Flocking motion in swarms with limited sensing radius and heterogeneous input constraints

This paper addresses the flocking motion problem for swarms of agents with two restrictions: limited communication/detection ranges and different input constraints. In this problem, the distance between pairs of agents determines if a communication/detection link exists among them, while each agent has a different control action bound. We use the notion of proximity graph to model communication/detection between agents and provide distributed controllers designed for leaderless and leader-followers flocking motion scenarios. Our proposed designs preserve the connectivity of the proximity graph while the control effort satisfies the bound of each particular agent. Unlike previous results, our protocols take advantage of the group’s input heterogeneity to use agents with a greater capacity to compensate for neighbors that are less capable of meeting the group’s requirements. Additionally, our designs are based only on local state errors and are robust to non-modeled edge failures. To illustrate the effectiveness of our proposal, we use numerical simulations of different flocking scenarios.     

Novel model reference adaptive control laws for improved transient dynamics and guaranteed saturation constraints

In classical model reference adaptive control (MRAC), the adaptive rates must be tuned to meet multiple competing objectives. Large adaptive rates guarantee rapid convergence of the trajectory tracking error to zero. However, large adaptive rates may also induce saturation of the actuators and excessive overshoots of the closed-loop system’s trajectory tracking error. Conversely, low adaptive rates may produce unsatisfactory trajectory tracking performances. To overcome these limitations, in the classical MRAC framework, the adaptive rates must be tuned through an iterative process. Alternative approaches require to modify the plant’s reference model or the reference command input. This paper presents the first MRAC laws for nonlinear dynamical systems affected by matched and parametric uncertainties that constrain both the closed-loop system’s trajectory tracking error and the control input at all times within user-defined bounds, and enforce a user-defined rate of convergence on the trajectory tracking error. By applying the proposed MRAC laws, the adaptive rates can be set arbitrarily large and both the plant’s reference model and the reference command input can be chosen arbitrarily. The user-defined rate of convergence of the closed-loop plant’s trajectory is enforced by introducing a user-defined auxiliary reference model, which converges to the trajectory tracking error obtained by applying the classical MRAC laws before its transient dynamics has decayed, and steering the trajectory tracking error to the auxiliary reference model at a rate of convergence that is higher than the rate of convergence of the plant’s reference model. The ability of the proposed MRAC laws to prescribe the performance of the closed-loop system’s trajectory tracking error and control input is guaranteed by barrier Lyapunov functions. Numerical simulations illustrate both the applicability of our theoretical results and their effectiveness compared to other techniques such as prescribed performance control, which allows to constrain both the rate of convergence and the maximum overshoot on the trajectory tracking error of uncertain systems.     

Visualization of the moving Pareto frontier in DSS

A new approach to decision support in a multiobjective problem arising in controlled dynamic system design is described. The approach basically extends the feasible goals method that implies specifying the goal point by visualizing the Pareto frontier that characterizes feasible objective values and objective tradeoffs to dynamic systems. The developed approach helps explore dynamic modifications of the Pareto frontier.     

A Review on Salivary Genomics and Proteomics Biomarkers in Oral Cancer

Oral cancer has emerged as an alarming public health problem with increasing incidence and mortality rates all over the world. Therefore, the implementation of newer screening and early detection approaches are of utmost importance which could reduce the morbidity and mortality associated with this disease. Sensitive and specific biomarkers for oral cancer are likely to be most effective for screening, diagnosis, staging and follow-up for this dreaded malignancy. Unlike other deep cancers, oral cancer is located in oral cavity. Hence, the direct contact between saliva and oral cancer lesion makes the measurement of tumor markers in saliva an attractive alternative to serum and tissue testing. The DNA, RNA and protein molecules derived from the living cancer cells can be conveniently obtained from saliva. Thus, salivary biomarkers, a non-invasive alternative to serum and tissue-based biomarkers may be an effective modality for early diagnosis, prognostication and monitoring post therapy status. In the current post-genomic era, various technologies provide opportunities for high-throughput approaches to genomics and proteomics; which have been used to evaluate altered expressions of gene and protein targets in saliva of oral cancer patients. The emerging field of salivary biomarkers has great potentials to prove its clinical significance to combat oral cancer. Hence, we have reviewed importance of several salivary genomics and proteomics biomarkers for oral cancer.     

New results on BIBO stability analysis for a class of neutral delay systems

This paper studies bounded input bounded output (BIBO) stability for a class of neutral systems with time-varying delays. Based on Lyapunov method and linear matrix inequalities, some new BIBO stability criteria are established. The numerical simulation is made to demonstrate the effectiveness of the theoretical results obtained in this paper.     

Growing where you are planted: Exogenous firms and the seeding of Silicon Valley

What are the respective roles of indigenous and exogenous factors in the development of high-tech regions? Entrepreneurs and their start-ups have dominated Silicon Valley's economy in recent decades, but a different dynamic was at work from 1940 to 1965, when the Valley emerged as a formidable high-tech region. In key industries (electronics, semiconductors, computers, and aerospace) that defined Silicon Valley as a high-tech cluster during that period, companies based elsewhere played critical roles in planting the organizations that would - through the innovations they made, the technical talent they attracted, and the start-ups they spun off - help make the Valley the world's most admired and emulated high-tech region.     

The impact of consumer trust on attitudinal loyalty and purchase intentions in B2C e-marketplaces: Intermediary trust vs. seller trust

The online merchant of an e-marketplace consists of an intermediary, providing the market infrastructure, and the community of sellers conducting business within that infrastructure. Typically, consumers willingly buy from unknown sellers within an e-marketplace, despite the apparent risk, since they trust the institutional mechanisms furnished by the relatively well-known intermediary. Consumers’ trust in one component of the e-marketplace merchant may not only affect their trust in the other, but also influence the way consumers make online purchases. This paper explores the impact of trust on consumer behavior in e-marketplaces. An empirical study has been conducted to accomplish our research objectives, using a questionnaire survey of 222 active e-marketplace shoppers in Korea. The results reveal that consumer trust in an intermediary has a strong influence upon both attitudinal loyalty and purchase intentions, although consumer trust in the community of sellers has no significant effect on the two constructs representing consumer behavior. In addition, it was found that trust is transferred from an intermediary to the community of sellers, implying that the trustworthiness of the intermediary plays a critical role in determining the extent to which consumers trust and accept the sellers in the e-marketplace. This paper offers some implications from the findings of the research.