Spatial Correlation Analysis on the Resource Allocation Efficiency of Regional Higher Education in China

This analysis may promote the regional development of higher education, resolve problems associated with inadequate resources, and improve efficiency of resource allocation. In this study, we examined the efficiency of resource allocation for higher education in 31 Chinese provinces using data envelopment analysis (DEA); spatial correlation was used concurrently for analytical purposes. Our aims through this study are to promote the optimal allocation of resources and healthy development of higher education

Mostly Beneficial Clustering: Aggregating Data for Operational Decision Making

With increasingly volatile market conditions and rapid product innovations, operational decision-making for large-scale systems entails solving thousands of problems with limited data. Data aggregation is proposed to combine the data across problems to improve the decisions obtained by solving those problems individually. We propose a novel cluster-based Shrunken-SAA approach that can exploit the cluster structure among problems when implementing the data aggregation approaches. We prove that, as the number of problems grows, leveraging the given cluster structure among problems yields additional benefits over the data aggregation approaches that neglect such structure. When the cluster structure is unknown, we show that unveiling the cluster structure, even at the cost of a few data points, can be beneficial, especially when the distance between clusters of problems is substantial. Our proposed approach can be extended to general cost functions under mild conditions. When the number of problems gets large, the optimality gap of our proposed approach decreases exponentially in the distance between the clusters. We explore the performance of the proposed approach through the application of managing newsvendor systems via numerical experiments. We investigate the impacts of distance metrics between problem instances on the performance of the cluster-based Shrunken-SAA approach with synthetic data. We further validate our proposed approach with real data and highlight the advantages of cluster-based data aggregation, especially in the small-data large-scale regime, compared to the existing approaches

Physiological Characterization of Cut-to-Cut Yield Variations of Alfalfa Genotypes under Controlled Greenhouse Conditions

In a temperate region, alfalfa (Medicago sativa) crops are usually harvested 3-6 times per annum. The biomass yields of first and second cuts in the spring are generally the high-est. However, in subsequent cuts the biomass yields decline, with the final 1 or 2 cuts producing the lowest yields (Wang et al. 2009). This seasonal reduction in alfalfa biomass yields could be associated with prevailing changes in environmental factors such as rainfall and heat stress or due to biological characteristics of alfalfa crop itself. In this study, alfalfa was grown under controlled greenhouse conditions with suitable temperature, light, water and nutrient supply to determine the driving force in cut-to-cut biomass yield variations among alfalfa genotypes

Network Embedding-Based Anomalous Density Searching for Multi-Group Collaborative Fraudsters Detection in Social Media

Detecting collaborative fraudsters who manipulate opinions in social media is becoming extremely important in order to provide reliable information, in which, however, the diversity in different groups of collaborative fraudsters presents a significant challenge to existing collaborative fraudsters detection methods. These methods often detect collaborative fraudsters as the largest group of users who have the strongest relation with each other in the social media, consequently overlooking the other groups of fraudsters that are with strong user relation yet small group size. This paper introduces a novel network embedding-based framework NEST and its instance BEST to address this issue. NEST detects multiple groups of collaborative fraudsters by two steps. In the first step, to disclose user collaboration, it represents users according to their social relations. Then, in the second step, to identify the collaborative fraudsters, it detects the user groups with anomalous large group density in its representation space. BEST instantiates NEST by using a bipartite network embedding method to represent users and adopting a fast density group detection method based on the k-dimensional tree. Our experiments show BEST (i) performs significantly better in detecting fraudsters on four real-word social media data sets, and (ii) effectively detects multiple groups of collaborative fraudsters, compared to three state-of-the-art competitors

Penaeid shrimp genome provides insights into benthic adaptation and frequent molting

Crustacea, the subphylum of Arthropoda which dominates the aquatic environment, is of major importance in ecology and fisheries. Here we report the genome sequence of the Pacific white shrimp Litopenaeus vannamei, covering similar to 1.66 Gb (scaffold N50 605.56 Kb) with 25,596 protein-coding genes and a high proportion of simple sequence repeats (>23.93%). The expansion of genes related to vision and locomotion is probably central to its benthic adaptation. Frequent molting of the shrimp may be explained by an intensified ecdysone signal pathway through gene expansion and positive selection. As an important aquaculture organism, L. vannamei has been subjected to high selection pressure during the past 30 years of breeding, and this has had a considerable impact on its genome. Decoding the L. vannamei genome not only provides an insight into the genetic underpinnings of specific biological processes, but also provides valuable information for enhancing crustacean aquaculture

Managing price and service rate in customer-intensive services under social interactions

This paper investigates the price and service rate decisions in a customer-intensive service in an M/M/1 queue system under the influence of social interactions, where a higher value of the service is perceived if more customers purchase the service. The customer-intensive nature of the service requires a low service speed to maintain its quality, which may increase the congestion of the system. Two cases where customers are either homogeneous or heterogeneous in terms of the customer intensity are considered. It is found that social interactions can always benefit the service provider as more expected revenue can be achieved, and potential profits would be lost if the influence of social interactions is ignored. For the case with heterogeneous customers, the optimal price and service rate decisions are solved with or without considering social interaction effect. The study finds the proportions of high and low sensitive customers and the social interaction intensity are critical to the operational decisions and the market coverage strategies. These results offer a better understanding on the interplay between the quality-speed conundrum and the influence of social interactions in customers’ purchase behaviour in managing customer-intensive services

Condensed Representation Learning for Interactive Driving Styles Recognition

Automated vehicle (AV) validation faces the "billions of miles" challenge, requiring high-fidelity simulations to replicate diverse interactive driving behaviors for safety. Traditional methods oversimplify by using uniform behavioral models, ignoring the diversity of human driving styles, which are deeply influenced by individual psychological traits. This research introduces a condensed framework for representing interactive driving styles, by incorporating these psychological dimensions, balancing completeness and complexity. Key features include: i) individual style recognition via attention mechanisms and hierarchical contrastive learning, capturing subtle cognitive-based interaction patterns that reflect underlying differences in driver psychology (e.g., risk tolerance, decision-making heuristics); ii) scenario-independent style compression, filtering external factors to extract intrinsic driver intentions; iii) dimensionality-aware refinement, mapping complex behaviors to low-dimensional psychological axes for efficient computation. Tests on the NGSIM dataset reduced testing complexity by decoupling styles from scenarios. Compared to traditional methods, style distinctiveness improves by 28% (entropy-based), with 85% edge-case behavior coverage. This framework supports scalable AV testing by integrating diverse, psychologically-informed driving styles without combinatorial complexity