Miracle-dz/UniFAR-FTUs huggingface dataset:
Global maximum power point tracking method based on sorting particle swarm optimizer

ABSTRACT This paper presents a novel global maximum power point tracking (MPPT) method based on the sorting particle swarm optimizer (PSO). At first, the output characteristics of photovoltaic (PV) array under the partial shading conditions are analyzed. As an artificial intelligence algorithm, the sorting PSO is proposed for global MPPT of PV array under partial shading conditions. The principle of the proposed algorithm is illustrated in detail. Second, a stand-alone PV system based on Z-source inverter (ZSI) is simulated to validate the proposed global MPPT method. Then, the experiments are implemented based on the established ZSI. Compared with the conventional PSO for MPPT, simulation and experimental results both reveal that the proposed global MPPT method based on the sorting PSO can reduce the fluctuation of voltage of PV array during MPPT. Simulation results reveal that 61.53% and 51.81% energy consumption during MPPT is reduced under two different typical partial shading conditions. The proposed MPPT method can also track the global maximum power point more rapidly. Approximately 21.21–39.96% time consumptions during MPPT are saved.
Toward a Fair Evaluation and Analysis of Feature Selection for Music Tag Classification

Accurate music tag classification of music clips has been attracting great attention recently, because it allows one to provide various music excerpts, including unpopular ones, to users based on the clips’ acoustic similarities. Given a user’s preferred music, acoustic features are extracted and then fed into the classifier, which outputs the related tag to recommend new music. Furthermore, the accuracy of the tag classifiers can be improved by selecting the best feature subset based on the domain to which the tag belongs. However, recent studies have struggled to evaluate the superiority of various classifiers because they utilize different feature extractors. In this study, to conduct a direct comparison of existing methods of classification, we create 20 music datasets with the same acoustic feature structure. In addition, we propose an effective evolutionary feature selection algorithm to evaluate the effectiveness of feature selection for tag classification. Our experiments demonstrate that the proposed method improves the accuracy of tag classification, and the analysis with multiple datasets provides valuable insights, such as the important features for general music tag classification in target domains.
Dengue and the heart.

BACKGROUND Peri-operative morbidity and mortality are increased in patients with rheumatic heart disease. Pre-operative risk stratification is imperative for optimisation and a better outcome. METHODS This was a descriptive, retrospective, contextual study. A consecutive convenience sampling method was used. Eighty-nine patients who underwent mitral valve surgery at Charlotte Maxeke Johannesburg Academic Hospital between January 2014 and December 2015 were enrolled. The objectives of the study were to describe the demographic profile of the patients presenting for rheumatic mitral valve surgery, describe their peri-operative cardiovascular and echocardiographic parameters, and risk stratify according to their clinical and echocardiographic parameters. Demographic, echocardiographic and laboratory data as well as the cardiovascular examination were analysed. Descriptive statistics using proportions (percentages), means (standard deviations) or medians (interquartile ranges) were used where appropriate. RESULTS A total of 102 patients were reviewed. Thirteen were excluded due to significant missing data. Of the 89 analysed, all had demographic data, 81 had cardiovascular clinical examination data, 82 had echocardiographic data and 52 had laboratory data. Forty-seven patients presented with mitral regurgitation (MR) and 35 had mitral stenosis (MS). Data included two mixed mitral valve disease patients with predominant regurgitation who were classified under the MR group. In total, 45% (39 patients) had arrhythmias and 49% (42 patients) had congestive cardiac failure at presentation for surgery. The overall mean (SD) pulmonary artery systolic pressure was 57 (20) mmHg and mean (SD) left atrial size was 53 (11) mm. Those with MS presented with mean (SD) mitral valve area of 0.9 (0.2) cm2. Of the analysed MR patients, 51% presented with left ventricular ejection fraction < 60% and 55% with left ventricular end-systolic diameter > 40 mm. Among the analysed MS patients, 59% had mitral valve area < 1 cm2. A substantial number (49% MR and 54% MS) of collected records were not eligible for analysis and stratification using the American Heart Association/American College of Cardiology (ACC/AHA) guidelines for valvular heart disease due to missing vital information. Of the 24 MR patients analysed utilising the 2014/2017 AHA/ACC guidelines, 13 had asymptomatic severe MR (stage C) and 11 had symptomatic severe MR (stage D). One patient had progressive MS (stage B), eight had asymptomatic severe MS (stage C) and seven had symptomatic severe MS (stage D). CONCLUSIONS The majority of those who could be stratified presented in stages C and D of disease progression; however, they also presented with concomitant clinical and echocardiographic features that placed them at high risk of perioperative morbidity.
EMPIRICAL ANALYSIS OF CAUSALITY BETWEEN R&D EXPENDITURES AND ECONOMIC GROWTH: A STUDY ON TURKEY, ITALY AND RUSSIA

In recent years, the changes in the world economy and the emerging competitive environment have increased the interest in innovation and approaches to innovation. The reflection of the scientific production process in daily life occurs with developing a new product or production process. These emerging new products, production, or product processes represent activities carried out on Research and Development (R&D). For this reason, countries contribute to the development of their economies by producing new technologies and products with their research and development (R&D) expenditures. This study aims to analyze the causal relationship between R&D expenditures and economic growth by using the Granger causality test, based on the annual data (WORLD BANK and Euro stat) of the 1990-2018 period for Turkey, Italy, and Russia. This study aims to analyze the causal relationship between R&D expenditures and economic growth by using the Granger causality test, based on the annual data of the 1990-2018 period for Turkey, Italy, and Russia. As a result of the analysis, a one-way causality relationship was determined between R&D expenditures and economic growth for Turkey and Russia. However, there was no causal relationship found between R&D expenditures and economic growth for Italy.
Is There Any Non-functional Training? A Conceptual Review

This conceptual review investigates whether functional training (FT) is a different approach from traditional strength, power, flexibility, and endurance (aerobic or cardiorespiratory) training already adopted in the physical training plan of professional, recreational athletes, healthy, and older adults. The 20 most recent papers published involving FT were searched in the PubMed/Medline database. Definition, concepts, benefits, and the exercises employed in FT programs were analyzed. The main results were: (a) there is no agreement about a universal definition for FT; (b) FT programs aim at developing the same benefits already induced by traditional training programs; (c) exercises employed are also the same. The inability to define FT makes the differentiation from traditional training programs difficult. Physical training programs can be easily described and classified as strength, power, flexibility, endurance, and the specific exercises employed (e.g., traditional resistance training, ballistic exercises, plyometrics and Olympic-style weightlifting, continuous and high-intensity interval training). This apt description and classification may provide consistent and clear communication between students, coaches, athletes, and sports scientists. Based on the current evidence and to avoid confusion and misconceptions, we recommend that the terms FT, high-intensity FT, and functional fitness training no longer describe any physical training program.
Sensor-Based Fall Risk Assessment: A Survey

Fall is a major problem leading to serious injuries in geriatric populations. Sensor-based fall risk assessment is one of the emerging technologies to identify people with high fall risk by sensors, so as to implement fall prevention measures. Research on this domain has recently made great progress, attracting the growing attention of researchers from medicine and engineering. However, there is a lack of studies on this topic which elaborate the state of the art. This paper presents a comprehensive survey to discuss the development and current status of various aspects of sensor-based fall risk assessment. Firstly, we present the principles of fall risk assessment. Secondly, we show knowledge of fall risk monitoring techniques, including wearable sensor based and non-wearable sensor based. After that we discuss features which are extracted from sensors in fall risk assessment. Then we review the major methods of fall risk modeling and assessment. We also discuss some challenges and promising directions in this field at last.
Diptool—A Novel Numerical Tool for Membrane Interactions Analysis, Applying to Antimicrobial Detergents and Drug Delivery Aids

The widespread problem of resistance development in bacteria has become a critical issue for modern medicine. To limit that phenomenon, many compounds have been extensively studied. Among them were derivatives of available drugs, but also alternative novel detergents such as Gemini surfactants. Over the last decade, they have been massively synthesized and studied to obtain the most effective antimicrobial agents, as well as the most selective aids for nanoparticles drug delivery. Various protocols and distinct bacterial strains used in Minimal Inhibitory Concentration experimental studies prevented performance benchmarking of different surfactant classes over these last years. Motivated by this limitation, we designed a theoretical methodology implemented in custom fast screening software to assess the surfactant activity on model lipid membranes. Experimentally based QSAR (quantitative structure-activity relationship) prediction delivered a set of parameters underlying the Diptool software engine for high-throughput agent-membrane interactions analysis. We validated our software by comparing score energy profiles with Gibbs free energy from the Adaptive Biasing Force approach on octenidine and chlorhexidine, popular antimicrobials. Results from Diptool can reflect the molecule behavior in the lipid membrane and correctly predict free energy of translocation much faster than classic molecular dynamics. This opens a new venue for searching novel classes of detergents with sharp biologic activity.
A Contrast Calibration Protocol for X-ray Speckle Visibility Spectroscopy

X-ray free electron lasers, with their ultrashort highly coherent pulses, opened up the opportunity of probing ultrafast nano- and atomic-scale dynamics in amorphous and disordered material systems via speckle visibility spectroscopy. However, the anticipated count rate in a typical experiment is usually low. Therefore, visibility needs to be extracted via photon statistics analysis, i.e., by estimating the probabilities of multiple photons per pixel events using pixelated detectors. Considering the realistic X-ray detector responses including charge cloud sharing between pixels, pixel readout noise, and gain non-uniformity, speckle visibility extraction relying on photon assignment algorithms are often computationally demanding and suffer from systematic errors. In this paper, we present a systematic study of the commonly-used algorithms by applying them to an experimental data set containing small-angle coherent scattering with visibility levels ranging from below 1% to ∼60%. We also propose a contrast calibration protocol and show that a computationally lightweight algorithm can be implemented for high-speed correlation evaluation.
Humidity-Responsive Photonic Films and Coatings Based on Tuned Cellulose Nanocrystals/Glycerol/Polyethylene Glycol

It has been extensively reported that cellulose nanocrystals (CNCs) can represent structural colors due to their unique chiral-nematic self-assembly. However, the application of this remarkable structure does need further investigation. It has been challenging to keep the selective reflection band (SRB) resulting from the CNC structure in the visible spectrum. Herein, composition of CNC colloidal suspensions with polyethylene glycol (PEG) and glycerol (Gly) have been studied to develop humidity-responsive sensors in the form of coatings and films. The fabricated samples were characterized for their mechanical properties, optical properties, water uptake capacity, water contact angle, and surface roughness. Additionally, the chemical structure of the samples was studied with FTIR spectroscopy. The produced humidity indicators on microbial glass slides were maintained and tested in a different relative humidity range from 20% to 98% with a different color response from blue to red, respectively. The color change of the humidity sensors was reversible for several cycles. It should be noted that the color change can be detected easily by the naked eye. The water uptake test showed that pure CNC and CNC/Gly had the lowest (34%) and highest (83%) water absorption levels. The mechanical tests for CNC/PEG composites showed the highest tensile strength (40.22 MPa). Moreover, microstructural characterizations confirmed the CNC pitch formation in all the samples. Addition of the fillers increased the CNC pitch, resulting in a mesoporous film formation. These produced humidity sensors are promising candidates in food and drug packaging due to their biodegradability, biocompatibility, and cost-effectiveness.
ATLANTIC: Making Database Differentially Private and Faster with Accuracy Guarantee

Differential privacy promises to enable data sharing and general data analytics while protecting individual privacy. Because the private data is often stored in the form of relational database that supports SQL queries, making SQL-based analytics differentially private is thus critical. However, the existing SQL-based differentially private systems either only focus on specific type of SQL queries such as COUNT or substantially modify the database engine, thus obstructing adoption in practice. Worse yet, these systems often do not guarantee the desired accuracy by the applications. In this demonstration, using the driving trace workload from Cambridge Mobile Telematics (CMT), we show that our ATLANTIC system, as a database middleware, enforces differential privacy for real-world SQL queries with provable accuracy guarantees and is compatible with existing databases. Moreover, using a sampling-based technique, ATLANTIC significantly speeds up the query execution, yet effectively amplifying the privacy guarantee.
Modern condition of biological diversity in near-shore zone of Crimea (the Black Sea sector)

Monograph is prepared on the basis of the materials collected in near-shore waters of Crimea in 2003, and the retrospective data. Modern condition of biological diversity in near-shore zone of Crimea is shown on the basis of analysis of qualitative and quantitative composition of micro- and zooplankton, phyto- and zoobenthos, ichthyofauna and parasite fauna; the recommendations on conservation of biological diversity are given. Responses of biota on the appearance of new species in communities and on the impact of some anthropogenic factors are analyzed. Characteristics of the Black Sea plankton bioluminescents and the bioluminescence field forming by them in neritic zone of Crimea are shown. Lists of species of main groups of flora and fauna from the different regions of near-shore waters of Crimea are made.
What’s Up and What’s Down for WhatsApp in Medical Education

WhatsApp is a smartphone messenger that employs the users’ existing internet data plan to connect the user to their social as well as learning community. This study aimed to explore the use of the WhatsApp instant messaging as a source of learning for postgraduate students enrolled in Master of Health Professions Education. This mixed method study involved postgraduate students enrolled in Master of Health Professions Education at University of Lahore, Pakistan from January 2017January 2018. Eighteen students filled the questionnaire after giving informed consent. This was followed by eight individual interviews of students consenting for interview. The findings of the research suggest that the use of WhatsApp instant messaging may assist students in learning as it fosters a social constructivist environment. This environment supported students in reinforcing various concepts by enhancing interaction within the groups, among teachers as well as assimilation of the content material shared. The study concluded that WhatsApp is perceived to be an acceptable supplementary application for educational purposes.
Flame Synthesis of Carbon Nanotubes on Different Substrates in Methane Diffusion Flames

Characterized by their unique electrical, mechanical, and photonic properties, carbon nanotubes (CNTs) have generated a high level of research interest. Based on the study of methane coaxial jet diffusion ﬂame, the effect of sampling substrate, sampling time and sampling height on the catalytic synthesis of CNTs was studied. The morphology and structure of carbon nanomaterials were characterized by ﬁeld emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM) and Raman spectroscopy. The interaction between the catalyst and the substrate was analyzed with nickel nitrate as catalyst precursor and copper, nickel-chromium and nickel foam as supported substrates, respectively. The experimental results showed that sampling time and sampling height had an important inﬂuence on CNTs. Copper and nickel-chromium substrates provide open space for the synthesis of CNTs, resulting in more impurities. The unique voids of nickel foam provide excellent growth space for CNTs. SEM showed that the multi-walled CNTs were dense and uniform. TEM displayed that the catalyst particles were coated inside the CNTs. Raman spectroscopy indicated that the CNTs synthesized on copper substrates had the highest crystallinity and purity, followed by nickel-chromium and nickel foam substrates. The interaction between the catalyst and the substrate remarkably affected the growth mechanism and morphology of CNTs. The ﬂame-synthesizing CNTs are based on the tip growth mode and the vapor-liquid-solid growth mechanism. Flame synthesis holds great potential for the cost-effective production of CNTs.
THE COUPLING OF THE SYSTEM DYNAMICS MODEL WITH GIS TO VISUALISE THE POTENTIAL OF RENEWABLE ENERGY

During the current economic growth and the global climate crisis, energy supply is a pressing concern. Local energy sources are an essential part of sustainable development, and they also provide workplaces and energy independence. Various biding legislative documents oblige prioritising renewable energy sources (RES) and focus on green, local energy. Society cannot do such a transition without estimating the available resources and understanding the links among various factors influencing the energy market. The study aims to visualise system dynamics (SD) model parameters using the interactive cartography tool to analyse the potential of renewable energy in Latvia. Data gaps in the model were filled using statistical estimations and ArcGIS Pro spatial analysis. A JavaScript library for interactive maps Leaflet and HTML were used for the graphic user interface. The coupling presented in this paper is not dynamic but provides an insight into the potential implementation of the spatial data into models.
Predictive Comparison Between Random Machines and Random Forests

Ensemble techniques have been gaining strength among machine learning models, considering supervised tasks, due to their great predictive capacity when compared with some traditional approaches. The random forest is considered to be one of the oﬀ-the-shelf algorithms due to its ﬂexibility and robust performance to both regression and classiﬁcation tasks. In this paper, the random machines method is applied over simulated data sets and benchmarking datasets in order to be compared with the consolidated random forest models. The results from simulated models show that the random machines method has a better predictive performance than random forest in most of the investigated data sets. Three real data situations demonstrate that the random machines may be used to solve real-world problems with competitive payoﬀ.
Mechanisms of prediction updating - preprint

One of the most compelling pieces of evidence for the presence of predictions in language comprehension comes from event-related potential (ERP) studies which show that encountering an adjective whose gender marking is inconsistent with that of a highly expectable noun leads to an effect at the adjective. Until now the mechanism underlying this effect has been unknown. The present study tests a novel hypothesis whereby the effect at adjectives reflects prediction updating, which in turn impacts the N400 generated by the upcoming noun. In two experiments, native speakers of Polish read short stories that made several nouns likely at the position of a direct object in the story’s final sentence. The noun was preceded by a gender-marked adjective which was shown in either of the two grammatical forms: a) informative about the following noun (adjectival gender marking constrained which nouns can follow), or b) less informative (the grammatical marking did not disambiguate the gender of the following noun). Importantly, the same stories and critical words were used in both conditions, which was possible thanks to exploiting syncretisms in Polish adjectival inflection. The results provide support for one or more mechanisms of prediction updating at the adjective that impact the amplitude of the N400 elicited by the following noun. Together, these results show that when only morphosyntactic cues are available, they are instantly used to update predictions about upcoming words.
“Smart” Guns: A Technological Fix For Regulating The Secondary Market

A "personalized" or "smart" gun will not fire unless it is being used by an authorized individual. Such guns have the potential to reduce the negative externalities of gun ownership while preserving the benefits. Ongoing efforts to develop practical "smart" designs make it timely to consider regulations that would favor or mandate them in the market for new guns. The likely consequences would depend on the design details, in particular the costs of transferring the "key" to firing such guns. With an "ideal" design, transferring the key would require special equipment that could be monitored by appropriate authorities. The result would be to block thefts and other transfers of such guns in the secondary market and, in the long run, reduce access by individuals who are proscribed from possessing a gun. Personalized guns, therefore, could make existing firearms regulations more effective and reduce the social costs associated with gun misuse. Though personalized guns have advantages relative to standard guns in a wide variety of situations, some of the potential benefits of personalized guns could be captured through alternative policy measures. Copyright 2002 Western Economic Association International.