arXiv subjects

José Halloy

Publications and source records attributed to José Halloy.

16 recordsLinked to original sources

arXiv2026

Connecting Electrical Grid Length and Material Stock to Population Density: Comparison of a French-Calibrated Scaling with 35 Electrified Countries

The expansion of global electricity distribution systems necessitates the deployment of massive infrastructure. Assessing its implications from a spatial and material perspective requires an understanding of the core drivers of a distribution grid configuration. Our model samples substation locations using a non-linear relationship with population density and constructs a proxy network applying the Kruskal algorithm. This streamlined approach generates a proxy grid layout at the local scale and provides reasonable aggregate estimates of the total network length at the national scale. Using highly granular population data, this local model reveals a connection between population spread and distribution grid, which appears to persist at larger scales. Potentially driven by the emergent properties of population scaling laws, the aggregated network characteristics appear to be well described by multivariate power laws on aggregated population and area. Benchmarked against reported aggregate data for 35 countries, these results provide new multi-scale tools for characterizing electrical infrastructure and reveal key determinants of distribution grid extent. Combining network length estimates with material intensity data, we derive country-scale inventories of copper invested in medium-voltage lines, bounded by aerial and underground cabling assumptions. The same scaling law directly yields a macro-level proxy for copper mass as a function of population and area, extending the framework toward prospective assessments of material demand in electricity grid expansion.

physics.soc-ph↗

arXiv2025

The Physics of Sustainability: Material and Power Constraints for the Long Term

Much of today's sustainability discourse emphasizes efficiency, clean technologies, and smart systems, but largely underestimates fundamental physical constraints relating to energy-matter interactions. These constraints stem from the fact that Earth is a materially closed yet energetically open system, driven by the sustained but low power-density flux of solar radiation. This Perspective reframes sustainability within these axiomatic limits, integrating relevant timescales and orders of magnitude. We argue that fossil-fueled industrial metabolism is inherently incompatible with long-term viability, while post-fossil systems are surface-, materials-, and power-intensive. Long-term sustainability must therefore be defined not only by how much energy or material is used, but also by how it is used: favoring organic, carbon-based chemistry with limited reliance on purified metals, operating at low power density, and maintaining low throughput rates. Achieving this requires radical technological shifts toward life-compatible systems and biogeochemical circular processes, and, likely as a consequence, a paradigm change toward degrowth to a steady-state. These two shifts are mutually reinforcing and together provide the necessary foundation for any viable future.

physics.soc-ph↗

arXiv2025

Beyond 2050: From deployment to renewal of the global solar and wind energy system

The global energy transition depends on large-scale photovoltaic (PV) and wind power deployment. While 2050 targets suggest a transition endpoint, maintaining these systems beyond mid-century requires continuous renewal, marking a fundamental yet often overlooked shift in industrial dynamics. This study examines the transition from initial deployment to long-term renewal, using a two-phase growth model: an exponential expansion followed by capacity stabilization. By integrating this pattern with a Weibull distribution of PV panel and wind turbine lifespans, we estimate the annual production required for both expansion and maintenance. Our findings highlight two key factors influencing production dynamics: deployment speed and lifespan. When deployment occurs faster than the average lifespan, production overshoots and exhibits damped oscillations due to successive installation and replacement cycles. In contrast, gradual deployment leads to a smooth increase before stabilizing at the renewal rate. Given current scenarios, the PV industry is likely to experience significant oscillations - ranging from 15 % to 60 % of global production - while wind power follows a monotonic growth trajectory. These oscillations, driven by ambitious energy targets, may result in cycles of overproduction and underproduction, affecting industrial stability. Beyond solar and wind, this study underscores a broader challenge in the energy transition: shifting from infrastructure expansion to long-term maintenance. Addressing this phase is crucial for ensuring the resilience and sustainability of renewable energy systems beyond 2050.

physics.soc-ph↗

arXiv2025

Modeling Technological Deployment and Renewal: Monotonic vs. Oscillating Industrial Dynamics

This study proposes a new model based on a classic S-curve that describes deployment and stabilization at maximum capacity. In addition, the model extends to the post-growth plateau, where technological capacity is renewed according to the distribution of equipment lifespans. We obtain two qualitatively different results. In the case of "fast" deployment, characterized by a short deployment time in relation to the average equipment lifetime, production is subject to significant oscillations. In the case of "slow" deployment, production increases monotonically until it reaches a renewal plateau. These results are counterintuitively validated by two case studies: nuclear power plants as a fast deployment and smartphones as a slow deployment. These results are important for long-term industrial planning, as they enable us to anticipate future business cycles. Our study demonstrates that business cycles can originate endogenously from industrial dynamics of installation and renewal, contrasting with traditional views attributing fluctuations to exogenous macroeconomic factors. These endogenous cycles interact with broader trends, potentially being modulated, amplified, or attenuated by macroeconomic conditions. This dynamic of deployment and renewal is relevant for long-life infrastructure technologies, such as those supporting the renewable energy sector and has major policy implications for industry players.

physics.soc-ph↗

arXiv2023

Investor-patent networks as mutualistic networks

Venture capital investments in startups have come to represent an important driver of technological innovation, in parallel to corporate- and government-directed efforts. Part of the future of artificial intelligence, medicine and quantum computing now depends upon a large number of venture investment decisions whose robustness against increasingly frequent crises has therefore become crucial. To shed light on this issue, and by combining large-scale financial, startup and patent datasets, we analyze the interactions between venture capitalists and technologies as an explicit bipartite patent-investor network. Our results reveal that this network is topologically mutualistic because of the prevalence of links between generalist investors, whose portfolios are technologically diversified, and general-purpose technologies, characterized by a broad spectrum of use. As a consequence, the robustness of venture-funded technological innovation against different types of crises is affected by the high nestedness and low modularity, with high connectance, associated with mutualistic networks.

physics.soc-ph↗

arXiv2022

Applicability of Hubbert model to global mining industry: Interpretations and insights

The Hubert's model has been introduced in 1956 as a phenomenological description of the time evolution of US oil fields production. It has since then acquired a vast notoriety as a conceptual approach to resource depletion. It is often invoked nowadays in the context of the energy transition to question the limitations induced by the finitude of mineral stocks. Yet, its validity is often controversial despite its popularity. This paper offers a pedagogical introduction to the model, assesses its ability to describe the current evolution of 20 mining elements, and discusses the nature and robustness of conclusions drawn from Hubbert's model considered either as a for cast or as a foresight tool. We propose a novel way to represent graphically these conclusions as a "Hubbert's map" which offers direct visualization of their main features.

physics.soc-ph↗

arXiv2020

Tracing the origins of SARS-CoV-2 in coronavirus phylogenies

SARS-CoV-2 is a new human coronavirus (CoV), which emerged in China in late 2019 and is responsible for the global COVID-19 pandemic that caused more than 59 million infections and 1.4 million deaths in 11 months. Understanding the origin of this virus is an important issue and it is necessary to determine the mechanisms of its dissemination in order to contain future epidemics. Based on phylogenetic inferences, sequence analysis and structure-function relationships of coronavirus proteins, informed by the knowledge currently available on the virus, we discuss the different scenarios evoked to account for the origin - natural or synthetic - of the virus. The data currently available is not sufficient to firmly assert whether SARS-CoV2 results from a zoonotic emergence or from an accidental escape of a laboratory strain. This question needs to be solved because it has important consequences on the evaluation of risk/benefit balance of our interaction with ecosystems, the intensive breeding of wild and domestic animals, as well as some lab practices and on scientific policy and biosafety regulations. Regardless of its origin, studying the evolution of the molecular mechanisms involved in the emergence of pandemic viruses is essential to develop therapeutic and vaccine strategies and to prevent future zoonoses. This article is a translation and update of a French article published in M{é}decine/Sciences, Aug/Sept 2020 (http://doi.org/10.1051/medsci/2020123).

q-bio.PE↗

arXiv2019

Automatic Calibration of Artificial Neural Networks for Zebrafish Collective Behaviours using a Quality Diversity Algorithm

During the last two decades, various models have been proposed for fish collective motion. These models are mainly developed to decipher the biological mechanisms of social interaction between animals. They consider very simple homogeneous unbounded environments and it is not clear that they can simulate accurately the collective trajectories. Moreover when the models are more accurate, the question of their scalability to either larger groups or more elaborate environments remains open. This study deals with learning how to simulate realistic collective motion of collective of zebrafish, using real-world tracking data. The objective is to devise an agent-based model that can be implemented on an artificial robotic fish that can blend into a collective of real fish. We present a novel approach that uses Quality Diversity algorithms, a class of algorithms that emphasise exploration over pure optimisation. In particular, we use CVT-MAP-Elites, a variant of the state-of-the-art MAP-Elites algorithm for high dimensional search space. Results show that Quality Diversity algorithms not only outperform classic evolutionary reinforcement learning methods at the macroscopic level (i.e. group behaviour), but are also able to generate more realistic biomimetic behaviours at the microscopic level (i.e. individual behaviour).

cs.NE↗

arXiv2018

Modelling zebrafish collective behaviours with multilayer perceptrons optimised by evolutionary algorithms

Collective movements are pervasive behaviours among social organisms and have led to the development of many models. However, modelling animal trajectories and social interactions in simple bounded environments remains a challenge. Moreover, advances in the understanding of the sensory-motor loop and the information processing by animals are leading to revisions of the traditional assumptions made in decision-making algorithms. In this context, we develop a methodology based on artificial neural networks (ANN) to describe the collective motion of small zebrafish groups in a bounded environment. Although ANN models are commonly used in artificial systems they are still under-explored to model animal collective behaviours. Here, we present a methodology to calibrate Multilayer Perceptrons by learning from real fish experimental data. The ANNs are trained using either supervised learning or various forms of evolutionary reinforcement learning methods (using the CMA-ES and NSGA-III algorithms). We reveal that ANN models trained using evolutionary methods are capable of generating realistic collective motions for groups of 5 zebrafish including the tank wall effects, a feature that is lacking in previous models. Finally, we also discuss the benefits of optimised ANNs as candidates for driving robotic lure with biologically realistic behaviour, a method that is becoming increasingly popular to gather data and validate assumptions on collective behaviours.

q-bio.NC↗

arXiv2018

Evolutionary optimisation of neural network models for fish collective behaviours in mixed groups of robots and zebrafish

Animal and robot social interactions are interesting both for ethological studies and robotics. On the one hand, the robots can be tools and models to analyse animal collective behaviours, on the other hand, the robots and their artificial intelligence are directly confronted and compared to the natural animal collective intelligence. The first step is to design robots and their behavioural controllers that are capable of socially interact with animals. Designing such behavioural bio-mimetic controllers remains an important challenge as they have to reproduce the animal behaviours and have to be calibrated on experimental data. Most animal collective behavioural models are designed by modellers based on experimental data. This process is long and costly because it is difficult to identify the relevant behavioural features that are then used as a priori knowledge in model building. Here, we want to model the fish individual and collective behaviours in order to develop robot controllers. We explore the use of optimised black-box models based on artificial neural networks (ANN) to model fish behaviours. While the ANN may not be biomimetic but rather bio-inspired, they can be used to link perception to motor responses. These models are designed to be implementable as robot controllers to form mixed-groups of fish and robots, using few a priori knowledge of the fish behaviours. We present a methodology with multilayer perceptron or echo state networks that are optimised through evolutionary algorithms to model accurately the fish individual and collective behaviours in a bounded rectangular arena. We assess the biomimetism of the generated models and compare them to the fish experimental behaviours.

q-bio.NC↗

arXiv2017

Collective departures in zebrafish: profiling the initiators

For animals living in groups, one of the important questions is to understand what are the decision-making mechanisms that lead to choosing a motion direction or leaving an area while preserving group cohesion. Here, we analyse the initiation of collective departure in zebrafish \textit{Danio rerio}. In particular, we observed groups of 2, 3, 5, 7 and 10 zebrafish swimming in a two resting sites arena and quantify the number of collective departure initiated by each fish. While all fish initiated at least one departure, the probability to be the first one to exit a resting site is not homogeneously distributed with some individuals leading more departures than others. We show that this number of initiation is linearly proportional to the number of attempts performed and that all fish have the same success rate to lead the group out of a resting sites after an attempt. In addition, by measuring the average swimming speed of all fish, we highlight that the intra-group ranking of a fish for its proportion of initiation is correlated to its intra-group ranking in average speed. These results highlight that the initiation of collective departure in zebrafish is a heterogeneously distributed process, even if all individual have the same success rate after attempting a departure.

q-bio.PE↗

arXiv2017

Loose social organisation of AB strain zebrafish groups in a two-patch environment

We explore the collective behaviours of 7 group sizes: 1, 2, 3, 5, 7, 10 and 20 AB zebrafish (Danio rerio) in a constraint environment composed of two identical squared rooms connected by a corridor. This simple set-up is similar to a natural patchy environment. We track the positions and the identities of the fish and compute the metrics at the group and at the individual levels. First, we show that the size of the population affects the behaviour of each individual in a group, the cohesion of the groups, the preferential interactions and the transition dynamics between the two rooms. Second, during collective departures, we show that the rankings of exit correspond to the topological organisations of the fish prior to their collective departure with no leadership. This spatial organisation emerge in the group a few seconds before a collective departure. These results provide new evidences on the spatial organisation of the groups and the effect of the population size on individual and collective behaviours in a patchy environment.

q-bio.PE↗

arXiv2016

Strains differences in the collective behaviour of zebrafish (Danio rerio) in heterogeneous environment

Recent studies show differences in individual motion and shoaling tendency between strains of the same species. Here, we analyse the collective motion and the response to visual stimuli in two morphologically different strains (TL and AB) of zebrafish. For both strains, we observe 10 groups of 5 and 10 zebrafish swimming freely in a large experimental tank with two identical attractive landmarks (cylinders or disks) for one hour. We track the positions of the fish by an automated tracking method and compute several metrics at the group level. First, the probability of presence shows that both strains avoid free space and are more likely to swim in the vicinity of the walls of the tank and the attractive landmarks. Second, the analysis of landmarks occupancy shows that AB zebrafish are more present in their vicinity than TL zebrafish and that both strains regularly transit from one landmark to the other with no preference on the long duration. Finally, TL zebrafish show a higher cohesion than AB zebrafish. Thus, landmarks and duration of the repicates allow to reveal collective behavioural variabilities among different strains of zebrafish. These results provide a new insight into the need to take into account individual variability of zebrafish strains for studying collective behaviour.

q-bio.QM↗

arXiv2016

Automated optimisation of multi-level models of collective behaviour in a mixed society of animals and robots

Animal and robotic collective behaviours can exhibit complex dynamics that require multi-level descriptions. Here, we are interested in developing a multi-level modeling framework for the use of robots in studies about animal collective decision-making. In this context, using robots can be useful for validating models in silico, inducing calibrated repetitive stimuli to trigger animal responses or modulating and controlling animal collective behaviour. However, designing appropriate biomimetic robotic behaviour faces a major challenge: how to go from the collective decision dynamics observed with animals to an actual algorithmic implementation in robots. In previous work, this was mainly done by hand, often by taking inspiration from human-designed models. Typically, models of behaviour are either macroscopic, differential equations of the population dynamics, or microscopic,explicit spatio-temporal state of each individual. Only microscopic models can easily be implemented as robot controllers. Here, we address the problem of automating the design of lower level description models that can be implemented in robots and exhibit the same collective dynamics as a given higher level model. We apply evolutionary algorithms to simultaneously optimise the parameters of models accounting for different levels of description. This methodology is applied to an experimentally validated shelter-selection problem solved by gregarious insects and robots. We successfully design and calibrate automatically both a microscopic and a hybrid model exhibiting the same dynamics as a macroscopic one. Our framework can be used for multi-level modeling of collective behaviour in animal or robot populations and bio-hybrid systems.

nlin.AO↗

arXiv2015

Zebrafish collective behaviour in heterogeneous environment modeled by a stochastic model based on visual perception

Collective motion is one of the most ubiquitous behaviours displayed by social organisms and has led to the development of numerous models. Recent advances in the understanding of sensory system and information processing by animals impel to revise classical assumptions made in decisional algorithms. In this context, we present a new model describing the three dimensional visual sensory system of fish that adjust their trajectory according to their perception field. Furthermore, we introduce a new stochastic process based on a probability distribution function to move in targeted directions rather than on a summation of influential vectors as it is classically assumed by most models. We show that this model can spontaneously transits from consensus to choice. In parallel, we present experimental results of zebrafish (alone or in group of 10) swimming in both homogeneous and heterogeneous environments. We use these experimental data to set the parameter values of our model and show that this perception-based approach can simulate the collective motion of species showing cohesive behaviour in heterogeneous environments. Finally, we discuss the advances of this multilayer model and its possible outcomes in biological, physical and robotic sciences.

physics.bio-ph↗

arXiv2013

The Nosoi commute: a spatial perspective on the rise of BSL-4 laboratories in cities

Recent H5N1 influenza research has revived the debate on the storage and manipulation of potentially harmful pathogens. In the last two decades, new high biosafety (BSL-4) laboratories entered into operation, raising strong concerns from the public. The probability of an accidental release of a pathogen from a BSL-4 laboratory is extremely low, but the corresponding risk -- defined as the probability of occurrence multiplied by its impact -- could be significant depending on the pathogen specificities and the population potentially affected. A list of BSL-4 laboratories throughout the world, with their location and date of first activity, was established from publicly available sources. This database was used to estimate the total population living within a daily commuting distance of BSL-4 laboratories, and to quantify how this figure changed over time. We show that from 1990 to present, the population living within the commuting belt of BSL-4 laboratories increased by a factor of 4 to reach up to 1.8% of the world population, owing to an increase in the number of facilities and their installation in cities. Europe is currently hosting the largest population living in the direct vicinity of BSL-4 laboratories, while the recent building of new facilities in Asia suggests that an important increase of the population living close to BSL-4 laboratories will be observed in the next decades. We discuss the potential implications in term of global risk, and call for better pathogen-specific quantitative assessment of the risk of outbreaks resulting from the accidental release of potentially pandemic pathogens

q-bio.PE↗