Separation of multiple secondary surveillance radar sources in a real environment for the near-far case

Multilateration systems based on Secondary Surveillance Radar (SSR) systems and omni-directional antennae are operational today [1,2]. Assuming the replacement of the single-element antenna by an array, we proposed new algorithms to discriminate overlapped signals in previous works [3,4,5]; other solutions were also proposed in the literature [6,7,8]. Unfortunately, all have either some shortcomings, or an expensive computational cost, or no simple practical implementation. Therefore, we proposed in [9] a reliable, simple, and effective projection algorithm. Nevertheless, some issues were overlooked: in particular the relative power ratio between the signals to be separated may be important, which we study in this paper with real-life signals

Coherent source separation based on sparsity: an application to SSR signals

Systems based on secondary surveillance radar (SSR) downlink signals, both with directional and with omni-directional antennae (such as in multilateration), are operational today and more and more installations are being planned. In this frame, high-density traffic leads to the reception of a mixture of several overlapping SSR replies. By nature, SSR sources are sparse, i.e. with amplitude equal to zero with significantly high probability. While in the literature several algorithms performing sources separation with an m-element antenna have been proposed, none has satisfactorily employed the full potential of sparsity for SSR signals. Most sparsity algorithms can separate only real-valued sources, although we present in this study two algorithms to separate the complex-valued SSR sources. Recorded signals in a live environment are used to demonstrate the effectiveness of the proposed techniques. Copyright © Cambridge University Press and the European Microwave Association 2009

Effectiveness of an Empathic Chatbot in Combating Adverse Effects of Social Exclusion on Mood

From past research it is well known that social exclusion has detrimental consequences for mental health. To deal with these adverse effects, socially excluded individuals frequently turn to other humans for emotional support. While chatbots can elicit social and emotional responses on the part of the human interlocutor, their effectiveness in the context of social exclusion has not been investigated. In the present study, we examined whether an empathic chatbot can serve as a buffer against the adverse effects of social ostracism. After experiencing exclusion on social media, participants were randomly assigned to either talk with an empathetic chatbot about it (e.g., “I’m sorry that this happened to you”) or a control condition where their responses were merely acknowledged (e.g., “Thank you for your feedback”). Replicating previous research, results revealed that experiences of social exclusion dampened the mood of participants. Interacting with an empathetic chatbot, however, appeared to have a mitigating impact. In particular, participants in the chatbot intervention condition reported higher mood than those in the control condition. Theoretical, methodological, and practical implications, as well as directions for future research are discussed

Ant colony optimisation and local search for bin-packing and cutting stock problems

The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization problems. Exact solution methods can only be used for very small instances, so for real-world problems, we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimization (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can compete with existing evolutionary methods, whereas the hybrid approach can outperform the best-known hybrid evolutionary solution methods for certain problem classes. The hybrid ACO approach is also shown to require different parameter values from the pure ACO approach and to give a more robust performance across different problems with a single set of parameter values. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO

Clustering methods for Mode S stations: Evaluation and perspectives

Grouping of a set of Secondary Surveillance Radar (SSR) Mode S stations into functional entities called clusters has significant operational facets; it calls for a constrained optimization, an important significant constraint being the scarcity of Interrogation Identifier (II) codes. This clustering problem can be approached by two ways, i.e., integer programming methods and heuristic approaches. The definition of a general, usable decision support tool to build up and evaluate clustering strategies in any operational airspace, e.g. the one of a nation or, even more complicated, of a system such as the European one, is a very challenging task. This paper describes some steps toward this envisaged result proposing a mathematical formulation and a heuristic approach for the problem

Constant-angle surfaces in liquid crystals

We discuss some properties of surfaces in R3 whose unit normal has constant angle with an assigned direction field. The constant angle condition can be rewritten as an Hamilton-Jacobi equation correlating the surface and the direction field. We focus on examples motivated by the physics of interfaces in liquid crystals and of layered fluids, and discuss the properties of the constant-angle surfaces when the direction field is singular along a line (disclination) or at a point (hedgehog defect

Green and efficient recovery of valuable metals from spent lithium-ion batteries using molasses: Parametric optimization and performance evaluation.

Supplementary data to this article can be found online at: https://doi.org/10.1016/j.hydromet.2023.106168.In this study, a green and efficient leaching system utilizing methanesulfonic acid (MSA) and sugarcane molasses is investigated, and conditions optimized using response surface methodology (RSM). Results show that molasses is capable of reducing the oxidation state thus facilitating the leaching of the transition metals and lithium. The dissolution rate of the metals in molasses-methanesulfonic acid media is observed to be fast and temperature dependent. >98% of all the metals can be dissolved under the optimized leaching condition of 2 M MSA, reductant dosage of 0.408 g (molasses)/g (cathode material) on a wet molasses (18.5% moisture) and dry cathode material basis, solid-liquid ratio of 50 g/L, 90 °C and 60 min. The proposed system provides an efficient waste-to-wealth approach that utilizes an agro-industrial by-product to recover hazardous metals present in spent LIBs

A randomized, controlled trial comparing ganciclovir to ganciclovir plus foscarnet (each at half dose) for preemptive therapy of cytomegalovirus infection in transplant recipients

Forty-eight patients who provided 2 consecutive blood samples that tested positive for cytomegalovirus DNA by polymerase chain reaction (PCR) were randomized to receive either full-dose ganciclovir ( 5 mg/kg intravenously [iv] twice daily) or half-dose ganciclovir (5 mg/kg iv once daily) plus half-dose foscarnet (90 mg/kg iv once daily) for 14 days. In the ganciclovir arm, 17 (71%) of 24 patients reached the primary end point of being CMV negative by PCR within 14 days of initiation of therapy, compared with 12 (50%) of 24 patients in the ganciclovir-plus-foscarnet arm (P = .12). Toxicity was greater in the combination-therapy arm. In patients who failed to reach the primary end point, baseline virus load was 0.77 log(10) higher, the replication rate before therapy was faster (1.5 vs. 2.7 days), and the viral decay rate was slower (2.9 vs. 1.1 days) after therapy. Bivariable logistic regression models identified baseline virus load, bone-marrow transplantation, and doubling time and half-life of decay as the major factors affecting response to therapy within 14 days. This study did not support a synergistic effect of ganciclovir plus foscarnet in vivo

Adaptive Horizon Model Predictive Control and Al'brekht's Method

A standard way of finding a feedback law that stabilizes a control system to an operating point is to recast the problem as an infinite horizon optimal control problem. If the optimal cost and the optmal feedback can be found on a large domain around the operating point then a Lyapunov argument can be used to verify the asymptotic stability of the closed loop dynamics. The problem with this approach is that is usually very difficult to find the optimal cost and the optmal feedback on a large domain for nonlinear problems with or without constraints. Hence the increasing interest in Model Predictive Control (MPC). In standard MPC a finite horizon optimal control problem is solved in real time but just at the current state, the first control action is implimented, the system evolves one time step and the process is repeated. A terminal cost and terminal feedback found by Al'brekht's methoddefined in a neighborhood of the operating point is used to shorten the horizon and thereby make the nonlinear programs easier to solve because they have less decision variables. Adaptive Horizon Model Predictive Control (AHMPC) is a scheme for varying the horizon length of Model Predictive Control (MPC) as needed. Its goal is to achieve stabilization with horizons as small as possible so that MPC methods can be used on faster and/or more complicated dynamic processes.Comment: arXiv admin note: text overlap with arXiv:1602.0861