Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelo matemático simplificado para determinar a capacidade erosiva de um escoamento lamoso em regime laminar pulsante

RESUMO Neste artigo foi proposto um modelo matemático simplificado para inferir sobre a capacidade erosiva de um escoamento lamoso em regime laminar com presença de roll waves (efeito pulsante) na superfície livre. Na perspectiva de aplicação de resultados no contexto de desastres naturais (corridas de lama), foi determinada a evolução da velocidade média e da tensão de cisalhamento no fundo do canal. O modelo proposto é baseado nas equações de águas rasas e na reologia de Herschel-Bulkley, como representativa das lamas. A validação e a aplicação do modelo foram feitas a partir de dados experimentais de roll waves geradas em canal no laboratório confrontados com resultados de simulações numéricas. Os resultados obtidos mostram um incremento na capacidade erosiva, na presença de roll waves, da ordem de 12% em relação ao escoamento base, valor este em concordância com os 10% apontados por Ng e Mei (1994) para uma reologia mais simplificada.
Defining and Measuring Microblogging Sentiment Investors on Stock Market: A Literature Review

The purpose of this paper is to review microblogging sentiment investors on a literature-based. Focusing on defining and measuring online sentiment investors and understanding their impact on financial market behavior—using scholarly articles to analyze, define, and measure microblogging sentiment investors. All the articles have been classified to measure and underpinnings online investor sentiment theory and other determinants in psychological measure. We have used citation and bibliographic coupling analysis to provide a structure of microblogging sentiment investors. This research also investigates how microblogging forums have become an online platform for exchanging stock information among investors. This paper's result defines investor sentiment research development will further reveal new ways of measuring investor sentiment through big data social media on microblogging sentiment investors construct by identifying authors, cite, references, or keywords.
Inputs from wastewater treatment plant effluent influence the temporal variability of nutrient uptake in an intermittent stream

Effluents from wastewater treatment plants (WWTP) affect water chemistry and in-stream nutrient uptake capacity from receiving freshwaters, thus altering the amount and fate of nutrients exported. In Mediterranean regions, the dilution capacity of receiving streams to buffer the WWTP biogeochemical fingerprint can vary seasonally due to changes in hydrologic conditions. We assessed the temporal patterns and controls on nutrient uptake in an intermittent Mediterranean stream receiving WWTP effluent inputs. We compiled data on longitudinal profiles of ambient concentrations of dissolved inorganic nitrogen and phosphorus along a 800 m reach on 47 sampling dates between 2001 and 2017 that cover a wide range of hydrological conditions. Data were used to estimate net nutrient uptake in the receiving stream. Ammonium concentration decreased along the reach in 72% of dates, and these decreases were coupled with increases of either nitrite or nitrate. This phenomenon suggests that the stream acted as a hot spot of nitrification. Conversely, concentration of phosphorus did not show any longitudinal pattern in 75% of dates, suggesting that uptake and release processes for this element were commonly counterbalanced. Finally, ammonium net uptake decreased when the stream had a low dilution capacity, suggesting that excess of available nutrients associated with WWTP inputs control de temporal variation of the bioreactive capacity of the receiving streams. Overall, this study suggests that water management should consider the biogeochemical interplay between WWTP operation and the functioning of receiving streams as a strategy to improve stream water quality in urban landscapes.
Effect of Dust and Shadow on Performance of Solar Photovoltaic Modules: Experimental Analysis

This study presents an experimental performance of a solar photovoltaic module under clean, dust, and shadow conditions. It is found that there is a significant decrease in electrical power produced (40% in the case of dust panels and 80% in the case of shadow panels) and a decrease in efficiency of around 6% in the case with dust and 9% in the case with the shadow, as compared to the clean panel. From the results, it is clear that there is a substantial effect of a partial shadow than dust on the performance of the solar panel. This is due to the more obstruction of the sunlight by the shadowed area compared to the dust. The dust being finer particles for the given local experimental condition did not influence the panel than the shadow. The main outcome of this study is that the shadowing effect may cause more harm to the PV module than dust for the given experimental conditions. However, Further long-term studies on the effect of dust and shadow are needed to understand the effect on performance degradation and module life.
KINETIC ANALYSIS OF MANGANESE REMOVAL FROM MINE WATER- CONTINUOUS FLOW REACTION SYSTEM

Manganese removal from mine water by oxidation-precipitation often requires significant contact time and the use of a high pH-value for reaction stage. In order to avoid reactor over sizing or under sizing, kinetic data for manganese oxidation using mine water or very similar aqueous systems are needed. As the batch type tests can be difficult to control (e.g. to set a certain pH value during the start-up), the continuous flow reaction systems seem to be more reliable as the direct determination of reaction rate is possible in this case. Results obtained using manganese sulphate solution and real mine water are presented here. A third order reaction rate law was obtained, taking into account homogenous and heterogeneous mechanism.
Surface Engineering of Chemically Exfoliated MoS2 in a “Click”: How To Generate Versatile Multifunctional Transition Metal Dichalcogenides-Based Platforms

The interest for transition metal dichalcogenides (TMDs) as two-dimensional (2D) analogues of graphene is steadily growing along with the need of efficient and easy tunable protocols for their surface functionalization. This latter aspect holds a key role in the widespread application of TMDs in various technological fields and it represents the missing step to bridge the gap between the more popular C sp2-based networks and their inorganic counterparts. Although significant steps forward have already been made in the field of TMDs functionalization (particularly for MoS2), a rational approach to their surface engineering for the generation of 2D organic–inorganic hybrids capable to accommodate various molecules featured by orthogonal groups has not been reported yet. The paper paves the way toward a new frontier for “click” chemistry in material science. It describes the post-synthetic modification (PSM) of covalently decorated MoS2 nanosheets with phenylazido pendant arms and the successful application .
RESEARCH PROGRESS ON TITANIUM-CONTAINING ORGANIC–INORGANIC HYBRID PROTECTIVE COATINGS

In this paper, we summarize research progress on the design and preparation of organic–inorganic hybrid coating molecular networks, which are based on the basic theory of organic–inorganic hybrid network construction. Among them, we focus on expounding the essential problems of hydrolysis during sol preparation particularly. In addition, the status of research related to titanium-containing organic–inorganic hybrid protective coatings for the surfaces of lightweight alloys such as steel, aluminum and magnesium is introduced here.
On the Supportive Role of the Warnke Method in Improving the Phonological Competence of a Bilingual Girl

It is known that bilingualism may cause some specific problems with the articulation of sounds and errors in reading and writing when acquiring linguistic skills. The paper presents the results and conclusions related to the use of the Warnke method in improving the phonological competence of a bilingual girl aged seven, which learned two languages simultaneously (Polish and Italian) in her family environment. The case study method was used. The main objective was to assess the effectiveness of the first stage of the Warnke method in improving reading and writing skills in a bilingual child. The Warnke method focuses primarily on the diagnosis and training of the phonological competence and was based on the following assumptions: (a) the automation of hearing, vision, and motor functions can be improved at the level of brain activity; (b) the development and automation of phonological analysis and synthesis are based on the cooperation of the brain hemispheres. The preliminary and final results of the diagnosis obtained for basic brain functions (visual, motor, and auditory) and the level of literacy skills were analyzed and compared. The reading and writing abilities, before and after training with the Warnke method, were assessed using the symptomatic tests. The obtained results showed that during and after training with the method, there was noticeable progress in eight basic functions (visual, motor and auditory). Improvement was also seen in the following areas: knowledge of letters; pairing phonemes with letters; technique, speed, and fluency of reading and writing. The quality of reading and writing was verified and confirmed by symptomatic tests. The conducted study suggests that the observed learning disabilities were caused by deficits in the central processing functions, which resulted in a lower level of phonological skills.
Use of Dynamic Event Trees and Deep Learning for Real-Time Emergency Planning in Power Plant Operation

Abstract An initiating event that disrupts regular nuclear power plant (NPP) operation can result in a variety of different scenarios as time progresses depending on the response of standby safety systems and operator actions to bring the plant to a safe, stable state, or the uncertainties in accident phenomenology. Depending on the severity of the accident and potential magnitude of release of radioactive material into the environment, off-site emergency response such as evacuation may be warranted. An approach that could be used for real-time emergency guidance to support the declaration of a site emergency and to guide off-site response is presented using observable plant data in the early stages of a severe accident. The approach is based on the simulation of the possible NPP behavior following an initiating event and projects the likelihood of different levels of off-site release of radionuclides from the plant using deep learning (DL) techniques. Training of the DL process is accomplished using results of a large number of scenarios generated with the Analysis of Dynamic Accident Progression Trees/MELCOR/Radiological Assessment System for Consequence Analysis (RASCAL) computer codes to simulate the variety of possible consequences following a station blackout event (similar to the Fukushima accident) for a large pressurized water reactor. The ability of the model to predict the likelihood of different levels of consequences is assessed using a separate test set of MELCOR/RASCAL calculations.
First-Principles Modeling in Heterogeneous Electrocatalysis

The last decade has witnessed tremendous progress in the development of computer simulation based on quantum mechanical description of the interactions between electrons and between electrons and atomic nuclei with electrode potentials taken into account–promoting the possibility to model electrocatalytic reactions. The cornerstone of this development was laid by the widely used computational hydrogen electrode method which involves a posteriori correction of standard constant charge first principles studies in solvent environment. The description of this technique and its contribution to our effort to understand electrocatalytic reactions on the active sites of metal-based nanoparticles are reviewed. The pathways and energetics of the relevant elementary reactions are presented. We also discussed a recent attempt in the literature to account for the inflow and outflow of electrons from the electrode as electrochemical reactions proceed, which has been greatly assisted by the development of density functional theory within the grand canonical framework. Going beyond the computational hydrogen electrode method by explicit incorporation of electrode potential within the calculations permits access to more detailed insights without requiring extra computational burden.
CNN based sensor fusion method for real-time autonomous robotics systems

: Autonomous Robotic Systems (ARS) serve in many areas of daily life. The sensors have critical importance 3 for these systems. The sensor data obtained from the environment should be as accurate and reliable as possible and 4 correctly interpreted by the autonomous robot. Since sensors have advantages and disadvantages over each other they 5 should be used together to reduce errors. In this study, Convolutional Neural Network (CNN) based sensor fusion was 6 applied to ARS to contribute the autonomous driving. In a real-time application, a camera and LIDAR sensor were 7 tested with these networks. The novelty of this work is that the uniquely collected data set was trained in a new CNN 8 network and sensor fusion was performed between CNN layers. The results showed that CNN based sensor fusion process 9 had more eﬀective than the individual usage of the sensors on the ARS.
The Geosciences in Europe’s Urban Sustainability: Lessons from Glasgow and Beyond (CUSP) Tracking natural organic carbon in the River Clyde, UK, using glycerol dialkyl glycerol tetraethers

Surface sediments from a 100-km stretch of the River Clyde, UK, and its estuary were analysed for glycerol dialkyl glycerol tetraethers (GDGTs) to track the downstream changes in the source of organic matter (OM) and to evaluate the impact of urbanisation. Bacterial membrane lipids, named branched GDGTs (brGDGTs), produced in soils and rivers ranged from 1.6 to 58 mg g 1 organic carbon (OC) and the isoprenoid GDGT crenarchaeol, mainly from marine archaea, ranged from 0.01 to 42 mg g 1 OC. The highest brGDGT concentrations were in the upper river, in Glasgow city and in the outer estuary, suggesting higher soil-derived OM input. By contrast, crenarchaeol concentrations gradually increased from the tidal weir in Glasgow towards the sea. This spatial distribution of the tetraethers was reflected in the branched and isoprenoid tetraether (BIT) index, a proxy for soil versus marine carbon. The highest BIT values (1.0) occurred upstream, estuarine values ranged from 0.9 to 0.6 and the lowest values (0.4) were found in the outer estuary. An independent proxy for soil-derived OM, stable carbon isotope (dC) values, showed a comparable decrease in terrigenous OM contribution towards the sea, but was more variable compared to the BIT. Conversely, carbon/nitrogen (C/N) showed a constant trend, suggesting that it is not a reliable indicator of OM source in the Clyde. Neither BIT, dC nor C/N were able to reflect the input of urban effluents from Glasgow.
Effects of Different Fermented Feeds on Production Performance, Cecal Microorganisms, and Intestinal Immunity of Laying Hens

Simple Summary Fermented feed exerts beneficial effects on intestinal microorganisms, host health, and production performance. However, the effect of fermented feed on laying hens is uncertain due to the different types of inoculated probiotics, fermentation substrates, and fermentation technology. Hence, this experiment was conducted to investigate the effects of fermented feed with different compound strains on the performance and intestinal health of laying hens. Supplement fermented feed reduced the feed conversion ratio and promoted egg quality. Both dietary treatment (fermented feed A produced Bacillus subtilis, Lactobacillus, and Yeast and fermented feed B produced by C. butyricum and L. salivarius) influenced intestinal immunity and regulated cecal microbial structure. This may be because the metabolites of microorganisms in fermented feed and the reduced pH value inhibited the colonization of harmful bacteria, improved the intestinal morphology, and then had a positive impact on the production performance and albumen quality of laying hens. Abstract This experiment was conducted to investigate the effects of different compound probiotics on the performance, cecal microflora, and intestinal immunity of laying hens. A total of 270 Jing Fen No.6 (22-week-old) were randomly divided into 3 groups: basal diet (CON); basal diet supplemented with 6% fermented feed A by Bacillus subtilis, Lactobacillus, and Yeast (FA); and with 6% fermented feed B by C. butyricum and L. salivarius (FB). Phytic acid, trypsin inhibitor, β-glucan concentrations, and pH value in fermented feed were lower than the CON group (p < 0.05). The feed conversion ratio (FCR) in the experimental groups was decreased, while albumen height and Haugh unit were increased, compared with the CON group (p < 0.05). Fermented feed could upregulate the expression of the signal pathway (TLR4/MyD88/NF-κB) to inhibit mRNA expression of pro-inflammatory cytokines (p < 0.05). Fermented feed promoted the level of Romboutsia (in the FA group) Butyricicoccus (in the FB group), and other beneficial bacteria, and reduced opportunistic pathogens, such as Enterocooccus (p < 0.05). Spearman’s correlations showed that the above bacteria were closely related to albumen height and intestinal immunity. In summary, fermented feed can decrease the feed conversion ratio, and improve the performance and intestinal immunity of laying hens, which may be related to the improvement of the cecal microflora structure.
Comprehensive Understanding of Roller Milling on the Physicochemical Properties of Red Lentil and Yellow Pea Flours

The development of convenience foods by incorporating nutrient-rich pulses such as peas and lentils will tremendously alter the future of pulse and cereal industries. However, these pulses should be size-reduced before being incorporated into many food products. Therefore, an attempt was made to adapt roller mill settings to produce de-husked yellow pea and red lentil flours. The milling flowsheets unique to yellow peas and red lentils were developed in producing small, medium, and large flours with maximum yield and flour quality. This study also investigated the differences in chemical composition, physical characteristics, and particle size distributions of the resultant six flour fractions. The kernel dimensions and physicochemical properties of the whole yellow pea and red lentils were also studied to develop customized mill settings. Overall, the mill settings had a significant effect on the physical properties of different particle-sized flours. The geometric mean diameters of different particle-sized red lentil flours were 56.05 μm (small), 67.01 μm (medium), and 97.17 μm (large), while for yellow pea flours they were 41.38 μm (small), 60.81 μm (medium), and 98.31 μm (large). The particle size distribution of all the flour types showed a bimodal distribution, except for the small-sized yellow pea flour. For both the pulse types, slightly more than 50% flour was approximately sizing 50 μm, 75 μm, and 100 μm for small, medium, and large settings, respectively. The chemical composition of the flour types remained practically the same for different-sized flours, fulfilling the objective of this current study. The damaged starch values for red lentil and yellow pea flour types increased with a decrease in flour particle size. Based on the Hausner’s ratios, the flowability of large-sized flour of red lentils could be described as passable; however, all the remaining five flour types were indicated as either poor or very poor. The findings of this study assist the millers to adapt yellow pea and red lentil milling technologies with minor modifications to the existing facilities. The study also helps in boosting the production of various baking products using pulse and wheat flour blends to enhance their nutritional quality.
Classification based levodopamine response prediction in parkinson’s disorder

ABSTRACT Parkinson’s disease (PD) is a progressive neurodegenerative disorder that affects a sizable fraction of the population and degrades the quality of life. Levodopamine (L-Dopa) is the first-line treatment drug for PD and remains ubiquitously used. However, the drug response prediction of L-Dopa is still an exigent task and there is an unresolved absence of any substantial biomarkers for a robust prediction of L-Dopa response for a robust prediction of L-Dopa response in Parkinson’s disease. The present study intends to develop a robust prediction model to predict the L-Dopa drug response in PD using machine learning approaches. This work intended to utilize the MJFF Levodopa Response Study data of Parkinson’s subjects with conclusive pre-clinical and clinical assessments for resolving the significantly impending task of drug response prediction. The problem was identified as a classification task which employed four different supervised machine learning classification algorithms for data analysis and predictive learning. The underlying task of predictive classification of drug response classified the responders as “good” and “bad,” based on comprehensive analysis on the selected feature space which identified the participants with an improvement in the symptoms as “good” responders and the ones with degraded or no improvement in the symptoms as “bad” responders. The decision tree’s (classification and regression tree) classification accuracy was 88.89% (area under receiver operating characteristics curve 0.9) for predicting levodopamine response. The presence or absence of the symptoms along with Unified Parkinson’s Disease Rating Scale (UPDRS) scores and Hoehn and Yahr scale (H and Y) scores were recognized as the most distinguishing feature subset. The research stipulates the required preliminary evidence to the adaptive advancement of decision trees as an illuminating technique that can facilitate the prediction of the drug treatment response for Parkinson’s disorder, however, an extended effort is necessary to provide efficient predictive performance.
Discovery of Potent Antiproliferative Agents Targeting EGFR Tyrosine Kinase Based on the Pyrido[3',2':4,5]thieno[3,2-d]pyrimidin-4-amine Scaffold.

A series of pyridothieno[3,2-d]pyrimidin-4-amines was designed and synthesized as congeners to the classical 4-anilinoquinazolines as ATP-competitive epidermal growth factor receptor (EGFR) inhibitors. Compound 5a exhibited the most potent and selective inhibitory activity against EGFR with an IC50 value of 36.7 nM. Moreover, compounds 4b and 5a showed remarkable cell growth inhibition against leukemia, central nervous system cancer, and non-small cell lung cancer cell lines that overexpress EGFR, with growth inhibition of 50% (GI50) values of around 10 nM in the full U.S. National Cancer Institute 60 cell panel assay. Cell cycle studies indicated that compounds 4b and 5a induced significant cell cycle arrest in the S-phase and G0/G1, respectively, in addition to boosting P27(kip) expression. Compound 5a did not alter the viability of placental trophoblasts, which reflects its safety for normal cells. The standard COMPARE analyses demonstrated considerable correlation levels between compounds 4b and 5a and erlotinib, with pyridinium chlorochromate (PCC) values of 0.707 and 0.727, respectively.