General Scenario Research Articles

Exponential output tracking via a saturated RLC for a class of nonlinear systems under PSD of periodic references

This paper considers the same class of zero-relative-degree nonlinear systems for which a saturated Repetitive Learning Control (RLC) has been recently shown to ensure exponential – and not just asymptotical – convergence to zero of both the output and the input tracking errors in the case of periodic output reference signals with a known single period. The explicit role of the nonlinear unstructured uncertainties is here investigated within the more general scenario in which the output reference signal is multi-periodic. Special emphasis is provided to the generation of the input reference whose effect has to be exponentially nullified by the RLC. In particular, the following question is answered. Restrict the design to the sub-class of periodic output reference signals that admit a Periodic Signal Decomposition (PSD) (namely, the ones that can be written as a finite sum of periodic functions) with pairwise-commensurable periods (so that the knowledge of the multiple periods characterizing the input reference is preserved once the common multiple among the single periods is additionally included). Then, which robustness and convergence properties can be still achieved by the same output-feedback (definitely saturated) RLC, once it is intuitively generalized to transiently include an additional bank of learning estimation schemes corresponding to the single periods? Interestingly, the theoretical tools of this paper can be also used to successfully address identifiability and convergence issues regarding the identification of periodic components of general multi-periodic signals.

Switched sampled-data-based membership function-dependent [formula omitted] control for PMSG-based WTS with actuator failures

This paper deals with the problem of sampled-data-based H∞ control design for nonlinear permanent magnet synchronous generator (PMSG)-based wind turbine system (WTS) subject to actuator failures. First, the Takagi-Sugeno (T-S) fuzzy system is employed to handle the nonlinearities of presented model. Next, by employing a Bernoulli stochastic variable, the general scenario of switched coupling memory sampled-data control (CMSDC) is designed which contains the sampled-data control (SDC) and memory SDC scheme as special cases. By combining the membership function-dependent (MFD) asymmetric looped-type Lyapunov-Krasovskii functional (LKF) and employing the available information of membership functions in H∞ performance, the delay-dependent stabilization conditions of considered PMSG-based WTS are derived through the switched CMSDC technique. Meanwhile, the disturbances of considered systems are attenuated by taking the advantage of the newly proposed MFD H∞ performance. Eventually, the simulation results are given to demonstrate the efficacy and applicability of the proposed CMSDC technique.

Challenges for the steel industry in terms of environmental control in the production of pig iron in the state of Minas Gerais - Brazil

This text highlights the importance of implementing an Environmental Management System (EMS) in the steel industry sector focused on pig iron production in the state of Minas Gerais, Brazil. The text refers to the general scenario of pig iron production in Minas Gerais, addresses the main environmental impacts observed in the area of influence of the enterprises and the legislation directed at the sector. It discusses the behavior of the steel industry in the face of the need for environmental management plans and programs, and points out the possible competitive advantages of industrial companies that meet acceptable environmental and production control standards.

Two-sample test for high-dimensional covariance matrices: A normal-reference approach

Testing the equality of the covariance matrices of two high-dimensional samples is a fundamental inference problem in statistics. Several tests have been proposed but they are either too liberal or too conservative when the required assumptions are not satisfied which attests that they are not always applicable in real data analysis. To overcome this difficulty, a normal-reference test is proposed and studied in this paper. It is shown that under some regularity conditions and the null hypothesis, the proposed test statistic and a chi-squared-type mixture have the same limiting distribution. It is then justified to approximate the null distribution of the proposed test statistic using that of the chi-squared-type mixture. The distribution of the chi-squared-type mixture can be well approximated using a three-cumulant matched chi-squared-approximation with its approximation parameters consistently estimated from the data. The asymptotic power of the proposed test under a local alternative is also established. Simulation studies and a real data example demonstrate that the proposed test works well in general scenarios and outperforms the existing competitors substantially in terms of size control.

Spatiotemporal variability of pesticides concentration in honeybees (Apis mellifera) and their honey from western Mexico. Risk assessment for honey consumption

The study conducted in the state of Colima, western Mexico, aimed to assess the 1) occurrence, 2) temporal variability, 3) spatial variability, and 4) potential risk for honeybees and human consumption of pesticide-contaminated honey. For that purpose, 48 pesticides were determined in bees and their honey during both dry and wet seasons. The research considered two variables: land use categorization (irrigated agriculture, rainfed agriculture, grassland, and forest area) and location (coastal, valley, and mountain). Bee and honey samples were collected, pre-treated using solid-phase extraction (SPE), and analyzed using LC-MS/MS and GC–MS techniques. Occurrence: of the total number of pesticides, 17 were detected in the bee samples and 12 in the honey samples. The pesticides with the highest concentrations in the bee samples were glufosinate ammonium, picloram, and permethrin, while in the honey samples, picloram, permethrin, and atrazine were the most prevalent. Temporal variability: analyses revealed significant differences between dry and wet seasons for glufosinate ammonium and DEET in bee samples and only for glufosinate ammonium in honey samples. Spatial variability: analyses showed a trend in the number of detected pesticides, with irrigated agriculture areas having the highest detection and grassland areas having the least. The human potential risk assessment of contaminated honey consumption indicated no risk. The bee's potential risk for consumption of pesticides contaminated honey revealed chronic effects due to permethrin in a general scenario, and carbofuran, diazinon and permethrin in the worst scenario, and potential risk of acute effects by permethrin. The findings of this study contribute to understanding the contamination levels of pesticides in bees and their honey, emphasizing the importance of monitoring and mitigating the adverse effects of pesticide exposure on bee populations and environmental health.

Superresolution imaging of two incoherent optical sources with unequal brightnesses

Resolving the separation between two incoherent optical sources with high precision is of great significance for fluorescence imaging and astronomical observations. In this paper, we focus on a more general scenario where two sources have unequal brightnesses. We give the ultimate precision limit with respect to separation by using the quantum Fisher information. Through the calculation of the classical Fisher information, we analyze and compare several specific measurement schemes including direct measurement, Gaussian mode measurement and zero-photon measurement. The results indicate that Gaussian mode measurement is the nearly optimal for a small separation. On this basis, the effects of relative brightness fluctuation are addressed. Our work provides a positive complement to the aspect of superresolution imaging of incoherent sources.

Error-corrected Hadamard gate simulated at the circuit level

We simulate the logical Hadamard gate in the surface code under a circuit-level noise model, compiling it to a physical circuit on square-grid connectivity hardware. Our paper is the first to do this for a logical unitary gate on a quantum error-correction code. We consider two proposals, both via patch-deformation: one that applies a transversal Hadamard gate (i.e. a domain wall through time) to interchange the logical X and Z strings, and another that applies a domain wall through space to achieve this interchange. We explain in detail why they perform the logical Hadamard gate by tracking how the stabilisers and the logical operators are transformed in each quantum error-correction round. We optimise the physical circuits and evaluate their logical failure probabilities, which we find to be comparable to those of a quantum memory experiment for the same number of quantum error-correction rounds. We present syndrome-extraction circuits that maintain the same effective distance under circuit-level noise as under phenomenological noise. We also explain how a SWAP-quantum error-correction round (required to return the patch to its initial position) can be compiled to only four two-qubit gate layers. This can be applied to more general scenarios and, as a byproduct, explains from first principles how the "stepping" circuits of the recent Google paper \cite{McEwenBaconGidney} can be constructed.

STTAR: Surgical Tool Tracking Using Off-the-Shelf Augmented Reality Head-Mounted Displays.

The use of Augmented Reality (AR) for navigation purposes has shown beneficial in assisting physicians during the performance of surgical procedures. These applications commonly require knowing the pose of surgical tools and patients to provide visual information that surgeons can use during the performance of the task. Existing medical-grade tracking systems use infrared cameras placed inside the Operating Room (OR) to identify retro-reflective markers attached to objects of interest and compute their pose. Some commercially available AR Head-Mounted Displays (HMDs) use similar cameras for self-localization, hand tracking, and estimating the objects' depth. This work presents a framework that uses the built-in cameras of AR HMDs to enable accurate tracking of retro-reflective markers without the need to integrate any additional electronics into the HMD. The proposed framework can simultaneously track multiple tools without having previous knowledge of their geometry and only requires establishing a local network between the headset and a workstation. Our results show that the tracking and detection of the markers can be achieved with an accuracy of 0.09±0.06 mm on lateral translation, 0.42 ±0.32 mm on longitudinal translation and 0.80 ±0.39° for rotations around the vertical axis. Furthermore, to showcase the relevance of the proposed framework, we evaluate the system's performance in the context of surgical procedures. This use case was designed to replicate the scenarios of k-wire insertions in orthopedic procedures. For evaluation, seven surgeons were provided with visual navigation and asked to perform 24 injections using the proposed framework. A second study with ten participants served to investigate the capabilities of the framework in the context of more general scenarios. Results from these studies provided comparable accuracy to those reported in the literature for AR-based navigation procedures.

Desarrollo y consenso de un cuadro de mando para evaluar la actividad investigadora en los servicios de farmacia hospitalaria en España

ObjectiveTo develop by consensus a dashboard model to standardize and promote the evaluation of research activity in Spanish Hospital Pharmacy Services. MethodsThe study was carried out in 5 phases following the modified Delphi methodology: constitution of the coordinating group, elaboration of a list of scenarios, selection of participating centers, evaluation of the list of scenarios, and analysis of the results.The coordinating group designed a questionnaire with 114 questions. General research questions and different scenarios (indicators) were included to form the dashboard. The Hospital Pharmacy Services with the highest number of publications were identified to participate in the Delphi consultation. Two rounds of consultations were conducted in which the "Need" and/or "Feasibility" of their measurement was evaluated for each of the scenarios, using a numerical scale from 1 (lowest score) to 9 (highest score). ResultsSixteen Hospital Pharmacy Services, belonging to 8 different autonomous communities, participated in the Delphi consultation. A total of 100% of them responded to all the questions in the 2 rounds of consultations. It was considered that the Hospital Pharmacy Services should have a research dashboard (need = 100%) with a basic structure and a common minimum set of data for all them (need = 87.5%). The consensus was reached on distinguishing research projects led by the Hospital Pharmacy Services from those led by other groups in which the Hospital Pharmacy Services collaborate (need = 87.5%), and a definition was approved on the leadership of these projects according to whether they are single-center or multicenter.A consensus was reached on 40 indicators to form the dashboard, which evaluates publications (13 indicators), human resources (12 indicators), research projects (9 indicators), doctoral theses (4 indicators), and patents and intellectual property registrations (2 indicators). ConclusionsThis is the first consensus dashboard developed to evaluate the research activity of the Hospital Pharmacy Services, which will help to analyze the productivity and impact of research systematically and continuously. In addition, it will allow comparison between them and will help to establish synergies and identify trends, patterns, and challenges.

[Translated article] Development and consensus of a dashboard model to evaluate research activity in Spanish Hospital Pharmacy Services

ObjectiveTo develop by consensus a dashboard model to standardise and promote the evaluation of research activity in Spanish Hospital Pharmacy Services. MethodsThe study was carried out in 5 phases following the modified Delphi methodology: constitution of the coordinating group, elaboration of a list of scenarios, selection of participating centres, evaluation of the list of scenarios, and analysis of the results.The coordinating group designed a questionnaire with 114 questions. General research questions and different scenarios (indicators) were included to form the dashboard. The Hospital Pharmacy Services with the highest number of publications were identified to participate in the Delphi consultation. Two rounds of consultations were conducted in which the “Need” and/or “Feasibility” of their measurement was evaluated for each of the scenarios, using a numerical scale from 1 (lowest score) to 9 (highest score). ResultsSixteen Hospital Pharmacy Services, belonging to 8 different Autonomous Communities, participated in the Delphi consultation. A total of 100% of them responded to all the questions in the 2 rounds of consultations. It was considered that the Hospital Pharmacy Services should have a research dashboard (Need=100%) with a basic structure and a common minimum set of data for all them (Need=87.5%). The consensus was reached on distinguishing research projects led by the Hospital Pharmacy Services from those led by other groups in which the Hospital Pharmacy Services collaborate (Need=87.5%), and a definition was approved on the leadership of these projects according to whether they are single-centre or multicentre.A consensus was reached on 40 indicators to form the dashboard, which evaluates publications (13 indicators), human resources (12 indicators), research projects (9 indicators), doctoral theses (4 indicators), and patents and intellectual property registrations (2 indicators). ConclusionsThis is the first consensus dashboard developed to evaluate the research activity of the Hospital Pharmacy Services, which will help to analyse the productivity and impact of research systematically and continuously. In addition, it will allow comparison between them and will help to establish synergies and identify trends, patterns, and challenges.

Modelling intracellular transport in crowded environments: effects of motor association to cargos.

In intracellular transports, motor proteins transport macromolecules as cargos to desired locations by moving on biopolymers such as microtubules. Recent experiments suggest that, while moving in crowded environments, cargos that can associate motor proteins during their translocation have larger run-length and association time compared to free motors. Here, we model the dynamics of a cargo that can associate at the most m free motors present on the microtubule track as obstacles to its motion. The proposed models display competing effects of association and crowding, leading to a peak in the run-length with the free-motor density. For and 3, we show that this feature is governed by the largest eigenvalue of the transition matrix describing the cargo dynamics. In all the above cases, free motors are assumed to be present on the microtubule as stalled obstacles. We finally compare simulation results for the run-length for general scenarios where the free motors undergo processive motion in addition to binding and unbinding to or from the microtubule.

Optimal Guidance for Heliocentric Orbit Cranking with E-Sail-Propelled Spacecraft

In astrodynamics, orbit cranking is usually referred to as an interplanetary transfer strategy that exploits multiple gravity-assist maneuvers to change both the inclination and eccentricity of the spacecraft osculating orbit without changing the specific mechanical energy, that is, the semimajor axis. In the context of a solar sail-based mission, however, the concept of orbit cranking is typically referred to as a suitable guidance law that is able to (optimally) change the orbital inclination of a circular orbit of an assigned radius in a general heliocentric three-dimensional scenario. In fact, varying the orbital inclination is a challenging maneuver from the point of view of the velocity change, so orbit cranking is an interesting mission application for a propellantless propulsion system. The aim of this paper is to analyze the performance of a spacecraft equipped with an Electric Solar Wind Sail in a cranking maneuver of a heliocentric circular orbit. The maneuver performance is calculated in an optimal framework considering spacecraft dynamics described by modified equinoctial orbital elements. In this context, the paper presents an analytical version of the three-dimensional optimal guidance laws obtained by using the classical Pontryagin’s maximum principle. The set of (analytical) optimal control laws is a new contribution to the Electric Solar Wind Sail-related literature.

Two Models on the Unsteady Heat and Fluid Flow Induced by Stretching or Shrinking Sheets and Novel Time-Dependent Solutions

Abstract This study sheds light on unsteady heat and fluid flow problems over stretching and shrinking surfaces, enriching our understanding of these complex phenomena. We derive two mathematical models using a rigorous approach. The first model aligns with the model commonly employed by researchers in this field, but its steady-state solution remains trivial. The second model, introduced in this work, demonstrably captures the physically relevant steady-state solutions well-studied in the literature. Notably, we introduce new similarity solutions for the temperature field specifically within the first model. We further demonstrate that a uniform wall temperature condition leads to the optimal heat transfer rate. While similarity solutions can be derived for specific cases with the second model, nonsimilar solutions may be necessary for more general scenarios. We discuss the implications of our analysis for stagnation-point flow and non-Newtonian viscoelastic fluid flow problems, illuminating future research directions in the open literature.

A Framework for Developing Systematic Testbeds for Multi-Fidelity Optimization Techniques

Abstract Multi-fidelity (MF) models abound in simulation-based engineering fields. Many MF strategies have been proposed to improve the efficiency in engineering processes, especially in design optimization. When it comes to assessing the performance of MF optimization techniques, existing practice usually relies on test cases involving contrived MF models of seemingly random math functions, due to limited access to real-world MF models. While it is acceptable to use contrived MF models, these models are often manually written up rather than created in a systematic manner. This gives rise to the potential pitfall that the test MF models may be not representative of general scenarios. We propose a framework to generate test MF models systematically and characterize tested MF optimization methods' performances comprehensively. In our framework, the MF models are generated based on given high-fidelity (HF) model and come with two parameters to control their fidelity levels and allow model randomization. In our testing process, MF case problems are systematically formulated using our model creation method. Running the given MF optimization technique on these problems produces what we call “savings curve” that characterizes the method's performance similarly to how ROC curves characterize machine learning classifiers. Our test results also allow plotting “optimality curves” that serve similar functionality to savings curves in certain types of problems. The flexibility of our MF model creation facilitates the development of testing processes for general MF problem scenarios, and our framework can be easily extended to other MF applications than optimization.

Variational model-based reconstruction techniques for multi-patch data in Magnetic Particle Imaging

Magnetic Particle Imaging is an emerging imaging modality through which it is possible to detect tracers containing superparamagnetic nanoparticles. The exposure of the particles to dynamic magnetic fields generates a non-linear response that is used to locate the particles and produce an image of their distribution. The bounding box that can be covered by a single scan curve depends on the strength of the gradients of the magnetic fields applied, which is limited due to the risk of causing peripheral nerve stimulation (PNS) in the patients. To address this issue, multiple scans are performed. The scan data must be merged together to produce reconstructions of larger regions of interest. In this paper we propose a mathematical framework which can deal with rather general multi-patching scenarios including rigid transformations of the field of view (FoV), the specimen and of the scanner. We show the flexibility of this framework in a variety of different scanning scenarios. Moreover, we describe an iterative reconstruction algorithm that yields a reconstruction of the target distribution by minimizing a convex functional which includes positivity constraints and sparsity enforcing priors. We show its convergence to a minimizer and perform numerical experiments on simulated data.

Super-resolution spatial spectrum reconstruction under unknown near-field interferences

In underwater acoustic signal processing, conventional spatial spectrum estimation and the associated direction-of-arrival estimation are often impaired by strong near-field interferences at the same frequency as the far-field target signal, leading to significant performance degradation and even detection failure. In contrast to prevalent near-field interference suppression algorithms that require prior knowledge of near-field interference positions, this paper proposes a super-resolution spatial spectrum reconstruction algorithm designed for more general scenarios where crucial information about the near-field interference, such as positions and magnitudes, is unknown. The proposed algorithm demonstrates its adaptability in mitigating unknown near-field interference and achieves this by leveraging rank constraint-based relaxation and alternating minimization, resulting in an effective spatial spectrum reconstruction strategy. The efficacy of the proposed spatial spectrum reconstruction method in handling strong near-field interference is confirmed through analysis of simulated and synthetic experimental data. It exhibits superiority over traditional competitors in terms of resolution, denoising capabilities, and estimation accuracy. Moreover, it achieves comparable results to algorithms that utilize prior information about near-field interference positions. The enhanced performance remains consistent even in challenging scenarios such as snapshot deficiency and low signal-to-noise ratios.

Algorithms for online fault tolerance server consolidation

We study a novel replication mechanism to ensure service continuity against multiple simultaneous server failures. In this mechanism, each item represents a computing task and is replicated into ξ+1 servers for some integer ξ≥1, with workloads specified by the amount of required resources. If one or more servers fail, the affected workloads can be redirected to other servers that host replicas associated with the same item, such that the service is not interrupted by the failure of up to ξ servers. This requires that any feasible assignment algorithm must reserve some capacity in each server to accommodate the workload redirected from potential failed servers without overloading, and determining the optimal method for reserving capacity becomes a key issue. Unlike existing algorithms that assume that no two servers share replicas of more than one item, we first formulate capacity reservation for a general arbitrary scenario. Due to the combinatorial nature of this problem, finding the optimal solution is difficult. To this end, we propose a Generalized and Simple Calculating Reserved Capacity (GSCRC) algorithm, with a time complexity only related to the number of items packed in the server. In conjunction with GSCRC, we propose a robust replica packing algorithm with capacity optimization (RobustPack), which aims to minimize the number of servers hosting replicas and tolerate multiple server failures. Through theoretical analysis and experimental evaluations, we show that the RobustPack algorithm can achieve better performance.

Semi-Device-Independently Characterizing Quantum Temporal Correlations.

We develop a framework for characterizing quantum temporal correlations in a general temporal scenario, in which an initial quantum state is measured, sent through a quantum channel, and finally measured again. This framework does not make any assumptions on the system nor on the measurements, namely, it is device independent. It is versatile enough, however, to allow for the addition of further constraints in a semi-device-independent setting. Our framework serves as a natural tool for quantum certification in a temporal scenario when the quantum devices involved are uncharacterized or partially characterized. It can hence also be used for characterizing quantum temporal correlations when one assumes an additional constraint of no-signalling in time, there are upper bounds on the involved systems' dimensions, rank constraints-for which we prove genuine quantum separations over local hidden variable models-or further linear constraints. We present a number of applications, including bounding the maximal violation of temporal Bell inequalities, quantifying temporal steerability, and bounding the maximum successful probability in quantum randomness access codes.

Knockoffs-SPR: Clean Sample Selection in Learning With Noisy Labels.

A noisy training set usually leads to the degradation of the generalization and robustness of neural networks. In this article, we propose a novel theoretically guaranteed clean sample selection framework for learning with noisy labels. Specifically, we first present a Scalable Penalized Regression (SPR) method, to model the linear relation between network features and one-hot labels. In SPR, the clean data are identified by the zero mean-shift parameters solved in the regression model. We theoretically show that SPR can recover clean data under some conditions. Under general scenarios, the conditions may be no longer satisfied; and some noisy data are falsely selected as clean data. To solve this problem, we propose a data-adaptive method for Scalable Penalized Regression with Knockoff filters (Knockoffs-SPR), which is provable to control the False-Selection-Rate (FSR) in the selected clean data. To improve the efficiency, we further present a split algorithm that divides the whole training set into small pieces that can be solved in parallel to make the framework scalable to large datasets. While Knockoffs-SPR can be regarded as a sample selection module for a standard supervised training pipeline, we further combine it with a semi-supervised algorithm to exploit the support of noisy data as unlabeled data. Experimental results on several benchmark datasets and real-world noisy datasets show the effectiveness of our framework and validate the theoretical results of Knockoffs-SPR.

A Reinforcement Learning Benchmark for Autonomous Driving in General Urban Scenarios

A Reinforcement Learning Benchmark for Autonomous Driving in General Urban Scenarios