The Development Trend of the Non-governmental Higher Education in China

In more than 30 years after China’s reform and opening to the outside world the non-governmental higher education has developed dramatically and enjoyed more and more favorable conditions, making great contributions to the popularization of higher education in China. According to the definition that “The non-governmental higher education is a major growth point for development of educational career and an important driving force in promoting educational reform”, quoting from The planning outline on the national medium and long-term educational reform and development (The Planning Outline for short) issued in 2010 by China’s State Council, China has launched policies of greatly supporting the non-governmental higher education and designed the reform goals of the government as the main body of education, with the active participation of the whole society, and the mutual development of the governmental and non-governmental higher education. Soon afterwards, the nation has started the move of the non-governmental educational system, and positively explored the management of classification of profit and non-profit non-governmental educational institutions. Thus, the development of the Chinese non-governmental institutions of higher learning has been greatly influenced because of the policies and relevant practices. With the opening of the 18th national congress and the political direction mentioned in the report as “encouraging and guiding social organizations to initiate education”, the pace of development of the non-governmental institutions of higher learning will be accelerated and some new development trends will become clearer

The Dynamic Change in Wage Gap between Urban Residents and Rural Migrants in Chinese Cities

Although a significant wage gap has been found in many previous studies between urban workers and rural migrants in Chinese cities, it is still not clear how such a wage gap may evolve over time. This paper uses both a dynamic wage decomposition method and economic assimilation model with pooled cross-sectional data from the China Household Income Project Survey (CHIPS) of 1999 and 2002 to investigate the change in the wage gap between urban workers and rural migrants over time and its determinants in Chinese cities. The estimation results show that (1) there is a widening on-average wage gap between urban workers and rural migrants across the two surveyed years in Chinese cities, mainly caused by the decline in the return to education for rural migrants; (2) rural migrants can catch up with the wage level of their urban counterparts as the time they reside in the host cities increases, but because of the decline in the speed of catching-up over time, rural migrants cannot obtain wages comparable totheir urban counterparts in their life time, and more importantly well-educated rural migrants do not seem to have a significant advantage in this wage assimilation process than the lowlypoorly-educated ones. Both findings suggest that there might be discrimination against well-educated rural migrants which prevents them from obtaining a fair wage in the Chinese urban labour market.Wage differential, Migration

Numerical analysis on global serviceability behaviours of tall Glulam frame buildings to the Eurocodes and UK National Annexes

Glued-laminated timber (Glulam) is an innovative engineered timber product and has been widely used for constructing spatial grand timber structures and tall timber buildings due to its exceptional natural attraction, easy processing, decent fire resistance and outstanding structural performance. However, global serviceability performances of tall timber buildings constructed from Glulam products for beams, columns and bracings and CLT products for lift core and floors under wind load are not well known yet though they are crucial in structural design and global analysis. In this study, finite element software SAP2000 is used to numerically simulate the global static and dynamic serviceability behaviours of a 105 m high 30-storey tall Glulam building with CLT lift core and floors assumed in Glasgow, Scotland, UK. The maximum horizontal storey displacement due to wind is 58.5% of the design limit and the maximum global horizontal displacement is 49.7% of the limit set to the Eurocodes. The first three lowest vibrational frequencies, modes and shapes of the building are obtained, with the fundamental frequency being 33.3% smaller than the code recommended value due to its low mass and stiffness. The peak acceleration of the building due to wind is determined to the Eurocodes and ISO 10137. The results show that the global serviceability behaviours of the building satisfy the requirements of the Eurocodes and other design standards. Parametric studies on the peak accelerations of the tall Glulam building are also conducted by varying timber material properties and building masses. Increasing the timber grade for CLT members, the generalised building mass and the generalised building stiffness can all be adopted to lower the peak accelerations at the top level of the building so as to reduce the human perceptions to the wind induced vibrations with respect to the peak acceleration

Few-shot Class-incremental Learning: A Survey

Few-shot Class-Incremental Learning (FSCIL) presents a unique challenge in machine learning, as it necessitates the continuous learning of new classes from sparse labeled training samples without forgetting previous knowledge. While this field has seen recent progress, it remains an active area of exploration. This paper aims to provide a comprehensive and systematic review of FSCIL. In our in-depth examination, we delve into various facets of FSCIL, encompassing the problem definition, the discussion of primary challenges of unreliable empirical risk minimization and the stability-plasticity dilemma, general schemes, and relevant problems of incremental learning and few-shot learning. Besides, we offer an overview of benchmark datasets and evaluation metrics. Furthermore, we introduce the classification methods in FSCIL from data-based, structure-based, and optimization-based approaches and the object detection methods in FSCIL from anchor-free and anchor-based approaches. Beyond these, we illuminate several promising research directions within FSCIL that merit further investigation

Damage Identification of Piles Based on Vibration Characteristics

A method of damage identification of piles was established by using vibration characteristics. The approach focused on the application of the element strain energy and sensitive modals. A damage identification equation of piles was deduced using the structural vibration equation. The equation contained three major factors: change rate of element modal strain energy, damage factor of pile, and sensitivity factor of modal damage. The sensitive modals of damage identification were selected by using sensitivity factor of modal damage firstly. Subsequently, the indexes for early-warning of pile damage were established by applying the change rate of strain energy. Then the technology of computational analysis of wavelet transform was used to damage identification for pile. The identification of small damage of pile was completely achieved, including the location of damage and the extent of damage. In the process of identifying the extent of damage of pile, the equation of damage identification was used in many times. Finally, a stadium project was used as an example to demonstrate the effectiveness of the proposed method of damage identification for piles. The correctness and practicability of the proposed method were verified by comparing the results of damage identification with that of low strain test. The research provided a new way for damage identification of piles

A Factored Similarity Model with Trust and Social Influence for Top-N Recommendation

Many trust-aware recommendation systems have emerged to overcome the problem of data sparsity, which bottlenecks the performance of traditional Collaborative Filtering (CF) recommendation algorithms. However, these systems most rely on the binary social network information, failing to consider the variety of trust values between users. To make up for the defect, this paper designs a novel Top-N recommendation model based on trust and social influence, in which the most influential users are determined by the Improved Structural Holes (ISH) method. Specifically, the features in Matrix Factorization (MF) were configured by deep learning rather than random initialization, which has a negative impact on prediction of item rating. In addition, a trust measurement model was created to quantify the strength of implicit trust. The experimental result shows that our approach can solve the adverse impacts of data sparsity and enhance the recommendation accuracy

CLEC11A expression as a prognostic biomarker in correlation to immune cells of gastric cancer

Gastric cancer (GC) is a prevalent malignant cancer characterized by a poor survival rate. The C-type lectin domain family 11 member A (CLEC11A) is part of the C-type lectin superfamily, and its dysregulation has been implicated in the progression of several cancers. The specific role of CLEC11A and its association with immune infiltration in GC, however, remains unclear. In this study, we employed The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, Tumor IMmune Estimation Resource (TIMER) database, Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, Kaplan–Meier plotter databases, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and the CIBERSORT algorithm to investigate CLEC11A expression, its prognostic significance, its association with tumor immune infiltration, and gene function enrichment in GC. We conducted western blotting, Cell Counting Kit-8 (CCK-8), wound healing, and transwell assays to validate CLEC11A's function. We found that CLEC11A expression was significantly elevated in GC when compared to adjacent non-tumor tissues. Elevated CLEC11A expression was strongly associated with poor survival outcomes and advanced clinicopathological stages.  Moreover, heightened CLEC11A expression positively correlated with immunomodulators, chemokines, and the infiltration of immune cells, especially M2 macrophages, in GC. Additionally, CLEC11A silencing suppressed GC cells proliferation, migration and invasion in vitro. Our results elucidate the functions of CLEC11A in GC, suggesting its potential as a valuable prognostic biomarker and therapeutic target for GC immunotherapy

Top-N Recommendation Based on Mutual Trust and Influence

To improve recommendation quality, the existing trust-based recommendation methods often directly use the binary trust relationship of social networks, and rarely consider the difference and potential influence of trust strength among users. To make up for the gap, this paper puts forward a hybrid top-N recommendation algorithm that combines mutual trust and influence. Firstly, a new trust measurement method was developed based on dynamic weight, considering the difference of trust strength between users. Secondly, a new mutual influence measurement model was designed based on trust relationship, in light of the social network topology. Finally, two hybrid recommendation algorithms, denoted as FSTA(Factored Similarity model with Trust Approach) and FSTI(Factored similarity models with trust and influence), were presented to solve the data sparsity and binarity. The two algorithms integrate user similarity, item similarity, mutual trust and mutual influence. Our approach was compared with several other recommendation algorithms on three standard datasets: FilmTrust, Epinions and Ciao. The experimental results proved the high efficiency of our approach