Understanding Distributed Leadership and Insights for Chinese Educational Institutions in the Context of Digital Transformation: A Literature Review

When education across all levels, is no exception for meeting the needs of industry 4.0 and the new demand of the digital economy and society, distributed leadership is an effective reform strategy for organization's transition to digital transformation. 174 articles related to distributed leadership were selected from eight core-international journals in the field of educational leadership and management with an average h-index of 45, and 64 articles with the keywords of distributed leadership published in the CSSCI and core journals were found. The 248 articles in total were reviewed for analysis with three aspects (research themes and theories; research methodology and analytical methods; discovery and revelation) which were synthesized from the systematic conceptual framework of literature review by Hallinger (2013,2014), the research conclusion frameworks by Bennett et al. (2003) and Tian et al. (2016). The literature review was conducted on four aspects (who, why, what and how) for knowing which most scholars are concerned and for informing educational institutions with insights on distributed leadership for future development

A Cross-project Defect Prediction Model Using Feature Transfer and Ensemble Learning

Cross-project defect prediction (CPDP) trains the prediction models with existing data from other projects (the source projects) and uses the trained model to predict the target projects. To solve two major problems in CPDP, namely, variability in data distribution and class imbalance, in this paper we raise a CPDP model combining feature transfer and ensemble learning, with two stages of feature transfer and the classification. The feature transfer method is based on Pearson correlation coefficient, which reduces the dimension of feature space and the difference of feature distribution between items. The class imbalance is solved by SMOTE and Voting on both algorithm and data levels. The experimental results on 20 source-target projects show that our method can yield significant improvement on CPDP

Mobile Robot Oriented Large-Scale Indoor Dataset for Dynamic Scene Understanding

Most existing robotic datasets capture static scene data and thus are limited in evaluating robots' dynamic performance. To address this, we present a mobile robot oriented large-scale indoor dataset, denoted as THUD (Tsinghua University Dynamic) robotic dataset, for training and evaluating their dynamic scene understanding algorithms. Specifically, the THUD dataset construction is first detailed, including organization, acquisition, and annotation methods. It comprises both real-world and synthetic data, collected with a real robot platform and a physical simulation platform, respectively. Our current dataset includes 13 larges-scale dynamic scenarios, 90K image frames, 20M 2D/3D bounding boxes of static and dynamic objects, camera poses, and IMU. The dataset is still continuously expanding. Then, the performance of mainstream indoor scene understanding tasks, e.g. 3D object detection, semantic segmentation, and robot relocalization, is evaluated on our THUD dataset. These experiments reveal serious challenges for some robot scene understanding tasks in dynamic scenes. By sharing this dataset, we aim to foster and iterate new mobile robot algorithms quickly for robot actual working dynamic environment, i.e. complex crowded dynamic scenes.Comment: This version has been accepted by ICRA2024 and the dataset has been published, where the link can be found in the pape

Deep reinforcement learning for real-time economic energy management of microgrid system considering uncertainties

The electric power grid is changing from a traditional power system to a modern, smart, and integrated power system. Microgrids (MGs) play a vital role in combining distributed renewable energy resources (RESs) with traditional electric power systems. Intermittency, randomness, and volatility constitute the disadvantages of distributed RESs. MGs with high penetrations of renewable energy and random load demand cannot ignore these uncertainties, making it difficult to operate them effectively and economically. To realize the optimal scheduling of MGs, a real-time economic energy management strategy based on deep reinforcement learning (DRL) is proposed in this paper. Different from traditional model-based approaches, this strategy is learning based, and it has no requirements for an explicit model of uncertainty. Taking into account the uncertainties in RESs, load demand, and electricity prices, we formulate a Markov decision process for the real-time economic energy management problem of MGs. The objective is to minimize the daily operating cost of the system by scheduling controllable distributed generators and energy storage systems. In this paper, a deep deterministic policy gradient (DDPG) is introduced as a method for resolving the Markov decision process. The DDPG is a novel policy-based DRL approach with continuous state and action spaces. The DDPG is trained to learn the characteristics of uncertainties of the load, RES output, and electricity price using historical data from real power systems. The effectiveness of the proposed approach is validated through the designed simulation experiments. In the second experiment of our designed simulation, the proposed DRL method is compared to DQN, SAC, PPO, and MPC methods, and it is able to reduce the operating costs by 29.59%, 17.39%, 6.36%, and 9.55% on the June test set and 30.96%, 18.34%, 5.73%, and 10.16% on the November test set, respectively. The numerical results validate the practical value of the proposed DRL algorithm in addressing economic operation issues in MGs, as it demonstrates the algorithm’s ability to effectively leverage the energy storage system to reduce the operating costs across a range of scenarios

Risk factors for virologic failure and persistent low-level viremia in people with HIV experiencing low-level viremia: Chongqing ART cohort study, 2019–2023

BackgroundLow-level viremia (LLV) during effective antiretroviral therapy (ART) presents ongoing management challenges globally, with reported prevalence rates of 10–46% in resource-limited settings. The clinical significance of LLV remains controversial: while some studies demonstrate that viral load (VL) levels exceeding 200 copies/mL predict virologic failure (VF), others report no significant association. This uncertainty underscores the need for clearer risk stratification in diverse clinical settings.ObjectiveTo investigate risk factors for VF and persistent low-level viremia (pLLV) in HIV-1-infected individuals experiencing LLV.DesignA retrospective cohort study between January 2019 and December 2023, consisting of 1,214 individuals with LLV (defined as plasma HIV-1 RNA levels of 50–999 copies/mL detected at two consecutive time points following previously undetected viral loads) at a large specialized hospital in Chongqing, China.MethodsClinical data, including demographics, ART regimens, adherence, baseline viral load (VL), CD4 + T-cell counts, and LLV characteristics, were extracted from medical records. Univariate and multivariate logistic regression models were used to identify factors associated with VF (defined as one or more HIV VLs of ≥1,000 copies/mL) and pLLV (defined as at least three consecutive measurements of VL within the range of 50 to 999 copies/mL), with adjustments for potential confounders.ResultsAmong 1,214 participants with LLV, 2.64% (32/1,214) developed VF, and 28.09% (341/1,214) developed pLLV. Protective factors against VF included baseline VL < 1,000 copies/mL (adjusted odds ratio [aOR] = 0.100, 95%CI: 0.013–0.765) and VL < 200 copies/mL during LLV (aOR = 0.157, 95%CI: 0.071–0.540). Viral blips (transient LLV) independently predicted VF (aOR = 4.6775, 95%CI: 1.392–15.704). For pLLV, baseline VL < 1,000 copies/mL remained protective (aOR = 0.569, 95% CI: 0.329–0.984), while primary education or lower was a risk factor (aOR = 2.052, 95%CI: 1.014–4.194).ConclusionVL levels during LLV and baseline VL predict VF risk, emphasizing the need for vigilant VL monitoring and adherence support

GWAS Identifies Novel Susceptibility Loci on 6p21.32 and 21q21.3 for Hepatocellular Carcinoma in Chronic Hepatitis B Virus Carriers

Genome-wide association studies (GWAS) have recently identified KIF1B as susceptibility locus for hepatitis B virus (HBV)–related hepatocellular carcinoma (HCC). To further identify novel susceptibility loci associated with HBV–related HCC and replicate the previously reported association, we performed a large three-stage GWAS in the Han Chinese population. 523,663 autosomal SNPs in 1,538 HBV–positive HCC patients and 1,465 chronic HBV carriers were genotyped for the discovery stage. Top candidate SNPs were genotyped in the initial validation samples of 2,112 HBV–positive HCC cases and 2,208 HBV carriers and then in the second validation samples of 1,021 cases and 1,491 HBV carriers. We discovered two novel associations at rs9272105 (HLA-DQA1/DRB1) on 6p21.32 (OR = 1.30, P = 1.13×$10^{−19}$) and rs455804 (GRIK1) on 21q21.3 (OR = 0.84, P = 1.86×$10^{−8}$), which were further replicated in the fourth independent sample of 1,298 cases and 1,026 controls (rs9272105: OR = 1.25, P = 1.71×$10^{−4}$; rs455804: OR = 0.84, P = 6.92×$10^{−3}$). We also revealed the associations of HLA-DRB1*0405 and 0901*0602, which could partially account for the association at rs9272105. The association at rs455804 implicates GRIK1 as a novel susceptibility gene for HBV–related HCC, suggesting the involvement of glutamate signaling in the development of HBV–related HCC

Self-passivated freestanding superconducting oxide film for flexible electronics

The integration of high-temperature superconducting YBa2Cu3O6+x (YBCO) into flexible electronic devices has the potential to revolutionize the technology industry. The effective preparation of high-quality flexible YBCO films therefore plays a key role in this development. We present a novel approach for transferring water-sensitive YBCO films onto flexible substrates without any buffer layer. Freestanding YBCO film on a polydimethylsiloxane substrate is extracted by etching the Sr3Al2O6 sacrificial layer from the LaAlO3 substrate. In addition to the obtained freestanding YBCO thin film having a Tc of 89.1 K, the freestanding YBCO thin films under inward and outward bending conditions have Tc of 89.6 K and 88.9 K, respectively. A comprehensive characterization involving multiple experimental techniques including high-resolution transmission electron microscopy, scanning electron microscopy, Raman and X-ray Absorption Spectroscopy is conducted to investigate the morphology, structural and electronic properties of the YBCO film before and after the extraction process where it shows the preservation of the structural and superconductive properties of the freestanding YBCO virtually in its pristine state. Further investigation reveals the formation of a YBCO passivated layer serves as a protective layer which effectively preserves the inner section of the freestanding YBCO during the etching process. This work plays a key role in actualizing the fabrication of flexible oxide thin films and opens up new possibilities for a diverse range of device applications involving thin-films and low-dimensional materials.Comment: 22 pages,4 figures,references adde

Analysis of Nutritional Composition, Texture Characteristics and Volatile Flavor Compounds of Yam from Different Origins

In this study, the basic nutrients, textures and volatile flavor compounds of 11 varieties of yams were analyzed using various analytical and measurement methods, and an evaluation model was established. The study revealed variations in the nutritional quality of the 11 yam species, highlighting both varietal and regional differences. The protein content of different yams ranged from 6.53% to 17.45%, polysaccharide content from 1.21% to 15.96%, fat content from 0.63% to 2.39%, total starch content from 52.03% to 82.01%, allantoin content from 3.16~11.5 mg/g, total saponin content from 0.66% to 10.47%, the ranking of trace element content was as follows: K>Fe>Mg>Ca>Na>Zn, the total amino acid content was 17.2%~74.85%, among which the essential amino acid/total amino acid was 23.22%~32.93%, which was slightly lower than the FAO/WHO ideal protein standard. Texture analysis results showed that iron stick yam had a relatively soft, glutinous, and smooth overall taste. A total of 64 volatile compounds were detected in the 11 types of yams, with higher contents of alcohols (0.64% to 85.04%) and aldehydes (1.76% to 28.12%). The content of volatile compounds varied greatly, the relative content of volatile components in Hebei hemp yam was the highest, while that in Chaoshan yam was the lowest. Correlation analysis results revealed significant correlations between different nutritional components and between nutritional components and texture characteristics. Principal component analysis results indicated that the top three in overall nutritional quality were Henan iron stick yam, Shandong Jiaxiang slender hairy yam, and Hebei hemp yam. The results of this paper indicate that the nutritional components, texture characteristics, and volatile components of yams from different regions vary, providing a reference basis for the processing and development of related yam products