Miracle-dz/UniFAR-FTUs huggingface dataset:
Preparation of surface-modified, micrometer-sized carboxymethyl chitosan drug-loaded microspheres

Drug-loaded microspheres have attracted much attention in embolization therapy for liver cancer in recent years. Carboxymethyl chitosan has obvious advantages for biomedical applications because of its exceptional biocompatibility and biodegradability. In this study, surface-modified carboxymethyl chitosan microspheres were prepared by the crosslinking reactions of carboxymethyl chitosan in a reverse suspension system with poly(ethylene glycol diglycidyl ether) (PEGDE) as the crosslinking agent; this was followed by the grafting polymerization of 2-acrylamido-2-methyl propane sulfonic acid on the surface of the microspheres. The microspheres showed regular spherical shapes with size distributions ranging from 300 to 600 μm. Ion-exchange groups (COOH, SO3H) were introduced into the microspheres; these groups could load doxorubicin with a loading rate as high as 34.6% in 24 h. This was an increase of 49.8% compared to that of the pure carboxymethyl chitosan microspheres. Additionally, the microspheres possessed large network structures because macromolecular PEGDE was used as the crosslinking agent. The drug-release profile showed that the surface-modified microspheres displayed a sustained-release manner compared with the nonmodified microspheres in phosphate-buffered saline. These microspheres have promising applications as drug-loaded arterial embolization agents for the interventional treatment of tumors. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45731.
Big Data Classification for the Analysis MEL Scale Features Using KNN Parameterization

The role of human speech is intensified by the emotion it conveys. The parameterization of the vector obtained from the sentence divided into the containing emotional-informational part and the informational part is effectively applied. There are several characteristics and features of speech that differentiate it among utterances, i.e. various prosodic features like pitch, timbre, loudness and vocal tone which categorize speech into several emotions. They were supplemented by us with a new classification feature of speech, which consists in dividing a sentence into an emotionally loaded part of the sentence and a part that carries only informational load. Therefore, the sample speech is changed when it is subjected to various emotional environments. As the identification of the speaker’s emotional states can be done based on the Mel scale, MFCC is one such variant to study the emotional aspects of a speaker’s utterances. In this work, we implement a model to identify several emotional states from MFCC for two datasets, classify emotions for them on the basis of MFCC features and give the comparison of both. Overall, this work implements the classification model based on dataset minimization that is done by taking the mean of features for the improvement of the classification accuracy rate in different machine learning algorithms.
Mineralization of Nacre-like Structures Mediated by Extrapallial Fluid on Pearl Nucleus

Nacre is renowned for its fascinating iridescence and superior mechanical properties owing to their hierarchically ordered structures at multiscale levels. However, mechanisms for constructing these ordered structures and bottom-up strategies to synthesize nacre-like materials are still lacking. Here, we developed a CaCO3 deposition system in vitro to mimic biomineralization process in vivo. Strikingly, in the presence of extrapallial fluid (EPF) from the pearl oyster Pinctada fucata, a multilevel layered nacre-like structure was formed on pearl nucleus. Particularly, aragonite nanocolumns with diameters of 30–60 nm aggregated by a parallel pattern and assembled into polygonal tablets in each layer. Moreover, linkers between adjacent laminated layers were observed, resembling mineral bridges in natural nacre. Thus, we found that EPF could induce the mineralization of nacre-like structures on pearl nucleus in vitro, which would be helpful for better understanding the mechanism for assembling layered struct.
Survivor interaction contrasts for errored response times: Non-parametric contrasts for serial and parallel systems

Systems Factorial Technology (SFT) is a theoretically-derived methodology that allows for strong inferences to be made about the underlying processing architecture (e.g., whether processing occurs in a pooled, coactive fashion or independently, in serial or in parallel). Measures of mental architecture using SFT have been restricted to the use of error-free response times. In this paper, through formal proofs and demonstrations, we extended the measure of architecture, the survivor interaction contrast (SIC), to response times conditioned on whether they are correct or incorrect. We show that so long as an ordering relation (between stimulus conditions of different difficulty) is preserved, unique conditional SIC predictions are found for several classes of processing models. We further prove that this ordering relation holds for the popular Wiener diffusion model for both correct and error RTs but fails under some instantiations of a Poisson counter model.
Mechanisms linking multi-year La Niña with preceding extreme El Niño

El Niño/La Niña, characterized by anomalous sea surface temperature warming/cooling in the central-eastern equatorial Pacific, is a dominant interannual variability with irregularity, impacting worldwide weather and socioeconomics. The observed records show that La Niña often persists for more than two years, called “multi-year La Niña” which tends to accompany extreme El Niño in the preceding year; however, the physical linkage between them remains unclear. Here we show using reanalysis data that an extreme El Niño excites atmospheric conditions that favor the generation of the multi-year La Niña in subsequent years. Easterly wind anomalies along the northern off-equator in the Pacific during the decay phase of an extreme El Niño are crucial. They act to discharge ocean heat content (OHC) via an anomalous northward Ekman transport; the negative OHC anomaly is large enough to be restored by a single La Niña and, therefore, causes another La Niña to occur in the second year. Furthermore, analyses of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models show that the occurrence frequencies of multi-year La Niña and extreme El Niño are highly correlated, supporting the abovementioned mechanism. Our results provide physical evidence that the increasing frequency of multi-year La Niña is explained by the increasing El Niño amplitude since the late 20th century.
A STUDY ON PRASEODYMIUM 4-HYDROXYCINNAMATE AS AN INHIBITOR FOR CARBON STEEL IN FRESH COOLING WATER SYSTEM OF CA MAU FERTILIZER PLANT

Praseodymium 4-hydroxycinnamate compound has been successfully studied as an effective corrosion inhibitor for carbon steel in 0.1 M NaCl solution using electrochemical techniques and surface analysis. The results of electrochemical techniques indicated that there were the decrease of current density and the appearance of protective film on the steel surface evidencing the presence of inhibitor and the effect of Pr(4OHCin)3 compound depending on its concentration in solution. The surface analysis show a confirmation of the protective film formation which is a result of adsorption between the metal and inhibitor components. In addition, inhibition performance of Pr(4OHCin)3 compound is also compared to the inhibition performance of TRACT 109, which has been added to a fresh cooling water system of Ca Mau fertilizer plant.
Immediate postpartum haemorrhage at the Libreville University Hospital Centre: Epidemiological profile of women

Background In Gabon, the frequency of maternal deaths directly related to Immediate PostPartum Haemorrhage (IPPH) is 15 to 25%, despite the different means that World Health Organization (WHO) has made available to the providers of the Emergency Obstetrical and Neonatal Care (EmONC). The objective of this study was to determine the prevalence and epidemiological characteristics of IPPH in order to improve its management and reduce the rate of maternal deaths. Methods An analytical retrospective study involved 42,728 records, whose data were collected using a chart collection form on the basis of information contained in partograms and other patient records. The description of the sample is made using numbers and proportions. The relationship between the etiologies of IPPH and certain characteristics of the women was established using the ORs with their 95% confidence interval. The difference was significant if p < 0.05. Results The prevalence of IPPH was 1.6%. The 20–39 age group was the most represented. Multiparous women accounted for 67.2%. Delivery hemorrhages accounted for 65.5% of IPPH. The main factors associated with delivery hemorrhages were multiparity (p < 0.001), preterm and postterm (p < 0.001), and child weight > 4,000 g (p < 0.001). Those associated with genital track injuries were young maternal age, multiparity (p < 0.001). Conclusions The prevalence of IPPH was 1.6%. The most common etiology was hemorrhage from delivery. Multiparity remains the most common risk factor and the young age of the patients. It is important to improve management through better assessment of blood loss in the immediate postpartum as well as capacity building of health providers on EmONC.
Evaluation of borhani prepared from whole milk, skim milk and full cream powdered milk curd

The present research work was carried out to evaluate the qualities of borhani prepared by curd using whole milk, skim milk and full cream powder milk. In this purposes, three types of borhani were prepared, these were grouped as; A: Borhani prepared from whole milk curd, B: Borhani prepared from skim milk curd and C: Borhani prepared from full cream powder milk curd. The prepared borhani samples were subjected to organoleptic, chemical and microbiological qualities. After performed physical examination, for sample A it was found that there was no significant difference in color and appearance (17.67±0.33) but there was significant difference (p<0.05) in smell and taste (47.33±0.33), body and consistency (26.33±0.33) and total physical scores (91.33±0.88) of the three borhani samples. Chemical analysis showed that for group A; there were significant differences with others (p<0.01) in contents (%) of fat (2.95±0.33), total solids (TS) (18.15±0.16), acidity (0.80±0.01), pH (4.25±0.02), moisture (81.85±0.16), CHO (10.50±0.93) and (p<0.05) in contents (%) of ash (1.82±0.02), protein (2.88±0.02). The result of chemical parameter showed that borhani prepared from whole milk (sample A) was best among the samples. However, there were no significant differences in coliform count and yeast and mold count but there were significant differences (p<0.01) in total viable count (60.67±2.33). Cost analysis showed that samples B and C was cheap than sample A. It can be concluded that even costly; the nutritive value and the consumer preference of borhani from whole milk was better than the others. Asian Australas. J. Food Saf. Secur. 2020, 4 (2), 58-65
On the Control of Electric Vehicle Charging in the Smart Grid

Over the last decade, the demand for electric vehicles (EVs) has surged across the globe. This spurred an increase in the installation of public and private EV charging points which are typically connected to low-voltage power distribution feeders. A high penetration of plug-in EVs in distribution networks is anticipated to cause several problems, such as transformer overloading, voltage limits violations, and increased heat losses. Hence, a demand-side management strategy is needed to control the real power drawn by the charging points. These control strategies can be classified as model-based and model-free depending on whether they rely on a model of the distribution network (i.e., the admittance matrix). This thesis aims to investigate how to control the charge power of EVs from user-centric and grid centric perspectives. We design and evaluate two control frameworks that are suitable for the smart grid. Given an approximate model of the distribution grid, we first propose a reputation-based framework for allocating power to EVs in the smart grid. In this framework, the available capacity of the distribution network measured by distribution-level phasor measurement units is divided in a proportionally fair manner among connected EVs, considering their demands and self-declared deadlines. To encourage users to estimate their deadlines more precisely and conservatively, a weight is assigned to each deadline based on the user’s reputation, which comprises two kinds of evidence: the deadlines declared before and after the actual departure times in the recent past. We design a decentralized algorithm that achieves quadratic convergence under specific conditions ii and evaluate it empirically on a test distribution network by comparing it with state of the art algorithms. In the second framework, we relax the assumption of having a model and propose a model-free, adaptive additive-increase multiplicative-decrease (AIMD)-like algorithm for controlled charging of EVs. This control algorithm is decentralized and merely relies on congestion signals generated by sensors deployed across the network. To dynamically adjust the parameter of this congestion control algorithm, we cast the problem as multi-agent reinforcement learning where each charging point is an independent agent which learns this parameter using an off-policy actor-critic deep reinforcement learning algorithm. Simulation results of both control algorithms on a test distribution network and on a parking station corroborate that the proposed algorithms track the available capacity of the network in real-time, prevents transformer overloading and voltage limits violation problems for an extended period of time, and outperforms several other decentralized feedback control algorithms proposed in the literature.
Stakeholders’ views on barriers and facilitators of optimal support of individuals with Autism Spectrum Disorder in Macedonia

The aim of presented study was to explore barriers and facilitators of optimal support for social and developmental outcomes for people with ASD. The survey was based on the semistructured interviews with parents of people with ASD, as well as professionals supporting persons with ASD. Thematic analysis of the data was viewed with regard to positive and negative practices that essentially serve as barriers and facilitators of optimal support of individuals with ASD. The results showed three levels of perceived barriers and facilitators of optimal support: (1) individual – related to the level of autistic impairment, (2) attitudinal – related to social interpretation of individual behaviour and needs, and (3) systemic – related to the education and welfare approach and practice at macro level. Insights from viewpoints of key stakeholders represent indispensable considerations in promotion of health, equity and well-beingof individuals with ASD and to inform the development of an individualized support system based on the person-centred planning approach.
A systematic review and meta-analysis protocol of observational studies to assess the association between sleep and mental resilience in healthy individuals

Background: Mental resilience has emerged as a recent focus within the context of positive psychology. Resilience is a potentially modifiable construct. Some preliminary evidence from experimental studies as well as clinical populations has indicated that sleep may be related to mental resilience, but little is known about this relationship in healthy populations. Sleep is a complex with multi-factorial outcome, although the majority of research effort has focused on sleep duration and sleep quality. Our systematic review and meta-analysis will identify original research data from observational studies to assess if sleep duration and/or sleep quality is associated with levels of psychological resilience amongst healthy children, adolescents and adults. We hypothesize that there will be a positive association between sleep duration/quality and mental resilience across all age groups. Methods: A comprehensive, systematic search for literature will be conducted in June 2020 (LÖ), covering a total of eight academic databases (PubMed, Embase, PsycInfo, PsycArticles, CINAHL, Scopus, Web of Science and Academic Search Complete). Sources for grey materials will also be included and experts in the field will also be contacted for inclusion of unpublished data. PubMed and PubMed’s MeSH terms were used to develop the search strategy and systematically identify relevant search terms. Two authors (IG & AA) will screen the articles independently, and a third researcher (TA) will resolve discrepancies. The primary outcome is mental/psychological resilience. The systematic review will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.Discussion: The results of our systematic review and meta-analysis are intended to be published in an appropriate scientific peer-reviewed journal and will be made publicly available. The findings from our project are envisioned to inform and educate the public as well as healthcare and education systems, globally. Our systematic review protocol has been peer-reviewed and is currently registered in the international database, PROSPERO.Systematic review registration: CRD42020191119 (submitted on 2 July 2020).
Analysis and Prediction of Air Quality based on LSTM Neural Network —— Take Beijing Temple of Heaven as an Example

LSTM is an excellent variant model of RNN, which inherits the characteristics of most RNN models and solves the problem of gradient disappearance caused by a gradual reduction in the process of gradient backpropagation. LSTM is very suitable for processing data highly related to time series. This paper selects meteorological elements and air quality data of the temple of heaven in Beijing from 2016 to 2018, analyse the correlation between meteorological elements and air quality, selects index modeling, and designs an air quality prediction model based on LSTM neural network. It is concluded that the model has better accuracy and robustness. Finally, reasonable air pollution control measures are proposed to provide a scientific theoretical basis and new prediction methods for the prevention and control of air pollution.
A Conceptual Model of Immersive Experience in Extended Reality

The term immersion or immersive is popularly used when describing and evaluating technologies in the area of extended reality (i.e., virtual/augmented/mixed reality). Much research been conducted on immersion over the last few decades. However, there is still a lack of consistency in how the term is defined in the literature. Presence and involvement are other prominent concepts studied in the field of extended reality. However, there is currently no consensus on their relationship with immersion among researchers. This paper first discusses different dimensions of immersion as well as those of presence and involvement, aiming to resolve potential confusion around the terms and synthesise a relationship among them. From this, a new conceptual model of immersive experience for future studies in extended reality is proposed. The model defines physical presence, social/self presence and involvement as the main high-level attributes that collectively lead to an immersive experience. Each pair of the three attributes shares a common lower-level attribute of sensory, narrative or task/motor engagement, which is an initial step towards the higher-level experience. Plausibility, interactivity and interestingness are defined as the main properties of immersive system and content, each of which is biased by a subjective factor: internal reference, skills/knowledge and personal preference, respectively.
Optimization of ALD ZnO for Antibacterial Activity

Extended Abstract There are many microorganisms which are potentially harmful for human in the polluted water. As these microorganisms can cause diseases to human, it is needed to treat them using a proper method. Metal oxides such as ZnO, TiO2, AgO have been reported to show antibacterial effects when they are exposed to UV light, although antibacterial effects are different depending on surface properties of the metal oxides. [2] An important factor for determining the antibacterial efficiency is reactive surface area. To enhance the reactive surface area, in our previous study, glass slides was coated with ZnO using atomic layer deposition (ALD). [1] A benefit using the ZnO thin films is no need for recycling of metal oxides after water treatment and require a lower light energy compared with conventional lights for killing microorganisms. We also demonstrated that glass slides coated with ZnO films by ALD were effective to produce reactive oxygen species and to kill a model microorganism. Nevertheless, the optimum condition of ZnO thin film formation has not been studied. Therefore, the main objective of this study was to investigate the optimum condition for maximizing antibacterial effects. There are two main parameters affecting the efficiency of antibacterial property of the ALD ZnO films: ALD cycles and synthesis temperature. In this study, ZnO thin films were formed on glass slides. ZnO films were synthesized under four different synthesis temperature conditions (100°C, 150°C, 200°C, and 250°C) and two different ALD cycles (100 cycles and 400 cycles), respectively. Conditions of the synthesis temperature and the ALD cycles are currently optimized. [3] The antibacterial activity of ALD ZnO decreases when the synthesizing temperature was about 250°C. Moreover, the antibacterial activity of different cycle (100 cylces and 400 cycles) has little difference. This study will provide the mechanisms of ROS productions in association with antibacterial properties for various ZnO thin films formed by ALD.
Modulation of visceral sensitivity by faecal microbiota transplantation (FMT): the active role of gut microbiota in the persistence of abdominal pain

Background. Recent findings linked gastrointestinal disorders characterized by abdominal pain to gut microbiota composition. The present work aimed to evaluate the power of gut microbiota as a visceral pain modulator and, consequently, the relevance of its manipulation as a therapeutic option in reversing the persistence of visceral hypersensitivity consequent to colitis induced by the intra-rectal injection of 2,4-dinitrobezenesulfonic acid (DNBS) in rats. Results: The effect of faecal microbiota transfer (FMT) from viscerally hypersensitive DNBS and naïve donors was evaluated in control rats after an antibiotic-mediated microbiota depletion. FMT from DNBS donors induced a long-lasting visceral hypersensitivity in control rats. Pain threshold trend correlated with major modifications in the composition and structure of the gut microbiota at phylum (Proteobacteria and Firmicutes to Bacteroides ratio) and family levels (Enterobacteriaceae, Akkermansiaceae and Lachnospiraceae). Acetic acid was significantly increased in the recipients FMT from DNBS donors. Gut cytokine profile, as well as tryptophan metabolism were similarly altered after FMT from both DNBS and naïve donors. By contrast, no significant alterations of colon histology, permeability and monoamines levels were detected. Finally, following FMT from healthy donors to DNBS-treated animals, a counteraction of persistent visceral pain was achieved. Conclusions: The present results provide novel insights into the relationship between intestinal microbiota and visceral hypersensitivity, highlighting the therapeutic potential of microbiota modulation on persistent abdominal pain.