The Architectural Path of Collaborative Management in Performing Arts Organizations

Under the background of the new era, performing arts organizations have become a cornerstone force in promoting the high-quality development of China’s culture. “How to explore the development models and general rules that promote internal and external collaborative management within organizations?” and “How to achieve dynamic balance within organizations with diverse collaborative models?” These are all crucial questions that managers of performing arts organizations should consider in this era. Based on positioning theory and the logical perspective of organizational structuring functional types, this article proposes a “three-step” development strategy for the internal and external collaborative management of performing arts organizations from the value logic of arts management and the research perspective of history. The article also establishes an evaluation system for the internal and external collaborative management of performing arts organizations using the DEA method and explores the full-process management architecture of integrated collaboration within and outside performing arts organizations. The aim is to provide theoretical support for the collaborative innovation and high-quality development of performing arts organization management

A Statistical Approach to Functional Connectivity Involving Multichannel Neural Spike Trains

The advent of the multi-electrode has made it feasible to record spike trains simultaneously from several neurons. However, the statistical techniques for analyzing large-scale simultaneously recorded spike train data have not developed as satisfactorily as the experimental techniques for obtaining these data. This dissertation contributes to the literature of modeling simultaneous spike train data and inferring the functional connectivity in two aspects. In the first part, we apply a point process likelihood method under the generalized linear model framework (Harris, 2003) for analyzing ensemble spiking activity from noncholinergic basal forebrain neurons (Lin and Nicolelis, 2008). The model can assess the correlation between a target neuron and its peers. The correlation is referred to as weight for each peer and is estimated through maximizing the penalized likelihood function. A discrete time representation is used to construct the point process likelihood, and the discrete 0-1 occurrence data are smoothed using Gaussian kernels. Ultimately, the entire peer firing information and the correlations can be used to predict the probability of target firing. In the second part, we propose a regression spline model, which directly makes use of the neural firing times instead of using the smoothed version of spike train. The primary contribution of the model is that it can both capture the spontaneous dynamics and also infer functional connectivity for an arbitrary number of interactive neurons in a given region or across different regions. In addition, it does not need discretization, relaxes the parametric assumption, and offers high flexibility for estimation via spline functions. The regression spline model selects the optimal spline knots adaptively using the spike train data. Our model incorporates adaptive model selection and is estimated through maximum likelihood. Asymptotic properties of the proposed estimator are investigated as well.Doctor of Philosoph

Projected Spatiotemporal Dynamics of Drought under Global Warming in Central Asia

Drought, one of the most common natural disasters that have the greatest impact on human social life, has been extremely challenging to accurately assess and predict. With global warming, it has become more important to make accurate drought predictions and assessments. In this study, based on climate model data provided by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), we used the Palmer Drought Severity Index (PDSI) to analyze and project drought characteristics and their trends under two global warming scenarios—1.5 °C and 2.0 °C—in Central Asia. The results showed a marked decline in the PDSI in Central Asia under the influence of global warming, indicating that the drought situation in Central Asia would further worsen under both warming scenarios. Under the 1.5 °C warming scenario, the PDSI in Central Asia decreased first and then increased, and the change time was around 2080, while the PDSI values showed a continuous decline after 2025 in the 2.0 °C warming scenario. Under the two warming scenarios, the spatial characteristics of dry and wet areas in Central Asia are projected to change significantly in the future. In the 1.5 °C warming scenario, the frequency of drought and the proportion of arid areas in Central Asia were significantly higher than those under the 2.0 °C warming scenario. Using the Thornthwaite (TH) formula to calculate the PDSI produced an overestimation of drought, and the Penman–Monteith (PM) formula is therefore recommended to calculate the index

Bayesian optimization with active learning of Ta-Nb-Hf-Zr-Ti system for spin transport properties

Designing materials with enhanced spin charge conversion, i.e., with high spin Hall conductivity (SHC) and low longitudinal electric conductivity (hence large spin Hall angle (SHA)), is a challenging task, especially in the presence of a vast chemical space for compositionally complex alloys (CCAs). In this work, focusing on the Ta-Nb-Hf-Zr-Ti system, we confirm that CCAs exhibit significant spin Hall conductivities and propose a multi-objective Bayesian optimization approach (MOBO) incorporated with active learning (AL) in order to screen for the optimal compositions with significant SHC and SHA. As a result, within less than 5 iterations we are able to target the TaZr-dominated systems displaying both high magnitudes of SHC (~-2.0 (10$^{-3}$ $\Omega$ cm)$^{-1}$) and SHA (~0.03). The SHC is mainly ascribed to the extrinsic skew scattering mechanism. Our work provides an efficient route for identifying new materials with significant SHE, which can be straightforwardly generalized to optimize other properties in a vast chemical space

Transcriptional peroxisome proliferator-activated receptor γ coactivator-1 (PGC-1α) regulates transformation of muscle fiber type in Schizothorax prenanti

Peroxisome proliferator-activated receptor γ coactivator (PGC)-1ɑ, a well-known member of PGC-1 transcriptional coactivator’s family, plays a key role in various metabolic pathways. Here, we investigated the role of PGC-1ɑ in the transformation of muscle fiber type in Schizothorax prenanti. The expression of PGC-1ɑ was induced in S. prenanti muscles following fasting. Following the induction of PGC-1ɑ, the expressions of mitochondrial-related enzyme cytochrome c oxidase (COX), citrate synthase (CS) and cytochrome c oxidase IV was also increased in white muscles, but the expression of carnitine palmitoyltransferase II (CPT II) has no change in this condition. Notably, when the levels of PGC-1ɑ was upregulated in the condition of fasting, muscle fibres type II showed the characteristics of muscle fibres type I, with expressed myosin heavy chain I (MyHC I) and myoglobin (Mb), and suppressed myosin heavy chain II (MyHC II) in response to fasting. Therefore, we can draw conclusion that PGC-1ɑ up-regulates slow fiber type formation during the transformation of muscle fiber type in S. prenanti.Keywords: PGC-1ɑ, muscle fiber type, transformation, Schizothorax prenanti, MyHC I, MyHC I

Magnetic properties of Nd6Fe13Cu single crystals

The understanding of coercivity mechanism in high performance Nd-Fe-B permanent magnets relies on the analysis of the magnetic properties of all phases present in the magnets. By adding Cu in such compounds, a new Nd6Fe13Cu grain boundary phase is formed, however, the magnetic properties of this phase and its role in the magnetic decoupling of the matrix Nd2Fe14B grains are still insufficiently studied. In this work, we have grown Nd6Fe13Cu single crystals by the reactive flux method and studied their magnetic properties in detail. It is observed that below the N\'eel temperature (TN = 410 K), the Nd6Fe13Cu is antiferromagnetic in zero magnetic field; whereas when a magnetic field is applied along the a-axis, a spin-flop transition occurs at approx. 6 T, indicating a strong competition between antiferromagnetic and ferromagnetic interactions in two Nd layers below and above the Cu layers. Our atomistic spin dynamics simulation confirms that an increase in temperature and/or magnetic field can significantly change the antiferromagnetic coupling between the two Nd layers below and above the Cu layers, which, in turn, is the reason for the observed spin-flop transition. These results suggest that the role of antiferromagnetic Nd6Fe13Cu grain boundary phase in the coercivity enhancement of Nd-Fe-B-Cu magnets is more complex than previously thought, mainly due to the competition between its antiferro- and ferro-magnetic exchange interactions.Comment: 15 pages, 4 figure