Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Sustainability of Dairy Slurry by A Commercial Additive Treatment

Ammonia (NH3), methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) emissions from livestock farms contribute to negative environmental impacts such as acidification and climate change. A significant part of these emissions is produced from the decomposition of slurry in livestock facilities, during storage and treatment phases. This research aimed at evaluating the effectiveness of the additive “SOP LAGOON” (made of agricultural gypsum processed with proprietary technology) on (i) NH3 and Greenhouse Gas (GHG) emissions, (ii) slurry properties and N loss. Moreover, the Life Cycle Assessment (LCA) method was applied to assess the potential environmental impact associated with stored slurry treated with the additive. Six barrels were filled with 65 L of cattle slurry, of which three were used as a control while the additive was used in the other three. The results indicated that the use of the additive led to a reduction of total nitrogen, nitrates, and GHG emissions. LCA confirmed the higher environmental sustainability of the scenario with the additive for some environmental impact categories among which climate change. In conclusion, the additive has beneficial effects on both emissions and the environment, and the nitrogen present in the treated slurry could partially displace a mineral fertilizer, which can be considered an environmental credit.
Designing and Running Concurrent Future Networks

In the future, many new services can be expected to be running on a multitude of devices and networks with diverse requirements. For example, in sensor-actuator networks energy awareness is important; in car-2-car communication, one possible goal is reliable low delay data dissemination of critical traffic events, and in inter-planetary networks, delay-tolerant delivery of data is one of the main issues. In the proposed demo, we will show how our concepts support the rapid development of application-tailored communication protocols for such network architectures and their easy deployment within a run-time environment. This way, the development of innovative services is no longer limited to the application, but can also easily happen further down the protocol stack. The key features presented in our demo include: (1) rapid creation of composed protocols using a graphical design tool; (2) reuse of existing protocol building blocks; (3) automatically selecting an appropriate Netlet based on application requirements at end nodes.
Introduction to Fog Computing

Pushing computing, control, data storage, and processing into the cloud has been a key trend in the past decade. However, the cloud alone encounters growing limitations, such as reduced latency, high mobility, high scalability, and real-time execution in order to meet the upcoming computing and intelligent networking demands. A new paradigm called fog computing has emerged to overcome these limits. Fog extends cloud computing and services to the edge of the network. It provides data, computing, storage, and application services to end-users that can be hosted at the network edge. It reduces service latency, and improves QoS/QoE, that results in superior user experience. This chapter is about introduction and overview of fog computing, comparison between fog computing and cloud computing, fog computing and mobile edge computing, possible fog computing architecture, applications of fog computing, and possible research directions.
Language Influences Thought? Or the Reverse?

The Whorfian Hypothesis has been a source of controversy since it was first articulated both in its strong form ‘language determines thought’ and its weaker form ‘language influences thought’, and it has influenced disciplines beyond linguistics. This article intends to regard the relationship between language and thought from a different perspective: namely, how thought may determine language. It analyzes how thought determines language lexically, semantically and structurally. Keywords—Whorfian Hypothesis; perception and thought; lexicon; expression; structure
The DiamondCandy Algorithm for Maximum Performance Vectorized Cross-Stencil Computation

An advance in the search for the 4D time-space decomposition that leads to an efficient vectorized cross-stencil implementation is presented here. The new algorithm is called DiamondCandy. It is built from the dependency and influence conoids of the scheme stencil. It has high locality in terms of the operational intensity, SIMD parallelism support, and is easy to implement. The implementation details are shown to illustrate how both instruction and data levels of parallelism are used for many-core CPU. The test run results show that it performs an order of magnitude better than the traditional approach, and that the performance does not decline with the increase of the data size.
Clients' Satisfaction with Maternal and Child Health Care Service at El - Minia Governorate, Egypt

Background: Health care services are being provided by health facilities to the community with the objective of fulfilling their satisfaction but sometimes the target population can not be always satisfied. Client satisfaction measurement is a routine tool in health care facilities to assess the quality of service and know whether it meets the clients' expectations. Aim: Our study was conducted to assess the satisfaction of clients receiving maternal and child health services at El-Minia MCH facilities in order to elicit suggestion for improving the services. Research design: It is a cross-sectional descriptive study Settings: This study was conducted during April-October 2010, at El-Minia governorate, in Upper Egypt, 234 Km south to Cairo, Egypt. In El-Minia governorate, 233 different health facilities are providing MCH care (10 urban centers and 223 rural health units). Sample: The target sample size 300 adult women. Tool: An interview questionnaire was developed by the authors and filled out from the answers of the female clients who attended the services at El-Minia MCH facilities during a period from April to October 2010. Results: As regard to client satisfaction with the provided MCH services, 56.3% of the clients were not satisfied while 43.7% of clients accepted the services and described it as good. Most of the clients (57%) expressed their responses as not satisfactory regarding the assessment of physicians and 66.7 for nursing staff. Conclusions: The majority of the interviewed women were not satisfied with most of the aspects that include dealing with the health care providers at all the urban and rural MCH facilities of ElMinia governorate. Consequently, the policy makers and health care providers must revise their quality and quantity of services, and accordingly actions must be taken to improve them to meet the clients' expectations. Recommendations: In order to overcome this low level of satisfaction we recommend holding regular mothers' meetings inviting opinions and suggestions from the clients. On the other hand, adoption of an extensive national educational and training program for the proper dealing and patients’ rights among health care service providers. Moreover, in the university education for medical and nursing students, there must be special emphasis on patients’ rights, human dealings and quality of service that satisfies all health care clients.
Modeling Consumers' Confidence and Inflation Expectations

This study analyzes the socio-economic factors that influence inflation expectations of Indian households. Using Consumer Confidence Survey data we discover that inflation expectations of households depend largely on inflation perceptions, income, education of the respondents, and outlook on economy, employment and spending. Women, lower income individuals and less educated persons tend to have higher inflation expectations. Macroeconomic variables like inflation, repo rate and GDP growth rate influence inflation expectations positively. Along with these variables, inflation projections by the Reserve Bank of India play a pivotal role in influencing inflation expectations of households. A significant role of the Reserve Bank in anchoring inflation expectations via communications is visible from this analysis.
Stress resilience, sleep and physical activity in older manual workers.

This thesis investigated the relationships between stress resilience, sleep and physical activity (PA) in older manual workers (OMW). The first study aimed to explore latent profiles of individuals at risk of ill health and being based on presenteeism, stress resilience and PA. Profiles of OMW differed on work-related affect and health perceptions, and high levels of PA were associated with lower levels of presenteeism. As such, implementing interventions that focus on high levels of PA may be efficient in the reduction of presenteeism in some groups of OMW and potentially in improving stress resilience. The aim of study two in this thesis was to explore the relationship between perceived psychological resilience and work-related factors (presenteeism and work engagement) and the physiological response to acute psychological stress in a group of OMW. The results indicated that psychological resilience was not associated with work-related factors. In contrast, cardiovascular reactivity was significantly related to work-related factors such that workers with high levels of presenteeism and low work engagement had high heart rate reactivity to stress. Thus, exaggerated cardiac reactivity in workers with high presenteeism may provide a pathway to cardiovascular disease (CVD). Study three examined the association of PA levels, BMI and undiagnosed obstructive sleep apnoea (OSA), which is common in OMW. Preliminary results indicated that undiagnosed OSA was associated with low levels of PA, although BMI was a stronger indicator, implying that PA may be a modifiable risk factor in OSA development. The aim of the fourth and final study of this thesis was to investigate the ability of a 12-week PA intervention to reduce moderate OSA independent of BMI in a group of older workers. The results indicated that the PA intervention was effective in the reduction of OSA independent of BMI and weight-loss and therefore, supports previous research that increasing PA may provide a means of managing OSA and improving cardiovascular health. In summary, increasing levels of PA, decreasing stress and promoting sufficient quality and duration of sleep may well be the pathway to promoting good health and wellbeing in older workers, which will enable them to continue a long and efficient working life.
Deep Learning Algorithms for Cardiac Image Classification and Landmark Detection

With the increase in computational power, deep learning algorithms have become an active field of research over the last 7 years. These data-driven machine learning algorithms have produced good results in many applications, image analysis being one of them. Today, many image analysis tasks in the medical field are done manually, taking valuable time and effort from professionals. Automating some of these tasks could relieve work-load and speed up the healthcare process. In this thesis, the potential use of deep learning algorithms for medical image analysis will be evaluated. Two problems will be investigated, cardiac image classification and landmark detection in cardiac images. The algorithms used will be based on two existing deep learning algorithms, the U-net and AlexNet. The deep learning algorithms will be implemented in the deep learning software Caffe, and all training and testing will be ran on an Amazon Web Services instance. The data used for training, testing and validation are provided by the Department of Clinical Physiology at Lund University Hospital. This data is augmented by scaling and rotation to provide a larger and more representative data set for training. The trained algorithm for image classification achieved 98.8% accuracy on validation data, while the algorithm for landmark detection achieved approximately 95 % accuracy on validation data. The image classification algorithms worked well, and it serves as a proof of concept with the potential of being able to solved more clinically difficult problems. With the high accuracy of the landmark detection algorithm largely being due to an imbalance in class distribution, this algorithm, while showing some promise, needs more work to be clinically useful.
Double-Tract-Rekonstruktion nach Resektion des gastroösophagealen Übergangs wegen AEG-II- und -III-Tumoren

Zusammenfassung Bei selektionierten AEG-Typ-II- und -III-Tumoren (AEG = Adenokarzinome des ösophagogastralen Übergangs) der T-Kategorien 1 und 2 kann der distawle Magen nach proximaler Resektion ohne Beeinträchtigung der Radikalität und der Prognose erhalten werden. Als Rekonstruktion wird die Doppeltraktanlage mit Seit-zu-Seit-Anastomosierung des distalen Magens in die Roux-Y-Jejunumschlinge nach Ösophagojejunostomie dargestellt. Nach der Literatur hat dieses Verfahren eine niedrige Rate von postoperativer Refluxösophagitis, erfordert gegenüber der totalen Gastrektomie signifikant geringere Dosen an Vitamin-B12-Substitution und die Hämoglobinwerte sind signifikant höher.
Uncertainty-aware similarity measures - properties and construction method

A concept of uncertainty–aware similarity measure is being defined and discussed. The aim of the paper is to support the opinion that both definition and construction of such measure should take into account the epistemic nature of compared incomplete (uncertain) information. This approach, however, generates new challenges resulted from the computational complexity of the problem. We define a set of properties to be satisfied by uncertainty–aware similarity measure and we propose a new technique of constructing such measures for Interval–Valued Fuzzy Sets.
Domestic Violence Through a Caribbean Lens: Historical Context, Theories, Risks and Consequences

ABSTRACT Studies show that increasing and escalating rates of violence prominently exist among Caribbean family and intimate relationships. As known to many, the social and cultural norms that underscore gendered disparities within Caribbean societies disproportionately increases the risk of violence for women and children. With general knowledge surrounding these issues within Caribbean communities and across its Diaspora, the scientific literature lacks critical in-depth analysis of the risks and consequences of violence, along with important steps in curbing this social and public health concern that continues to impact the lives and well-being of individuals who experience higher than usual victimization rates. This manuscript highlights historical perspectives, theories, prevalence, risks and consequences of violence among Caribbean descendants. The article further proposes recommendations for prevention and intervention measures that might be useful in addressing the escalating rate of violence within the family and relationship contexts.
Exploring the Lived Experiences of Career Oriented Military Spouses Pursuing Education for Career Advancement

EXPLORING THE LIVED EXPERIENCES OF CAREER ORIENTED MILITARY SPOUSES PURSUING EDUCATION FOR CAREER ADVANCEMENT Melody D. Agbisit Old Dominion University, 2019 Director: Dr. Tim Grothaus In the United States, the number of dual career couples has been increasing due to economic and personal growth needs (Pixley, 2009). Furthermore, more careers are requiring specialized education than before (O'Neill & Thomson, 2013). However, there is a limited understanding of the trailing career oriented military spouse as a student population (Gleiman & Swearengen, 2012). This phenomenological qualitative study aimed to explore the lived experiences of 11 military spouses pursuing education to advance their career options. Participants were military spouses who valued career development and who were currently enrolled or admitted in a post-secondary educational or training program. In addition, participants each had experienced at least one relocation as a military spouse. Data were analyzed with a research team. To increase rigor, four main strategies of trustworthiness were utilized: credibility, transferability, dependability, and confirmability (Hays & Singh, 2012). Three superordinate themes as well as 10 themes and 28 subthemes were constructed and explained. The first superordinate theme, the self, focused on the participants’ inner dialog. The second superordinate theme, the circumstances, focused more on external factors impacting participants’ lives. The final superordinate theme, the choices, focused on the decisions made given the inner dialog and the external circumstances. The findings suggest that military spouses may have more needs than has been noted in the military spouse literature. Implications for practitioners and institutions, limitations of the study, and future research were
Filtrado inteligente de noticiasbasado en Deep Learning

El analisis de posicionamiento de noticias consiste en identificar la disposicion de una noticia con respecto a un hecho o entidad, y supone una tarea fundamental para determinar la veracidad de esta. Este trabajo presenta una solucion sobre el problema presentado en el desaf´io de Fake News Challenge, proponiendo un servicio web de filtrado de noticias basado en redes neuronales hacia delante. La herramienta esta orientada a usuarios finales y les permitira obtener la posicion de una noticia concreta respecto del titular. Para el desarrollo del clasificador se han comparado representaciones basadas en el metodo de bag of words y vectores de medias de embedding de word2vec. La representacion del titular y el cuerpo de la noticia alimenta un modelo de red hacia delante que realiza una clasificacion multiclase. El modelo final consiste en una red neuronal optimizada con tres capas ocultas aplicando regularizacion L2, tomando como entrada la representaci´on de vectores de medias. Se consigue un resultado de 87% en valor predictivo positivo y 86% en terminos de recall. ---ABSTRACT---Analysis of news stance consists on addressing the position on a report towards a fact or entity, and takes an important role in the assessment of veracity of the report itself. This project proposes a solution relating to the Fake News Challenge problem, and proposes a web service for news filtering, based on feedforward neural networks. This tool is conceived to end-users and will let them obtain the stance of a text with respect a claim. To develop the classifier, text representations based on bag-of-words and vector embedding mean have been considered. Text representations of headline and article body are used as input for a feedforward neural network trained to make a multiclass classification. The final model proposed consists on a hand-tuned 3-layer feedforward network applying L2 regularization, taking as input two vectors of means of word embeddings. This model achieves a 87% in terms on precision and a 86% in terms on recall.
Optical Properties and Light-Emission Device Applications of 2-D Layered Semiconductors

Two-dimensional layered semiconductors have attracted a great deal of attention recently from many fields of science and technology. This article overviews the major progress from the perspective of light-emission properties and related device applications of such 2-D materials. We begin with the overview of basic optical properties, including emission features of various excitonic complexes, many-body effects, light-emission enhancement due to plasmonic coupling, and mechanisms of optical gain. This is followed by discussions of coupling of 2-D materials with an optical cavity, including cavity-enhanced emission due to the Purcell effect, strong and weak coupling, as well as lasing behavior. We then discuss the design and the fabrication of various heterostructures by stacking layers of different 2-D materials for the purpose of confining and injecting charge carriers. Such structures are indispensable for light-emitting devices under electrical injection, the ultimate goal of any semiconductor-based light-emitting diode (LED) or lasers. The progress in electrical injection devices is reviewed next, where LEDs under lateral and vertical electrical injection schemes are discussed. The review is concluded with an outlook and future perspectives.
Robust Identification of Nonlinear Systems With Missing Observations: The Case of State-Space Model Structure

This paper investigates the robust identification of nonlinear systems in state-space setting with output measurements contaminated with outliers and part of output measurements missing at random. The problems of outliers and missing observations are often encountered in practical industrial processes and are taken into consideration comprehensively in this work. The robust Student's t-based observation model is built to model the output measurements with stochastic outliers, and the impact of outliers imposed on nonlinear system identification can be suppressed through inference of the degree of freedom in Student's t-distribution. The proposed robust nonlinear system identification method with an incomplete dataset is derived with the expectation–maximization (EM) algorithm, and the particle filter is employed to numerically approximate the Q-function of the EM algorithm. A numerical example and a chemical process are utilized to demonstrate the superiority of the proposed strategy.
Intelligent ECG processing and abnormality detection using adaptive ensemble models

This thesis explores the automated Electrocardiogram (ECG) signal analysis and the feasibility of using a set of computationally inexpensive algorithms to process raw ECG signals for abnormality detection. The work is divided into three main stages which serve towards the main aim of this research, i.e. the abnormality detection from single channel raw ECG signals. In the first stage, a lightweight baseline correction algorithm is proposed along with a modified moving window average method for real-time noise reduction. Additionally, for further offline analysis, a wavelet transform and adaptive thresholding based method is proposed for noise reduction to improve signal-to-noise ratio. In the second stage, a sliding window based lightweight algorithm is proposed for real-time heartbeat detection on the raw ECG signals. It includes max-min curve and dynamic (adaptive) threshold generation, and error correction. The thresholds are adapted automatically. Moreover, a sliding window based search strategy is also proposed for real-time feature extraction. Subsequently, a hybrid classifier is proposed, which embeds multiple ensemble methods, for abnormality classification in the final stage. It works as a meta classifier which generates multiple instances of base models to improve the overall classification accuracy. The proposed hybrid classifier is superior in performance, however, it is dedicated to offline processing owing to high computational complexity. Especially, the proposed hybrid classifier is also further extended to conduct novel class detection (i.e. unknown newly appeared abnormality types). A modified firefly algorithm is also proposed for parameter optimization to further improve the performance for novel class detection. The overall proposed system is evaluated using benchmark ECG databases to prove its efficiency. To illustrate the advantage of each key component, the proposed feature extraction, classification and optimization algorithms are compared with diverse state-of-the-art techniques. The empirical results indicate that the proposed algorithms show great superiority over existing methods.