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.

The work was accomplished within a research project which aimed to streamline the resource management on the campus of the University of Craiova by creating a GIS database using a new technology that is increasingly being applied in topo-cadastral works, namely aerial scanning using drones. The main objectives of the research were represented by obtaining an orthophotomap using air scanning (the orthophotomap becoming the GIS raster graphical support) designing, validating and implementing a data model (database), using specialized software products. In the case study carried out at the University of Craiova, the following steps were also taken: obtaining raw data, processing raw data and obtaining an orthophotomap, designing the data model (database) and implementation of the data model (database). The research proves of interest primarily through the conceptual foundation of the applicable data model, a model that will allow the recording, storage, extraction, processing and efficient analysis of the desired data. The system is based on a relational open model, which permits it to adapt according to specific needs. The design data model has applicability on the structure of any student campus and can be implemented in other locations (with the provision of the related graphic support).

There have been made soil analysis with samples taken from 0-20 cm depth from Centrotrans s.r.l. company from Valea Stanciului – Dolj; the surface was split in 23 homogenous plots. The fertilization recommendations have been calculated after actual methodology in Romania, for the following crops: wheat, corn, soybean, oilseed rape and sunflower.

Soil represents the upper layer of the Earth’s crust, consisting of a complex mixture of mineral particles, organic matter, water, air, and microbial communities and living organisms, which interact to support the biological, chemical, and physical processes essential to ecosystems.The use of soil as a medium for the storage and disposal of liquid and solid waste raises a series of significant environmental issues. Among the factors contributing to waste generation and soil degradation are mining activities, particularly lignite extraction, which affects the structure and fertility of soils.Lignite mining, whether in open-pit mines or at the surface, represents the most aggressive form of soil degradation. It leads to the inversion and mixing of geological soil layers, nutrient migration, and landscape degradation, with waste dumps replacing productive land.The areas affected by lignite extraction in Romania amount to over 26,200 hectares, with Gorj County ranking first (~14,505 ha), followed by Vâlcea (~2,166 ha) and Mehedinți (~586 ha). These areas are mainly occupied by tailings dumps, composed of a homogeneous mixture of geological deposits depending on the exploitation zone, which have a significant environmental impact.Under these conditions, the recultivation of tailings dumps becomes an essential necessity, also requiring detailed research aimed at identifying the most appropriate, efficient, and cost-effective solutions for their ecological restoration.

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.