Miracle-dz/UniFAR-FTUs huggingface dataset:
Veblen Goods and Urban Distinction

A fundamental observation of 21st century cities is that they have become great centers of consumption. Some argue that their ability to generate consumer options is part and parcel of their success, particularly with regard to attracting highly-skilled human capital (Glaeser et al. 2001; Clark 2004; Handbury 2012; Diamond 2016). While extant studies of metropolitan consumer options lump them together as a large “amenities” variable associated with growth and skills, previous work has not highlighted the relationship between different types of consumer goods and the geographical location of where they are most consumed. These distinctions have implications for the relationship between consumerism and economic development both as an extension of the literature and in its practical application. In this paper, we seek to understand the geographic variations of consumer behavior. Using Consumer Expenditure Survey (CEX), we analyze how consumption differs across 21 major U.S. metropolitan areas, and the association between urban characteristics and consumption. We expand previous geographic analysis of consumption to include luxury goods that are socially visible (conspicuous consumption) and luxury goods that are not visible (inconspicuous consumption). Our analysis shows that conspicuous consumption is more sensitive to an urban context and the variables associated with cities influence status spending. Overall, results suggest that the conspicuous consumption of a household is positively associated with size, density, residential segregation, and regional specialization of eating and drinking places of the metropolitan area in which the household is located; it is negatively associated with the metropolitan area’s income inequality, income level of reference group, and housing cost. The results indicate that the importance of surrounding urban context in understanding what people consume, especially on status goods which often attain value through social visibility.
Aroma Composition and Sensory Quality of Cabernet Sauvignon Wines Fermented by Indigenous Saccharomyces cerevisiae Strains in the Eastern Base of the Helan Mountain, China

In order to improve the regional flavor peculiarity of Cabernet Sauvignon wine in the eastern base of the Helan Mountain, an Appellation of Origin in China, eight indigenous Saccharomyces cerevisiae strains were studied and evaluated. Compared to commercial control F15, indigenous N11424 present significant difference in the concentration of hexyl acetate, isoamyl acetate, ethyl hexanoate, diethyl succinate, and methyl benzoate, and the highest content of esters and least of alcohols were found in the wine made by N11424. By sensory analysis, N11424 wine was characterized with honey, pepper, as well as more fruit flavor, such as peach, cherry, clove, redcurrant, and blackcurrant. By odor activity value analysis, most of the high impact odorants were esters and the odor activity values of esters in most of indigenous strains were higher than in control F15.
Sarcasm Detection in English and Hindi Sentences

In the growing advancement of Natural Language Processing, sarcasm detection, a part of sentiment analysis is still a challenge. It is important to detect sarcasm in the statement, especially in open platforms where people are free to express themselves, to identify the correct intended meaning of the statement mentioned. Sarcasm is an important processing problem in the NLP field of sentiment analysis, as it serves as an interface between communicating humans and machines, also such sentences can change the polarity of a sentence, which might result in wrong sentiment analysis. Here, in this project, we going to work upon sarcasm detection in news headlines, which we can get online or in news articles. We made use of LSTM[1][4], an artificial recurrent neural network architecture used in the field of deep learning[8], built around our collected dataset. The model is trained over the pre-processed dataset, cleaned with the help of the nltk library enabling it to create word embeddings, remove stopwords, etc.
Socioeconomic Restructuring and Regional Change - Rethinking Growth in the European-Community

Scientific literature on the spatial effects of socioeconomic restructuring has tended to highlight three key components in what has been considered to be the emergence of a new territorial model: the significance of subnational units (regions and cities) as the genuine "post-Fordist" unit of analysis; the growing prominence of service activities in the configuration of new development areas; and, finally, either the "randomizing" of the location of growth poles or the appearance of the social dimension as a new determinant of growth. Most studies of spatial restructuring have been confined, however, to the theoretical dimension or to case study analyses. There is a lack of large empirical cross-sectional studies on the topic. In this article, I try to discern, using a cross-sectional method, the extent to which a new growth pattern is developing in the European Community (EC) and the influence of previously existing conditions in current territorial changes in wealth levels. I conclude that a flexible production spatial model is not yet the dominant pattern. Many characteristics of the mass production system are still evident in growth trends. Furthermore, this territorial restructuring is by no means a global and homogeneous process, since it implies different transformations in core, intermediate, and lagging areas.
TDA layer: Impact of persistent homology on the performance of Convolutional Neural Networks

In this work, we introduce the topological data analysis layer that estimates persistent homology on attributes extracted from convolutional layers for image classification. This method shows that topological information can be utilized to upgrade network performance. This work focuses on applying persistent images on the deep convolutional layer to learn topological features and also exploring the behavior of topological data analysis on various convolutional neural network architectures like sequential architecture and extended width architecture. Based on our empirical analysis, we exhibit the significance of topological data analysis on convolutional neural networks by attaining reliable scores on classification tasks on benchmark datasets.
Augmented Reality Adapted Book (AREmotion) Design as Emotional Expression Recognition Media for Children with Autistic Spectrum Disorders (ASD)

One of the Autism Spectrum Disorder (ASD) characteristics is their difficulty understanding other people's emotions. Their lack of skill of understanding emotion includes expression and appropriate emotional response for a certain situation. This paper proposes an adapted book that helps therapists and parents guide ASD children to learn facial emotional expression. The adapted book combined with video, animation, and Augmented Reality increases children with ASD at recognizing emotional expression. This research uses UserCentered Design (UCD) approach to design the AR Adapted Book application and design the social story to be observable in school and family environment. Based on usability testing tried on the application, the result shows that AREmotion has an average score of 82.73 percent, and the learnability aspect has the highest score of 86.7 percent. This preliminary usability testing proves that the design of the AREmotion application is ready to use in real implementation for children with ASD. Keywords—Autism Spectrum Disorder (ASD); adapted book; Augmented Reality (AR); User-Centered Design (UCD)
Syntheses, Crystal Structure, Spectroscopic, and Electrochemical Properties of a Novel Manganese Porphyrin

A new porphyrin complex, Mn(TBrPP)(DMF) (1) (H2TBrPP = meso-tetra(4-bromophenyl)porphyrin), has been obtained and characterized by single-crystal X-ray diffraction. The title complex is composed of Mn(TBrPP)(DMF) units, which are connected by multiform hydrogen bonds leading to a 3D supramolecular network structure. Furthermore, we investigated the properties of the compound by means of UV-vis spectra, infrared spectra, fluorescence spectra and cyclic voltammetry. These studies indicate that the substituent and metal ion have much effect on the properties of porphyrin compounds.
Prediction of thermodynamic properties of gas mixtures using the Peng-Robinson equation of state

This bachelor thesis summarizes basic thermodynamic concepts necessary for calculation of speed of sound in mixtures of real gases using the Peng Robinson equation of state. Moreover, two different group contribution methods are used to estimate thermodynamic properties of refrigerants C2F6 and C3F8. The analytical prediction of speed of sound in mixtures of N2 and C3F8 is then confronted with experimental data measured on sunar tube gas analyzer. Finally, the accuracy, sources of error and overall potential of this approach is discussed.
Dietary Acid Load and Its Interaction With IGF1 and IL6 Polymorphisms on Metabolic Traits Among Postmenopausal Women

Background: Despite considerable work has been done to understand the underlying disease mechanisms by elucidating its genetic etiology, still the pathophysiology of the metabolic syndrome remains unclear. While numerous studies have shown that Insulin growth factors-1 (IGF1), Interleukin-6 (IL-6) gene polymorphisms and high dietary acid load (DAL) were related to undesirable profile of metabolic traits; it remains controversial whether DAL is associated with genetic polymorphisms or risk of metabolic traits. Thus, the objective of this study was to explore the effects of DAL, IGF1 and IL6 gene polymorphisms and their potential diet-gene interactions on metabolic traits. Methods: A total of 211 community-dwelling postmenopausal women were recruited. DAL was estimated using potential renal acid load (PRAL). Blood was drawn for biochemical parameters and DNA was extracted and Agena® MassARRAY was used for genotyping analysis to identify the signaling of IGF1 (rs35767, rs7136446) and IL6 (rs1800796) polymorphisms. Interactions between diet and genetic polymorphisms were assessed using hierarchical multiple linear regression analysis.Results: Mean age of respondents was 67 ± 6.7 years. A total of 68.2%, 59.2%, 49.3%, 23.2%, 23.2% and 10.9% had elevated systolic blood pressure (SBP), hyperglycemia, excessive waist circumference (WC), diastolic blood pressure (DBP), triglycerides (TG) and low high-density lipoprotein (HDL), respectively. DAL was positively associated with FBG (β = 0.147, p < 0.05) and there was significant interaction effect between DAL and IL6 with SBP (β = 0.19, p = 0.041). On the other hand, there were no significant main effects of DAL and single nucleotide polymorphisms (SNPs) (rs35767, rs7136446 and rs1800796) on SBP, DBP, WC, TG or HDL and cholesterol ratio. There were also no significant interaction effects between DAL and IGF1 SNPs (rs35767 and rs7136446) on SBP, DBP and FBG, TG, HDL and cholesterol ratio. Conclusion: These findings did not support the interaction effects between DAL and IGF1 and IL6 SNPs (s35767, rs7136446 and rs1800796) on metabolic traits, except for SBP. Besides, higher DAL was associated with higher FBG, allowing us to postulate high DAL is a potential risk factor for diabetes. Future research delineating foods that contribute to high DAL, and how IL6 gene polymorphism influences the association with DAL on hypertension are warranted.
Investigation on the photocatalytic activity of chemically synthesized zirconium doped cadmium selenide nanoparticles for indigo carmine dye degradation under solar light irradiation

A simple chemical methodology has been adopted for the synthesis of zirconium (Zr) doped and un-doped cadmium selenide (CdSe) nanoparticles for the application towards photocatalytic degradation of indigo carmine (IC) dye under solar light irradiation. The as prepared Zr-CdSe (doped) and CdSe (un-doped) nanoparticles were characterized by ultraviolet visible spectroscopy (UV-vis), X-ray diffraction (XRD), Scanning electron microscopy (SEM) coupled with energy dispersive X-ray analysis (EDAX) and Transmission electron microscopy (TEM) studies. The inclusion of Zr ion into the CdSe nanoparticles matrix was confirmed by SEM-EDAX and XRD studies. TEM studies confirm the zirconium ions are uniformly doped over the CdSe surface. The photocatalytic degradation performance of Zr doped and un-doped CdSe nanoparticles were examined for the degradation of IC dye under solar light irradiation. The experimental results showed that the Zr doped CdSe possessed greater photocatalytic activity in comparison to un-doped CdSe. Photodegradation process parameters such as the initial concentration of the dye, as well as the amount of catalyst and time were investigated. The photocatalytic degradation rate was favored by a high concentration of solution in respect to Langmuir–Hinshelwood model.
Title : Generating polyphonic music using neural networks

The aim of this thesis is to explore new ways of generating unique polyphonic music using neural networks. Music generation, either in raw audio waveforms or discretely represented, is very interesting and under a heavy exploration in recent years. This thesis works with midi represented polyphonic classical music for piano as training data. We introduce the problem, show relevant neural network architectures and describe our numerous ideas, out of which one idea, our experiment with three versions of skip residual LSTM connections for music composition, we consider a good contribution to the field. In related work, skip-connections were explored mostly for classification tasks, however, our results show a solid improvement for music composition (e.g. 47% of respondents considered our samples real). We also show that skip-connections have rather diverse hyperparameter space for future tuning. Apart from standard automated test set evaluation, which is hard to design and interpret for creativity mimicking models, we also did a complex evaluation through surveys. The evaluation was specifically designed to not only to show results for our samples, but to reveal information about expectancy, preconceptions and influence of personal characteristics of the respondents. We consider this a valuable resource for future works in automated music generation.
A comparative study and classification on different underwater image enhancement techniques

Underwater images play a vital role in marine engineering, marine research etc. These images can be used for control of underwater vehicles, study of corals, fishes and other specious. Underwater images suffer poor visibility and some important details may loss because of scattering and absorption. Obtaining a clear underwater image is very important and it is difficult too. Underwater image enhancement has been an active research topic over many years and several techniques has been proposed. This paper introduces a study on various techniques used to enhance the underwater images and classified the techniques into four categories. Keywords⸻ Underwater images, Image enhancement, Enhancement techniques
Mechanics defines the spatial pattern of compensatory proliferation

The number of cells in tissues is tightly controlled by cell division and cell death, and misregulation of cell numbers could lead to pathological conditions such as cancer. To maintain cell numbers in a tissue, a cell elimination process named programmed cell death or apoptosis, stimulates the proliferation of neighboring cells. This mechanism is called apoptosis-induced compensatory proliferation, which was originally reported more than 40 years ago. While only a limited number of the neigboring cells need to divide to compensate for apoptotic cell loss, the mechanisms that select cells for undergoing division remain an open question. Here we found that the spatial inhomogeneity in mechanotransduction through a growth-promoting transcription co-activator Yes-associated protein (YAP) in the neighboring tissue, accounts for the inhomogeneity of compensatory proliferation. Such inhomogeneous mechanotransduction arises from the combination of the non-uniform distribution of nuclear size, which is inherent in tissues, and the non-uniform pattern of mechanical force applied to the neighboring cells upon apoptosis. Our findings from a mechanical perspective complement the current biochemical understanding of compensatory growth and provide additional insights into cellular functions of how tissue precisely maintains its homeostasis.
A study on network intrusion detection system

In this study, we have tried to survey the methods to detect network intrusions. The Intrusion detection system is important in computer networks for protecting the data. There are several algorithms using neural networks, deep learning, and machine learning to detect intrusion detection in the system. There are many methods for detecting intrusions by ensemble methods, classifiers, anomaly detection giving accuracy according to the methods. The goal of the study is to give a comparison of the methods for an accuracy rate of low rate false alarm and high detection rate. Also, the data sets used in the methods.
Guest editorial

Over the past few years, the concepts of sustainability, corporate social responsibility (CSR) and management control systems (MCSs) have continued to draw the attention of scholars and practitioners alike. While a great deal of research has been conducted to explore the separate areas of sustainability, CSR and MCSs, very few concerted research efforts have beenmade to systematically integrate these concepts (Gond et al., 2012; Lueg and Radlach, 2016).
THE ROLE OF INTELLECTUAL CAPITAL IN SHAPING BUSINESS PERFORMANCE: MEDIATING ROLE OF INNOVATION AND LEARNING

Organizational performance and their relationship with intellectual capital is becoming interesting, particularly in times of intense economic turbulence, when companies are looking for new solutions to maintain and grow their business. The aim of this study is to explore the impact of intellectual capital on business performance. Self-administered questionnaire containing the measures of human capital, structural capital, relational capital, business performance, innovation & creation and learning & education has been used for data collection. Quantitative data have been analyzed through PLS-SEM techniques. This study has explored that intellectual capital has positive and significant association with business performance. The study has also examined that intellectual has direct impact on intellectual capital. Moreover, human capital has also indirect impact on business performance as innovation & creation and learning & education positively and significantly mediate the relationship between human capital and business performance. Outcomes of this research are providing insights to higher education institutions, firms and policymakers to consider these factors while making strategies and policies to boost the firm’s value.
Multiscale assessment of TRMM (3B42 V7) and GPM (IMERG V5) satellite precipitation products over a Mediterranean mountainous watershed with sparse rain gauges in the Moroccan High Atlas (case study of Zat basin)

Abstract. The performance of Tropical Precipitation Measurement Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), has provided hydrologists with a source of critical precipitation data for hydrological applications in basins where ground-based observations of precipitation are sparse, or spatially undistributed. The very high temporal and spatial resolution satellite precipitation products have therefore become a reliable alternative that researchers are increasingly using in various hydro-meteorological and hydro-climatological applications. This study aims to evaluate statistically and hydrologically the TRMM (3B42 V7) and GPM (IMERG V5) satellite precipitations products (SPPs), at multiple temporal scales from 2010 to 2017, in a mountainous watershed characterized by the Mediterranean climate. The results show that TRMM (3B42 V7) and GPM (IMERG V5) satellite precipitation products have a significant capacity for detecting precipitation at different time steps. However, the statistical analysis of SPPs against ground observation shows good results for both statistical metrics and contingency statistics with notable values (CC > 0.8), and representative values relatively close to 0 for the probability of detection (POD), critical success index (CSI), and false alarm ratio (FAR). Moreover, the sorting of the events implemented on the hydrological model was performed seasonally, at daily time steps. The calibrated episodes showed very good results with Nash values ranging from 53.2 % to 95.5 %. Nevertheless, the (IMERG V5) product detects more efficiently precipitation events at short time steps (daily), while (3B42 V7) has a strong ability to detect precipitation events at large time steps (monthly and yearly). Furthermore, the modeling results illustrate that both satellite precipitation products tend to underestimate precipitation during wet seasons and overestimate them during dry seasons, while they have a better spatial distribution of precipitation measurements performance, which shows the importance of their use for basin modeling and potentially for flood forecasting in Mediterranean catchment areas.
Invasive Fungal Infections After Liver Transplantation: A Retrospective Matched Controlled Risk Analysis

Background Invasive fungal infections (IFI) are major risks for mortality after liver transplantation (LT). The aim of this study was to evaluate possible risk factors for the development of IFI after LT. Material/Methods All adult patients with IFI after LT between January 2012 and December 2016 at Essen University were identified. Pre-, intra-, and postoperative data were reviewed. These were compared to a 1-to-3 matched control group. Multinominal univariate and multivariate regression analyses were performed. Results Out of the 579 adults who underwent LT, 33 (5.6%) developed postoperative IFI. Fourteen had invasive aspergillosis with 7 (50%) mortality, and 19 had Candida sepsis with 7 (37%) mortality. The overall mortality due to invasive fungal infections was 42%. Perfusion fluid contamination with yeast was detected in 5 patients (15%). Multivariate regression analyses showed that preoperative dialysis (OR=1.163; CI: 1.038–1.302), Eurotransplant donor risk index (OR=0.04; CI=0.003–0.519), length of hospital stay (OR=25.074; CI: 23.99–26.208), and yeast contamination of the preservation fluid (OR=47.8; CI: 4.77–478, 96) were associated with IFI in the Candida group, whereas duration of surgery (OR=1.013; CI: 1.005–1.022), ventilation hours (OR=0.993; CI=0.986–0.999), and days of postoperative dialysis (OR=1.195; CI: 1.048–1,362) were associated with IFI in the aspergillosis group. Conclusions Post-LT IFI had 42% mortality in our cohort. Prophylactic antifungal therapy should be expanded to broader risk groups as defined above.
Machine Learning and NLP based System for Medical Data Analytics and Prediction

In the present technical era, healthcare providers generate large amounts of medical data on a day-to-day basis. Produced clinical information is placed away carefully as Electronic Health Record (EHR) as essential information archive of medical clinics. These days Artificial Intelligence (AI) have been developing rapidly in recent years. Particularly, wellbeing data framework can get most advantages from the AI benefits. Specifically, symptom based disease prediction expectation exploration and creation turned out to be progressively mainstream in the medical care area as of late. In the paper, we have proposed a structure to assess the proficiency of applying both Natural Language Processing (NLP) and Machine learning (ML) advances for disease prediction framework. As an example we have interpreted n2c2 heart related disease symptom datasets from DBMI portal. The acquired patient records is in XML format which is parsed and converted to structured format and naive Bayes algorithm is applied for training.