Miracle-dz/UniFAR-FTUs huggingface dataset:
Formation and Measurement of Ozone and Nitric Acid in a High Voltage DC Negative Metallic Point-to-Aqueous-Plane Continuous Corona Reactor

Abstract Ozone and nitric acid were formed when high voltage was applied across a metal (stainless steel) point and liquid (water) plane at atmospheric pressure and room temperature. Ozone was observed in the gas-phase and nitric acid in the liquid-phase. The gas-phase concentration of ozone reached a power-dependent steady-state whereas the nitric acid concentration in solution increased linearly with time. The rate of formation of ozone increased with increasing current. Aqueous solutions of potassium iodide were exposed to corona discharge to determine the amount of gas-phase ozone available to oxidize liquid-phase solutes. The result of the test with KI suggests that ozone is the major oxidant in the solution phase. An optimum point-to-plane distance of 1.5 cm was observed for both ozone production and solution phase oxidation. Corona discharge effectively destroyed both non-volatile organic compounds such as methylene blue and volatile compounds such as carbon tetrachloride in aqueous solutions.
Cationic montmorillonite: Preparation and synergy with anionic polymer in filler flocculation

La bentonite activee ou montmorillonite anionique, en tant que partie des systemes de retention des microparticules, a ete bien acceptee par l'industrie des pâtes et papiers. Toutefois, la modification de la montmorillonite en microparticules cationiques pour la floculation ou la retention a rarement fait l'objet d'un rapport. Dans le present rapport, deux types de montmorillonite cationique ont ete synthetises en incorporant des groupes amines primaires ou quaternaires dans les couches de montmorillonite. La montmorillonite modifiee a ete caracterisee a l'aide de la diffraction des rayons X et de la titration par charge Mutek. Les resultats ont demontre que le reactif 1,12-diaminododecane est en effet insere dans la montmorillonite. De plus, le polyaminoamide obtenu de la reaction du diethylenetriamine avec l'acide adipique est lie dans la montmorillonite. La densite de charge de la montmorillonite modifiee a encore ete augmentee en faisant reagir du 2-(chloromethyl)oxirane avec de la polyaminoamide liante. En conjonction avec un polymere anionique a base de polyacrylamide, la montmorillonite modifiee a ameliore la floculation du kaolin.
AN EMPIRICAL INVESTIGTION OF SERVICE QUALITY IMPACTS ON PASSENGERS’ SATISFACTION IN AIR INDUSTRY

Purpose: This paper aims to address how passengers’ satisfaction can be measured in relative to quality services provided by airlines using SERVQUAL methodology. Research Approach: An exploratory approach is applied to identify those key variables that influence passengers’ satisfaction. Empirical Study has applied in reference to Egypt Air as a case study. A questionnaire is developed for collecting the required data and samples and the SERVQUAL methodology (Reliability, Tangibility, Response, Assurance, and Empathy) is used as an instrument to determine the effectiveness of service development and improvement initiatives in targeted dimensions. Findings and Originality: The main findings conclude that passengers are mostly concerned with the Assurance and Response dimensions, while Reliability is the most dominant dimension affecting the service quality in Egypt Air. Research Impact: Passenger’s satisfaction service arises when a company can provide passengers with benefits that exceed their expectations. A number of studies have conducted in service quality related theories and methods in this field. Few studies were examined and prioritised those dimensions that affect passengers’ satisfaction. Practical Impact: Service quality is one of the most important determinants, which affect the world competitiveness of the aviation industry. This paper aims to help decision makers at Egypt Air to identify the key dimensions that influence the service quality level provided.
Anaerobic-Aerobic Wastewater Treatment System Suitable for Variable Population in Coastal Areas: The Ananox® Process

Results of a four year experience on a combined anaerobic-anoxic-oxic municipal wastewater treatment process - named ANANOX® - are presented. This process demonstrated to be highly efficient, with 89.6% CODt, 89.2% TSS and 81.2% N removal, and a sludge production of only 0.2 kg TSS.kg COD removed−1, a value which is roughly 50% less if compared with traditional nitrification/denitrification processes. Energy needs are reduced with respect to aerobic plants, because the anaerobic COD degradation plays a major role, particularly during the warm season. Sulphates play a very significant role in the process because, after being reduced in the anaerobic step, where they give a contribution to the organic matter degradation, they are reoxidized in the anoxic step by nitrates, reducing the organic matter need for denitrification. Due to the high dependence of efficiency on temperature, the system proposed has advantageous uses for sewage treatment particularly in warm climates and in tourist and recreational areas where the population increases during the warm season.
Bispecific antibodies and their potential for cancer immunotherapy

3 Introduction . 4 Development . 5 Bispecific antibody targets & effects . 9 Bispecific antibody based medicine . 10 Catumaxomab . 10 Blinatumomab . 11 Emicizumab . 12 Flotetuzumab . 12 Discussion . 13 Future perspectives . 14 Conclusion . 14 Experience writing bachelor thesis . 14 Acknowledgements . 14 References . 15
A Critical Analysis of Conversational Floors In Post- Corona Digital Discourse Among Pakistani Students

This article intends to investigate conversational patterns of communication in multi-participant text-based digital discourse in the post-corona scenario. Much of ComputerMediated Communication (CMC) research motivated the researchers to understand how computer mediation and cellular phones influence human conversation. This research is inspired by the conversational floors discussed by Simpson (2005). In this present study, the researchers focus on the WhatsApp groups created for Academic purposes among English-major students in Pakistan after a sudden outbreak of coronavirus (henceforth, COVID-19). WhatsApp, primarily developed in 2009 as an informal instant chat app. It has widely been used by youth worldwide. Manufacturers of WhatsApp brought many changes in its format and introduced many new features over the years, i.e., written text messages, audio recordings, picture and video sharing, use of emoticons, document sharing, and status updating. Soon after realizing the liking of people for instant messaging application, WhatsApp launched 2.0 in its beta stage (Pahwa, 2019). People love the notion of using WhatsApp for sending messages via the internet instead of using the Operator's SMS plans by using just phone numbers. This is how WhatsApp replaced SMS and emails in many countries like Pakistan. In Pakistan, WhatsApp has been used for both formal and informal purposes. Consequently, right after the widespread of the novel coronavirus in March 2020 leading to the suspension of traditional classes, the university teachers and students in Pakistan moved to WhatsApp instantly to resume Academic Abstract: This study investigates the pattern of conversational floors of digital discourse in WhatsApp groups among Pakistan's students after the outbreak of Covid-19. Gender differences were also focused while generating digital discourse in the post corona scenario. Non-probability sampling techniques are used, as our research design is qualitative. Data is taken from universities of Punjab and is in the form of interviews and screenshots of students' five days chat in Whatsapp groups. Researchers chiefly focused on conversational floors, gender roles, and post corona discourse. Results show the effects of Covid-19 as students have problems involving in more meaningful conversations, and due to conflicts among members, a breakdown in communication occurs. The connection between argumentation and floor holding gives a deep analysis of the dynamics of online discussion. From the gender perspective, males maintained the central stage in noncollaborative discourse while there are almost equal or more active females in collaborative discourse.
Habitat structure mediates vulnerability to climate change through its effects on thermoregulatory behavior

Tropical ectotherms are thought to be especially vulnerable to climate change because they are thermal specialists, having evolved in aseasonal thermal environments. However, even within the tropics, habitat structure can influence opportunities for behavioral thermoregulation. Open (and edge) habitats likely promote more effective thermoregulation due to the high spatial heterogeneity of the thermal landscape, while forests are thermally homogenous and may constrain opportunities for behavioral buffering of environmental temperatures. Nevertheless, the ways in which behavior and physiology interact at local scales to influence the response to climate change are rarely investigated. We examined the thermal ecology and physiology of two lizard species that occupy distinct environments in the tropics. The brown anole lizard (Anolis sagrei) lives along forest edges in The Bahamas, whereas the Panamanian slender anole (Anolis apletophallus) lives under the canopy of mature forests in Panama. We combined detailed estimates of environmental variation, thermoregulatory behavior, and physiology to model the vulnerability of each of these species. Our projections suggest that forest‐dwelling slender anoles will experience severely reduced locomotor performance, activity time, and energy budgets as the climate warms over the coming century. Conversely, the forest‐edge‐dwelling brown anoles may use behavioral compensation in the face of warming, maintaining population viability for many decades. Our results indicate that local habitat variation, through its effects on behavior and physiology, is a major determinant of vulnerability to climate change. When attempting to predict the impacts of climate change on a given population, broad‐scale characteristics such as latitude may have limited predictive power.
Personalized Response Generation via Generative Split Memory Network

Despite the impressive successes of generation and dialogue systems, how to endow a text generation system with particular personality traits to deliver more personalized responses remains under-investigated. In this work, we look at how to generate personalized responses for questions on Reddit by utilizing personalized user profiles and posting histories. Specifically, we release an open-domain single-turn dialog dataset made up of 1.5M conversation pairs together with 300k profiles of users and related comments. We then propose a memory network to generate personalized responses in dialogue that utilizes a novel mechanism of splitting memories: one for user profile meta attributes and the other for user-generated information like comment histories. Experimental results show the quantitative and qualitative improvements of our simple split memory network model over the state-of-the-art response generation baselines.
Sighting survey procedures for abundance estimates of large whales in JARPA and JARPAII, and results for Antarctic minke whales

This paper provides an overview of the sighting surveys and analytical procedures for abundance estimates of large baleen whales under the non-lethal component of JARPA/JARPAII. The paper also summarizes the abundance estimates and abundance trends for Antarctic minke whales based on sighting data collected by JARPA and JARPAII surveys between 1989/90 and 2008/09.
Using Robust Ridge Regression Diagnostic Method to Handle Multicollinearity Caused High Leverage Points

Statistics practitioners depends on the Ordinary Least Squares (OLS) method to estimate the parameters because of its optimal properties and simplicity to Computation. However, the OLS estimator can be strongly affected by the existence of multicollinearity. Even though in the presence of multicollinearity the OLS estimate still remained unbiased. But the standard errors of the estimated parameter become inaccurate. In this paper, we proposed some alternative methods to estimate the parameters in presence of multiple high leverage points which cause the multicollinearity problem. The procedure used the ordinary least squares method to estimate the parameters as the initial followed by ridge regression estimator. We incorporated the robust Least Trimmed Squares (LTS) estimator to down weight the effects of multiple high leverage points which leads to the reduction of the effects of multicollinearity. The result seemed that the suggested method Ridge Least Trimmed Square (RLTS) gives a substantial improvement over the Ridge Regression.
Free Radical Scavenging Activity of Salvadora persica Linn.

This study comprises of in vitro antioxidant activity on hydro alcoholic (SPHA) and aqueous (SPW) extracts from shade dried roots of Salvadora persica L. by various models. Both SPHA and SPW extracts showed significantly high scavenging ability on 1,1-diphenyl2-picryl hydrazyl (DPPH) radical (87.60 and 72.29 % at 400 µg/mL), 2,2-azino bis(3-ethyl benzothiazoline-6-sulphonic acid (ABTS) radical (87.50 and 78.45 % at 500 µg/mL), superoxide nitroblue-tetrazoliun (NBT) radical (54.39 and 42.25 % at 600 µg/mL) and reduction of ferric ion solution (73.10 and 64.90 % at 500 µg/mL). Result indicates the powerful electron donating and radical scavenging activity of S. persica L. Hence both the extracts can be considered as effective chain breaking in vivo antioxidants.
Interpretability Rules: Jointly Bootstrapping a Neural Relation Extractorwith an Explanation Decoder

We introduce a method that transforms a rule-based relation extraction (RE) classifier into a neural one such that both interpretability and performance are achieved. Our approach jointly trains a RE classifier with a decoder that generates explanations for these extractions, using as sole supervision a set of rules that match these relations. Our evaluation on the TACRED dataset shows that our neural RE classifier outperforms the rule-based one we started from by 9 F1 points; our decoder generates explanations with a high BLEU score of over 90%; and, the joint learning improves the performance of both the classifier and decoder.
PERFORMANCE ANALYSIS and PREDICTION of NEPAL STOCK MARKET (NEPSE) for INVESTMENT DECISION using MACHINE LEARNING TECHNIQUES

Predicting behaviour of Stock Market is a challenging task. It is a hot topic in machine learning. It assists the investor to minimize the risk and fluctuation in Stock Market. Five years data from different Companies from different sectors stock information’s are analyzed. The information such as stock close price, volume of shares, interest rate, stock sentiments are main attributes and features for this research. Regression Machine Learning is applied for predicting the behaviour of Nepal Stock Exchange, NEPSE index which is totally a new concept in Nepal Stock Exchange. NEPSE is one and only stock exchange in Nepal to trade equity and stocks. There are at least 231 public companies currently listed in NEPSE index. In this research, the performance of Support Vector Regression Kernels (linear, polynomial and RBF) is used to evaluate predicting ability. The various performance parameters such as Moving Average, Interest Rate Correlation, and Stock news sentiment analysis are evaluated. Validation is performed by taking the different timeframe behavior of the stock and comparing the actual and predicted stock prices. The investment decision is made based on the investment strategy developed to allow minimum risk investment for investors. Machine learning predictions for stock market are rare in developing country like Nepal. Different attributes and parameter are taken as suitable to Nepal stock market which provides better prediction result. It ensures that investors invest the money with little or no risk and able to make profit.
Mediating Effect of Entrepreneurial Orientation between Intellectual Capital and Performance in Cultural Industry

There are plenty of empirical researches testifying association between intellectual capital, along with its components, and performance, but the conclusions were contradiction, and most researches are not focus on innovative industries, like cultural sector. This paper, with samples in Chinese cultural industry, attempted to reveal the mediating effects of entrepreneurial orientation (EO) between intellectual capital and performance, in order to introduce organizational culture as a transition or bridge between intellectual capital and performance. The purpose of this research is to direct Chinese cultural firms to successfully transfer strategic resources as intellectual capital into competitive advantages and performance, with the suitable entrepreneurial orientation.
Narratives and/or Mechanisms in the Explanation of the Origin of Eukaryotes

The arrival of eukaryotic cells, cells with a nucleus, is considered a major evolutionary event. Explaining the emergence of eukaryotes has been a subject of interest amongst biologists from the early 20th century to the present. This paper explores the form, rather than the content, of these explanations. I focus on two representative hypotheses: the phagotrophic theory from Tom Cavalier-Smith (first formulated in 1975) and the hydrogen hypothesis from Bill Martin (first formulated in 1998). I argue that these two explanations contain a combination of contingency, order, and sketchiness. I confront this state of affairs with epistemological tools that have been identified as useful for the explanation of unique events, namely “narrative explanations” and “ephemeral mechanisms”. From my case study, I argue that elements of each explanation can coexist at different stages of a given explanation. I also argue that both narratives and mechanistic explanations are unable to account for the sketchiness present in both Cavalier-Smith and Martin’s explanations. This paper, then, sheds light on the work needed to refine our understanding of (a) the relation between narrative and mechanistic explanations and (b) the explanation of unique events in historical sciences.
Fecal storage condition induces variations of microbial composition and differential interpretation of metagenomic analysis

Advances in metagenomics have facilitated population studies of associations between microbial compositions and host properties, but strategies to minimize biases in these population analyses are needed. However, the effects of storage conditions, including freezing and preservation buffer, on microbial populations in fecal samples have not been studied sufficiently. In this study, we investigated metagenomic differences between fecal samples stored in different conditions. We collected 46 fecal samples from patients with lung cancer. DNA quality and microbial composition within different storage Methods were compared throughout 16S rRNA sequencing and post analysis. DNA quality and sequencing results for two storage conditions (freezing and preservation in buffer) did not differ significantly, whereas microbial information was better preserved in buffer than by freezing. In a metagenomic analysis, we observed that the microbial compositional distance was small within the same storage condition. Taxonomic annotation revealed that many microbes differed in abundance between frozen and buffer-preserved feces. In particular, the abundances of Firmicutes and Bacteroidetes varied depending on storage conditions. Microbes belonging to these phyla differed, resulting in biases in population metagenomic analysis. We suggest that a unified storage Methods is requisite for accurate population metagenomic studies.
Model Predictive Control of Nonlinear MIMO Systems Based on Adaptive Orthogonal Polynomial Networks

This paper considers a new design of model predictive control based on specific models in the form of adaptive orthogonal polynomial networks, built around a specially tailored basis of generalized orthogonal functions. Polynomial model has a single layer structure and a smaller number of model parameters than classical neural networks, usually used for model predictive control design, leading to lower complexity and shorter calculation time. Desired property of adaptability of the model is achieved by using additional variable factors inside the orthogonal basis. The designed controller was applied in control of twin-rotor aero-dynamic system as a representative of nonlinear multiple input-multiple output systems and compared to the other state-of-the-art control algorithms.
Towards Generative Illustration of Text

Generative illustration of text is a task that can be viewed as related to both information visualisation and computational design. In this paper, we present an exploratory study towards text illustration, describing our experiments to visually represent data about characters, objects and emotions. We present our conclusions in regards to representing data using attributes such as colour, shape and position.
CONTRIBUTIONS TO THE STABILITY ANALYSIS OF EXPLICIT ENRICHED MESHFREE METHODS

Meshfree methods have certain advantages with respect to other more classical numerical methods. The Element Free Galerkin Method (EFGM), in particular, seems to offer a good performance when dealing with problems that include the treatment of large deformations together with the presence of strong or weak discontinuities to model cracks or material interfaces. A possible approach is to introduce the discontinuity to the discretization by adding special enrichment functions to the standard shape functions. The cracking particles method, based on EFGM, is an example of such approaches. When dealing with dynamic applications by means of explicit time integration schemes, a lumped mass matrix is necessary for an efficient numerical simulation. However, when the shape functions include discontinuous enrichment, studying the behavior of the critical time step becomes problematic. Moreover, it may tend to very small values, thus leading to computationally expensive simulations. In this work, we present some contributions to the study of these stability issues for an explicit enriched EFGM for oneand two-dimensional problems. Mecánica Computacional Vol XXIX, págs. 4781-4800 (artículo completo) Eduardo Dvorkin, Marcela Goldschmit, Mario Storti (Eds.) Buenos Aires, Argentina, 15-18 Noviembre 2010 Copyright © 2010 Asociación Argentina de Mecánica Computacional http://www.amcaonline. org.ar
Emotion Recognition using Convolution Neural Network

- Human computer interaction has been an important field of study. If computer could understand the feelings of humans, it can cater the services based on the feedback received. In this model we give the overview of the work done in the past related to Emotion Recognition using Facial expressions along with our approach towards solving the problem. The approaches used for facial expression include classifiers like Support Vector Machine