The hydrolysis behaviour of magnides, aluminides and sodides and its application to production of nanoparticles

The hydrolysis phenomenon of transition metal (including Si and Ge) magnides, aluminides and sodides has been investigated in this study, and has been successfully developed to produce both transition metal and semiconductor element nanoparticles. Both in-situ synthesized Mg2Ni and as-cast Mg2Ni exhibited a close to zero discharge capacity due to hydrolysis of Mg 2Ni and Mg2NiH4. The hydrolysis characteristics of Mg2Ni and Mg2NiH4 suggest that they are not suitable for use as electrodes in rechargeable batteries. The hydrolysis byproduct of transition metal and semiconductor element magnides, Mg(OH)2, can be easily removed by a dilute acid. After removal of Mg(OH)2 from the hydrolysis product of Mg2Ni, Ni nanoparticles were obtained. Besides Ni nanoparticles, Cu, Au and Ag nanoparticles have been successfully prepared by this method. The hydrolysis byproduct of magnides, Mg(OH)2, has a very small solubility in water, and thus the newly-formed Mg(OH)2 precipitates from water in the vicinity of the Mg dissolution sites. The existence of the Mg(OH)2 particles, and the low mobility of transition metal atoms at room temperature, give rise to the formation of very fine transition metal nanoparticles. Therefore, the particle size of these transition metal nanoparticles prepared by this method was not sensitive to the concentration of the initial materials in aqueous solution. Al3Ni spontaneously undergoes hydrolysis in water at room temperature, and forms Al(OH)3, Ni nanoparticles and hydrogen in distilled water at room temperature. Due to chemical characteristics of Al(OH)3 including its low acidity, chemically active transition metal nanoparticles, such as Fe, Co, and Ni, can not be produced by using dilute hydrochloric acid to remove Al(OH)3. However, chemically inert transition metal nanoparticles such as Au and Ag could be prepared by this method. The transition metal-sodium intermetallic compounds, i.e. sodides, undergo severe hydrolysis in water at room temperature. The reaction byproduct, NaOH, has a high solubility and is easier to remove than Mg(OH)2, the hydrolysis byproduct of magnides. This method, therefore, offers a simpler method of preparing transition metal nanoparticles. The drawback of this method is the difficulty in controlling the reaction rate

Mineralogy, Micro-fabric and the Behavior of the Completely Decomposed Granite Soils

The main objective of this study is to investigate the impact of the micro-fabric and soil mineralogy on the overall macro-behavior of the completely decomposed granite soil through a set of drained and undrained triaxial shearing and isotropic compression tests on a medium-coarse grading completely decomposed granite soil. The mineral composition of the soil was a substantial factor governing the compressive behavior. The soil compressibility increased significantly in the case of existence crushable and weak minerals within the soil minerals like fragile feldspar, as well as the high content of fines, especially the plastic fines. The scanning electron microscopic photos indicated that the micro-fabric of the soil had a paramount impact on the compressive behavior. The mechanism of the volumetric change depended on the stress levels, the soil mineral composition and the grain morphology. In the low consolidated stress levels, the soils’ grains rearrangement was the prevailing mechanism of the volumetric change, particularly with the absence of weak and crushable minerals. On the other hand, at the high consolidated stress levels, particles’ crushing was the prevailing mechanism in the volumetric change. Both the mechanisms of volume change could occur simultaneously at the low stress levels in the case of presence crushable minerals in addition to micro-cracks in the soil grains. The soil showed an isotropic response after 250 kPa, as this stress level erased the induced anisotropy from the moist tamping preparation method. Under the drained shearing conditions, the soil showed a contractive response, while during the undrained shearing conditions, the soil exhibited both the contractive and dilative responses with phase transformation points. The studied soil showed a unique critical state line, irrespective of the drainage conditions and initial states, the critical state line was parallel to the isotropic compression line in the void ratio effective stress space. In the deviator effective mean stresses space, the studied soil approached a unique CSL with a critical stress ratio equal 1.5, corresponding to critical friction angle of 36.8°

Systematic analysis of the Hippo pathway organization and oncogenic alteration in evolution.

The Hippo pathway is a central regulator of organ size and a key tumor suppressor via coordinating cell proliferation and death. Initially discovered in Drosophila, the Hippo pathway has been implicated as an evolutionarily conserved pathway in mammals; however, how this pathway was evolved to be functional from its origin is still largely unknown. In this study, we traced the Hippo pathway in premetazoan species, characterized the intrinsic functions of its ancestor components, and unveiled the evolutionary history of this key signaling pathway from its unicellular origin. In addition, we elucidated the paralogous gene history for the mammalian Hippo pathway components and characterized their cancer-derived somatic mutations from an evolutionary perspective. Taken together, our findings not only traced the conserved function of the Hippo pathway to its unicellular ancestor components, but also provided novel evolutionary insights into the Hippo pathway organization and oncogenic alteration

Studies on the Property and Application of Starch Sugar Ester Dodecenylsuccinic

In this study, we have prepared starch and Brown algae sugar ester dodecenylsuccinic, and by using infrared rays, scanning electron microscopy (SEM), and differential scanning calorimetry (DSC), we studied the structures and properties of the starch and Brown algae sugar ester dodecenylsuccinic. In addition, we studied the possibility of using this modified starch and Brown algae as emulsifier that can be used in ice cream

A novel control system design for automatic feed drilling operation of the PLC-based oil rig

Aiming at the difficulties in realizing the accurate control due to the nonlinearity of the automatic drilling system of oil drilling rig, a design scheme is proposed by giving a constant drilled-pressure to the rig for fuzzy control. Sampling error with changes in the signal was sent into the fuzzy controller, which turned the signal into a fuzzy volume. Subsequently, a precise volume was obtained accordingly and then added to an actuator for the motor control. According to the MATLAB simulation results, the response could be faster and more stable compared with the traditional control

Multi-feature fusion learning for Alzheimer's disease prediction using EEG signals in resting state

IntroductionDiagnosing Alzheimer's disease (AD) lesions via visual examination of Electroencephalography (EEG) signals poses a considerable challenge. This has prompted the exploration of deep learning techniques, such as Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs), for AD prediction. However, the classification performance of CNN-based methods has often been deemed inadequate. This is primarily attributed to CNNs struggling with extracting meaningful lesion signals from the complex and noisy EEG data.MethodsIn contrast, ViTs have demonstrated proficiency in capturing global signal patterns. In light of these observations, we propose a novel approach to enhance AD risk assessment. Our proposition involves a hybrid architecture, merging the strengths of CNNs and ViTs to compensate for their respective feature extraction limitations. Our proposed Dual-Branch Feature Fusion Network (DBN) leverages both CNN and ViT components to acquire texture features and global semantic information from EEG signals. These elements are pivotal in capturing dynamic electrical signal changes in the cerebral cortex. Additionally, we introduce Spatial Attention (SA) and Channel Attention (CA) blocks within the network architecture. These attention mechanisms bolster the model's capacity to discern abnormal EEG signal patterns from the amalgamated features. To make well-informed predictions, we employ a two-factor decision-making mechanism. Specifically, we conduct correlation analysis on predicted EEG signals from the same subject to establish consistency.ResultsThis is then combined with results from the Clinical Neuropsychological Scale (MMSE) assessment to comprehensively evaluate the subject's susceptibility to AD. Our experimental validation on the publicly available OpenNeuro database underscores the efficacy of our approach. Notably, our proposed method attains an impressive 80.23% classification accuracy in distinguishing between AD, Frontotemporal dementia (FTD), and Normal Control (NC) subjects.DiscussionThis outcome outperforms prevailing state-of-the-art methodologies in EEG-based AD prediction. Furthermore, our methodology enables the visualization of salient regions within pathological images, providing invaluable insights for interpreting and analyzing AD predictions

Contributions of photosynthetic organs to the seed yield of hybrid rice: The effects of gibberellin application examined by carbon isotope technology

The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Contributions of photosynthetic organs to the seed yield of hybrid rice: The effects of gibberellin application examined by carbon isotope technology. Seed Science and Technology, 46(3), (2018): 533-546, doi:10.15258/sst.2018.46.3.10.Changes in the structure and quality of a hybrid combination population have been observed after the application of gibberellins. Such changes would affect the accumulation and distribution of photosynthetic products, which would subsequently affect the yield during hybrid rice seed production. In this study, photosynthetic physiological characteristics and the distribution of photosynthetic products were evaluated in a field experiment. The transport of panicle photosynthetic products to grain was demonstrated using a 14C isotope tracer technique.The contribution ratios of the panicle and leaf to yield in the hybrid rice seed production were 32.3 and 42.1%, respectively. Through isotope tracing technology, it was determined that about 90% of the photosynthetic products of the panicle and 50% of those of the leaf were delivered to the panicle. During the filling period, the contribution of panicle to yield was concentrated in the early period (0–10 days after pollination), and the contribution of leaf to yield was more significant in the late period (10 days after pollination to maturity). These results suggest that the panicle makes an important photosynthetic contribution (equivalent to that of the flag leaf) during the process of grain filling, especially at 0–5 days after the heading stage.We are thankful to anonymous reviewers and editors for their helpful comments and suggestions. This research was part of the project for the National Natural Science Foundation of China (No. 31271666), “12th 5-year plan” Agro-Scientific Research in the Public Interest (Grant No. 201303002) and the Earmarked Fund for China Agriculture Research System (Grant No. CARS-01-26)

SEPT: Towards Scalable and Efficient Visual Pre-Training

Recently, the self-supervised pre-training paradigm has shown great potential in leveraging large-scale unlabeled data to improve downstream task performance. However, increasing the scale of unlabeled pre-training data in real-world scenarios requires prohibitive computational costs and faces the challenge of uncurated samples. To address these issues, we build a task-specific self-supervised pre-training framework from a data selection perspective based on a simple hypothesis that pre-training on the unlabeled samples with similar distribution to the target task can bring substantial performance gains. Buttressed by the hypothesis, we propose the first yet novel framework for Scalable and Efficient visual Pre-Training (SEPT) by introducing a retrieval pipeline for data selection. SEPT first leverage a self-supervised pre-trained model to extract the features of the entire unlabeled dataset for retrieval pipeline initialization. Then, for a specific target task, SEPT retrievals the most similar samples from the unlabeled dataset based on feature similarity for each target instance for pre-training. Finally, SEPT pre-trains the target model with the selected unlabeled samples in a self-supervised manner for target data finetuning. By decoupling the scale of pre-training and available upstream data for a target task, SEPT achieves high scalability of the upstream dataset and high efficiency of pre-training, resulting in high model architecture flexibility. Results on various downstream tasks demonstrate that SEPT can achieve competitive or even better performance compared with ImageNet pre-training while reducing the size of training samples by one magnitude without resorting to any extra annotations.Comment: Accepted by AAAI 202