We describe experimental vignette methodology (EVM) as a way to address the dilemma of conducting experimental research that results in high levels of confidence regarding internal validity but is challenged by threats to external validity versus conducting nonexperimental research that usually maximizes external validity but whose conclusions are ambiguous regarding causal relationships. EVM studies consist of presenting participants with carefully constructed and realistic scenarios to assess dependent variables including intentions, attitudes, and behaviors, thereby enhancing experimental realism and also allowing researchers to manipulate and control independent variables. We describe two major types of EVM aimed at assessing explicit (i.e., paper people studies) and implicit (i.e., policy capturing and conjoint analysis) processes and outcomes. We offer best practice recommendations regarding the design and implementation of EVM studies based on a multidisciplinary literature review, discuss substant...

Best Practice Recommendations for Designing and Implementing Experimental Vignette Methodology Studies

With much advancement in the field of nanotechnology, bioengineering, and synthetic biology over the past decade, microscales and nanoscales devices are becoming a reality. Yet the problem of engineering a reliable communication system between tiny devices is still an open problem. At the same time, despite the prevalence of radio communication, there are still areas where traditional electromagnetic waves find it difficult or expensive to reach. Points of interest in industry, cities, and medical applications often lie in embedded and entrenched areas, accessible only by ventricles at scales too small for conventional radio waves and microwaves, or they are located in such a way that directional high frequency systems are ineffective. Inspired by nature, one solution to these problems is molecular communication (MC), where chemical signals are used to transfer information. Although biologists have studied MC for decades, it has only been researched for roughly 10 year from a communication engineering lens. Significant number of papers have been published to date, but owing to the need for interdisciplinary work, much of the results are preliminary. In this survey, the recent advancements in the field of MC engineering are highlighted. First, the biological, chemical, and physical processes used by an MC system are discussed. This includes different components of the MC transmitter and receiver, as well as the propagation and transport mechanisms. Then, a comprehensive survey of some of the recent works on MC through a communication engineering lens is provided. The survey ends with a technology readiness analysis of MC and future research directions.

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A Comprehensive Survey of Recent Advancements in Molecular Communication

Chemotactic bacteria are guided towards the source of a nutrient by local concentration gradients. That works on the microscopic scale, but at larger scales such local cues are unreliable pointers — for example, wind or water currents may disperse odours sought by foraging animals. Using statistical techniques, Vergassola et al. have developed a general search algorithm for movement strategies based on the detection of sporadic cues and partial information. The strategy, termed 'infotaxis' as it maximizes the expected rate of information gain, could find application in the design of 'sniffer' robots. A computational model of odour plume propagation and experimental data are used to devise a general search algorithm for movement strategies in chemotaxis, based on sporadic cues and partial information. The strategy is termed 'infotaxis' as it locally maximizes the expected rate of information gain. Chemotactic bacteria rely on local concentration gradients to guide them towards the source of a nutrient1. Such local cues pointing towards the location of the source are not always available at macroscopic scales because mixing in a flowing medium breaks up regions of high concentration into random and disconnected patches. Thus, animals sensing odours in air or water detect them only intermittently as patches sweep by on the wind or currents2,3,4,5,6. A macroscopic searcher must devise a strategy of movement based on sporadic cues and partial information. Here we propose a search algorithm, which we call ‘infotaxis’, designed to work under such conditions. Any search process can be thought of as acquisition of information on source location; for infotaxis, information plays a role similar to concentration in chemotaxis. The infotaxis strategy locally maximizes the expected rate of information gain. We demonstrate its efficiency using a computational model of odour plume propagation and experimental data on mixing flows7. Infotactic trajectories feature ‘zigzagging’ and ‘casting’ paths similar to those observed in the flight of moths8. The proposed search algorithm is relevant to the design of olfactory robots9,10,11, but the general idea of infotaxis can be applied more broadly in the context of searching with sparse information.

"Infotaxis" as a strategy for searching without gradients

A comprehensive review is presented on the development and state of the art of colorimetric and fluorometric sensor arrays. Optical arrays based on chemoresponsive colorants (dyes and nanoporous pigments) probe the chemical reactivity of analytes, rather than their physical properties. This provides a high dimensionality to chemical sensing that permits high sensitivity (often down to ppb levels), impressive discrimination among very similar analytes and exquisite fingerprinting of extremely similar mixtures over a wide range of analyte types, both in the gas and liquid phases.

https://zenodo.org/record/895426/files/article.pdf

Optical sensor arrays for chemical sensing: the optoelectronic nose

A comprehensive review on the development and state of the art of colorimetric and fluorometric sensor arrays is presented Chemical sensing aims to detect subtle changes in the chemical environment by transforming relevant chemical or physical properties of molecular or ionic species (ie, analytes) into an analytically useful output Optical arrays based on chemoresponsive colorants (dyes and nanoporous pigments) probe the chemical reactivity of analytes, rather than their physical properties (eg, mass) The chemical specificity of the olfactory system does not come from specific receptors for specific analytes (eg, the traditional lock-and-key model of substrate-enzyme interactions), but rather olfaction makes use of pattern recognition of the combined response of several hundred olfactory receptors In a similar fashion, arrays of chemoresponsive colorants provide high-dimensional data from the color or fluorescence changes of the dyes in these arrays as they are exposed to analytes This provides chemical sensing with high sensitivity (often down to parts per billion levels), impressive discrimination among very similar analytes, and exquisite fingerprinting of extremely similar mixtures over a wide range of analyte types, in both the gas and liquid phases Design of both sensor arrays and instrumentation for their analysis are discussed In addition, the various chemometric and statistical analyses of high-dimensional data (including hierarchical cluster analysis (HCA), principal component analysis (PCA), linear discriminant analysis (LDA), support vector machines (SVMs), and artificial neural networks (ANNs)) are presented and critiqued in reference to their use in chemical sensing A variety of applications are also discussed, including personal dosimetry of toxic industrial chemical, detection of explosives or accelerants, quality control of foods and beverages, biosensing intracellularly, identification of bacteria and fungi, and detection of cancer and disease biomarkers

The Optoelectronic Nose: Colorimetric and Fluorometric Sensor Arrays.

We present a mechanical design and implementation of spherical ultrasonic motor (SUSM) that is an actuator with multiple rotational degrees of freedom (multi-DOF). The motor is constructed of 3 annular stators and a spherical rotor and is much smaller and simpler than conventional multi-DOF mechanisms such as gimbals using servomotors. We designed a novel SUSM using experimental data from a single annular stator and a finite element method. The SUSM using a spherical rotor of diameter 20 mm without any reduction gear has demonstrated advantages of high responsiveness, good accuracy, and high torque at low speed. The dynamic implementation of SUSM was consistent with the driving model of SUSM based on a friction drive.

Design and implementation of spherical ultrasonic motor

The use of a dynamic gas sensor network is proposed for air pollution monitoring, and its auto-calibration is discussed to achieve the maintenance-free operation. Although the gas sensor outputs generally show drift over time, frequent recalibration of a number of sensors in the network is a laborious task. To solve this problem, instead of the static network proposed in the related works, we propose to realize a dynamic gas sensor network by, e.g., placing sensors on vehicles running on the streets or placing some of them at fixed points and the others on vehicles. Since each sensor in the dynamic network often meets other sensors, calibration of that specific sensor can be performed by comparing the sensor outputs in such occasions. The sensors in the whole network can thus be calibrated eventually. The simulation results are presented to show that adjusting the sensor outputs to the average values of the sensors sharing the same site improves the measurement accuracy of the sensor network.

Dynamic gas sensor network for air pollution monitoring and its auto-calibration

Gas sensors provide the artificial sense of smell for a mobile robot to track an airborne gas/odor plume and to locate its source. However, a slow response of gas sensors has been the major factor limiting the development of plume-tracking robots. This paper describes a new control algorithm that overcomes the limitation. The basic idea is to detect the onset of a gas sensor response and start recovery by monitoring the relative change in each sensor output. Fast plume tracking is accomplished by making the robot take appropriate actions immediately when the sensor outputs start changing from one state to another. The detection of output change also leads to reliable plume detection and comparison of multiple sensor outputs since it is less affected by drift or sensitivity mismatch than the previous algorithm based on the absolute levels of sensor outputs. Experimental results have shown that the robot can track down a gas source within the distance of 2 m in 30 s even though semiconductor gas sensors with a long recovery time (> 60 s) are used.

Controlling a gas/odor plume-tracking robot based on transient responses of gas sensors

Sensors are devices to receive physical or chemical signals and to convert them into electrical signals. Physical signals are carried by waves such as electromagnetic, optical, and acoustic ones. Sensing technology for physical signals has been well-understood and has already been established. On the other hand, sensing technology for chemical signals, which are carried by chemical substances, is not matured. It is thought that chemical substances are moved by molecular diffusion. However, diffusion velocities of gas molecules are too slow to transport chemical signals under many conditions. The transport of chemical substances onto chemical sensors is actually governed by fluid dynamics. Fluid dynamics offers two aspects such as a signal in spatial domain and one in time domain. A signal in spatial domain is tightly coupled with a plume, a flowing trail of a chemical substance. Although the electromagnetic and acoustic waves mainly propagate straight and their behaviors are easily predicted, it is difficult to predict the behavior of the plume. Thus, it is helpful to see the plume dynamics so that people can understand the plume behavior in spatial domain. Then, the gas distribution can be measured using a homogeneous sensor array. The two types of sensor arrays such as sparse and packed sensor arrays are available. The sparse sensor array can show the global behavior of the plume, whereas the packed one reveals the local detailed behavior of the plume. Although the measurement of gas distribution is the typical method to reveal the chemical-signal behavior in spatial domain, one of the recent topics is the plume generated in a virtual environment, where people perceive sensory stimuli even if they do not stay in the actual environment. In virtual reality, people can perceive an object with smell. The direction to an odor source, the feeling of approaching or going away from it, might be realized even if the actual smell source is not in front of people; it is difficult for a chemical sensor to follow the true dynamic concentration change of the chemical substance. Generally, temporal behavior of a chemical sensor has not been well-studied in comparison with steady-state response. However, the temporal signal sometimes has information of chemical substance. Thus, the technique to know the sensor dynamics such as time constant is required. In some cases, time constant must be obtained even if the concentration profile is irregular and is not known. In some cases, the peaks of the chemical signal over time provide information of chemical substance. The temporal data from preconcentrator is also useful to obtain information on the chemical substance. In addition to raising the sensitivity, the preconcentrator with variable temperature can be used to enhance the pattern separation among odor samples. The sensing in both spatial and time domains is complicated. Although there have been many works in a single domain, a limited number of works have been addressed to combining both domains. The straightforward method to understand the combination of both domains is to observe change in spatial distribution with time. Another approach * Corresponding author. Tel.: +81-3-5734-2579. Fax: +81-3-5734-2828. E-mail: nakamoto@mn.ee.titech.ac.jp. † Tokyo Institute of Technology. ‡ Tokyo University of Agriculture and Technology. 680 Chem. Rev. 2008, 108, 680−704

Chemical sensing in spatial/temporal domains.

Convolutional Long Short-Term Memory Neural Networks (CNN-LSTM) are a variant of recurrent neural networks (RNN) that can extract spatial features in addition to classifying or making predictions from sequential data. In this paper, we analyzed the use of CNN-LSTM for gas source localization (GSL) in outdoor environments using time series data from a gas sensor network and anemometer. CNN-LSTM is used to estimate the location of a gas source despite the challenges created from inconsistent airflow and gas distribution in outdoor environments. To train CNN-LSTM for GSL, we used temporal data taken from a 5 × 6 metal oxide semiconductor (MOX) gas sensor array, spaced 1.5 m apart, and an anemometer placed in the center of the sensor array in an open area outdoors. The output of the CNN-LSTM is one of thirty cells approximating the location of a gas source. We show that by using CNN-LSTM, we were able to determine the location of a gas source from sequential data. In addition, we compared several artificial neural network (ANN) architectures as well as trained them without wind vector data to estimate the complexity of the task. We found that ANN is a promising prospect for GSL tasks.

Application of Convolutional Long Short-Term Memory Neural Networks to Signals Collected from a Sensor Network for Autonomous Gas Source Localization in Outdoor Environments.

Hiroshi Ishida

Papers

Design and implementation of spherical ultrasonic motor

Dynamic gas sensor network for air pollution monitoring and its auto-calibration

Controlling a gas/odor plume-tracking robot based on transient responses of gas sensors

Chemical sensing in spatial/temporal domains.

Application of Convolutional Long Short-Term Memory Neural Networks to Signals Collected from a Sensor Network for Autonomous Gas Source Localization in Outdoor Environments.