The Universidad Azteca (also known as Universidad Azteca de Chalco) is a private university in Chalco, Mexico in a community in Mexico State in the greater Mexico City area. Universidad Azteca is a private university with recognition of the Official Validity of Studies awarded by the Federal Secretary of Education (RVOE), accredited by the Federal Ministry of Education of the Republic and recognized by the Federal Government to provide higher education and award graduate and postgraduate university degrees. According to the Mexican Higher Education Laws, Universidad Azteca is authorized to offer study programs and award degrees with RVOE and offer autonomous programs and award academic degrees of the university. The study areas with RVOE (accreditation) are Administration Informatics; Architecture; Business Administration; Education Sciences; International Commerce; Law; Pedagogics; Psychology; Public Accounting. The University awards undergraduate Bachelor, graduate Master, postgraduate Master and Doctoral degrees in international programs in accordance with the Bologna Process and issues a Diploma Supplement. Universidad Azteca International Network System is the university extension, collaborating with other universities globally and branch campus facilities in Austria, Switzerland, United Arab Emirates, India.
Financial fraud in receivables financing presents challenges for identifying documentary manipulation, tracing financial responsibility, and assessing evidence of criminal intent. This study develops an integrated forensic accounting and legal assessment framework to examine Mexico’s Oceanografía–Banamex case, focusing on the relationship between financial irregularities, authorization practices, and evidence relevant to deliberate deception. The framework combines transaction reconstruction, invoice verification, documentary triangulation, transaction network analysis, and an evidence matrix to distinguish indicators of intentional misconduct from negligence and weaknesses in internal controls. The study dataset comprises 12,000 receivables financing records, 2,400 invoice documents, 180 authorization logs, and 60 correspondence files covering a three year observation period. Data preparation involves record standardization, duplicate identification, chronological alignment, and reconciliation of invoices with financing and approval information. Twenty four forensic indicators assess unsupported receivables, repeated invoices, inconsistent approval dates, unusual financing concentrations, documentary discrepancies, and authorization exceptions. Rule based screening and network analysis identify potentially irregular transactions, which are subsequently assessed against reference labels through independent documentary review. Legal analysis organizes the resulting evidence according to its relevance to knowledge, deception, participation, and financial benefit, while considering alternative explanations. The integrated framework identifies 912 of 960 labelled irregular records, achieving 95.0% recall. Among 1,000 flagged records, 912 correspond to reference irregularities, yielding 91.2% precision and a 93.1% F1 score. Conventional checklist screening identifies 720 irregular records, producing 75.0% recall. The proposed approach therefore improves recall by 20 percentage points, demonstrating stronger identification of irregular financing patterns within the analyzed dataset. Documentary inconsistencies and recurring authorization exceptions provide priorities for further investigation, although these indicators alone cannot establish criminal intent or individual guilt. The study contributes an auditable framework connecting financial analysis with legally relevant evidentiary assessment. Its findings support integrated verification, transaction tracing, and contextual review as complementary tools for investigating complex financial fraud. The framework emphasizes evidence provenance, document authentication, corroboration, and distinctions between suspicious transactions and established responsibility. Its practical value lies in strengthening investigative prioritization and audit transparency while supporting carefully qualified legal interpretation of financial evidence within the examined case context.
In the hybrid cloud environment cost, scalability, and performance are major obstacles for the quick expansion of data-intensive applications. Standard techniques of workload scheduling generally fail in dynamically optimizing resource demands and complex hybrid infrastructure, leading to low efficiency and substandard costs. Cost reduction while preserving or enhancing the performance of data-intensive applications in hybrid clouds is investigated in this paper. Scheduled SC can reduce needless data movement, comprehend workload in both public and private cloud resources, and minimize dynamic infrastructure to satisfy a range of application needs by utilizing future-stating analysis, real-time resource monitoring, and MLoperated adaptive decision-making. The analysis finds a number of machine learning techniques that allow for accurate, automated charge placements, resulting in a notable reduction in computation. These methods include learning reinforcement and multi-object optimization. A comprehensive grasp of the benefits and drawbacks of applying ML-based scheduling in complex cloud ecosystems is provided by research integration, which also addresses operational concepts like safety, complexity, and ongoing model maintenance. These findings show how intelligent automation can assist companies in the Big Data era in finding a balance between cost-effectiveness and technical agility.
The integration of Artificial Intelligence (AI) into supply chain systems has emerged as a transformative force in advancing sustainability objectives, reducing environmental impact, and promoting more responsible resource use across global logistics networks. As supply chains grow more complex—spanning continents, involving a diverse network of suppliers, and responding to increasingly dynamic consumer demands—traditional management approaches struggle to maintain efficiency and sustainability simultaneously. AI addresses these limitations through its capacity for intelligent data processing, real-time decision-making, and predictive analytics, enabling organizations to transition towards greener, more transparent, and more ethical supply chain practices. One of the most notable contributions of AI to supply chain sustainability lies in its role in optimizing resource consumption. AI-driven tools facilitate inventory optimization, ensuring the right quantity of goods is produced, transported, and stocked, thus minimizing waste associated with overproduction and excess storage.
Extended-spectrum β-lactamase (ESBL), AmpC β-lactamase and carbapenem resistance in Enterobacterales threaten the reliability of empirical therapy and the interpretation of routine antimicrobial-resistance surveillance in Sri Lanka. This critical narrative review evaluates the available human, molecular, environmental and policy evidence, with particular attention to whether studies genuinely establish temporal trends. Peer-reviewed research and relevant institutional documents published through 24 August 2026 were identified through scholarly-source searching, reference tracing and verification of bibliographic records. Sri Lankan studies document substantial ESBL production among selected urinary and hospital isolates, alongside clinically important plasmid-mediated AmpC enzymes and diverse carbapenemase mechanisms, including OXA-48-like and NDM variants. Nevertheless, the reported proportions cannot be arranged into a coherent national time series because sampling frames, specimen types, geographical coverage, organism composition, phenotypic definitions and confirmatory testing differ markedly. Some studies intentionally enrolled resistant isolates, whereas others assessed all isolates in a laboratory population. Molecular investigations offer stronger evidence for the presence of specific enzymes and mobile genetic contexts than for their population prevalence or direction of spread. Studies of cancer patients, hospital wastewater and animal-associated environments expose additional reservoirs and high-risk settings but do not independently prove intersectoral transmission. Diagnostic co-production, altered permeability and variable carbapenemase expression further complicate the equivalence of resistance phenotype and resistance mechanism. The most defensible interpretation is that clinically significant ESBL, AmpC and carbapenemase-mediated resistance is well documented in Sri Lanka, while the magnitude and direction of national change remain inadequately established. A harmonised, denominator-explicit surveillance framework, targeted molecular confirmation and prospectively linked clinical and One Health datasets are priorities for translating isolated findings into actionable trends.
