This study aims to use IGA (Improved Genetic Algorithm) to optimize the design and construction of a personalized practical teaching platform for school enterprise integration. This platform aims to meet the personalized learning needs of different students and effectively connect with the needs of enterprises. We first introduced the basic principles of genetic algorithms and their applications in optimization problems. Then, we described in detail the modeling of personalized practical teaching needs as a multi-objective optimization problem, including student satisfaction, education quality, enterprise needs, etc. Next, we proposed an application method for IGA to search for the optimal platform design. The experimental results show that the resource scheduling efficiency using IGA can more effectively meet the practical needs of students, with a resource scheduling efficiency of about 86%. Compared to traditional methods, the resource scheduling efficiency has reached 70%. IGA has improved resource utilization efficiency and increased student satisfaction. This indicates that IGA has potential in resource scheduling and schedule optimization in school enterprise integrated education, which can help provide a better learning experience. The results of this study are of great significance for promoting school enterprise cooperation, improving education quality, and cultivating graduates with practical skills.

Construction of Personalized Practice Teaching Platform for School-Enterprise Integration Based on Improved Genetic Algorithm

OBJECTIVE
To assess the probability of being cured from pancreatic ductal adenocarcinoma (PDAC) by pancreatic surgery.


SUMMARY BACKGROUND DATA
Statistical cure implies that a patient treated for a specific disease will have the same life expectancy as if he/she never had that disease.


METHODS
Patients who underwent pancreatic resection for PDAC between 2010 and 2021 were retrospectively identified using a multi-institutional database. A non-mixture statistical cure model was applied to compare disease-free survival to the survival expected for matched general population.


RESULTS
Among 2554 patients, either in the setting of upfront (n=1691) or neoadjuvant strategy (n=863), the cure model showed that the probability that surgery would offer the same life-expectancy (and tumor-free) as the matched general population was 20.4% (95%CI: 18.3, 22.5). Cure likelihood reached the 95% of certainty (time-to-cure) after 5.3 years (95%CI: 4.7, 6.0). A preoperative model was developed based on tumor stage at diagnosis (P=0.001), radiological size (P=0.001), response to chemotherapy (P=0.007), American Society of Anesthesiology class (P=0.001) and pre-operative Ca19-9 (P=0.001). A post-operative model with the addition of surgery type (P=0.015), pathological size (P=0.001), tumour grading (P=0.001), resection margin (P=0.001), positive lymphnode ratio (P=0.001) and the receipt of adjuvant therapy (P=0.001) was also developed.


CONCLUSIONS
Patients operated for PDAC can achieve a life-expectancy similar to that of general population and the likelihood of cure increases with the passage of recurrence-free time. An online calculator was developed and available at https://aicep.website/?cff-form=15.

Cure Probabilities After Resection Of Pancreatic Ductal Adenocarcinoma: A Multi-Institutional Analysis Of 2554 Patients.

A dynamic analysis of empirical survival outcomes after pancreatectomy for pancreatic ductal adenocarcinoma.

Genetic algorithms (GAs) are a type of computational optimization algorithms that emulate natural selection to "evolve" candidate solutions to a given problem.The GENETIS collaboration applies GAs to experimental design to efficiently optimize for improved performance.To this end, GENETIS has begun by designing GAs for the evolution of vertically polarized (VPol) antennas used in ultra-high energy (UHE) neutrino observatories.Due to the low flux of UHE neutrinos, as well as their small cross sections, it is essential to maximize the sensitivity of neutrino observatories at every step of the experiment.Neutrino observatories make use of radio signals produced by UHE neutrino interactions by observing large volumes of ice.The Askaryan Radio Array (ARA) achieves this by distributing stations of antennas across vast areas near the South Pole.Experiments like ARA measure their expected performance using simulation software that incorporates properties of the experiment and the physics of neutrino interactions in ice.The Physical Antenna Evolutionary Algorithm (PAEA) evolves the geometric properties of antennas within the physical constraints of specific UHE neutrino experiments and simulates their responses using EM simulation software XFdtd.To measure the performance of antenna designs, PAEA uses neutrino observatories' simulation software with the evolved antennas' responses included.This proceeding will discuss GENETIS' evolution of VPol antenna designs for ARA and upcoming work on evolving more antenna designs for ARA and the Payload for Ultrahigh Energy Observations (PUEO).New efforts to capitalize on the birefringent properties of Antarctic ice to evolve experimental design will also be discussed. 

Using Genetic Algorithms to Optimize Antenna Designs for Improved Sensitivity to Ultra-High Energy Neutrinos

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A dynamic analysis of empirical survival outcomes after pancreatectomy for pancreatic ductal adenocarcinoma.

Using Genetic Algorithms to Optimize Antenna Designs for Improved Sensitivity to Ultra-High Energy Neutrinos