Abstract:
We evolve topology of a network of N fully-coupled nodes that interact according to repulsion-attractiondynamics within a confining wall. The dynamics portrays each node’s tendency to keep distance from itscompetitors while maintaining a lighter tendency to resist relative isolation. Each node is characterizedby two parameters: an intrinsic mobility μ and a preferred neighboring distance ρ. Onset of clustering isfound to occur at a critical variance in mobility, σμ2 = 1, and in preferred neighboring distance, σμ2 = 10.This result implies that small-world behavior manifested in clustering can be triggered by the diversity ofnode population.

Abstract:
We derive a general formulation of the self-organized branching process by considering sandpile dynamics in an evolving population characterized by "birth" (excitation) and "death" (de-excitation) of active sites ($z=1$). New active sites are born in empty sites ($z=0$) with a probability of $\eta$, whereas active sites die, thus becoming empty, with a probability $\lambda$. Subsequently, when an active site becomes unstable ($z=2$), it topples by transferring two grains to two randomly chosen sites with probability $\alpha$ or, by transferring only one grain to a randomly selected site (while retaining the other) with probability $\beta=1+\frac{\lambda}{\eta}-2\alpha$, thus remaining active after toppling. We show that when sandpile dynamics occurs in an evolving population, self-organized criticality, characterized by a power-law avalanche size distribution with exponent $\tau_s=3/2$ and power-law avalanche duration distribution with exponent $\tau_T=2$ at very high dimension $n >> 1$, is achieved even in the presence of dissipation ($\epsilon = 1-\alpha - \beta > 0$), contrary to previous claims.

Abstract:
The study was conducted to assess the effectiveness of career-oriented performance task (COPT) approach against the traditional teaching approach (TTA) in enhancing students’ critical thinking skills. Specifically, it sought to find out if students exposed to COPT have higher critical thinking skills than those students exposed to the traditional teaching approach (TTA). COPT approach aims to integrate career-oriented examples and inquiry-based activities in general inorganic chemistry. The study used the quasi-experimental pretest-posttest control group design. The sample of the study consisted of two (2) intact sections of first-year students in a private higher education institution in Manila who are enrolled in general inorganic chemistry during the second semester of school year 2011-2012. Thirty-nine (39) students are in the COPT class while thirty-eight (38) students are in the TTA class. The instrument used in the study is the Watson-Glaser Critical Thinking Appraisal (WGCTA) to evaluate students’ critical thinking skills. The study found out that the mean posttest score in the WGCTA was not significantly higher for students exposed to COPT than for students exposed to TTA. The COPT approach in teaching chemistry was not effective in enhancing students’ critical thinking skills given the limited time of intervention. Longer exposure to intervention is necessary to enhance students’ critical thinking skills. 1. Background of the Study In the Philippines, results of the national achievement test in secondary science were reported to be 51.8% in 2007 and 57.8% in 2008. Although there has been an evident increase in students’ mastery level of six percentage points, it is still far from the government’s target criterion level, which is 75% [1] Moreover, out of 45 participating countries in the Trends in International Mathematics and Science Study (TIMSS) in 2003, the Philippines ranked 41st and 42nd in mathematics and science, respectively. This suggests that Filipino students are weak in terms of mastery level in mathematics and science when they graduate from high school [2]. Specifically, in chemistry, Filipino students have 30% average correct answers in TIMSS which is way below the international average of 45% correct answers. One of the most relevant skills in science learning is student’s critical thinking skills. Critical thinking skills are those requiring students to apply information in new situations and in solving problems. Critical thinking is an intellectually disciplined process that is characterized by creative conceptualization,

Abstract:
We examine avalanche statistics of rain- and vibration-driven granular slides in miniature sand mounds. A crossover from power-law to non power-law avalanche-size statistics is demonstrated as a generic driving rate $\nu$ is increased. For slowly-driven mounds, the tail of the avalanche-size distribution is a power-law with exponent $-1.97\pm 0.31$, reasonably close to the value previously reported for landslide volumes. The interevent occurrence times are also analyzed for slowly-driven mounds; its distribution exhibits a power-law with exponent $-2.670\pm 0.001$.

Abstract:
A simple algorithm for constructing an effective traffic model is presented. The algorithm uses statistically well-defined quantities extracted from the flow-density plot, and the resulting effective model naturally captures and predicts many quantitative and qualitative empirical features of the highway traffic, especially with the presence of an on-ramp bottleneck. The simplicity of the effective model provides strong evidence that stochasticity, diversity of vehicle types and modeling of complicated driving behaviors are \emph{not} fundamental to many observations in the complex real traffic dynamics. We also propose the nature of the congested phase can be well characterized by the long lasting transient states of the effective model, from which the wide moving jams evolve.

Abstract:
We show that all existing deterministic microscopic traffic models with identical drivers (including both two-phase and three-phase models) can be understood as special cases from a master model by expansion around well-defined ground states. This allows two traffic models to be compared in a well-defined way. The three-phase models are characterized by the vanishing of leading orders of expansion within a certain density range, and as an example the popular intelligent driver models (IDM) is shown to be equivalent to a generalized optimal velocity (OV) model. We also explore the diverse solutions of the generalized OV model that can be important both for understanding human driving behaviors and algorithms for autonomous driverless vehicles.

Abstract:
A sufficient knowledge of the demographics of a commuting public is essential in formulating and implementing more targeted transportation policies, as commuters exhibit different ways of traveling. With the advent of the Automated Fare Collection system (AFC), probing the travel patterns of commuters has become less invasive and more accessible. Consequently, numerous transport studies related to human mobility have shown that these observed patterns allow one to pair individuals with locations and/or activities at certain times of the day. However, classifying commuters using their travel signatures is yet to be thoroughly examined. Here, we contribute to the literature by demonstrating a procedure to characterize passenger types (Adult, Child/Student, and Senior Citizen) based on their three-month travel patterns taken from a smart fare card system. We first establish a method to construct distinct commuter matrices, which we refer to as eigentravel matrices, that capture the characteristic travel routines of individuals. From the eigentravel matrices, we build classification models that predict the type of passengers traveling. Among the models explored, the gradient boosting method (GBM) gives the best prediction accuracy at 76%, which is 84% better than the minimum model accuracy (41%) required vis-\`a-vis the proportional chance criterion. In addition, we find that travel features generated during weekdays have greater predictive power than those on weekends. This work should not only be useful for transport planners, but for market researchers as well. With the awareness of which commuter types are traveling, ads, service announcements, and surveys, among others, can be made more targeted spatiotemporally. Finally, our framework should be effective in creating synthetic populations for use in real-world simulations that involve a metropolitan's public transport system.

Abstract:
Animal and human clusters are complex adaptive systems and many are organized in cluster sizes $s$ that obey the frequency-distribution $D(s)\propto s^{-\tau}$. Exponent $\tau$ describes the relative abundance of the cluster sizes in a given system. Data analyses have revealed that real-world clusters exhibit a broad spectrum of $\tau$-values, $0.7\textrm{(tuna fish schools)}\leq\tau\leq 2.95\textrm{(galaxies)}$. We show that allelomimesis is a fundamental mechanism for adaptation that accurately explains why a broad spectrum of $\tau$-values is observed in animate, human and inanimate cluster systems. Previous mathematical models could not account for the phenomenon. They are hampered by details and apply only to specific systems such as cities, business firms or gene family sizes. Allelomimesis is the tendency of an individual to imitate the actions of its neighbors and two cluster systems yield different $\tau$ values if their component agents display different allelomimetic tendencies. We demonstrate that allelomimetic adaptation are of three general types: blind copying, information-use copying, and non-copying. Allelomimetic adaptation also points to the existence of a stable cluster size consisting of three interacting individuals.

Abstract:
Interdependent networks are ubiquitous in our society, ranging from infrastructure to economics, and the study of their cascading behaviors using percolation theory has attracted much attention in the recent years. To analyze the percolation phenomena of these systems, different mathematical frameworks have been proposed including generating functions, eigenvalues among some others. These different frameworks approach the phase transition behaviors from different angles, and have been very successful in shaping the different quantities of interest including critical threshold, size of the giant component, order of phase transition and the dynamics of cascading. These methods also vary in their mathematical complexity in dealing with interdependent networks that have additional complexity in terms of the correlation among different layers of networks or links. In this work, we review a particular approach of simple self-consistent probability equations, and illustrate that it can greatly simplify the mathematical analysis for systems ranging from single layer network to various different interdependent networks. We give an overview on the detailed framework to study the nature of the critical phase transition, value of the critical threshold and size of the giant component for these different systems.

Abstract:
We propose a framework for constructing microscopic traffic models from microscopic acceleration patterns that can in principle be experimental measured and proper averaged. The exact model thus obtained can be used to justify the consistency of various popular models in the literature. Assuming analyticity of the exact model, we suggest that a controlled expansion around the constant velocity, uniform headway "ground state" is the proper way of constructing various different effective models. Assuming a unique ground state for any fixed average density, we discuss the universal properties of the resulting effective model, focusing on the emergent quantities of the coupled non-linear ODEs. These include the maximum and minimum headway that give the coexistence curve in the phase diagram, as well as an emergent intrinsic scale that characterizes the strength of interaction between clusters, leading to non-trivial cluster statistics when the unstable ground state is randomly perturbed. Utilizing the universal properties of the emergent quantities, a simple algorithm for constructing an effective traffic model is also presented. The algorithm tunes the model with statistically well-defined quantities extracted from the flow-density plot, and the resulting effective model naturally captures and predicts many quantitative and qualitative empirical features of the highway traffic, especially in the presence of an on-ramp bottleneck. The simplicity of the effective model provides strong evidence that stochasticity, diversity of vehicle types and modeling of complicated individual driving behaviors are \emph{not} fundamental to many observations of the complex spatiotemporal patterns in the real traffic dynamics. We also propose the nature of the congested phase can be well characterized by the long lasting transient states of the effective model, from which the wide moving jams evolve.