Seven definitions of bipartite bound entanglement

An entangled state is bound entangled, if one cannot combine any number of copies of the state to a maximally entangled state, by using only local operations and classical communication. If one formalizes this notion of bound entanglement, one arrives immediately at four different definitions. In addition, at least three more definitions are commonly used in the literature, in particular so in the very first paper on bound entanglement. Here we review critical distillation protocols and we examine how different results from quantum information theory interact in order to prove that all seven definitions are eventually equivalent. Our self-contained analysis unifies and extends previous results scattered in the literature and reveals details of the structure of bound entanglement.Comment: 10+6 Page

Is there an association between metabolic syndrome and rotator cuff related shoulder pain? A systematic review”.

Objectives Rotator cuff-related shoulder pain (RCRSP) is a common upper limb complaint. It has been suggested that this condition is more common among people with cardiometabolic risk factors. This systematic review has synthesised evidence from case–control, cross-sectional and cohort studies on the association between metabolic syndrome (MetS) and RCRSP. Design and data sources Five medical databases (MEDLINE, EMBASE, SCOPUS, CINAHL and AMED) and reference checking methods were used to identify all relevant English articles that considered MetS and RCRSP. Studies were appraised using the Newcastle-Ottawa Scale (NOS). Two reviewers performed critical appraisal and data extraction. Narrative synthesis was performed via content analysis of statistically significant associations. Results Three cross-sectional, two case–control and one cohort study met the inclusion criteria, providing a total of 1187 individuals with RCRSP. Heterogeneity in methodology and RCRSP or MetS definition precluded a meaningful meta-analysis. Four of the included studies identified associations between the prevalence of MetS and RCRSP. Studies consistently identified independent cardiometabolic risk factors associated with RCRSP. All studies were level III evidence. Summary and conclusion The low-moderate quality evidence included in this review suggests an association between MetS and RCRSP. Most studies demonstrated moderate quality on appraisal. The direction of association and cardiometabolic factors influencing should be investigated by longitudinal and treatment studies. These preliminary conclusions and clinical utility should be treated with caution due to limitations of the evidence base.Peer reviewedFinal Published versio

Predicting organic acid concentration from UV/vis spectrometry measurements – A comparison of machine learning techniques

The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data

Predicting organic acid concentration from UV/vis spectrometry measurements – A comparison of machine learning techniques

The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data

Optimal Control of Biogas Plants using Nonlinear Model Predictive Control

Optimal control of biogas plants is a complex and challenging task due to the nonlinearity of the anaerobic digestion process involved in the conversion of biodegradable input material to biogas (a mixture of the energy carrier methane and carbon dioxide). In this paper a nonlinear model predictive control (NMPC) algorithm is developed to optimally control the substrate feed of the anaerobic digestion process on biogas plants. The implemented algorithm is investigated in a simulation study using a validated simulation model of a full-scale biogas plant with an electrical power of 750 kW, where the control objective is to achieve high biogas production and quality while maintaining stable plant operation. Results are presented demonstrating the feasibility of the proposed approach. The optimal operating state identified by the controller provides an additional return of investment of 650 €/day compared to a nominal operating state. Using the proposed algorithm it will be possible in the near future to optimize full-scale biogas plants using nonlinear model predictive control and therefore to advance the use of anaerobic digestion for eco-friendly energy production

Online-measurement systems for agricultural and industrial AD plants – A review and practice test

Online-measurement systems for AD plants in general are crucial to allow for detailed and comprehensive process monitoring and provide a basis for the development and practical application of process optimisation and control strategies. Nevertheless, the online measurement of key process variables such as Volatile Fatty Acids (VFA) and Total Alkalinity (TA) has proven to be difficult due to extreme process conditions. High Total Solids (TS) concentrations and extraneous material often damage the sensors or have a strong negative impact on measurement quality and long-term behaviour. Consequently, there is a need for new robust and accurate online-measurement systems. The purpose of this paper is to give an overview of existing online-measurement systems, to present the current state of research and to show the results of practice tests at an agricultural and industrial AD plant. It becomes obvious that a broad variety of measurement solutions have been developed over the past few years, but that the main problem is the upscaling from lab-scale to practical application at full-scale AD plants. Results from the practice tests show that an online-measurement of pH, ORP, TS is possible

Multi-objective nonlinear model predictive substrate feed control of a biogas plant

In this paper a closed-loop substrate feed control for agricultural biogas plants is proposed. In this case, multi-objective nonlinear model predictive control is used to control composition and amount of substrate feed to optimise the economic feasibility of a biogas plant whilst assuring process stability. The control algorithm relies on a detailed biogas plant simulation model using the Anaerobic Digestion Model No. 1. The optimal control problem is solved using the state-of-the-art multi-objective optimization method SMS-EGO. Control performance is evaluated by means of a set point tracking problem in a noisy environment. Results show, that the proposed control scheme is able to keep the produced electrical energy close to a set point with an RMSE of 0.9 %, thus maintaining optimal biogas plant operation

Observation of fluctuation-mediated picosecond nucleation of a topological phase

peer reviewedTopological states of matter exhibit fascinating physics combined with an intrinsic stability. A key challenge is the fast creation of topological phases, which requires massive reorientation of charge or spin degrees of freedom. Here we report the picosecond emergence of an extended topological phase that comprises many magnetic skyrmions. The nucleation of this phase, followed in real time via single-shot soft X-ray scattering after infrared laser excitation, is mediated by a transient topological fluctuation state. This state is enabled by the presence of a time-reversal symmetry-breaking perpendicular magnetic field and exists for less than 300 ps. Atomistic simulations indicate that the fluctuation state largely reduces the topological energy barrier and thereby enables the observed rapid and homogeneous nucleation of the skyrmion phase. These observations provide fundamental insights into the nature of topological phase transitions, and suggest a path towards ultrafast topological switching in a wide variety of materials through intermediate fluctuating states. © 2020, The Author(s), under exclusive licence to Springer Nature Limited.Leibniz Association Grant no. K162/2018 (OptiSPIN