Giorgio Kaniadakis, Tiziana Di Matteo, Antonio Maria Scarfone & Giampiero Gervino

The European Physical Journal B Volume 97, article number 203, (2024)

This issue contains peer-reviewed papers based on selected contributions presented at the International Conference on Statistical Physics (SigmaPhi) held in Chania-Crete (Greece) from July 10th to July 14th, 2023 (http://sigmaphisrv.polito.it/). The challenge facing statistical physics today is expanding beyond conventional conceptions of physics, bringing together multiple research streams that were thought to be separate and independent for the majority of the 20th century. In this topical issue, we present a collection of papers that demonstrate the current applications of statistical physics in a variety of different fields, including networks, biophysics, statistical mechanics, kinetic theory, and cosmology.

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Stuart Bartlett, Andrew W. Eckford, Matthew Egbert, Manasvi Lingam, Artemy Kolchinsky, Adam Frank, Gourab Ghoshal

This paper explores the idea that information is an essential and distinctive feature of living systems. Unlike non-living systems, living systems actively acquire, process, and use information about their environments to respond to changing conditions, sustain themselves, and achieve other intrinsic goals. We discuss relevant theoretical frameworks such as “semantic information” and “fitness value of information”. We also highlight the broader implications of our perspective for fields such as origins-of-life research and astrobiology. In particular, we touch on the transition to information-driven systems as a key step in abiogenesis, informational constraints as determinants of planetary habitability, and informational biosignatures for detecting life beyond Earth. We briefly discuss experimental platforms which offer opportunities to investigate these theoretical concepts in controlled environments. By integrating theoretical and experimental approaches, this perspective advances our understanding of life’s informational dynamics and its universal principles across diverse scientific domains.

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Babak Ravandi, Gordana Ispirova, Michael Sebek, Peter Mehler, Albert-László Barabási & Giulia Menichetti
Nature Food (2025)

The offering of grocery stores is a strong driver of consumer decisions. While highly processed foods such as packaged products, processed meat and sweetened soft drinks have been increasingly associated with unhealthy diets, information on the degree of processing characterizing an item in a store is not straightforward to obtain, limiting the ability of individuals to make informed choices. GroceryDB, a database with over 50,000 food items sold by Walmart, Target and Whole Foods, shows the degree of processing of food items and potential alternatives in the surrounding food environment. The extensive data gathered on ingredient lists and nutrition facts enables a large-scale analysis of ingredient patterns and degrees of processing, categorized by store, food category and price range. Furthermore, it allows the quantification of the individual contribution of over 1,000 ingredients to ultra-processing. GroceryDB makes this information accessible, guiding consumers toward less processed food choices.

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Roberto Murcio, Balamurugan Soundararaj

The accurate estimation of human activity in cities is one of the first steps towards understanding the structure of the urban environment. Human activities are highly granular and dynamic in spatial and temporal dimensions. Estimating confidence is crucial for decision-making in numerous applications such as urban management, retail, transport planning and emergency management. Detecting general trends in the flow of people between spatial locations is neither obvious nor easy due to the high cost of capturing these movements without compromising the privacy of those involved. This research intends to address this problem by examining the movement of people in a SmartStreetSensors network at a fine spatial and temporal resolution using a Transfer Entropy approach.

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Yiding Cao, Yingjun Dong, Minjun Kim, Neil G. MacLaren, Sriniwas Pandey, Shelley D. Dionne, Francis J. Yammarino & Hiroki Sayama
npj Complexity

Human collective tasks in teams and organizations increasingly require participation of members with diverse backgrounds working in networked social environments. However, little is known about how network structure and the functional diversity of member backgrounds would interact with each other and affect collective processes. Here we conducted three sets of human-subject experiments which involved 617 university students who collaborated anonymously in a collective ideation task on a custom-made online social network platform. We found that spatially clustered collectives with assortative background distribution tended to explore more diverse ideas than in other conditions, whereas collectives with random background distribution consistently generated ideas with the highest utility. We also found that higher network connectivity may improve individuals’ overall experience but may not improve the collective performance regarding idea generation, idea diversity, and final idea quality.

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