The Libyan civil conflict : selected case series of orthopaedic trauma managed in Malta in 2014

Aim: The purpose of this series of cases was to analyse our management of orthopaedic trauma casualties in the Libyan civil war crisis in the European summer of 2014. We looked at both damage control orthopaedics and for case variety of war trauma at a civilian hospital. Due to our geographical proximity to Libya, Malta was the closest European tertiary referral centre. Having only one Level 1 trauma care hospital in our country, our Trauma and Orthopaedics department played a pivotal role in the management of Libyan battlefield injuries. Our aims were to assess acute outcomes and short term mortality of surgery within the perspective of a damage control orthopaedic strategy whereby aggressive wound management, early fixation using relative stability principles, antibiotic cover with adequate soft tissue cover are paramount. We also aim to describe the variety of war injuries we came across, with a goal for future improvement in regards to service providing.Methods: Prospective collection of six interesting cases with severe limb and spinal injuries sustained in Libya during the Libyan civil war between June and November 2014.Conclusions: We applied current trends in the treatment of war injuries, specifically in damage control orthopaedic strategy and converting to definitive treatment where permissible. The majority of our cases were classified as most severe (Type IIIB/C) according to the Gustilo-Anderson classification of open fractures. The injuries treated reflected the type of standard and improved weaponry available in modern warfare affecting both militants and civilians alike with increasing severity and extent of damage. Due to this fact, multidisciplinary team approach to patient centred care was utilised with an ultimate aim of swift recovery and early mobilisation. It also highlighted the difficulties and complex issues required on a hospital management level as a neighbouring country to war zone countries in transforming care of civil trauma to military trauma.peer-reviewe

Stability and Performance Verification of Optimization-based Controllers

This paper presents a method to verify closed-loop properties of optimization-based controllers for deterministic and stochastic constrained polynomial discrete-time dynamical systems. The closed-loop properties amenable to the proposed technique include global and local stability, performance with respect to a given cost function (both in a deterministic and stochastic setting) and the $\mathcal{L}_2$ gain. The method applies to a wide range of practical control problems: For instance, a dynamical controller (e.g., a PID) plus input saturation, model predictive control with state estimation, inexact model and soft constraints, or a general optimization-based controller where the underlying problem is solved with a fixed number of iterations of a first-order method are all amenable to the proposed approach. The approach is based on the observation that the control input generated by an optimization-based controller satisfies the associated Karush-Kuhn-Tucker (KKT) conditions which, provided all data is polynomial, are a system of polynomial equalities and inequalities. The closed-loop properties can then be analyzed using sum-of-squares (SOS) programming

Quantization Design for Distributed Optimization

We consider the problem of solving a distributed optimization problem using a distributed computing platform, where the communication in the network is limited: each node can only communicate with its neighbours and the channel has a limited data-rate. A common technique to address the latter limitation is to apply quantization to the exchanged information. We propose two distributed optimization algorithms with an iteratively refining quantization design based on the inexact proximal gradient method and its accelerated variant. We show that if the parameters of the quantizers, i.e. the number of bits and the initial quantization intervals, satisfy certain conditions, then the quantization error is bounded by a linearly decreasing function and the convergence of the distributed algorithms is guaranteed. Furthermore, we prove that after imposing the quantization scheme, the distributed algorithms still exhibit a linear convergence rate, and show complexity upper-bounds on the number of iterations to achieve a given accuracy. Finally, we demonstrate the performance of the proposed algorithms and the theoretical findings for solving a distributed optimal control problem

A Parametric Non-Convex Decomposition Algorithm for Real-Time and Distributed NMPC

A novel decomposition scheme to solve parametric non-convex programs as they arise in Nonlinear Model Predictive Control (NMPC) is presented. It consists of a fixed number of alternating proximal gradient steps and a dual update per time step. Hence, the proposed approach is attractive in a real-time distributed context. Assuming that the Nonlinear Program (NLP) is semi-algebraic and that its critical points are strongly regular, contraction of the sequence of primal-dual iterates is proven, implying stability of the sub-optimality error, under some mild assumptions. Moreover, it is shown that the performance of the optimality-tracking scheme can be enhanced via a continuation technique. The efficacy of the proposed decomposition method is demonstrated by solving a centralised NMPC problem to control a DC motor and a distributed NMPC program for collaborative tracking of unicycles, both within a real-time framework. Furthermore, an analysis of the sub-optimality error as a function of the sampling period is proposed given a fixed computational power.Comment: 16 pages, 9 figure

An Alternating Trust Region Algorithm for Distributed Linearly Constrained Nonlinear Programs, Application to the AC Optimal Power Flow

A novel trust region method for solving linearly constrained nonlinear programs is presented. The proposed technique is amenable to a distributed implementation, as its salient ingredient is an alternating projected gradient sweep in place of the Cauchy point computation. It is proven that the algorithm yields a sequence that globally converges to a critical point. As a result of some changes to the standard trust region method, namely a proximal regularisation of the trust region subproblem, it is shown that the local convergence rate is linear with an arbitrarily small ratio. Thus, convergence is locally almost superlinear, under standard regularity assumptions. The proposed method is successfully applied to compute local solutions to alternating current optimal power flow problems in transmission and distribution networks. Moreover, the new mechanism for computing a Cauchy point compares favourably against the standard projected search as for its activity detection properties