Miracle-dz/UniFAR-FTUs huggingface dataset:
Agro-Waste Extract Based Solvents: Emergence of Novel Green Solvent for the Design of Sustainable Processes in Catalysis and Organic Chemistry

Water extract of agro-waste ash (AWEs) has emerged as an efficient non-conventional solvent for practicing green reactions in catalysis and organic chemistry. The recent advancements made in the field of agro-waste-derived reaction media have witnessed a major advancement in reactions like Suzuki-Miyaura, Sonogashira, Dakin, Henry, peptide synthesis and ipso-hydroxylation. Traditionally, these reactions were conducted in conventional volatile organic solvents (VOCs) derived from non-renewable petroleum sources. During the course of these reactions AWEs displays distinct properties, such as, in situ base, homogeneous catalyst, nano reactor and reducing agent. This review summarizes the recent developments made in the application of AWEs in important reactions emphasizing on the key utilities and limitations in order to facilitate the further progress of this exciting area of research.
THE CONNOTATIVE MEANING OF CORONAVIRUS IMPACT EXPRESSIONS IN TEMPO ENGLISH MAGAZINE

This research focuses on analyzing coronavirus impact expressions in Tempo English magazine by using theory of connotative meaning from Leech as the grand theory and from Hook as the supporting theory. The focus is on two kinds of connotative meaning, namely, positive and negative connotative meaning. The objectivities of this research are to find out and to explain the use of negative and connotative meaning. The research is qualitative by which helps the researcher to describe the data being studied. In analyzing qualitative data, there are various kinds of methods, including qualitative content analysis. The use of a qualitative content analysis in this research is to know, to describe, and to analyze the use of connotative meaning applied in Tempo English magazine, April 14, 2020 edition. The result of this research is that Tempo English magazine uses many connotative meaning of coronavirus impact expressions. There are 15 expressions as negative connotative meaning and 12 expressions as positive connotative meaning. The findings indicates that the use negative connotative meaning of coronavirus impact expressions in Tempo English magazine are associated with threats in economy sector and associated with inappropriate government policies in an effort to prevent the spread of covid-19 as well as the impact of pandemic in many sectors. Besides, the use positive connotative meaning of coronavirus impact expressions in Tempo English magazine are associated with solutions to avoid spread of Covid-19 as well as solutions to avoid the impact of pandemic, and associated with the government’s responsibility and seriousness in dealing with preventing the spread of coronavirus as well as in dealing with the impact of coronavirus. Keywords: semantics; connotative meaning; coronavirus impact
Effects of catechol-O-methyltransferase on reinforcement learning and mesolimbic dopamine transmission

The enzyme catechol-O-methyltransferase (COMT) plays an important role in the breakdown of dopamine in prefrontal cortex (PFC), but relatively little is known about its effects on nucleus accumbens (NAc) dopamine function. In this study, a transgenic mouse model of the Val158Met polymorphism found in the human COMT gene was used (a) to investigate the effects of COMT on associative learning, a process dependent on NAc dopamine, and (b) to relate this to potential changes in phasic dopamine release in the NAc core in awake behaving mice. In the first set of experiments (Chapter 3), animals were trained on a Pavlovian auditory discrimination task with white noise (WN) and a constant tone (Tone) as the rewarded (CS+) or unrewarded (CS-) conditioned stimulus (assignment of stimulus type counterbalanced between subjects). Whilst both COMT-Met mice and wild-type (WT) littermates acquired the cue-reward associations, learning rates of COMT-Met mice were significantly higher than those of WTs when WN was the CS+ and significantly lower when Tone was the CS+. Further experiments revealed that this effect was mediated by an interaction between COMT genotype and the unconditioned salience of the CS+, an effect that persisted even in the absence of a CS- (Chapter4). Cue-dependency of learning could not be explained by NAc dopamine release in response to auditory stimuli, as both genotypes showed similar response patterns as measured by fast-scan cyclic voltammetry at the start of training (Chapter 6). In a separate experiment, COMT-Met mice showed greater phasic dopamine levels in NAc core evoked by unsignalled reward than WT mice, especially when rewards were smaller than expected. Taken together, behavioural studies of associative learning in COMT-Met and WT mice revealed that COMT genotype affected the impact unconditioned cue salience had on associative learning. Direct measurements of phasic release of dopamine in the NAc core further showed a task-dependent effect of COMT genotype on dopamine release.
DETERMINATION OF PHYSICOCHEMICAL PARAMETERS AND ANTIOXIDANT PROPERTIES OF RHUS JAVANICA LINN.

Objective: The present work presents determination of physicochemical parameters of the leaves and the antioxidant properties of methanolic extract of leaves, fruits and bark of Rhus javanica L. Methods: The leaves of Rhus javanica L. was used for the determination of physicochemical parameters using standard protocol. The leaves, fruits and bark of the plant were extracted successively with petroleum ether, chloroform and methanol. The methanolic extracts were used to determine the antioxidant properties. The radical scavenging activity was determined with different methods like DPPH, hydrogen peroxide and hydroxyl radical scavenging assay. The reducing power was determined by reduction of ferric cyanide complex (Fe3+) to the ferrous cyanide form (Fe2+). Total phenols and flavonoids concentrations were analysed using Folin-Ciocalteu’s and aluminium chloride methods. Results: The moisture content, extractive value (alcohol and water), total ash, acid insoluble ash and water-soluble ash were 0.29%, 24.502%, 19.314%, 8.2%, 0.45% and 4.75% respectively. The fruit extract exhibits the highest scavenging activity presented as percentage inhibition, 75.00±0.89% (100 μg/ml) for DPPH free radical and 74.7±0.68% (100 μg/ml) for hydrogen peroxide radical. The standard BHT (100 μg/ml) shows 78.5±0.43% inhibition for DPPH free radical and 82.5±0.50% inhibition for hydrogen peroxide free radical. The leaves show the highest hydroxyl radical scavenging activity, 65.00±0.40% (100 μg/ml) while the standard Ascorbic acid exhibit 73.81±0.61% (100 μg/ml) inhibition. The highest reducing power was shown by the bark extract but lower than the standard Ascorbic acid. The bark extract shows the highest phenolic and flavonoid content 49.86 mg GAE/g and 19.38 mg QE/g respectively of the dry weight of the extract. Conclusion: The results of quantitative pharmacognostic parameters in this study is expected to be useful for setting standards for correct identification of the plant. The radical scavenging properties, total phenolic and flavonoid content also suggest that the extracts provide substantial antioxidant activities.
Imbalanced data classification using support vector machine based on simulated annealing for enhancing penalty parameter

For pattern cataloguing and regression issues, the support vector machine (SVM) is an eminent and computationally prevailing machine learning method. It’s been effectively addressing several concrete issues across an extensive gamut of domains. SVM possesses a key aspect called penalty factor C. The choice of these aspects has a substantial impact on the classification precision of SVM as unsuitable parameter settings might drive substandard classification outcomes. Penalty factor C is required to achieve an adequate trade-off between classification errors and generalisation performance. Hence, formulating an SVM model having appropriate performance requires parameter optimisation. The simulated annealing (SA) algorithm is employed to formulate a hybrid method for evaluating SVM parameters. Additionally, the intent is to enhance system efficacy to obtain the optimal penalty parameter and balance classification performance at the same time. Our experiments with many UCI datasets indicate that the recommended technique could attain enhanced classification precision.
Antarctic snow-covered sea ice topography derivation from TanDEM-X using polarimetric SAR interferometry

Abstract. Single-pass interferometric synthetic aperture radar (InSAR) enables the possibility for sea ice topographic retrieval despite the inherent dynamics of sea ice. InSAR digital elevation models (DEM) are measuring the radar scattering centre height. The height bias induced by the penetration of electromagnetic waves into snow and ice leads to inaccuracies of the InSAR DEM, especially for multi-year sea ice with snow 5 cover. In this study, an elevation difference between the satellite-measured InSAR DEM and the airborne-measured optical DEM is observed from a coordinated campaign over the western Weddell Sea in Antarctica. The objective is to correct the penetration bias and generate a precise sea ice topographic map from the single-pass InSAR data. With the potential of retrieving sea ice geophysical information by the polarimetric-interferometry (Pol-InSAR) technique, a two-layer plus volume model is proposed to represent the sea ice vertical structure and its scattering mechanisms. Furthermore, a simplified version of the model is derived, to allow its inversion with limited a priori knowledge, which is then applied to a topographic retrieval scheme. The model-retrieved performance is validated with the optical DEM of the sea ice topography, showing an excellent performance with root-mean-square error as low as 0.22 m. The experiments are performed across four polarizations: HH, VV, Pauli-1 (HH+VV), and Pauli-2 (HH-VV), indicating the polarization-independent volume scattering property of the sea ice in the investigated co-polarized data.
Uses of the Novel Small Peptide, KTH-222, in Treating Human Pancreatic Cancer: Evaluation of Different Treatment Regimens using a Mouse Xenograft Model

the effects of these peptides on treating cancer in humans has not well-explored, metastases the of ANP and ABSTRACT KTH-222 is a novel, 8-amino acid length peptide derived from atrial natriuretic peptide and a group of related peptides that reduce cancer cell growth. It was previously reported that KTH-222 reduces the rate of tumor growth and prolongs survival in mice implanted with MIA PaCa-2 human pancreatic cancer cells more effectively than gemcitabine when used as primary therapy. The goal of the present study is to explore the effectiveness of KTH-222 for treating pancreatic cancer when used in two other roles: combination primary therapy and salvage therapy. Female athymic nude mice were inoculated subcutaneously in the right flank with MIA-PaCa-2 tumor cells. KTH-222 was administered as part of a combination primary therapy together with gemcitabine and paclitaxel or as a salvage therapy. When used in combination with gemcitabine and paclitaxel as primary therapy, KTH-222 further reduced tumor volume, although it did not extend the interval until terminal tumor volume was reached. When used a salvage therapy, KTH-222 did extend the interval until terminal tumor volume was reached and, in one case, produced a sustained regression in tumor growth. The results of this study, together with earlier results, suggest that KTH-222 could be used in several different ways in the treatment of pancreatic cancer. Small KTH-222, Treatment Mouse Xenograft Model.
Effect of NH2OH on Etching Characteristics of Si{100} in KOH Solution

Potassium hydroxide (KOH) and tetramethylammonium hydroxide (TMAH) are most extensively used etchants for wet anisotropic etching process. Amongst these two etchants, KOH is a low cost etchant and provides high etch rate anisotropy between Si{111} and Si{100} planes. Increasing the etch rate is an important research problem for both academic and industrial applications. In this research, we study the effect of hydroxylamine (NH2OH) in 20 wt% KOH on the etching characteristics of Si{100}. This type of wafer orientation (i.e. Si{100}) is selected owing to its popularity in fabricating planar semiconductor devices (e.g. diode, transistor, complementary metal oxide semiconductor (CMOS), etc.) and MEMS components (e.g. cantilever, diaphragm, etc.). A systematic parametric analysis of various concentrations of hydroxylamine (from 0 to 20% in step of 5%) added 20 wt% KOH is carried out and its effect on the etching characteristics is discussed. We have found that the etch rate of Si{100} is increased threefold when 15% NH2OH is added to 20 wt% KOH solution. Additionally, this concentration also exhibits improved etching characteristics including undercutting rate, etch selectivity, and surface morphology. We have obtained improved etching characteristics when NH2OH is added to 20 wt% KOH. Due to an improvement in etching characteristics, present research is very useful to pave the path toward application in microfabrication industries as well as academic research laboratories.
Drought Cascade in the Terrestrial Water Cycle: Evidence From Remote Sensing

Droughts are complex phenomena that typically evolve slowly over time. They originate through interactions between the atmosphere and the land surface, but show delayed evolution as they cascade through soil moisture, surface, and groundwater supplies, sometimes resulting in prolonged and insidious impacts on regional hydrological systems. While previous studies have observed this cascade phenomenon at select in situ locations, the temporal characteristics of drought cascades have never been quantitatively estimated at large scales using primarily remote sensing techniques. Here, we use satellite observations of vapor pressure deficit, Precipitation, and terrestrial water storage, and reanalysis‐based estimates of column soil moisture to characterize cascade phenomena for four major US drought case studies. By mapping the time evolution through this suite of variables, we observe large‐scale behavior generally consistent with theory and are able to quantify emergent patterns in the evolution of drought signals that may support improved future predictability.
The Reconstruction of the Author’s Movement Through the Text, or How to Encode Keystroke Logged Writing Processes in TEI-XML

This essay demonstrates how the use of the keystroke logging tool Inputlog allows for a fine-grained analysis of literary writing processes. But before the writing process can be studied, the keystroke logging data needs to be transformed into an output that is suitable for a textual genetic analysis. For this purpose, this essay investigates the potential of combining text with keystroke logging data in TEI-conformant XML. Besides discussing how revisions can be specified in the encoding, the author asks herself how traces of digital writing processes differ from analogue traces (and, taking it one step further, how keystroke logging can be used to record more details about the genesis of a text), what kind of decisions need to be made when encoding keystroke logging data, and how the peculiarities of digital authorship leave their mark on its encoding — as well as on the interpretation and argumentation that underlies the transcription. This will demonstrate that the level of detail that is recorded in keystroke logging data requires us to consider the way in which the text was typed when we design our encoding schemas. The goal of the TEI-XML encoding of the keystroke logging data is to provide transcriptions of writing processes that could be used to analyse (the sequence of) revisions and text production in each logged writing session in relation to their specific location in the text.
AllFactory: An Aquaponics 4.0 Transdisciplinary Educational and Applied Research Learning Factory at the University of Alberta

Learning factories have presented an effective environment to develop theoretical and practical knowledge and skills, especially in engineering and technologies. It supports many of the challenges that engineering education faces regarding teaching, training, skills development, and learning novel technologies brought by the Industry 4.0 paradigm. However, most current learning factories focus on conventional manufacturing environments, i.e., CNC machining, additive manufacturing, or assembly processes, limiting the impact of the learning factory concept in engineering education and transdisciplinary areas focusing on the frontiers between sciences and engineering. This paper introduces the 'AllFactory' (Aquaponics 4.0 Learning Factory) developed at the University of Alberta, focusing on transdisciplinary engineering problems and Industry 4.0 implementation in vertical indoor farming (known as Aquaponics 4.0). The elements, systems, processes, and technology used are presented and the expected research, education and learning, and future training outcomes.
Evaluating Precipitation Errors Using the Environmentally Conditioned Intensity‐Frequency Decomposition Method

A fundamental issue when evaluating the simulation of precipitation is the difficulty of quantifying specific sources of errors and recognizing compensation of errors. We assess how well a large ensemble of high‐resolution simulations represents the precipitation associated with strong cyclones. We propose a framework to breakdown precipitation errors according to different dynamical (vertical velocity) and thermodynamical (vertically integrated water vapor) regimes and the frequency and intensity of precipitation. This approach approximates the error in the total precipitation of each regime as the sum of three terms describing errors in the large‐scale environmental conditions, the frequency of precipitation and its intensity. We show that simulations produce precipitation too often, that its intensity is too weak, that errors are larger for weak than for strong dynamical forcing and that biases in the vertically integrated water vapor can be large. Using the error breakdown presented above, we define four new error metrics differing on the degree to which they include the compensation of errors. We show that convection‐permitting simulations consistently improve the simulation of precipitation compared to coarser‐resolution simulations using parameterized convection, and that these improvements are revealed by our new approach but not by traditional metrics which can be affected by compensating errors. These results suggest that convection‐permitting models are more likely to produce better results for the right reasons. We conclude that the novel decomposition and error metrics presented in this study give a useful framework that provides physical insights about the sources of errors and a reliable quantification of errors.
Study of flow and combustion characteristics of optimized low swirl premixed nozzle for micro gas turbine

ABSTRACT With the micro gas turbine is used more and more widely, the operating conditions become complex and fluctuating which impacts the environmental friendliness of combustor. In this work, a low swirl premixed nozzle (LSPN) is designed based on the original premixed nozzle (OPN) of a 60 kW micro gas turbine, in order to adapt the flexibility of load and operating environment by improving the mixing performance of fuel and air. The flow and combustion characteristics of LSPN and OPN fed by natural gas is numerically studied using the standard k-ε model and the combined Finite Rate Chemistry/Eddy Dissipation Model (FRC/EDM) under various operating conditions. The results show that the mixing performance of air and fuel in LSPN has been improved. The flow unmixedness in LSPN is always smaller than OPN, and it is 41.74% lower at the outlet of the nozzle. While the equivalence ratio decreases, the mean temperature in the combustor decline. Furthermore, under the majority of operating conditions, the emission performance of LSPN is better than OPN.
Phase Envelopes for Reservoir Fluids with Asphaltene Onset Lines: An Integral Computation Strategy for Complex Combinations of Two- and Three-Phase Behaviors

Despite the fact that many publications have dealt with asphaltene onset pressure (AOP) lines during the last 2 or 3 decades, no explicit method for tracing these and other related boundaries from equation of state (EoS) models has been proposed in the literature. In this work, a new integral algorithmic strategy for the construction of complete phase envelope diagrams based on an EoS is presented. The method of Michelsen for tracing two-phase boundaries is used, while for three-phase saturation lines, an equivalent method was designed, including a second set of K factors and a phase fraction as extra independent variables. The double-saturation point is defined, and its importance is highlighted, from both phase behavior and algorithmic perspectives. Specific issues as recognition of unstable segments, such as false bubble curves, are also discussed. Three different fluids from the literature are taken as case studies to illustrate the application of the proposed strategy and discuss different types of b.
Clinical Investigation and Molecular Diagnosis of Microsporidian Enterocytozoon hepatopenaei (EHP) in Shrimps from Selangor, Malaysia

In recent years, shrimp aquaculture production in Southeast Asia countries was highly infected by, microsporidian parasite, EHP. Recently, shrimps from farms located in Selangor were encountering growth retardation and it highly concerned us to carry out a clinical investigation in both farms (Location A and B). EHP infected P. monodon samples were collected from both locations. A total of 43 shrimp samples were collected and diagnosed via PCR using the 18S rRNA gene. Environmental parameters were found relatively higher than the advised values. It indicates that the farms are highly infected and toxic which had led to growth retardation and mortality. However, no significant differences were observed (except water temperatures) between the locations. The phylogenetic analysis expressed EHP detected from Malaysia formed in one clade and all the reference sequences clustered based on geographical continents (Asian-Latin America). Minimum-spanning network analysis shows that Malaysia samples Location A are genetically related with other Asian EHP samples even though the total positive rate of EHP infection was lower. However, samples from Location B were not clustered within the EHP clade and shows the highest genetic variance within Malaysia and among Asian samples. This preliminary investigation results confirmed the incidence of EHP outbreak in Malaysia.
Incorporating MNL Model into Random Forest for Travel Mode Detection

Mode choice models have been used widely to forecast the relative probabilities of using available travel modes. These depend on mode-related and traveler-related characteristics. On the other hand, smartphones are increasingly being used to collect sensors’ data relating to trips made after selection of a suitable mode. Such sensors’ data may be correlated with decision-making process of travelers regarding travel mode selection. Discrete Choice Modelling is used to simulate this decision-making process by computing utilities of various travel alternatives, and then calculating their respective probabilities of being selected. In this paper, multinomial logit (MNL) mode choice model is utilized to enhance the prediction capacity of supervised learning algorithm i.e. Weighted Random Forest. To make the procedure less energy-intensive, GPS data was used only to locate the origin and destination of any trip, to be incorporated in mode choice model. Afterwards only accelerometer data was utilized in feature selection for the learning algorithm. One tenth of the classified data was used to train the algorithm whereas rest was used to test it. Results suggested that with incorporation of MNL, the overall prediction accuracy of learning algorithm was increased from 93.75% to 99.08%.