Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of social media in value cocreation and innovation in service ecosystems

Understanding how to plan and execute crowdsourcing challenges of complex industrial products is crucial for receiving useful information and knowledge from crowds. This qualitative single-case study was, thus, initiated to analyse the factors impacting the usefulness of crowdsourcing outcomes. The data were collected by means of semi-structured in-depth interviews with representatives of a crowdsourcing company and a crowdsourcing platform provider. From these data, a number of factors impacting the usefulness of information and knowledge acquired from crowdsourcing were identified, and their significances were evaluated, thereby increasing our understanding of value creation from crowdsourcing activities. The need for novel skills and competences was highlighted. Though more validation is needed to generalise these preliminary results, this study generates a new qualitative understanding of the use of crowdsourcing in the development of complex (new) product development.
Editorial Epilogue: Daniel Hjorth in Conversation with Nada Endrissat and Claus Noppeney

Biographical notes: Daniel Hjorth is a Professor of Entrepreneurship and Organisation at the Department of Management, Politics and Philosophy, Copenhagen Business School, where he also is the Academic Director of the across CBS Entrepreneurship Business in Society Platform. He is the Editor of the four Movements in Entrepreneurship books for Edward Elgar Publ. (together with Professor Chris Steyaert), initiating a European School of Entrepreneurship Research. He has also contributed to the opening up of Organisational Entrepreneurship (2012) as research field, and recently co-edited the Oxford University Press Handbook on Process Philosophy and Organization Studies (2014). He is a Senior Editor of Organization Studies and member of the editorial boards of, e.g., Organization, Entrepreneurship and Regional Development, and Entrepreneurship, Theory & Practice.
Characterization of Yeasts Capable of Neutralizing Acidic Media from Natural Neutral Environments

Acid-tolerant yeasts often inhabit extremely acidic environments: mine drain-ages, hot springs, and even fermented foods. Some of them also possess the ability to neutralize acidic media. However, the examples of these yeasts that are already known were isolated from acidic environments. In this study, the isolation of acid-tolerant yeasts from natural neutral aquatic environments and the identification of yeasts able to neutralize an acidic medium (acid-neutralizing yeast) in Japan were carried out. Various kinds of acid-tolerant and acid-neutralizing yeasts were obtained. In a neutralizing test using an acidic casamino acid solution adjusted to a pH of 4.0 with sulfuric acid, the obtained acid-neutralizing yeasts elevated the pH to approximately 7.0, and their neutralizing abilities were similar to those of previously reported yeasts that had been isolated from acidic environments. These results showed that acid-tolerant yeasts and acid-neutralizing yeasts exist widely in neutral environments, and little difference was found in the neutralizing abilities of yeasts obtained from neutral environments in comparison to those obtained from acidic environments.
Assessment of ready to eat cereal snacks consumption pattern of school children

A study was carried out to assess ready to eat cereal snacks consumption pattern of school children of Dharwad. Total of 160 school children in the age group of 11-14 years comprising 80 children each from both rural and urban locality were selected during 2015-18. Ready to eat cereal snacks consumption pattern was assessed by personal interview method using structured schedule. Results indicated that among the total of 160 children, more than 50 per cent of children were boys (52.50 %) and remaining 47.50 per cent were girls. The consumption of snacks by urban and rural children was 85 and 90 per cent respectively. More than 40 per cent of children from rural (43.06 %) and urban (48.53 %) liked to consume both savory and sweet snacks. Papadi was liked by majority of rural and urban children (76.47 and 69.41 %, respectively) followed by milk pedha, ghate (66.67 %, respectively) in rural and milk pedha and rasgulla (63.24 and 64.71 %, respectively) in urban. Total of twelve snacks were recorded as most liked snacks by children, among them 40 per cent of snacks were purchased one to two piece or packet per visit to the shop, remaining were purchased one piece per visit (60%). Among the daily consumed snacks were benne biscuits (80.71 %) followed by chakli (32.14 %) and papadi (21.43 %). Maximum per cent of children consumed snacks during school leisure time (92.14 %) followed by when relatives give amount (85.71 %) and while going to school (61.43 %). All the children consumed snacks due to the taste and more than 90 per cent of children consumed snacks as they like eating again and again (97.22 and 97.06 %, respectively), because of color (95.83 and 95.59 %, respectively) and to use the amount given by parents or relatives. Children looked for the quantity (97.86 %), price (97.14 %), color (94.29 %) and taste (91.43 %) while purchasing snacks. The study concludes that, along with esthetic quality, well-portioned nutri-snacks between meals can help children to develop healthy eating habits and to maintain good health.
Fertility Awareness Based Methods (FABMs): Evaluating and Promoting Female Interest for Purposes of Health Monitoring and Family Planning

Natural or fertility awareness-based methods (FABMs) help users monitor, interpret, and systematically chart female biological markers. FABMs are used as family planning methods and sometimes as reproductive health monitoring methods. The American College of Obstetricians and Gynecologists (ACOG) encourages medical professionals to use female reproductive cycle patterns, ‘the female fifth-vital sign’, as a means of improving the diagnosis and treatment of reproductive health diseases, disorders, and conditions. Patients need to have accurate knowledge of healthy and unhealthy cycle patterns. The general public does not have a sufficient level of fertility health knowledge. This study examined whether females that are interested and not interested in using FABMs for health monitoring and family planning differ in their mean reproductive health knowledge scores (average of six knowledge questions), and whether certain demographic characteristics or having been prescribed the hormonal birth control pill for reproductive health abnormalities predict female interest in using FABMs for health monitoring or family planning. A 52-item survey was administered to a national sample of 665 reproductive age women in March 2018. An independent samples t-test and two binary regression models were run using the Statistical Package for Social Sciences software (SPSS). Of the total 665 female participants 279 (41.9%) females reported not being interested in using FABMs for family planning and 386 (58%) females are interested. Females who were not interested in using FABMs for health monitoring (n=352) had a statistically significantly (p<.05) higher mean knowledge scores (73.6%) compared with the knowledge scores (68.8%) of females who were interested (n=237). Similarly, females who were not interested in using FABMs for family planning (n=279) had a statistically significantly (p<.05) higher mean knowledge scores (75.1%) compared with the knowledge scores (69%) of females (n= 386) who were interested. No demographic characteristics nor having been prescribed the hormonal birth control pill for reproductive health abnormalities significantly predicted female interest in using FABMs for health monitoring or family planning. These findings suggest that interest in FABMs for family planning or health monitoring is not isolated to a specific demographic of reproductive age women.
Sovereign Borrowing by Developing Countries; What Determines Market Access?

What determines the ability of governments from developing countries to access international credit markets? We examine this question using detailed data on sovereign bond issuances and public syndicated bank loans since 1982. We find that traditional measures of a country’s links with the rest of the world (such as trade openness) and traditional liquidity and macroeconomic indicators do not help much in explaining market access. However, a country’s vulnerability to shocks and the perceived quality of its policies and institutions appear to be important determinants of its government’s ability to tap the markets. We are unable to detect strong punishment of defaulting countries by credit markets.
Putting Peirce’s Theory to the Test: Peircean Evolutionary Algorithms

While Charles Darwin proposed one of the first systematic arguments evidencing evolution, alternatives with a strong empirical grounding exist. These alternatives deserve our attention, if only because some of them claim to correct the oversights in Darwin’s theory. Charles Sanders Peirce put forth one such theory. This article devises a novel Peircean Evolutionary Algorithm for finding the solution to a class of optimization problems in the domain of Evolutionary Computation (EC). The results are computed against a benchmark high-dimensional set of mathematical functions and compared with standard Darwinian Evolutionary Algorithms (EAs) and their advanced variants. Although there is further work to be done, this research meets four different but interrelated objectives: 1) To highlight the difference between Darwinian and Peircean understandings of natural evolution, 2) To show how cutting-edge work in molecular biology, symbiogenesis, epigenetics, and systems biology are moving in the direction of Peirce’s ideas, 3) To propose a Peircean evolutionary framework that can act as a theoretical foundation for EC, and 4) To meet at least one major challenge facing the Evolutionary Computation community: convergence to local minima due to stagnation.
The Mechanical Design of Drowsiness Detection Using Color Based Features

This paper demonstrates design and fabrication of a mechatronic system for human drowsiness detection. This system can be used in multiple places. For example, in factories, it is used on some dangerous machinery and in cars in order to prevent the operator or driver from falling asleep. This system is composed of three parts: (1) mechanical, (2) electrical and (3) image processing system. After processing the input image and eye position detection, the system investigates the state of the eye, and in the case of drowsiness, the system activates the alarm. It also has the ability to track the eyes.
Development of HRM Training Practice under Saudi Arabia’s Vision 2030

This paper studies the development in the human resources management training of domestic enterprises (DEs) Vs. multinational subsidiaries (MNSs) under Saudi Vision 2030 was formed in 25 April 2016 by Crown Prince Mohammad bin Salman. Based on the literature MNSs are more rigorous in their training practices. So, the hypotheses were predicted from literature as MNSs are more interested in their training. Findings were confirmed that predicted hypotheses were statistically significant. MNSs utilized more sophisticated training practice. Also, this paper considered the comparative in terms of local Saudi workforce. When MNSs have more local force were found to place more significance on training development . The literature was explored in order to confirm this study is only one in the context of Saudi Arabia. This study was added to understand of HRM practices between two different types of organizations. Furthermore, this study had suggestion for future research.
Effectiveness of plyometric exercises to improve muscular strength and digital achievement for students in shot put

To identify the effectiveness of plyometric exercises to improve muscular strength and achievement in shot put event by students (20-22 years old). The researchers used an experimental method; the sample was formed by students belonging to the institute of physical education & sports, University of Mostaganem (Algeria). Thus it was the application of the experimental method was applied on a sample of third-year students (males) of 45 students divided into two groups, first control group of 22 students and an experimental group of 23 students. While the tests used were (strength test, achievement test). After the treatment of the results by statistical means, and through these results it have been reached several conclusions of them, plyometric Exercises to develop muscular strength and achievement in shot put [discussion significant difference (*p≤0.05)]. And best improve of the experimental sample based on plyometric exercises compared to the control sample, which rely more on the technical side only in shot put event.
Variability and stability in early language acquisition: Comparing monolingual and bilingual infants' speech perception and word recognition

Many human infants grow up learning more than one language simultaneously but only recently has research started to study early language acquisition in this population more systematically. The paper gives an overview on findings on early language acquisition in bilingual infants during the first two years of life and compares these findings to current knowledge on early language acquisition in monolingual infants. Given the state of the research, the overview focuses on research on phonological and early lexical development in the first two years of life. We will show that the developmental trajectory of early language acquisition in these areas is very similar in mono- and bilingual infants suggesting that these early steps into language are guided by mechanisms that are rather robust against the differences in the conditions of language exposure that mono- and bilingual infants typically experience.
Improving Efficiency in Deep Learning for Large Scale Visual Recognition

The emerging recent large scale visual recognition methods, and in particular the deep Convolutional Neural Networks (CNN), are promising to revolutionize many computer vision based artificial intelligent applications, such as autonomous driving and online image retrieval systems. One of the main challenges in large scale visual recognition is the complexity of the corresponding algorithms. This is further exacerbated by the fact that in most real-world scenarios they need to run in real time and on platforms that have limited computational resources. This dissertation focuses on improving the efficiency of such large scale visual recognition algorithms from several perspectives. First, to reduce the complexity of large scale classification to sub-linear with the number of classes, a probabilistic label tree framework is proposed. A test sample is classified by traversing the label tree from the root node. Each node in the tree is associated with a probabilistic estimation of all the labels. The tree is learned recursively with iterative maximum likelihood optimization. Comparing to the hard label partition proposed previously, the probabilistic framework performs classification more accurately with similar efficiency. Second, we explore the redundancy of parameters in Convolutional Neural Networks (CNN) and employ sparse decomposition to significantly reduce both the amount of parameters and computational complexity. Both inter-channel and inner-channel redundancy is exploit to achieve more than 90% sparsity with approximately 1% drop of classification accuracy. We also propose a CPU based efficient sparse matrix multiplication algorithm to reduce the actual running time of CNN models with sparse convolutional kernels. Third, we propose a multi-stage framework based on CNN to achieve better efficiency than a single traditional CNN model. With a combination of cascade model and the label tree framework, the iii proposed method divides the input images in both the image space and the label space, and processes each image with CNN models that are most suitable and efficient. The average complexity of the framework is significantly reduced, while the overall accuracy remains the same as in the single complex model.
College students' conditional knowledge about reading.

Strategic reading requires not only a repertoire of processing strategies but also knowledge about the conditions under which a given strategy is relevant. This study examined college students' conditional knowledge about reading. Subjects sorted descriptions of reading situations according to how they believe they read in each situation. Cluster analyses of the resulting distance matrixes identified 10 distinct categories of reading situations. Ratings of the cognitive demands of the reading situations supported the interpretations of the sorting data and suggested how reading patterns vary across different texts and task demands
Influence of Perceived Stress and Organizational Justice on Employee Wellbeing amongst Academia: A Conceptual Paper

This paper proposes a conceptual model and states valid propositions to be tested by future researchers empirically. The model explicates the influence of perceived stress and organizational justice on employee wellbeing amongst academia. Being a conceptual paper, it reviewed scholarly articles on the variables of the study in accordance with theoretical perspectives. The paper draws inference from both job demands resources (JDR) theory and Adams equity theory to explain and support the conceptual model. This study would assist policy makers and university authorities in particular to comprehend the theoretical importance of sound wellbeing of academics in higher educational institutions (HEIs). The proposed model would have several significant implications for understanding, creating awareness and promoting employee wellbeing as well as contributes to existing works on wellbeing.
Fracture Toughness and Charpy Impact Test of MIM Steels and Correlation of Results by KIC- CVN Relationships

This paper presents results of fracture toughness and Charpy impact tests of 4 steels prepared by metal injection moulding (MIM) method: AISI 4140, 4605, 17-4 PH and SS 420W Grade. Charpy impact testing was performed on the series of machined specimens with sub-size cross-section to obtain values of CVN impact energy. Fracture toughness test method according to ASTM E1820 was performed on SENB specimens, fatigue pre-cracked, to obtain values of KIC. All tests were performed at the room temperature. After the material testing, next step was to evaluate the results and apply known correlation relationships between fracture toughness KIC and Charpy impact CVN energies. Many empirical relations exist and describe existence of such relationship. Procedure was used to correlate energies of sub-size Charpy specimens to full-size specimens. Several proposed correlation relationships from transition and upper shelf region of transition curve were used, where the best correlation was found to be Robert-Newtons with average deviation of 22% in comparison to tested values.
How Does Foreign Direct Investment Affect Economic Growth

We test the effect of foreign direct investment (FDI) on economic growth in a cross-country regression framework, utilizing data on FDI flows from industrial countries to 69 developing countries over the last two decades. Our results suggest that FDI is an important vehicle for the transfer of technology, contributing relatively more to growth than domestic investment. However, the higher productivity of FDI holds only when the host country has a minimum threshold stock of human capital. In addition, FDI has the effect of increasing total investment in the economy more than one for one, which suggests the predominance of complementarity effects with domestic firms.
HTTP/2 Semantics Using The QUIC Transport Protocol

The QUIC transport protocol has several features that are desirable in a transport for HTTP/2, such as stream multiplexing, per-stream flow control, and low-latency connection establishment. This document describes a mapping of HTTP/2 semantics over QUIC. Specifically, this document identifies HTTP/2 features that are subsumed by QUIC, and describes how the other features can be implemented atop QUIC.
The psychosis risk timeline: can we improve our preventive strategies? Part 2: adolescence and adulthood

SUMMARY Current understanding of psychosis development is relevant to patients' clinical outcomes in mental health services as a whole, given that psychotic symptoms can be a feature of many different diagnoses at different stages of life. Understanding the risk factors helps clinicians to contemplate primary, secondary and tertiary preventive strategies that it may be possible to implement. In this second article of a three-part series, the psychosis risk timeline is again considered, here focusing on risk factors more likely to be encountered during later childhood, adolescence and adulthood. These include environmental factors, substance misuse, and social and psychopathological aspects. LEARNING OBJECTIVES: After reading this article you will be able to: • understanding the range of risk factors for development of psychotic symptoms in young people and adults• understand in particular the association between trauma/abuse and subsequent psychosis• appreciate current evidence for the nature and strength of the link between substance misuse and psychosis. DECLARATION OF INTEREST: None.
The Pricing of Composition Uncertainty

The "quant crisis" of 2007 and subsequent unfolding of the global financial crisis highlighted the importance of the "crowded-trade" problem (not being able to know how many others are taking the same position). To investigate the crowded trading, we present a model in which informed and uninformed traders face uncertainty about the composition of traders (composition uncertainty). We characterize the equilibrium in both the information and financial markets. Composition uncertainty distorts traders' information acquisition, demands, and perceived equity premium, resulting in a security price mispricing. The model helps to understand a linkage between liquidity and asset prices, proposes plausible explanations for large price swings, and demonstrates how current regulations to enhance market efficiency may not work in the presence of composition uncertainty.
Extended Technology Acceptance Model to Predict Mobile-Based Money Acceptance and Sustainability: A Multi-Analytical Structural Equation Modeling and Neural Network Approach

This research is a pioneering study into the adoption of mobile-based money services for financial inclusion and sustainability in developing countries like Togo. Owing to their differences from more usual mobile-based banking and payment services, such technology is being aggressively promoted by providers of network telecommunication companies. However, the factors influencing its sustainable acceptance remain largely unknown. This paper extends the original Technology Acceptance Model (TAM), by integrating self-efficacy (SEMM), technology anxiety (TAMM), and personal innovativeness (PIMM). The research model is assessed with survey data of 539 actual and prospective mobile money users employing structural equation modeling–artificial neural networks (SEM–ANN) approach. A feed-forward-back-propagation (FFBP) multi-layer perceptron (MLP) ANN with significant predictors obtained from SEM as the input units and the root mean square of errors (RMSE) indicated that the ANN method achieves high prediction accuracy. The results present conclusive evidence that perceived ease-of-use (PEMM) is the most significant factor affecting consumers’ attitudes to mobile-based money. While perceived usefulness (PUMM) and PIMM affect adoption decisions, their impact is much lower. Consumer attitudes and intentions were found to have a significant relationship with TAM. SEMM and TAMM; however, they showed mixed results. These findings will be useful to retain prevailing users and attract new ones.