with a biophysical signal. Computational modeling of DBS in neural network models is an increasingly important tool in the development of new protocols that aid researchers in animal and clinical studies. In this computational study, we seek to implement a novel technique of DBS where we stimulate the STN in an adaptive fashion using the interspike time of the neurons to control stimulation. Our results show that our protocol eliminates bursts in the synchronized bursting neuronal activity of the STN, which is hypothesized to cause the failure of thalamocortical neurons (TC) to respond properly to excitatory cortical inputs. Further, we are able to significantly decrease the TC relay errors, representing potential therapeutics for Parkinson's disease. [ABSTRACT FROM AUTHOR]

Autonomous parking is an active field of automatic driving in both industry and academia. Parking slot detection (PSD) based on a panoramic image can effectively improve the perception of a parking space and the surrounding environment, which enhances the convenience and safety of parking. The challenge of PSD implementation is identifying the parking slot in real-time based on images obtained from the around view monitoring (AVM) system, while maintaining high recognition accuracy. This paper proposes a real-time parking slot detection (RPSD) network based on semantic segmentation, which implements real-time parking slot detection on the panoramic surround view (PSV) dataset and avoids the constraint conditions of parking slots. The structural advantages of the proposed network achieve real-time semantic segmentation while effectively improving the detection accuracy of the PSV dataset. The cascade structure reduces the operating parameters of the whole network, ensuring real-time performance, and the fusion of coarse and detailed features extracted from the upper and lower layers improves segmentation accuracy. The experimental results show that the final mIoU of this work is 67.97% and the speed is up to 32.69 fps, which achieves state-of-the-art performance with the PSV dataset. [ABSTRACT FROM AUTHOR]

Rolling enables the directional alignment of the reinforcements in graphene/Cu composites while achieving uniform graphene dispersion and matrix grain refinement. This is expected to achieve a breakthrough in composite performance. In this paper, the process parameters of rolling are investigated, and the defects, thickness variations of graphene and property changes of the composite under different parameters are analyzed. High-temperature rolling is beneficial to avoid the damage of graphene during rolling, and the prepared composites have higher electrical conductivity. The properties of graphene were investigated. Low-temperature rolling is more favorable to the thinning and dispersion of graphene; meanwhile, the relative density of the composites is higher in the low-temperature rolling process. With the increase of rolling deformation, the graphene defects slightly increased and the number of layers decreased. In this paper, the defect states of graphene and the electrical conductivity with different rolling parameters is comprehensively investigated to provide a reference for the rolling process of graphene/copper composites with different demands. [ABSTRACT FROM AUTHOR]