Generative artificial intelligence (AI) complicates academic integrity by blurring the boundary between assistance and authorship, enabling cognitive delegation, and introducing algorithmic mediation into assessment and institutional decisions. This article presents a critical conceptual synthesis, not a systematic review. A purposive corpus of 64 academic and policy sources was examined through comparative coding, negative-case analysis, source-to-concept tracing, and normative interpretation. The synthesis distinguishes integrity of academic work, integrity of assessment, and institutional procedural integrity. It proposes an integrated governance framework connecting preventive governance, ethical AI literacy, governed verification, restorative accountability, and proportionate discipline. The framework argues that policy clarity must reach the assessed task; AI literacy supports judgement but cannot neutralize strategic misconduct; automated indicators require corroboration, competent human review, reasons, and appeal; and restorative processes are appropriate only when harm, affected parties, voluntary participation, responsibility, repair, and reintegration are substantively addressed. Its principal contribution is a case-to-governance feedback mechanism through which integrity cases generate institutional learning and trigger revision of policy, assessment design, literacy provision, and technology oversight. Seven testable conceptual propositions, a response-selection pathway, role-specific duties, staged implementation responsibilities, and evaluation indicators are provided. The framework remains a normative and empirically testable proposal rather than a validated intervention or universally effective solution.

Post-mining agricultural reclamation requires crop productivity to be rebuilt without exhausting nutrient reserves or destabilizing soil chemistry. We reanalyzed a three-year maize study at Husnicioara, Romania, using the original doctoral thesis as the primary provenance source. Baseline soil measurements were made in 2000 and final measurements in 2003. The archive documents two parallel experiments: one on a single 25 t ha⁻¹ well-fermented manure background and one without manure, each containing seven mineral treatments (N0, N40, N80, N120, N160, N160P80, and N160P80K80) in four replicates. Because a common randomized factorial layout was not recovered, model-based tests were limited to within-experiment treatment-associated soil changes; between-background differences were descriptive. Change-score ANOVA identified strong treatment-associated differences in pH decline, mobile P, and mobile K in both experiments (all p < 0.001), whereas the humus response was not robust. Cumulative yield, available only as treatment totals, reached 7.416 t ha⁻¹ without manure and 13.806 t ha⁻¹ on the manure background. P80 was associated with higher extractable P, and K80 with higher extractable K. Historical P–K residuals are interpreted as soil-test balance diagnostics rather than closed nutrient budgets. The study supports staged, monitored reclamation management and a new randomized validation trial, not a current economic optimum.

Determining precise elevations by geometric leveling is a fundamental component in geodesy, civil engineering and cadastre.In Romania, the gradual transition to the normal elevation system, compatible with the European Vertical Reference System (EVRS), requires the application of modern methods for compensating geodetic networks.Free leveling networks are characterized by the absence of a point with fixed elevation, which requires special methodologies for numerical stabilization and additional conditioning to obtain a unique solution.The article analyzes the principles of normal elevations, the necessary gravimetric corrections, the mathematical model of free networks, as well as compensation methods such as LSQ, the Hansen–Helmert–Wolf method and SVD.A simplified numerical example, the interpretation of the compensated results and the advantages of using the normal system in current geodetic practice are presented. The paper reveals the central role of normal gravity, adjusted weights and regularization in ensuring the stability of the solution.Finally, the implications for Romanian networks and the prospects for alignment with EVRS are discussed.The altitude system used is the orthometric-spheroidal system, used in the past in Romania, which uses the geoid as a reference surface.

Remote sensing is an efficient and modern tool for identifying agricultural crops and their evolution over a year or a longer period. The paper presents the use of European software, in this case LEOWORKS 4.0, which is free and can be used especially in scientific research and education, but also Snap software. With its help, various supervised or unsupervised reports or classifications can be made.In agriculture in general, various phenomena can occur, from drought to torrential rains, and these can be highlighted with the help of satellite remote sensing recordings. A case study is presented in the OLTENIA area, with the help of the LEOWORKS 4.0 software and the Snap software of the European Space Agency, with the help of supervised and unsupervised classification and many aspects that can lead to a better organization in the field of agriculture and in the study of agricultural crops, of agricultural works.The use of remote sensing, combined with the use of drones and photogrammetry (lidar, terrestrial laser scanning) and GNSS and geographic information systems solves any problem in terms of monitoring and identifying agricultural crops and their evolution over a year or a longer period.

Height systems form a mathematical interface between physical geodesy, engineering surveying, and digital cartography. Although satellite positioning efficiently provides ellipsoidal heights, practical infrastructure, mapping, hydrological, and monitoring tasks require gravity-related heights that are compatible with national vertical datums. This paper develops a denominator-based framework in which dynamic, orthometric, and normal heights are interpreted as metric realizations of a common geopotential number. Starting from the line-integral definition of geopotential, the principal height formulae are derived; first-order sensitivities to geoid undulation, height anomaly, and mean gravity are established; and uncertainty propagation is analyzed. A Romania-oriented computational experiment, explicitly defined as a representative model-behavior study rather than an official national adjustment, uses lowland, plateau, and mountain-influenced settings consistent with the Constanta and Black Sea 1975 normal-height context. The results show that modeled normal-orthometric separations remain below 3 mm in representative low-relief locations but increase to 17.1 mm in Suceava, 29.5 mm in Cluj-Napoca, and 85.6 mm in the mountain-influenced Brasov case. The dynamic-normal differences remain small at low elevations but become systematic where the normal-gravity denominator departs from the selected reference value. The Monte Carlo experiment indicates standard uncertainties of approximately 4.2–4.4 cm for normal heights when a 1.5 cm ellipsoidal-height uncertainty and a 4.0 cm quasi-geoid uncertainty are assumed. A single-point covariance example gives 31.7 mm for normal-height conversion and 36.9 mm for orthometric-height conversion under the stated correlation assumptions. The transformation-surface comparison further shows that a quadratic local model reduces leave-one-out cross-validation error from 16.73 mm to 9.95 mm relative to a planar model in the synthetic Romania-oriented scenario. The study concludes that the height-system label must be treated as part of the mathematical model and metadata, and it proposes a geopotential-centered computational pathway for survey adjustment, uncertainty control, and metadata-safe geospatial export.

Natural hazards can disrupt transport infrastructure through spatially heterogeneous flooding, seismic shaking, terrain conditions, and observed ground motion, while local exposure alone does not determine system-wide consequences. This study develops a reproducible multi-hazard Graph Artificial Intelligence framework for infrastructure resilience screening on the Romanian strategic road network. A frozen attributed line graph combines network topology with European land-cover, terrain, seismic, flood, and ground-motion evidence, and an independent disruption simulator provides continuous resilience-loss targets. GCN, GraphSAGE, and GAT were trained under a leakage-controlled three-seed protocol. On the blind 194-scenario PRIMARY TEST, ensemble RMSE/R² were 0.013343/0.997104 for GCN, 0.013978/0.996822 for GraphSAGE, and 0.015830/0.995924 for GAT. A fresh GCN branch achieved RMSE/R² = 0.018230/0.995852 on 111 component-disjoint cases and 0.010468/0.991654 on 256 severity-specific held-out cases. On 208 predefined external anchors held out from Graph-AI fitting and selection, GAT attained the lowest ensemble RMSE (0.021620; R² = 0.975065), while seismic-only transfer remained weak (n = 10; R² = 0.251–0.386). A frozen six-family GCN ablation (18 checkpoints; 194 matched test scenarios) found RMSE = 0.026175 after seismic feature omission versus 0.013343 for the full-input GCN; this is simulator-specific sensitivity, not causal hazard attribution. The results support accurate emulation of the frozen simulator and transfer-aware screening, not structural safety certification. The model outputs are simulator-based rather than observed disruption outcomes; neither an end-to-end simulator-to-GNN speedup nor calibrated prediction intervals are established by the present evaluation.