Living organisms have developed and optimized ingenious defense strategies based on positional entropy. One of the most significant examples in this respect is known as protean behavior, where a prey animal under threat performs unpredictable zigzag movements in order to confuse, delay or escape the predator. This kind of defensive behavior can inspire efficient strategies for patrolling robots evolving in the presence of adversaries. The main goal of our proposed bioinspired method is to implement the protean behavior by altering the reference path of the robot with sudden and erratic direction changes without endangering the robot's overall mission. By this, a foe intending to target and destroy the mobile robot from a distance has less time for acquiring and retaining the proper sight alignment. The method uses the chaotic dynamics of the 2D Arnold's cat map as a primary source of positional entropy and transfers this feature to every reference path segment using the kinematic relative motion concept. The effectiveness of this novel biologically inspired method is validated through extensive and realistic simulation case studies.

Identifying possible research gaps is a main step in problem framing, however it is increasingly tedious and expensive considering the continuously growing amount of published material. This situation suggests the critical need for methodologies and tools that can assist researchers in their selection of future research topics. Related work mostly focuses on trend analysis and impact prediction but less on research gap identification. This paper presents our first approach in automated identification of feasible research gaps by using a double-threshold procedure to eliminate the research gaps that are currently difficult to study or offer little novelty. Gaps are then found by extracting subgraphs for the less-frequent co-occurrences and correlations of key terms describing domains. A case study applying the methodology for electronic design automation (EDA) domain is also discussed in the paper.

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There has been increasing interest in the study of research communities with the goal of optimizing their outcomes and impact. While current methods can predict future trends, they offer little insight about the causes of the trends. However, causal insight is important for strategic decision making to improve a community. This paper presents a new method to predict the possible causes for inefficiencies in a community by relating them to disconnections between trends, like trends in the number of publications, patents, citations, and so on. The method combines traditional scientometric and webometric metrics and metric predictions with a recent model for trend analysis in a community. The proposed method was used to analyze electronic design automation (EDA) domain. The analysis showed intriguing disconnections between the trends of the number of papers, number of granted patents, and impact of its main publications. The analysis suggests a slightly decreasing impact and visibility of EDA, while having less novel, commonly-accepted knowledge in the area. The gained insight suggests three possible strategic decisions to improve EDA community: avoiding to ignore new ideas, reducing the complexity of framed problems, and keeping a minimal gap between real-life needs and academic solutions.

Investigating the research trends within a scientific domain by analyzing semantic information extracted from scientific journals has been a topic of interest in the natural language processing (NLP) field. A research trend evaluation is generally based on the time evolution of the term occurrence or the term topic, but it neglects an important aspect—research publication latency. The average time lag between the research and its publication may vary from one month to more than one year, and it is a characteristic that may have significant impact when assessing research trends, mainly for rapidly evolving scientific areas. To cope with this problem, the present paper is the first work that explicitly considers research publication latency as a parameter in the trend evaluation process. Consequently, we provide a new trend detection methodology that mixes auto-ARIMA prediction with Mann–Kendall trend evaluations. The experimental results in an electronic design automation case study prove the viability of our approach.