Message passing for distributed optimisation of power allocation with renewable resources

The increase in renewable energy generators introduced into the electricity grid is putting pressure on its stability and management as predictions of renewable energy sources cannot be accurate or fully controlled. This, with the additional pressure of fluctuations in demand, presents a problem more complex than the current methods of controlling electricity distribution were designed for. A global approximate and distributed optimisation method for power allocation that accommodates uncertainties and volatility is suggested and analysed. It is based on a probabilistic method known as message passing [1], which has deep links to statistical physics methodology. This principled method of optimisation is based on local calculations and inherently accommodates uncertainties; it is of modest computational complexity and provides good approximate solutions.We consider uncertainty and fluctuations drawn from a Gaussian distribution and incorporate them into the message-passing algorithm. We see the effect that increasing uncertainty has on the transmission cost and how the placement of volatile nodes within a grid, such as renewable generators or consumers, effects it

Network optimisation - A statistical physics perspective

Inference and optimisation of real-value edge variables in sparse graphs are studied using the tree based Bethe approximation optimisation algorithms. Equilibrium states of general energy functions involving a large set of real edge-variables that interact at the network nodes are obtained for networks in various cases. These include different cost functions, connectivity values, constraints on the edge bandwidth and the case of multiclass optimisation

From the physics of interacting polymers to optimizing routes on the London Underground

Optimizing paths on networks is crucial for many applications, ranging from subway traffic to Internet communication. Because global path optimization that takes account of all path choices simultaneously is computationally hard, most existing routing algorithms optimize paths individually, thus providing suboptimal solutions. We use the physics of interacting polymers and disordered systems to analyze macroscopic properties of generic path optimization problems and derive a simple, principled, generic, and distributed routing algorithm capable of considering all individual path choices simultaneously. We demonstrate the efficacy of the algorithm by applying it to: (i) random graphs resembling Internet overlay networks, (ii) travel on the London Underground network based on Oyster card data, and (iii ) the global airport network. Analytically derived macroscopic properties give rise to insightful new routing phenomena, including phase transitions and scaling laws, that facilitate better understanding of the appropriate operational regimes and their limitations, which are difficult to obtain otherwise

Distributed optimization in transportation and logistics networks

Many important problems in communication networks, transportation networks, and logistics networks are solved by the minimization of cost functions. In general, these can be complex optimization problems involving many variables. However, physicists noted that in a network, a node variable (such as the amount of resources of the nodes) is connected to a set of link variables (such as the flow connecting the node), and similarly each link variable is connected to a number of (usually two) node variables. This enables one to break the problem into local components, often arriving at distributive algorithms to solve the problems. Compared with centralized algorithms, distributed algorithms have the advantages of lower computational complexity, and lower communication overhead. Since they have a faster response to local changes of the environment, they are especially useful for networks with evolving conditions. This review will cover message-passing algorithms in applications such as resource allocation, transportation networks, facility location, traffic routing, and stability of power grids

Optimal load shedding in electricity grids with renewable sources via message passing

The increased penetration of volatile and intermittent renewable energy sources challenges existing power-distribution methods as current dispatch methods were not designed to consider high levels of volatility. We suggest a principled algorithm called message passing, which complements existing techniques. It is based on statistical physics methodology and passes probabilistic messages locally to find the approximate global optimal solution for a given objective function. The computational complexity of the algorithm increases linearly with the system size, allowing one to solve large-scale problems. We show how message passing considers fluctuations effectively and prioritise consumers in the event of insufficient resource. We demonstrate the efficacy of the algorithm in managing load-shedding and power-distribution on synthetic benchmark IEEE data and discuss the role of weights in the trade-off between minimising load-shedding and transmission costs

Correction to: Position statement: minimum archiving requirements for emergency medicine point‑of‑care ultrasound—a modified Delphi‑derived national consensus (Canadian Journal of Emergency Medicine, (2021), 23, 4, (450-454), 10.1007/s43678-021-00109-8)

A correction to this paper has been published: https://doi.org/10.1007/s43678-021-00117-8

Commodity prices rise sharply at turning points

Commodity prices depend on supply and demand. With an uneven distribution of resources, prices are high at locations starved of commodity and low where it is abundant. We introduce an agent-based model in which agents set their prices to maximize profit. At steady state, the market self-organizes into three groups: excess producers, consumers, and balanced agents. When resources are scarce, prices rise sharply at a turning point due to the disappearance of excess producers. Market data of commodities provide evidence of turning points for essential commodities, as well as a yield point for non-essential ones

Rescue from excitotoxicity and axonal degeneration accompanied by age-dependent behavioral and neuroanatomical alterations in caspase-6-deficient mice

Apoptosis, or programmed cell death, is a cellular pathway involved in normal cell turnover, developmental tissue remodeling, embryonic development, cellular homeostasis maintenance and chemical-induced cell death. Caspases are a family of intracellular proteases that play a key role in apoptosis. Aberrant activation of caspases has been implicated in human diseases. In particular, numerous findings implicate Caspase-6 (Casp6) in neurodegenerative diseases, including Alzheimer disease (AD) and Huntington disease (HD), highlighting the need for a deeper understanding of Casp6 biology and its role in brain development. The use of targeted caspase-deficient mice has been instrumental for studying the involvement of caspases in apoptosis. The goal of this study was to perform an in-depth neuroanatomical and behavioral characterization of constitutive Casp6-deficient (Casp6−/−) mice in order to understand the physiological function of Casp6 in brain development, structure and function. We demonstrate that Casp6−/− neurons are protected against excitotoxicity, nerve growth factor deprivation and myelin-induced axonal degeneration. Furthermore, Casp6-deficient mice show an age-dependent increase in cortical and striatal volume. In addition, these mice show a hypoactive phenotype and display learning deficits. The age-dependent behavioral and region-specific neuroanatomical changes observed in the Casp6−/− mice suggest that Casp6 deficiency has a more pronounced effect in brain regions that are involved in neurodegenerative diseases, such as the striatum in HD and the cortex in AD

CXCR4 identifies transitional bone marrow premonocytes that replenish the mature monocyte pool for peripheral responses

It is well established that Ly6C(hi) monocytes develop from common monocyte progenitors (cMoPs) and reside in the bone marrow (BM) until they are mobilized into the circulation. In our study, we found that BM Ly6C(hi) monocytes are not a homogenous population, as current data would suggest. Using computational analysis approaches to interpret multidimensional datasets, we demonstrate that BM Ly6C(hi) monocytes consist of two distinct subpopulations (CXCR4(hi) and CXCR4(lo) subpopulations) in both mice and humans. Transcriptome studies and in vivo assays revealed functional differences between the two subpopulations. Notably, the CXCR4(hi) subset proliferates and is immobilized in the BM for the replenishment of functionally mature CXCR4(lo) monocytes. We propose that the CXCR4(hi) subset represents a transitional premonocyte population, and that this sequential step of maturation from cMoPs serves to maintain a stable pool of BM monocytes. Additionally, reduced CXCR4 expression on monocytes, upon their exit into the circulation, does not reflect its diminished role in monocyte biology. Specifically, CXCR4 regulates monocyte peripheral cellular activities by governing their circadian oscillations and pulmonary margination, which contributes toward lung injury and sepsis mortality. Together, our study demonstrates the multifaceted role of CXCR4 in defining BM monocyte heterogeneity and in regulating their function in peripheral tissues

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries