Physecology: A Conceptual Framework to Describe Data Physicalizations in their Real-World Context

The standard definition for “physicalizations” is “a physical artifact whose geometry or material properties encode data” [ 47 ]. While this working definition provides the fundamental groundwork for conceptualizing physicalization, in practice many physicalization systems go beyond the scope of this definition as they consist of distributed physical and digital elements that involve complex interaction mechanisms. In this article, we examine how “physicalization” is part of a broader ecology—the “physecology”—with properties that go beyond the scope of the working definition. Through analyzing 60 representative physicalization papers, we derived six design dimensions of a physecology: (i) represented data type, (ii) way of information communication, (iii) interaction mechanisms, (iv) spatial input–output coupling, (v) physical setup, and (vi) audiences involved. Our contribution is the extension of the definition of physicalization to the broader concept of “physecology,” to provide conceptual clarity on the design of physicalizations for future work.

Archives and AI: An Overview of Current Debates and Future Perspectives

The digital transformation is turning archives, both old and new, into data. As a consequence, automation in the form of artificial intelligence techniques is increasingly applied both to scale traditional recordkeeping activities, and to experiment with novel ways to capture, organise, and access records. We survey recent developments at the intersection of Artificial Intelligence and archival thinking and practice. Our overview of this growing body of literature is organised through the lenses of the Records Continuum model. We find four broad themes in the literature on archives and artificial intelligence: theoretical and professional considerations, the automation of recordkeeping processes, organising and accessing archives, and novel forms of digital archives. We conclude by underlining emerging trends and directions for future work, which include the application of recordkeeping principles to the very data and processes that power modern artificial intelligence and a more structural—yet critically aware—integration of artificial intelligence into archival systems and practice.

Evolutionary Machine Learning: A Survey

Evolutionary Computation (EC) approaches are inspired by nature and solve optimization problems in a stochastic manner. They can offer a reliable and effective approach to address complex problems in real-world applications. EC algorithms have recently been used to improve the performance of Machine Learning (ML) models and the quality of their results. Evolutionary approaches can be used in all three parts of ML: preprocessing (e.g., feature selection and resampling), learning (e.g., parameter setting, membership functions, and neural network topology), and postprocessing (e.g., rule optimization, decision tree/support vectors pruning, and ensemble learning). This article investigates the role of EC algorithms in solving different ML challenges. We do not provide a comprehensive review of evolutionary ML approaches here; instead, we discuss how EC algorithms can contribute to ML by addressing conventional challenges of the artificial intelligence and ML communities. We look at the contributions of EC to ML in nine sub-fields: feature selection, resampling, classifiers, neural networks, reinforcement learning, clustering, association rule mining, and ensemble methods. For each category, we discuss evolutionary machine learning in terms of three aspects: problem formulation, search mechanisms, and fitness value computation. We also consider open issues and challenges that should be addressed in future work.

Pose Generation for Social Robots in Conversational Group Formations

We study two approaches for predicting an appropriate pose for a robot to take part in group formations typical of social human conversations subject to the physical layout of the surrounding environment. One method is model-based and explicitly encodes key geometric aspects of conversational formations. The other method is data-driven. It implicitly models key properties of spatial arrangements using graph neural networks and an adversarial training regimen. We evaluate the proposed approaches through quantitative metrics designed for this problem domain and via a human experiment. Our results suggest that the proposed methods are effective at reasoning about the environment layout and conversational group formations. They can also be used repeatedly to simulate conversational spatial arrangements despite being designed to output a single pose at a time. However, the methods showed different strengths. For example, the geometric approach was more successful at avoiding poses generated in nonfree areas of the environment, but the data-driven method was better at capturing the variability of conversational spatial formations. We discuss ways to address open challenges for the pose generation problem and other interesting avenues for future work.

The Control of Movements via Motor Gamma Oscillations

The ability to perform movements is vital for our daily life. Our actions are embedded in a complex environment where we need to deal efficiently in the face of unforeseen events. Neural oscillations play an important role in basic sensorimotor processes related to the execution and preparation of movements. In this review, I will describe the state of the art regarding the role of motor gamma oscillations in the control of movements. Experimental evidence from electrophysiological studies has shown that motor gamma oscillations accomplish a range of functions in motor control beyond merely signaling the execution of movements. However, these additional aspects associated with motor gamma oscillation remain to be fully clarified. Future work on different spatial, temporal and spectral scales is required to further understand the implications of gamma oscillations in motor control.

Smart Bioinks for the Printing of Human Tissue Models

3D bioprinting has tremendous potential to revolutionize the field of regenerative medicine by automating the process of tissue engineering. A significant number of new and advanced bioprinting technologies have been developed in recent years, enabling the generation of increasingly accurate models of human tissues both in the healthy and diseased state. Accordingly, this technology has generated a demand for smart bioinks that can enable the rapid and efficient generation of human bioprinted tissues that accurately recapitulate the properties of the same tissue found in vivo. Here, we define smart bioinks as those that provide controlled release of factors in response to stimuli or combine multiple materials to yield novel properties for the bioprinting of human tissues. This perspective piece reviews the existing literature and examines the potential for the incorporation of micro and nanotechnologies into bioinks to enhance their properties. It also discusses avenues for future work in this cutting-edge field.

Evaluation of an Active LF Tracking System and Data Processing Methods for Livestock Precision Farming in the Poultry Sector

Tracking technologies offer a way to monitor movement of many individuals over long time periods with minimal disturbances and could become a helpful tool for a variety of uses in animal agriculture, including health monitoring or selection of breeding traits that benefit welfare within intensive cage-free poultry farming. Herein, we present an active, low-frequency tracking system that distinguishes between five predefined zones within a commercial aviary. We aimed to evaluate both the processed and unprocessed datasets against a “ground truth” based on video observations. The two data processing methods aimed to filter false registrations, one with a simple deterministic approach and one with a tree-based classifier. We found the unprocessed data accurately determined birds’ presence/absence in each zone with an accuracy of 99% but overestimated the number of transitions taken by birds per zone, explaining only 23% of the actual variation. However, the two processed datasets were found to be suitable to monitor the number of transitions per individual, accounting for 91% and 99% of the actual variation, respectively. To further evaluate the tracking system, we estimated the error rate of registrations (by applying the classifier) in relation to three factors, which suggested a higher number of false registrations towards specific areas, periods with reduced humidity, and periods with reduced temperature. We concluded that the presented tracking system is well suited for commercial aviaries to measure individuals’ transitions and individuals’ presence/absence in predefined zones. Nonetheless, under these settings, data processing remains a necessary step in obtaining reliable data. For future work, we recommend the use of automatic calibration to improve the system’s performance and to envision finer movements.

Exploring the Causal Relationship between Factors Affecting US Army Recruitment

Every year, United States Army Recruiting Command (USAREC) dedicates considerable resources to recruiting and accessing soldiers. As the largest branch of the United States Armed Forces, the Army must meet a high recruiting quota while competing in the free-labor market for quality recruits. Over the past two decades, the Army’s success in recruiting ebbed and flowed within the broader context of society and global events. While numerous studies have examined the statistical relationship between factors associated with recruitment, these studies are observational and definitively ascribing causality in retrospect is difficult. With this in mind, we apply fuzzy cognitive mapping (FCM), a graphical method of representing uncertainty in a dynamic system, to model and explore the complex causal relationships between factors. We conclude our paper with implications for USAREC’s efforts, as well as our model’s limitations and opportunities for future work.

An Entropy-Based Approach for Nonparametrically Testing Simple Probability Distribution Hypotheses

In this paper, we introduce a flexible and widely applicable nonparametric entropy-based testing procedure that can be used to assess the validity of simple hypotheses about a specific parametric population distribution. The testing methodology relies on the characteristic function of the population probability distribution being tested and is attractive in that, regardless of the null hypothesis being tested, it provides a unified framework for conducting such tests. The testing procedure is also computationally tractable and relatively straightforward to implement. In contrast to some alternative test statistics, the proposed entropy test is free from user-specified kernel and bandwidth choices, idiosyncratic and complex regularity conditions, and/or choices of evaluation grids. Several simulation exercises were performed to document the empirical performance of our proposed test, including a regression example that is illustrative of how, in some contexts, the approach can be applied to composite hypothesis-testing situations via data transformations. Overall, the testing procedure exhibits notable promise, exhibiting appreciable increasing power as sample size increases for a number of alternative distributions when contrasted with hypothesized null distributions. Possible general extensions of the approach to composite hypothesis-testing contexts, and directions for future work are also discussed.

Geodesign Approaches to City Resilience Planning: A Systematic Review

The increased frequency of extreme events facing society is placing mounting pressure on cities and regions that need more robust resilience planning against growing uncertainty. Data augmented participatory methods, such as geodesign, offer much promise in supporting strategic planning to make our cities and regions more resilient. In that context, this study aims to contribute to a deeper understanding of geodesign practices in resilience planning, through a systematic review of the selected 487 studies available from various bibliographic databases. The results indicate that a total of 75 studies were connected to resilience thinking, with a focus on climate change, floods, and sea level rise risks. A significant cluster of those resilience-related studies worked, especially, on improving sustainability. A detailed analysis of 59 relevant geodesign case studies revealed a strong underlying emphasis on disaster risk reduction and management activities. This study also noticed two prominent approaches among the analysed case studies to future city scenario planning: computational (41 studies), and collaborative (18 studies). It is recommended that an explicit integration of these two approaches into the geodesign approach can assist future city resilience planning endeavours. Thus, future research should further investigate the utility of integrating data-driven modelling and simulation within a collaborative scenario planning process, the usability of digital tools such as planning support systems within a collaborative geodesign framework, and the value of the plan’s performance evaluation during resilience decision-making. Another area for future work is increased community engagement in city resilience practices. The geodesign approach can provide a comprehensive framework for bringing communities, decision-makers, experts, and technologists together to help plan for more resilient city futures. Finally, while geodesign’s explicit role in empirical resilience implementations has been found to be low in this systematic review study, there are significant opportunities to support evidence-based and collaborative city resilience planning and decision-making activities.