Using Lumsden’s Thurstonian fluctuation model as a starting point, this paper attempts to develop a unidimensional item response theory model intended for binary personality items. Under some additional assumptions, a new model is obtained in which the item characteristic curves are defined by a cumulative Pearson-Type-VII distribution, and the person response curves are two-parameter normal ogives. Procedures for fitting the new model are proposed. Furthermore, the relations between individual fluctuation and scalability are discussed, and a scalability index based on the new model is proposed. All the developments in this paper are illustrated using two empirical examples.

Climate change (CC) challenges food and climate through reduced crop yields and increasing production risk. Regenerative agriculture (RA) emerged as a pivotal strategy for enhancing crop productivity and soil organic carbon (SOC) sequestration, contributing to agriculture’s CC mitigation and resilience. Nevertheless, expanding RA’s main challenges is providing sufficient science-based decision support for farmers and other stakeholders. In this context, we present herein the largest public-private partnership in Brazil to conduct research in a multidisciplinary collaborative scientific network on RA and describe the Carbon Farming Program approaches. Bayer SA leads the initiative, which also includes 11 partner institutions (i.e., Universities, Research Institutions and Foundations, and Farmers organisations). The programme aims to assess the benefits of improvement of cropland management, intensified and biodiverse crop rotation plans on SOC, soil health, crop productivity, and profitability in a no-till system. The programme has a multi-scale approach with three main steps (‘Research Partners’, ‘On-Farm Research Sites’, and ‘Carbon Program at Scale’). In total, it encompasses 1,906 farmers and 232 000 hectares across the Brazilian edaphoclimatic conditions. The programme has gathered a large database, integrating SOC and fertility determinations, and crop yields, to derive a quantitative evaluation of the impacts of sustainable agricultural land management practices adoption. Moreover, the programme enabled breaking through the gap of quantitative knowledge for the development of a novel mathematical model to predict SOC dynamics for tropical agroecosystems. This is worth supporting assertive decisions along the specific planning to promote scalability in the insertion of Brazilian agriculture in the global C market.

Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space–time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems.

Self-organizing systems (SOS) are developed to perform complex tasks in unforeseen situations with adaptability. Predefining rules for self-organizing agents can be challenging, especially in tasks with high complexity and changing environments. Our previous work has introduced a multiagent reinforcement learning (RL) model as a design approach to solving the rule generation problem of SOS. A deep multiagent RL algorithm was devised to train agents to acquire the task and self-organizing knowledge. However, the simulation was based on one specific task environment. Sensitivity of SOS to reward functions and systematic evaluation of SOS designed with multiagent RL remain an issue. In this paper, we introduced a rotation reward function to regulate agent behaviors during training and tested different weights of such reward on SOS performance in two case studies: box-pushing and T-shape assembly. Additionally, we proposed three metrics to evaluate the SOS: learning stability, quality of learned knowledge, and scalability. Results show that depending on the type of tasks; designers may choose appropriate weights of rotation reward to obtain the full potential of agents’ learning capability. Good learning stability and quality of knowledge can be achieved with an optimal range of team sizes. Scaling up to larger team sizes has better performance than scaling downwards.

With the increasing availability of large amounts of data in the livestock domain, we face the challenge to store, combine and analyse these data efficiently. With this study, we explored the use of a data lake for storing and analysing data to improve scalability and interoperability. Data originated from a 2-day animal experiment in which the gait score of approximately 200 turkeys was determined through visual inspection by an expert. Additionally, inertial measurement units (IMUs), a 3D-video camera and a force plate (FP) were installed to explore the effectiveness of these sensors in automating the visual gait scoring. We deployed a data lake using the IMU and FP data of a single day of that animal experiment. This encompasses data from 84 turkeys for which we preprocessed by performing an ‘extract, transform and load’ (ETL-) procedure. To test scalability of the ETL-procedure, we simulated increasing volumes of the available data from this animal experiment and computed the ‘wall time’ (elapsed real time) for converting FP data into comma-separated files and storing these files. With a simulated data set of 30 000 turkeys, the wall time reduced from 1 h to less than 15 min, when 12 cores were used compared to 1 core. This demonstrated the ETL-procedure to be scalable. Subsequently, a machine learning (ML) pipeline was developed to test the potential of a data lake to automatically distinguish between two classses, that is, very bad gait scores v. other scores. In conclusion, we have set up a dedicated customized data lake, loaded data and developed a prediction model via the creation of an ML pipeline. A data lake appears to be a useful tool to face the challenge of storing, combining and analysing increasing volumes of data of varying nature in an effective manner.

Recent technological advances have led to unprecedented amounts of generated data that originate from the Web, sensor networks, and social media. Analytics in terms of defeasible reasoning – for example, for decision making – could provide richer knowledge of the underlying domain. Traditionally, defeasible reasoning has focused on complex knowledge structures over small to medium amounts of data, but recent research efforts have attempted to parallelize the reasoning process over theories with large numbers of facts. Such work has shown that traditional defeasible logics come with overheads that limit scalability. In this work, we design a new logic for defeasible reasoning, thus ensuring scalability by design. We establish several properties of the logic, including its relation to existing defeasible logics. Our experimental results indicate that our approach is indeed scalable and defeasible reasoning can be applied to billions of facts.

In 2009, Greenland obtained self-government, terminating Danish supremacy, which had taken many forms over the centuries. This article analyses significant moments in Greenlandic-Danish relations by unpacking distinct contact zones that have emerged through the many encounters between locals and newcomers. Contact zones are always emplaced and create their own logic, which is not always easily readable as “colonialism” in the sense of appropriating lands and extracting surplus. While wrongs may have been perpetrated and ill-informed actions towards the inhabitants taken, relations between Greenland and Denmark illustrate how “colonialism” is neither uniform nor scalable. The analytical focus on colonial moments stresses the nonlinearity of colonial relations and allows for a subtler understanding of the postcolonial remains.

An extension of the well-known Particle Swarm Optimization (PSO) to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefited from the dynamical partitioning of the whole population of robots. Although such strategy allows decreasing the amount of required information exchange among robots, a further analysis on the communication complexity of the RDPSO needs to be carried out so as to evaluate the scalability of the algorithm. Moreover, a further study on the most adequate multi-hop routing protocol should be conducted. Therefore, this paper starts by analyzing the architecture and characteristics of the RDPSO communication system, thus describing the dynamics of the communication data packet structure shared between teammates. Such procedure will be the first step to achieving a more scalable implementation of RDPSO by optimizing the communication procedure between robots. Second, an ad hoc on-demand distance vector reactive routing protocol is extended based on the RDPSO concepts, so as to reduce the communication overhead within swarms of robots. Experimental results with teams of 15 real robots and 60 simulated robots show that the proposed methodology significantly reduces the communication overhead, thus improving the scalability and applicability of the RDPSO algorithm.