Fast acoustic tweezers for the two-dimensional manipulation of individual particles in microfluidic channels

This paper presents a microfluidic device that implements standing surface acoustic waves in order to handle single cells, droplets, and generally particles. The particles are moved in a very controlled manner by the two-dimensional drifting of a standing wave array, using a slight frequency modulation of two ultrasound emitters around their resonance. These acoustic tweezers allow any type of motion at velocities up to few 10mm/s, while the device transparency is adapted for optical studies. The possibility of automation provides a critical step in the development of lab-on-a-chip cell sorters and it should find applications in biology, chemistry, and engineering domains

Sustainable Growth and Ethics: a Study of Business Ethics in Vietnam Between Business Students and Working Adults

Sustainable growth is not only the ultimate goal of business corporations but also the primary target of local governments as well as regional and global economies. One of the cornerstones of sustainable growth is ethics. An ethical organizational culture provides support to achieve sustainable growth. Ethical leaders and employees have great potential for positive influence on decisions and behaviors that lead to sustainability. Ethical behavior, therefore, is expected of everyone in the modern workplace. As a result, companies devote many resources and training programs to make sure their employees live according to the high ethical standards. This study provides an analysis of Vietnamese business students’ level of ethical maturity based on gender, education, work experience, and ethics training. The results of data from 260 business students compared with 704 working adults in Vietnam demonstrate that students have a significantly higher level of ethical maturity. Furthermore, gender and work experience are significant factors in ethical maturity. While more educated respondents and those who had completed an ethics course did have a higher level of ethical maturity, the results were not statistically significant. Analysis of the results along with suggestions and implications are provided

On the perspective transformation for efficient relay placement in wireless multicast networks

This letter investigates the relay placement problem in wireless multicast networks consisting of multiple sources, relays, and destinations. The data transmission from the sources to the destinations is carried out via the relays employing physical-layer network coding technique. Hybrid automatic repeat request protocol with incremental redundancy is applied for reliable communication. In particular, considering a general setting of nodes in irregularly shaped network, an efficient relay placement algorithm is proposed based on perspective transformation technique to find optimal relay positions for minimizing either the total energy consumption or the total delay in the whole network. The proposed algorithm not only helps reduce the relay searching complexity but also facilitates the relay placement for optimizing networks of any shape

Model-based learning for point pattern data

This article proposes a framework for model-based point pattern learning using point process theory. Likelihood functions for point pattern data derived from point process theory enable principled yet conceptually transparent extensions of learning tasks, such as classification, novelty detection and clustering, to point pattern data. Furthermore, tractable point pattern models as well as solutions for learning and decision making from point pattern data are developed

Optimal interdiction of urban criminals with the aid of real-time information

Most violent crimes happen in urban and suburban cities. With emerging tracking techniques, law enforcement officers can have real-time location information of the escaping criminals and dynamically adjust the security resource allocation to interdict them. Unfortunately, existing work on urban network security games largely ignores such information. This paper addresses this omission. First, we show that ignoring the real-time information can cause an arbitrarily large loss of efficiency. To mitigate this loss, we propose a novel NEtwork purSuiT game (NEST) model that captures the interaction between an escaping adversary and a defender with multiple resources and real-time information available. Second, solving NEST is proven to be NP-hard. Third, after transforming the non-convex program of solving NEST to a linear program, we propose our incremental strategy generation algorithm, including: (i) novel pruning techniques in our best response oracle; and (ii) novel techniques for mapping strategies between subgames and adding multiple best response strategies at one iteration to solve extremely large problems. Finally, extensive experiments show the effectiveness of our approach, which scales up to realistic problem sizes with hundreds of nodes on networks including the real network of Manhattan

Predictive Collision Management for Time and Risk Dependent Path Planning

Autonomous agents such as self-driving cars or parcel robots need to recognize and avoid possible collisions with obstacles in order to move successfully in their environment. Humans, however, have learned to predict movements intuitively and to avoid obstacles in a forward-looking way. The task of collision avoidance can be divided into a global and a local level. Regarding the global level, we propose an approach called "Predictive Collision Management Path Planning" (PCMP). At the local level, solutions for collision avoidance are used that prevent an inevitable collision. Therefore, the aim of PCMP is to avoid unnecessary local collision scenarios using predictive collision management. PCMP is a graph-based algorithm with a focus on the time dimension consisting of three parts: (1) movement prediction, (2) integration of movement prediction into a time-dependent graph, and (3) time and risk-dependent path planning. The algorithm combines the search for a shortest path with the question: is the detour worth avoiding a possible collision scenario? We evaluate the evasion behavior in different simulation scenarios and the results show that a risk-sensitive agent can avoid 47.3% of the collision scenarios while making a detour of 1.3%. A risk-averse agent avoids up to 97.3% of the collision scenarios with a detour of 39.1%. Thus, an agent's evasive behavior can be controlled actively and risk-dependent using PCMP.Comment: Extended version of the SIGSPATIAL '20 pape