Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship between fetal biometric assessment by ultrasonography and neonatal lamb vitality, birth weight and growth

Abstract Ultrasonographic examination of pregnant ewes can enable the identification of perinatal abnormalities and establish prenatal assistance responsible for minimizing morbidity and perinatal mortality. Therefore, we aimed to evaluate the feasibility of a fetal biometric analysis by ultrasonography to predict neonatal vitality and lamb growth during the first month of life. A longitudinal study was conducted with 13 healthy ewes, subjected to ultrasonographic examination every 15 days from 60th day of pregnancy until lambing, evaluating thoracic diameter, abdominal diameter, biparietal diameter, humerus, femur and placentome length. At birth, 22 lambs were assessed through Apgar score at 5 minutes and after 1 hour. Measurement of body weight was also carried out immediately at birth and weekly during 30 days after birth. Thoracic diameter showed a significant increase between 91-105 days and 121-135 days. Conversely, abdominal diameter had a progressive growth until 106-120 days, and then, a steady development was observed. Biparietal diameter showed progressive growth only towards days 91 and 105. For the humerus length, we verified a significant increase between 106-120 days and 121-135 days, remaining unaltered onwards; while femur length continued to grow until lambing. The linear regression analysis between birth weight and biparietal diameter at 60-75 days was high (R2=0.96; P<0.0001; coefficient of variability of 3.3%). In conclusion, ultrasonographic analysis of fetal biparietal diameter at mid-pregnancy can be used as a predictor of lamb weight at birth. Moreover, assessment of femur length at final pregnancy can be employed for fetal and neonatal development estimation.
The relationships between total, electricity and biofuels residential energy consumption and income in Latin America and the Caribbean Countries

Controlling residential energy consumption in Latino America and the Caribbean countries is crucial to reduce CO2 emissions, as it has an important energy-saving potential, and its environmental controls are difficult to displace offshore. The aim of this study is to analyze the relationships between residential energy consumption and income for 22 Latin America and the Caribbean countries in the period 1990-2013. For this purpose, residential energy environmental Kuznets curves (EKC) are estimated by taking into account the heterogeneity among the countries by including two control variables: one representing the possible effect of urbanization on residential energy use and the second representing the possible effect of petrol production.
Modified Recovery Algorithms Using proposed algorithm for Compressive Sampling

Compressive sampling (CS) has been an effective research area which plays an efficient role in many applications such as cognitive radio, imaging, radar and many other applications. In CS only a small number of linear measurements are used for reconstruction of the signal. The significant condition for dealing with compressed sensing system is that the signal in the input must be sparse. Most signals in nature are sparse or can be transformed to sparse by using any transform domain. This paper modifies all the recovery algorithms by using the proposed complex to real transformation algorithm. Conversion from not sparse signal to sparse by using Fourier transform will produce complexity, where this complexity can be removed using complex to real transformation algorithm and then applying it on all recovery algorithms to enhance their performance. By using the proposed algorithm, the sparse signal will be recovered in minimum error and less time. Also, the signal to error ratio from the recovery process is increased.
D 4 . 2 : Full API and Information Model Specification Deliverable

This deliverable presents the final version of the NECOS Resource Marketplace related specifications, including the resource discovery and matching methods, the information model, as well as the associated client-to-cloud and cloud-to-cloud APIs. D4.2 further includes more detailed examples of the Marketplace operation, a cost model formulation for resource pricing, and new state-of-the-art descriptions, related to the new contributions. In addition, D4.2 provides evaluations results regarding the performance and operation of the NECOS Marketplace. Document ID NECOS-D4.2
Quantitative phytoplankton physiology: implications for primary production and phytoplankton growth

A review of quantitative information on phytoplankton growth and photosynthesis obtained from studies employing clonal phytoplankton cultures has been undertaken. Where data permit, both chlorophyll-specific and carbon-spccific photosynthesis rates have been obtained. This analysis uses as its basis the photosynthesis-light response curve employed within the context of an energy budget for phytoplankton growth. Variability in the chlorophyll-specific light absorption coefficient (a ), the maximum photon efficiency of photosynthesis (0 m), and the chlorophyll-specific light-saturated photosynthesis rate ( P ^ 1) contribute to variations in photosynthesis rate. Much of the variability in a chl can be attributed to interspecific variability in a*chl, whereas variability in PjJ1 arises from both genetic and adaptive sources. The maximum quantum efficiency of photosynthesis appears to be largely independent of irradiance, but declines in response to nitrate limitation. Variability in the chlorophyll axarbon ratio, and the respiration and excretion rates contribute to uncoupling of growth from gross photosynthesis. Finally, the relationship between the traditional approach to the photosynthesis-irradiance (PI) curve based on the light-saturated photosynthesis rate (Pm1) and the light-limited initial slope (achl), and the biophysical approach based on the photosynthetic unit (PSU) cross-section (ctpsu), minimum turnover time for photosynthetic electron transfer (r) and the photosynthctic unit size (chlorophyll: PSU), is summarized.
X-REEact: Fighting Runtime Variances across Time and Space

Emerging CMPs (chip multiprocessors) are not only taking over the desktop and enterprise computing market but they are also positioning themselves as building blocks of future exascale systems due to the exceptional computing efficiency they offer in a single chip. An inherent characteristic of CMPs that presents a significant obstacle is runtime variation: energy consumption, thermal behavior, and process variation will vary across identically-designed components of a CMP, producing a negative impact on application power consumption, reliability and performance. Runtime variation has been identified as one of the key problems that could block further scaling of circuits if not properly addressed. It not only causes reliability problems itself, but it also makes power-saving techniques more susceptible to errors. Furthermore, diverse applications react to runtime variations differently. For example, while a class of HPC applications have self-healing properties which help them cope with variations in a natural fashion, other classes of HPC applications can experience substantial error rates under high runtime variance. Clearly, this observation rules out any one-size-fits-all type of solution. For example, conventional circuit/microarchitecture level solutions built upon the worst-case assumptions will not fly in an exascale regime since they would cause too much power at runtime and this power consumption would not be justified for many applications. Instead, what is needed is a low cost, low footprint, scalable and adaptive solution that can be reconfigured at runtime based on the dynamic needs of threads, applications, and workloads. Motivated by this pressing need, we propose X-REEact, a novel lightweight and scalable application-level runtime system with the following unique characteristics:
The implications of loan maturity on the probability of default: evidence from Peruvian long-term loans∗

This paper analyzes the impact of a limited set of credit risk drivers (collateral and number of bank-debtor relationships) on long-term loans, and evaluates the effect of loan maturity on the probability of default (PD). In our estimates, we treat households and firms separately and include variables that reflect repayment ability, debtor characteristics, loan conditions, and macroeconomic factors as controls. Our dataset includes more than twenty-six million loans granted by Peruvian financial institutions for the period 2012-2016. Using a set of logit models, we find evidence of a positive correlation between loan maturity and the PD for firms and households. Overall, credit risk drivers appear as heterogeneous when different loan maturities are considered. Furthermore, our results suggest that the impact of collateral on the PD is negative for firm loans, but positive for household loans, while the number of bank-debtor relationships has a positive impact among all models estimated. These findings can ultimately result in policy actions to mitigate the scarcity of long-term loans in the country. JEL classification: C35, C55, E51, G21
Global-scale benefit–cost analysis of coastal flood adaptation to different flood risk drivers using structural measures

Abstract. Coastal flood hazard and exposure are expected to increase over the course of the 21st century, leading to increased coastal flood risk. In order to limit the increase in future risk, or even reduce coastal flood risk, adaptation is necessary. Here, we present a framework to evaluate the future benefits and costs of structural protection measures at the global scale, which accounts for the influence of different flood risk drivers (namely sea-level rise, subsidence, and socioeconomic change). Globally, we find that the estimated expected annual damage (EAD) increases by a factor of 150 between 2010 and 2080 if we assume that no adaptation takes place. We find that 15 countries account for approximately 90 % of this increase. We then explore four different adaptation objectives and find that they all show high potential in cost-effectively reducing (future) coastal flood risk at the global scale. Attributing the total costs for optimal protection standards, we find that sea-level rise contributes the most to the total costs of adaptation. However, the other drivers also play an important role. The results of this study can be used to highlight potential savings through adaptation at the global scale.
USING ENVIRONMENTAL STANDARDS TO COMMUNICATE SECURITY, QUALITY AND ECO-DESIGN IN THE FURNITURE SECTOR: AN ITALIAN CASE STUDY

In today's society, environmental sustainability is becoming increasingly important. It raises the need for sustainable development in which the environment is both a constraint and a driving force for product and process innovations. Nevertheless, the effectiveness of these innovations depends strongly on the way in which they are communicated to the consumers. The aim of this study is to identify the role of environmental standards in the communication strategies of eco-sustainable products by using a case-study method related to the furniture sector.
Music and Literacy Prioritizing Instrumental Music Education at the Middle School Level

This paper explores the relationship between instrumental music training and literacy. Research shows that instrumental music training (such as band) has a positive impact on literacy rates and on learning in general. This relationship was not seen for choral studies but specifically for instrumental music education. When implemented correctly, music training supports the learning of students and boosts their reading levels. The quality of the music instruction also affects the outcomes. Through an exploration of these studies and their results, this paper argues that quality instrumental music training (i.e., quality band programs) leads to higher literacy rates, as measured by state test scores.
Self-Aware OS and Runtime in Exascale Systems

Future exascale systems will be composed by millions of heterogeneous components that will be dynamically activated and deactivated as result of contingent conditions, such as opportunities for power/energy efficiency, poor reliability, task/data movement and/or re-allocation of hardware resources. In this context, understanding whether an applications is executing at the maximum expected speed and efficiently performing computation is a hard task. Moreover, each individual component may operate in different states (DVFS, number of active cores/hardware threads, availability of memory), which further increases the complexity of efficiently managing system’s resources and may lead to sub-optimal performance or even performance degradation if not well guided. The programmer could theoretically provide low-level information (when and which core to power off, where to allocate a particular data structure) to guide the system’s actions but this would considerably increase the complexity of writing exascale applications. More importantly, some of the information, such as the allocation of global data structures, are not directly under the control of the programmer but are determined by the runtime system [1]. Finally, this effort may have to be repeated for each application/kernel development and whenever external conditions (new system configuration, different input set) change the characteristics of the application. A self-aware, automated system software (operating and runtime system) has the potentiality of extracting the necessary information from the application execution and the current system conditions. Solving the problem at OS/RT level has a direct impact on programmers’ productivity: first the programmer need not have to deal with the system complexity of scheduling a large number of tasks, dealing with faults and efficiently managing hardware resources. Second, an automated system can analyze different parameters, some of which are only known at run time and depends on the particular system System Monitor Interface Runtime System
Learning Entailment-Based Sentence Embeddings from Natural Language Inference

Large datasets on natural language inference are a potentially valuable resource for inducing semantic representations of natural language sentences. But in many such models the embeddings computed by the sentence encoder goes through an MLP-based interaction layer before predicting its label, and thus some of the information about textual entailment is encoded in the interpretation of sentence embeddings given by this parameterised MLP. In this work we propose a simple interaction layer based on predefined entailment and contradiction scores applied directly to the sentence embeddings. This parameter-free interaction model achieves results on natural language inference competitive with MLP-based models, demonstrating that the trained sentence embeddings directly represent the information needed for textual entailment. The inductive bias of this model leads to better generalisation to other natural language inference datasets, and the resulting sentence embeddings are better for other semantic tasks.
Third Law of Thermodinamics / Entropy and Financial Trading through Quantum and Shanon's Theory of Information

We have great knowledge of self-organization at the levels of physical, chemical, biological and social phenomena. Research, complexity analysis is an intellectual challenge and very useful. The paper presents a theoretical insight into the scientific complexity in specific areas of financial trading through the third law of thermodynamics / entropy and the relation between quantum and Shannon theory of information. Theoretical analysis gives implications for entropy through the information of pillars of reality. Financial Trading (different financial models of the stock market, some based on competition between informed investors and "noise trader" -noisy coding theory, game theory, and extreme theory) are followed by analysis and synthesis of the interrelationship of the third law of thermodynamics / entropy and their meaning following Ludwig Boltzmann formulas for physical entropy (S = k log W-relation between the microscopic and macroscopic world view) and the evolution of biology and biological complexity (John Kelly's formula for maximizing profits by Fredrik Burton and Richard Dawkins), which is the same as the profit of a successful gambler / investor (grows the same way). Physics and information create interplay; Real-world information is different in comparison to what seems to us at first glance, the world is in quantum mechanics ultimately (quantum theory information is over-set Shannon's information by somehow falling into Shannon's information). Information is synonymous with knowledge. The network of human behavior is based on theoretical principles of information technology (such as biology and physics). We wonder what is the entropy of the stock? The role of intelligence / human brain and entropy?
PERFORMANCE OF ARTIFICIAL NEURAL NETWORKS AND FUZZY INFERENCE SYSTEMS FOR PREDICTING CONCRETE COMPRESSIVE STRENGTH

Artificial neural networks (ANN) have been traditionally utilized for developing prediction models based on experimental data; however, fuzzy logic theory is a novel tool that could also be applied as well in this cases. The present study compares the performance of two different approaches; namely, ANN and fuzzy inference systems (FIS) for predicting concrete compressive strength. Adaptive neuro fuzzy inference systems (ANFIS) was the technique based on input – output experimental data that was used to create two Sugeno type fuzzy models for estimating concrete compressive strength and then compared with several ANN prediction models created through different methods. Subtractive clustering method was the clustering procedure for establishing the number of membership functions and fuzzy rules. Predicted data resulting from all models were presented in a comparative manner and validation analyses were conducted in order to observe the performance of ANN and ANFIS. The results indicate that both fuzzy models performed very well when estimating concrete compressive strength (i.e., all R2 values are greater than 90%) while for ANN, only one ANN method had an R2 value greater than 90%.
An innovative STEM outreach model (OH-Kids) to foster the next generation of geoscientists, engineers, and technologists

Abstract. Childhood education programmes aiming at incorporating topics related to science, technology, engineering, and mathematics (STEM) have gained recognition as key levers in the progress of education for all students. Inspiring young people to take part in the discovery and delivery of science is of paramount importance not only for their well-being but also for their future human development. To address this need, an outreach model entitled OH-Kids was designed to empower pupils through the development of high-quality STEM learning experiences based on a research project. The model is an opportunity for primary school learners to meet geoscientists while receiving the take-home message that anyone can get involved in scientific activities. The effort is part of a research project aimed at the real-time monitoring of precipitation in Mexico City, which is a smart solution to rainfall monitoring using information and communications technologies. The argument behind this effort is that in order to produce the next generation of problem-solvers, education should ensure that learners develop an appreciation and working familiarity with a real-world project. Results show success at introducing the role of researchers and STEM topics to 6–12-year-old learners.
A Grouping Hotel Recommender System Based on Deep Learning and Sentiment Analysis

Recommender systems are important tools for users to identify their preferred items and for businesses to improve their products and services. In recent years, the use of online services for selection and reservation of hotels have witnessed a booming growth. Customer’ reviews have replaced the word of mouth marketing, but searching hotels based on user priorities is more time-consuming. This study is aimed at designing a recommender system based on the explicit and implicit preferences of the customers in order to increase prediction’s accuracy. In this study, we have combined sentiment analysis with the Collaborative Filtering (CF) based on deep learning for user groups in order to increase system accuracy. The proposed system uses Natural Language Processing (NLP) and supervised classification approach to analyze sentiments and extract implicit features. In order to design the recommender system, the Singular Value Decomposition (SVD) was used to improve scalability. The results show that our proposed method improves CF performance.
Development of a virtual reality toolkit to enhance community walking after stroke

Community walking remains compromised in the majority of stroke survivors even after rehabilitation. We developed a virtual reality (VR) toolkit prototype to provide intensive, task-specific training of complex locomotor tasks as required for community walking in safe yet ecological virtual environments. The toolkit, supported by best evidence and developed using low-cost VR tools, comprises of 6 dimensions that include endurance, walking speed, postural transitioning environmental encumbrance, cognitive load and a more complex dimension combining encumbrance and cognitive load. Feedback collected through hands-on interaction and focus groups with stroke participants and clinicians will be used to refine the prototype. The resulting VR toolkit will be combined to field training practice as part of a multi-centered intervention study to enhance community walking in stroke survivors.
Operation Planning of Wind Farms with Pumped Storage Plants Based on Interval Type-2 Fuzzy Modeling of Uncertainties

AbstractThe operation planning problem encounters several uncertainties in terms of the power system’s parameters such as load, operating reserve and wind power generation. The modeling of those uncertainties is an important issue in power system operation. The system operators can implement different approaches to manage these uncertainties such as stochastic and fuzzy methods. In this paper, new fuzzy based modeling approach is implemented to develop the new formulation of power system problems under an uncertain environment with energy storage systems. Interval type2 fuzzy membership function (MF) is implemented to model the uncertainty of available wind power generation and the type-1 fuzzy MF is used to model the other parameters in weekly unit commitment (UC) problem. The proposed approach is applied to two different test systems which have conventional generating units, wind farms and pumped storage plants to consider differences between the type-1 and type-2 fuzzy approaches for uncertainty modeling. The results show that the total profit of UC problem using type-2 fuzzy MF is better than type-1 fuzzy MF.
Simulative Study of Wavelength Division Multiplexing Fiber Bragg Grating in Nuclear Reactors Monitoring

The technologies of wavelength division multiplexing (WDM) have been theoretically studied and analyzed for multiplexing fiber Bragg grating (FBG) in a single optical fiber. This method allows a single fiber to carry many of identical FBGs, making this sensor more appropriate in the nuclear reactors. The analysis demonstrates that the multiplexing capacity can be incredibly enhance small data rates and high channel spacing. The interference effect among FBGs multi-reflections channels must be taken into account. This paper simulate WDM based FBG for a channel spacing of 0.1, 0.3, 0.5, 0.8, 1 nm Gaussian apodized FBGs at data rates of 2.5, 10, 40,100,160, 250 Gb/s respectively for nuclear applications. All simulations were performed in Optisystem software.