Search Results (4)

This paper focuses on the acceptance of ADASs in the traffic safety and human factor domain. More specifically, it examines the predictive validity of the Unified Model of Driver Acceptance (UMDA) for an ADAS bundle that includes forward collision warning, headway monitoring and warning, and lane-keeping assistance in Belgium and Vietnam, two substantially different geographical, socio-cultural, and macroeconomic settings. All systems in the studied ADAS bundle are located at the Society of Automotive Engineer (SAE)-level 0 of automation. We found moderate acceptance towards such an ADAS bundle in both countries, and respondents held rather positive opinions about system-specific characteristics. In terms of predictive validity, the UMDA scored quite well in both countries, though better in Belgium than in Vietnam. Macroeconomic factors and socio-cultural characteristics could explain these differences between the two countries. Policymakers are encouraged to prioritise initiatives that stimulate the purchase and use of the ADAS, rather than on measures meant to influence the underlying decisional balance. Full article

Mathematical models are used for simulating the electrochemical phenomena of proton-exchange-membrane (PEM) fuel cells. They differ in the scale, modeling variables, precision in specific features, and the required parameters. Often, the input parameters are not measurable and need to be estimated by minimizing the error between the model output and experimental data; however, the estimated parameters could differ from one model to another, hence provoking uncertainty about the correct values and the model’s suitability for simulating the real phenomenon. To address these issues, we introduced a self-validating methodology using three different mathematical models: The first set of parameters was estimated with a semi-empirical (SE) model; then, it was used for computing several points of the polarization curve (PC). The SE parameters and points were used to estimate a second set of parameters and to compute a single point of the PC with a macro-homogeneous (MH) model. The parameters and concentration profiles from the MH solution were used to estimate the last set of parameters with the reaction–convection–diffusion (SP-RCD) model, increasing the detail of the simulation. The SP-RCD parameters were returned to the MH model to recover the complete PC. The proposed methodology requires a few data points to consistently recover the same PC from the three models through estimating parameters in one model and validating them in the others. As output, the method provides complete information about several variables and the physical properties of the catalysts. In addition to the consistent simulation, the numerical results are consistent with those reported in the literature, thus validating the proposed method. Full article

The problem of scheduling parallel tasks graphs (PTGs) represented by directed acyclic graphs (DAGs) in heterogeneous distributed computing systems (HDCSs) is considered an nondeterministic polynomial time (NP) problem due to the diversity of characteristics and parameters, generally opposed, intended to be optimized. The PTGs are scheduled by a scheduler that determines the best location for the sub-tasks that constitute the PTGs and is responsible for allocating the resources of the HDCS to the sub-tasks of the PTGs. To optimize scheduling and allocations, the scheduler extracts characteristics from the internal structure of the PTGs. The prevailing characteristic in existing research is the critical path (CP), which is limited to providing execution paths of PTGs; considering this limitation, we extend the array method proposed in Velarde, which extracts two additional characteristics to the CP: the layering and the density of the graph for scheduling. These characteristics are represented as integer values of the PTGs to be scheduled; the values obtained from the characteristics are stored in arrays representing populations that are evaluated with the heuristic univariate marginal distribution algorithm (UMDA) and in terms of comparison with the genetic algorithm. With the best allocations produced by the algorithms, two performance parameters are evaluated: makespan and waiting time. The results indicate that when more PTGs characteristics are considered, resource allocations are optimized, and scheduling times are reduced. The results obtained with the heuristic algorithms show that UMDA provides shorter scheduling and allocation times compared with the genetic algorithm; UMDA widely distributes the sub-tasks in the clusters, whereas the genetic algorithm compacts the assignments of the PTGs in the clusters with a longer convergence time that translates into longer scheduling and allocation times. Extensive explanations of these conclusions are provided in this work, based on the conducted experiments. Full article

Quelites are Mexican wild plants, reported as excellent sources of nutritional compounds such as amino acids (serine, glycine, and cysteine), minerals (Mg, Fe, and Zn), and phytochemicals, as phenolic acids (chlorogenic acid) and flavonoids (phloridzin and naringenin); on the other hand, high biological activity has been shown in these compounds. This work aimed to evaluate the effect of a supplementation with two endemic quelites of Mexico (Chenopodium berlandieri L. and Portulaca Oleracea L.); in addition to supplementation, a nutritional intervention was performed; the biomarkers of hemoglobin (Hb), urinary malondialdehyde (UMDA), and urinary arsenic (UAs) were measured in adolescents exposed to arsenic. A clinical intervention study was conducted in 27 adolescents ages 11 to 12 years for 4 weeks. Weekly anthropometric and dietary evaluations were carried out, as well as the concentration of Hb; the UMDA and UAs were performed by plate-based colorimetric measurement and atomic absorption spectrophotometry with the hydrides generation system, respectively. The results showed that UMDA concentrations had a significant improvement in the supplemented group (SG) vs. control group (CG) (SG = 1.59 ± 0.89 µM/g creatinine vs. CG = 2.90 ± 0.56 µM/g creatinine) in the second week of intervention; on the other hand, the supplemented group showed an increase in Hb levels (15.12 ± 0.99 g/dL) in the same week; finally after the second week, an increase in UAs levels was observed significantly compared to the baseline value (Baseline: 56.85; Week 2: 2.02 µg/g creatinine). Therefore, the results show that the mixture of quelites (a rich source of phytochemicals and nutrients) improved hemoglobin and UMDA levels, and urinary arsenic excretion from the second week in the exposed population. Full article