Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis of biocapped CuO nanoparticles: An investigation on biorganic-Cu2+ interactions, in vitro antioxidant and antimicrobial aspects

ABSTRACT The present research reports an eco-friendly, cost-effective, rapid, and easy method for preparing CuO nanoparticles (CuONPs) using the fruit extract of rose hip as a biocapping and reducing agent. The CuONPs are crystalline in nature and possess average sizes near 40 nm. Next, the biorganic-Cu2+ interactions were investigated based on FT IR results, X-Ray diffraction patterns, and potentiometric data during capping of cupric species. The free-radical scavenging potential of CuONPs as well as their antibacterial activity was tested against four pathogenic microbes. The CuONPs prepared from rose hip fruit metabolites showed promising results, accordingly proving their biomedical importance.
Robust GNSS Shadow Matching for Smartphones in Urban Canyons

GNSS is being widely used in different applications in navigation. However, GNSS positioning is greatly challenged by notorious multipath effects and non-line-of-sight (NLOS) receptions. The signal blockage and reflection by buildings cause these effects. In other words, the more urbanized the city is, the more challenge on the GNSS positioning. The conventional multipath mitigation approaches, such as the sophisticated design of GNSS receiver correlator, can efficiently mitigate the most of multipath effects. However, it has less capability against NLOS reception, potentially leading to several tens of positioning errors. Therefore, the 3D mapping aided (3DMA) GNSS positioning is introduced to exclude or even use the NLOS signal. Shadow matching is to make use of the similarity between building geometry and satellite visibility to improve the positioning performance. This paper introduces a machine learning intelligent classifier with features to distinguish LOS and NLOS. With the NLOS reception classification, the positioning accuracy of shadow matching can be increased. In addition, this paper develops several indicators to label the unreliable solution of shadow matching. These indicators are to examine the complexity of the surrounding environment, which is the key factor relating to the proposed shadow matching performance. Several designed experiments were done in Hong Kong to evaluate the proposed method. With the intelligent classifier, the average positioning accuracy is about 15m and 6m on 2D and the across-street direction, respectively. Simultaneously, the reliability evaluation rules can exclude unreliable epoch and improve the positioning results, especially on smartphone data.
IONICMECHANISMS OF ACTION POTENTIALPROLONGATION AT LOW TEMPERATURE INGUINEA-PIGVENTRICULAR MYOCYTES

SUMMARY 1.We studied theeffects oflowtemperature ontheaction potentials and membrane currents ofguinea-pig ventricular myocytes, using atight-seal whole-cell clamptechnique. 2.Theaction potential duration at95% repolarization wasprolonged from 146+33ms(mean+S.D., n= 6)at33-34 °C(control temperature) to314+83msat 24-25°C(lowtemperature). 3.Inwhole-cell clampexperiments, lowtemperature decreased thecalcium current (ICa), thedelayed rectifier potassium current (IK)' andtheinwardly rectifying potassium current(IK.) with'apparent' Qlo(temperature coefficient) values of 2-3+06forICa' 4-4+1-2for'Ktail current and1-5+03forIK. (n= 7). 4.Theeffect oflowtemperature onIKwasfurther studied inthepresence of06,(M nicardipine toblock ICa. ThedecayphaseoftheIKtail consisted oftwoexponential components. Thefastbutnottheslowcomponent washighly sensitive tothe temperature change withanapparent Q1o of4-5. 5.Wefoundthatacomponent oftime-independent current isalso sensitive tothe temperature. Thecurrent hada linear I-Vrelationship andremained almost unchanged after inhibition ofNa+-K+pumpinK+-free external solution. 6.Usingourmathematical modeloftheventricular actionpotential (a modification fromtheDiFrancesco-Noble model), wesimulated theaction potential atlowtemperature bymodifying someofthemembrane currents, namely IK'IK1, ICa andacomponent ofbackground current. Itwasshownthatsimultaneous changes in thesecurrents couldreproduce approximately 75% oftheaction prolongation induced bylowtemperature.
Taxpayer Compliance Based on Awareness and Policy

Article History: Received October 22, 2020 Accepted February 27, 2021 Available March 15, 2021 This study examines the effect of tax policy aspects and awareness on taxpayer compliance in Kuningan Regency. The policy aspects in this study have manifested by tax sanction and Fiscus service quality. This study uses questionnaires which have distributed by random sampling technic. By using the multiple linear regression analysis, this study reveals that tax sanctions, Fiscus service quality, and taxpayer awareness positively affect taxpayer compliance. Aspects of government policy in tax sanctions are more significant than taxpayers’ awareness to affect taxpayer compliance. It means that strict tax sanctions have been necessary accompanied by improving the Fiscus services quality as part of the tax services paradigm. This study can be used as a reference to reexamine the determinants of taxpayer compliance, especially in areas with the same characteristics as the object of this study. Future research is expected to examine the equilibrium between economic and non-economic variables more deeply in increasing taxpayer compliance. It is essential because which variable most determines taxpayer compliance is still unclear, whether theoretically or practically.
PHYTOPLANKTON FUNCTIONAL GROUPS AND THEIR RELATIONSHIP WITH ENVIRONMENTAL FACTORS IN A EUTROPHIC AND HIGH-ALKALINITY LAKE IN THE COLD REGIONS OF NORTHERN CHINA

The investigation of phytoplankton was carried out from May to October month by month in 2018 in Hulun Lake, north China. Our purpose is to explore the community structure characteristics of phytoplankton by using the method of functional groups, combined with the monitoring results of water environmental factors, discussed the correlation between them in this special lake with high alkalinity, turbidity and eutrophication, so as to further explore the mechanisms of cyanobacteria bloom. Altogether 21 functional groups were identified, including 7 predominant functional groups. The predominant functional groups demonstrated strong spatio-temporal variations. It should also be noted that group H1 and M were the main culprits for the cyanobacteria bloom in the lake. Redundancy and Pearson analysis revealed that WT, DO, TP and TN were the most significant environmental factors controlling the distribution of phytoplankton functional groups. Furthermore, this study also evaluated the water quality in Hulun Lake by using Q index which shows it in a “medium” state. In a word, our results evidenced that a timely water environment dynamic can be reflected by monitoring the phytoplankton in Hulun Lake, which is also a superb way for the real-time monitoring of cyanobacteria bloom in this area.
QuizBot: A Dialogue-based Adaptive Learning System for Factual Knowledge

Advances in conversational AI have the potential to enable more engaging and effective ways to teach factual knowledge. To investigate this hypothesis, we created QuizBot, a dialogue-based agent that helps students learn factual knowledge in science, safety, and English vocabulary. We evaluated QuizBot with 76 students through two within-subject studies against a flashcard app, the traditional medium for learning factual knowledge. Though both systems used the same algorithm for sequencing materials, QuizBot led to students recognizing (and recalling) over 20% more correct answers than when students used the flashcard app. Using a conversational agent is more time consuming to practice with, but in a second study, of their own volition, students spent 2.6x more time learning with QuizBot than with flashcards and reported preferring it strongly for casual learning. Our results in this second study showed QuizBot yielded improved learning gains over flashcards on recall. These results suggest that educational chatbot systems may have beneficial use, particularly for learning outside of traditional settings.
Why Evolutionary Psychology Should Abandon Modularity

A debate surrounding modularity—the notion that the mind may be exclusively composed of distinct systems or modules—has held philosophers and psychologists captive for nearly 40 years. Concern about this thesis—which has come to be known as the massive modularity debate—serves as the primary grounds for skepticism of evolutionary psychology’s claims about the mind. In this article we argue that the entirety of this debate, and the very notion of massive modularity itself, is ill-posed and confused. In particular, it is based on a confusion about the level of analysis (or reduction) at which one is approaching the mind. Here we provide a framework for clarifying at what level of analysis one is approaching the mind and explain how a systemic failure to distinguish between different levels of analysis has led to profound misunderstandings of not only evolutionary psychology but also of the entire cognitivist enterprise of approaching the mind at the level of the mechanism. We furthermore suggest that confusions between different levels of analysis are endemic throughout the psychological sciences—extending well beyond issues of modularity and evolutionary psychology. Therefore, researchers in all areas should take preventive measures to avoid this confusion in the future.
Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model

Accurate global solar radiation prediction is highly essential for related research on renewable energy sources. The cost implication and measurement expertise of global solar radiation emphasize that intelligence prediction models need to be applied. On the basis of long-term measured daily solar radiation data, this study uses a novel regularized online sequential extreme learning machine, integrated with variable forgetting factor (FOS-ELM), to predict global solar radiation at Bur Dedougou, in the Burkina Faso region. Bayesian Information Criterion (BIC) is applied to build the seven input combinations based on speed (Wspeed), maximum and minimum temperature (Tmax and Tmin), maximum and minimum humidity (Hmax and Hmin), evaporation (Eo) and vapor pressure deficiency (VPD). For the difference input parameters magnitudes, seven models were developed and evaluated for the optimal input combination. Various statistical indicators were computed for the prediction accuracy examination. The experimental results of the applied FOS-ELM model demonstrated a reliable prediction accuracy against the classical extreme learning machine (ELM) model for daily global solar radiation simulation. In fact, compared to classical ELM, the FOS-ELM model reported an enhancement in the root mean square error (RMSE) and mean absolute error (MAE) by (68.8–79.8%). In summary, the results clearly confirm the effectiveness of the FOS-ELM model, owing to the fixed internal tuning parameters.
High catalytic activity of monometallic Ag, Cu nanostructures in the degradation of acid blue 113 dye: an electron relay effect

This report discusses the observed faster decoloration of an azo dye with an enhanced degradation rate constant achieved using metal nanostructures as a catalyst. Silver and copper nanostructures were synthesized by reducing the corresponding nitrate salts using hydrazine and hexamethylenetetramine (HMTA) in sodium hydroxide (NaOH) solution. The influence of HMTA was clearly evident from the scanning electron microscopy (SEM) images; with increasing concentration it caused agglomeration and the formation of net-like nanostructures. An x-ray diffraction study confirmed the formation of monometallic Ag and Cu nanostructures. The prepared nanostructures exhibited dipole and multipole surface plasmon resonance-related optical absorption bands which were size and shape dependent. The degradation of the azo dye acid blue 113 (AB113) in the presence of sodium borohydride (NaBH4) was taken as model system for studying the catalytic activity of the metal nanostructures. From the optical absorption spectral studies of dye degradation it was observed that the rate constant (k) was of the order of kCu > kAg > kno catalyst. From the dye degradation studies a high catalytic activity was observed for Cu nanostructures with a rate constant of 20.93 × 10–4 s−1.
Population size and trend, and habitat selection of Common Moorhens (Gallinula chloropus) in Oslo and Akershus, southeastern Norway

The Common Moorhen Gallinula chloropus is listed as vulnerable (VU) in Norway due to a small population size (estimated at 110–215 pairs in 2015). The population size is considered stable. More than one quarter of the population (30–70 pairs) is thought to occur in the region of Oslo and Akershus, SE Norway. In 2018, I conducted a comprehensive survey of known and potential breeding sites to assess current population size in Oslo and Akershus. I recorded 74 pairs in 58 sites. To analyse the population trend, I collected all known records of Common Moorhen during the breeding season for the period 1995–2018. Analyses indicated that population size was stable overall. However, compared to data from 1982, sites with the largest number of pairs in 1982 have had declining population size, and these sites also had high nutrient levels. On the other hand, several new breeding sites in recently created ponds in parks, on golf courses and wastewater treatment plants have been established. Dammed ponds were occupied more often than natural waterbodies, and occupied sites were in general at nutrient-rich sites at low elevation close to the coast. Occupancy rate (proportion of years surveyed with Common Moorhen presence) during 1995–2018 was higher for dammed ponds than for natural waterbodies, and higher for smaller wetlands. Thus, analyses suggested that the most suitable sites for Common Moorhen were nutrient-rich small ponds at low elevation close to the coast, and in such sites the Common Moorhen appears to have a stable, but small population size.
Morality in the flesh: on the link between bodily self-consciousness, moral identity and (dis)honest behaviour

The sense of owning a body (ownership) and controlling its actions (agency) are two main pillars of bodily self-consciousness (BSC). Although studies suggest that BSC signals and morality may be associated, whether such association has a positive or negative direction remains unclear. To investigate this issue, we conducted two pre-registered, online studies, in which a total of 1309 participants completed BSC- and morality-related questionnaires and undertook a task where they could cheat for monetary gain. We found that participants with high sense of ownership displayed high moral identity, which supports the notion that ownership is used to associate the self with positive characteristics. Moreover, high agency was associated with increased moral identity when sense of power is high. Results regarding deception are less clear, and might relate to the impact of COVID-19. Our results concerning moral identity may inspire policies that rely on changes of corporeal awareness to contrast immorality.
Decentralized Event-Triggered Adaptive Fuzzy Control for Nonlinear Switched Large-Scale Systems With Input Delay Via Command-Filtered Backstepping

This article presents a decentralized fuzzy adaptive event-triggered command-filtered control scheme for switched large-scale nonlinear systems with input delay. Fuzzy logic systems are employed to approximate uncertain nonlinearities and a novel observer is constructed to estimate unmeasured states. The “explosion of complexity” defect inherent in the backstepping approach is overcome by employing command filter technology. An auxiliary system is designed to compensate for the effect of input delay, and a novel event-triggered decentralized controller is derived based on the common Lyapunov function method. This article shows that the presented strategy guarantees that all closed-loop variables are semiglobally uniformly ultimately bounded. Finally, simulation results are shown to further confirm the presented strategy’s validity.
A local sensitivity analysis for the kinetic Kuramoto equation with random inputs

We present a local sensivity analysis for the kinetic Kuramoto equation with random inputs in a large coupling regime. In our proposed random kinetic Kuramoto equation (in short, RKKE), the random inputs are encoded in the coupling strength. For the deterministic case, it is well known that the kinetic Kuramoto equation exhibits asymptotic phase concentration for well-prepared initial data in the large coupling regime. To see a response of the system to the random inputs, we provide propagation of regularity, local-in-time stability estimates for the variations of the random kinetic density function in random parameter space. For identical oscillators with the same natural frequencies, we introduce a Lyapunov functional measuring the phase concentration, and provide a local sensitivity analysis for the functional.
An Evaluation of a Markov Chain Monte Carlo Method for the Two-Parameter Logistic Model.

The accuracy of the Markov Chain Monte Carlo (MCMC) procedure Gibbs sampling was considered for estimation of item parameters of the two-parameter logistic model. Data for the Law School Admission Test (LSAT) Section 6 were analyzed to illustrate the MCMC procedure. In addition, simulated data sets were analyzed using the MCMC, marginal Bayesian estimation, and marginal maximum likelihood estimation methods. Data were simulated with 100 or 300 examinees and 15 or 45 items. Two different priors, informative and uninformative, were employed in the MCMC procedure. Marginal Bayesian estimation yielded consistently smaller root mean square differences and mean Euclidean distances than the other estimation methods. An appendix provides additional information about the computations. (Contains 6 tables, 5 figures, and 52 references.) (SLD) ******************************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ********************************************************************************
Taskflow: A General-Purpose Parallel and Heterogeneous Task Programming System

Taskflow tackles the long-standing question: How can we make it easier for developers to program parallel and heterogeneous computer-aided design (CAD) applications with high performance and simultaneous high productivity? Taskflow introduces a new powerful task graph programming model to assist developers in the implementation of parallel and heterogeneous algorithms with complex control flow. We develop an efficient system runtime to solve many of the new scheduling challenges arising out of our models and optimize the performance across latency, energy efficiency, and throughput. Taskflow has demonstrated promising performance on both micro-benchmarks and real-world applications. As an example, Taskflow solved a large-scale circuit placement problem up to 17% faster, with <inline-formula> <tex-math notation="LaTeX">$1.3\times $ </tex-math></inline-formula> fewer memory, <inline-formula> <tex-math notation="LaTeX">$2.1\times $ </tex-math></inline-formula> less power consumption, and <inline-formula> <tex-math notation="LaTeX">$2.9\times $ </tex-math></inline-formula> higher throughput than two industrial-strength systems, oneTBB and StarPU, on a machine of 40 CPUs and 4 GPUs.
Preference of Use of Transportation Mode in Travelling

Mode of transportation facilities have an important role for the development of the tourism sector. However, there are several regions in Central Java, not showing this. There are some regions that have more complete modes of transportation facilities and a greater number compared to other regions, but do not have as many tourist arrivals as other areas with fewer facilities. The guess is that there is a preference for certain modes of transportation in traveling to tourist attractions. Therefore, it is necessary to study how individual preferences in using modes of transportation in traveling in Central Java. The objective of this study is to identify and analyze individual preferences in using modes of transportation when traveling in Central Java. The analytical method used is the mix method, which is a combination of qualitative analysis and quantitative analysis. In detail, the analytical method used is the Analysis Hierarchy Process and Descriptive Analysis. Analysis Hierarchy Process is used to obtain preference for the use of transportation modes. Descriptive analysis is used to describe, explain and reinforce the results of the Analysis Hierarchy Process. The results obtained priority scale of preference in the use of transportation modes in traveling. Furthermore, there are criteria that serve as reasons for choosing transportation modes. Then the criteria are known to be the priority of individuals in a tour.
Intrusion Detection, Measurement Correction, and Attack Localization of PMU Networks

Accurate state estimation is essential for correct supervision of power grids. With the existence of cyber-attacks, state estimation may become inaccurate, which can eventually lead to wrong supervisory decision making. To detect cyber-attacks in power grids equipped with phasor measurement units (PMUs), a new intrusion detection system based on clustering approach, called PMU Intrusion Detection System (PMUIDS), is proposed in this article. After solving the optimal PMU placement in N-1 contingency, several static state estimations are obtained by removing the measurements of one PMU in each time. The resulting state vectors are clustered in two steps: First, subtractive clustering is employed to obtain the number of clusters, which determines the number of integrity attacks; and second, fuzzy C-means clustering assigns the state vectors to the corresponding clusters, which determines the attacked PMUs. In addition, two theorems are proved, which indicate that the attacker cannot coordinate successful stealth attacks in cases that by removing attacked PMUs from state estimation, the power system still remains full observable. Furthermore, in the case of possible stealth attacks, the attacker cannot falsify the estimation of any arbitrary state variable. The hardware-in-the-loop results on a sample power system show that the proposed approach can detect integrity attacks, determine the number of attacks, obtain the correct state vector, and localize the attacks, even in case of multiple simultaneous attacks.
MINIMUM-TIME LOW-EARTH ORBIT TO HIGH-EARTH ORBIT LOW-THRUST TRAJECTORY OPTIMIZATION

The problem of many revolution low-thrust Earth-orbit trajectory optimization is considered. The objective is to transfer a spacecraft from a parking orbit to a desired terminal orbit in minimum time. The minimum-time orbital transfer problem is posed as a nonlinear optimal control problem, and the optimal control problem is solved using a direct transcription variable-order Gaussian quadrature collocation method. It is found that the thrust-to-mass ratio holds a power relationship to the transfer time, final mass, and final true longitude. Using these power relationships obtained through regression on the collected results, it is possible to estimate the performance of a given thrust-to-mass ratio without solving for the optimal trajectory. In addition, the key structure of the optimal orbital transfers are identified. The results presented provide insight into the structure of the optimal performance for a range of small thrust-to-mass ratios and highlight the interesting features of the optimal solutions. Finally, a discussion of the performance of the variableorder Gaussian quadrature collocation method is provided.