Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical and biogeochemical impacts of RCP8.5 scenario in the Peru upwelling system

Abstract. The northern Humboldt Current system (NHCS or Peru upwelling system) sustains the world's largest small pelagic fishery. While a nearshore surface cooling has been observed off southern Peru in recent decades, there is still considerable debate on the impact of climate change on the regional ecosystem. This calls for more accurate regional climate projections of the 21st century, using adapted tools such as regional eddy-resolving coupled biophysical models. In this study three coarse-grid Earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are selected based on their biogeochemical biases upstream of the NHCS, and simulations for the RCP8.5 climate scenario are dynamically downscaled at ∼12 km resolution in the NHCS. The impact of regional climate change on temperature, coastal upwelling, nutrient content, deoxygenation, and the planktonic ecosystem is documented. We find that the downscaling approach allows us to correct major physical and biogeochemical biases of the ESMs. All regional simulations display a surface warming regardless of the coastal upwelling trends. Contrasted evolutions of the NHCS oxygen minimum zone and enhanced stratification of phytoplankton are found in the coastal region. Whereas trends of downscaled physical parameters are consistent with ESM trends, downscaled biogeochemical trends differ markedly. These results suggest that more realism of the ESM circulation, nutrient, and dissolved oxygen fields is needed in the eastern equatorial Pacific to gain robustness in the projection of regional trends in the NHCS.
End-to-End Distortion Analysis of Nonorthogonal Layered Coding Over Relay-Assisted Networks

This article investigates cross-layer design of nonorthogonal layered (NOL) coding schemes over relay-assisted downlink of cellular networks that incorporates with successive refinement (SR) source code for transmission of adaptive multiresolution multimedia content. Two novel-layered relaying strategies, namely NOL compress-forward (NOL-CF) and NOL decode-forward (NOL-DF) coding, concatenated to the SR source coding scheme are outlined. The network parameters are jointly optimized, to derive optimal end-to-end system performance. Taking the expected distortion (ED) as the end-to-end system performance metric, the explicit analytical solution achieving optimal ED under the proposed NOL-CF scheme with two layers is derived. For the NOL-DF coding, the numerical analysis demonstrates the merits of the proposed scheme, which dynamically adapts the transmission to the instantaneous channel realization in the absence of channel knowledge at the source node. The superiority of the proposed layered coding over several available nonorthogonal multiple access relaying schemes is demonstrated for Gaussian channels. Also, simulation results confirm the effectiveness of the proposed relay-assisted NOL codes to improve the ED over Rayleigh fading channel.
A frequentist and a Bayesian approach to estimating HIV prevalence accounting for non-response using population-based survey data.

Enhanced and novel frequentist and Bayesian approaches to estimating disease measures such as HIV prevalence utilizing the recent advances in statistical computing software are explored and applied making use of population-based complex survey data. In particular design-consistent estimates and logistic regression models for HIV prevalence are respectively computed and tted using each of the approaches. Practical survey data are rarely obtained using simple random sampling schemes, instead complex sampling designs, that are designed to re ect complex underlying population structures, are employed. These designs usually involve strati cation, multistage sampling and unequal selection probability of sampling units giving rise to data that are hierarchical (multilevel), clustered, and hence correlated. This is particularly true for large-scale population-based surveys. Consequently this often gives rise to units that are correlated within clusters as well as multiple sources of variability rendering standard statistical methods based on the assumption of independence of units inappropriate. Survey logistic regression models built from a generalized linear modelling framework were used to explain the variation in HIV prevalence accounting for the nonindependence of the units. In addition, a hierarchical logistic regression model built from a generalized linear mixed modelling framework was used to capture the variability and correlation of the units within clusters and further determine how di erent layers interact and impact on a response variable. In particular, the logistic regression models for HIV prevalence on demographic, behavioural and socio-economic variables were developed from a frequentist and a Bayesian perspective. Statistical methods that incorporate prior known information about unknown parameters are vital in most scienti c and biological research especially in studies where replicative experimental investigations are not possible. The Bayesian statistical paradigm o ers a framework upon which a prior distribution of a parameter can be combined with the likelihood of the observed data to obtain a posterior distribution for explaining the
Multiple-Choice Question Answering Over Semi-Structured Tables

Question answering (QA) is the task of automatically finding answers to natural language questions. A QA system requires access to some form of knowledge in order to find the answers. Most QA tasks use raw text corpora or structured knowledge bases as knowledge. However, raw text corpora, although easy to get in large quantities, are hard to reason with by machines. Structured knowledge bases are easy to reason with, but require manual effort to normalize. We view semi-structured tables as a compromise between raw text corpora and structured knowledge bases. Semi-structured tables require less manual effort to build comparing with structured knowledge bases, and their structured properties make it easy for automated reasoning. In this thesis, we build a QA system that can answer multiple-choice questions based on semi-structured tables. We tackle the task in two steps: table retrieval and answer selection. To retrieve the most relevant table to the questions, we build a feature-based model that can effectively take the candidate choices into account. To find the best answer based on the retrieved table, we first measure the relevance between the question and rows in the table, then extract the best answer from the most relevant rows. Evaluation on the TabMCQ benchmark shows that our system achieves a huge improvement over the previous state-of-the-art system.
Magnesium nanocomposites reinforced with rare earth element nanoparticles: nanoindentation-driven response

Abstract Magnesium (Mg), as the lightest metallic material, is 33% lighter than aluminum which makes it, potentially, a great replacement for aluminum and its alloys. However, Mg in pure and alloyed conditions is brittle at ambient temperature which largely limits their applications. One key solution to enhance the strength and ductility of Mg and its alloys is to embed thermally-stable nano-size reinforcements within the Mg matrix to produce so-called “Mg nanocomposites”. The Mg nanocomposites are considering revolutionizing energy-saving lightweight materials of the future with enhanced strength and ductility properties. Mg nanocomposites are, however, at the initial degrees of improvement and consequently, systematic research is required to set up microstructure/property relationships at distinct potential conditions (i.e. temperatures and strain rates). In the present study, a nanoindentation testing approach is adopted to assess ambient-temperature small scale mechanical properties of a group of Mg nanocomposites reinforced with rare-earth element nanoparticles (NPs), i.e. Sm2O3. This paper tried to assess various nanoindentation-driven properties of the Mg-Sm2O3 nanocomposites and compare them with the pure Mg as the baseline. Graphical Abstract
Online learning and the effects on functional health: a pilot study

iv Chapter 1 . 1 Purpose Statement . 3 Null Hypothesis . 3 Operational Definitions . 4 Limitations and Delimitations . 4 Assumptions . 4 Significance . 4 Summary . 5 Chapter 2 . 6 Introduction . 6 Online Learning . 6 Physical Activity Guidelines . 9 Sedentary Lifestyle Risks . 10 Functional Health and the SF-36 . 11 Summary . 12 Chapter 3 . 14 Introduction . 14
The Association between Smoking Habits and Candida in the Oral Cavity

The current study examined the effect of smoking habits on the presence of candida in the oral cavity. The study group included 50 healthy smokers (age range 20-50 years, mean 26.78 years, median 25 years and SD ± 6.9). As reference we examined a group of 50 healthy nonsmokers with similar form of age and gender. This study was the first to use the Diaslide device, which was modified to provide a quantitative assessment of candidal colonies. Of all the parameters which evaluated the influence of smoking on candida, the number of colonies in saliva was the only one that was significantly higher in smokers compared to non-smokers (p=0.037). The current study had demonstrated that smoking appears to have a minor influence on candidal colony growth, compared to other known factors such as dry mouth, immunodeficiency disorders or antibiotic treatment. It is recommended that the study will be repeated with participants of different combinations of age and smoking habits, and possibly research differences between varied candida species in association with tobacco smoking in the oral cavity.
Profiling Language Learners in the Big Data Era

The educational data revolution has empowered universities and educational institutes with rich data on their students, including information on their academic data (e.g., program completion, course enrolment, grades), learning activities (e.g., learning materials reviewed, discussion forum interactions, learning videos watched, projects conducted), learning process (i.e., time, place, path or pace of learning activities), learning experience (e.g., reflections, views, preferences) and assessment results. In this paper, we apply clustering to profile students from one of the largest Massive Open Online Courses (MOOCs) in the field of Second Language Learning. We first analyse the profiles, revealing the diversity among students taking the same course. We then, referring to the results of our analysis, discuss how profiling as a tool can be utilised to identify at-risk students, improve course design and delivery, provide targeted teaching practices, compare and contrast different offerings to evaluate interventions, develop policy, and improve self-regulation in students. The findings have implications for the fields of personalised learning and differentiated instruction.
Design Rules, Volume 2: Chapter 17 The Wintel Standards-based Platform

The purpose of this chapter is to use the theory of bottlenecks laid out in previous chapters to better understand the dynamics of an open standards-based platform. I describe how the Wintel platform evolved from 1990 through 2000 under joint sponsorship of Intel and Microsoft. I first describe a series of technical bottlenecks that arose in the early 1990s concerning the “bus architecture” of IBM-compatible PCs. Intel’s management of buses demonstrates how, under conditions of distributed supermodular complementarity, a platform sponsor can reconfigure the modular structure of a technical system, property rights within the system, and its own zone of authority to increase system-wide throughput, while protecting its own strategic bottleneck from disintermediation. I go on to describe how Microsoft used platform envelopment to establish a second strategic bottleneck in productivity software and later to respond to the threat of disintermediation from platform-independent Internet browsers. I end the chapter by discussing the conditions under which shared platform sponsorship can be a long-term dynamic equilibrium.
Adaptive bandwidth control for multi-band OFDM transmission with spectrum sharing

: This paper proposes an adaptive bandwidth control method in a transmitter for multi-band OFDM transmission with spectrum sharing. The proposed method controls the bandwidth used by a multi-band system to reduce out-of-band distortion noise which interferes to other spectrum sharing systems. In addition, the method uses clipping followed by in-band and out-of-band filtering to satisfy required interference power conditions on other system bands. The evaluation results by computer simulations show that adaptive band selection in the proposed bandwidth control can reduce out-of-band distortion noise power so that the required conditions can be satisfied in other spectrum sharing systems.
Inverse Inclusion: Transforming Dispositions of Disability and Inclusion

Inverse inclusion, a novel pedagogy, transforms preservice teachers’ dispositions about disability and inclusion during an action research study of two university intercession service-learning course collaborations with a communitybased art program for adults with disabilities (clients). In this approach, university students (preservice teachers) rotate and reflect on roles as student, teacher, teacher’s assistant, and observer within an inclusive art class. Among these rotations, the student position relinquishes their hierarchical perspective as teacher, assistant, and observer, and situates them as a collaborative learner, conducive to building egalitarian relationships with clients. Based on qualitative data from university student participants in the form of pre and postquestionnaires, reflections, and focus group interviews, most students transformed their perceptions about disability, increased their own confidence and advocacy for teaching in an inclusion setting, and were most influenced during their role as student working alongside clients.
Using Reinforcement Learning and Simulation to Develop Autonomous Vehicle Control Strategies

W hile machine learning in autonomous vehicles development has increased significantly in the past few years, the use of reinforcement learning (RL) methods has only recently been applied. Convolutional Neural Networks (CNNs) became common for their powerful object detection and identification and even provided end-to-end control of an autonomous vehicle. However, one of the requirements of a CNN is a large amount of labeled data to inform and train the neural network. While data is becoming more accessible, these networks are still sensitive to the format and collection environment which makes the use of others’ data more difficult. In contrast, RL develops solutions in a simulation environment through trial and error without labeled data. Our research expands upon previous research in RL and Proximal Policy Optimization (PPO) and the application of these algorithms to 1/18th scale cars by expanding the application of this control strategy to a full-sized passenger vehicle. By using this method of unsupervised learning, our research demonstrates the ability to learn new control strategies while in a simulated environment without the need for large amounts of real-world data. The use of simulation environments for RL is important as the unsupervised learning methodology requires many trials to learn appropriate desired behavior. Running this in the real-world would be expensive and impractical, however the simulation enables the solutions to be developed at low cost and time as the process can be accelerated beyond real-time. The simulation environment has high-fidelity to model vehicle dynamics as well as rendering capability for domain adaptation, and guarantees successful simulation-to-real
Understanding Image Caption Algorithms: A Review

The technology of Image caption is developing rapidly. In order to review the recent advancement in this field, this article briefly summarize several typical works in image caption researching, in which they figured out new ways to improve the accuracy or efficiency. We describe the methods, organize the results of experiments in one form, and then analyse the data. Besides, a novel quantitative metric which can measure the quality of image caption more objectively is also introduced.
Feature Extraction Using Discriminant Graph Laplacian Principal Component Analysis with Application to Biomedical Datasets

In this paper, we propose a manifold learning method called discriminant graph Laplacian principal component analysis (DGLPCA) for feature extraction. The proposed method projects high dimensional data into a lower dimensional subspace while preserving much of the intrinsic structure of the data. Moreover, DGLPCA integrates maximum margin criterion into its objection function to improve class separability in the lower dimensional space. The effectiveness of the proposed method is demonstrated on two publicly available biomedical datasets taken from UCI machine learning repository. The results show that our proposed method provides more discriminative power compared to other similar approaches.
Habitat use of an endangered beetle Carabus hungaricus assessed via radio telemetry

Carabus hungaricus is an endangered habitat specialist of tall-grass steppe occurring in Pannonian region. In this study, we used radio telemetry to examine whether habitat type (different habitat patches in steppe mosaic), sex, daytime, temperature and air pressure affect movement activity of the species. For seven-day tracking during the reproductive period of C. hungaricus in October 2017, we used 10 individuals, five males and five females, equipped by 0.3 g VHF transmitters. We found that the average speed was 1.29 m/h for the fastest individual and 0.21 m/h for the slowest one. Considering the shape of trajectories, beetles mostly randomly walked around; only in few cases they covered larger distances between two tracking sessions. Habitat type significantly affected beetles’ movement; the average speed was higher in grassy patches and under common juniper stands, while it decreased in mosses and litter under bushes. Although there was no effect of sex, daytime and air pressure on beetles’ activity the temperature had a positive effect on movement behaviour. We showed that preferred habitat observed from the human perspective was not in fact equally utilized by C. hungaricus. The availability of suitable patches within steppe mosaic can be therefore crucial for species persistence.
Effects of emotion and semantic relatedness on recognition memory: Behavioral and electrophysiological evidence

Some aspects of our memory are enhanced by emotion, whereas other can be unaffected or even hindered. Previous studies report impaired associative memory of emotional content, an effect termed associative “emotional interference”. The current study used EEG and an associative recognition paradigm to investigate the cognitive and neural mechanisms associated with this effect. In two experiments, participants studied negative and neutral stimulus-pairs that were either semantically related or unrelated. In Experiment 1 emotions were relevant to the encoding task (valence judgment) whereas in Experiment 2 emotions were irrelevant (familiarity judgment). In a subsequent associative recognition test, EEG was recorded while participants discriminated between intact, rearranged, and new pairs. An associative emotional interference effect was observed in both experiments, but was attenuated for semantically related pairs in Experiment 1, where valence was relevant to the task. Moreover, a modulation of an early associative memory ERP component (300-550 ms) occurred for negative pairs when valence was task-relevant (Experiment 1), but for semantically related pairs when valence was irrelevant (Experiment 2). A later ERP component (550-800 ms) showed a more general pattern, and was observed in all experimental conditions. These results suggest that both valence and semantic relations can act as an organizing principle that promotes associative binding. Their ability to contribute to successful retrieval depends on specific task demands.
Heated, humidified high-flow nasal cannula vs. nasal CPAP in infants with moderate respiratory distress

Background Respiratory distress is the most common cause of morbidity in premature babies in the delivery room. Nasal continuous positive airway pressure (nCPAP) is widely used as the preferred modality of treatment, although it may cause nasal trauma. Heated, humidified high-flow nasal (HHHFN) cannula is an alternative oxygen therapy, yet the safety and efficacy has not been widely studied. Objective To compare the safety and efficacy of HHHFN and nCPAP in premature babies with gestational age > 28 to < 35 weeks and moderate respiratory distress. Methods We conducted a randomized, non-inferiority, clinical trial using HHHFN vs. nCPAP as a treatment for moderate respiratory distress within 72 hours after they had been used. The efficacy endpoints were treatment failure, length of device use, length of Kangaroo Mother Care (KMC), and full enteral feeding time. Safety assessment included pain score, nasal trauma, and systemic complications. Results No differences were found in terms of incidence of endotracheal intubation within < 72 hours of HHHFN (20%) compared to nCPAP (18%) (P=0.799). However, there was a significant difference in moderate nasal trauma in nCPAP (14%) compared to HHHFN (0%)(P=0.006). There were no significant differences of blood gas analysis results, full enteral feeding time, length of KMC, length of device use, and rate of complications (bronchopulmonary dysplasia/BPD, intraventricular hemorrhage/IVH, patent ductus arteriosus/PDA, necrotizing enterocolitis/NEC and late onset neonatal sepsis/LONS) between the nCPAP and HHHFN groups. Conclusion The HHHFN is not inferior to nCPAP in terms of the safety and efficacy as primary non-invasive therapy in premature babies of gestational age > 28 to < 35 weeks with moderate respiratory distress . Compared to nCPAP, HHHFN induced lower nasal trauma.
The Mediating Role of Intention to Adopt in the Relationship between the Information Processing Requirements and Information Processing Capacity: The Application of Cloud Computing in Thai Auto Supply Chain

The main purpose of this study is to examine the relationship between information processing requirements and information processing capacity. Additionally, the intention to adopt is examined the mediating role of intention to adopt in the relationship between information processing requirements and information processing capacity in the Thai automobile industry is examined. The study has broached the conceptual model as an application of cloud computing in the Thai automobile supply chain. The SEM-PLS is adopted to examine the model. The Thai automobile industry is taken as the sample of the study. The study is of the view that the industry of cloud computing is growing rapidly to support the supply chain activities. However, there is lack of academic research on analyzing the application of cloud computing in managing supply chain theoretically. It has been revealed through literature review that the focus of researches is on identifying the applications and architectures of cloud environment. Moreover, the study proposes different limitations and opportunities related to cloud computing. The adoption of cloud computing by an organization has been examined theoretically in this research through use of information processing view and classical diffusion theory. Practitioners and academicians are given an understanding of the circumstances, which influence the firm to adopt cloud-computing technology within its supply chain.