This systematic literature review investigates the factors influencing cloud computing adoption within both educational and organizational settings. By synthesizing a comprehensive body of research, this study finds and analyzes the determinants that shape the decision-making process about cloud technology adoption. Security, cost-effectiveness, scalability, interoperability, and regulatory compliance are examined across educational institutions and organizational contexts. Additionally, socio-economic, political, and technological factors specific to each context are explored to provide a nuanced understanding of the challenges and opportunities associated with cloud computing adoption. The review reveals commonalities and differences in adoption drivers and barriers between education and organizational environments, offering insights into tailored strategies for effective implementation. This research contributes to the existing literature by shedding light on the multifaceted nature of cloud adoption and offering valuable guidance for educators, organizational leaders, policymakers, and technology providers looking to use cloud computing to enhance operations and services.

An intelligent dynamic range compression (DRC) algorithm, using the CA-DAFX processing architecture, produces the optimal amount of dynamic range for multitrack recordings. The algorithm exploits the interdependence of input audio features, incorporates best practices, and uses subjective evaluation. The classical parameters of a typical compressor (ratio, threshold, knee, attack, and release) are dynamically adjusted depending on extracted features and control rules. Two new audio weighting...

Software engineering education requires students to develop technical knowledge and advanced cognitive and behavioral skills, particularly in the transition from novice to proficient. In distance learning, the hurdles are greater because students require greater autonomy, adopting strategies of self and co-regulation of learning. Facing these challenges, the SimProgramming approach has been transposed into the context of DL: e-SimProgramming. In the second iteration of e-SimProgramming implementation (2019/2020), one adaptation was inclusion of metacognitive challenges (MC) to promote students' self-reflection on their learning process. We explain the design of the two types of implemented MCs. We provide qualitative and quantitative analysis of: 1) evolution of MCs submission throughout the semester, identifying regularity and completion within deadlines and their relationship to student success; 2) students' perceptions of MCs. Results show a positive correlation between high MC submission and student success, greater interest and involvement of students in type 2 MCs and positive perceptions of students about MCs.

Logistic and Gompertz growth equations are the usual choice to model sustainable growth and immoderate growth causing depletion of resources, respectively. Observing that the logistic distribution is geo-max-stable and the Gompertz function is proportional to the Gumbel max-stable distribution, we investigate other models proportional to either geo-max-stable distributions (log-logistic and backward log-logistic) or to other max-stable distributions (Fréchet or max-Weibull). We show that the former arise when in the hyper-logistic Blumberg equation, connected to the Beta $(p,q)$ function, we use fractional exponents $p-1=1\mp 1/\alpha$ and $q-1=1\pm 1/\alpha$, and the latter when in the hyper-Gompertz-Turner equation, the exponents of the logarithmic factor are real and eventually fractional. The use of a BetaBoop function establishes interesting connections to Probability Theory, Riemann–Liouville’s fractional integrals, higher-order monotonicity and convexity and generalized unimodality, and the logistic map paradigm inspires the investigation of the dynamics of the hyper-logistic and hyper-Gompertz maps.

Modeling the vulnerabilities lifecycle and exploitation frequency are at the core of security of networks evaluation. Pareto, Weibull, and log-normal models have been widely used to model the exploit and patch availability dates, the time to compromise a system, the time between compromises, and the exploitation volumes. Random samples (systematic and simple random sampling) of the time from publication to update of cybervulnerabilities disclosed in 2021 and in 2022 are analyzed to evaluate the goodness-of-fit of the traditional Pareto and log-normal laws. As censoring and thinning almost surely occur, other heavy-tailed distributions in the domain of attraction of extreme value or geo-extreme value laws are investigated as suitable alternatives. Goodness-of-fit tests, the Akaike information criterion (AIC), and the Vuong test, support the statistical choice of log-logistic, a geo-max stable law in the domain of attraction of the Fréchet model of maxima, with hyperexponential and general extreme value fittings as runners-up. Evidence that the data come from a mixture of differently stretched populations affects vulnerabilities scoring systems, specifically the common vulnerabilities scoring system (CVSS).