Miracle-dz/UniFAR-FTUs huggingface dataset:
A 28-nm 6-GHz 2-bit Continuous-Time ΔΣ ADC With −101-dBc THD and 120-MHz Bandwidth Using Blind Digital DAC Error Correction

In this article, a 6-GHz, 2-bit, fourth-order continuous-time delta–sigma (CT $\Delta \Sigma $ ) analog-to-digital converter (ADC) fabricated in 28-nm CMOS is presented. It achieves −101- and −105-dBc total harmonic distortion (THD)/third-order inter-modulation (IM3) typically and 72.3-dB signal to noise and distortion ratio (SNDR) in 120-MHz bandwidth (BW). The ADC comprises four cascaded integrators with inverter-based amplifiers, an offset compensated 2-bit quantizer, and calibrated 2-bit feedback (FB) digital-to-analog converter (DAC). The DAC and quantizer employ blind digital calibration techniques enabling the wideband linearity performance. The ADC does not require any external test signal during calibration. The power dissipation of the modulator core, including demultiplexer, is 108.8 mW.
Reinforcement Learning-Based Adaptive Optimal Fuzzy MPPT Control for Variable Speed Wind Turbine

A reinforcement learning-based adaptive optimal fuzzy controller is proposed for maximum power point tracking (MPPT) control of a variable-speed permanent magnet synchronous generator-based wind energy generation system. The algorithm consists of a critic, an adaptive optimal fuzzy controller, and an adaptive optimal fuzzy estimator. The critic is built based on an adaptive neuro-fuzzy inference system (ANFIS) network instead of the neural network as normal to reduce the computation. The error between the system output and the estimator output is used as the input of the critic. In addition, the critic is used to calculate the update law for the parameters of the adaptive optimal fuzzy controller and adaptive optimal fuzzy estimator based on minimizing the input error function. Moreover, the proposed control scheme is output feedback instead of state feedback, which does not require a system model as well as system parameters, so the system is robust to uncertainties and external disturbances. Besides, the stabilization proof is accomplished by using the Lyapunov stability theorem for the closed-loop system and the convergence of the update law. Finally, the effectiveness of the proposed reinforcement learning-based adaptive optimal fuzzy control scheme is verified through simulation with various scenarios such as step wind speed, random wind speed, and system parameter variations. Also, the comparisons with other control schemes in the state-of-art (neural network reinforcement learning based adaptive optimal fuzzy controller, PI controller) are executed to demonstrate the advantages of the proposed control scheme.
The Theory of Open Quantum Systems

PREFACE ACKNOWLEDGEMENTS PART 1: PROBABILITY IN CLASSICAL AND QUANTUM MECHANICS 1. Classical probability theory and stochastic processes 2. Quantum Probability PART 2: DENSITY MATRIX THEORY 3. Quantum Master Equations 4. Decoherence PART 3: STOCHASTIC PROCESSES IN HILBERT SPACE 5. Probability distributions on Hilbert space 6. Stochastic dynamics in Hilbert space 7. The stochastic simulation method 8. Applications to quantum optical systems PART 4: NON-MARKOVIAN QUANTUM PROCESSES 9. Projection operator techniques 10. Non-Markovian dynamics in physical systems PART 5: RELATIVISTIC QUANTUM PROCESSES 11. Measurements in relativistic quantum mechanics 12. Open quantum electrodynamics
Taxonomy and phylogeny of Exobasidium pentasporium causing witches' broom of Rhododendron species

Exobasidium pentasporium was first found on Rhododendron kaempferi in Nikko, Tochigi Prefecture, Japan and described only with a brief mentions and illustration of a specimen in 1896. This fungus causes a witches' broom disease of Rhododendron species. To stabilize the concept of this species, the specimen in the protologue was located, carefully examined, and illustrated. In addition, the name was epitypified based on a newly collected topotype specimen. A phylogenetic tree using ITS and LSU sequences showed that our isolates of E. pentasporium grouped with other Exobasidium species on Rhododendron forming a monophyletic clade with strong statistical support and were unrelated to E. nobeyamense, another causal agent of witches' broom disease on Rhododendron species.
The Influence of Crisis Management, Risk-Taking, and Innovation in Sustainability Practices: Empirical Evidence From Iraq

Aim/Purpose: This study examines the impact of decision-making, crisis management, and decision-making on sustainability through the mediation of open innovation in the energy sector. Background: Public companies study high-performance practices, requiring overcoming basic obstacles such as financial crises that prevent the adoption and development of sustainability programs. Methodology: Due to the COVID-19 pandemic, which has led to the closure of businesses in Iraq, a survey was distributed. To facilitate responses, free consultations were offered to help complete the questionnaire quickly. Of the 435 questionnaires answered, 397 were used for further analysis. Contribution: The impact of crises that impede the energy sector from adopting sustainable environmental regulations is investigated in this study. Its identification of specific constraints to open innovation leads to the effectiveness of adopting environmentally friendly policies and reaching high levels of sustainable performance. Findings: The impacts of risk-taking, crisis management, and decision-making on sustainability have been explored. Results show that open innovation fully mediates the relationship between the factors of risk-taking, crisis management, decision-making, and sustainability. Recommendations for Practitioners: The proposed model can be used by practitioners to develop and improve sustainable innovation practices and achieve superior performance. Recommendation for Researchers: Researchers are recommended to conduct in-depth studies of the phenomenon based on theoretical and empirical foundations, especially in light of the relationship between crisis management, decision-making, and risk-taking and their impact on sustainability based on linear and non-compensatory relationships. Impact on Society: This study provides a reference for organizations with similar cultural backgrounds in adopting sustainable practices to minimize pollution in the Iraqi context. Future Research: A more in-depth study can be performed using a larger sample, which not only includes the energy industry but also other industries.
High-Fluence Multi-Energy Ion Irradiation for Testing of Materials

Structural materials of the new generation of nuclear reactors, fission as well as fusion, must often cope with high production rates of transmutation helium. Their testing hence requires either a powerful source of fast neutrons or a high-fluence ion-irradiation facility providing sufficient amounts of high-energy helium to investigate its effect on the material. Most ion irradiation studies, however, concentrate on basic effects such as defect evolution or bubble swelling in narrow near-surface regions modified by ion bombardment. Studies on bulk samples with a relatively thick implanted region, which would enable, for instance, micromechanical testing, are underrepresented. This gap might be filled by high-fluence multi-energy ion irradiations modifying several tens of micrometres of the investigated substrate. High-energy ion accelerators providing reasonable currents with energies of tens of MeV are rarely employed in such studies due to their scarcity or considerable beamtime costs. To contribute to this field, this article reports a unique single-beam He implantation experiment aimed at obtaining quasi-uniform displacement damage across >60 μm with the He/dpa ratio roughly one order of magnitude above the typical spallation neutron target irradiation conditions. Some technical aspects of this irradiation experiment, along with recent developments and upgrades at the 6 MV Tandetron accelerator of the Slovak university of technology in Bratislava, are presented.
The Midcourse Space Experiment Point Source Catalog, Version 1.2

Abstract : This Explanatory Guide to the MSX Point Source Catalog briefly describes the instrumentation, and the experiments, which emphasized the areas that IRAS did not survey, and the Galactic Plane. A detailed discussion of the calibration and data processing follows. This guide then describes the catalog contents and presents analyses of the reliability of the quoted fluxes and positions, and the completeness and reliability of the catalog as a whole.
On reconstruction of a matrix by its minors

ABSTRACT In this paper, we reconstruct matrices from their minors, and give explicit formulas for the reconstruction of matrices of orders 2 × 3, 2 × 4, 2 × n, 3 × 6 and m × n. We also formulate the Plücker relations, which are the conditions of the existence of a matrix related to its given minors.
LogST: Log Semi-supervised Anomaly Detection Based on Sentence-BERT

Semantics extraction is a very important part in the field of log anomaly detection, how to accurately obtain the semantics representation of log events will have a direct impact on the final anomaly detection results. However, existing research focuses on using word embedding to extract the semantics of log events, this method based on weighted aggregation of all word vectors may lose the semantic relationship of word order, and does not consider the interaction between words in log events, which cannot fully represent the semantics of log events. To solve this problem and further improve the anomaly detection performance, this paper proposes a novel practical log-based anomaly detection approach, LogST, which uses the SBERT model to extract the semantics representation of log events, considering the semantic and word order relationship of each word of log events, which is convenient to understand the log sequence context, and then design a GRU model for anomaly detection. Experimental results conducted on the public HDFS datasets demonstrate that LogST outperforms other methods in the case of a sufficient number of labeled normal logs, and can still guarantee the stability of anomaly detection accuracy in the case of a small number of labeled normal logs.
The Support Vector Machine and Mixed Integer Linear Programming: Ramp Loss SVM with L1-Norm Regularization

The support vector machine (SVM) is a flexible classification method that accommodates a kernel trick to learn nonlinear decision rules. The traditional formulation as an optimization problem is a quadratic program. In efforts to reduce computational complexity, some have proposed using an L1-norm regularization to create a linear program (LP). In other efforts aimed at increasing the robustness to outliers, investigators have proposed using the ramp loss which results in what may be expressed as a quadratic integer programming problem (QIP). In this paper, we consider combining these ideas for ramp loss SVM with L1-norm regularization. The result is four formulations for SVM that each may be expressed as a mixed integer linear program (MILP). We observe that ramp loss SVM with L1-norm regularization provides robustness to outliers with the linear kernel. We investigate the time required to find good solutions to the various formulations using a branch and bound solver.
Efforts to Develop Bicycle Transportation in Supporting the Internal Transportation System

The Unhas Tamalanrea Campus has green open space which is one of the best urban forest areas in Makassar. This is supported by the implementation of Non-Motorized-Transport by re-developing bicycle transportation management. Campus Green Open Space is an attraction for the community to cycle, for this reason, attention to bicycle transportation facilities and connectivity with other transportation is needed so that vehicle access continues to run safely and comfortably, at the Unhas Tamalanrea Campus. This study aims to find out how the services of bicycle transportation facilities, the suitability of bicycle lanes, and development concepts can be applied at the Unhas Tamalanrea Campus. The analysis methods used are Importance Performance Analysis (IPA), Scoring Analysis, and Map interpretation. Data collection was carried out through observation, interviews, and questionnaires. The respondents taken were cyclists at the Unhas Tamalanrea Campus using accidental sampling techniques. The results showed that the condition of bicycle transportation facilities at the Unhas Tamalanrea Campus was quite good but still needed to be improved by the guidelines and concept of campus bicycle transportation.
An Improved Triplet Network for Electromechanical Actuator Fault Diagnosis Based on Similarity Strategy

Deep learning-based methods have been widely used and achieved state-of-the-art performance in fault diagnosis of an electromechanical actuator (EMA). Traditional deep learning methods face three major challenges, i.e., when the training dataset is limited, unbalanced, and/or when the model is applied in the different working conditions from the training dataset. In this article, we propose an improved deep learning-based fault diagnosis framework for EMAs based on triplet network with coupled cluster losses. Unlike the traditional approaches, the proposed framework learns to predict similarity between samples, rather than the fault labels directly. The trained model is used to calculate the distance between testing samples and a set of benchmark samples with known labels. Fault diagnosis is, then, conducted based on the $K$ -nearest neighbor algorithm. Experiments on a real-world EMA dataset from NASA show that the developed framework can improve the performance of traditional deep learning-based approaches under the three challenges.
Design of Low Power Adaptive Path Changing Glitch Free Radix-4, Radix-8 Multipliers

The emerging growth of digital systems depends on the errorless implementation of signal processing circuits. These circuits are implemented in VLSI platforms that provide reconfigurable clocking circuits. The clock signals are required to be stable and glitch free. In most of the digital circuit developments, multiplier plays a major role. The design implemented with less number of multipliers is highly demanded. These multiplier circuits are highly sensitive to glitches that occur due to fluctuations in power and floating elements. The proposed approach uses the RADIX-4 and RADIX-8 models with a glitch optimization circuit to construct a low power adjustable path selective Booth Multiplier architecture. The system utilized low power techniques like clock gating and sequenced latching process. The design is simulated using Modelsim tool and validated with test and evaluation of multiplier circuit with equivalent test bench. In comparison to the circuit with glitches, this model achieves low power elements after removal of glitch.
Prevention Detection for Cyclists based on Faster R-CNN

Cycling is becoming increasingly common on the roads, and the growth of this activity raises safety concerns. Bicycles are becoming one of the most common ways of transportation worldwide. Almost every day, though, there is an incident in which a driver hits and kills a rider. In the past few years, the number of avoidable deaths from bicycle transportation events has climbed by 37%. (National Safety Council). Thus, prediction prevention is an essential task in order to avoid such incidents. This article provides a summary of recent developments in commercial devices for cyclists as well as intent estimation to improve rider safety. Understanding the intentions of vehicles enables cyclists to take precautionary measures to prevent collisions. To accomplish this, the development of methods/ techniques for the autonomous vehicle, such as machine learning (ML), will be investigated. For example, employing DL techniques like R-CNN, and Faster R-CNN, the development of pedestrian detection has improved dramatically. While machine learning techniques have been around for decades, the technology required to implement them has just recently been made available. However, using a different approach by employing these ML approaches for vehicle detection and applying it for prediction estimations can allow for an effective and accurate way of vehicle intent estimation. A standard generic object identification framework is used to recognize the vehicle and assess the speed and distance from the cyclist. Then warn the rider ahead of time if it is speeding up or slowing down, calculating if it can slow down at that pace. To tackle the given challenge, we employed a two-stage Faster R-CNN detector.
THE PHYSICS OF ELEMENTARY PARTICLES AND FIELDS: Time-domain analytic solutions of two-wire transmission line excited by a plane-wave field

This paper reports that an analytic method is used to calculate the load responses of the two-wire transmission line excited by a plane-wave directly in the time domain. By the frequency-domain Baum–Liu–Tesche (BLT) equation, the time-domain analytic solutions are obtained and expressed in an infinite geometric series. Moreover, it is shown that there exist only finite nonzero terms in the infinite geometric series if the time variate is at a finite interval. In other word, the time-domain analytic solutions are expanded in a finite geometric series indeed if the time variate is at a finite interval. The computed results are subsequently compared with transient responses obtained by using the frequency-domain BLT equation via a fast Fourier transform, and the agreement is excellent.
Finite‐element discretisation of the eddy‐current term in a 2D solver for radially symmetric models

This paper discusses the discretisation of the eddy‐current term of the magnetoquasistatic subset of the Maxwell equations in a 2D setting with radial symmetry. It is shown that dedicated finite element shape functions are needed to make sure that particular distributions of the magnetic flux density and the electric field strength can be resolved exactly. Moreover, the shape functions should obey a partition‐of‐unity property and should achieve a prescribed convergence order. The 2D solver with radial symmetry is applied to calculate the performance of a multi‐coil induction cooking system. Copyright © 2013 John Wiley & Sons, Ltd.
Therapeutic Neuromodulation toward a Critical State May Serve as a General Treatment Strategy

Brain disease has become one of this century’s biggest health challenges, urging the development of novel, more effective treatments. To this end, neuromodulation represents an excellent method to modulate the activity of distinct neuronal regions to alleviate disease. Recently, the medical indications for neuromodulation therapy have expanded through the adoption of the idea that neurological disorders emerge from deficits in systems-level structures, such as brain waves and neural topology. Connections between neuronal regions are thought to fluidly form and dissolve again based on the patterns by which neuronal populations synchronize. Akin to a fire that may spread or die out, the brain’s activity may similarly hyper-synchronize and ignite, such as seizures, or dwindle out and go stale, as in a state of coma. Remarkably, however, the healthy brain remains hedged in between these extremes in a critical state around which neuronal activity maneuvers local and global operational modes. While it has been suggested that perturbations of this criticality could underlie neuropathologies, such as vegetative states, epilepsy, and schizophrenia, a major translational impact is yet to be made. In this hypothesis article, we dissect recent computational findings demonstrating that a neural network’s short- and long-range connections have distinct and tractable roles in sustaining the critical regime. While short-range connections shape the dynamics of neuronal activity, long-range connections determine the scope of the neuronal processes. Thus, to facilitate translational progress, we introduce topological and dynamical system concepts within the framework of criticality and discuss the implications and possibilities for therapeutic neuromodulation guided by topological decompositions.
LC–MS/MS Phytochemical Profiling, Antioxidant Activity, and Cytotoxicity of the Ethanolic Extract of Atriplex halimus L. against Breast Cancer Cell Lines: Computational Studies and Experimental Validation

Atriplex halimus L. , also known as Mediterranean saltbush, and locally as “Lgtef”, an halophytic shrub, is used extensively to treat a wide variety of ailments in Morocco. The present study was undertaken to determine the antioxidant activity and cytotoxicity of the ethanolic extract of A. halimus leaves (AHEE). We first determined the phytochemical composition of AHEE using a liquid chromatography (LC)–tandem mass spectrometry (MS/MS) technique. The antioxidant activity was evaluated using different methods including DPPH scavenging capacity, β-carotene bleaching assay, ABTS scavenging, iron chelation, and the total antioxidant capacity assays. Cytotoxicity was investigated against human cancer breast cells lines MCF-7 and MDA-MB-231. The results showed that the components of the extract are composed of phenolic acids and flavonoids. The DPPH test showed strong scavenging capacity for the leaf extract (IC50 of 0.36 ± 0.05 mg/mL) in comparison to ascorbic acid (IC50 of 0.19 ± 0.02 mg/mL). The β-carotene test determined an IC50 of 2.91 ± 0.14 mg/mL. The IC50 values of ABTS, iron chelation, and TAC tests were 44.10 ± 2.92 TE µmol/mL, 27.40 ± 1.46 mg/mL, and 124 ± 1.27 µg AAE/mg, respectively. In vitro, the AHE extract showed significant inhibitory activity in all tested tumor cell lines, and the inhibition activity was found in a dose-dependent manner. Furthermore, computational techniques such as molecular docking and ADMET analysis were used in this work. Moreover, the physicochemical parameters related to the compounds’ pharmacokinetic indicators were evaluated, including absorption, distribution, metabolism, excretion, and toxicity prediction (Pro-Tox II).
Optimising number resolving photo-detectors using classical post-processing

Centre for Quantum Computation and Communication Technology,School of Mathematics and Physics, University of Queensland, QLD 4072, Australia(Dated: July 15, 2011)Many present day quantum optics experiments, particularly in optical quantum information pro-cessing, rely on number-resolving photo-detection as a basic building block. In this paper we demon-strate that a simple classical optimisation technique can sometimes be employed to post-process thedetector signature and improve the con dence of the measurement outcome in the presence ofphoton-number errors such as loss or dark-counts. While the regime in which this technique is appli-cable is rather restrictive, and will likely not be very useful for the large-scale quantum informationprocessing applications of the future, the ideas presented might be employed in some present-dayexperiments where photo-detectors are typically very poor.INTRODUCTION