The authors report on the experimental demonstration of a $\mathrm{Ga}\mathrm{As}∕\mathrm{Al}\mathrm{Ga}\mathrm{As}$ -based quantum cellular automata cell fabricated using electron beam lithographically defined gates. These surface metallic gates form a pair of double quantum dots, as well as a pair of quantum point contacts (QPCs) that act as noninvasive voltage probes. Measurements at cryogenic temperatures show that an electron transfer in the input dots induces the relocation of a single electron in the output dots. Using the QPCs they were also able to determine the operating limits of the cell.

Considering both biological and non-biological polygonal shape organizations, in this paper we introduce a quantitative method which is able to determine informational entropy as spatial differences between heterogeneity of internal areas from simulation and experimental samples. According to these data (i.e., heterogeneity), we are able to establish levels of informational entropy using statistical insights of spatial orders using discrete and continuous values. Given a particular state of entropy, we establish levels of information as a novel approach which can unveil general principles of biological organization. Thirty-five geometric aggregates are tested (biological, non-biological, and polygonal simulations) in order to obtain the theoretical and experimental results of their spatial heterogeneity. Geometrical aggregates (meshes) include a spectrum of organizations ranging from cell meshes to ecological patterns. Experimental results for discrete entropy using a bin width of 0.5 show that a particular range of informational entropy (0.08 to 0.27 bits) is intrinsically associated with low rates of heterogeneity, which indicates a high degree of uncertainty in finding non-homogeneous configurations. In contrast, differential entropy (continuous) results reflect negative entropy within a particular range (−0.4 to −0.9) for all bin widths. We conclude that the differential entropy of geometrical organizations is an important source of neglected information in biological systems.

In this work, the authors developed procedures to explain mean extra travel time (T) and extra travel time variability (V). This was carried out for situations (through simulations) where the fast vehicles’ travel time, whose speed tendency (sp₁) is the permitted speed limit, is negatively affected (i.e., increasing travel time) by the presence of slow vehicles, whose speed tendency (sp₂) is half the speed limit. The speed limit was set in the range of 60 km/h to 90 km/h, with seven cases, and every case had eight simulations, each with different p₁ (fast vehicles’ percentage) and p₂= 100% − p₁ (slow vehicles’ percentage) values. p₂ ranged from 10% to 80% at intervals of 10%, for a total of 56 simulations. From the simulations’ data, we calculated the fast vehicles’ extra travel time, which is the additional time to traverse an avenue segment owing to the presence of slow vehicles. The fast and slow vehicles recreate heterogenous traffic in terms of speed. We developed procedures for modeling T and V with p₂, and V with T. For modeling, $~$71.42% of the data from simulations was used. We find that the models’ parameters values can be used for explaining the remaining data. In addition, we discovered that the pattern of p₂ vs. V, for p₂ ranging from 50% to 80%, is different among sp₁ cases and not linear.

It is known that many variables influence traffic, yet very little is known about the weight of each factor in the dynamics of traffic in cities of developing countries, in many cases due to their peculiar traffic regulations. In this work, we search for the variables that have the most significant impact on the average travel speed of three distinct types of vehicles: passenger cars, taxis, and buses. First, we developed a tool featuring algorithms that simulate ordinary overtaking and car-following behaviors, along with controls for setting vehicles’ actions, particularly buses’ and taxis’ stops. Then, we chose a particular zone to study, based on its common geometry and the particular traffic infrastructure (speed bumps, traffic lights, and bus stops) inside it. Later on, three experiments were carried out, with the following results. (1) Both the buses’ arrival frequency and curbside bus stops affect the passenger cars’ average travel speed. The buses’ response was affected by the bus bay and curbside bus stops. The buses’ speed tendency influenced neither the passenger cars’ nor buses’ response. (2) Taxis’ arrival frequency, stopping frequency, and speed tendency were found to influence the passenger cars’ response. Taxis’ response was altered by taxis’ speed tendency, while buses’ response was affected by taxis’ arrival frequencies. (3) The number of speed bumps, the arrival frequency of passenger cars, and their speed conditions (homogeneous and heterogeneous) affect the passenger cars’ response. We expect that the findings presented in this study, along with the recommendations made from the results, may pave the way for better road design public policies.