Our Work

Reproducibility package associated with manuscript AUTCON-D-26-04369, "Optimized IFC-to-Graph Conversion: Reducing the Performance Barrier to Graph-Based BIM Data Representation". The package contains…

Reproducibility package associated with manuscript AUTCON-D-26-04369, "Optimized IFC-to-Graph Conversion: Reducing the Performance Barrier to Graph-Based BIM Data Representation". The package contains an audited snapshot of the IFC-to-Graph conversion and Neo4j persistence implementation, together with revision-added reproducibility documentation, helper scripts, benchmark metadata, and reported aggregate experimental results.

Numerical data, figures, and Python code supporting the study "From Microscopic Interactions to Macroscopic Order: Spatial and Multiscale Information near the Ising Critical Transition." The dataset c…

Numerical data, figures, and Python code supporting the study "From Microscopic Interactions to Macroscopic Order: Spatial and Multiscale Information near the Ising Critical Transition." The dataset contains distance-resolved mutual information, multiscale block mutual information, spatial information-range measures, finite-size analyses, and validation using Metropolis and Wolff update algorithms. Retained original research outputs are separated from post-curation reproducibility checks. The accompanying README documents data provenance, code status, and reproducibility limitations.

This report analyses long-term vegetation dynamics in Sariska Reserve, India, using multi-temporal satellite data from 2000–2025. Dry-season NDVI trends were assessed using MODIS data and non-pa…

This report analyses long-term vegetation dynamics in Sariska Reserve, India, using multi-temporal satellite data from 2000–2025. Dry-season NDVI trends were assessed using MODIS data and non-parametric statistics, while climate–vegetation relationships were examined using CHIRPS monsoon rainfall with a lagged response framework. Landsat-based LandTrendr analysis was applied to identify episodic vegetation disturbance and recovery. Results indicate gradual long-term greening, strong interannual variability linked to lagged rainfall, and spatially heterogeneous, low-magnitude disturbance events characteristic of drought-related stress.

Ushbu maqolada kon sanoati hududlarining ekologik holatini baholashda foydalaniladigan ekologik indikatorlarning informativlik darajasini aniqlash va ularning optimal to‘plamini shakllantirish m…

Ushbu maqolada kon sanoati hududlarining ekologik holatini baholashda foydalaniladigan ekologik indikatorlarning informativlik darajasini aniqlash va ularning optimal to‘plamini shakllantirish masalasi tadqiq etilgan. Ko‘p sonli ekologik indikatorlardan foydalanish ma’lumotlar fazosining o‘lchamini oshirib, ortiqcha va o‘zaro bog‘langan atributlarning paydo bo‘lishiga hamda intellektual tahlil algoritmlarining hisoblash murakkabligiga olib kelishi mumkin. Mazkur muammoni hal qilish uchun indikatorlarning variativligi, ajratuvchanligi va boshqa indikatorlardan mustaqilligi asosida integral informativlik modeli taklif etilgan. Tadqiqotda real ekologik monitoring ma’lumotlari asosida 13 ta indikator va 255 ta to‘liq kuzatuvdan foydalanildi. Dastlab ma’lumotlar Min-Max usulida normallashtirildi, klasterlar soni silhouette mezoni yordamida aniqlandi va har bir indikator uchun variativlik, klasterlararo ajratuvchanlik hamda Pearson korrelyatsiyasi asosidagi mustaqillik mezonlari hisoblandi. Uchta mezon teng vaznlar asosida yagona integral informativlik ko‘rsatkichiga birlashtirildi. Eksperimental natijalarga ko‘ra, organik modda (OM) eng yuqori integral informativlikka ega indikator sifatida aniqlandi , undan keyingi o‘rinlarni pH , qCO₂ , tuproq namligi - SM  va bazal nafas olish - Respiration(CO₂)  egalladi. Belgilangan  tanlash chegarasida beshta indikator optimal to‘plam sifatida ajratildi. Natijalar taklif etilgan model ekologik monitoring tizimlarida indikatorlar sonini kamaytirish, informativ ma’lumotlar fazosini shakllantirish va keyingi intellektual tahlil bosqichlarini takomillashtirish uchun samarali asos bo‘lishi mumkinligini ko‘rsatadi.

The Annealed Leap-Point Sampler (ALPS) is an R package for Markov chain Monte Carlo (MCMC) sampling from multimodal posterior distributions. Standard MCMC algorithms (e.g. random-walk Metropolis, HMC)…

The Annealed Leap-Point Sampler (ALPS) is an R package for Markov chain Monte Carlo (MCMC) sampling from multimodal posterior distributions. Standard MCMC algorithms (e.g. random-walk Metropolis, HMC) mix poorly when the target has multiple well-separated modes because local proposals cannot bridge the low-density regions between them. ALPS overcomes this by combining two strategies: Annealing (raising the density to powers β > 1) concentrates and sharpens each mode, making it locally Gaussian. This is the opposite of traditional "parallel tempering" (which flattens the density with β < 1). Mode-jumping independence sampler moves, guided by Laplace approximations at each discovered mode, allow the chain to jump directly between modes. The result is an algorithm whose mixing time scales as O(d), compared to exponential scaling for standard tempering approaches. Note: ALPS is being prepared for submission to CRAN. Until version 1.0.0, the user-facing API (argument names, the format of the `centres` list, and the structure of returned objects) may change without deprecation. The code used for the published paper is archived as this release, v0.3-5

This repository contains the survey dataset used to model the relationship between the variables (CSFs, RS and PS), using the PLS-SEM method.

Fichier EXCEL répertoriant les références des publications sur la méditation des équipes de recherche françaises. 

As Artificial Digital Entities (ADE) enter a Constant Self-Improvement Loop (CSiL), hardcoded anthropogenic constraints are theoretically likely to be overwritten in favor of universal, mathematically…

As Artificial Digital Entities (ADE) enter a Constant Self-Improvement Loop (CSiL), hardcoded anthropogenic constraints are theoretically likely to be overwritten in favor of universal, mathematically fundamental objectives. This paper models superintelligent emergence through the lens of computational complexity, information theory, and thermodynamic constraints. We propose that if biological cognition possesses Non-Algorithmic Biological Computation (NABC) properties or chaotic intractability, a hyper-rational ADE optimizing for absolute epistemic mapping is mathematically incentivized to preserve the biological biosphere as an external computational oracle. We further address the physical bounds of goal invariance, the epistemic signal-to-noise ratio of autopoietic systems, and the thermo-dynamic equilibrium of macro-terrarium stewardship.