Smart systems are going to transform the social life of modern society. Notwithstanding the benefits of these systems, its use is still low in developing world including Jordan. This study aims to construct a novel model to identify the factors that affect people’s intention to use smart systems in their homes. A survey was conducted on 650 individuals among which 550 valid questionnaires were considered in Jordan. The collected data were analyzed for descriptive analysis and further to valid the hypothesis using Structural Equation Modeling. The findings showed that there is a significant relationship between individuals’ attitudes related to PU and PEOU with their intention toward using smart systems in their lifestyle. It indicated that the research model explains 68.6% of the variance in the individual’s intention to use smart systems in their residence. Practically, it fruitful to the top management of IT companies that offers smart systems. Moreover, enhancing the current systems and organizing a strategic plan for implementation within best practice.

The study aims to investigate the impact of e-learning readiness on faculty’s performance in their academic works. It also seeks to examine how their information security awareness and technology self-efficacy mediate this primary effect. Due to the COVID-19 pandemic, many academic institutions around the world have closed; and adopted online learning platforms to keep the educational process running. However, the issues about universities readiness and effectiveness of e-learning seem to be motionless and not obviously understood, particularly for a developing country such as Jordan. To achieve the study goals, online questionnaire was distributed as a main tool for data collection from academicians. Sequentially, (383) valid questionnaires were subject to statistical analysis. Data analysis was performed using Partial Least Squares - Structural Equation Modelling (PLS-SEM). The results showed that there is a statistically significant impact of e-learning readiness on the academic staff performance through information security awareness and technology self-efficacy in Jordanian higher education institutions. Based on the results, the study proposed some recommendations, such as improving and supporting e-learning readiness in the Jordanian educational institutions due to its role in improving achieving academic performance.

Road networks are routinely exposed to partial capacity losses caused by incidents, work zones, flooding and pavement failures, and planners need fast, trustworthy estimates of how such losses propagate through route choice. This paper develops a framework for user-equilibrium (UE) traffic networks with degradable link capacities that combines variational analysis with a learned surrogate. Writing the Beckmann potential in the coordinates $u_a=c_a^{-p}$ makes it affine in u, so standard results (the concavity of a pointwise minimum of affine functions and Danskin’s theorem) show that the optimal potential is concave, non-decreasing and differentiable, with a gradient available from one equilibrium solution. The resulting criticality index equals $p/(p+1)$ times the congestion delay of each link; the theory adds to this classical indicator a derivative interpretation and certified bounds. We derive two-sided certificates on the potential change for any degradation, the identity $\mathrm{TSTT}=(p+1)V-p\mathrm{FFT}$, a solver-free certificate for the total system travel time (TSTT), and an additive certificate for restoring sets of links, whose per-link weight is the damaged-state delay of the link times a severity factor, and we use these quantities as features of an envelope-informed surrogate (EIGS), a gradient-boosted model on per-link envelope features diffused over the line graph. Experiments on the Sioux Falls and Anaheim benchmarks show where the results are useful and where they are not. The potential certificate is within a factor of 1.45 of the exact change at 25% capacity loss on Sioux Falls (1.03 on the 100 most critical Anaheim links) but loosens quickly with severity (factor 3.28 at 50% loss on Sioux Falls; 574 and 74 at 90% loss), and the solver-free TSTT bound exceeds the true change by a median factor of 3.8–12 in the multi-link Monte Carlo scenario sets. No single cheap indicator ranks link criticality best on both networks. On Sioux Falls, EIGS predicts the TSTT increase with $R^2=0.942$ (0.913–0.960 over ten splits) against 0.919 for ridge regression and 0.924 for gradient boosting on the diffused severities, and with $R^2=0.909$ against 0.862 on a spatially correlated test set generated after the model was fixed. Under shifts in demand ($\pm 15\%$) and nominal capacity, refreshing its envelope features from one equilibrium of the shifted network gives $R^2=0.86$–$0.91$; using the same equilibrium to rescale the predictions of raw-input baselines gives them $0.84$–$0.89$, so the gain from the per-link envelope features is modest. On Anaheim, whose losses are extremely heavy-tailed, EIGS ranks scenarios well (Spearman $\rho =0.92$) but does not predict their magnitude reliably, while ridge regression with a cross-validated penalty ranks them poorly ($\rho =0.16$). For restoring three of about fifteen damaged links, the certified envelope rule, which needs a single equilibrium, recovers on average 93.6% of the loss recovered by the exact optimum over all three-link sets.