Applied Science Private University

About: Applied Science Private University is a education organization based out in Amman, Jordan. It is known for research contribution in the topics: Catalysis & Population. The organization has 4124 authors who have published 5299 publications receiving 116167 citations.

Fine-tuning ADAS algorithm parameters for optimizing traffic safety and mobility in connected vehicle environment.

Abstract: Under the Connected Vehicle environment where vehicles and road-side infrastructure can communicate wirelessly, the Advanced Driver Assistance Systems (ADAS) can be adopted as an actuator for achieving traffic safety and mobility optimization at highway facilities. In this regard, the traffic management centers need to identify the optimal ADAS algorithm parameter set that leads to the optimization of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. Once the ADAS-equipped drivers implement the optimal parameter set, they become active agents that work cooperatively to prevent traffic conflicts, and suppress the development of traffic oscillations into heavy traffic jams. Measuring systematic effectiveness of this traffic management requires am analytic capability to capture the quantified impact of the ADAS on individual drivers' behaviors and the aggregated traffic safety and mobility improvement due to such an impact. To this end, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state-of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through a multi-objective optimization approach that uses the Genetic Algorithm. The developed methodology is tested at a freeway facility under low, medium and high ADAS market penetration rate scenarios. The case study reveals that fine-tuning the ADAS algorithm parameter can significantly improve the throughput and reduce the traffic delay and conflicts at the study site in the medium and high penetration scenarios. In these scenarios, the ADAS algorithm parameter optimization is necessary. Otherwise the ADAS will intensify the behavior heterogeneity among drivers, resulting in little traffic safety improvement and negative mobility impact. In the high penetration rate scenario, the identified optimal ADAS algorithm parameter set can be used to support different control objectives (e.g., safety improvement has priority vs. mobility improvement has priority).

A Superiority-Inferiority-Based Inexact Fuzzy Stochastic Programming Approach for Solid Waste Management Under Uncertainty

Abstract: A superiority–inferiority-based inexact fuzzy stochastic programming (SI-IFSP) model was developed for planning municipal solid waste management systems under uncertainty. The SI-IFSP approach represents a new attempt to tackle multiple uncertainties in objective function coefficients which are beyond the capabilities of existing inexact programming methods. Through introducing the concept of fuzzy random boundary interval, SI-IFSP is capable of reflecting multiple uncertainties (i.e., interval values, fuzzy sets, probability distributions, and their combinations) in both the objective function and constraints, leading to enhanced system robustness. The developed SI-IFSP method was applied to a case study of long-term municipal solid waste management. Useful solutions were generated. A number of decision alternatives could be generated based on projected applicable conditions, reflecting the compromise between system optimality and reliability as well as the tradeoffs between economic and environmental objectives. Moreover, the consequences of system violations could be quantified through introducing a set of economic penalties, reflecting the relationships between system costs and constraint violation risks. The results suggest that the proposed SI-IFSP method can explicitly address complexities in municipal solid waste management systems and is applicable to practical waste management problems.

Saturated Fatty Acids Modulate Autophagy's Proteins in the Hypothalamus

Abstract: Autophagy is an important process that regulates cellular homeostasis by degrading dysfunctional proteins, organelles and lipids. In this study, the hypothesis that obesity could lead to impairment in hypothalamic autophagy in mice was evaluated by examining the hypothalamic distribution and content of autophagic proteins in animal with obesity induced by 8 or 16 weeks high fat diet to induce obesity and in response to intracerebroventricular injections of palmitic acid. The results showed that chronic exposure to a high fat diet leads to an increased expression of inflammatory markers and downregulation of autophagic proteins. In obese mice, autophagic induction leads to the downregulation of proteins, such as JNK and Bax, which are involved in the stress pathways. In neuron cell- line, palmitate has a direct effect on autophagy even without inflammatory activity. Understanding the cellular and molecular bases of overnutrition is essential for identifying new diagnostic and therapeutic targets for obesity.