The Motivation Theory of Life-Span Development and Its Implications for Career Education

Based on the Selective Optimization with Compensation (SOC) and control model, the Motivational Theory of Life-Span Development (MTLSD) proposes adaptive development criteria and objectives. It asserts that the pursuit of perpetual development is the primary control and that the life cycle is an action field with an opportunity and constraint structure of time organization. Opportunity is a process characterized by the change processes of escalating and waning and by the adaptation consistency processes of goal participation and goal separation. The MTLSD has endeavored to elucidate how people actively promote their own personal lifelong development throughout its entirety. This provides illumination for China’s career education. The importance of individual initiative in career development, the process of opportunity in the continuity of career education, the difference of career education in different career (major) life cycles, and the fairness of career education during the transitional period of social mobility (opportunity) must be emphasized

The Construction of Career Education in Senior High School under the Field Perspective

In the deep-seated transformation of senior high school education catalysed by the reform of the college entrance examination, the function of career education in senior high school has garnered attention. This paper examines career education in senior high school through the lens of Bourdieu's theory of the field. It is not only significant for theoretical reconstruction but also for re-understanding of methodology. It highlights the necessity of viewing the propelling role of capital and power, particularly cultural capital, from a relational perspective within its structural context. In addition, the process is examined in terms of the significance of the distinct habits that shaped its construction and practical logic. The career field in senior high school emphasize ‘value leadership’ and ‘personal growth’, moulding field habits through the interplay of relationships within the field and interactions with other fields, such as the ‘college field’ and the ‘workplace field’. This interaction contributes to the development of field behaviours and stimulates the psychological field's growth and transformation. It aids actors in generating their patterns of practice, thereby attaining the dual objectives of intellectual and social construction and development

Coherence of ion cyclotron resonance for damping ion cyclotron waves in space plasmas

Ion cyclotron resonance is one of the fundamental energy conversion processes through field-particle interaction in collisionless plasmas. However, the key evidence for ion cyclotron resonance (i.e., the coherence between electromagnetic fields and the ion phase space density) and the resulting damping of ion cyclotron waves (ICWs) has not yet been directly observed. Investigating the high-quality measurements of space plasmas by the Magnetospheric Multiscale (MMS) satellites, we find that both the wave electromagnetic field vectors and the bulk velocity of the disturbed ion velocity distribution rotate around the background magnetic field. Moreover, we find that the absolute gyro-phase angle difference between the center of the fluctuations in the ion velocity distribution functions and the wave electric field vectors falls in the range of (0, 90) degrees, consistent with the ongoing energy conversion from wave-fields to particles. By invoking plasma kinetic theory, we demonstrate that the field-particle correlation for the damping ion cyclotron waves in our theoretical model matches well with our observations. Furthermore, the wave electric field vectors ($\delta \mathbf{E'}_{\mathrm {wave,\perp}}$), the ion current density ($\delta \mathbf{J}_\mathrm {i,\perp}$) and the energy transfer rate ($\delta \mathbf{J}_\mathrm {i,\perp}\cdot \delta \mathbf{E'}_{\mathrm {wave,\perp}}$) exhibit quasi-periodic oscillations, and the integrated work done by the electromagnetic field on the ions are positive, indicates that ions are mainly energized by the perpendicular component of the electric field via cyclotron resonance. Therefore, our combined analysis of MMS observations and kinetic theory provides direct, thorough, and comprehensive evidence for ICW damping in space plasmas

Statistical Study of Anisotropic Proton Heating in Interplanetary Magnetic Switchbacks Measured by Parker Solar Probe

Magnetic switchbacks, which are large angular deflections of the interplanetary magnetic field, are frequently observed by Parker Solar Probe (PSP) in the inner heliosphere. Magnetic switchbacks are believed to play an important role in the heating of the solar corona and the solar wind as well as the acceleration of the solar wind in the inner heliosphere. Here, we analyze magnetic field data and plasma data measured by PSP during its second and fourth encounters, and select 71 switchback events with reversals of the radial component of the magnetic field at times of unchanged electron-strahl pitch angles. We investigate the anisotropic thermal kinetic properties of plasma during switchbacks in a statistical study of the measured proton temperatures in the parallel and perpendicular directions as well as proton density and specific proton fluid entropy. We apply the “genetic algorithm” method to directly fit the measured velocity distribution functions in field-aligned coordinates using a two-component bi-Maxwellian distribution function. We find that the protons in most switchback events are hotter than the ambient plasma outside the switchbacks, with characteristics of parallel and perpendicular heating. Specifically, significant parallel and perpendicular temperature increases are seen for 45 and 62 of the 71 events, respectively. We find that the density of most switchback events decreases rather than increases, which indicates that proton heating inside the switchbacks is not caused by adiabatic compression, but is probably generated by nonadiabatic heating caused by field–particle interactions. Accordingly, the proton fluid entropy is greater inside the switchbacks than in the ambient solar wind

SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI

Diffusion models are a leading method for image generation and have been successfully applied in magnetic resonance imaging (MRI) reconstruction. Current diffusion-based reconstruction methods rely on coil sensitivity maps (CSM) to reconstruct multi-coil data. However, it is difficult to accurately estimate CSMs in practice use, resulting in degradation of the reconstruction quality. To address this issue, we propose a self-consistency-driven diffusion model inspired by the iterative self-consistent parallel imaging (SPIRiT), namely SPIRiT-Diffusion. Specifically, the iterative solver of the self-consistent term in SPIRiT is utilized to design a novel stochastic differential equation (SDE) for diffusion process. Then $\textit{k}$-space data can be interpolated directly during the reverse diffusion process, instead of using CSM to separate and combine individual coil images. This method indicates that the optimization model can be used to design SDE in diffusion models, driving the diffusion process strongly conforming with the physics involved in the optimization model, dubbed model-driven diffusion. The proposed SPIRiT-Diffusion method was evaluated on a 3D joint Intracranial and Carotid Vessel Wall imaging dataset. The results demonstrate that it outperforms the CSM-based reconstruction methods, and achieves high reconstruction quality at a high acceleration rate of 10

TF-Cluster: A pipeline for identifying functionally coordinated transcription factors via network decomposition of the shared coexpression connectivity matrix (SCCM)

<p>Abstract</p> <p>Background</p> <p>Identifying the key transcription factors (TFs) controlling a biological process is the first step toward a better understanding of underpinning regulatory mechanisms. However, due to the involvement of a large number of genes and complex interactions in gene regulatory networks, identifying TFs involved in a biological process remains particularly difficult. The challenges include: (1) Most eukaryotic genomes encode thousands of TFs, which are organized in gene families of various sizes and in many cases with poor sequence conservation, making it difficult to recognize TFs for a biological process; (2) Transcription usually involves several hundred genes that generate a combination of intrinsic noise from upstream signaling networks and lead to fluctuations in transcription; (3) A TF can function in different cell types or developmental stages. Currently, the methods available for identifying TFs involved in biological processes are still very scarce, and the development of novel, more powerful methods is desperately needed.</p> <p>Results</p> <p>We developed a computational pipeline called TF-Cluster for identifying functionally coordinated TFs in two steps: (1) Construction of a shared coexpression connectivity matrix (SCCM), in which each entry represents the number of shared coexpressed genes between two TFs. This sparse and symmetric matrix embodies a new concept of coexpression networks in which genes are associated in the context of other shared coexpressed genes; (2) Decomposition of the SCCM using a novel heuristic algorithm termed "Triple-Link", which searches the highest connectivity in the SCCM, and then uses two connected TF as a primer for growing a TF cluster with a number of linking criteria. We applied TF-Cluster to microarray data from human stem cells and <it>Arabidopsis </it>roots, and then demonstrated that many of the resulting TF clusters contain functionally coordinated TFs that, based on existing literature, accurately represent a biological process of interest.</p> <p>Conclusions</p> <p>TF-Cluster can be used to identify a set of TFs controlling a biological process of interest from gene expression data. Its high accuracy in recognizing true positive TFs involved in a biological process makes it extremely valuable in building core GRNs controlling a biological process. The pipeline implemented in Perl can be installed in various platforms.</p

Synthesis of Ni nanoparticles by dc magnetron sputtering

Magnetic materials have been used with grain sizes down to the nanoscale for longer than any other type of material. The biomedical applications cover from magnetic separation of specific biological entities from their native environment to drug delivery, hyperthermia treatments or MRI contrast enhancement [1]. There are many synthesis methods depending on the final applications of the magnetic nanoparticles [2]. Sputtering methods are less extensively used, maybe due to the low efficiency of the process, however these methods have the advantage of a good control on the composition and size of the particles. Research has focused mainly on Fe [3,4], Co [5] and FeCo alloys [6]. In this work we apply the dc magnetron sputtering technique to the growth of Ni nanoparticles

High-Frequency Space Diffusion Models for Accelerated MRI

Diffusion models with continuous stochastic differential equations (SDEs) have shown superior performances in image generation. It can serve as a deep generative prior to solving the inverse problem in magnetic resonance (MR) reconstruction. However, low-frequency regions of $k$-space data are typically fully sampled in fast MR imaging, while existing diffusion models are performed throughout the entire image or $k$-space, inevitably introducing uncertainty in the reconstruction of low-frequency regions. Additionally, existing diffusion models often demand substantial iterations to converge, resulting in time-consuming reconstructions. To address these challenges, we propose a novel SDE tailored specifically for MR reconstruction with the diffusion process in high-frequency space (referred to as HFS-SDE). This approach ensures determinism in the fully sampled low-frequency regions and accelerates the sampling procedure of reverse diffusion. Experiments conducted on the publicly available fastMRI dataset demonstrate that the proposed HFS-SDE method outperforms traditional parallel imaging methods, supervised deep learning, and existing diffusion models in terms of reconstruction accuracy and stability. The fast convergence properties are also confirmed through theoretical and experimental validation. Our code and weights are available at https://github.com/Aboriginer/HFS-SDE.Comment: accepted for IEEE TM

Super resolution dual-layer CBCT imaging with model-guided deep learning

Objective: This study aims at investigating a novel super resolution CBCT imaging technique with the dual-layer flat panel detector (DL-FPD). Approach: In DL-FPD based CBCT imaging, the low-energy and high-energy projections acquired from the top and bottom detector layers contain intrinsically mismatched spatial information, from which super resolution CBCT images can be generated. To explain, a simple mathematical model is established according to the signal formation procedure in DL-FPD. Next, a dedicated recurrent neural network (RNN), named as suRi-Net, is designed by referring to the above imaging model to retrieve the high resolution dual-energy information. Different phantom experiments are conducted to validate the performance of this newly developed super resolution CBCT imaging method. Main Results: Results show that the proposed suRi-Net can retrieve high spatial resolution information accurately from the low-energy and high-energy projections having lower spatial resolution. Quantitatively, the spatial resolution of the reconstructed CBCT images of the top and bottom detector layers is increased by about 45% and 54%, respectively. Significance: In future, suRi-Net provides a new approach to achieve high spatial resolution dual-energy imaging in DL-FPD based CBCT systems