SafeCodeBench is a reproducible longitudinal benchmark for evaluating how software security changes across iterative AI-assisted development. It defines 60 base applications across six categories and five staged evolution steps: initial implementation, persistence/data, authentication, integration, and privilege/admin expansion. The full design supports three LLM families and three independent generations per stage, corresponding to 2,700 artifacts and 2,160 revision transitions. The archive provides machine-readable task specifications, standardized evolution prompts, artifact and vulnerability schemas, experiment configuration, SDI computation code, prediction-evaluation scaffolding, dynamic-testing guidance, and a two-reviewer manual-validation protocol. Version 1.0 contains the benchmark/test-bench design and analysis infrastructure; it does not present synthetic calibration measurements as empirical LLM findings.
Halil Dursunoglu· Zenodo (CERN European Organi...· 0 citations
Learning fractions remains a significant challenge in elementary education, often hindered by specific learning obstacles. This study aims to develop GeoGebra-assisted teaching materials integrated with a Hypothetical Learning Trajectory (HLT) to bridge the gap between these obstacles and conceptual understanding. Adopting an Educational Design Research (EDR) approach, the study progressed through three iterative phases: analysis and exploration, design and construction, and evaluation and reflection. The development was grounded in an initial pedagogical analysis of student learning obstacles, which informed the HLT design. Data were collected through expert validation, teacher interviews, and field testing with 161 elementary students (divided into small-scale and large-scale trials), then analyzed qualitatively using retrospective analysis and constant comparative methods.The quantitative results indicate that the GeoGebra-based materials are highly valid (92.75% from media experts) and practical (81.75% in large-scale trials), effectively transforming the HLT into a verified learning path. However, qualitative findings revealed a critical nuance: while students engaged enthusiastically with dynamic visualizations, some faced challenges in transitioning from visual area models to formal symbolic notation. This underscores the necessity of teacher-led instrumental orchestration to scaffold mathematical generalizations. This research concludes that the synergy between dynamic geometry software and a structured learning trajectory provides a robust framework for overcoming cognitive barriers, provided it is integrated within a dialogic instructional environment.
Andhin Dyas Fitriani, - Lea Christina Br Ginting, Harsa Wara Prabawa· Journal of Mathematics Instr...· 0 citations
This paper explores the application of Deep Reinforcement Learning (DRL) to automate software testing. Traditional software testing methodologies heavily rely on predefined rules and templates, often proving inadequate against the complexity and variability of modern software systems. This research proposes a novel approach leveraging DRL to intelligently generate test cases and execute test procedures automatically. The core mechanism involves training a deep reinforcement learning agent to learn optimal testing strategies and methods. The agent learns through trial and error, maximizing reward based on test execution outcomes. We present a framework for implementing this approach, focusing on the challenges and potential benefits of automating the testing process with a learning agent. The results suggest that DRL can significantly improve the efficiency and effectiveness of software testing, particularly in scenarios with high complexity and evolving requirements. This work contributes to the growing field of intelligent software testing and offers a promising avenue for reducing testing costs and improving software quality.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Code and dataset for DeepThermo-Meta-assisted inverse design of dynamic meta-regulators Description System requirements Dependencies Python 3.11 PyTorch 2.5.1+cu121 torchvision 0.20.1+cu121 Pydantic >= 2.6, < 3 Loguru >= 0.7, < 1 FastAPI >= 0.115, < 1 Uvicorn >= 0.34, < 1 Ollama Python package >= 0.5, < 1 Tested environments DeepThermo-Meta Agent: Windows, Python 3.11, CPU devices Ollama with the qwen3.6 local model tag (optional; model not included in this archive) Inverse-design computation: WSL2 Ubuntu 20.04, Python 3.11, PyTorch 2.5.1+cu121, CUDA 12.1, NVIDIA GeForce RTX 4070 Ti MATLAB: MATLAB (version compatible with RETICOLO V10) - RETICOLO V10 (third-party MATLAB package for RCWA; required for patterned-structure optical simulations and not included in this archive) Other software CST Studio Suite 2025 and a compatible MATLAB environment for joint simulation and validation screening Installation Miniconda conda create -n deepthermo-meta python=3.11 conda activate deepthermo-meta python -m pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu121 DeepThermo-Meta Agent installation: cd agent python -m pip install -r requirements.txt DeepThermo-Meta Agent operation Path: agent 1. Start the Web Interface python -m uvicorn app.webapp.backend.main:app --host 127.0.0.1 --port 8000 Open http://127.0.0.1:8000 in a Web browser 2. Environment Selection - Selects Arbitrary spectral selection, Temperature resistance, Mechanical flexibility, Corrosion resistance, or Space survivability before entering the design request - Locks the corresponding material preset for an environment-constrained request - Uses preset 3 (colorful) for DMR-1 to DMR-12 under Arbitrary spectral selection when no material is specified 3. Requirement Analysis and Clarification - Parses the natural-language request and extracts the visible and infrared spectral objectives - Requests only the unresolved spectral information and accepts free-form user answers 4. DMR and Material Mapping - Maps the confirmed spectral requirement to one of twelve DMR tasks - Confirms the environment-authorized or project-approved material preset 5. Downstream Input - Generates a structured DMR and material-preset contract for the inverse-design backend - The publication Web interface reports the resolved contract without automatically starting the scientific backend Command-line demonstration (optional) python -m app.interactive_clarify_main "Use DMR-10." --environment arbitrary To enable the optional local LLM, set PUBLICATION_USE_OLLAMA=true and OLLAMA_MODEL=qwen3.6 before running the Agent. Instructions and demo Dataset collection (~15 mins per model) 1. Generate Turing Pattern Database Run TULINpattern.m in MATLAB 2. DMR Generation and Spectral Analysis Run DMR_runing.m in MATLAB - Generates DMR structure modes - Obtains IR spectral responses 3. Optical Analysis Run RCWA_optical_simulation.m in MATLAB with RETICOLO V10 installed. - Calculates reflectivity and transmittance of patterned DMR structures 4. Data Integration and Preprocessing Run data_integrate.m in MATLAB - Integrates DMR results - Normalizes spectral data - Exports processed data for further analysis Dimensional reduction (~1 hour) Path: Dimensional reduction 1. Spectral Data Dimensionality Reduction Run AE_DR.py in Python - Compresses high-dimensional IR spectral data into a low-dimensional latent representation 2. Graphic Parameter Data Dimensionality Reduction Run VAE-main2.py in Python - Compresses high-dimensional DMR graphic parameters into a low-dimensional latent vector that follows a probabilistic distribution This combined latent representation serves as the input for subsequent MCGAN training and generation. Prediction (~1 hour) Path: Prediction Run Simulator_VIS_ref.py - Trains and saves the visible reflectivity prediction module. Run Simulator_VIS_trans.py - Trains and saves the visible transmittance prediction module. Run Simulator_IR.py - Trains and saves the IR emissivity prediction module. Inverse design (~8 hours) Path: MCGAN Run General_GAN_con_VIS_IR.py - Loads the concatenated low-dimensional data and pre-trained prediction modules (Simulators) - Trains the Multi-Conditional Generative Adversarial Network (MCGAN) - Uses TensorBoard to monitor training progress - Saves the trained generator and discriminator models Run General_GAN_con_VIS_IR_test.py - Loads the trained generator model - Performs inverse design for specific targets (DMR-1 to DMR-12) - Outputs the designed DMR parameters and their predicted optical performance Simulation and Validation Path: Validation Run the supplied MATLAB validation scripts - Screens inverse-designed DMR candidates - Performs RCWA optical analysis of patterned DMR structures - Uses CST Studio Suite for high-fidelity electromagnetic validation when the licensed local environment is configured
Shaowen Chen, Yuqian Wang, Haotian Han et al.· Zenodo (CERN European Organi...· 0 citations
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PyFgsea 0.2.0 Release date: 2026-09-02. PyFgsea 0.2.0 updates the statistical core used by the high-level Python API and narrows the trajectory interface to functions that are implemented and tested. The paper remains reproducible with its original software pair; current comparisons use a separate, newer reference pair. Highlights mode="aligned" and score_type="std" are the high-level defaults. Aligned mode uses exact pathway sizes (bin_width=0). Gene-ID ordering makes tied ranks deterministic. Unresolved and failed pathways remain visible in the result table. Results include tail-depth, error, termination, approximation, pathway-size, ranking, and algorithm-revision fields. The supported trajectory API is run_trajectory_gsea; unfinished public helpers and the incomplete trajectory CLI have been removed. DPT computation requires an explicit root and preserves root identity when cells are filtered or subsetted. The low-level build_tail_curve and query_tail_curve helpers remain available but are deprecated. They implement the approximate legacy-empirical-tail-v1 method, not the aligned estimator. Reference comparisons The two reference lanes answer different questions and should not be pooled: | Lane | PyFgsea | R | Bioconductor | fgsea | Purpose | | --- | --- | --- | --- | --- | --- | | publication | 0.1.4 | 4.4.3 | 3.20 | 1.32.2 | reproduce the comparison reported in the paper | | current | 0.2.0 | 4.6.0 | 3.23 | 1.38.0 | evaluate the current implementation | On the 100-pathway publication input, the publication lane reproduced the reported values after rounding: ES RMSE 3.84e-16; NES Pearson 0.999967, Spearman 0.999676, and RMSE 0.01333; transformed-p Pearson 0.997199, Spearman 0.988318, and RMSE 0.16960. On the same input, the current lane gave ES RMSE 3.21e-16; NES Pearson 0.999952, Spearman 0.999652, and RMSE 0.01567; transformed-p Pearson 0.999069, Spearman 0.996628, and RMSE 0.11826. A separate 60-pathway tie-heavy case exposes a 0.1.4 limitation. Its legacy NES RMSE was 0.29868; the current implementation reduced it to 0.01558 while restoring machine-precision ES agreement. This sensitivity case was not the publication input. Across 20 fixed score-permutation replicates, the current installed package returned all 2,000 planned null pathways. The pooled mean and median nominal p-values were 0.4991 and 0.5010; the proportions below 0.01, 0.05, and 0.10 were 0.0065, 0.0430, and 0.0935. These are descriptive summaries, not a fitted acceptance threshold. The original speed and memory values are not carried forward because the current run did not measure all tools under one matched scope. Figure 2 scope The accepted trajectory parameters are window_size=500, step=50, nperm_nes=2000, score_type="std", exact pathway sizes, bin_width=0, and NES caching off. The reassembled figure uses a processed 3,576-cell erythroid subset from GSE155254 with precomputed UMAP coordinates and DPT. Panel D ranks genes from scaled adata.X; the frozen object has no separate raw or named log1p layer. Public assignment tables match 2,097 control and 816 gata307mut cells, while 663 cells remain unmatched. The figure pools these groups and is descriptive, not a control-versus-mutant comparison. Benjamini-Hochberg adjustment is performed across pathways within each window and is not trajectory-wide error control. Migration from 0.1.4 The defaults intentionally differ: | Setting | 0.1.4 | 0.2.0 | | --- | --- | --- | | high-level score type | two_sided_abs | std | | estimator | implicit legacy behavior | mode="aligned" | | pathway-size binning | None | 0 (exact size) | | trajectory step | 100 | 50 | | trajectory NES permutations | 100 | 2000 | | trajectory NES cache | on | off | For new analyses, state the numerical contract explicitly: result = pyfgsea.run_gsea( data=ranks, gmt=pathways, gene_col="gene", score_col="score", mode="aligned", score_type="std", bin_width=0, seed=42, ) Install trajectory dependencies with: python -m pip install "pyfgsea[trajectory]" score_type="two_sided_abs" remains available for a bounded legacy-like calculation, but it is approximate and is not equivalent to fgseaMultilevel(scoreType="std"). The Python API does not accept an n_threads keyword. Packaging and paper impact Cargo.toml is the single version source for the Rust crate and Python distribution. Release packages are built as source distribution and platform wheels, installed into fresh environments, and tested through the installed Python package and native core. The two R reference images are also started and checked at runtime. The manuscript review is classified as a numerical and methods correction whose central software-concordance conclusion remains intact. The detailed decision note and claim matrix identify the text and values to keep, qualify, update, or remove. Limits The formal comparison covers the publication input and one predeclared tie-heavy sensitivity case; it is not a universal equivalence claim. Correlation and pathway overlap do not establish algorithmic identity or biological correctness. The Figure 1 run does not provide matched current benchmarks against GSEApy or BlitzGSEA. Runtime and peak memory remain hardware- and scope-dependent. Fast mode and the legacy empirical-tail helpers are approximate.
Kuanghao Wang, Hong Shi· Zenodo (CERN European Organi...· 0 citations
Sovereign Cyber-Physical Hypervisor Platform: Hardened Asymmetric Multiprocessing Co-Design Matrix Framework (v1 / REV14-FROZEN) This repository contains the absolute, production-frozen version 1 (v1) parent codebase for the Sovereign Cyber-Physical Hypervisor, implemented in strict compliance with the SEC-AMP-TRILOGY-SECURE-REV14-FROZEN master specification. The framework is engineered to enforce pure silicon determinism, absolute data integrity, and complete fault isolation across heterogeneous dual-core Asymmetric Multiprocessing (AMP) infrastructures. It targets mission-critical deployment environments prone to physical vulnerability vectors such as Electromagnetic Fault Injection (EMFI) and electrical voltage transients. This frozen v1 release establishes the uncompromised architectural foundation that will be continued under version 2 (v2) to support downstream child project integration layers. The platform splits execution domains between a high-overhead, non-deterministic primary Application Core (480 MHz ARM Cortex-M7 domain running Linux/RTOS abstractions for optical telemetry and SCADA logging) and an ultra-fast, strictly deterministic Sentinel Core (240 MHz bare-metal ARM Cortex-M4 domain handling real-time control loops and critical fluid line actuation). Key Microarchitectural Hardening Invariants:1. Strict Parallel Harvard Segregation: Instruction text and case targets execute natively inside zero-wait-state Instruction Tightly-Coupled Memory (ITCM) over the I-Code bus, while all static lookup tables, function pointer arrays, mutable tracking matrices, and buffers live strictly within Data Tightly-Coupled Memory (DTCM) to eliminate crossbar cross-contamination.2. Pure Arithmetic Determinism: Enforces strict compile-time soft-float constraints (#pragma GCC poison float double) combined with explicit linker-stage symbol exclusions (*(__aeabi_f*)) to completely paralyze software float-emulation timing jitter loops.3. Asymmetric Mailbox Protection: Communication interfaces are physically air-gapped using separate unidirectional APB peripheral bus structures. Slave Interface A (Application side) is bound to Shareable Normal Non-Cacheable Memory (TEX=001, C=0, B=0, XN=1) to prevent speculative prefetches, while Slave Interface B (Sentinel side) is mapped strictly to Strongly-Ordered Memory (TEX=000, C=0, B=0) to ensure un-buffered immediate register retirement.4. Word-Driven Stride Packings: Telemetry serialization utilizes strict 32-bit word-aligned strides to satisfy peripheral bus alignment restrictions, backed by an atomic four-step read-after-write pipeline register interlock (Atomic Write -> Compiler Fence -> Dummy Read -> Assembly DSB SY/ISB SY Barrier) to eliminate cross-boundary data tearing.5. Branchless Math Engine: Features an un-preemptible supervisor loop running a 7th-order fixed-point Taylor expansion Arrhenius decay engine. Upper and lower saturation clamping limits are resolved via portable, branchless unsigned relational bitmask selections to maintain single-clock execution profiles.6. Automated Disassembly Audit Gating: Includes a target-prefixed automated continuous integration parser (verify_pipeline.py) that evaluates post-link object text segments to mathematically prove the complete absence of non-deterministic conditional branch opcodes (bne, beq, cbz, cbnz) inside verified runtime code boundaries, while accepting deterministic IT blocks. The repository zip payload contains the full, production-tested 15-file blueprint manifest, including the global technical constitution (master_specification.md), the race-condition isolated Makefile build matrix, the target GNU Linker Script (sentinel_flash.ld), and the complete, verified C/Assembly source environments. --- 📋 SUBMISSION METADATA DIRECTIVES [Deposit Parameters]* Version Tag: v1.0.0-frozen* Upload Type: Software* Access Right: Open Access* License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) [Target Keywords]Bare-Metal Hypervisor, Asymmetric Multiprocessing (AMP), MISRA-C:2012, Cyber-Physical Systems (CPS), Deterministic Execution, Fault-Injection Mitigation, Tightly-Coupled Memory (TCM), High-Assurance Firmware, Harvard Bus Matrix Isolation, Silicon-Determinism. [Target Curation Communities]1. Cyber-Physical Systems Security2. Open-Source Hardware/Software Co-Design3. High-Assurance Firmware --- Licensing & Open-Science Compliance:This archive is published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0). Any derivative works, downstream sub-systems, or secondary version 2 (v2) child projects must inherit these identical legal protection attributes and attribute this parent platform record accordingly. Code Verification Token: [ysD9J5, 0.1.1].
Zachary August Jacoby· Zenodo (CERN European Organi...· 0 citations
Why Fisher's exact test is not exact: achieved size, wasted power, and a routing rule for 2×2 tables William J. Dwyer, MD, MPH, FAAP — Department of Mathematics and Statistics, University of Massachusetts Lowell. ORCID 0009-0004-0855-7222. Concept DOI (always resolves to the latest version): 10.5281/zenodo.22238930. This release (v1.0.5):10.5281/zenodo.22238931. What this is The reproducibility deposit for the m02d paper. "Fisher's exact test" is exactly valid — its true rejection rate never exceeds the nominal α — but it is not size-exact: because the 2×2 reference distribution is discrete, no table boundary falls at α, so the achieved (unconditional) size sits below nominal. At small designs the test spends only 26–66% of its α budget, and wasted size is wasted power. The paper computes the achieved size, the wasted-power cost, and the significance-flip behaviour of Fisher's exact against a conservative→liberal roster (Yates χ², Fisher, mid-p, Boschloo, Barnard, Pearson χ², Cressie–Read λ=2/3, likelihood-ratio G), and distills the result into a routing rule for the 2×2. Everything is deterministic exhaustive enumeration — two independent binomials, no Monte Carlo, no random seed — so there is no simulation error: every number, table, and figure reproduces exactly from a named script. The keynote A real-data scan of 6,030 public 2×2 tables (757 pydataset/Rdatasets datasets) shows the tests agree essentially everywhere away from the threshold but disagree in ~84% of cases inside the Fisher-p band 0.05–0.07 — that is, the tests are miscalibrated relative to one another exactly where we use them to draw the significant/not-significant line. Flips are read on a three-tier taxonomy relative to Boschloo (the valid frontier): conservative below it, valid on it, liberal above it. What the deposit contains Manuscript (author + anonymized, .md), the novelty / prior-art companion, and the cover letter (The American Statistician). Reproducibility apparatus (rerun/) — the exact-enumeration engine achieved_size.py (tie-aware conditional p-values, validated against SciPy to 2.2e-16) and the deterministic sweep drivers: power_and_flips.py, size_skew_sweep.py, flip_boundary_sweep.py, roster_sweep.py (the full comparator roster, including the Cressie–Read λ=2/3 power-divergence member), and public_flip_scan.py (the real-corpus scan). Each writes its locked JSON/CSV. Figures (figures/) — the achieved-size and wasted-power panels, the size×skew heat maps, the α=0.05 flip-boundary maps, the χ²-inclusive roster figures, the real-data flip-incidence figure, and the routing flowchart (Figure 7). Interactive demonstrator honest_fisher.html — a self-contained in-browser calculator with the full comparator taxonomy and the live-highlight routing flowchart; its JavaScript engine reproduces the deposited numbers and self-checks a parity badge on load. Deep-dive record (deepdives/) — the supporting analyses, including the size/skew, flip-boundary, χ²-roster, real-data, and computed-clearance write-ups, and the M0u-bundle assessment that reconciles m02d with the author's earlier Monte-Carlo program (below). LICENSE, CITATION.cff, MANIFEST.txt (SHA-256 of every file). The Cressie–Read λ=2/3 reconciliation The roster now carries Cressie–Read λ=2/3, the best-calibrated member of the power-divergence family that also contains Pearson (λ=1) and the G-test (λ=0). Across R×C shapes it is the best-calibrated test in the author's companion Monte-Carlo study; on the pure 2×2 that is m02d's subject, its exact achieved size is 0.062 / 0.053 / 0.055 / 0.055 at the representative designs — still mildly liberal, because small-sample discreteness bites it too. So the paper names λ=2/3 as the calibrated member of the χ² family, scopes the "common χ² defaults overshoot" claim to Pearson/Yates/G, and shows that even the well-chosen asymptotic statistic does not escape the 2×2 discreteness the exact unconditional tests handle — turning a potential referee objection into a strengthening of the routing rule. The companion 18-test Monte-Carlo study independently confirms m02d's Boschloo/Barnard power gap over Fisher (+3 to +10 pp by simulation vs+3.7 to +11.1 pp by exact enumeration here). All computation is deterministic exact enumeration; code is released under the MIT License, and text, figures, and data under CC BY 4.0. The public 2×2 corpus is built from the Rdatasets collection via pydataset; no data are redistributed beyond the illustrative tables cited in the paper. How to cite Please cite this deposit if you use the package or the method. Citing the concept DOI references the work in general and always resolves to the latest version; cite a specific version DOI to point at an exact snapshot. Dwyer, W. J. (2026). Why Fisher's exact test is not exact: achieved size, wasted power, and a routing rule for 2×2 tables — reproducibility deposit [Software]. Zenodo. https://doi.org/10.5281/zenodo.22238930 BibTeX: bibtex @software{dwyer_m02d_2026, author = {Dwyer, William J.}, title = {Why Fisher's exact test is not exact: achieved size, wasted power, and a routing rule for 2x2 tables --- reproducibility deposit}, year = {2026}, publisher = {Zenodo}, doi = {10.5281/zenodo.22238930}, url = {https://doi.org/10.5281/zenodo.22238930}, orcid = {0009-0004-0855-7222} } The DOI above is the concept DOI (resolves to the latest version); to cite a specific release use that version's DOI in its place. When the accompanying journal article appears, please cite it as the primary reference for the method and this deposit as the reproducibility archive. Version history v1.0.6 — staged, not yet uploaded (m02d_fishers_exact_not_exact_reproducibility_v1.0.6.zip, md5 fc3d9dcd5574606e97e5e7f20b434024, 1,848,064 B, 58 files): prior-art citation-completeness pass. Added eight Crossref-verified references — Chen (2011), Neuhäuser & Ruxton (2025), Rudas (1986), Upton (1982), Little (1989), Berger (1996), Routledge (1994), and Hirji (2006) — and, in §8, framed the real-corpus keynote as the empirical, at-scale demonstration of the test-disagreement result Chen (2011) characterized theoretically; §6 now attributes the λ=2/3 calibration to Rudas (1986). No number, figure, table, or claim changed. Upload this version to mint its DOI. v1.0.5 — ✅ 10.5281/zenodo.22238931 (2026-09-01) (m02d_fishers_exact_not_exact_reproducibility_v1.0.5.zip, md5 31f2a7bfeda570ed402dd1aa3fb619e6, 1,846,004 B, 58 files): added the Cressie–Read λ=2/3 member to the roster engine (exact 2×2 achieved size 0.062/0.053/0.055/0.055) and its column to Table 1; scoped the χ²-overshoot claim to the common defaults and named λ=2/3 as the calibrated member (§6); added the Cressie & Read (1984) / Read & Cressie (1988) references and the companion Monte-Carlo self-citation (§9); folded in the M0u-bundle vs m02d assessment deep-dive. Numbers re-verified against the deposited JSON; deterministic (identical md5 on two runs). v1.0.4 — md5 b59f667573823962f999ff040ec591e4, 1,837,352 B, 57 files: routing-flowchart computed-clearance finalizer (Figure 7 and the demonstrator SVG from one audited spec). v1.0.3 — md5 0f01c96e0eb836b2572109446db1b20d, 1,833,339 B: manuscript body expanded to fold the figures/tables with organized in-text callouts; anon regenerated; audits PASS. v1.0.2 — md5 29caf50da2d7bec2409f42098d979e64, 1,830,784 B: full comparator roster and the real-data flip scan added; demonstrator enriched with the taxonomy. v1.0.1 — md5 e0733897cad80d8178d17c8ffcb6943c, 1,714,057 B: size×skew and flip-boundary sweeps + figures. v1.0.0 — md5 54286e0b6841778c71cbea2b20a56897, 1,703,321 B, 48 files: first build — the exact achieved-size engine, the power/flip computation, and the honest_fisher.html demonstrator. Provenance: every number traces to a named, seed-free exact-enumeration script and is validated against SciPy; the demonstrator reproduces the deposited Python. Related work: the companion 18-test Monte-Carlo contingency-table program (M0/M0u), folded into the repo as m0u_work and cited for the Cressie–Read result, the Rényi negative result, and the independent power-gap confirmation.
William Dwyer· Zenodo (CERN European Organi...· 0 citations
Objectives: Certain symptoms of neuropathic pain and their combinations respond differently to various treatments. The Neuropathic Pain Symptom Inventory (NPSI) performs sensory phenotyping, providing a detailed assessment of neuropathic pain qualities, demonstrating responsiveness to changes in pain, and classifying patients for clinical trials in both central and peripheral conditions. This study aimed to perform a psychometric validation of the Turkish version of the NPSI (NPSI-T). Patients and Methods: In this cross-sectional study, the NPSI-T, Douleur Neuropathique 4, and Numeric Rating Scale were administered twice to 275 patients with central and peripheral neuropathic pain (PNP) at 1-week intervals. All reliability and validity analyses, including factor analyses, Cronbach's alpha, Hotelling's T2, Tukey's test of nonadditivity, and item analysis, were performed using SPSS 27.0 (IBM Corp., Armonk, NY, USA). Software to compute descriptive statistics. Results: The NPSI-T showed high reliability with a Cronbach's alpha of 0.89, an internal consistency Spearman-Brown coefficient of 0.779, and a significant 27% difference between upper and lower groups. The total explained variance was 61.58%, and five subdimensions were identified through exploratory factor analysis. Confirmatory factor analysis confirmed that the structure, consisting of four subdimensions with 9 items, is valid, as indicated by excellent goodness-of-fit indices. Conclusion: The results of this study indicate that the NPSI-T is a reliable and valid instrument for assessing Turkish patients with central and PNP syndromes.
Serpil Savaş, Ayşegül Garip Honamlıoğlu, Adnan Karaibrahimoğlu et al.· Turkish Journal of Physical...· 0 citations
This study aims to determine the influence of Work Discipline (X1) and Work Motivation (X2) on Employee Performance (Y) at CV. Kombos Tendean Manado, both partially and simultaneously. The study is grounded in the crucial role of human resources as the primary driver for achieving organizational goals; consequently, employee work discipline and motivation are hypothesized to be factors that help determine company performance. A quantitative approach was employed, utilizing descriptive, field, and library research methods. Data were collected through observation and questionnaires distributed to 53 employee respondents at CV. Kombos Tendean Manado. The data were then analyzed using classical assumption tests, multiple linear regression analysis, t-tests, F-tests, and the coefficient of determination (R-Square) with the aid of SPSS software. The results indicate that, partially, Work Discipline does not have a significant effect on Employee Performance (t-count 1.482 < t-table 2.009; Sig. 0.145 > 0.05), nor does Work Motivation (t-count 0.467 < t-table 2.009; Sig. 0.642 > 0.05). Simultaneously, Work Discipline and Work Motivation also do not significantly affect Employee Performance (F-count 1.174 < F-table 3.183; Sig. 0.317 > 0.05). The R-Square value is 0.045, meaning the two independent variables explain only 4.5% of the variation in Employee Performance, while the remaining 95.5% is explained by factors outside this research model. These results must be interpreted in light of the research instrument's low reliability (Cronbach's Alpha below 0.60 for all variables); therefore, it cannot be immediately concluded that Work Discipline and Work Motivation have no conceptual impact on Employee Performance.
Whole-exome sequencing (WES) enables the identification of rare germline variants contributing to pediatric diseases. Trio-based sequencing, comparing affected children with their parents, is particularly effective for rare disease genetics. However, WES data analysis requires bioinformatics expertise, varies across institutions, and is often incompatible with clinical workflows. We developed T-Rex (Trio Rare variant analysis of EXomes), a cross-platform desktop application that enables the standardized and local analysis of WES germline Trio data without the need for programming knowledge. T-Rex integrates state-of-the-art tools for alignment, dual-variant calling (GATK HaplotypeCaller + VarScan2), annotation (SNPEff/SNPSift), rare-variant filtering based on population frequencies (gnomAD), and family-based statistical testing, including the Transmission Disequilibrium Test with multiple-testing correction. Benchmarking of the dual-caller strategy on the Genome in a Bottle Ashkenazim Trio demonstrates high precision (99.2%) while maintaining robust sensitivity (91.1%). User testing (n = 13) confirmed quick learning across clinicians and researchers. Application to a cohort of n = 121 pediatric cancer Trio datasets, filtering for rare protein-coding variants (MAF ≤ 0.1% in gnomAD v4.1), validated all assessable previously reported pathogenic variants. Overall, T-Rex enables clinicians to robustly analyze WES Trio data in compliance with data protection regulations without requiring additional software licenses. As one of the first platforms for comprehensive WES Trio analysis that requires no programming expertise while providing reproducible, end-to-end workflows for clinical genomics, T-Rex facilitates collaborative research between clinics and reduces reliance on external providers.
Sara-Luisa Reh, C. Walter, J. Lohse et al.· Scientific Reports· 0 citations
Preliminary evidence from multi-vehicle collaborative experimentation, code and experimental-skill sharing, and software-hardware collaborative testing shows that experimental records can be examined together with their operating conditions, providing a reviewable basis for Apollo-on-Hongqi EV research.
Dianjing Cheng, Yike Li, Lan Yang et al.· 0 citations
This work introduces nonlinear Fourier retraction, which uses QSP completion and phase synthesis to turn a nearly feasible polynomial into phase factors for a feasible QSP polynomial without increasing the degree.
Yulong Dong, James B. Larsen, Lin Lin et al.· 0 citations
A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems.
