Timing of Water Deficit Limits Maize Kernel Setting in Association With Changes in the Source-Flow-Sink Relationship

The kernel setting of maize varies greatly because of the timing and intensity of water deficits. This variation can limit leaf productivity (source), the translocation of assimilated sugars (flow), and yield formation (sink). To explain the decline in kernel setting of maize under water deficits from the perspective of source-flow-sink, a 3-year experiment was conducted under a rain shelter. Five water regimes were studied. One regime included well-irrigated (CK) treatment. Four regimes involved water deficits: irrigation was withheld during the 6- to 8-leaf stage (V6−8), the 9- to 12-leaf stage (V9−12), the 13-leaf stage to tasseling stage (V13−T), and the silking stage to blister stage (R1−2). Water deficit effects on kernel setting began when the water deficit occurred at V9 and became more significant with time. Kernel weight was reduced by 12 and 11% when there were water deficits during V9−12 and V13−T, respectively. This was the result of reduced leaf area (limited source) and an altered vascular bundle in the ear peduncles (limited assimilate flow). The reduced vascular bundle number, rather than the ear peduncle cross-sectional area, significantly affected the final kernel weight when exposed to a water deficit prior to the silking stage. The water deficits prior to and close to the flowering stage significantly reduced ear kernel number; that is, 14 and 19% less during V13−T and R1−2, respectively, compared with the kernel number during the CK treatment. This reflects a smaller sink under water deficit conditions. Additionally, ovary size was reduced the most in the V13−T water deficit compared with other treatments. After rewatering, the water deficit before or during flowering stage continued to have residual effects on grain-filling in the late growth period. The grain-filling rate decreased under the V9−12 water deficit; the grain-filling duration shortened under the R1−2 water deficit; and both negative effects occurred under the V13−T water deficit. This study clearly indicated that (1) the water deficit during the vegetative organ rapid growth period both limited leaf source development and assimilate flow and slowed down kernel development, and (2) the water deficit just before and during flowering reduced kernel sink. Deficits at both times could retard grain-filling and reduce maize yield. The results of the present study might guide irrigation practices in irrigated maize or inform the management of sowing time in rainfed maize, to desynchronize the water deficit and the plant’s reactions to such deficits at different stages

Private-Library-Oriented Code Generation with Large Language Models

Large language models (LLMs), such as Codex and GPT-4, have recently showcased their remarkable code generation abilities, facilitating a significant boost in coding efficiency. This paper will delve into utilizing LLMs for code generation in private libraries, as they are widely employed in everyday programming. Despite their remarkable capabilities, generating such private APIs poses a formidable conundrum for LLMs, as they inherently lack exposure to these private libraries during pre-training. To address this challenge, we propose a novel framework that emulates the process of programmers writing private code. This framework comprises two modules: APIFinder first retrieves potentially useful APIs from API documentation; and APICoder then leverages these retrieved APIs to generate private code. Specifically, APIFinder employs vector retrieval techniques and allows user involvement in the retrieval process. For APICoder, it can directly utilize off-the-shelf code generation models. To further cultivate explicit proficiency in invoking APIs from prompts, we continuously pre-train a reinforced version of APICoder, named CodeGenAPI. Our goal is to train the above two modules on vast public libraries, enabling generalization to private ones. Meanwhile, we create four private library benchmarks, including TorchDataEval, TorchDataComplexEval, MonkeyEval, and BeatNumEval, and meticulously handcraft test cases for each benchmark to support comprehensive evaluations. Numerous experiments on the four benchmarks consistently affirm the effectiveness of our approach. Furthermore, deeper analysis is also conducted to glean additional insights

Experimental study on mechanical properties of filling-bulk ce-menting combination body

In order to study the influence of caved rocks in the goaf on the backfilling body in the backfilling mining, uniaxial compression test are carried out on the backfilling body-cemented granular body combination with different granular heights, discrete element lithology and backfilling body strength. The uniaxial compression failure of the combination body specimen is monitored in real time by using the three-dimensional acoustic emission positioning technology. The deformation and failure corresponding to the AE events in the loading process is characterized by combining the time parameters of AE events with the starting time points of the four stages of the stress-strain curve. Based on this, the failure model for the interface of the combination body is established. The results show that the height of granular is negatively correlated with the strength of the combination body, and the uniaxial compressive strength of the combination body with the backfilling height ratio of 1:4 is only 55.0 % of that of the single backfilling body. The discrete element lithology and the strength of backfilling body are positively correlated with the strength of the combination body. Although high-strength backfilling body can improve the uniaxial compressive strength of the combination body, the higher the strength of filling body in the combination body, the more serious the strength reduction of the combination body. When the particle lithology in cemented bulk is siltstone with low strength, the uniaxial compressive strength of the combination body is only 42.9% of that of single combination body. The siltstone with smaller compressive strength will have a fracture plane due to shear failure during the failure, and the limestone with larger compressive strength can withstand shear load by using the shear strength of the granular particles. When the cementing matrix in the cemented granular fails or the particles in the cemented granular are broken, the interface of the backfilling body and the cemented granular undergoes non-uniform compression deformation, resulting in the stress concentration on the backfilling body on the interface damaged by the cemented granular, resulting in the shear failure of the upper backfilling body locally, and the failure of backfilling body is the contribution of both axial stress and non-uniform deformation of the interface

HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images

Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter- and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and two vision transformer models via five-fold cross validation. It achieves state-of-the-art classification performance with the best model interpretability. In the meanwhile, our proposed model outperforms two senior sonographers on the breast cancer diagnosis when only one BUS image is given

The neural correlates of apathy in the context of aging and brain disorders: a meta-analysis of neuroimaging studies

IntroductionApathy is a prevalent mood disturbance that occurs in a wide range of populations, including those with normal cognitive aging, mental disorders, neurodegenerative disorders and traumatic brain injuries. Recently, neuroimaging technologies have been employed to elucidate the neural substrates underlying brain disorders accompanying apathy. However, the consistent neural correlates of apathy across normal aging and brain disorders are still unclear.MethodsThis paper first provides a brief review of the neural mechanism of apathy in healthy elderly individuals, those with mental disorders, neurodegenerative disorders, and traumatic brain injuries. Further, following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, the structural and functional neuroimaging meta-analysis using activation likelihood estimation method is performed on the apathy group with brain disorders and the healthy elderly, aiming at exploring the neural correlates of apathy.ResultsThe structural neuroimaging meta-analysis showed that gray matter atrophy is associated with apathy in the bilateral precentral gyrus (BA 13/6), bilateral insula (BA 47), bilateral medial frontal gyrus (BA 11), bilateral inferior frontal gyrus, left caudate (putamen) and right anterior cingulate, while the functional neuroimaging meta-analysis suggested that the functional connectivity in putamen and lateral globus pallidus is correlated with apathy.DiscussionThrough the neuroimaging meta-analysis, this study has identified the potential neural locations of apathy in terms of brain structure and function, which may offer valuable pathophysiological insights for developing more effective therapeutic interventions for affected patients

Impact of Underground Coal Seam Mining on Stability and Slippage of the Loess Slope

How to quantitatively characterise the impact of underground coal mining on the stability and slippage of loess slopes is a key problem in the evaluation of mining damage under loess slopes, but it is more difficult to study this problem under the impact of the particular mechanical properties and topographical features of loess slopes. In order to clarify the impact of underground coal seam mining on the stability and slippage of the loess slope, theoretical analysis, numerical simulation and physical similarity simulation experiments are used to address the problem based on the theory of slope stability and strata movement. The results show that the stability coefficient of a mining slope (Kms) is introduced to quantitatively characterise the stability of a mining loess slope, and to measure the degree of landslide risk. Due to the superposition of slope movement caused by mining subsidence and slope sliding tendency, the slope is more unstable when mining along the slope than when mining against the slope. The slope angle and slope height are the most important factors influencing the Kms. The ratio of rock stratum thickness to mining height and the ratio of rock stratum thickness to soil stratum thickness are positively correlated with Kms, and the correlation is relatively strong. The range of variation of the volume weight, internal friction angle and cohesion of the loess is small, and the influence on Kms is relatively weak. Probability integral theory is used to construct the relationship between stability and slippage of mining loess slopes. Taking the mining of a working face under the loess slope of Ningtiaota Coal Mine (China) as an example, the predicted results of the slope movement and deformation theory are in good agreement with the similar simulation test results, reaching 93.57~97.97%

A Comprehensive Evaluation and Analysis of Ground Surface Damage Due to Mining under Villages Based on GIS

This paper aims to evaluate the severities and causes of ground surface building and cropland damages after coal mining in a better way, and to clarify the correlation between the damage assessment indexes that influence mining. Against the backdrop of multi-seam mining in certain coal mines in China, the estimated results of each displacement and deformation were analyzed using GIS technology. The damage range determined for each deformation index is divided according to the displacement and deformation combined with the virtue of damage judgment threshold. The damage ranges on the ground surface based on the comprehensive value of each displacement and deformation index were obtained through superimposing those ranges delineated by each displacement and deformation index, and the law on influence from displacement indexes upon various levels of damage was analyzed in a quantitative manner accordingly. The results showed that coal mining destroyed 14 buildings and a cropland area of 11.96 hm2; among them, building damage was only associated with displacement indexes E (horizontal deformation) and T (inclined deformation). Seven buildings were solely destroyed by T alone; five buildings were solely damaged by E; two buildings were damaged jointly by E and T; and, moreover, with the aggravation in building damage level, the proportion of building damage due to E decreased while the proportion of building damage under the same level due to T increased. Regarding cropland destruction, the damage due to T accounted for 33.48% while the damage jointly caused by W (Subsidence), E and T accounted for 30.45%. Moreover, the proportion of damaged cropland area due to inclined deformation T was positively correlated with cropland damage level. These findings can provide a reference for rational judgment regarding civilian building and cropland destruction on the ground surface after coal mining

Solid-state cooling: Thermoelectrics

The growing demand of thermal management in various fields such as miniaturized 5G chips has motivated researchers to develop new and high-performance solid-state refrigeration technologies, typically including multicaloric and thermoelectric (TE) cooling. Among them, TE cooling has attracted huge attention owing to its advantages of rapid response, large cooling temperature difference, high stability, and tunable device size. Bi2Te3-based alloys have long been the only commercialized TE cooling materials, while novel systems SnSe and Mg3(Bi,Sb)2 have recently been discovered as potential candidates. However, challenges and problems still require to be summarized and further resolved for realizing better cooling performance. In this review, we systematically investigate TE cooling from its internal mechanism, crucial parameters, to device design and applications. Furthermore, we summarize the current optimization strategies for existing TE cooling materials, and finally provide some personal prospects especially the material-planification concept on future research on establishing better TE cooling

Multi-Constrained Seismic Multi-Parameter Full Waveform Inversion Based on Projected Quasi-Newton Algorithm

The multi-parameter full waveform inversion (FWI) that integrates velocity and density can make full use of the kinematic and dynamic information of the measured data to reconstruct the underground model. However, it faces problems of crosstalk between multiple parameters and strong nonlinearity. This research proposes a multi-constrained, multi-parameter FWI framework based on the projected quasi-Newton algorithm. This framework can introduce multiple types of prior geological information, which can effectively improve the problem of multi-parameter inversion. Additionally, the quasi-Newton method can eliminate the crosstalk phenomenon to further improve the inversion convergence speed. Taking the 1994BP model as an example, the results show that the projected quasi-Newton method has a faster convergence speed than the spectral projected gradient method, and reduces the crosstalk between parameters; multiple constraint sets are uniquely projected onto the intersection to ensure that the estimated values of model parameters meet multiple constraints. We also experiment with the overthrust model, which shows that the framework we proposed can improve the inversion accuracy and has good adaptability. The proposed multi-parameter inversion framework can be compatible with more prior information to obtain an inversion model that conforms to geological understanding and shows great potential in seismic exploration