Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of the Diurnal Change Using a Multimodel Superensemble. Part I: Precipitation

Modeling the geographical distribution of the phase and amplitude of the diurnal change is a challenging problem. This paper addresses the issues of modeling the diurnal mode of precipitation over the Tropics. Largely an early morning precipitation maximum over the oceans and an afternoon rainfall maximum over land areas describe the first-order diurnal variability. However, large variability in phase and amplitude prevails even within the land and oceanic areas. This paper addresses the importance of a multimodel superensemble for much improved prediction of the diurnal mode as compared to what is possible from individual models. To begin this exercise, the skills of the member models, the ensemble mean of the member models, a unified cloud model, and the superensemble for the prediction of total rain as well as its day versus night distribution were examined. Here it is shown that the distributions of total rain over the earth (tropical belt) and over certain geographical regions are predicted reasonably well (RMSE less than 18%) from the construction of a multimodel superensemble. This dataset is well suited for addressing the diurnal change. The large errors in phase of the diurnal modes in individual models usually stem from numerous physical processes such as the cloud radiation, shallow and deep cumulus convection, and the physics of the planetary boundary layer. The multimodel superensemble is designed to reduce such systematic errors and provide meaningful forecasts. That application for the diurnal mode appears very promising. This paper examines some of the regions such as the Tibetan Plateau, the eastern foothills of the Himalayas, and the Amazon region of South America that are traditionally difficult for modeling the diurnal change. In nearly all of these regions, errors in phase and amplitude of the diurnal mode of precipitation increase with the increased length of forecasts. Model forecast errors on the order of 6–12 h for phase and 50% for the amplitude are often seen from the member models. The multimodel superensemble reduces these errors and provides a close match (RMSE 6 h) to the observed phase. The percent of daily rain and their phases obtained from the multimodel superensemble at 3-hourly intervals for different regions of the Tropics showed a closer match (pattern correlation about 0.4) with the satellite estimates. This is another area where the individual member models conveyed a much lower skill.
Contemporary notes on metamaterials

The essence of the metamaterial concept from the structural point of view. Particular attention is paid to the macroscopic description of metamaterials and to the corresponding requirements and precautions for using that concept. General advantages of metamaterials are also pointed out. Next, the main research directions related to metamaterials are briefly overviewed and the key references are provided. Finally, the most probable sources of disagreement and misunderstanding within the field are summarised.
Bilinear Mixed-Effects Models for Dyadic Data

This article discusses the use of a symmetric multiplicative interaction effect to capture certain types of third-order dependence patterns often present in social networks and other dyadic datasets. Such an effect, along with standard linear fixed and random effects, is incorporated into a generalized linear model, and a Markov chain Monte Carlo algorithm is provided for Bayesian estimation and inference. In an example analysis of international relations data, accounting for such patterns improves model fit and predictive performance.
Accelerating the Discovery of Anticancer Peptides through Deep Forest Architecture with Deep Graphical Representation

Cancer is one of the leading diseases threatening human life and health worldwide. Peptide-based therapies have attracted much attention in recent years. Therefore, the precise prediction of anticancer peptides (ACPs) is crucial for discovering and designing novel cancer treatments. In this study, we proposed a novel machine learning framework (GRDF) that incorporates deep graphical representation and deep forest architecture for identifying ACPs. Specifically, GRDF extracts graphical features based on the physicochemical properties of peptides and integrates their evolutionary information along with binary profiles for constructing models. Moreover, we employ the deep forest algorithm, which adopts a layer-by-layer cascade architecture similar to deep neural networks, enabling excellent performance on small datasets but without complicated tuning of hyperparameters. The experiment shows GRDF exhibits state-of-the-art performance on two elaborate datasets (Set 1 and Set 2), achieving 77.12% accuracy and 77.54% F1-score on Set 1, as well as 94.10% accuracy and 94.15% F1-score on Set 2, exceeding existing ACP prediction methods. Our models exhibit greater robustness than the baseline algorithms commonly used for other sequence analysis tasks. In addition, GRDF is well-interpretable, enabling researchers to better understand the features of peptide sequences. The promising results demonstrate that GRDF is remarkably effective in identifying ACPs. Therefore, the framework presented in this study could assist researchers in facilitating the discovery of anticancer peptides and contribute to developing novel cancer treatments.
Identification of medicinal plants used for chronic kidney disease: An update of reported literature in South Africa

year to year (Khan et al. 2022). Chronic kidney disease rates rise rapidly with age, with prevalence in those aged 75 years and up being twice as high as those aged 65–74 years, and nearly seven times higher than those aged 18–54 years (42%, 21% and 6%, respectively). The prevalence of CKD is generally higher in lower socio-economic groups (14% vs. 8% in higher socio-economic groups) (Khan et al. 2022). Indigenous populations are frequently at a higher risk of CKD than nonindigenous populations because of their limited access to hospitals and healthcare centres (Herrera-Añazco et al. 2019; Khan et al. 2022). Current therapies using commercially available drugs only delay the progression of CKD, ultimately creating a financial burden on healthcare systems and an expectation of a rise in hospitalisations and treatments. Therefore, poorer countries Background: Chronic kidney disease (CKD) is a debilitating condition that is becoming more common around the world, as well as a financial and social burden on healthcare systems. If not treated with kidney replacement therapies, kidney failure, the final stage of CKD, can be fatal. Chronic kidney disease patients are now seeking the use of alternative remedies, including medicinal plants, as the primary source of healthcare. Aim: This review aimed to evaluate the use of medicinal plants in the treatment of CKD and other associated kidney diseases in South Africa. Method: This article summarises previous research (2010–2021) on the impact of traditional plant-based medicine in CKD treatment and identifies the context between traditional and conventional medicines. Various scientific databases were used to source key literature. Results: The findings of this study revealed 10 medicinal plant species from nine different botanical families that are commonly used for the treatment of CKD and other kidney-related diseases in South Africa. In addition, the study demonstrated that despite medicinal plants having toxic impacts, they were still the preferred choice of medication for CKD, especially in developing countries. Conclusion: It is crucial to validate the balance between the risks and benefits of medicinal plants in CKD treatment to further enhance the credibility of medical plants in drug development. Contribution: This study contributes to the existing knowledge of medicinal plants used in CKD treatment primarily in South Africa.
Comparison of methods to determine electrocatalysts’ surface area in gas diffusion electrode setups: a case study on Pt/C and PtRu/C

In recent years, gas diffusion electrode (GDE) half-cell setups have attracted increasing attention, bridging the gap between fundamental and applied fuel cell research. They allow quick and reliable evaluation of fuel cell catalyst layers and provide a unique possibility to screen different electrocatalysts at close to real experimental conditions. However, benchmarking electrocatalysts’ intrinsic activity and stability is impossible without knowing their electrochemical active surface area (ECSA). In this work, we compare and contrast three methods for the determination of the ECSA: (a) underpotential deposition of hydrogen (Hupd); (b) CO-stripping; and (c) underpotential deposition of copper (Cuupd) in acidic and alkaline electrolytes, using representative electrocatalysts for fuel cell applications (Pt and PtRu-alloys supported on carbon). We demonstrate that, while all methods can be used in GDE setups, CO-stripping is the most convenient and reliable. Additionally, the application of Cuupd offers the possibility to derive the atomic surface ratio in PtRu-alloy catalysts. By discussing the advantages of each method, we hope to guide future research in accurately determining surface area and, hence, the intrinsic performance of realistic catalyst layers.
Regional Differences in the Prediction of Extratropical Cyclones by the ECMWF Ensemble Prediction System

A regional study of the prediction of extratropical cyclones by the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) has been performed. An objective feature-tracking method has been used to identify and track the cyclones along the forecast trajectories. Forecast error statistics have then been produced for the position, intensity, and propagation speed of the storms. In previous work, data limitations meant it was only possible to present the diagnostics for the entire Northern Hemisphere (NH) or Southern Hemisphere. A larger data sample has allowed the diagnostics to be computed separately for smaller regions around the globe and has made it possible to explore the regional differences in the prediction of storms by the EPS. Results show that in the NH there is a larger ensemble mean error in the position of storms over the Atlantic Ocean. Further analysis revealed that this is mainly due to errors in the prediction of storm propagation speed rather than in direction. Forecast storms propagate too slowly in all regions, but the bias is about 2 times as large in the NH Atlantic region. The results show that storm intensity is generally overpredicted over the ocean and underpredicted over the land and that the absolute error in intensity is larger over the ocean than over the land. In the NH, large errors occur in the prediction of the intensity of storms that originate as tropical cyclones but then move into the extratropics. The ensemble is underdispersive for the intensity of cyclones (i.e., the spread is smaller than the mean error) in all regions. The spatial patterns of the ensemble mean error and ensemble spread are very different for the intensity of cyclones. Spatial distributions of the ensemble mean error suggest that large errors occur during the growth phase of storm development, but this is not indicated by the spatial distributions of the ensemble spread. In the NH there are further differences. First, the large errors in the prediction of the intensity of cyclones that originate in the tropics are not indicated by the spread. Second, the ensemble mean error is larger over the Pacific Ocean than over the Atlantic, whereas the opposite is true for the spread. The use of a storm-tracking approach, to both weather forecasters and developers of forecast systems, is also discussed.
Constraints on Ωm and σ8 from Weak Lensing in Red-Sequence Cluster Survey Fields

We have analyzed 53 deg2 of RC-band imaging data from the Red-Sequence Cluster Survey (RCS) and measured the excess correlations in the shapes of galaxies on scales out to ~1. °5. We separate the signal into an "E" (lensing) and "B" (systematics) mode, which allows us to study the contribution from residual systematics and intrinsic alignments. On scales larger than 10', we find no B mode, suggesting that the signal at those scales is caused solely by gravitational lensing. On smaller scales we find a small but significant B mode. This signal is also present when we select a sample of bright (20 < RC < 22) galaxies. These galaxies are rather insensitive to observational distortions, and we therefore conclude that the observed B mode is likely to be caused by intrinsic alignments. To minimize the effect of intrinsic alignments, we limit the cosmic shear analysis to galaxies with 22 < RC < 24. We derive joint constraints on Ωm and σ8 by marginalizing over Γ, ΩΛ, and the source redshift distribution, using different priors. Marginalizing over Γ and ΩΛ, and using a flat prior for the source redshift distribution, yields a conservative constraint of σ8 = 0. 45Ω (95% confidence). A better constraint is derived when we use Gaussian priors for Γ (from the Two-Degree Field survey) and Ωm + ΩΛ (from cosmic microwave background [CMB]), and the source redshift distribution. For this choice of priors, we find σ8 = (0.46)Ω (95% confidence). We also investigated whether the RCS data can be used to constrain Γ. Using our set of Gaussian priors, we find that we can only place a lower bound on Γ for which we find Γ > 0.1 + 0.16Ωm (95% confidence). Comparison of the RCS results with three other recent cosmic shear measurements shows excellent agreement. The current weak-lensing results are also in good agreement with CMB measurements, when we allow the reionization optical depth τ and the spectral index ns to vary. We present a simple demonstration of how the weak-lensing results can be used as a prior in the parameter estimation from CMB measurements to derive constraints on the reionization optical depth τ.
Development of Teacher Education Candidates’ Self-Efficacy for Culturally Responsive Teaching Through Extended Clinical Experiences

ABSTRACT Teacher self-efficacy is a construct that exerts a powerful influence on the behaviors of teachers. Yet, few studies have been conducted examining the impact of contextual structures within educator preparation programs on the development of general and domain-specific teaching efficacy. This study investigated the impact of an embedded internship in culturally- and linguistically-diverse elementary schools on self-efficacy for culturally responsive teaching. The results indicated candidates who participated in the internship demonstrated differences in self-efficacy for culturally responsive teaching that were significantly different in comparison to candidates who completed the traditional teacher education program. Implications are discussed as related considerations for teacher education programs as they seek to structure clinical experiences with focused opportunities for mastery experiences and relationship-building.
Radial Velocity and Wind Measurement with NIMA–NWCA: Comparisons with Human Estimation and Aircraft Measurements

Abstract The National Center for Atmospheric Research (NCAR) Improved Moments Algorithm (NIMA) calculates the first and second moments (radial velocity and spectral width) of wind-profiler Doppler spectra and provides an evaluation of confidence in these calculations. The first moments and their confidences are used by the NCAR Winds And Confidence Algorithm (NWCA), to estimate the horizontal wind. NIMA–NWCA has been used for several years in a real-time application for three wind profilers in Juneau, Alaska. This paper presents results of an effort to evaluate the first moments produced by NIMA and horizontal winds produced by NIMA–NWCA through comparison with estimates from “human experts” and also presents a comparison of NIMA–NWCA winds with in situ aircraft measurements. NIMA uses fuzzy logic to separate the atmospheric component of Doppler spectra from ground clutter and other sources of interference. The fuzzy logic rules are based on similar features humans consider when identifying atmospheric an.
Transport coefficients of helium and argon-helium plasmas

Calculated values of the viscosity, thermal conductivity, and electrical conductivity of helium, and mixtures of argon and helium, at high temperatures are presented. In addition, combined ordinary, pressure, and thermal diffusion coefficients are given for the mixtures. The calculations, which assume local thermodynamic equilibrium, are performed for atmospheric pressure plasmas in the temperature range from 300 to 30000 K. The results are compared with those of previously published studies. Significant discrepancies are found; these are attributed to the improved values of the collision integrals used here in calculating the transport coefficients.
The 2020-2022 Inflation Surge Across Europe: A Phillips-Curve-Based Dissection

: In 2021-22, inflation in Europe soared to multidecade highs, consistently exceeding policymakers’ forecasts and surprising with its wide cross-country dispersion. This paper analyzes the key drivers of the inflation surge in Europe and its variation across countries. The analysis highlights significant differences in Phillips curve parameters across Europe’s economies. Inflation is more sensitive to domestic slack and external price pressures in emerging European economies compared to their advanced counterparts, which contributed to a greater passthrough of global commodity price shocks into domestic prices
Directions in planning: understanding the flow of time in planning

Since the notion of general purpose planning became one of the touchstones of artificial intelligence, surprisingly little improvement has been made in the efficiency of planning algorithms. Efficient high-speed search is essential to most planning algorithms proposed to date: this can only be achieved if the search algorithms used are based on a solid understanding of the search space. The concept of search directionality—searching temporally or causally forward or backward—has been quite important to designers of planning algorithms. Nonetheless, this concept appears to be poorly understood. Through a series of constructions and experiments, it is shown here that successful planners must be capable of both forward chaining and backward chaining behavior, and that understanding directionality issues in planning is a necessary precursor to the construction of efficient planners. This work begins by discussing some of the underpinnings of directionality in planning: the physical, psychological, and computational temporal arrows that directionally orient planning problems. A previously unappreciated property of directionality in planning is then described, namely that the direction of planning problems is not a fundamental property of the standard formalism. Planning problems can be reversed, allowing a planner to search in the opposite of its normal direction without a change in planning algorithm. Next, a novel technique is described for determining the directional behavior of existing planners, and experimental results using an implementation of this technique are reported. This analysis of planner direction can be used to better understand the search strategy used in modern planning algorithms such as satisfiability-based planning. Finally, some consequences and extensions of the results are given. Together, these results shed new light on the construction of planning algorithms using high-speed search, and thus move us closer to making planning practical for real problems. DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE UNIVERSITY OF OREGON Copyright 1999 Bart Massey This Technical Report is a reformatted version of the author’s Doctoral Dissertation, completed June 1999. Examining committee: Dr. Matthew Ginsberg, Co-chair Dr. Amr Sabry, Co-chair Dr. Christopher Wilson Dr. John Orbell
Wavelet Threshold Estimators for Data with Correlated Noise

Wavelet threshold estimators for data with stationary correlated noise are constructed by applying a level‐dependent soft threshold to the coefficients in the wavelet transform. A variety of threshold choices is proposed, including one based on an unbiased estimate of mean‐squared error. The practical performance of the method is demonstrated on examples, including data from a neurophysiological context. The theoretical properties of the estimators are investigated by comparing them with an ideal but unattainable `bench‐mark', that can be considered in the wavelet context as the risk obtained by ideal spatial adaptivity, and more generally is obtained by the use of an `oracle' that provides information that is not actually available in the data. It is shown that the level‐dependent threshold estimator performs well relative to the bench‐mark risk, and that its minimax behaviour cannot be improved on in order of magnitude by any other estimator. The wavelet domain structure of both short‐ and long‐range dependent noise is considered, and in both cases it is shown that the estimators have near optimal behaviour simultaneously in a wide range of function classes, adapting automatically to the regularity properties of the underlying model. The proofs of the main results are obtained by considering a more general multivariate normal decision theoretic problem.
Research Progress of the Ion Activity Coefficient of Polyelectrolytes: A Review

Polyelectrolyte has wide applications in biomedicine, agriculture and soft robotics. However, it is among one of the least understood physical systems because of the complex interplay of electrostatics and polymer nature. In this review, a comprehensive description is presented on experimental and theoretical studies of the activity coefficient, one of the most important thermodynamic properties of polyelectrolyte. Experimental methods to measure the activity coefficient were introduced, including direct potentiometric measurement and indirect methods such as isopiestic measurement and solubility measurement. Next, progress on the various theoretical approaches was presented, ranging from analytical, empirical and simulation methods. Finally, challenges for future development are proposed on this field.
Determination of Extratropical Tropopause Height in an Idealized Gray Radiation Model

AbstractThis paper investigates the mechanisms that determine the extratropical tropopause height, extending previous results with a Newtonian cooling model. A primitive equation model forced by a meridional gradient of incoming solar radiation, with the outgoing infrared radiation calculated using a simple gray radiation scheme, is now used. The tropopause is defined as the top of the boundary layer over which dynamical heat transport moves the temperature away from radiative equilibrium, and its height is estimated from the isentropic mass flux. Depending on parameters, this tropopause may or may not be associated with a sharp stratification change, and it may or may not be possible to define a thermal tropopause. The mass flux and thermal tropopause display similar sensitivity to external parameters when the latter can be defined; this is a sensitivity in good agreement with predictions by a radiative constraint. In some contrast to the Newtonian model, the radiative constraint is now quite effective i.