The Modified Trapezoidal Rule for Computing Hypersingular Integral on Interval

The modified trapezoidal rule for the computation of hypersingular integrals in boundary element methods is discussed. When the special function of the error functional equals zero, the convergence rate is one order higher than the general case. A new quadrature rule is presented and the asymptotic expansion of error function is obtained. Based on the error expansion, not only do we obtain a high order of accuracy, but also a posteriori error estimate is conveniently derived. Some numerical results are also reported to confirm the theoretical results and show the efficiency of the algorithms

Strategies for Talent’s Digital Competence Development at Higher Vocational Colleges for Digital Transformation

Digital transformation has brought unprecedented opportunities and challenges to economic and social development, and the development of talent’s digital competence is of growing importance. Under an ideal situation, digitization, digitalization and digital transformation are three stages of gradual digital development. However, influenced by the difference in social and economic development among regions, higher vocational colleges, as an educational system closest to the labor market, vary by their own strengths and digital competence. From the perspective of game theory and based on the model of the boxed pig game, this paper provides strategies and suggestions for the cultivation of talent’s digital competence in terms of non-cooperative strategy at higher vocational colleges. The supply-demand model of talent’s digital competence cultivation is established to provide further suggestions for the top-down cooperation strategy among higher vocational colleges

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

Prediction of complex super-secondary structure βαβ motifs based on combined features

AbstractPrediction of a complex super-secondary structure is a key step in the study of tertiary structures of proteins. The strand-loop-helix-loop-strand (βαβ) motif is an important complex super-secondary structure in proteins. Many functional sites and active sites often occur in polypeptides of βαβ motifs. Therefore, the accurate prediction of βαβ motifs is very important to recognizing protein tertiary structure and the study of protein function. In this study, the βαβ motif dataset was first constructed using the DSSP package. A statistical analysis was then performed on βαβ motifs and non-βαβ motifs. The target motif was selected, and the length of the loop-α-loop varies from 10 to 26 amino acids. The ideal fixed-length pattern comprised 32 amino acids. A Support Vector Machine algorithm was developed for predicting βαβ motifs by using the sequence information, the predicted structure and function information to express the sequence feature. The overall predictive accuracy of 5-fold cross-validation and independent test was 81.7% and 76.7%, respectively. The Matthew’s correlation coefficient of the 5-fold cross-validation and independent test are 0.63 and 0.53, respectively. Results demonstrate that the proposed method is an effective approach for predicting βαβ motifs and can be used for structure and function studies of proteins

A Rough VIKOR-Based QFD for Prioritizing Design Attributes of Product-Related Service

Many manufacturers today are striving to offer high value-added product-related services (PRS) due to increasing competition and environmental pressure. PRS can reduce the negative impact on the environment, because it extends the life of products and minimizes the cost. Product and service planning has been considered as the critical factor to the success of PRS. Quality function deployment (QFD) has been recognized as an efficient planning tool which can convert customer needs (CNs) into design attributes of PRS involving product attributes (PAs) and service attributes (SAs). However, the subjective and vague information in the design of PRS with QFD may lead to inaccurate priority of PAs and SAs. To solve this problem, a novel rough VIKOR- (VIseKriterijumska Optimizaciji I Kompromisno Resenje-) based QFD is proposed. The proposed approach integrates the strength of rough number (RN) in manipulating vague concepts with less a priori information and the merit of VIKOR in structuring framework of compromise decision-making. Finally, an application in compressor-based service design is presented to illustrate the potential of the proposed method

Harnessing Context for Budget-Limited Crowdsensing with Massive Uncertain Workers

Crowdsensing is an emerging paradigm of ubiquitous sensing, through which a crowd of workers are recruited to perform sensing tasks collaboratively. Although it has stimulated many applications, an open fundamental problem is how to select among a massive number of workers to perform a given sensing task under a limited budget. Nevertheless, due to the proliferation of smart devices equipped with various sensors, it is very difficult to profile the workers in terms of sensing ability. Although the uncertainties of the workers can be addressed by standard Combinatorial Multi-Armed Bandit (CMAB) framework through a trade-off between exploration and exploitation, we do not have sufficient allowance to directly explore and exploit the workers under the limited budget. Furthermore, since the sensor devices usually have quite limited resources, the workers may have bounded capabilities to perform the sensing task for only few times, which further restricts our opportunities to learn the uncertainty. To address the above issues, we propose a Context-Aware Worker Selection (CAWS) algorithm in this paper. By leveraging the correlation between the context information of the workers and their sensing abilities, CAWS aims at maximizing the expected total sensing revenue efficiently with both budget constraint and capacity constraints respected, even when the number of the uncertain workers are massive. The efficacy of CAWS can be verified by rigorous theoretical analysis and extensive experiments

Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity

Spatial crowdsourcing (SC) engages large worker pools for location-based tasks, attracting growing research interest. However, prior SC task allocation approaches exhibit limitations in computational efficiency, balanced matching, and participation incentives. To address these challenges, we propose a graph-based allocation framework optimized for massive heterogeneous spatial data. The framework first clusters similar tasks and workers separately to reduce allocation scale. Next, it constructs novel non-crossing graph structures to model balanced adjacencies between unevenly distributed tasks and workers. Based on the graphs, a bidirectional worker-task matching scheme is designed to produce allocations optimized for mutual interests. Extensive experiments on real-world datasets analyze the performance under various parameter settings

Different plant species drive the microbial methane and nitrogen cycles and CH4 and N2O emissions on coasts affected by brackish water

Peer reviewedPublisher PD