Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial neural network for Gaussian and non-Gaussian random fatigue loading analysis

There has been a lot of work done on the analysis of Gaussian loading analysis perhaps because its occurrence is more common than non-Gaussian loading problems. It is nevertheless known that non-Gaussian load occurs in many instances especially in various forms of transport, land, sea and space. Part of the challenge with non-Gaussian loading analysis is the increased number of variables that are needed to model the loading adequately. Artificial neural network approach provides a versatile means to develop models that may require many input variables in order to achieve applicable predictive generalisation capabilities. Artificial neural network has been shown to perform much better than existing frequency domain methods for random fatigue loading under stationary Gaussian load forms especially when mean stress effects are included. This paper presents an artificial neural network model with greater predictive capability than existing frequency domain methods for both Gaussian and non-Gaussian loading analysis. Both platykurtic and leptokurtic non-Gaussian loading cases were considered to demonstrate the scope of application. The model was also validated with available SAE experimental data, even though the skewness and kurtosis of the signal in this case were mild.
Swords and sorcery: a structural gamification framework for higher education using role-playing game elements

Research in Learning Technology 2019. © 2019 K. Ntokos. Research in Learning Technology is the journal of the Association for Learning Technology (ALT), a UK-based professional and scholarly society and membership organisation. ALT is registered charity number 1063519. http://www.alt. ac.uk/. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.
Design compact and efficient recurrent neural networks for natural language processing tasks

The present work takes into account the compactness and efficiency of Recurrent Neural Networks (RNNs) for solving Natural Language Processing (NLP) tasks. RNNs are a class of Artificial Neural Networks (ANNs). Compared to Feed-forward Neural Networks (FNNs), RNN architecture is cyclic, i.e. the connection between nodes form cycles. This subtle difference has actually a huge impact on solving sequence-based problems, e.g. NLP tasks.In particular, the first advantage of RNNs regards their ability to modellong-range time dependencies, which is a very desirable property for natural languagedata, where word’s meaning is highly dependent on its context. The second advantage of RNNs is that are flexible and accept as input many different datatypes and representation. This is again the case of natural language data, whichcan come in different sizes, e.g. words with different lengths, and types, e.g. sequences or trees.
Transition from education to the labour market

The statistical system for studying transition from school to work described in this paper depends on the existence of an identification system which makes it possible to combine data from different registers and to follow large nation-wide representative cohorts from the educational system through to adulthood and the labour market. This paper focuses on the Swedish system for education and training statistics. Three measurement methods are used: registers; student follow-up surveys; and sample panels of students. The paper finds that a great advantage of statistics based on administrative sources is the ability to offer many subdivisions of the data.
On the surprising capacity of linear combinations of embeddings for natural language processing

As Webster’s classic 1900 text “English: Composition and Literature” states “A sentence is a group of words expressing a complete thought. ” People use natural language to represent thoughts. Thus the representation of natural language, in turn, is of fundamental importance in the field of artificial intelligence. Natural language understanding is a research area which revolves around how to represent text in a form that an algorithm can manipulate in such a way as to mimic the ability of a human to truly understand the text’s meaning. In this dissertation, we aim to extend the practical reach of this area, by exploring a commonly overlooked method for natural language representation: linear combinations (i.e. weighted sums) of embedded representations. This dissertation is organised as a collection of research publications: with our novel contributions published in conference proceedings or journals; and with a comprehensive literature review published as part of a book. When considering how to represent English input into a natural language processing system, a common response is to view it as a sequential modelling problem: as a discrete time-series of words. A more complex alternative is to base the input model on the grammatical tree structures used by linguists. But there are also simpler models: systems based on just summing the word embeddings. Word embeddings are dense vector representations of words, generally learned as part of a neural network or related system. They are commonly available pretrained, and are used in a variety of systems for different tasks to those they were originally trained for as a form of transfer learning. On a variety of tasks, simply summing these word embeddings work very well – often better than the more complex models. This dissertation examines these linear combinations of embeddings for natural language understanding tasks. In brief, it is found that a sum of embeddings is a particularly effective dimensionalityreduced representation of a bag of words. The dimensionality reduction is carried out at the word level via the implicit matrix factorization on the collocation probability matrix. It thus captures into the dense word embeddings the key features of lexical semantics: words that occur in similar contexts have similar meanings. We find that summing these representations of words gives us a very useful representation of structures built upon words: such as sentences, phrases, and word senses. A limitation of the sum of embedding representation is that it is unable to represent word order. This representation does not capture any order related information; unlike for example a recurrent neural network. Recurrent neural networks, and other more complex models, are outperformed by sums of embeddings in tasks where word order is not highly significant. It is found that even in tasks where word order does matter to an extent, the improved training capacity of the simpler model still means that it performs better than more complex models. This limitation thus impacts surprisingly little.
Holistically improving screening decisions under uncertainty in aircraft conceptual design and technology assessment: Insights on bottom-up uncertainty quantification and propagation and integrated socio-technical group decision making

In the highly uncertain environment of conceptual aircraft design and technology assessment and selection, making good decisions is of utmost importance. Aiming to contribute to improving decision quality, the problem was researched from both a technical as well as a socio-psychological perspective. In the technical perspective, the uncertainty associated to three two-dimensional aerodynamic solvers -- following from physical flow model assumptions or numerical solution method differences -- was quantified and propagated. It was found that the influence of flow model clearly outweighs the impact of solution method. Although the aircraft synthesis program used for uncertainty propagation showed very little sensitivity to the effects of the assumptions investigated, these results raise critical questions about the currently observed push for higher-fidelity analysis methods in aircraft conceptual design. In the socio-psychological perspective, document review, interviews and observations were used to define what socio-psychological factors negatively impact group decision making in an international public-private research consortium aiming to contribute to aircraft fuel burn reduction through RaD concept development. Based on vigilant interaction theory and (the conceptual foundations of) effective intercultural workgroup communication theory, a theoretical framework was used to develop the TARE-model, which helps groups balance task (TA) and relationship (RE) outcomes in an uncertain environment. Using a thought experiment, the inherent integration and inseparability of the two perspectives was found. Uncertainty was put forward as the primary variable impacting the interwovenness of the two perspectives. Applying this conclusion to the results from the technical and socio-psychological domains, the question is raised whether increased computational power and additional models are the most effective way to substantially improve the quality of decisions in conceptual design and technology assessment and selection projects.
Attitudes to Mental Health in Scotland: Scottish Social Attitudes Survey 2013

■ ■ There have been quite substantial changes since 2008 in the factors identified as supporting an individual’s recovery. In particular, there was a sharp decline in the proportion choosing ‘family or friends’ (from 62% in 2008 to 41% in 2013) and ‘having belief in myself’ (from 31% in 2008 to 15% in 2013). By contrast there was a substantial increase since 2008 in the proportion selecting ‘other forms of treatment/therapy’ (from 19% in 2008 to 27% in 2013) and ‘finding out more about mental health’ (from 8% in 2008, to 19% in 2013). Attitudes to mental health in Scotland: Scottish Social Attitudes Survey 2013
The Impact of Self-Efficacy on Accountants' Behavioral Intention to Adopt and Use Accounting Information Systems

Digitalization increasingly affects the accounting profession as it engages with pervasive technologically enabled systems that support business processes and financial management. Given these systems commonly result in less than voluntary use, mandating compliance is challenging. In this context, it is important to understand the attitudes of prospective users, as their negativity may waste resources through ambivalence, frustration, and under-use. Our study of Libyan accountants shows that in adopting a mandated technologically enabled accounting information system, they were influenced by a range of perceptional, dispositional, and environmental factors. By combining components of the unified theory of acceptance and use of technology with institutional theory, results show that 63.4 percent of the variance regarding behavioral intention is attributable to self-efficacy, effort expectancy, coercive, and mimetic pressures. Our findings confirming the significance of self- efficacy and disconfirming experience support calls to consider the influence of self-efficacy upon the use of restrictive decision aids.
Adaptive FOV Control of Laparoscopes With Programmable Composed Constraints

Field of view (FOV) is important for minimally invasive surgeries. However, manual laparoscope manipulation is laborious and requires a lot of training. Assistants’ fatigue and misunderstanding during collaboration will also compromise the quality of FOV. To improve these drawbacks, we developed a new algorithm to automatically manipulate 2-D laparoscopes. The innovative aspect lies in the following ways: the algorithm designs adaptive laws for on-line estimation of depth information, which is difficult to calibrate accurately in advance; to follow moving tools and stabilize FOV around local operations at the same time, a region-based visual servoing method is proposed. We design a novel region factor to deal with regions of different shapes; Aside from the remote center of motion (RCM), the eye-hand coordination problems, especially misorientation, are also key issues for MIS. However, how to reduce the misorientation effects is still an open problem in the literature. We propose a new constraint called the intuitive virtual plane (IVP) constraint to minimize the misorientation; in contrast with the most related works assuming the optical axis and physical axis of a laparoscope are perfectly coincident to each other, our method can be extended to different kinds of laparoscopes without any assumptions on its optical and physical axis with null-space method to decouple image-based visual servoing (IBVS) and RCM-IVP constraints. Experiments are presented to validate the method. Moreover, user studies and interviews are conducted with medical professionals and inexperienced subjects, and the results show that our system is able to improve efficiency, eye-hand coordination, and safety for FOV control of laparoscopes.
Acute Respiratory Distress Syndrome in a Neonate: A case report

Acute respiratory distress syndrome (ARDS) describes an acute diffuse inflammatory lung injury caused by permeability pulmonary edema which occurs as a result of alveoli capillary endothelial damage that develops in response to an initiating injury or illness.This condition occurs unnoticed among seriously ill patients in the hospital wards. Despite advance research in inflammatory mechanism , ventilatory support and haemodynamic control,the mortality rate of ARDS remains high,50 -75%. In this report, we present a term,3kg live female baby who became breathless immediately after birth. There was generalized refractory cyanosis(despite adequate ventilation),tachycardia (≥140b/minute) and bibasal coarse crepitations and rhonchi. Apgar score was 4 1 3 10 . She was delivered by a 29year old Primi gravida via emergency caesarian section(CS) indicated by passage of fresh meconium stained liquor.Findings during Surgery were cord round neck and meconium filled uterine cavity. Mother was not diabetic nor hypertensive.She neither had any major medical therapy nor trauma prior to delivery.
Point beauty contest: measuring the distribution of focal points on the individual level

We propose the Point Beauty Contest, a mechanism to identify the distribution of focal points on the individual level. By contrast to conventional coordination, subjects coordinate by the distribution of points. This allows for nuanced coordination strategies, as subjects can invest in multiple alternatives at the same time and weigh their choice. A subject´s strategy choice then reveals her perception of the distribution of focal points. In an experiment on the elicitation of social norms, we compare the mechanism with conventional coordination. The data confirms the theoretical predictions regarding coordination behavior and demonstrates that the proposed technique is suited to identify the distribution of focal points on the individual level. Using Monte Carlo simulations, we find that the proposed mechanism identifies focal points on the population level more efficiently than conventional coordination. We point to the possibility of using the mechanism as a simple method to directly measure strategic uncertainty.
Large volume multicolor nonlinear microscopy of neural tissues

Multiphoton microscopy has transformed neurobiology since the 1990s by enabling 3D imaging of thick tissues at subcellular resolution. However the depths provided by multiphoton microscopy are limited to a few hundreds of micrometers inside scattering tissues such as the brain. In the recent years, several strategies have emerged to overcome this depth limitation and to access larger volumes of tissue. Although these novel approaches are transforming brain imaging, they currently lack efficient multicolor and multicontrast modalities. This work aims at developing large-scale and deep-tissue multiphoton imaging modalities with augmented contrast capabilities. In a first chapter, we present the challenges of high-content large-volume brain imaging, with a particular emphasis on powerful multicolor labeling strategies which have so far been restricted to limited scales. We then introduce chromatic serial multiphoton (Chrom-SMP) microscopy, a method which combines automated histology with multicolor two-photon excitation through wavelength-mixing to access multiple nonlinear contrasts across large volumes, from several mm3 to whole brains, with submicron resolution and intrinsic channel registration. In a third chapter, we explore the potential of this novel approach to open novel experimental paradigms in neurobiological studies. In particular, we demonstrate multicolor volumetric histology of several mm3 of Brainbow-labeled tissues with preserved diffraction-limited resolution and illustrate the strengths of this method through color-based tridimensional analysis of astrocyte morphology, interactions and lineage in the mouse cerebral cortex. We further illustrate the potential of the method through multiplexed whole-brain mapping of axonal projections labeled with distinct tracers. Finally, we develop multimodal three-photon microscopy as a method to access larger depths in live settings.
Risk factors of Gestational Diabetes in Karnataka

Gestational diabetes affects 3-14% of pregnancies, depending on the population studied. Indian ethnic groups have shown to have an escalating incidence of GDM. Glucose intolerance during pregnancy predisposes the offspring for increased risk of developing glucose intolerance in the future in addition to various other complications like cardiac problems. A case control study was conducted among pregnant ladies admitted in VIMS Hospital. 45 cases and 90 controls were selected and interviewed with a structured questionnaire. Data was statistically analyzed with Chi-square test for finding association. The strength of association, so found, was estimated with Odds ratio and 95% confidence interval. Logistic regression was also done to remove the effect of confounding. Analysis with binary logistic regression gave the following findings: GDM is significantly associated only with previous history of GDM (P value=0.001, Adjusted OR=17.332), family history of Diabetes mellitus (P value= 0.004, OR=6.249), previous history of Caesarian sections (P value=0.006, OR=5.886). The major risk factors for developing gestational diabetes mellitus in our setup are history of gestational diabetes mellitus in previous pregnancies, family history of diabetes mellitus and history of caesarian sections.
Computer Vision Approach for Detection and Extraction of Text from Video Frames Using Color Continuity, Color Variations and HV Projections Methods

- M ultimedia data retrieval and indexing is an important aspect for information processing and information extraction. In the field of multimedia data indexing, video data indexing is considered as a challenging task for various type of real-time applications. In order to improve the retrieval and indexing performance, text detection and extraction has gained attraction from research community. In this work, we presented text detection and extraction process from video scene frames. In order to accomplish the desired objective, the complete process is divided into two stages as (a) text detection and (b) text recognition. For text detection, a combined approach is presented where color continuity, color variations are computed and later text localization is performed using horizontal and vertical projections. In the next phase, text recognition is performed where DWT based features are extracted and trained using RBF classifier. The complete experimental study is carried out using MATLAB simulation and tested for open source datasets. The comparative study shows that the proposed approach achieves better performance when compared with the existing techniques.
Shift work and the United States female workforce: The relationship between shift work and ill-health effects

The present study was designed to determine the magnitude of the relationship between amount, frequency, and length of shift work completed by female transportation employees and the number, degree, and extent of problems related to physical, menstrual and psychological health including depression. It was hypothesized that workers that are employed in areas such as transportation who are working shift work on a regular basis place themselves at higher risk for developing health or psychosocial related effects. These health related outcomes can have a profound impact on an employee’s job performance, daily functioning, and personal life. The present study sought to understand the potential relationship between working shift work and higher disturbances to the bodies’ natural functioning. The present study has the potential for explaining new ways to decrease the risk factors for those working shift work by contributing to the overall understanding of this multifaceted relationship. This study has many important findings and implications. This study has implications for explaining that the effects of disturbances to the circadian rhythm as a result of certain shift work schedules can result in ill-related health effects. Additionally, this study sought to challenge limitations to current research that has been conducted on
The Effect of Regular Breakfast on Body Mass Index in 9- to 10-year-old Czech Children

Background: Eating habits play a crucial role in weight control management; however, little research has examined whether frequency of breakfast consumption influences body mass index (BMI) in middle childhood. Purpose: The purpose of this study was to (a) determine the relationship between BMI and the frequency of breakfast consumption, (b) examine gender differences in the frequency of eating breakfast and starting time of breakfast after waking up, and (c) explore breakfast composition. Methods: The research group consisted of 298 children aged 9–10 years (164 boys and 134 girls). Frequency of breakfast consumption, starting time of breakfast after waking up, and breakfast composition were determined by questionnaire. Results: A statistically significant difference in the regularity of breakfast consumption was found between children of the underweight, healthy weight, and overweight/obese groups (P < .001). There was no statistical gender effect for the frequency and timing of the breakfast (P>.05). Discussion: Our data indicate that regular consumption of breakfast is related to BMI values in children aged 9–10 years independent of gender. Translation to Health Education Practice: Our finding confirmed the importance of breakfast consumption and a healthy eating pattern in middle childhood.