Employment Core Competencies of Chinese Higher Vocational Students: A Systematic Review and Research Agenda

This study explores the number of publications, major researchers, major research institutions, research hotspots and research trends in this field of study by conducting thematic searches on the Scopus knowledge service platform and bibliometric analyses using CiteSpace.The results indicate that the number of publications on research on structural equation modelling in the area of employment core competencies is increasing, but the total amount of literature is still too small and there are many gaps. Research on the application of structural equation modelling in the field of employment core competencies focuses on topics such as higher education, healthcare, human resources, and behaviour. Future colleges and universities are the main research participants. Considering the lack of richness of current research perspectives, this review also provides suggestions for future research directions in order to strengthen the Chinese higher vocational students’ employment core competency

Disruption of gradient expression of Zic3 resulted in abnormal intra-retinal axon projection

The targeting of retinal ganglion axons toward the optic disc is the first step in axon pathfinding in the visual system. The molecular mechanisms involved in guiding the retinal axons to project towards the optic disc are not well understood. We report that a gene encoding a zinc-finger transcription factor, Zic3, is expressed in a periphery-high and center-low gradient in the retina at the stages of active axon extension inside the retina. The gradient expression of Zic3 recedes towards the periphery over the course of development, correlating with the progression of retinal cell differentiation and axonogenesis. Disruption of gradient expression of Zic3 by retroviral overexpression resulted in mis-targeting of retinal axons and some axons misrouted to the sub-retinal space at the photoreceptor side of the retina. Misexpression of Zic3 did not affect neurogenesis or differentiation inside the retina, or grossly alter retinal lamination. By stripe assay, we show that misexpression of Zic3 may induce the expression of an inhibitory factor to the retinal axons. Zic3 appears to play a role in intra-retinal axon targeting, possibly through regulation of the expression of specific downstream genes involved in axon guidance

An adaptation reference-point-based multiobjective evolutionary algorithm

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.It is well known that maintaining a good balance between convergence and diversity is crucial to the performance of multiobjective optimization algorithms (MOEAs). However, the Pareto front (PF) of multiobjective optimization problems (MOPs) affects the performance of MOEAs, especially reference point-based ones. This paper proposes a reference-point-based adaptive method to study the PF of MOPs according to the candidate solutions of the population. In addition, the proportion and angle function presented selects elites during environmental selection. Compared with five state-of-the-art MOEAs, the proposed algorithm shows highly competitive effectiveness on MOPs with six complex characteristics

An improved memory prediction strategy for dynamic multiobjective optimization

The file attached to this record is the author's final peer reviewed version.In evolutionary dynamic multiobjective optimization (EDMO), the memory strategy and prediction method are considered as effective and efficient methods. To handling dynamic multiobjective problems (DMOPs), this paper studies the behavior of environment change and tries to make use of the historical information appropriately. And then, this paper proposes an improved memory prediction model that uses the memory strategy to provide valuable information to the prediction model to predict the POS of the new environment more accurately. This memory prediction model is incorporated into a multiobjective evolutionary algorithm based on decomposition (MOEA/D). In particular, the resultant algorithm (MOEA/D-MP) adopts a sensor-based method to detect the environment change and find a similar one in history to reuse the information of it in the prediction process. The proposed algorithm is compared with several state-of-the-art dynamic multiobjective evolutionary algorithms (DMOEA) on six typical benchmark problems with different dynamic characteristics. Experimental results demonstrate that the proposed algorithm can effectively tackle DMOPs

Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System

The rapid growth of the ride-hailing industry has revolutionized urban transportation worldwide. Despite its benefits, equity concerns arise as underserved communities face limited accessibility to affordable ride-hailing services. A key issue in this context is the vehicle rebalancing problem, where idle vehicles are moved to areas with anticipated demand. Without equitable approaches in demand forecasting and rebalancing strategies, these practices can further deepen existing inequities. In the realm of ride-hailing, three main facets of fairness are recognized: algorithmic fairness, fairness to drivers, and fairness to riders. This paper focuses on enhancing both algorithmic and rider fairness through a novel vehicle rebalancing method. We introduce an approach that combines a Socio-Aware Spatial-Temporal Graph Convolutional Network (SA-STGCN) for refined demand prediction and a fairness-integrated Matching-Integrated Vehicle Rebalancing (MIVR) model for subsequent vehicle rebalancing. Our methodology is designed to reduce prediction discrepancies and ensure equitable service provision across diverse regions. The effectiveness of our system is evaluated using simulations based on real-world ride-hailing data. The results suggest that our proposed method enhances both accuracy and fairness in forecasting ride-hailing demand, ultimately resulting in more equitable vehicle rebalancing in subsequent operations. Specifically, the algorithm developed in this study effectively reduces the standard deviation and average customer wait times by 6.48% and 0.49%, respectively. This achievement signifies a beneficial outcome for ride-hailing platforms, striking a balance between operational efficiency and fairness.Comment: 31 pages, 6 figure

Handling Dynamic Multiobjective Optimization Environments via Layered Prediction and Subspace-Based Diversity Maintenance

Peer reviewedPostprin

Fairness-enhancing deep learning for ride-hailing demand prediction

Short-term demand forecasting for on-demand ride-hailing services is one of the fundamental issues in intelligent transportation systems. However, previous travel demand forecasting research predominantly focused on improving prediction accuracy, ignoring fairness issues such as systematic underestimations of travel demand in disadvantaged neighborhoods. This study investigates how to measure, evaluate, and enhance prediction fairness between disadvantaged and privileged communities in spatial-temporal demand forecasting of ride-hailing services. A two-pronged approach is taken to reduce the demand prediction bias. First, we develop a novel deep learning model architecture, named socially aware neural network (SA-Net), to integrate the socio-demographics and ridership information for fair demand prediction through an innovative socially-aware convolution operation. Second, we propose a bias-mitigation regularization method to mitigate the mean percentage prediction error gap between different groups. The experimental results, validated on the real-world Chicago Transportation Network Company (TNC) data, show that the de-biasing SA-Net can achieve better predictive performance in both prediction accuracy and fairness. Specifically, the SA-Net improves prediction accuracy for both the disadvantaged and privileged groups compared with the state-of-the-art models. When coupled with the bias mitigation regularization method, the de-biasing SA-Net effectively bridges the mean percentage prediction error gap between the disadvantaged and privileged groups, and also protects the disadvantaged regions against systematic underestimation of TNC demand. Our proposed de-biasing method can be adopted in many existing short-term travel demand estimation models, and can be utilized for various other spatial-temporal prediction tasks such as crime incidents predictions

Irx4-mediated regulation of Slit1 expression contributes to the definition of early axonal paths inside the retina

Although multiple axon guidance cues have been discovered in recent years, little is known about the mechanism by which the spatiotemporal expression patterns of the axon guidance cues are regulated in vertebrates. We report that a homeobox gene Irx4 is expressed in a pattern similar to that of Slit1 in the chicken retina. Overexpression of Irx4 led to specific downregulation of Slit1 expression, whereas inhibition of Irx4 activity by a dominant negative mutant led to induction of Slit1 expression, indicating that Irx4 is a crucial regulator of Slit1 expression in the retina. In addition, by examining axonal behavior in the retinas with overexpression of Irx4 and using several in vivo assays to test the effect of Slit1, we found that Slit1 acts positively to guide the retinal axons inside the optic fiber layer (OFL). We further show that the regulation of Slit1 expression by Irx4 is important for providing intermediate targets for retinal axons during their growth within the retina