Miracle-dz/UniFAR-FTUs huggingface dataset:
Grandfather Clause and Customer Loyalty: Evidence from a Quasi-Experiment

In customer relationship management (CRM), the grandfather clause is a common practice that differentiates between existing and new customers and exempts existing customers from a price hike to maintain their loyalty. Since the existing customers do not experience any price change, however, the effect of the grandfather clause on their loyalty is not theoretically straightforward. We explore this research question with a quasi-experiment involving a subscription-based video-streaming firm. The results suggest that the grandfather clause positively impacts subscribers’ behavioral loyalty: the grandfathered subscribers who were notified of this policy (hereafter, “addressed subscribers�? ) kept their subscription for 22.71 days more than the grandfathered subscribers who weren’t notified (hereafter, “unaddressed subscribers�? ). Further analyses of video consumption and content contribution behavior revealed that both economic considerations and attitudinal changes contributed to the increase in subscription duration. Addressed subscribers extended their subscription because they wanted to avoid the future cost of churning and re-subscribing for a higher price. Since the grandfather clause was a preferential treatment for the grandfathered subscribers, it also led to positive attitudinal changes, which manifested as an increase in subscription duration and content contribution behavior. The attitudinal changes were stronger for communal-oriented subscribers than exchange-oriented subscribers, consistent with the relationship norm theory. This paper contributes to the CRM literature by establishing the grandfather clause as an effective CRM strategy upon a price increase. It also contributes to the behavior-based pricing literature by providing empirical evidence for the validity of forward-looking behavior and the role of the fairness concern in the model.
Deformation, Stretching, and Relaxation of Single‐Polymer Chains: Fundamentals and Examples

Abstract We review the basic theory for the static and dynamic properties of ideal and real chains. We describe the stretching of single ideal chains in both the strain and the stress ensembles, give scaling arguments for the deformation of real chains, and also consider the impact of chain rigidity by discussing the stretching of worm-like chains. We complement this theoretical outlook with specific examples that highlight the relevance of the single-molecule deformation in a practical perspective: the translocation of a macromolecule over a potential barrier, the collision of a single chain with fixed obstacles or other chains, the dynamics of a thethered chain in a strong shear flow, and some remarkable predictions concerning the deformation of composite chain molecules during free-solution electrophoresis. #This article is a corrected version of a paper which has already appeared in Soft Materials [G. W. Slater, Y. Gratton, M. Kenward, L. McCormick, F. Tessier, Soft Materials 1, 365 (2003)]. Due to problems in the publishing process at the time, the original publication contained an excess of errors. This was not the fault of the authors. The editors wish to apologise to authors and readers. Florian Müller‐Plathe, Editor‐in‐Chief.
Immaturity

We introduce conceptions of ‘enacted cognition’,‘practical authorship’ and ‘maturity’ that help us to investigate entrepreneurship as an activity. The first two stem from social constructionist perspectives on patterns of recognition and the articulation of knowledge respectively (Shotter, 1993; Vygotsky, 1986). The third is found in Kant’s (1784/1991) essay ‘What is Enlightenment? ’ By combining these, our research highlights those factors that prevent entrepreneurs developing maturity and so embedding their ideas within the wider economic and social activities of their community. A novel e-postcard methodology is employed with 44 UK entrepreneurs to investigate these factors from the entrepreneurs’ own judgements. Implications for the development of a new methodology, for the conceptualization and development of entrepreneurial activity and learning among small firm managers, and for policy are discussed.
MULTISCALE MODEL FOR HURRICANE EVACUATION AND FUEL SHORTAGE

Recent hurricanes in Southeast United States have led to mass evacuations. During the last year’s hurricane Irma, 23 counties in Florida issued mandatory evacuation orders, with the remaining 44 counties putting in place voluntary orders. Our analysis of hurricane Irma traffic data obtained from Florida Department of Transportation (FDOT) indicates a net exodus of 550,000vehicles from the southern parts of Florida. It is estimated that approximately 6.8 million Floridians and tourist took to the roads in the days leading up to the storm. Such mass evacuations have also been observed during recent hurricane Florence affecting North and South Carolina as well as during this month’s hurricane Michael. Hurricane evacuees tend to make longer, intercity trips to stay with friends and family outside the impacted area and to completely move out of the storm path. The high volume mass evacuations, disruptions to the supply chain, long distances travelled, fuel hording from non‐evacuees have led to localized fuel shortages lasting several days in all of recent hurricanes including Irma, Florence and Michael. Fuel shortages further exacerbate safe evacuation of residents in affected areas. While news reports have documented fuel shortages during past hurricanes, crowd‐sourced data from social media platform gas buddy has quantified the shortages during recent hurricanes. A predictive model of fuel shortages during hurricane combined with on‐ground traffic and fuel data will be effective in providing policy suggestions for both effective evacuation and back‐up fuel storage locations. We propose a computational model at multiple scales to predict the fuel shortages that develop during hurricane evacuation. Our preliminary analysis of such fuel shortages indicates that models developed for predicting epidemic dynamics can adequately represent fuel shortages. We will develop a multiscale framework with multiple components including: I. Compartmental stochastic epidemic spread model for fuel shortages at city and state level. II. Traffic data from past hurricanes (e.g. Irma and Florence), combined with a Monte Carlo fuel consumption model to estimate fuel usage. III. Self‐excitation point process model to delineate and estimate baseline and contagious fuel consumption (e.g. fuel hoarding, effect of nearby refueling stations emptying). IV. Agent based discrete event simulation model for detailed analysis at the scale of few intersections. V. Policy analysis using a predictive model for future hurricanes based on the above. The project outcome will be a predictive capability for fuel shortages in the event of future hurricanes which can be used for effective analysis of available policy options and for guiding mass evacuations. CATM Research Affiliates: Sirish Namilae (ERAU: Lead) Dahai Liu (ERAU) Scott Parr (ERAU)
Actin assembly-inducing protein ActA promotes FcγRIa-mediated Listeria internalization

Listeria monocytogenes is a Gram-positive intracellular pathogen and the causative agent of human listeriosis. While the ability of L. monocytogenes to enter and survive in professional phagocytes is critical to establish a successful infection, mechanisms of invasion are poorly understood. Our previous investigation into the role of type I interferon-stimulated genes in bacterial infection revealed that the human immunoglobulin receptor FcγRIa served as a L. monocytogenes invasion factor. FcγRIa-mediated L. monocytogenes entry occurred independently of immunoglobulin interaction or bacterial internalins. However, the bacterial determinants that mediate FcγRIa interaction remain unclear. Using a comparative genomics approach, we identify actin assembly-inducing protein ActA as a pathogen specific ligand of FcγRIa. FcγRIa enhanced entry of pathogenic L. monocytogenes and L. ivanovii strain but not non-pathogenic L. innocua. We found that the major virulence regulator PrfA is required for pathogen entry into FcγRIa-expressing cells and identify its gene target actA as the critical Listeria ligand. ActA alone was sufficient to promote entry into FcγRIa-expressing cells, and this function is independent of its actin nucleating activity. Together, these studies present an unexpected role of ActA beyond its canonical function in actin-based motility and expand our understanding of Listeria strategies for host cell invasion. Importance Listeria monocytogenes is a food-borne bacterial pathogen and a causative agent of listeriosis with up to 50% mortality rate in immunocompromised individuals. While the mechanisms of Listeria entry into non-phagocytic cells have been extensively characterized, the details of phagocytic cell invasion are still poorly understood. We have recently discovered that human immunoglobulin receptor FcγRIa mediates Listeria uptake by monocytic cells. This process occurred independently of canonical immunoglobulin interactions as well as classic Listeria internalization factors. Importantly, molecular determinants of Listeria-FcγRIa interaction leading to bacterial entry, remained unknown. In this study, we demonstrate that Listeria virulence factor actin-assembly inducing protein ActA is required for FcγRIa-mediated entry. Further, ActA was found to be sufficient for the internalization, suggesting its role as a bacterial ligand of FcγRIa. Together, these findings expand our knowledge of mechanisms that Listeria has evolved to exploit cellular signaling pathways and immune defense of the host.
Raman and infrared spectroscopy studies of carbon nitride films prepared on Si (100) substrates by ion beam assisted deposition

Filmes de nitreto de carbono, preparados por deposicao assistida por feixe de ions, foram estudados pela espectroscopia de Raman e infravermelho, em funcao de energia de ions (200, 400, e 600 eV) e razao de chegada de ion a atomo R(I/A) de 0,9 a 2,5. A razao de composicao N/C no filme, determinada por analise de deteccao de recuo elastico, foi encontrada proporcional a R(I/A); porem, a formacao de filme e possivel somente se R(I/A) for menor do que um valor critico da eficiencia de "sputtering" quimico. Esse valor encontrado foi de 0,21. O valor maximo de N/C obtido foi de 0,9 (47% atomica de nitrogenio) para o filme depositado com 400 eV. Para entender o efeito de incorporacao de nitrogenio na estrutura dos filmes, os parâmetros foram determinados pela analise dos espectros Raman e encontrados fortes dependencias da quantidade de nitrogenio nos filmes; os comportamentos da posicao e da largura do pico G, e da razao ID/IG sao correlacionados com as mudancas estruturais nos filmes.
Review on Developing Corpora for Sentiment Analysis Using Plutchik’s Wheel of Emotions with Fuzzy Logic

- Internet is the global system which is increasing day by day with a faster rate. With the increasing internet, social networking increases and people started to share information through different kinds of social media. In recent years several efforts were devoted for mining opinions and sentiments automatically from natural language in social media messages, news and commercial product reviews. This task involves a deep understanding of the explicit and implicit information, conveyed by the language. Most of these approaches refer to annotated corpora. The use of Opinion mining is to identify and extract the information, which is in the subjective form from the internet. This can be done with the help of data, required for processing. The methods used are natural language processing, text analysis. Sentiments are also extracted from the feedbacks. Feedback is important for selling or purchasing any product. While shopping whenever someone wants to choose any product, the opinion of others will always help him/her to choose the best product. But it is very difficult for customer to read thousands of reviews at a time and it also creates confusion. So some data mining techniques must be applied to solve these problems. Sentiment analysis also helps in identifying the attitude of the person. In our work, we present a system which develops a corpus for opinion and sentiment analysis. We will take the product reviews and classify them as positive, negative and objective. The system will further classify the positive and negative sentiments into emotions using Plutchik’s wheel of emotions and makes a dictionary. It uses fuzzy logic approach for prediction and generates output.
Op2Vec: An Opcode Embedding Technique and Dataset Design for End-to-End Detection of Android Malware

Android is one of the leading operating systems for smartphones in terms of market share and usage. Unfortunately, it is also an appealing target for attackers to compromise its security through malicious applications. To tackle this issue, domain experts and researchers are trying different techniques to stop such attacks. All the attempts of securing the Android platform are somewhat successful. However, existing detection techniques have severe shortcomings, including the cumbersome process of feature engineering. Designing representative features require expert domain knowledge. There is a need for minimizing human experts’ intervention by circumventing handcrafted feature engineering. Deep learning could be exploited by extracting deep features automatically. Previous work has shown that operational codes (opcodes) of executables provide key information to be used with deep learning models for the detection process of malicious applications. The only challenge is to feed opcodes information to deep learning models. Existing techniques use one-hot encoding to tackle the challenge. However, the one-hot encoding scheme has severe limitations. In this paper, we introduce (1) a novel technique for opcodes embedding, which we name Op2Vec, and (2) based on the learned Op2Vec, we have developed a dataset for end-to-end detection of Android malware. Introducing the end-to-end Android malware detection technique avoids expert-intensive handcrafted feature extraction and ensures automation. Some of the recent deep learning-based techniques showed significantly improved results when tested with the proposed approach and achieved an average detection accuracy of 97.47%, precision of 0.976, and F1 score of 0.979.
Prediction of Food Preparation Time for Smart City

With advancements in digital technologies, very large volumes of big data can be generated and collected from a wide variety of rich data sources in various real-life applications, including those from many of our daily routines. Embedded in these big data are useful information and valuable knowledge. Analyzing and mining these big data helps discover useful information and valuable knowledge, which may enhance performance and well-being, as well as reduce costs and resource consumption (e.g., time), of citizens. Quality of food could enhance our lifestyle, and thus our health. While home cooking is considered healthy, occasion take-out may save time. When ordering take-out, it is challenging for the customers or delivery persons to predict the right time for picking up the order. In this paper, we present a smart city system that makes good use of big data mining to predict food preparation time. Evaluation results show that our system is effective and practical in predicting food preparation time, and thus leading to benefits towards building a smart city.
Serious Games in High School Mathematics Lessons: An Embedded Case Study in Europe

In this article, we will describe the embedded case study of the European project “E-Magic” carried out within the Erasmus+ project and which involved 12-16 years old students and teachers from three nations: “Acharnes Special Education Vocational School” in Athens (Greece), IIS “L. Da Vinci-Nitti”, a high school in Potenza (Italy), and from Gabinete de Modernizao das Tecnologias Educativas (G.M.T.E.) in Madeira Islands (Portugal). The project is finalized to enhance Mathematical Competencies by Serious Games learning. We carried out both empirical qualitative and quantitative methods to highlight Serious Games’ effectiveness, as a teaching methodology, in motivating students to learn mathematics. We also hypothesize that prompting a strategic way of thinking within a didactical intervention by game-approach could improve students’ proving processes and support them in mathematical thinking production. Rabardel’s cognitive ergonomics and the Technological Pedagogical Content Knowledge model are the lenses for reading educational experiments’ data. Data seems to show that simulation helps students find alternative and creative solutions to problems. Therefore, Serious Games’ use in education promotes significant changes and mental resources activation.
COVID-19 Prediction using X-Ray Images

Coronavirus disease (COVID-19) is a pandemic caused by the coronavirus SARS -CoV-2 that was not previously seen in humans. COVID-19 is spreading rapidly throughout the world. COVID-19 can be detected by a lung infection of the patients. The standard method for detecting COVID-19 is the Reverse transcription-polymerase chain reaction (RT-PCR) test. But the availability of RT-PCR tests is in short supply. As a result of this, the early detection of the disease is difficult. The easily obtainable modes like X-rays are often used for detecting infections in the lungs. It is confirmed that X-ray scans can be widely used for efficient COVID-19 diagnosis. But a physical diagnosis of X-rays of an outsized number of patients is a longterm process. A deep learning-based diagnosis process can help radiologists in detecting COVID-19 from X-ray scans. Pre-trained CNNs are commonly used in detecting diseases from datasets. This paper proposes a CNN model with a parallelization strategy that extracts the features in the X-ray images by applying filters parallelly through the images. Our proposed method aims to attain higher accuracy and a less loss rate with precision. To do so, the accuracy and loss rates of three types of CNN - VGG-16, MobileNet, and CNN are compared with the parallelization technique. Since, VGG-16 and MobileNet are pre-trained models; those two models are directly imported from Keras. Moreover, this paper utilizes two datasets consisting of COVID X-ray images and Non-COVID X-ray images for the prediction of COVID-19 using Convolution Neural Network [CNN].
New vector/scalar Overhauser DNP magnetometers POS-4 for magnetic observatories and directional oil drilling support

V.A. Sapunov 1, *, A.Y. Denisov 1, D.V. Saveliev 1, А. A. Soloviev 2, 3, S.Y. Khomutov 4, P.B. Borodin 5, E.D. Narkhov 1, А. V. Sergeev 1, A.N. Shirokov 1 1 Ural Federal University, Quantum Magnetometry Laboratory, Mira 21, Ekaterinburg 620002, Russia 2 Geophysical Center, RAS, Molodezhnaya 3, Moscow 119296, Russia 3Schmidt Institute of Physics of the Earth, RAS, B. Gruzinskaya 10, Moscow 123242, Russia 4 Institute of Cosmophysical Research and Radio Wave Propagation, FEB RAS, Mirnaya 7, Kamchatskiy Kray, Paratunka 684034, Russia 5 Geophysical Observatory "Arti", Institute of Geophysics, UB RAS, Geofizicheskaya 2a, Sverdlovskaya oblast, Arti 623341, Russia *E-mail: vasapunov@urfu.ru
Epenthesis in Jerash Fallaahi Dialect: An Autosegmental Analysis

The study aims to investigate the phonological epenthesis processes which are manifested in Jerash Falla:ħi Dialect (JFD), a rural Jordanian dialect spoken in the north of Jordan by almost 130,000 people and has never been investigated before. The study uses a non-linear approach, namely, the autosegemental approach to analyze the phonological epenthesis processes. The data are collected by recording spontaneous conversations of twenty participants of Jerash Falla:ħi people who are native speakers of this dialect. The analysis shows that JFD displays two types of epenthesis, namely, vowel epenthesis and consonant epenthesis. Vowel epenthesis involves the insertion of the high short vowel /i/, or the high back vowel /u/, or the short low central vowel /a/ in certain cases within the word boundary or within an adjacent utterance; word-finally and wordmedially. The second type is consonant epenthesis, which involves the insertion of the glottal stop /? / within the word boundary. In this type, the glottal stop can only occur word initially.
Acute Effects of Upper Extremity Static Stretching and Dynamic Warm-up Protocols on Range of Motion, Strength, and Power Output

INTRODUCTION: Overhead throwing athletes develop muscular and capsular tightness of the posterior shoulder and an altered arc of motion in their dominant shoulder due to repetitive overhead throwing. Stretching has been suggested as a way to improve soft tissue flexibility and reduce the risk of shoulder pathology associated with posterior shoulder tightness (PST). Baseball players commonly perform upper extremity acute static stretching exercises during warm-up to increase glenohumeral (GH) range of motion (ROM), prevent injury, and enhance performance. However, previous literature has demonstrated that acute static stretching may be detrimental to performance. The purpose of this study was (1) to compare upper extremity static stretching and dynamic warm-up protocols and (2) determine the most appropriate protocol to increase GH ROM, decrease PST, and maintain GH strength and power. METHODS: Upper extremity static and dynamic protocols were compared in 15 healthy and physically active males using a within-subject, repeated measures, and counterbalanced design. GH internal rotation (IR) and external rotation (ER) ROM, PST, and GH isokinetic concentric strength and power were measured before and after each protocol. Post-test assessments occurred over four time intervals (post-0, post-5, post-15, and post-30 minutes). RESULTS: The results of this study demonstrated no significant test x time interactions between the static and dynamic protocols at any time interval for any of the dependent variables. However, a significant main effect occurred where GH IR ROM group mean significantly increased at the post-0 (p= CONCLUSIONS: This study did not identify a stretching or warm-up protocol that increased or decreased muscular force output. However, both protocols acutely increased GH IR and ER ROM for up to 30 minutes, suggesting that static stretching and dynamic warm-up may be similarly effective at increasing ROM. Clinicians and researchers must continue to work together to guide future research and determine the most effective stretching or warm-up protocol to maximize upper extremity performance.
A Single-Stage Sensorless Control of a PV-Based Bore-Well Submersible BLDC Motor

Permanent magnet (PM) brushless dc (BLDC) motor-based photovoltaic water pumps are becoming popular in rural areas due to their higher efficiency and reliability compared to induction motor-based pumps. The water table level in these rural areas is typically more than 15 m. Hence, deep bore-well submersible motors are used for extracting potable water from the water table. The motor and controller are submerged in the water table, and have more temperature rise due to the poor ambience. Thus, control of a PM BLDC motor with hall-effect-based position sensors is unreliable in these environments due to the temperature sensitivity of the hall-effect sensors. In this paper, a single-stage sensorless control of a deep bore-well submersible ferrite PM BLDC motor drive is presented. A position sensorless control scheme is implemented to eliminate the use of hall sensors, thus improving reliability of the overall system. Furthermore, the challenges involved in the control of a deep bore-well submersible ferrite PM BLDC motor are elaborated. The prototype of a 1.5-kW submersible BLDC motor drive is fabricated and the experimental results are presented.