A repeated non-coding DNA sequence d(TTAGGG)n is present in the telomeric ends of all human chromosomes. These repeats can adopt multiple inter and intramolecular non-B-DNA conformations that may play an important role in biological processes. Two intramolecular structures of the telomeric oligonucleotide dAGGG(TTAGGG)3, antiparallel and parallel, have been solved by NMR and X-ray crystallography. In both structures, the telomeric sequence adopts an intramolecular quadruplex structure that is stabilized by G-4 quartets, but the ways in which the sequence folds into the quadruplex are different. The folds of the human telomeric DNA were described as an anti-parallel basket-type and a parallel propeller-type. We applied 125I-radioprobing to determine the conformation of the telomeric quadruplex in solution, in the presence of either Na+ or K+ ions. The probability of DNA breaks caused by decay of 125I is inversely related to the distance between the radionuclide and the sugar unit of the DNA backbone; hence, the conformation of the DNA backbone can be deduced from the distribution of breaks. The probability of breaks measured in the presence of Na+ and K+ were compared with the distances in basket-type and propeller-type quadruplexes obtained from the NMR and crystal structures. Our radioprobing data demonstrate that the antiparallel conformation was present in solution in the presence of both K+ and Na+. The preferable conformation in the Na+-containing solution was the basket-type antiparallel quadruplex whereas the presence of K+ favored the chair-type antiparallel quadruplex. Thus, we believe that the two antiparallel and the parallel conformations may coexist in solution, and that their relative proportion is determined by the type and concentration of ions.

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Background Surging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions. Methods Tractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives. Results Our data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7–20.5% higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2–17.3%). Conclusions In general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off.

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Electrification, automation, and connectivity in the automotive and transport industries are gathering momentum, but there are escalating concerns over their need for co-optimization to improve energy efficiency, traffic safety, and ride comfort. Previous approaches to these multi-objective co-optimization problems often overlook trade-offs and scale differences between the objectives, resulting in misleading optimizations. To overcome these limitations, this study proposes a Pareto-based framework that demonstrably optimizes the system parameters of the cooperative adaptive cruise control (CACC) and the energy management strategy (EMS) for PHEVs. The high-level Pareto knowledge assists in finding a best-compromise solution. The results of this work suggest that the energy and the comfort targets are harmonious, but both conflict with the safety target. Validation using real-world driving data shows that the Pareto optimum for CACC and EMS systems, relative to the baseline, can reduce energy consumption (by 7.57 %) and tracking error (by 68.94 %), while simultaneously satisfying ride comfort needs. In contrast to the weighted-sum method, the proposed Pareto method can optimally balance and scale the multiple objective functions. In addition, sensitivity analysis proves that the vehicle reaction time impacts significantly on tracking safety, but its effect on energy saving is trivial.

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Driver-identified supervisory control system of hybrid electric vehicles based on spectrum-guided fuzzy feature extraction', IEEE Transactions on Fuzzy Systems.