Our Work

What's Changed Bump actions/download-artifact from 7 to 8 by @dependabot[bot] in https://github.com/cirKITers/jaqsi/pull/2 GPU support by @stroblme in https://github.com/cirKITers/jaqsi/pull/4 Depend…

What's Changed Bump actions/download-artifact from 7 to 8 by @dependabot[bot] in https://github.com/cirKITers/jaqsi/pull/2 GPU support by @stroblme in https://github.com/cirKITers/jaqsi/pull/4 Dependency Cleanup by @stroblme in https://github.com/cirKITers/jaqsi/pull/5 Gate Fusion & Custom JVP by @stroblme in https://github.com/cirKITers/jaqsi/pull/10 Cache & tiling by @stroblme in https://github.com/cirKITers/jaqsi/pull/11 Commutable drive term by @stroblme in https://github.com/cirKITers/jaqsi/pull/12 Drag pulse by @stroblme in https://github.com/cirKITers/jaqsi/pull/13 Full Changelog: https://github.com/cirKITers/jaqsi/compare/0.1.0...0.1.1

# Reproducibility package This archive contains the R code and generated numerical outputs supportingthe manuscript: > Dinesh Kumar and Sudesh K. Srivastav,  > *Uniform-Spacing Contraction …

# Reproducibility package This archive contains the R code and generated numerical outputs supportingthe manuscript: > Dinesh Kumar and Sudesh K. Srivastav,  > *Uniform-Spacing Contraction Bounds for Randomized Interval Bracketing.* The manuscript studies randomized bracket-preserving interval updates forderivative-free scalar root finding. At each update, uniformly sampledinterior points induce an order-statistic spacing partition of the currentbracket. Under continuity, strict monotonicity, a valid initial sign-changingbracket, and exact sign evaluations, the selected interval is almost surelythe adjacent order-statistic spacing containing the root. The archive supports the manuscript's numerical experiments and resourceanalyses, including pooled and per-function summaries, evaluation-countaudits, finite-worker evaluation-wave calculations, manuscript tables, andfigures. ## Contents - `ric_bit_final.R`    The authoritative manuscript-matched program for the main numerical study.  It runs the exact-evaluation randomized interval-contraction simulations,  deterministic benchmark comparisons, pooled and per-function summaries,  evaluation-count audits, finite-worker wave calculations, and generation of  manuscript tables and figures. - `noisy_experiment.R`    Program for the manuscript's exploratory noisy-evaluation experiment. This  script supports the noisy-study result reported in Table 8. It is separate  from the main exact-evaluation program because observation noise can alter  signs and therefore falls outside the assumptions used in the formal  analysis. - `output_bit_final/`    Output directory generated by `ric_bit_final.R`. It contains raw simulation  paths, tolerance summaries, evaluation-count audits, CSV summaries,  generated LaTeX table rows, and PDF figures supporting the main  exact-sign monotone numerical study. - `output_noisy/`    Output directory generated by `noisy_experiment.R`. It contains  `noisy_experiment_raw_results.csv` and  `table_noisy_performance.csv`, which support the exploratory  results in Table 8. - `README.md`    Instructions for installing dependencies, running the programs, and  interpreting the outputs. - `CITATION.cff`    Machine-readable citation metadata for this reproducibility package. - `LICENSE`    The license governing reuse of the code and archive contents. ## Requirements The programs were written for R. The package requirements are declared nearthe beginning of each script. The main program requires: ```rdplyrtidyrpurrrggplot2readrgridExtra``` Install any missing R package with: ```rinstall.packages("package_name")``` For example: ```rinstall.packages("dplyr")``` ## Main reproduction Run the authoritative exact-evaluation program from the directory containing`ric_bit_final.R`: ```rsource("ric_bit_final.R")``` The script creates `output_bit_final/` and its subdirectories automatically.It uses fixed pseudo-random seeds to make the Monte Carlo simulationsreproducible. The main study evaluates randomized interval contraction for: ```textm = 2, 3, 5, 10, 20, 50``` across five continuous, strictly monotone test functions. The default designuses 1,000 replications per test function and value of \(m\), aninterval-width tolerance of \(10^{-8}\), and a maximum of 100 iterations. The script also produces reference results for bisection, secant iteration,and R's `uniroot` implementation. ## Main outputs The main script writes, among other files: ```textoutput_bit_final/raw/ric_all_paths.csvoutput_bit_final/raw/ric_tolerance_summaries.csvoutput_bit_final/raw/ric_evaluation_audit.csv output_bit_final/tables/ric_pooled_scaling_summary.csvoutput_bit_final/tables/ric_per_function_summary.csvoutput_bit_final/tables/ric_finite_worker_wave_table.csvoutput_bit_final/tables/table4_scaling_rows.texoutput_bit_final/tables/table5_wave_rows.texoutput_bit_final/tables/table6_rows.texoutput_bit_final/tables/table7_rows.texoutput_bit_final/tables/figure2_scaling_with_Bm_data.csv output_bit_final/figures/figure_scaling_m_pooled.pdfoutput_bit_final/figures/figure_scaling_m_pooled.pngoutput_bit_final/figures/figure_avg_interval_length.pdfoutput_bit_final/figures/figure_avg_root_error.pdfoutput_bit_final/figures/figure_efficiency_frontier.pdf``` The data underlying the pooled scaling figure include the empirical pooledcontraction estimate \(\widehat{\rho}_m\) and the maximal-spacing benchmark: \[B_m = \frac{H_{m+1}}{m+1},\] where \(H_{m+1}\) is the \((m+1)\)-st harmonic number. ## Evaluation-count convention For RIC-\(m\) runs, the two initial endpoint function values are evaluatedonce and cached. Each subsequent iteration evaluates exactly \(m\) newinterior points. The main program verifies, for every simulated path, theidentity: \[N_{\mathrm{feval}} = 2 + mN_{\mathrm{iter}}.\] The script stops with an error if this invariant fails. Midpoint residuals arenot separately evaluated in the main RIC simulations, so they do not inflatethe reported function-evaluation totals. Reported mean RIC evaluation countssatisfy the corresponding identity up to displayed rounding. ## Noisy experiment Run the exploratory noisy-evaluation study separately: ```rsource("noisy_experiment.R")``` This script writes results to `output_noisy/`. It is included to reproduce themanuscript's noisy-evaluation illustration, including Table 8 whereapplicable. ## Scope The theorem-backed analysis in the manuscript assumes: - a continuous, strictly monotone function;- a valid initial sign-changing bracket; and- exact sign evaluations. Accordingly, `ric_bit_final.R` is the authoritative reproduction program forthe main theorem-aligned numerical results. The noisy-evaluation experiment represented by `noisy_experiment.R` isexploratory: noise can change observed signs, and the formal exact-signtheory does not supply a root-preservation guarantee, a bracket-preservationguarantee, or a controlled head-to-head efficiency guarantee in that setting.

This dataset accompanies the manuscript "An operational river-stage forecasting system using spatiotemporal graph neural network (ST-GNN) in Ascension Parish, Louisiana", prepared for submission to Hy…

This dataset accompanies the manuscript "An operational river-stage forecasting system using spatiotemporal graph neural network (ST-GNN) in Ascension Parish, Louisiana", prepared for submission to Hydrology and Earth System Sciences (HESS). It provides the evaluation data for a 68-gauge forecasting network comprising 51 scored in-parish gauges and 17 supporting boundary gauges. The paired forecast and observed stage series cover 5,832 hourly forecast origins from 1 January to 31 August 2026, with 96 lead steps at 15-minute intervals over a 24-hour horizon. The evaluated ST-GNN, GRU, and LSTM use final weights fitted to eligible data through 31 December 2025 and frozen during the 2026 evaluation, with three training seeds (101, 202, and 303). Model columns contain the P80 forecasts scored in the study; persistence is included as a benchmark. The event-only subset comprises 927 origins when at least one parish pump was running at issue time. Three compressed CSV files provide the observed-rainfall hindcast, the retrospective simulated operational forecast using issue-time HRRR rainfall and operational postprocessing, and the ST-GNN rainfall-forcing comparison without postprocessing. The deposit also includes the gauge inventory; graph definitions for final fitting, chronological epoch selection, and gauge-network sensitivity; per-gauge evaluation metrics; full-precision inputs for paired bootstrap uncertainty analysis; archived rainfall forcings; and figure inputs. Directory contents are packaged in the corresponding RAR archives. README.md describes file schemas, scoring and aggregation rules, and the mapping to the manuscript results. FILE_MANIFEST.csv and public_source_manifest.csv document file integrity and public input sources. Stages are expressed in metres and the paired CSV values are rounded to 0.1 mm. Observed stages are masked for missing or stale gauge readings under the documented screening rule. The deposit supports evaluation reproducibility; the complete feature-engineered training matrix is not included. Internal Ascension Parish Government HEC-RAS terrain, simulated water-surface, and mesh files are not redistributed, although their derived graphs are included. Public hydrologic and meteorological inputs are documented by retrieval source; rainfall forecasts are included as used. The companion code and trained-weight record is on github https://github.com/awesomemfg/River_ST_GNN_Forecast. It contains the data preparation, graph construction, chronological training, inference, postprocessing, and evaluation workflow. This data record is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

This repository contains spatial data from the research paper "Climate refugia identification as high‐priority areas to improve ecosystem conservation in central Chile", published in Conservation Scie…

This repository contains spatial data from the research paper "Climate refugia identification as high‐priority areas to improve ecosystem conservation in central Chile", published in Conservation Science and Practice. File description: Climate_Refugia.rar = Climate refugia shapefile for the study area. This layer was built from prioritization using geodiversity, climate change velocity and biotic velocity criteria. The attribute table contains the climate refugia typology: headwater forests, riparian forests, forests and non-forest. Conservation_Feasibility_Index.rar = Conservation Feasibility Index shapefile for the study area. Values categorized from 0 to 5; 0 corresponds to the lowest priority and 5 to the highest. This layer was built using floristic and socioeconomic criteria to adress refugia potential for conversion to protected areas within the study area. Conservation_Opportunities.rar = Conservation opportunities shapefile for the study area. This layer was built by intersecting the Conservation Feasibility Index with a 50% cutoff value with climate refugia. The attribute table contains the conservation opportunities typology: headwater forests, riparian forests, forests and non-forest.  

This release includes all relevant codes to operate the StreamScope instrument.

Software and reproduction package accompanying the manuscript “pdicert: outcome-independent certification and selection for reproducible delivery of biomedical prediction scores.” The arch…

Software and reproduction package accompanying the manuscript “pdicert: outcome-independent certification and selection for reproducible delivery of biomedical prediction scores.” The archive contains Python source code, tests, locked configurations, retained predictions, reference results, provenance manifests, and documentation for outcome-independent prediction-delivery certification, minimum-byte certified text selection, and outcome-aware table–summary verification. Its default replay starts from retained predictions and checks 21 unit tests, 1,728 delivery cases, 13,824 checker decisions, 1,680 precision exports, and 48 selected representations; it does not refit the models. Full refitting requires separate downloads of the public GEO expression datasets documented in README.md; source expression matrices are not redistributed. The original software and accompanying original documentation are licensed under the MIT License by Chung-I Huang and Iris Yang. Third-party dependencies, datasets, and other materials retain their own terms.

NF Metadata Dictionary v12.0 Breaking Changes Removed deprecated ManifestationEnum values (#997) — any data annotated with those values will fail validation against this version. New Features Added…

NF Metadata Dictionary v12.0 Breaking Changes Removed deprecated ManifestationEnum values (#997) — any data annotated with those values will fail validation against this version. New Features Added PortalDatasetCandidate template for externally-indexed datasets (#999) Added KAPA EvoPrep Kit to LibraryPreparationMethodEnum Expanded and harmonized ManifestationEnum for dataset-level tumorType rollup (#979, #983) Added two new Synapse schemas: org.synapse.nf-portaldataset, org.synapse.nf-portaldatasetcandidate Internal / Infrastructure Refactored duplicated slot bundles into mixins (#928, #973) Scoped PR checks to changed files, sped up schema generation, unified CI report (#1009) Issue 954 follow-up fixes (#1006) Updated contributing docs, design docs, and development guides

The dynamics of digital transformation require vocational educational institutions, particularly office administration education programs, to continuously update their curricula to produce graduates w…

The dynamics of digital transformation require vocational educational institutions, particularly office administration education programs, to continuously update their curricula to produce graduates who possess both technological proficiency and high moral integrity. To support the achievement of the Sustainable Development Goals (SDGs), especially regarding quality education (SDG 4) and decent work and economic growth (SDG 8), this study analyzes the implementation of digital-based learning models and literacy enhancement aligned with Islamic work ethics. Employing a descriptive qualitative approach utilizing literature review methods, the findings indicate that the utilization of digital learning facilities significantly boosts students' technical competencies and work readiness (Handarini & Wulandari, 2020; Lasmini et al., 2023). Nevertheless, such technological mastery must be balanced with the internalization of prophetic values to ensure that professional ethics and character are maintained in the modern era (Yustika & Iswati, 2020). Overall, this study offers a framework for office administration learning development that is adaptive to technological innovation, oriented toward sustainable development, and firmly rooted in Islamic values. Keywords: Office Administration Education, Digital Literacy, Islamic Work Ethics,

Education is a process which revolves around teacher, student and curriculum where teacher has to play a very important role. The content and techniques of teaching depend to a great extent on what we…

Education is a process which revolves around teacher, student and curriculum where teacher has to play a very important role. The content and techniques of teaching depend to a great extent on what we want to achieve. With the changing scenario of education, it is supposed to find ways and means to improve the teaching techniques to cope with the need of the times. Technology benefited us in every aspect of life from communication to education. The new revolutionary programme in school education system “smart class” has changed the concept of learning. It is an innovative technology that has aimed to revolutionize the way of teaching learning process. Smart classroom learning scale by constructed and standardized by Senthilraja and Pandian (2021) The SPSS Program was used to analyze the data that were obtained from the study. Smart classroom schools have randomly selected by the investigator in the same district and the size of the sample is 300 standard IX and X rural school students from high schools.The result of the study showed, there is significant difference in smart classroom learning in rural students in terms of gender, and medium of instruction. Further the result of the study showed, there is no significant difference in smart classroom learning in rural students in terms of location of student, type of school, father’s education, mother’s education, parents occupation and type of family. 

Two-minute highlight video accompanying "Flood Exposure and Structure Damage under Deterministic Design-Storm and Stochastic Storm Transposition Rainfall Scenarios: A Case Study in South Louisiana, US…

Two-minute highlight video accompanying "Flood Exposure and Structure Damage under Deterministic Design-Storm and Stochastic Storm Transposition Rainfall Scenarios: A Case Study in South Louisiana, USA", accepted for publication in Natural Hazards and Earth System Sciences (NHESS) and selected as a highlight paper. Using figures from the study, the video explains how the assumption of spatially uniform design rainfall affects estimates of building flood exposure and structural damage in Louisiana’s Vermilion River Basin. It presents the comparison of NOAA Atlas 14 rainfall with 50 stochastic storm transposition (SST) realizations, the controlled modeling and depth–damage analysis, differences in inundation and spatial damage clusters, and the relationship between those differences and Height Above Nearest Drainage (HAND). This version replaces the earlier study video with a concise, figure-based highlight video. Duration: 2 min. Format: MP4, 1920 × 1080 pixels, with English audio narration. The analysis code and SST rainfall data are provided as a separate research output. This material is based upon work supported by the National Science Foundation under Award No. 2418434: “RII FEC: Research on Impacts of Hydroclimatic Extremes in Water-Rich Regions and Collaborative Adaptation Planning” (NSF EPSCoR). https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2418434 Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Interactive companion: https://melsaadani1.github.io/modules/nhess-2026/ Credits and rights: Video produced by Mohamed ElSaadani. Figures © ElSaadani, Habib and Morsy (2026), https://doi.org/10.5194/egusphere-2026-1388, used under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Cropping, zooming, highlights, captions and narration were added. CC BY 4.0 applies to the authors’ original content; third-party material and institutional logos retain their respective rights. Map credits (as credited in the source figures): Esri, NASA, NGA, USGS, CONANP, TomTom, Garmin, SafeGraph, FAO, METI, NOAA, EPA, NPS, USFWS, © OpenStreetMap contributors (https://www.openstreetmap.org/copyright), and the GIS User Community. NSF EPSCoR, HydroRISE and UL Lafayette Louisiana Watershed Flood Center logos identify funding and institutional support; their inclusion does not grant reuse rights. Video narration was generated using Microsoft Edge via edge-tts, available at https://github.com/rany2/edge-tts.