Selection processes in academic institutions and public organizations are frequently managed through email and spreadsheets, creating legal vulnerabilities, version-control errors, and data protection non-compliance. This paper presents avalia+Tec, an open-source SaaS platform that enforces cryptographic integrity verification (SHA-256), tamper-evident hash-chained audit trails with external RFC 3161 timestamp anchoring, and end-to-end traceability for selection workflows. The system operates through a responsive web portal for candidate submissions and administrative oversight, and an Electron-based desktop application for structured blind evaluation. A preliminary single-institution deployment at Universidade Estadual de Feira de Santana processing 60 submissions with 6 evaluators was associated with a 65% reduction in review-to-decision cycle time (95% CI: [55%, 73%], Mann–Whitney U = 0 , p = 0.029 , r = 1.0 ), no formal candidate disputes during the observation period, and a 45.4% reduction in evaluator time per submission (95% CI: [26%, 59%]). The platform provides technical controls supporting regulatory compliance with Brazil’s LGPD. The scientific contribution resides not in novel cryptographic primitives but in the systematic integration engineering of well-established techniques to address a concrete, underserved institutional governance problem. avalia+Tec is freely available under the MIT License. • Auditable selection workflow using SHA-256 hashing and tamper-evident hash-chained logs. • HMAC-SHA256 signatures ensure integrity in blind peer-review scorecards. • Automation was associated with a 65% reduction in administrative cycle time. • External RFC 3161 timestamp anchoring strengthens tamper-evidence beyond internal controls. • Technical controls supporting LGPD compliance via role-based data segregation and automatic PII redaction.

TreeEyed was developed to facilitate tree monitoring in silvopastoral systems as an open source and easy to use QGIS Plugin that leverages state of the art AI models for tree detection, tree segmentation and canopy height estimation. Over six thousand downloads from >20 different countries from the QGIS plugin repository demonstrate great interest and user feedback has been useful to identify different areas of improvement. This software update to version 0.2.0 improves the processing and usability of the plugin in terms of model integration capabilities, tiling approach and cache system, user interface improvements with the addition of QGIS processing algorithm, and features for developers.

Advances in Life Cycle Assessment (LCA) toward greater automation and methodological integration have intensified challenges in standardizing heterogeneous raw Life Cycle Inventory (LCI) data, which rarely aligns with LCI database nomenclature. Rule-based mapping approaches struggle with linguistic variations, typographical errors, unit inconsistencies, and location granularity mismatches. Furthermore, they fail to adapt automatically when data or terminology change. FAULDIER (Framework for lArge langUage modeL assisteD lIfe cyclE inventoRy) is proposed as a framework to bridge heterogeneities between raw LCI data and LCI database requirements. It aims to automate data transformation by resolving naming inconsistencies, classifying flow types, and harmonizing locations and units. By using LLMs, FAULDIER supports handling multilingual inputs, correcting typographical errors, resolving location granularity mismatches, and choosing proxies for missing processes. In a test scenario using the open LCI database FORWAST and a use case characterized by non-standardized multilingual entries, unit inconsistencies, and typographical errors, FAULDIER achieved approximately 57% process and elementary flow mapping accuracy (single-expert validated), with unit conversion error rates below 1%. Current limitations include LCI database constraints, LLM token limitations, performance variability of open-weight LLMs, mapping ability, and reproducibility across runs. Within these limitations, FAULDIER indicates the feasibility of LLM-assisted LCI construction for LCA modeling, particularly for non-standardized raw LCI data. Future work could focus on developing confidence metrics for mapped LCI data, optimizing LLM query efficiency, and expanding testing across additional LCI databases, use cases, and LLMs.