A stochastic network with mobile users in heavy traffic

We consider a stochastic network with mobile users in a heavy-traffic regime. We derive the scaling limit of the multi-dimensional queue length process and prove a form of spatial state space collapse. The proof exploits a recent result by Lambert and Simatos which provides a general principle to establish scaling limits of regenerative processes based on the convergence of their excursions. We also prove weak convergence of the sequences of stationary joint queue length distributions and stationary sojourn times.Comment: Final version accepted for publication in Queueing Systems, Theory and Application

The surprising attractiveness of tearing mode locking in tokamaks

Tearing modes in tokamaks typically rotate while small and then lock at a fixed location when larger. Research on present-day devices has focused almost exclusively on stabilisation of rotating modes, as it has been considered imperative to avoid locked modes. However, in larger devices, such as those contemplated for tokamak reactors, the locking occurs at a smaller island size, and the island can be safely stabilised after locking. The stabilisation of small locked modes can be performed at lower wave power and broader deposition compared to rotating islands. On large devices, it thus becomes surprisingly advantageous to allow the mode to grow and lock naturally before stabilising it. Calculations indicate that the ITER international megaproject would be best stabilised through this approach.Comment: 6 pages, 4 figure

ANALISA KERUSAKAN JALAN DAN PENANGANANNYA DENGAN METODE PCI (PAVEMENT CONDITION INDEX) (Studi Kasus: Ruas Jalan Kauditan (by pass) – Airmadidi ; STA 0+770 – STA 3+770 )

Anggaran perbaikan kerusakan jalan yang dikeluarkan pemerintah pada tahun 2018 telah mencapai Rp 23,7 triliun untuk merehabilitasi jalan sepanjang 154.576 km untuk Sulawesi Utara Sendiri dialokasikan Rp. 651 miliar dengan jalan sepanjang 4.254 km. Jumlah ini menunjukkan banyaknya pengeluaran negara untuk menangani kerusakan jalan, oleh karena itu diperlukannya pemeriksaan kondisi kerusakan jalan untuk menentukan penanganan yang tepat di waktu yang tepat.Analisa kerusakan jalan sangat penting dilakukan demi tercapainya penanganan yang tepat, sehingga penggunaan anggaran dapat digunakan dengan efektif dan efisien. Metode Pavement Condition Index (PCI) di pilih untuk menjadi pedoman/acuan dalam menentukan kondisi perkerasan serta menentukan metode perbaikan tindakan yang akan di ambil pada jalan yang di tinjau. PCI adalah sistem penilaian kondisi perkerasan jalan berdasarkan jenis, tingkat dan luas kerusakan yang terjadi dan dapat digunakan sebagai acuan dalam usaha pemeliharaan. Nilai PCI ini memiliki rentang 0 sampai 100 dengan kriteria sempurna (excellent), sangat baik (very good), baik (good), sedang (fair), jelek (poor), sangat jelek (very poor) dan gagal (failed).Dalam penelitian ini ruas jalan yang akan di tinjau yaitu Ruas Jalan Kauditan (by pass) – Airmadidi ; STA 0+770 – STA 3+770, ruas jalan ini merupakan penghubung antara 2 kota besar yaitu Manado dan Bitung, dimana kota Bitung merupakan kota industri dan pelabuhan terbesar yang ada di Sulawesi Utara. Hal ini menjadikan ruas jalan Manado – Bitung harus memikul beban lalulintas yang besar.Penelitian ini dilakukan langsung secara visual dengan panjang ruas jalan yang diamati sepanjang 3km dan dibagi menjadi 60 segmen dengan ukuran persegmen 50 x 6. Untuk analisa beban yang diterima di ruas jalan yang diteliti, dilakukan survey lalulintas 12 jam selama 3 hari didapat 3.645.267,17 ESAL. Dari hasil penelitian didapat Nilai Index kondisi Perkerasan dengan menggunakan metode PCI pada tahun 2020 sebesar 76,7 (Sangat Baik) dengan beban ESA/Tahun kumulatif dari tahun terakhir dilakukan Overlay sebesar 22.155.288,47 ESAL. Kata kunci: Pavement Condition Index (PCI), beban lalulinta

Deep Learning of Sea Surface Temperature Patterns to Identify Ocean Extremes

We performed an out-of-distribution (OOD) analysis of ∼12,000,000 semi-independent 128 × 128 pixel2 sea surface temperature (SST) regions, which we define as cutouts, from all nighttime granules in the MODIS R2019 Level-2 public dataset to discover the most complex or extreme phenomena at the ocean’s surface. Our algorithm (ULMO) is a probabilistic autoencoder (PAE), which combines two deep learning modules: (1) an autoencoder, trained on ∼150,000 random cutouts from 2010, to represent any input cutout with a 512-dimensional latent vector akin to a (non-linear) Empirical Orthogonal Function (EOF) analysis; and (2) a normalizing flow, which maps the autoencoder’s latent space distribution onto an isotropic Gaussian manifold. From the latter, we calculated a log-likelihood (LL) value for each cutout and defined outlier cutouts to be those in the lowest 0.1% of the distribution. These exhibit large gradients and patterns characteristic of a highly dynamic ocean surface, and many are located within larger complexes whose unique dynamics warrant future analysis. Without guidance, ULMO consistently locates the outliers where the major western boundary currents separate from the continental margin. Prompted by these results, we began the process of exploring the fundamental patterns learned by ULMO thereby identifying several compelling examples. Future work may find that algorithms such as ULMO hold significant potential/promise to learn and derive other, not-yet-identified behaviors in the ocean from the many archives of satellite-derived SST fields. We see no impediment to applying them to other large remote-sensing datasets for ocean science (e.g., SSH and ocean color)