Post-sternotomy pain syndrome following cardiac surgery. case report

Over 2 million people undergo sternotomy worldwide for heart surgery each year, and many develop post-sternotomy pain syndrome (PSPS) which persists in the anterior thorax. In some patients, PSPS lasts for many years or suddenly reappears a long time after the sternotomy. The exact etiology of PSPS is unknown. This article presents a case report of a patient with a diagnosis of PSPS (after cardiac surgery 4 years prior) for whom an osteopathic approach was used, which successfully eliminated the pain. In a previous study, we demonstrated that this osteopathic procedure could reduce sternal pain associated with a recent surgical wound. Further efforts are needed to understand the reasons for PSPS. In light of new scientific data, these osteopathic techniques could contribute to a multidisciplinary approach to solve the proble

Image fusion techniqes for remote sensing applications

Image fusion refers to the acquisition, processing and synergistic combination of information provided by various sensors or by the same sensor in many measuring contexts. The aim of this survey paper is to describe three typical applications of data fusion in remote sensing. The first study case considers the problem of the Synthetic Aperture Radar (SAR) Interferometry, where a pair of antennas are used to obtain an elevation map of the observed scene; the second one refers to the fusion of multisensor and multitemporal (Landsat Thematic Mapper and SAR) images of the same site acquired at different times, by using neural networks; the third one presents a processor to fuse multifrequency, multipolarization and mutiresolution SAR images, based on wavelet transform and multiscale Kalman filter. Each study case presents also results achieved by the proposed techniques applied to real data

Risk-aware and Robust Approaches for Machine Learning-supported Model Predictive Control for Iterative Processes

The recent advances in machine learning have catalyzed a renewed interest in machine-learning-supported model predictive control. Machine learning promises to facilitate modeling and improve the process' performance. Nevertheless, it brings some challenges: For instance, as the connection with physics law is (partially) lost, machine learning models can provide wildly inaccurate results. It is therefore necessary to provide control methods that take the model uncertainty of these models into account. Uncertainties are even more important for iterative processes - processes that do not operate at a steady state - due to the large changes in the process conditions during operation. In this work, two methods for data-driven uncertainty modelling are proposed. The first method uses Gaussian processes to learn the model uncertainty and neural networks to learn the nominal model. It provides an simple way to summarize the uncertainty of the model into a single parameter, which can be used by a model predictive controller to make risk-aware decisions. This method, while being simple, does not guarantee constraint satisfaction. The second method is based on tube-based model predictive control and can guarantee constraint satisfaction. It is based on the concept of the "safe set": a set where a tube-based MPC has a feasible solution. We show that, under some assumptions, the safe set enlarges at every iteration of the process, potentially allowing increased performance. Finally, a novel Python library for machine-learning-based model predictive control, called HILO-MPC, is presented. This library interfaces with TensorFlow and PyTorch and provides easily-accesible tools for defining control and estimation problem using machine learning model

A fast optmization algorithm for Moving Horizon Estimation.

The Moving Horizon Estimation (MHE) is a technique that allows to estimate the states of a system considering constraints, either when they are effected by noise or are not measured. This method can be associated with control techniques such as Model Predictive Control. The core of the mathematics formulation of MHE consists of an optimization problem that can easily become huge as the horizon and the number of states of the system increase. This leads inevitably to a large computational time that makes dicult the implementation of the algorithm for on-line purpose. In this work we show through several simulations on linear random systems that if we assume box constraints on the states and output noises, we can eciently apply the Nesterov's Fast Gradient method for solving the optimization problem faster than using the standard optimization algorithms such as Interior Point Method or Active Set Method

New Proposal to Define the Fascial System.

At the beginning of the third millennium, we still do not have a definition of 'fascia' recognized as valid by every researcher. This article attempts to give a new definition of the fascial system, including the epidermis, by comparing the mechanical-metabolic characteristics of the connective tissue and the skin. In fact, according to the latest classification deriving from the Fascia Nomenclature Committee, the outer skin layer is not considered as part of the fascial continuum. This article highlights the reasons for taking the functional characteristics of the tissue into consideration, rather than its mere structure. A brief discussion will address the questions as to what is considered as fascial tissue and from which embryonic germ layer the epidermis is formed. The notion that all the layers intersect will be highlighted, demonstrating that quoting precise definitions of tissue stratification in the living organism probably does not correspond to what happens in vivo. What we propose as a definition is not to be regarded as a point of arrival but as another departure

Proposal for a New Osteopathic Treatment Method for the Eustachian Tube

The Eustachian tube ET plays an important role in the function of the middle ear stimulating drainage and ventilation The drainage of secretions from the middle ear is carried out by the mucociliary system of the tube and the mucus membrane of the middle ear the muscular system of the tube and the tension of the tubal lumen The ET is normally closed by periluminal pressure which is greater than the outside pressure while it is rhythmically opened by muscle activity to which it is directly or indirectly linked There are six muscles recognised as having an active role in the functions of the tuba tensor veli palatini levator veli palatini tensor tympani salpingopharyngeus lateral and medial pterygoids Literature available on an osteopathic approach to treating the ET is lacking The article reviews the literature on the osteopathic treatment of the Eustachian tube and proposes a new hypothesis of treatment for a non pediatric population This may lead to new scientific findings and improved results to the patient s overall healt

Ship Routing with Pickup and Delivery for a Maritime Oil Transportation System: MIP Modeland Heuristics

This paper examines a ship routing problem with pickup and delivery and time windows for maritime oil transportation, motivated by the production and logistics activities of an oil company operating in the Brazilian coast. The transportation costs from offshore platforms to coastal terminals are an important issue in the search for operational excellence in the oil industry, involving operations that demand agile and effective decision support systems. This paper presents an optimization approach to address this problem, based on a mixed integer programming (MIP) model and a novel and exploratory application of two tailor-made MIP heuristics, based on relax-and-fix and time decomposition procedures. The model minimizes fuel costs of a heterogeneous fleet of oil tankers and costs related to freighting contracts. The model also considers company-specific constraints for offshore oil transportation. Computational experiments based on the mathematical models and the related MIP heuristics are presented for a set of real data provided by the company, which confirm the potential of optimization-based methods to find good solutions for problems of moderate sizes

Coarse Grained Density Functional Theories for Metallic Alloys: Generalized Coherent Potential Approximations and Charge Excess Functional Theory

The class of the Generalized Coherent Potential Approximations (GCPA) to the Density Functional Theory (DFT) is introduced within the Multiple Scattering Theory formalism for dealing with, ordered or disordered, metallic alloys. All GCPA theories are based on a common ansatz for the kinetic part of the Hohenberg-Kohn functional and each theory of the class is specified by an external model concerning the potential reconstruction. The GCPA density functional consists of marginally coupled local contributions, does not depend on the details of the charge density and can be exactly rewritten as a function of the appropriate charge multipole moments associated with each lattice site. A general procedure based on the integration of the 'qV' laws is described that allows for the explicit construction the same function. The coarse grained nature of the GCPA density functional implies great computational advantages and is connected with the O(N) scalability of GCPA algorithms. Moreover, it is shown that a convenient truncated series expansion of the GCPA functional leads to the Charge Excess Functional (CEF) theory [E. Bruno, L. Zingales and Y. Wang, Phys. Rev. Lett. {\bf 91}, 166401 (2003)] which here is offered in a generalized version that includes multipolar interactions. CEF and the GCPA numerical results are compared with status of art LAPW full-potential density functional calculations for 62, bcc- and fcc-based, ordered CuZn alloys, in all the range of concentrations. These extensive tests show that the discrepancies between GCPA and CEF are always within the numerical accuracy of the calculations, both for the site charges and the total energies. Furthermore, GCPA and CEF very carefully reproduce the LAPW site charges and the total energy trends.Comment: 19 pages, 11 figure