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opencc-by-4.0Jan 2023View details →

PERTEMUAN 13_05 JANUARY_2023_PPh Pasal 4 Ayat (2)&PPh Final Lainya-PERPAJAKAN _UWP_WIYUNG_JAM 08.00-09.30

Capaian Pembelajaran Mata Kuliah (CPMK) Mahasiswa Mampu menjelaskan siapa Pemungut dan dan teknis menghitung PPh Pasal 4 (ayat 2) dan PPh Final lainnya Indikator Pembelajaran PPh Pasal Pasaal 4 (ayat 2) dan PPh Final lainnya 1.Pemungut dan pihak yang dipungut PPh Pasal 4 (ayat)2 2.Objek Yang Dipotong PPh  3.Pasal 4 (ayat 2); 4.Tarif, Dasar Pengenaan Pajak dan Penghitungan PPh Pasal 4 (ayat 2); 5. Contoh dan Penerapannya

opencc-by-4.0Jan 2023View details →

PERTEMUAN 14_12 JANUARY 2023_pengertian dan dasar hukum PPN dan PPn BM_UNIVERSITAS WIJAYA PUTRA_WIYUNG JAM 8.00-9.30

Capaian Pembelajaran Mata Kuliah (CPMK) Mahasiswa Mampu menjelaskan pengertian dan dasar hukum PPN dan PPn BM; Objek, Pengusaha Kena Pajak; Indikator Pembelajaran Gambaran Umum Kebijakan PPN dan PPn BM <ol> <li>Pengertian dan Dasar Hukum;</li> <li>Objek, Pengusaha Kena Pajak;</li> <li>Bukan Objek PPN dan Bukan Pengusaha Kena Pajak;</li> <li>Tarif PPN dan PPn BM;</li> <li>Mekanisme Penghitungan PPN;</li> <li>Pencatatan dan Pembukuan;</li> <li>Saat dan Tempat Terutang</li> </ol>

opencc-by-4.0Jan 2023View details →

Causal Analysis of Google Code Jam Contest Data

This archive is the replication package for the paper: Carlo A. Furia, Richard Torkar, Robert Feldt: Towards Causal Analysis of Empirical Software Engineering Data — The Impact of Programming Languages on Coding Competitions. <a href="https://arxiv.org/abs/2301.07524">arXiv:2301.07524</a>. January 2023.

PERTEMUAN 6_02 FEBRUARY 2023_PPh WAJIB PAJAK_UNIVERSITAS WIJAYA PUTRA _PRIGEN_JAM 18-00-20.30

Capaian Pembelajaran Mata Kuliah (CPMK) Mahasiswa Mampu mengetahui PPh WP Badan dan Bentuk Usaha Tetap (BUT).   Indikator Pembelajaran Konsep PPh Wajib Pajak Badan : Mampu menguasai dan menjelaskan mengenai PPh WP Badan 1.Subjek PPh Wp Badan 2.Obyek PPh Badan dan BUT

PERTEMUAN PERTAMA_6 FEBRUARY 2023_ Penjelasan Rencana Pembelajaran Semester (RPS) Kontrak Kuliah Statistik_JAM 20.00-21.30

Capaian Pembelajaran Mata Kuliah ICPMK) Setelah mengikuti perkuliahan ini mahasiswa mampu mengaplikasikan statistika dalam memecahkan masalah, dan  menggunakan serta menerapkannya dalam pengolahan dan analisa data dalam penelitian kuantitatif dengan tepat.

BUKU DASAR DASAR STATISTIK PENELITIAN UNIVERSITAS GRESIK _JAM 20.00-21.30

Gambaran Umum Buku Dasar dasar Statistik Penelitian  SEJARAH STATISTIKA Ilmu statistika mempunyai sejarah yang sangat panjang seiring peradaban manusia. Pada zaman sebelum Masehi, bangsa-bangsa di Mesopotamia (Babilonia), Mesir, dan Cina telah mengumpulkan data statistic untuk memperoleh informasi tentang berapa besar pajak yang harus dibayar oleh setiap penduduk, beberapa banyak hasil pertanian yang mampu diproduksi, dan lain sebagainya. Pada abad pertengahan, lembaga gereja menggunakan statistika untuk mencatat jumlah kelahiran, kematian, dan pernikahan. Statistika pertama kali di temukan oleh Aristoteles  dalam bukunya yang berjudul “politea”, dalam buku tersebut ia menjelaskan data tentang keadaan 158 negara yang di sebut sebagai statistika. Pada abad ke-17 di Inggris, statistika di sebut sebagai political aritmatic. Pada abad ke-18, istilah statistika dipopulerkan oleh Sir John Sinclair dalam bukunya berjudul “statistical account of Scotland (17911799)”, setelah terlebih dahulu dikemukakan oleh seorang ahli hitung asal Jerman yang bernama Gottfried Achenwell (1719-1772).

PERTEMUAN 1_ 7 FEBRUARY 2023_PENJELASAN RPS, RPP, DAN KONTRAK KULIAH AKUNTANSI KEUANGAN LANJUTAN 2_UNIVERSITAS GRESIK JAM 18.00-20.00

Capaian Pembelajaran Mata Kuliah (CPMK) Setelah mengikuti perkuliahan ini mahasiswa mampu mengaplikasikan penyusunan laporan keuangan untuk Akuntansi Penggabungan Usaha baik secara marger maupun konsolidasi, akuisisi langsung / tidak langusung, menctat laba transaksi antar perusahan serta laporan keuangan perubahan kepemilikan dari berbagai macam organisasi bisnis

PERTEMUAN 1_07 FEBRUARY 2023_PENJELASAN RPS, RPP, KONTRAK KULIAH AKUNTANSI KEUANGAN MENENGAH 2_UNIVERSITAS GRESIK JAM 20.00-21.30

Capaian Pembelajarn Mata Kuliah (CPMK) Setelah mengikuti perkuliahan ini mahasiswa Mampu menyusun laporan keuangan sesuai kaidah pengakuan, pengukuran, penyajian dan pengungkapan dalam PSAK dan SAK ETAP terkini. Pendukung Mata Ajar Mampu menggunakan dengan konsep professional judgement dalam tatanan lokal maupun global dalam menyusun dan memeriksa laporan keuangan

opencc-zeroApr 2023View details →

dryad36/100

Mathematical model results for: Dynamic fibronectin assembly and remodeling by leader neural crest cells prevents jamming in collective cell migration

Collective cell migration plays an essential role in vertebrate development, yet the extent to which dynamically changing microenvironments influence this phenomenon remains unclear. Observations of the distribution of the extracellular matrix (ECM) component fibronectin during the migration of loosely connected neural crest cells (NCCs) lead us to hypothesize that NCC remodeling of an initially punctate ECM creates a scaffold for trailing cells, enabling them to form robust and coherent stream patterns. We evaluate this idea in a theoretical setting by developing an agent-based model that incorporates reciprocal interactions between NCCs and their ECM. ECM remodeling, haptotaxis, contact guidance, and cell-cell repulsion are sufficient for cells to establish streams in silico, however additional mechanisms, such as chemotaxis, are required to consistently guide cells along the correct target corridor. Further investigations of the model imply that contact guidance and differential cell-cell repulsion between leader and follower cells are key contributors to robust collective cell migration by preventing stream breakage. Global sensitivity analysis and simulated underexpression/overexpression experiments suggest that long-distance migration without jamming is most likely to occur when leading cells specialize in creating ECM fibers, and trailing cells specialize in responding to environmental cues by upregulating mechanisms such as contact guidance. This dataset contains summary statistics, movies, parameter values, and photos obtained from individual realizations of the mathematical model.

dryad36/100

Mathematical model results for: Dynamic fibronectin assembly and remodeling by leader neural crest cells prevents jamming in collective cell migration

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publicApr 2023View details →

dryad36/100

Data from: Wood jam characteristics influence but do not fully explain wood jam morphologic functions

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publicFeb 2025View details →

opencc-by-4.0Apr 2020View details →

IO Islamic 1877.Kitâb-almu'jam fî âthâr-i-mulûk al'ajam, History of the Kings of Persia

IO Islamic 1877.Kitâb-almu’jam fî âthâr-i-mulûk al’ajam, History of the Kings of Persia

opencc-by-4.0Dec 2023View details →

Directed percolation and puff jamming near the transition to pipe turbulence

The onset of turbulence in pipe flow has defied detailed understanding ever since Reynolds' first observations revealed the spatially-heterogeneous nature of the transition. While recent theoretical studies and experiments in simpler, shear-driven flows suggest that the onset of turbulence is a directed percolation non-equilibrium phase transition, whether these findings are generic and apply also to open or pressure-driven flows is unknown. In pipe flow, the extremely long time scales near the transition make direct observations of critical behavior virtually impossible. Here, we circumvent these limitations by experimentally characterizing all pairwise interactions between localized patches of turbulence ("puffs"), and using these interactions as input to renormalization group and computer simulations of minimal models that extrapolate to long length and time scales. We show that the universality class of the transition is directed percolation, from which emerges a jammed phase of puffs above the critical point. The stronger interactions in the jamming regime enable us to explicitly measure the turbulent fraction and confirm model predictions. Our work shows that directed percolation scaling applies beyond simple closed shear flows, and underscores how statistical mechanics can lead to profound, quantitative and predictive insights on turbulent flows and their phases.

opencc-by-4.0Feb 2022View details →

Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning Dataset

Data files were used in support of the research paper titled “Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning" which has been submitted to the IET Communications journal. --------------------------------------------------------------------------------------------- All data was collected using the SDR implementation shown here: https://github.com/mainland/dragonradio/tree/iet-paper. Particularly for antenna state selection, the files developed for this paper are located in 'dragonradio/scripts/:' <ul> <li>'ModeSelect.py': class used to defined the antenna state selection algorithm</li> <li>'standalone-radio.py': SDR implementation for normal radio operation with reconfigurable antenna</li> <li>'standalone-radio-tuning.py': SDR implementation for hyperparameter tunning</li> <li>'standalone-radio-onmi.py': SDR implementation for omnidirectional mode only</li> </ul> --------------------------------------------------------------------------------------------- Authors: Marko Jacovic, Xaime Rivas Rey, Geoffrey Mainland, Kapil R. Dandekar Contact: krd26@drexel.edu --------------------------------------------------------------------------------------------- Top-level directories and content will be described below. Detailed descriptions of experiments performed are provided in the paper. --------------------------------------------------------------------------------------------- classifier_training: files used for training classifiers that are integrated into SDR platform <ul> <li>'logs-8-18' directory contains OTA SDR collected log files for each jammer type and under normal operation (including congested and weaklink states)</li> <li>'classTrain.py' is the main parser for training the classifiers</li> <li>'trainedClassifiers' contains the output classifiers generated by 'classTrain.py'</li> </ul> post_processing_classifier: contains logs of online classifier outputs and processing script <ul> <li>'class' directory contains .csv logs of each RTE and OTA experiment for each jamming and operation scenario</li> <li>'classProcess.py' parses the log files and provides classification report and confusion matrix for each multi-class and binary classifiers for each observed scenario - found in 'results->classifier_performance'</li> </ul> post_processing_mgen: contains MGEN receiver logs and parser <ul> <li>'configs' contains JSON files to be used with parser for each experiment</li> <li>'mgenLogs' contains MGEN receiver logs for each OTA and RTE experiment described. Within each experiment logs are separated by 'mit' for mitigation used, 'nj' for no jammer, and 'noMit' for no mitigation technique used. File names take the form *_cj_* for constant jammer, *_pj_* for periodic jammer, *_rj_* for reactive jammer, and *_nj_* for no jammer. Performance figures are found in 'results->mitigation_performance'</li> </ul> ray_tracing_emulation: contains files related to Drexel area, Art Museum, and UAV Drexel area validation RTE studies. <ul> <li>Directory contains detailed 'readme.txt' for understanding.</li> <li>Please note: the processing files and data logs present in 'validation' folder were developed by Wolfe et al. and should be cited as such, unless explicitly stated differently.  <ul> <li>S. Wolfe, S. Begashaw, Y. Liu and K. R. Dandekar, "Adaptive Link Optimization for 802.11 UAV Uplink Using a Reconfigurable Antenna," MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM), 2018, pp. 1-6, doi: 10.1109/MILCOM.2018.8599696.</li> </ul> </li> </ul> results: contains results obtained from study <ul> <li>'classifier_performance' contains .txt files summarizing binary and multi-class performance of online SDR system. Files obtained using 'post_processing_classifier.'</li> <li>'mitigation_performance' contains figures generated by 'post_processing_mgen.'</li> <li>'validation' contains RTE and OTA performance comparison obtained by 'ray_tracing_emulation->validation->matlab->outdoor_hover_plots.m'</li> </ul> tuning_parameter_study: contains the OTA log files for antenna state selection hyperparameter study <ul> <li>'dataCollect' contains a folder for each jammer considered in the study, and inside each folder there is a CSV file corresponding to a different configuration of the learning parameters of the reconfigurable antenna. The configuration selected was the one that performed the best across all these experiments and is described in the paper.</li> <li>'data_summary.txt'this file contains the summaries from all the CSV files for convenience.</li> </ul>

opencc-by-nc-sa-2.0May 2021View details →

Jam Roll Bay

Jam Roll Bay, South of Te Kawau Pa, North Taranaki, New Zealand. Exposed in the cliffs are 10 million year old rocks from the Late Miocene aged Mount Messenger Formation. These sedimentary rocks formed on an ancient sea floor where they originally laid flat. We can see the rocks and been bent and folded here, this happended to the rocks when they were part of an enourmous submarine landslide. This has left the rocks deformed in a variety of interesting ways. Source: Objaverse 1.0 / Sketchfab

restrictedcc-by-4.0May 2024View details →

Memory of shear flow in soft jammed materials

Dataset contains flow curve, stress relaxation and residual stress of experimental system (2w% Carbopol (974p (Lubrizol)) in propylene glycol). Simulation dataset contains flow curves, stress relaxation, residual stress, nematic order parameter, mean squared displacement, spatial correlations of displacement fluctuation, fraction of icosahedrons for different shear rates.  Data description is in "Memory of shear flow in soft jammed materials" arXiv preprint arXiv:2312.00251 (2023)