Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Repetitive Control of Nonlinearly Parameterized Systems Based on Fuzzy Basis Function Networks

In this note, an adaptive repetitive control scheme is presented for a class of nonlinearly parameterized systems based on fuzzy basis function networks (FBFNs). The parameters of the fuzzy rules are tuned with adaptive schemes. The adaptive repetitive control law is derived by using Lyapunov synthesis method to guarantee the closed-loop stability and the tracking performance. By means of FBFNs, the controller singularity problem is solved as it avoids the nonlinear parameterization from entering into the adaptive control and repetitive control. The proposed approach has the added advantage that it does not require an exact structure of the system dynamics. The proposed controller is applied to control a model of permanent-magnet linear synchronous motor (PMLSM) subject to significant disturbances and parameter uncertainties. The simulation results demonstrate the effectiveness of the proposed method in terms of significant reduction in chattering while maintaining asymptotic convergence.
Simultaneous Determination of Risperidone and Trihexhyphenidyl Hydrochloride from Bulk and Tablet Dosage Form by RP-HPLC

A simple, reverse phase high performance liquid chromatographic (HPLC) method was developed for the determination of Risperidone and Trihexyphenidyl hydrochloride in combined tablet dosage form. A octadecyl silane (ODS) C18 Phenomenex Gemini (250mm X 4.6mm, i.d., 5μ) column, with mobile phase of 0.3% ortho phosphoric acid buffer, acetonitrile and methanol (50:30:20%v/v/v) adjusted to pH3 with triethylamine was used. The flow rate was 1mL/minute and the effluent was monitored at 220nm. The retention times for Risperidone and Trihexyphenidyl hydrochloride were 2.685±0.012 and 5.912±0.030 minutes respectively. The linearity range was found to be 4.8 μg/mL to 19.2 μg/mL for Risperidone and 3.2 μg/mL to 12.8 μg/mL for Trihexyphenidyl hydrochloride. The proposed method was validated as per ICH guidelines.
Atorvastatin Can Modulate DNA Damage Repair in Endothelial Cells Exposed to Mitomycin C

HMG-CoA reductase inhibitors (statins) are widely used in the therapy of atherosclerosis and have a number of pleiotropic effects, including DNA repair regulation. We studied the cytogenetic damage and the expression of DNA repair genes (DDB1, ERCC4, and ERCC5) in human coronary artery (HCAEC) and internal thoracic artery endothelial cells (HITAEC) in vitro exposed to mitomycin C (MMC) (positive control), MMC and atorvastatin (MMC+Atv), MMC followed by atorvastatin treatment (MMC/Atv) and 0.9% NaCl (negative control). MMC/Atv treated HCAEC were characterized by significantly decreased micronuclei (MN) frequency compared to the MMC+Atv group and increased nucleoplasmic bridges (NPBs) frequency compared to both MMC+Atv treated cells and positive control; DDB1, ERCC4, and ERCC5 genes were upregulated in MMC+Atv and MMC/Atv treated HCAEC in comparison with the positive control. MMC+Atv treated HITAEC were characterized by reduced MN frequency compared to positive control and decreased NPBs frequency in comparison with both the positive control and MMC/Atv group. Nuclear buds (NBUDs) frequency was significantly lower in MMC/Atv treated cells than in the positive control. The DDB1 gene was downregulated in the MMC+Atv group compared to the positive control, and the ERCC5 gene was upregulated in MMC/Atv group compared to both the positive control and MMC+Atv group. We propose that atorvastatin can modulate the DNA damage repair response in primary human endothelial cells exposed to MMC in a cell line- and incubation scheme-dependent manner that can be extremely important for understanding the fundamental aspects of pleoitropic action of atorvastatin and can also be used to correct the therapy of patients with atherosclerosis characterized by a high genotoxic load.
SEE-CSOM: Sharp-Edged and Efficient Continuous Semantic Occupancy Mapping for Mobile Robots

Generating an accurate and continuous semantic occupancy map is a key component of autonomous robotics. Most existing continuous semantic occupancy mapping methods neglect the potential differences between voxels, which reconstruct an overinflated map. What is more, these methods have high computational complexity due to the fixed and large query range. To address the challenges of overinflation and inefficiency, this article proposes a novel sharp-edged and efficient continuous semantic occupancy mapping algorithm (SEE-CSOM). The main contribution of this work is to design the Redundant Voxel Filter Model (RVFM) and the Adaptive Kernel Length Model (AKLM) to improve the performance of the map. RVFM applies context entropy to filter out the redundant voxels with a low degree of confidence, so that the representation of objects will have accurate boundaries with sharp edges. AKLM adaptively adjusts the kernel length with class entropy, which reduces the amount of data used for training. Then, the multientropy kernel inference function is formulated to integrate the two models to generate the continuous semantic occupancy map. The algorithm has been verified on indoor and outdoor public datasets and implemented on a real robot platform, validating the significant improvement in accuracy and efficiency.
A Fast SRR Algorithm Based on Recursive Least Square Estimation and Simultaneous Image Registration

A Fast SRR Algorithm Based on Recursive Least Square Estimation and Simultaneous Image Registration by Stephane Kirchner This thesis proposed a Super Resolution Reconstruction (SRR) algorithm based on recursive least square estimation and simultaneous image registration. This recursive least square estimation algorithm is computationally fast and effective, and the simultaneous image registration algorithm is more efficient for real practical use. So this thesis will try to present the advantages by combining these two algorithms into a single framework. The thesis is structured into five main parts: • The first chapter introduces the super resolution reconstruction (SRR). • Later, the chapter 2 examines the super resolution reconstruction algorithm using the stochastic regularization approach. • The chapter 3 reviews and examines three SRR algorithms using the stochastic regularization approach (Classical SRR algorithm, Fast SRR algorithm based on Recursive Least Square and SRR algorithm based on Simultaneous Image Registration) and also the proposed algorithm based on recursive least square estimation and simultaneous image registration. • The chapter 4 gives some experimental comparisons among different algorithms. • And finally, the chapter 5 will give the conclusion and the future works directions.
A Dual-Function “TRE-Lox” System for Genetic Deletion or Reversible, Titratable, and Near-Complete Downregulation of Cathepsin D

Commonly employed methods for reversibly disrupting gene expression, such as those based on RNAi or CRISPRi, are rarely capable of achieving >80–90% downregulation, making them unsuitable for targeting genes that require more complete disruption to elicit a phenotype. Genetic deletion, on the other hand, while enabling complete disruption of target genes, often produces undesirable irreversible consequences such as cytotoxicity or cell death. Here we describe the design, development, and detailed characterization of a dual-function “TRE-Lox” system for effecting either (a) doxycycline (Dox)-mediated downregulation or (b) genetic deletion of a target gene—the lysosomal aspartyl protease cathepsin D (CatD)—based on targeted insertion of a tetracycline-response element (TRE) and two LoxP sites into the 5′ end of the endogenous CatD gene (CTSD). Using an optimized reverse-tetracycline transrepressor (rtTR) variant fused with the Krüppel-associated box (KRAB) domain, we show that CatD expression can be disrupted by as much as 98% in mouse embryonic fibroblasts (MEFs). This system is highly sensitive to Dox (IC50 = 1.46 ng/mL) and results in rapid (t1/2 = 0.57 d) and titratable downregulation of CatD. Notably, even near-total disruption of CatD expression was completely reversed by withdrawal of Dox. As expected, transient expression of Cre recombinase results in complete deletion of the CTSD gene. The dual functionality of this novel system will facilitate future studies of the involvement of CatD in various diseases, particularly those attributable to partial loss of CatD function. In addition, the TRE-Lox approach should be applicable to the regulation of other target genes requiring more complete disruption than can be achieved by traditional methods.
Phase-dependent intersubband optical properties of asymmetric AlGaAs/GaAs coupled quantum wells

In this article, we have investigated the optical properties of intersubband transitions in an asymmetric AlGaAs/GaAs coupled quantum wells system. We solved the Schrödinger equation numerically using homemade finite difference codes to determine the electronic structure. The susceptibilities were calculated in frame of density matrix equations. The effect of relative phase variation between applied fields, on the linear absorption coefficients, relative refractive index changes, and group velocity has been investigated. The results show that the linear optical properties are extremely phase-dependent. By adjusting the phase, it is possible to obtain absorption curves with dispersion-like profile and vice versa.
Efficacy of the online Mindful Self-Compassion for Healthcare Communities program for surgical trainees: a prospective pilot study

Purpose The efficacy of the Mindful Self-Compassion (MSC) for Healthcare Communities program has not been verified. This study aims to evaluate the feasibility and efficacy of the online MSC for Healthcare Communities program on burnout, stress-related health, and resilience among surgical trainees. Methods A single-arm pilot study was conducted at a tertiary referral academic hospital in Korea. Surgical trainees were recruited through flyer postings; therefore, a volunteer sample was used. Thus, 15 participants participated, among whom 9 were women and 11 were doctor-residents. The Self-Compassion for Healthcare Communities (SCHC) program was conducted from September to October 2021 via weekly online meetings (1 hour) for 6 weeks. The efficacy of the program was evaluated using validated scales for burnout, stress, anxiety, depression, self-compassion, and resilience before and after the intervention and 1 month later. Results The results showed significantly reduced burnout, anxiety, and stress scores. After the program, high emotional exhaustion and depersonalization rates decreased, and personal accomplishment increased. Eight participants showed reduced anxiety postintervention, and 9 showed reduced stress. Improvements were observed between pre- and postintervention in resilience, life satisfaction, and common humanity. Changes in self-compassion predicted higher gains in resilience and greater reductions in burnout and stress. Conclusion The SCHC is a feasible and effective program to improve resilience, self-compassion, and life satisfaction and reduce stress, anxiety, depression, and burnout in surgical trainees. This study highlights the need to include specific mental health programs in surgical training to improve trainees’ well-being.
Sharpening of Jordan's inequalities and its applications

In this paper, we establish the following inequalities sin r r + sin r − r cos r 2r3 (r − x) sin x x sin r r + r − sin r r3 (r − x) for x ∈ (0, r], r π/2 . An application of inequalities above leads to the following refinement of Yang Le inequalities: 4C n[ sin r r λ 2 π + sin r − r cos r 2r3 (r λ 2 π − λ 3 8 π)] cos λ 2 π (n − 1) n ∑ k=1 cos2 λAk − 2 cos λπ ∑ 1 i 0(i = 1, 2, · · · , n), ∑n i=1 Ai π, 0 λ 1 and n 2 is a natural number. Mathematics subject classification (2000): 26D15.
Waves and Currents

Much progress in analyzing the interaction between surface waves and currents has come from assuming that the waves behave locally like plane-waves. That is, the solution for waves in still water with a flat bottom is applied, modified only to account for uniform flow (i.e., translation in x or y). This assumption is typically applied in two opposite limits: i) separate regions of uniform flow and flat bottoms, connected at a thin vertical boundary where suitable matching conditions can be derived; and ii) flow and topography varying slowly compared to the time and length scales of the waves, so the errors are bounded (and quantifiably small). In view of this, it’s worthwhile to review the plane-wave solution for surface waves, so the quantities of interest (and the potential shortcomings) are more or less clear. Notation is as follows: vector locations and velocities are
Pseudo‐predictor feedback control of discrete‐time linear systems with a single input delay

This paper studies the problems of stabilization of discrete‐time linear systems with a single input delay. By developing the methodology of pseudo‐predictor feedback, which uses the (artificial) closed‐loop system dynamics to predict the future state, memoryless state feedback control laws are constructed to solve the problem. Necessary and sufficient conditions are obtained to guarantee the stability of the closed‐loop system in terms of the stability of a class‐difference equations. It is also shown that the proposed controller achieves semi‐global stabilization of the system if its actuator is subject to either magnitude saturation or energy constraints under the condition that the open‐loop system is only polynomially unstable. Numerical examples have been worked out to illustrate the effectiveness of the proposed approaches. Copyright © 2015 John Wiley & Sons, Ltd.
A Global Modeling Framework for Load Forecasting in Distribution Networks

With the increasing numbers of smart meter installations, scalable and efficient load forecasting techniques are critically needed to ensure sustainable situation awareness within the distribution networks. Distribution networks include a large amount of different loads at various aggregation levels, such as individual consumers, low-voltage feeders, and transformer stations. It is impractical to develop individual (or so-called local) forecasting models for each load separately. Additionally, such local models also $(i)$ (largely) ignore the strong dependencies between different loads that might be present due to their spatial proximity and the characteristics of the distribution network, $(ii)$ require historical data for each load to be able to make forecasts, and $(iii)$ are incapable of adjusting to changes in the load behavior without retraining. To address these issues, we propose a global modeling framework for load forecasting in distribution networks that, unlike its local competitors, relies on a single global model to generate forecasts for a large number of loads. The global nature of the framework, significantly reduces the computational burden typically required when training multiple local forecasting models, efficiently exploits the cross-series information shared among different loads, and facilitates forecasts even when historical data for a load is missing or the behavior of a load evolves over time. To further improve on the performance of the proposed framework, an unsupervised localization mechanism and optimal ensemble construction strategy are also proposed to localize/personalize the global forecasting model to different load characteristics. Our experimental results show that the proposed framework outperforms naive benchmarks by more than 25% (in terms of Mean Absolute Error) on real-world dataset while exhibiting highly desirable characteristics when compared to the local models that are predominantly used in the literature. All source code and data are made publicly available to enable reproducibility: https://github.com/mihagrabner/GlobalModelingFramework.
Towards a holographic description of pulsar glitch mechanism

This work aims to review the progress in understanding the underlining physics of pulsar glitches: beginning from the pedagogical development of the subject to eventually motivating the use of AdS/CFT techniques in studying a certain class of condensed matter systems. The foundation of this work is built upon the Gross Pitaevskii (GP) model of superWuidity applied to the interior matter of neutron stars, where the condensate wave function acts as the order parameter of the macroscopic coherence theory. The excitation modes of the Veld equations are found to be solitonic vortices, which then go on to present a theoretical basis to the plausible theories of pulsar glitches involving vortex dynamics. The second major thrust of this thesis is in reviewing the application of AdS/CFT in study of strongly-coupled condensed matter systems, with special attention to the models of holographic superWuidity that admit vortex-like solutions. The basic identiVcation of the characteristic free energy conVguration of global vortices in the AdS/CFT prescription enables to motivate its use in studying the pulsar glitch mechanism. The last part of this work traces the conclusions of this review and attempts to present the current state-of-progress of the Veld with its extensive domain of purview and open lines of inquiry.
Randomised multichannel singular spectrum analysis of the 20th century climate data

In this article, we introduce a new algorithm called randomised multichannel singular spectrum analysis (RMSSA), which is a generalisation of the traditional multichannel singular spectrum analysis (MSSA) into problems of arbitrarily large dimension. RMSSA consists of (1) a dimension reduction of the original data via random projections, (2) the standard MSSA step and (3) a recovery of the MSSA eigenmodes from the reduced space back to the original space. The RMSSA algorithm is presented in detail and additionally we show how to integrate it with a significance test based on a red noise null-hypothesis by Monte-Carlo simulation. Finally, RMSSA is applied to decompose the 20th century global monthly mean near-surface temperature variability into its low-frequency components. The decomposition of a reanalysis data set and two climate model simulations reveals, for instance, that the 2–6 yr variability centred in the Pacific Ocean is captured by all the data sets with some differences in statistical significance and spatial patterns.
Pricing European options with stochastic volatility under the minimal entropy martingale measure

In this paper, a closed-form pricing formula in the form of an infinite series for European call options is derived for the Heston stochastic volatility model under a chosen martingale measure. Given that markets with the stochastic volatility are incomplete, there exists a number of equivalent martingale measures and consequently investors face a problem of making a choice of appropriate measure when they price options. The one we adopt here is the so-called minimal entropy martingale measure shown to be related to the expected utility maximization theory (Frittelli 2000 Math. Finance 10(1), 39–52) and the financial rationality for choosing this measure will be further illustrated in this paper. A great advantage of our newly-derived pricing formula is that the convergence of the solution in series form can be proved theoretically; such a proof of the convergence is also complemented by some numerical examples to demonstrate the speed of convergence. To further show the validity of our formula, a comparison of prices calculated through the newly derived formula is made with those obtained directly from the Monte Carlo simulation as well as those from solving the PDE (partial differential equation) with the finite difference method.
Hedging of contingent claims written on non traded assets under Markov-modulated models

ABSTRACT This paper studies the hedging problem of European contingent claims when the underlying asset is non traded. We assume that the share prices of the assets are governed by Markov-modulated processes; that is, the market parameters switch over the time according to a finite-state continuous time Markov chain. Due to the presence of Markov chain the non traded asset, the market which we consider is incomplete, we shall use the local risk minimization method to obtain an optimal hedging strategy in a closed-form for an investor. Finally, numerical illustrations of an optimal hedging strategy are given by the Monte Carlo simulation.
Accrual-Based Accounting and Fiscal Performance: Empirical Evidence from Higher Education Institutions

Abstract This study aims to advance our knowledge about the role of accrual accounting regimes in financial decision-making process at Higher Education Institutions. The advantages of accrual accounting in the public sector are well documented from the theoretical perspective. However, empirical findings are relatively rare and primarily rely on either normative arguments or storytelling narratives drawn from case studies. Our paper intends to fill this void by combining archival and survey data from higher education institutions. We investigate whether and to what extent universities with accrual accounting enhance fiscal performance. Based on the results of Mahalanobis distance matching and regression with matched pairs, accrual accounting regimes have a small but positive impact on reducing the debt ratio of such institutions. This paper has the potential to provide information regarding the intended effects of accrual accounting on public organizations.
Impact of Teaching a Neuro-Computerized Course Through VLE to Develop Computational Thinking Among Mathematics Pre-service Teachers

The current study aims to determine the effect of teaching a mechanic neuro-computerized course through virtual learning environments (VLE) to develop computational thinking among mathematics pre-service teachers. The neuro-computerized virtual learning environments (NCVLE) model was designed to be used to teach the mechanics course to third-year students of the mathematics department. To achieve the targeted learning outcomes, the study recruited (102) third-year students of the Faculty of Education and classified them into a control group of (50) students and an experimental group of (52) students. The experiment lasted for 14 weeks during one semester of the 2021-2022 academic year. The results agreed with most of what has been found from relevant literature and studies. Also, the results indicated that the NCVLE model played a vital role in the purposeful teaching, learning, and assessment processes and enhanced the learning of computational thinking.
Adopting Coding in Early Childhood Education: Teachers’ Perspectives

The recent push to introduce coding in early childhood education has been the subject of heated debate. A lack of theoretical and empirical research devoted to understanding coding education acceptance provided the motivation for this article. As a way to engage in the debate, this article investigated kindergarten teachers’ acceptance of coding education by drawing on the theory of planned behavior framework, with self-efficacy, pedagogical beliefs, perceived ease of use, perceived usefulness, student readiness, teacher readiness, and parent readiness serving as external factors. The data were obtained from 236 in-service and preservice teachers in South Korea. Partial least square was employed to evaluate the research hypotheses. We find that self-efficacy and pedagogical beliefs exert a positive effect on perceived behavioral control. Perceived ease of use and perceived usefulness are pertinent for forming positive attitudes. Teacher readiness and parent readiness matter for generating positive subjective norm. Perceived behavioral control, attitude, and subjective norm all have a positive effect on behavioral intentions. The positive indirect effect of perceived usefulness on behavioral intentions through attitude is also noteworthy. In this article, we provide exploratory results as to how teachers believe and perceive coding in early childhood education. Practical insights are provided based on these initial findings.