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The ant colony algorithm, a heuristic algorithm that mimics ants’ foraging, can optimize problem-solving. Applying this algorithm to the design of innovation and entrepreneurship projects in colleges and universities can enhance the effectiveness and feasibility of these projects. Based on an analysis of the college innovation and entrepreneurship project design process, the article explores the entrepreneurial path through the use of big data analysis. Then, it discusses the ant colony algorithm’s basic concept and mathematical model and proposes a two-population ant colony algorithm for multi-objective resource-constrained project scheduling problems. When compared to the traditional algorithm, the improved ant colony algorithm runs 25–33 ms faster, and Company A’s operating costs drop by 1,176,000 yuan from 2020 to 2023. This shows that the improved ant colony algorithm suggested in this paper can make university innovation and entrepreneurship programs more efficient at allocating resources.

Off-site research institutes serve as crucial platforms for universities to facilitate the translation and application of their scientific research outcomes. Additionally, these institutes act as vital intermediaries for local governments to harmonize scientific inputs with economic development and play a key role in the industrial transformation and elevation of regional science and innovation levels. Drawing on the triple helix theory, this paper outlines strategic planning for off-site research institutes and develops a multi-objective linear programming model aimed at optimizing resource allocation. This model focuses on enhancing both the efficiency of resource utilization and the efficiency of resource allocation at these institutes. To address the issue of local minima commonly encountered in optimization algorithms, this study employs a simulated annealing algorithm to refine the performance of the particle swarm optimization algorithm. The resulting hybrid algorithm termed the simulated annealing particle swarm algorithm, is applied to solve the proposed model and investigate the determinants of optimal resource allocation. The findings indicate a significant improvement in resource allocation efficiency, with the coefficient for heterogeneous research institutes decreasing from an average of 0.84 in 2020 to 0.68. This optimization has led to a more effective and rational distribution of resources, better meeting the needs of the institutes. Furthermore, the analysis reveals that financial support and talent introduction and development account for approximately 69.7% of the variance in the optimized development of resource allocation at these institutes. The study provides actionable insights that could guide the optimal development of off-site research institutes, offering valuable references for future applications.

Enhancing research efficiency by managing related knowledge and forming standardized applications has always been an effort to optimize the work of off-site research institutes, and knowledge sharing is one of the most important methods in many practices. In this paper, the entity matching algorithm is used to identify and match the nodes in the knowledge graph, the connection algorithm is used to calculate and sort the data volume of each node, and all the nodes in the knowledge graph are connected in series. After introducing the update mechanism to establish the decentralized global knowledge graph, the knowledge graph has been integrated into the knowledge-sharing system to enable knowledge sharing between off-site research institutes. In this paper, the accuracy of the full-text search for knowledge graphs can reach 87%, which meets the demand for knowledge sharing. The speed of researchers’ research plan formulation in heterogeneous research institutes has been accelerated by 85.40%, and the efficiency of the research institutes has significantly improved. At the same time, the institute’s innovation ability also increased significantly, with a relative increase of 57.21% in the average number of patents filed annually. This paper provides a reference path for the realization of knowledge sharing among heterogeneous research institutes. It establishes a foundation for significant improvements in the efficiency and innovation ability of research institutes.

Context Accurately distinguishing between benign thyroid nodules (BTNs) and papillary thyroid cancers (PTCs) with current conventional methods poses a significant challenge. Objective We identify DNA methylation markers of immune response–related genes for distinguishing BTNs and PTCs. Methods In this study, we analyzed a public reduced representative bisulfite sequencing dataset and revealed distinct methylation patterns associated with immune signals in PTCs and BTNs. Based on these findings, we developed a diagnostic classifier named the Methylation-based Immune Response Signature (MeIS), which was composed of 15 DNA methylation markers associated with immune response–related genes. We validated MeIS's performance in 2 independent cohorts: Z.S.'s retrospective cohort (50 PTC and 18 BTN surgery-leftover samples) and Z.S.'s preoperative cohort (31 PTC and 30 BTN fine-needle aspiration samples). Results The MeIS classifier demonstrated significant clinical promise, achieving areas under the curve of 0.96, 0.98, 0.89, and 0.90 in the training set, validation set, Z.S.'s retrospective cohort, and Z.S.'s preoperative cohort, respectively. For the cytologically indeterminate thyroid nodules, in Z.S.'s retrospective cohort, MeIS exhibited a sensitivity of 91% and a specificity of 82%; in Z.S.'s preoperative cohort, MeIS achieved a sensitivity of 84% and a specificity of 74%. Additionally, combining MeIS and BRAF V600E detection improved the detecting performance of cytologically indeterminate thyroid nodules, yielding sensitivities of 98% and 87%, and specificities of 82% and 74% in Z.S.'s retrospective cohort and Z.S.'s preoperative cohort, respectively. Conclusion The 15 markers we identified can be employed to improve the diagnostic of cytologically indeterminate thyroid nodules. [ABSTRACT FROM AUTHOR]

Particle Investigations at a Northern Ozarks Tower: NOx, Oxidant, Isoprene Research (PINOT NOIR) were conducted in a Missouri forest dominated by isoprene emissions from May to October 2012. This study presents results of new particle formation (NPF) and the growth of new particles to cloud condensation nuclei (CCN)-active sizes (∼100 nm) observed during this field campaign. The measured sub-5 nm particles were up to ∼20,000 cm-3 during a typical NPF event. Nucleation rates J1 were relatively high (11.0 ± 10.6 cm-3 s-1), and one order of magnitude higher than formation rates of 5 nm particles (J5). Sub-5 nm particle formation events were observed during 64% of measurement days, with a high preference in biogenic volatile organic compounds (BVOCs)- and SO2-poor northwesterly (90%) air masses than in BVOCs-rich southerly air masses (13%). About 80% of sub-5 nm particle events led to the further growth. While high temperatures and high aerosol loadings in the southerly air masses were not favorable for nucleation, high BVOCs in the southerly air masses facilitated the growth of new particles to CCN-active sizes. In overall, 0.4-9.4% of the sub-5 nm particles grew to CCN-active sizes within each single NPF event. During a regional NPF event period that took place consecutively over several days, concentrations of CCN size particles increased by a factor of 4.7 in average. This enhanced production of CCN particles from new particles was commonly observed during all 13 regional NPF events during the campaign period.