Miracle-dz/UniFAR-FTUs huggingface dataset:
GPI observer‐based composite current‐constrained control approach for DC–DC buck converters

In this study, overcurrent protection technique is investigated for the voltage regulation problem of the DC–DC buck converter system with time-varying disturbances and uncertainties. Normally, fast dynamics during the start-up phase generates large transient current. The overlarge transient current may lead to risks of hardware damage. By selecting conservative control parameters, the current constrained performance can be realised. However, the transient performance and disturbance rejection ability are sacrificed. It is of significant importance to design a controller which keeps a good balance between dynamic performance and overcurrent protection. Moreover, the control accuracy is inevitable affected by the time-varying disturbances effects, including load variations, input voltage fluctuations and circuit parameter perturbations. In this paper, the control design is divided into two parts. Firstly, by inserting a dynamic penalty term into the state feedback controller, a baseline current-constrained scheme is developed. Rigorous stability analysis and current constraint performance are given under the presented baseline controller. Secondly, taking the time-varying disturbances into account, a novel GPI observer-based composite current-constrained controller can be developed by combining disturbance estimations and the baseline controller. Experimental results are provide to demonstrate the effectiveness of the presented control strategy.
Habitat-Suitability Models for Cavity-Nesting Birds in a Postfire Landscape

Abstract Models of habitat suitability in postfire landscapes are needed by land managers to make timely decisions regarding postfire timber harvest and other management activities. Many species of cavity-nesting birds are dependent on postfire landscapes for breeding and other aspects of their life history and are responsive to postfire management activities (e.g., timber harvest). In addition, several cavity nesters are designated as species at risk. We compare the ability of 2 types of models to distinguish between nest and non-nest locations of 6 cavity-nesting bird species (Lewis's woodpecker [Melanerpes lewis], black-backed woodpecker [Picoides arcticus], hairy woodpecker [P. villosus], northern flicker [Colaptes auratus], western bluebird [Sialia mexicana], and mountain bluebird [S. currucoides]) in the early postfire years for a ponderosa pine (Pinus ponderosa) forest in Idaho, USA. The 2 model sets consisted of 1) models based on readily available remotely sensed data and 2) models containing field-collected data in addition to remotely sensed data (combination models). We evaluated models of nesting habitat by quantifying the model's ability to correctly identify nest and non-nest locations and by determining the percentage of correctly identified nest locations. Additionally, we developed relative habitat-suitability maps for nesting habitat of black-backed and Lewis's woodpeckers from the best models. For all species except Lewis's woodpeckers, model performance improved with the addition of field-collected data. Models containing remotely sensed data adequately distinguished between nest and non-nest locations for black-backed woodpecker and Lewis's woodpecker only, whereas models containing both field-collected and remotely sensed data were adequate for all 6 species. Improvements in the availability of more accurate remote sensing technology would likely lead to improvements in the ability of the models to predict nesting locations. External validation with data from other wildfires is necessary to confirm the general applicability of our habitat-suitability models to other forests. Land managers responsible for maintaining habitat for cavity-nesting birds in postfire landscapes can use these models to identify potential nesting areas for these species and select areas in burned forests where postfire salvage logging is most likely to have minimal impacts on cavity-nesting bird habitats.
An Analysis on Readability of English Reading Texts With Automated Computer Tool

Selecting appropriate textbook to the students is important because textbook have dominant role in the success of foreign language teaching and learning. The purpose this article is to analyze the readability level of English reading texts with automated computer tools. There are eight reading texts that will be analyzed in this qualitative study. The data and source of data of this research were adopted from an English textbook entitled Pathway to English 2 Textbook for the Eighth Grade of Junior High School Students Published by Erlangga. The results reveal that only 1 text was appropriate for seven or eight grade of Junior High School students. There were 6 texts appropriate elementary students. And there was 1 text suitable for Senior High School students.
A Cook's tour of duality in logic: from quantifiers, through Vietoris, to measures

We identify and highlight certain landmark results in Samson Abramsky's work which we believe are fundamental to current developments and future trends. In particular, we focus on the use of (i) topological duality methods to solve problems in logic and computer science; (ii) category theory and, more particularly, free (and co-free) constructions; (iii) these tools to unify the `power' and `structure' strands in computer science.
Clustering and Power Allocation for UAV-assisted NOMA-VLC Systems: A Swarm Intelligence Approach

Integrating unmanned aerial vehicles (UAV) to non-orthogonal multiple access (NOMA) visible light communications (VLC) exposes many potentials over VLC and NOMA-VLC systems. In this circumstance, user grouping is of importance to reduce the NOMA decoding complexity when the number of users is large; however, this issue has not been considered in the existing study. In this paper, we aim to maximize the weighted sum-rate of all the users by jointly optimizing UAV placement, user grouping, and power allocation in downlink NOMA-VLC systems. We first consider an efficient user clustering strategy, then apply a swarm intelligence approach, namely Harris Hawk Optimization (HHO), to solve the joint UAV placement and power allocation problem. Simulation results show outperformance of the proposed algorithm in comparison with four alternatives: OMA, NOMA without pairing, NOMA-VLC with fixed UAV placement, and random user clustering.
Connecting actuarial judgment to probabilistic learning techniques with graph theory

Graphical models have been widely used in applications ranging from medical expert systems to natural language processing. Their popularity partly arises since they are intuitive representations of complex inter-dependencies among variables with efficient algorithms for performing computationally intensive inference in high-dimensional models. It is argued that the formalism is very useful for applications in the modelling of non-life insurance claims data. It is also shown that actuarial models in current practice can be expressed graphically to exploit the advantages of the approach. More general models are proposed within the framework to demonstrate the potential use of graphical models for probabilistic learning with telematics and other dynamic actuarial data. The discussion also demonstrates throughout that the intuitive nature of the models allows the inclusion of qualitative knowledge or actuarial judgment in analyses.
Accuracy and stability of solar variable selection comparison under complicated dependence structures

In this paper we focus on the variable-selection peformance of solar on the empirical data with complicated dependence structures and, hence, severe multicollinearity and grouping effect issues. We choose the prostate cancer data and the Sydney house price data and apply two lasso solvers, elastic net and solar on them (code can be found at \url{this https URL}). The results shows that (i) lasso is affected by the grouping effect and randomly drop variables with high correlations, resulting unreliable and uninterpretable results; (ii) elastic net is more robust to grouping effect; however, it completely lose variable-selection sparsity when the dependence structure of the data is complicated; (iii) solar demonstrates its superior robustness to complicated dependence structures and grouping effect, returning variable-selection results with better stability and sparsity. Also, such stability and sparsity make solar a reliable variable pre-estimation filter of a linear dependence structure esimation (linear probablistic graph learning). The linear probablistic graph estimated on the variable selected by solar returns an intuitive, sparse and stable dependence structure.
Effects of photoperiod on egg hatching and growth and survival of larvae fed with different diets in the Asian catfish, Clarias macrocephalus (Gunther) and the african catfish, C. gariepinus (Burche)

Experiments were conducted in two Clarias species to determine the effects of: (1) photoperiod onegg fertilization and hatching, and (2) photoperiod and diet on larval growth and survival. Artificiallyfertilized eggs of C. macrocephalus and C. gariepinus exposed to combinations of different photope-riod regimes had similar fertilization rates (89â€“95%), but the highest hatching rates (70â€“76%) wererecorded at 6 h light (6L):18 h dark (18D) photoperiod and the shortest hatching time (24â€“25 h) undercontinuous light (24L:0D). Ten-day-old larvae of both species were reared for 35 d under differentphotoperiods and fed three types of diet. Clarias macrocephalus larvae were the largest (p Key Words: catfish,Clarias macrocephalus, Clarias gariepinus, diet, growth, egg hatching rates, photoperiod, aqua-culture
SOME EXTREMAL RESULTS ON THE CHROMATIC STABILITY INDEX

Abstract The $\chi $ -stability index $\mathrm {es}_{\chi }(G)$ of a graph G is the minimum number of its edges whose removal results in a graph with chromatic number smaller than that of G. We consider three open problems from Akbari et al. [‘Nordhaus–Gaddum and other bounds for the chromatic edge-stability number’, European J. Combin. 84 (2020), Article no. 103042]. We show by examples that a known characterisation of k-regular ( $k\le 5$ ) graphs G with $\mathrm {es}_{\chi }(G) = 1$ does not extend to $k\ge 6$ , and we characterise graphs G with $\chi (G)=3$ for which $\mathrm { es}_{\chi }(G)+\mathrm {es}_{\chi }(\overline {G}) = 2$ . We derive necessary conditions on graphs G which attain a known upper bound on $\mathrm { es}_{\chi }(G)$ in terms of the order and the chromatic number of G and show that the conditions are sufficient when $n\equiv 2 \pmod 3$ and $\chi (G)=3$ .
Concentration of trace elements vs redox status in blood, liver, and muscles of turkey hens fed diets with the addition of soybean or linseed oil

Abstract The undertaken study aimed at analysing the effect of linseed oil used in diets for turkey hens on contents of pro- and antioxidants in their tissues. Additionally, correlations were analysed between the contents of these compounds in blood, liver, and muscles of the birds. The experiment was conducted on 240 one-week-old turkey hens reared until 16 weeks of age, randomly allocated into two feeding groups. Hens from the first group received soybean oil in their complete feed mixture, whereas linseed oil was the source of fat for turkey hens from the second group. The oil content of the diet was on the level appropriate for the period of feeding: Starter - 0.5%; Grower I - 1.0%, Grower II - 2.5%; Finisher I - 3%. The redox parameters were assayed in breast and thigh muscles, blood, and liver. Introduction of PUFA n-3 high linseed oil to a feed mixture contributed to the enhancement of lipid peroxidation processes, which was indicated by a significant increase in concentrations of H2O2 and malondialdehyde in muscles, and by an increase of superoxide dismutase activity and concentrations of vitamin C and minerals (Cu+2, Zn+2, Fe+2, Se+2). This points out the need of strengthening the antioxidative defence by the use of suitable antioxidants. The use of linseed oil as a feed material for turkey hens additionally contributed to changes in parameters determining the ferric reducing ability of plasma (FRAP). The increased FRAP values resulted from a significant increase in vitamin E level.
A word‐level language identification strategy for resource‐scarce languages

This study is based on the premise that it is possible to train computers to predict the language of a word (textual or audio) by learning from its character n‐gram pattern, without recourse to the language's dictionary. With the growth of multilingual collections and a need for automatic means of cleaning textual datasets, this paper presents a strategy for language identification of individual words in a body of texts. This strategy is suitable for resource‐scarce languages that do not have large electronic datasets that are required for machine learning and natural language processing studies and whose dictionaries may not be available. In this study, we focused on three African languages, namely Hausa, Igbo, and Yoruba. A training corpus in each of these languages was used to obtain the probabilities of character trigrams in the language. Given that English is a common language that is often mixed with these resource‐scarce languages in texts, we also obtained the probabilities of trigrams in an English training corpus. These probabilities were then used in identifying the language of each word in test corpora containing bilingual texts. Our strategy achieved average precision, recall and F1 values of about 97%, 91% and 94% respectively.
Distraction to illumination: Mining biomedical publications for serendipity in research

As our technological capabilities for filling information needs improve, developers seek to more effective ways to support different aspects of the users' experience. One aspect that is gaining attention as an emerging support area is serendipity. However, supporting serendipity within a recommender system is difficult because the experience is unexpected and, therefore unpredictable. While researchers agree that algorithms to support serendipity need to be able to provide a balance of surprise and value to the end user (Niu & Abbas, 2017), an understanding of how to provide that balance has not yet been realized. Information that could be puzzling or distracting to someone as they go about their research activities may provide the trigger someone else needs to make a serendipitous connection in their research. Reports of serendipitous occurrences in research settings have been identified in research commentaries (Campanario, 1996) and within full‐text research articles (Allen, Erdelez, & Marinov, 2013). This paper investigates the feasibility of automating the identification of information encounters in full‐text research articles. This study contributes to the development of algorithms for supporting serendipity in information systems. We identified four variables that are useful for predicting information encounters in 25‐35% of the instances. While we should continue to search for additional predictive variables, these findings present a novel approach to undertaking the support of serendipity in information systems.
On the modularity of 3‐regular random graphs and random graphs with given degree sequences

The modularity of a graph is a parameter that measures its community structure; the higher its value (between 0 and 1), the more clustered the graph is. In this paper we show that the modularity of a random 3‐regular graph is at least 0.667026 asymptotically almost surely (a.a.s.), thereby proving a conjecture of McDiarmid and Skerman. We also improve the a.a. s. upper bound given therein to 0.789998. For a uniformly chosen graph Gn over a given bounded degree sequence with average degree d(Gn) and with |CC(Gn)| many connected components, we distinguish two regimes with respect to the existence of a giant component. In the subcritical regime, we compute the second term of the modularity. In the supercritical regime, we prove that there is ε>0 , for which the modularity is a.a. s. at least 21−μd(Gn)+ε,where μ is the asymptotically almost sure limit of |CC(Gn)|n .
Foveation for Segmentation of Ultra-High Resolution Images

Segmentation of ultra-high resolution images is challenging because of their enormous size, consisting of millions or even billions of pixels. Typical solutions include dividing input images into patches of fixed size and/or down-sampling to meet memory constraints. Such operations incur information loss in the field-of-view (FoV) i.e., spatial coverage and the image resolution. The impact on segmentation performance is, however, as yet understudied. In this work, we start with a motivational experiment which demonstrates that the trade-off between FoV and resolution affects the segmentation performance on ultra-high resolution images---and furthermore, its influence also varies spatially according to the local patterns in different areas. We then introduce foveation module, a learnable "dataloader" which, for a given ultra-high resolution image, adaptively chooses the appropriate configuration (FoV/resolution trade-off) of the input patch to feed to the downstream segmentation model at each spatial location of the image. The foveation module is jointly trained with the segmentation network to maximise the task performance. We demonstrate on three publicly available high-resolution image datasets that the foveation module consistently improves segmentation performance over the cases trained with patches of fixed FoV/resolution trade-off. Our approach achieves the SoTA performance on the DeepGlobe aerial image dataset. On the Gleason2019 histopathology dataset, our model achieves better segmentation accuracy for the two most clinically important and ambiguous classes (Gleason Grade 3 and 4) than the top performers in the challenge by 13.1% and 7.5%, and improves on the average performance of 6 human experts by 6.5% and 7.5%. Our code and trained models are available at $\text{this https URL}$.
Efficacy of Triple Combination Antihypertensive Therapy in Hypertensive Patients with Pre-Dabetes

Aim of the study: evaluation of antihypertensive, organprotective efficacy and influence on metabolic disorders of triple combination antihypertensive therapy with valsartan, indapamide and amlodipine in hypertensive patients with pre-diabetes, who are resistant to bi-combination therapy. Material and Methods: The study included 66 men with hypertension in average age of 56.06 ± 7.86 years, diagnosed abdominal obesity (waist circumference 103.11 ± 6.4 cm and body mass index 34.54 ± 3.83 kg/m2) and metabolic disorders, which were resistant to bi-combination antihypertensive therapy. The hypertensive patients with associated clinic conditions didn’t include to the study. Target organ damage (TOD) was diagnosed by Echocardiography and Ultrasound of brachiocephalic arteries. Microalbuminuria was determined on daily urine and glomerular filtration rate was calculated using EPI formulae. Lipids and fasten glucose levels, serum creatinine and uric acid were measurement by biochemical methods. All measurements were before and during 6 monthly therapies with valsartan (Nortivan, “Gedeon Richter”, Hungary), indapamide (Indap, PRO.MED. CS Praha, Czech Republic) and amlodipine (Normodipine, “Gedeon Richter”, Hungary) in average daily doses 135.65 ± 37.63 / 2.52 ± 1.4 / 7.5 ± 2.6 mg respectively. Results: According to AH degree and founded risk factors and TOD, hypertensive patients with pre-diabetes were characterized as with high cardiovascular risk. Triple combination therapy showed high antihypertensive efficacy, achieving goal blood pressure level on 94% patients with decreased systolic BP on -22.26 ± 5.84% and diastolic BP on -21.1 ± 5.85%, significantly decreased of pulse BP. Heart rate beat didn’t change. Valsartan, indapamide and amlodipine combination characterized with significantly high cardioprotective, renoprotective efficacies and metabolic neutrality. Additional non-pharmacological interference, contributed to decrease of epicardial fat thickness and waist circumference, and positive influence to dyslipidemia. Conclusion: During 6-monthly triple combination antihypertensive therapy with valsartan, indapamide and amlodipine 94% of hypertensive patients with pre-diabetes, who were resistant to bi-combination therapy achieved goal BP level with significantly target organ protection and positive influence to metabolic disorders. ISSN 2639-9326 Research Article Citation: Nagay AV, Khamidullaeva GA, Srojidiniva NZ. Efficacy of Triple Combination Antihypertensive Therapy in Hypertensive Patients with Pre-Diabetes . Diabetes Complications. 2017; 1-4.
Pichia anomala as a biocontrol agent of Penicillium roqueforti in high-moisture wheat, rye, barley, and oats stored under airtight conditions

Pichia anomala inhibits growth of Penicillium roqueforti in high-moisture winter wheat, barley, and oats in cases where a malfunctioning airtight feed-storage system allows air to leak in. To imitate air leakage to such a storage system, grain was inoculated, packed in glass tubes with a restricted air supply, and incubated at 25°C. Yeast and mold colony-forming units (CFU) were counted on selective media after 14 days. Pichia anomala reached a density of about 5 x 107 CFU/g on all tested cereals except in spring wheat (cv. Dragon), where a density of 109 CFU/g was reached. In winter wheat (cv. Kosack), Penicillium roqueforti reached a density of 106 CFU/g in grain that had not been inoculated with yeast and 105 CFU/g in co-culture with 5 x 103 CFU/g of Pichia anomala. At 5 x 104 Pichia anomala, growth of Penicillum roqueforti was totally inhibited. Similar results were obtained with spring wheat (cv. Dragon), barley (cv. Golf), and oats (cv. Svea). However, spring wheat cv. Dragon was generally much less.
Development and use of a system for predicting the macroinvertebrate fauna in flowing waters

Abstract River InVertebrate Prediction And Classification System (rivpacs) is a software package developed by the Institute of Freshwater Ecology (IFE) for assessing the biological quality of rivers in the United Kingdom. The system can be used to generate site-specific predictions of the macroinvertebrate fauna to be expected in the absence of major environmental stress. Each prediction is based on a small number of environmental features that are used to characterize the site. The fauna predicted can then be compared with the fauna observed at the same site. This offers a procedure for evaluating biological quality with application in river management both at the local level and for national surveys. Close collaboration between the IFE team and biologists in the water industry during the project had a beneficial influence on the operational development of the system. A second feature of RIVPACS is the national classification of sites, based on the macro-invertebrate fauna. Although the classification is currently a pre-requisite for the prediction system, it also has intrinsic value because newly sampled sites of high biological quality can be placed within the national framework, based on their macroinvertebrate fauna. This facility is of interest to the statutory nature conservation bodies as an element in their site appraisal procedures. The predictive component of the current version of the system (RIVPACS n) was used in the 1990 River Quality Survey to assess the biological quality of almost 9000 sites throughout the United Kingdom. Further developmental work is now under way to provide a more comprehensive version of the system for the 1995 survey.