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Generative Relational Economics (GRE) is developed as a domain-specific research programme whose explanatory objects are economically relevant relational processes, structures, transformations, and co…

Generative Relational Economics (GRE) is developed as a domain-specific research programme whose explanatory objects are economically relevant relational processes, structures, transformations, and conditions of generation. Such a programme cannot remain intellectually isolated, however, because economic phenomena are historically formed and are frequently constituted, constrained, mediated, or evaluated through legal, political, social, educational, ecological, technological, epistemic, and aesthetic relations. This chapter develops a preliminary architecture for cross-domain inquiry while resisting the reduction of neighboring domains to economic categories or the premature expansion of GRE into a general theory of relational reality. The chapter first formulates a grammar of disciplinary interfaces based on domain containment, translation, translation loss, non-translation, domain-specific remainder, and reciprocal correction. It then examines interfaces between GRE and generative-relational approaches to history, governance, law, political theory, education, ecology, artificial intelligence studies, social theory, and aesthetics. Across these interfaces, economic incidence is distinguished from disciplinary jurisdiction: a phenomenon may have economic consequences without thereby becoming exhaustively economic in its constitution, meaning, validity, or normative status. The chapter consequently develops mechanisms for preserving external warrant, provenance, and correction authority when GRE relies on concepts or premises supplied by neighboring domains. The later sections turn from individual interfaces to the architecture of cross-domain reasoning itself. They distinguish portable analytical kernels from domain-specific claims, formulate conditions for importing externally warranted premises into GRE, and develop criteria for assessing semantic equivalence among recurrent concepts such as relation, subject, value, constraint, power, justice, generativity, and institution. Disciplinary boundary disputes are treated at the level of particular predicates and claims rather than through exclusive ownership of empirical objects. The chapter further argues that recurrence across related GR domains is insufficient by itself to establish a general Generative Relational Theory (GRT), especially where shared conceptual ancestry creates a common-origin confound. A preliminary generalization framework therefore distinguishes designed recurrence from independently rediscovered recurrence and gives substantive evidential roles to failed translations, counterexamples, historical variation, and irreducible domain-specific remainders. The chapter concludes by applying the same discipline to GRE itself. It distinguishes direct GRE explanation, partial GRE explanation, domain deference, and current non-claim, while proposing that disciplinary scope should remain revisable at the level of specific claims. GRE can therefore participate in interdisciplinary inquiry without claiming explanatory jurisdiction over legal validity, political legitimacy, educational or aesthetic quality, ecological value, consciousness, moral standing, ultimate ontology, or other questions whose warrants arise elsewhere. The resulting framework presents cross-domain openness not as theoretical expansion for its own sake, but as a structure of reciprocal answerability through which GRE may become more precise about both what it can explain and where its explanations should stop.

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.