Miracle-dz/UniFAR-FTUs huggingface dataset:
Value-Added and Student Growth Percentile Models: What Drives Differences in Estimated Classroom Effects?

ABSTRACT This study shows value-added models (VAM) and student growth percentile (SGP) models fundamentally disagree regarding estimated teacher effectiveness when the classroom distribution of test scores conditional on prior achievement is skewed (i.e., when a teacher serves a disproportionate number of high- or low-growth students). While conceptually similar, the two models differ in estimation method which can lead to sizable differences in estimated teacher effects. Moreover, the magnitude of conditional skewness needed to drive VAM and SGP models apart often by three and up to 6 deciles is within the ranges observed in actual data. The same teacher may appear weak using one model and strong with the other. Using a simulation, I evaluate the relationship under controllable conditions. I then verify that the results persist in observed student–teacher data from North Carolina.
The Ru Impurity Effect on Electronic, Optical and Thermoelectric Properties of MoS2 Nano-Sheet: a DFT Study

The electronic, optical and thermoelectric properties of Mos 2 nano-sheet in presence of the ru impurity have been calculated by density functional theory framework with generalized gradient approximation. The Morus 2 nano-sheet electronic structure was changed to the n-type semiconductor by 1.3 eV energy gap. The optical coefficients were shown that the loosing optical energy occurred in the higher ultraviolet region, so this compound is a promising candidate for optical sensing in the infrared and visible range. The thermoelectric behaviors were implied to the good merit parameter in the 100K range and room temperatures and also has high amount of power factor in 600K which made it for power generators applications.
Projection Based Models for High Dimensional Data

In recent years, many machine learning applications have arisen which deal with the problem of finding patterns in high dimensional data. Principal component analysis (PCA) has become ubiquitous in this setting. PCA performs dimensionality reduction by estimating latent factors which minimise the reconstruction error between the original data and its low-dimensional projection. We initially consider a situation where influential observations exist within the dataset which have a large, adverse affect on the estimated PCA model. We propose a measure of “predictive influence” to detect these points based on the contribution of each point to the leave-one-out reconstruction error of the model using an analytic PRedicted REsidual Sum of Squares (PRESS) statistic. We then develop a robust alternative to PCA to deal with the presence of influential observations and outliers which minimizes the predictive reconstruction error. In some applications there may be unobserved clusters in the data, for which fitting PCA models to subsets of the data would provide a better fit. This is known as the subspace clustering problem. We develop a novel algorithm for subspace clustering which iteratively fits PCA models to subsets of the data and assigns observations to clusters based on their predictive influence on the reconstruction error. We study the convergence of the algorithm and compare its performance to a number of subspace clustering methods on simulated data and in real applications from computer vision involving clustering object trajectories in video sequences and images of faces. We extend our predictive clustering framework to a setting where two highdimensional views of data have been obtained. Often, only either clustering or 5 predictive modelling is performed between the views. Instead, we aim to recover clusters which are maximally predictive between the views. In this setting two block partial least squares (TB-PLS) is a useful model. TB-PLS performs dimensionality reduction in both views by estimating latent factors that are highly predictive. We fit TB-PLS models to subsets of data and assign points to clusters based on their predictive influence under each model which is evaluated using a PRESS statistic. We compare our method to state of the art algorithms in real applications in webpage and document clustering and find that our approach to predictive clustering yields superior results. Finally, we propose a method for dynamically tracking multivariate data streams based on PLS. Our method learns a linear regression function from multivariate input and output streaming data in an incremental fashion while also performing dimensionality reduction and variable selection. Moreover, the recursive regression model is able to adapt to sudden changes in the data generating mechanism and also identifies the number of latent factors. We apply our method to the enhanced index tracking problem in computational finance.
Insight into Cancer Immunity: MHCs, Immune Cells and Commensal Microbiota

Cancer cells circumvent immune surveillance via diverse strategies. In accordance, a large number of complex studies of the immune system focusing on tumor cell recognition have revealed new insights and strategies developed, largely through major histocompatibility complexes (MHCs). As one of them, tumor-specific MHC-II expression (tsMHC-II) can facilitate immune surveillance to detect tumor antigens, and thereby has been used in immunotherapy, including superior cancer prognosis, clinical sensitivity to immune checkpoint inhibition (ICI) therapy and tumor-bearing rejection in mice. NK cells play a unique role in enhancing innate immune responses, accounting for part of the response including immunosurveillance and immunoregulation. NK cells are also capable of initiating the response of the adaptive immune system to cancer immunotherapy independent of cytotoxic T cells, clearly demonstrating a link between NK cell function and the efficacy of cancer immunotherapies. Eosinophils were shown to feature pleiotropic activities against a variety of solid tumor types, including direct interactions with tumor cells, and accessorily affect immunotherapeutic response through intricating cross-talk with lymphocytes. Additionally, microbial sequencing and reconstitution revealed that commensal microbiota might be involved in the modulation of cancer progression, including positive and negative regulatory bacteria. They may play functional roles in not only mucosal modulation, but also systemic immune responses. Here, we present a panorama of the cancer immune network mediated by MHCI/II molecules, immune cells and commensal microbiota and a discussion of prospective relevant intervening mechanisms involved in cancer immunotherapies.
Synthesis and Antibacterial Activity of Polyalthic Acid Analogs

Polyalthic acid (PA) is a diterpene found in copaiba oil. As a continuation of our work with PA, we synthesized PA analogs and investigated their antibacterial effects on preformed biofilms of Staphylococcus epidermidis and determined the minimal inhibitory concentration (MIC) of the best analogs against planktonic bacterial cells. There was no difference in activity between the amides 2a and 2b and their corresponding amines 3a and 3b regarding their ability to eradicate biofilm. PA analogs 2a and 3a were able to significantly eradicate the preformed biofilm of S. epidermidis and were active against all the Gram-positive bacteria tested (Enterococcus faecalis, Enterococcus faecium, S. epidermidis, Staphylococcus aureus), with different MIC depending on the microorganism. Therefore, PA analogs 2a and 3a are of interest for further in vitro and in vivo testing to develop formulations for antibiotic drugs against Gram-positive bacteria.
Adaptive Fuzzy Output Feedback Quantized Control for Uncertain Nonlinear Hysteretic Systems Using a New Feedback-Based Quantizer

This paper focuses on the problem of adaptive fuzzy output feedback quantized tracking control for a class of uncertain nonlinear systems preceded by unknown actuator hysteresis. A new feedback-based quantizer together with a new adaptive strategy is proposed to resolve the veiling of unknown actuator hysteresis and the conflicts between quantization and hysteresis. By using fuzzy logic systems, a novel output feedback control scheme is developed via backstepping technique, combining with new a output feedback control strategy to deal with the complexity of the quantized control hysteretic systems. It is proved that the proposed control scheme guarantees that the tracking error converge to a neighborhood of the origin and all the closed-loop signals in the systems are uniformly ultimately bounded. Finally, simulations are conducted to illustrate the effectiveness of the proposed methodology.
Generalized approach to Hurst exponent estimating by time series

This paper presents a generalized approach to the fractal analysis of self-similar random processes by short time series. Several stages of the fractal analysis are proposed. Preliminary time series analysis includes the removal of short-term dependence, the identification of true long-term dependence and hypothesis test on the existence of a self-similarity property. Methods of unbiased interval estimation of the Hurst exponent in cases of stationary and non-stationary time series are discussed. Methods of estimate refinement are proposed. This approach is applicable to the study of self-similar time series of different nature.
First results of HF radio science with e‐POP RRI and SuperDARN

The first results from coordinated experiments between the Radio Receiver Instrument (RRI) on the Enhanced Polar Outflow Probe (e‐POP) and the Super Dual Auroral Radar Network (SuperDARN) Saskatoon high frequency (HF) radar are examined for a conjunction on 8 July 2014. e‐POP, a payload on the CAScade, Smallsat and IOnospheric Polar Explorer spacecraft, was located at 380 km altitude, approximately 10° north (geographic) and 2° west of Saskatoon, Canada, moving in a southeast direction. We use a matched filter technique to extract individual received SuperDARN pulses from the RRI data stream. The pulses show characteristics of propagation through the F region ionosphere: they are heavily dispersed, they show significant pulse‐to‐pulse variability in magnitude, and there is clear evidence that they experienced multipath propagation. We calculate the polarization parameters of the pulses and use them to identify magnetoionic phenomena such as mode‐splitting and single‐mode fading. These first RRI results provide compelling insight into HF radio wave propagation and show RRI's potential to significantly advance radio science.
A Cross-Field Review of State Abstraction for Markov Decision Processes

Complex real-world systems pose a signiﬁcant challenge to decision making: an agent needs to explore a large environment, deal with incomplete or noisy information, generalize the experience and learn from feedback to act optimally. These processes demand vast representation capacity, thus putting a burden on the agent’s limited computational and storage resources. State abstraction enables eﬀective solutions by forming concise representations of the agents world. As such, it has been widely investigated by several research communities which have produced a variety of diﬀerent approaches. Nonetheless, relations among them still remain unseen or roughly deﬁned. This hampers potential applications of solution methods whose scope remains limited to the speciﬁc abstraction context for which they have been designed. To this end, the goal of this paper is to organize the developed approaches and identify connections between abstraction schemes as a fundamental step towards methods generalization. As a second contribution we discuss general abstraction properties with the aim of supporting a uniﬁed perspective for state abstraction.
PHENOTYPIC TRENDS FOR MILK YIELD IN NILI RAVI BUFFALOES REGISTERED UNDER BULL MOTHER SCHEME AND AT LIVESTOCK EXPERIMENT STATION BAHADURNAGAR- OKARA

Efficient breeding plans depend upon the availability of reliable and accurate data.To evaluate the productive and reproductive performance of the Nili-Ravi buffalo, registered under thebull mother scheme, was evaluated from 1980-2020. General Linear Model is used to study the effectof phenotypic characters on milk yield i.e., year of calving, age at first calving, and dam’s parity onproductive traits under consideration. The lactation records of all parity are used for the analysis. Thedata on pedigrees, breeding, and performance records through analysis of variance procedure showsstatistically significant (P < 0.05) differences for the effect of 305 days milk yield and lactation length.The Least Squares Mean for lactation milk yield, age at first calving and lactation length are 2108.62 ±12.80 liters, 1574.49 ± 8.1 and 295.50 ± 1.47 days, respectively. The inclusive results depict thatbuffaloes calve in the winter season have the highest and most significant total milk yield. The data setcould be used as a phenotype for genome-wide association studies as a reference population.
Effect of fermented sarco oyster extract on age induced sarcopenia
 muscle repair by modulating regulatory T cells

Sarcopenia is an age-related, progressive skeletal muscle disorder involving the loss of muscle mass and strength. Previous studies have shown that γ-aminobutyric acid (GABA) from fermented oysters aids in regulatory T cells (Tregs) cell expansion and function by enhancing autophagy, and concomitantly mediate muscle regeneration by modulating muscle inflammation and satellite cell function. The fermentation process of oysters not only increases the GABA content but also enhances the content of branched amino acids and free amino acids that aid the level of protein absorption and muscle strength, mass, and repair. In this study, the effect of GABA-enriched fermented sarco oyster extract (FSO) on reduced muscle mass and functions via Treg modulation and enhanced autophagy in aged mice was investigated. Results showed that FSO enhanced the expression of autophagy markers (autophagy-related gene 5 [ATG5] and GABA receptor-associated protein [GABARAP]), forkhead box protein 3 (FoxP3) expression, and levels of anti-inflammatory cytokines (interleukin [IL]-10 and transforming growth factor [TGF]-β) secreted by Tregs while reducing pro-inflammatory cytokine levels (IL-17A and interferon [IFN]-γ). Furthermore, FSO increased the expression of IL-33 and its receptor IL-1 receptor-like 1 (ST2); well-known signaling pathways that increase amphiregulin (Areg) secretion and expression of myogenesis markers (myogenic factor 5, myoblast determination protein 1, and myogenin). Muscle mass and function were also enhanced via FSO. Overall, the current study suggests that FSO increased autophagy, which enhanced Treg accumulation and function, decreased muscle inflammation, and increased satellite cell function for muscle regeneration and therefore could decrease the loss of muscle mass and function with aging.
Binary Image Fingerprint: Stable Structure Identifier for 3D LiDAR Place Recognition

Place recognition is considered as an effective strategy to reduce robot drift errors. In this work, a place recognition method that uses binary features to match loop closure frames is proposed for 3D LiDAR. The method extracts important information from structural feature matrix by image compression, and the resulting binary matrix with small size is used as a unique identifier for each frame of the point cloud. This matrix is called the binary image fingerprint (BIF). The Hamming distance between two binary image fingerprints is used for similarity matching when performing place recognition. The use of logical operations to match loop closure frames shows high efficiency. The proposed method has been extensively experimented on KITTI, NCLT, and MulRan datasets and widely compared with state-of-the-art methods. The experimental results show that the proposed method exhibits superior performance under most sequences. Notably, the proposed method has high robustness to sparse point clouds.
Another look at volume self-calibration: calibration and self-calibration within a pinhole model of Scheimpflug cameras

We address calibration and self-calibration of tomographic PIV experiments within a pinhole model of cameras. A complete and explicit pinhole model of a camera equipped with a 2-tilt angles Scheimpflug adapter is presented. It is then used in a calibration procedure based on a freely moving calibration plate. While the resulting calibrations are accurate enough for Tomo-PIV, we confirm, through a simple experiment, that they are not stable in time, and illustrate how the pinhole framework can be used to provide a quantitative evaluation of geometrical drifts in the setup. We propose an original self-calibration method based on global optimization of the extrinsic parameters of the pinhole model. These methods are successfully applied to the tomographic PIV of an air jet experiment. An unexpected by-product of our work is to show that volume self-calibration induces a change in the world frame coordinates. Provided the calibration drift is small, as generally observed in PIV, the bias on the estimated velocity field is negligible but the absolute location cannot be accurately recovered using standard calibration data.
Removal of nickel(II) from industrial wastewater using selected methods: a review

Due to the increasing problem resulting from environmental pollution with heavy metals, great emphasis is placed on the development of removal methods of these pollutants from the environment. This study presents a literature review on the methods for the removal of nickel ions from aqueous solutions such as sorption, especially using low-cost sorbents which are very popular in 21 𝑠𝑡 century, electrochemical processes and membrane techniques. It is often impossible to use a single technique for eﬃcient removal of heavy metals from wastewater as the process depends on many factors, such as wastewater composition, pH, temperature and many others. The aim of this review is to present some selected removal techniques of nickel(II) from wastewater from the point of view of their eﬃciency and applicability.
Development of a Set of Indicators for Measuring and Improving Quality of Rehabilitation Care after Ischemic Stroke

Stroke is the leading global cause of permanent disability and the second leading cause of dementia within the first year of the event. Systematic quality improvement interventions such as Audit & Feedback (A&F) can monitor and improve the performance of post-stroke care in conjunction with the use of quality indicators (QIs). The scientific literature shows limited studies on quality improvement and QIs design for poststroke rehabilitation. In Italy, the National Outcomes Evaluation Programme (PNE) annually provides several QIs concerning the acute wards. On the contrary, indicators for quality assessment of post-acute stroke rehabilitation are not available nationwide. In recent years, the Italian Ministry of Health has funded a national network project, the aim of which is to provide and evaluate the effectiveness of A&F strategies in healthcare improvement. Part of this project is the development of a set of IQs for ischemic stroke rehabilitation used to conduct an A&F. In this study, we describe the design and development process of these QIs from administrative databases and report the results of the pilot test conducted on a small sample of Sicilian rehabilitation facilities, comparing them from 2019 to 2021. Feedback from the participating centers was mainly positive, and the quality indicators were found to be comprehensible and appreciated. However, the study highlighted the need for better adherence to indicators measuring processes of rehabilitation care. The set of quality indicators presented in this study, relevant to inpatient settings, could be considered a starting point on which to base quality improvement initiatives both nationally and internationally.
On the Impact of Unmanned Aerial System Observations on Numerical Weather Prediction in the Coastal Zone

AbstractThe Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experim.
Floral Calendar of Honeybee Plants in Kellem and West Wollega Zone, Western Ethiopia

Beekeeping has significantly contributed to environmental conservation and the preservation of natural resources. Although the quality and quantity of flora available play a major role in the success of the sector, the botanical makeup of natural vegetation varies greatly. This study was conducted targeting the identification and documentation of major honeybee floras and their flowering calendar. Midland and lowland agroecologies were purposively selected. Continuous field plant registration was performed. Melissopalynological analysis from bee pollen and honey were used to identify floral origin. Field observations identified 59 and 63 plants in the midland and lowlands, respectively. Season 1 had the highest pollen yields, ranging from 11051.8 ± 56.4 g (midlands) to 878.3 ± 18.3 g (lowlands), while season 4 ranged from 16.8 ± 6.3 g (midlands) to 15.6 ± 7.4 g (lowlands) and had the lowest pollen yield. In both regions, February, March, July, and August are the months when pollen is not brought into the hive and could be used as starvation periods. A total of 1430.8 ± 75.4 and 1291.8 ± 71.4 g of bee pollen/hive were collected throughout the year in midland and lowland, respectively, and Asteraceae was the richest family accounting around 90% of pollen weight. In both agroecologies, honey is harvested three times a year. In the midland, monofloral honey, namely, Guizotia spp (64.42%) and Croton macrostychus (47.42%), was harvested in November and May, respectively, while honey harvested in February was multifloral type. Similarly, in the lowlands, monofloral honey of Guizotia spp (51.85%), Coffee arabica (55.22%), and Croton macrostychus (50.42%) was harvested in December, March, and June, respectively. Based on the results, Bidens prestinaria, Bidens pilosa, Guizotia spp, C. macrostachyus, Eucalyptus spp, Lepidium sativum, Zea mays, Hypostes trifolia, Vernonia spp, Trifolium spp, Helianthus annuus, C. arabica, Brassica abyssinica, Andropogon abyssinicus, Sorghum bicolor, Cordia africana, Syzygium guineense, and Terminalia spp are major bee plants. It is found that the study area is rich in bee plant diversity and hence has a potential for honey production.
Étude des performances de la détection d'anomalie magnétique généralisée au cas des cibles multipolaires

La detection d'anomalie magnetique designe une methode utilisee dans le cadre de la lutte anti-sous-marine afin de reveler la presence d'une masse magnetique , classiquement modelisee par un dipole. Afin d'obtenir une description plus precise de l'anomalie, nous proposons une generalisation de cette methode basee sur une modelisation multipolaire de la cible. Ce modele est alors utilise pour reevaluer la strategie de detection ainsi que les performances des detecteurs utilises. L'apport de notre generalisation est illustre sur plusieurs scenarios, qui montrent une amelioration des performances de detection par rapport a l'approche dipolaire classique.