Smart balancing of E-scooter sharing systems via deep reinforcement learning: a preliminary study

Nowadays, micro-mobility sharing systems have become extremely popular. Such systems consist in fleets of dockless electric vehicles which are deployed in cities, and used by citizens to move in a more ecological and flexible way. Unfortunately, one of the issues related to such technologies is its intrinsic load imbalance, since users can pick up and drop off the electric vehicles where they prefer. In this paper we present ESB-DQN, a multi-agent system for E-Scooter Balancing (ESB) based on Deep Reinforcement Learning where agents are implemented as Deep Q-Networks (DQN). ESB-DQN offers suggestions to pick or return e-scooters in order to make the fleet usage and sharing as balanced as possible, still ensuring that the original plans of the user undergo only minor changes. The main contributions of this paper include a careful analysis of the state of the art, an innovative customer-oriented rebalancing strategy, the integration of state-of-the-art libraries for deep Reinforcement Learning into the existing ODySSEUS simulator of mobility sharing systems, and preliminary but promising experiments that suggest that our approach is worth further exploration

The effects of shrub encroachment on arthropod communities depend on grazing history

Unsustainable grazing is a major driver of biodiversity loss worldwide. Conservation actions such as grazing exclusion are effective strategies for halting such decline. However, we still know little how the long-term impact of grazing exclusion depends on plant–animal interactions such as those between encroaching unpalatable shrubs and ground arthropods. Here, we assessed how encroaching, unpalatable shrub species (Sarcopoterium spinosum) mediates the effects of grazing exclusion on the recovery of arthropod communities. We used a large-scale, long-term (15–25 years) grazing exclusion experiment complemented with local-scale treatments that consider the presence or absence of shrubs. We found that halting overgrazing supported the recovery of biodiversity in the long-term. Notably, the impacts of shrubs on arthropod diversity vary with grazing history. Shrubs decreased arthropod abundance by three folds, affecting particularly flies, butterflies, hymenopteran, and beetles in protected areas. Yet, shrubs had positive effects on animal diversity, particularly centipedes and millipeds in grazed areas. On the one hand, shrubs may enhance biodiversity recovery in overgrazed systems; on the other hand, shrubs may be detrimental in protected areas, in the absence of grazing. Understanding how plant–animal interactions vary with historical land-use change is key for biodiversity conservation and recovery and for integrated management of agroecosystems

Life in harsh environments : carabid and spider trait types and functional diversity on a debris-covered glacier and along its foreland

1. Patterns of species richness and species assemblage composition of ground-dwelling arthropods in primary successions along glacier forelands are traditionally described using a taxonomic approach. On the other hand, the functional trait approach could ensure a better characterisation of their colonisation strategies in these types of habitat. 2. The functional trait approach was applied to investigate patterns of functional diversity and life-history traits of ground beetles and spiders on an alpine debris-covered glacier and along its forefield in order to describe their colonisation strategies. 3. Ground beetles and spiders were sampled at different successional stages, representing five stages of deglaciation. 4. The results show that the studied glacier hosts ground beetle and spider assemblages that are mainly characterised by the following traits: walking colonisers, ground hunters and small-sized species. These traits are typical of species living in cold, wet, and gravelly habitats. The diversity of functional traits in spiders increased along the succession, and in both carabids and spiders, life-history traits follow the \u2018addition and persistence model\u2019. Accordingly, there is no turnover but there is an addition of new traits and a variation in their proportion within each species assemblage along the succession. The distribution of ground beetles and spiders along the glacier foreland and on the glacier seems to be driven by dispersal ability and foraging strategy. 5. The proposed functional approach improves knowledge of the adaptive strategies of ground-dwelling arthropods colonising glacier surfaces and recently deglaciated terrains, which represent landforms quickly changing due to global warming

Smart balancing of E-scooter sharing systems via deep reinforcement learning

Nowadays, micro-mobility sharing systems have become extremely popular. Such systems consist in fleets of electric vehicles which are deployed in cities, and used by citizens to move in a more ecological and flexible way. Unfortunately, one of the issues related to such technologies is its intrinsic load imbalance; since the users can pick up and drop off the electric vehicles where they prefer. We present ESB-DQN, a multi-agent system based on Deep Reinforcement Learning that offers suggestions to pick or return e-scooters in order to make the fleet usage and sharing as balanced as possible

The mixed virtual element method for grids with curved interfaces in single-phase flow problems

In many applications the accurate representation of the computational domain is a key factor to obtain reliable and effective numerical solutions. Curved interfaces, which might be internal, related to physical data, or portions of the physical boundary, are often met in real applications. However, they are often approximated leading to a geometrical error that might become dominant and deteriorate the quality of the results. Underground problems often involve the motion of fluids where the fundamental governing equation is the Darcy law. High quality velocity fields are of paramount importance for the successful subsequent coupling with other physical phenomena such as transport. The virtual element method, as solution scheme, is known to be applicable in problems whose discretizations requires cells of general shape, and the mixed formulation is here preferred to obtain accurate velocity fields. To overcome the issues associated to the complex geometries and, at the same time, retaining the quality of the solutions, we present here the virtual element method to solve the Darcy problem, in mixed form, in presence of curved interfaces in two and three dimensions. The numerical scheme is presented in detail explaining the discrete setting with a focus on the treatment of curved interfaces. Examples, inspired from industrial applications, are presented showing the validity of the proposed approach

The mixed virtual element method on curved edges in two dimensions

In this work, we propose an extension of the mixed Virtual Element Method (VEM) for bi-dimensional computational grids with curvilinear edge elements. The approximation by means of rectilinear edges of a domain with curvilinear geometrical feature, such as a portion of domain boundary or an internal interface, may introduce a geometrical error that degrades the expected order of convergence of the scheme. In the present work a suitable VEM approximation space is proposed to consistently handle curvilinear geometrical objects, thus recovering optimal convergence rates. The resulting numerical scheme is presented along with its theoretical analysis and several numerical test cases to validate the proposed approach

Data from: Resistance of plant–plant networks to biodiversity loss and secondary extinctions following simulated environmental changes

1. Plant interactions are fundamental processes for structuring plant communities and are an important mechanism governing the response of plant species and communities to environmental changes. Thus, understanding the role played by the interaction network in modulating the impact of environmental changes on plant community composition and diversity is crucial. Here, we aimed to develop a new analytical and conceptual framework to evaluate the responses of plant communities to environmental changes. 2. This framework uses functional traits as sensitivity measures for simulated environmental changes and assesses the consequences of microhabitat loss. We show here its application to an alpine plant community where we recorded functional traits (specific leaf area [SLA] and leaf dry matter content [LDMC]) of all plants associated with three foundation species or the surrounding open areas. We then simulated primary species loss based on different scenarios of environmental change and explored community persistence to the loss of foundation species. 3. Generally, plant community responses differed among environmental change scenarios. In a scenario of increasing drought alone (i.e. species with lower LDMC were lost first) or increasing drought with increasing temperature (i.e. species with lower LDMC and higher SLA were lost first), the plant community resisted because drought-tolerant foundation species tolerated those deteriorating conditions. However, in a scenario with increasing nitrogen input (i.e. species having lower SLA were lost earlier), foundation species accelerated species loss due to their early primary extinctions and the corresponding secondary extinctions of species associated to their microhabitat. 4. The resistance of a plant community depends on the driver of environmental change, meaning that the prediction of the fate of this system is depending on the knowledge of the main driver of environmental change. Our framework provides a mechanistic understanding of an ecosystem response to such environmental changes thanks to the integration of biology-informed criteria of species sensitivities to environmental factors into a network of interacting species

Learning new physics efficiently with nonparametric methods

We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. Based on the original proposal by D’Agnolo and Wulzer (Phys Rev D 99(1):015014, 2019, arXiv:1806.02350 [hep-ph]), the model evaluates the compatibility between experimental data and a reference model, by implementing a hypothesis testing procedure based on the likelihood ratio. Model-independence is enforced by avoiding any prior assumption about the presence or shape of new physics components in the measurements. We show that our approach has dramatic advantages compared to neural network implementations in terms of training times and computational resources, while maintaining comparable performances. In particular, we conduct our tests on higher dimensional datasets, a step forward with respect to previous studies

New Frontiers in Sepsis-Induced Acute Kidney Injury and Blood Purification Therapies: The Role of Polymethylmethacrylate Membrane Hemofilter

Acute kidney injury (AKI) is a common consequence of sepsis with a mortality rate of up to 40%. The pathogenesis of septic AKI is complex and involves several mechanisms leading to exacerbated inflammatory response associated with renal injury. A large body of evidence suggests that inflammation is tightly linked to AKI through bidirectional interaction between renal and immune cells. Preclinical data from our and other laboratories have identified in complement system activation a crucial mediator of AKI. Partial recovery following AKI could lead to long-term consequences that predispose to chronic dysfunction and may also accelerate the progression of preexisting chronic kidney disease. Recent findings have revealed striking morphological and functional changes in renal parenchymal cells induced by mitochondrial dysfunction, cell cycle arrest via the activation of signaling pathways involved in aging process, microvascular rarefaction, and early fibrosis. Although major advances have been made in our understanding of the pathophysiology of AKI, there are no available preventive and therapeutic strategies in this field. The identification of ideal clinical biomarkers for AKI enables prompt and effective therapeutic strategy that could prevent the progression of renal injury and promote repair process. Therefore, the use of novel biomarkers associated with clinical and functional criteria could provide early interventions and better outcome. Several new drugs for AKI are currently being investigated; however, the complexity of this disease might explain the failure of pharmacological intervention targeting just one of the many systems involved. The hypothesis that blood purification could improve the outcome of septic AKI has attracted much attention. New relevant findings on the role of polymethylmethacrylate-based continuous veno-venous hemofiltration in septic AKI have been reported. Herein, we provide a comprehensive literature review on advances in the pathophysiology of septic AKI and potential therapeutic approaches in this field