Miracle-dz/UniFAR-FTUs huggingface dataset:
TSO-DSO Operational Coordination Using a Look-Ahead Multi-Interval Framework

With the rise of distributed energy resources and the increasing activation of flexibility resources by Distribution Systems Operators (DSOs), the Transmission System Operators (TSOs) need to co-ordinate their actions with those of the DSOs. This research uses a look-ahead multi-interval (LA-MI) framework for analyzing this coordination and explores two formulations. Firstly in the exogenous DSO model, a mixed-integer linear program is developed to reflect the pragmatic approach in many real situations whereby the TSO can only anticipate statistically the actions of the DSO. In the embedded DSO model, as a comparator, we propose a new organizational setup for the TSO-DSO operational coordination mechanism. In the resulting bilevel decomposition, a new method to calculate Benders cuts is developed and tested on a modified IEEE 118-bus test system as a transmission network and two modified IEEE 33-bus test systems as distribution networks. The benefits of the LA-MI coordination framework are substantial in comparison with the current Look-Ahead Single-Interval (LA-SI) coordination framework widely used in Europe.
On Consistency, Viability, and Admissibility in Constrained Ensemble and Hierarchical Control Systems

Several control architectures, such as decentralized, distributed, and hierarchical control, have been elaborated over the past decades for controlling systems composed of a set of subsystems. However, computational complexity and constraint satisfaction are still challenging tasks. We present an approach to control an ensemble of similar heterogeneous systems with input and state constraints via an identical control input. This control input is globally admissible and computed based on an aggregated system that reflects the overall behavior of the ensemble. To limit the computational complexity of the control task, the aggregated system is designed such that its dimension is independent of the number of subsystems. To guarantee viability, i.e., state constraint satisfaction for all times, appropriate consistency conditions are derived based on invariant set theory. The presented approach is illustrated with a numerical example.
Analyzing Continuous Stochastic Models—Diffusion in Time and Space

In this chapter we present techniques for constructing and analyzing stochastic models with continuous state spaces. All of the models presented in Chapters 13 and 14 assume that the state of the system takes on discrete values. Some processes are more naturally modeled by allowing the state space to be continuous. For example, suppose we want to model the movement of an individual within its home range. There will likely be a great deal of stochasticity in movement, and at each point in time, the individual might be in any of a continuum of possible locations. Consequently, it would be appropriate to use a stochastic model with a continuous state space, representing the different possible spatial locations. Aside from the fact that some processes are more naturally described by a continuous state space, some discrete models that are difficult to analyze can be well approximated using a stochastic model with a continuous state space. For example, the Wright-Fisher model for allele frequency change in a finite population (section 13.4) is difficult to analyze exactly, but it can be well approximated using a stochastic model with a continuous state space representing the allele frequency. Thus, techniques for analyzing models with a continuous state space often provide an alternative route for the approximate analysis of discrete-state models. In section 15.2, we begin by describing different ways in which stochastic models with a continuous state space can be constructed. We focus on models referred to as diffusion models, which are based on the idea that variables disperse (or diffuse) away from their original location or state. In section 15.3, we then develop techniques allowing us to obtain general solutions, stationary distributions, probabilities of absorption, and waiting times for many diffusion models. Finally, in section 15.4 we illustrate how these mathematical techniques can be used to model the deterministic dynamics of populations in spatial settings.
On Relative Derived Categories

The paper is devoted to study some of the questions arises naturally in connection to the notion of relative derived categories. In particular, we study invariants of recollements involving relative derived categories, generalize two results of Happel by proving the existence of AR-triangles in Gorenstein-derived categories, provide situations for which relative derived categories with respect to Gorenstein projective and Gorenstein injective modules are equivalent, and finally study relations between the Gorenstein-derived category of a quiver and its image under a reflection functor. Some interesting applications are provided.
The Troublesome Ticks Research Protocol: Developing a Comprehensive, Multidiscipline Research Plan for Investigating Human Tick-Associated Disease in Australia

In Australia, there is a paucity of data about the extent and impact of zoonotic tick-related illnesses. Even less is understood about a multifaceted illness referred to as Debilitating Symptom Complexes Attributed to Ticks (DSCATT). Here, we describe a research plan for investigating the aetiology, pathophysiology, and clinical outcomes of human tick-associated disease in Australia. Our approach focuses on the transmission of potential pathogens and the immunological responses of the patient after a tick bite. The protocol is strengthened by prospective data collection, the recruitment of two external matched control groups, and sophisticated integrative data analysis which, collectively, will allow the robust demonstration of associations between a tick bite and the development of clinical and pathological abnormalities. Various laboratory analyses are performed including metagenomics to investigate the potential transmission of bacteria, protozoa and/or viruses during tick bite. In addition, multi-omics technology is applied to investigate links between host immune responses and potential infectious and non-infectious disease causations. Psychometric profiling is also used to investigate whether psychological attributes influence symptom development. This research will fill important knowledge gaps about tick-borne diseases. Ultimately, we hope the results will promote improved diagnostic outcomes, and inform the safe management and treatment of patients bitten by ticks in Australia.
Dual Reconstructive Autoencoder for Crowd Localization and Estimation in Density and FIDT Maps

This paper proposes crowd estimation technology to help authorities make the right decisions in times of crisis. Specifically, deep learning models have faced these challenges, achieving excellent results. In particular, the trend of using single-column Fully Convolutional Networks (FCNs) has increased in recent years. A typical architecture that meets these characteristics is the autoencoder. However, this model presents an intrinsic difficulty: the search for the optimal dimensionality of the latent space. In order to alleviate such difficulty, we propose a dual architecture consisting of two cascaded autoencoders. The first autoencoder is responsible for carrying out the masked reconstruction of the original images, whereas the second obtains crowd maps from the outputs of the first one. In this way, our architecture improves the location of people and crowds in Focal Inverse Distance Transform (FIDT) maps, resulting in more accurate count estimates than estimates obtained through a single autoencoder architecture.
G\"odel on Deduction

This is an examination, a commentary, of links between some philosophical views ascribed to G\"odel and general proof theory. In these views deduction is of central concern not only in predicate logic, but in set theory too, understood from an infinitistic ideal perspective. It is inquired whether this centrality of deduction could also be kept in the intensional logic of concepts whose building G\"odel seems to have taken as the main task of logic for the future.
An Adversarial Perspective on Accuracy, Robustness, Fairness, and Privacy: Multilateral-Tradeoffs in Trustworthy ML

Model accuracy is the traditional metric employed in machine learning (ML) applications. However, privacy, fairness, and robustness guarantees are crucial as ML algorithms increasingly pervade our lives and play central roles in socially important systems. These four desiderata constitute the pillars of Trustworthy ML (TML) and may mutually inhibit or reinforce each other. It is necessary to understand and clearly delineate the trade-offs among these desiderata in the presence of adversarial attacks. However, threat models for the desiderata are different and the defenses introduced for each leads to further trade-offs in a multilateral adversarial setting (i.e., a setting attacking several pillars simultaneously). The first half of the paper reviews the state of the art in TML research, articulates known multilateral trade-offs, and identifies open problems and challenges in the presence of an adversary that may take advantage of such multilateral trade-offs. The fundamental shortcomings of statistical association-based TML are discussed, to motivate the use of causal methods to achieve TML. The second half of the paper, in turn, advocates the use of causal modeling in TML. Evidence is collected from across the literature that causal ML is well-suited to provide a unified approach to TML. Causal discovery and causal representation learning are introduced as essential stages of causal modeling, and a new threat model for causal ML is introduced to quantify the vulnerabilities introduced through the use of causal methods. The paper concludes with pointers to possible next steps in the development of a causal TML pipeline.
Long time dynamics for weakly damped nonlinear Klein-Gordon equations

We continue our study of damped nonlinear Klein-Gordon equations. In our previous work we considered fixed positive damping and proved a form of the soliton resolution conjecture for radial solutions. In contrast, here we consider damping which decreases in time to 0. In the class of radial data we again establish soliton resolution provided the damping goes to 0 sufficiently slowly. While our previous work relied on invariant manifold theory, here we use the Lojasiewicz-Simon inequality applied to a suitable Lyapunov functional.
Fault detection and identification—A filter investigation

This paper develops a new active fault‐tolerant control system based on the concept of analytical redundancy. The novel design presented here consists of an observation filter–based fault detection and identification system integrated with a nonlinear model predictive controller. A number of observation filters are designed, integrated with the nonlinear controller, and tested before reaching the final design, which comprises an unscented Kalman filter for fault detection and identification together with a nonlinear model predictive controller to form an active fault‐tolerant control system.
UAV-Assisted Wireless Information and Power Transfer for Self-Sustained IoT Communications

This article investigates the performance of an unmanned aerial vehicle (UAV)-assisted wireless power transfer (WPT)-enabled communications for energy-constrained wireless devices. Herein, a UAV serves as a radio frequency (RF) energy harvesting (EH) transmitter for a pair of Internet-of-Things devices (IoDs). It also provides relay cooperation to IoDs using decode-and-forward (DF) operation. The composite fading channels associated with the UAV and IoDs are modeled by considering the probability of line-of-sight (LoS)/non-LoS (NLoS) and shadowing components, assuming log-normal distribution. Besides, the small-scale fading scenarios for LoS and NLoS communications are assumed to follow Nakagami- ${m}$ and Rayleigh distributions, respectively. In addition, considering relative mobility between the UAV and IoDs, we exploit the first-order autoregressive process to model the fading channels during the information transmission (IT) phase. For such a realistic system setup, we derive the expressions for the outage probability (OP) at the IoDs using Gauss-Hermite quadrature under different shadowing and fading channel combinations. The system throughput and energy efficiency are also quantified using the derived OP expressions. The analytical findings are verified through numerical and simulation results. Our results highlight the impact of various system/channel parameters on the system’s performance and provide significant insights into the system’s behavior.
Orbital Angular Momentum Data Transmission Using a Silicon Photonic Mode Multiplexer

We designed and fabricated a silicon photonic orbital angular momentum (OAM) multiplexer (MUX) chip that enables simultaneous wavelength-division multiplexing (WDM) and OAM mode-division multiplexing (MDM) in optical fiber communication systems. We successfully utilized the fabricated MUX chip to demonstrate simultaneous 3 OAM MDM transmission as well as OAM MDM/WDM transmission with an 800-m OAM ring-core fiber. In both cases, 10 Gbit/s OOK signals were utilized and BERs below the 7% FEC limit were obtained.
Marketing of library resources and its impact on the library usage of distance-learning students

Purpose Access to learning resources in higher education is a prerequisite for effective learning. The purpose of this study is to investigate how marketing of library resources can influences library usage of students from the University of Education Winneba and University of Cape Coast in Ghana. Design/methodology/approach The study was grounded in the pragmatic paradigm and guided by the mixed methods research approach. The sequential explanatory design was used in that, quantitative data was collected first, followed by the collection of qualitative data. The respondents were selected using the multi-stage and purposive sampling techniques. A total of 1,170 students were involved in the quantitative study, and the qualitative study comprised 12 students and 6 librarians. The quantitative data were analysed descriptively using the Statistical Package for the Social Sciences (SPSS), while the qualitative data were analysed thematically. Findings The study found that the marketing of library resources and services increased awareness and motivates students from the University of Education Winneba and University of Cape Coast to use them. In addition, the study found no relationship between competencies in information literacy and the use of library resources and services beyond developing students’ capacity to use library resources The study further established a positive relationship between improved learning activities and the use of library resources and services by students at the University of Education Winneba and University of Cape Coast in Ghana. Originality/value The study is unique in its scope and focus and will lead understanding to the use of library services and resources to achieving positive learning outcomes in Ghanaian distance learning institutions. Thus, the expanded benefit to students’ use of library services not only will improve their academic activities, performances, retention, programme completion, employment opportunities and lifelong learning but also is important for institutions to increase students’ enrolments, increase university world-wide visibility and influence national development.
Low Power Neural Network by Reducing SRAM Operating Voltage

With advancements in machine learning technology, networks are becoming increasingly complex, and the extent of the computation involved is increasing. Consequently, the computation time and power consumption of the learning process are increased. The error tolerance of neural networks has attracted attention as an approach to solving this problem. Because neural networks can tolerate small errors, it is possible to reduce the calculation speed and power consumption at the expense of accuracy. In this study, we propose a method to reduce the power consumption of the circuit by lowering the operating voltage of the static random-access memory (SRAM) that is utilized to store the weights. In the proposed method, using two different operating voltages of SRAM, we used different bit error rates (BERs) for error-tolerant and non-error-tolerant. We demonstrated the relationship between the BER and recognition rate, and the appropriate combination of the BER and circuit configuration that maintains a high recognition rate.
Nonsymmetric Reduction-Based Algebraic Multigrid

Algebraic multigrid (AMG) is often an effective solver for symmetric positive definite (SPD) linear systems resulting from the discretization of general elliptic PDEs, or the spatial discretization of parabolic PDEs. However, convergence theory and most variations of AMG rely on $A$ being SPD. Hyperbolic PDEs, which arise often in large-scale scientific simulations, remain a challenge for AMG, as well as other fast linear solvers, in part because the resulting linear systems are often highly nonsymmetric. Here, a novel convergence framework is developed for nonsymmetric, reduction-based AMG, and sufficient conditions derived for $\ell^2$-convergence of error and residual. In particular, classical multigrid approximation properties are connected with reduction-based measures to develop a robust framework for nonsymmetric, reduction-based AMG. Matrices with block-triangular structure are then recognized as being amenable to reduction-type algorithms, and a reduction-based AMG method is developed for upwind discretizations of hyperbolic PDEs, based on the concept of a Neumann approximation to ideal restriction ($n$AIR). $n$AIR can be seen as a variation of local AIR ($\ell$AIR) introduced in previous work, specifically targeting matrices with triangular structure. Although less versatile than $\ell$AIR, setup times for $n$AIR can be substantially faster for problems with high connectivity. $n$AIR is shown to be an effective and scalable solver of steady state transport for discontinuous, upwind discretizations, with unstructured meshes, and up to 6th-order finite elements, offering a significant improvement over existing AMG methods. $n$AIR is also shown to be effective on several classes of `nearly triangular' matrices, resulting from curvilinear finite elements and artificial diffusion.
Optimal Branch Exchange for Distribution System Reconfiguration

The feeder reconfiguration problem chooses the on/off status of the switches in a distribution network in order to minimize a certain cost such as power loss. It is a mixed integer nonlinear program and hence hard to solve. A popular heuristic search consists of repeated application of branch exchange, where some loads are transferred from one feeder to another feeder while maintaining the radial structure of the network, until no load transfer can further reduce the cost. Optimizing each branch exchange step is itself a mixed integer nonlinear program. In this paper we propose an efficient algorithm for optimizing a branch exchange step. It uses an AC power flow model and is based on the recently developed convex relaxation of optimal power flow. We provide a bound on the gap between the optimal cost and that of our solution. We prove that our algorithm is optimal when the voltage magnitudes are the same at all buses. We illustrate the effectiveness of our algorithm through the simulation of real- world distribution feeders.
Size estimates for fat inclusions in an isotropic Reissner–Mindlin plate

In this paper we consider the inverse problem of determining, within an elastic isotropic thick plate modelled by the Reissner–Mindlin theory, the possible presence of an inclusion made of a different elastic material. Under some a priori assumptions on the inclusion, we deduce constructive upper and lower estimates of the area of the inclusion in terms of a scalar quantity related to the work developed in deforming the plate by applying simultaneously a couple field and a transverse force field at the boundary of the plate. The approach allows us to consider plates with a boundary of Lipschitz class.
Fuzzy Observer-Based Consensus Tracking Control for Fractional-Order Multi-Agent Systems Under Cyber-Attacks and Its Application to Electronic Circuits

Consensus control of multi-agent systems (MASs) has applications in various domains. As MASs work in networked environments, their security control becomes critically desirable in response to cyber-attacks. In this paper, the observer-based consensus tracking control problem is investigated for a class of Takagi-Sugeno fuzzy fractional-order multi-agent systems (FOMASs) under cyber-attacks. The malicious cyber attacks can impact the security of topologies of the communication networks of both controllers and observers. To estimate unmeasurable system states, a fuzzy observer is built. It is found that the topology of contact for observer states may be different from that of the feedback signals. A novel mathematical model for T-S fuzzy FOMASs with cyber-attacks is proposed. By using algebraic graph theory, Lyapunov functional, and fractional calculus theory, a distributed feed-back controller is developed for each agent, which guarantee the secure performance of tracking consensus error and observer error. Finally, two numerical examples demonstrate the effectiveness of the suggested control scheme, and the controller design for electronic network circuits shows the applicability of the proposed theoretical results. Simulations results for different differential-orders and coupling strength scenarios are given.
Hamilton–Jacobi equations for mean-field disordered systems

We argue that Hamilton-Jacobi equations provide a convenient and intuitive approach for studying the large-scale behavior of mean-field disordered systems. This point of view is illustrated on the problem of inference of a rank-one matrix. We compute the large-scale limit of the free energy by showing that it satisfies an approximate Hamilton-Jacobi equation with asymptotically vanishing viscosity parameter and error term.
Extension of frames and bases - I

We extend the theory of operator-valued frames (resp. bases), hence the theory of frames (resp. bases), for Hilbert spaces and Hilbert C*-modules, in two folds. This extension leads us to develop the theory of operator-valued frames (resp. bases) for Banach spaces. We give a characterization for the operator-valued frames indexed by a group-like unitary system. This answers an open question asked in the paper titled “Operator-valued frames” by Kaftal, Larson, and Zhang in Trans. Amer. Math. Soc. (2009). We study stability of the extension. We also extend Riesz-Fischer theorem, Bessel’s inequality, variation formula, dimension formula, and trace formula. Further, notions of p-orthogonality, p-orthonormality and Riesz p-bases have been developed in Banach spaces and Paley-Wiener theorem has also been generalized. We derive ‘4-inequality,’ ‘4-parallelogram law,’ and ‘4-projection theorem. ’
to defaulted corporate bonds”

The paper studies the return patterns for defaulted bonds at default time and nine months after. The author identifies and describes an strong overshooting effect, where the large negative return at default is followed by strong positive returns for a number of time periods. The article tests statistically for short-term excess return in a sample of 374 defaulted U.S. corporate bonds. There is a robust overshooting effect also after the author has controlled for market and liquidity risk. The average expected recovery rate during the time period of 2001-2010 is estimated to be seven percentage points lower at the first month after default than the present value of the recovery rate nine months later.