Assessing and Tracking Success of Advancing Age Inclusivity

Assessment is an important component of advancing age inclusivity on your campus, and the AFU Principles are a useful guiding framework. Assessment helps move the campus from making a commitment to endorse the principles to actually taking stock of current campus practices and movement toward achieving the vision of an age-friendly institution of higher education. To establish a baseline of campus practices, assessment can be done before or after an institution joins the AFU Global Network. Evaluation also follows periodically to assess how well a campus is adhering to the AFU Principles once measurable goals are established and priorities are integrated within an institution’s strategic plan. The toolkit contains examples from multiple campuses of mapping the principles, conducting an audit, doing a photovoice evaluation, holding listening tours, and using the newly developed AFU Inventory and Campus Climate Survey (ICCS).

Equipment Maintenance Support Effectiveness Evaluation Based on Improved Generative Adversarial Network and Radial Basis Function Network

Due to the lack of maintenance support samples, maintenance support effectiveness evaluation based on the deep neural network often faces the problem of small sample overfitting and low generalization ability. In this paper, a neural network evaluation model based on an improved generative adversarial network (GAN) and radial basis function (RBF) network is proposed to amplify maintenance support samples. It adds category constraint based on category probability vector reordering function to GAN loss function, avoids the simplification of generated sample categories, and enhances the quality of generated samples. It also designs a parameter initialization method based on parameter components equidistant variation for RBF network, which enhances the response of correct feature information and reduces the risk of training overfitting. The comparison results show that the mean square error (MSE) of the improved GAN-RBF model is 5.921 × 10 − 4 , which is approximately 1/2 of the RBF model, 1/3 of the Elman model, and 1/5 of the BP model, while its complexity remains at a reasonable level. Compared with traditional neural network evaluation methods, the improved GAN-RBF model has higher evaluation accuracy, better solves the problem of poor generalization ability caused by insufficient training samples, and can be more effectively applied to maintenance support effectiveness evaluation. At the same time, it also provides a good reference for evaluation research in other fields.

ПРИМЕНА RONET МОДЕЛА НА МРЕЖИ ДРЖАВНИХ ПУТЕВА РЕПУБЛИКЕ СРБИЈЕ

RONET (Road Network Evaluation Tools) модел представња управљачки алат, финансиран од стране SSАTP и Светске Банке. Помаже доносиоцима одлука јер процењује тренутно стање мреже, њен релативни значај за економију и прорачунава скуп индикатора праћења за процену перформанси путне мреже. Најбитнији су квалитетни подаци, које проверавају надлежне агенције за путеве. Они омогућавају већу тачност и прецизност система за управљање коловозом,као и стицање поверења у податаке и анализе. Резултати RONET модела показују важност сталне подршке иницијативама за одржавање путева. Улагање у путну инфраструктуру има развојни значај, јер подстиче привредне токове. Радови на одржавању проистичу из потребе да пут одговара намени због које је грађен, да омогући безбедно одвијање саобраћаја, транспорт људи и материјалних добара.