Miracle-dz/UniFAR-FTUs huggingface dataset:
Hadoop based Demography Big Data Management System

Demography relates to statistical measures in social sciences, which is the study of human population in a certain period. Its main focus is to calculate birth & death rate, mobility, literacy ratio, and marriages. The immense population growth is presenting new challenges to handle massive quantities of demographic data (big data). With the evolution of computer age and software development, different procedures have been introduced to deal with big data. Most of the systems are based on traditional rational database, which lack in their capability to handle big data efficiently and accurately. Apache Hadoop platform is becoming famous for handling big data problems. Herein, we propose a demography big data handling system under Hadoop ecosystem to solve the issue of demography large-scale data handling. The main components of our system are Sqoop, HBase and Hive. Our system defines a method to export data from different RDBMS to Hadoop HBase. Moreover, HBase- Hive integration is a good architecture to store and query data for analysis efficiently. The results indicate that the presented approach better handles the distributed datasets as a single dataset to analyze for future estimates. Our approach will provide advancement in handling sophisticated demography big data and will eventually improve analytics such as promoting sustainable agriculture, preventing poverty, and decreasing death rate.
BIPV Power Plants and Policy recommendatons

The radiant light energy from the sun is converted into useful electricity using photovoltaic technology. Harnessing solar energy is simple and easy operation with the installation of PV modules in various configurations (open rack, roof mount, tracking, building integrated, building applied, floating). Among these configurations, BIPV is the most trending and ecologically efficient installation methods. This paper presents a study on the building integrated photovoltaic systems with the electric power system classification. This classification includes on-grid, off-grid, and hybrid BIPV power plants. The studied three classifications were compared in terms of the operation, components, installation configuration etc. General recommendations and various policy options for BIPV are also discussed. This study shows, BIPV power systems discussed in three combinations would contribute to the energy needs of the developed/developing nations by adding pleasing look to the buildings without having negative impacts on the ecology.
cAMP Response Element-Binding Protein (CREB): A Possible Signaling Molecule Link in the Pathophysiology of Schizophrenia

Dopamine is a brain neurotransmitter involved in the pathology of schizophrenia. The dopamine hypothesis states that, in schizophrenia, dopaminergic signal transduction is hyperactive. The cAMP-response element binding protein (CREB) is an intracellular protein that regulates the expression of genes that are important in dopaminergic neurons. Dopamine affects the phosphorylation of CREB via G protein-coupled receptors. Neurotrophins, such as brain derived growth factor (BDNF), are critical regulators during neurodevelopment and synaptic plasticity. The CREB is one of the major regulators of neurotrophin responses since phosphorylated CREB binds to a specific sequence in the promoter of BDNF and regulates its transcription. Moreover, susceptibility genes associated with schizophrenia also target and stimulate the activity of CREB. Abnormalities of CREB expression is observed in the brain of individuals suffering from schizophrenia, and two variants (-933T to C and -413G to A) were found only in schizophrenic patients. The CREB was also involved in the therapy of animal models of schizophrenia. Collectively, these findings suggest a link between CREB and the pathophysiology of schizophrenia. This review provides an overview of CREB structure, expression, and biological functions in the brain and its interaction with dopamine signaling, neurotrophins, and susceptibility genes for schizophrenia. Animal models in which CREB function is modulated, by either overexpression of the protein or knocked down through gene deletion/mutation, implicating CREB in schizophrenia and antipsychotic drugs efficacy are also discussed. Targeting research and drug development on CREB could potentially accelerate the development of novel medications against schizophrenia.
Joint Tracking of Multiple Frequency Signals from the same GNSS satellite

We propose a joint tracking architecture for multiple frequency signals originated from the same GNSS satellite in order to improve the reliability of position solution by making use of redundancy of multiple frequency signals. A joint higher order tracking loop is used to track the signal dynamics common across multiple frequency signals and a narrow individual signal tracking loop is used to track an uncommon residual carrier phase variation due to frequency dependent error sources. A suitable collaboration across multi-frequency signal tracking improves the individual signal tracking sensitivity and the robustness to frequency selective interference. Furthermore, joint signal tracking improves the receiver resistance to intentional interference such as jamming and spoofing. Performance of the proposed architecture is verified with live multi-frequency satellite signals collected from GPS Block-IIF satellites under the influence of frequency selective matched spectrum jamming signal.
Automated Deep Reinforcement Learning Environment for Hardware of a Modular Legged Robot

In this paper, we present an automated learning environment for developing control policies directly on the hardware of a modular legged robot. This environment facilitates the reinforcement learning process by computing the rewards using a vision-based tracking system and relocating the robot to the initial position using a resetting mechanism. We employ two state-of-the-art deep reinforcement learning (DRL) algorithms, Trust Region Policy Optimization (TRPO) and Deep Deterministic Policy Gradient (DDPG), to train neural network policies for simple rowing and crawling motions. Using the developed environment, we demonstrate both learning algorithms can effectively learn policies for simple locomotion skills on highly stochastic hardware and environments. We further expedite learning by transferring policies learned on a single legged configuration to multi-legged ones.
Internet of Things (IoT) Operating Systems Support, Networking Technologies, Applications, and Challenges: A Comparative Review

The Internet of Things (IoT) has become a reality. As the IoT is now becoming a far more common field, the demand for IoT technologies to manage the communication of devices with the rest of the world has increased. The IoT is connecting various individual devices called things and wireless sensor networks is also playing an important role. A thing can be defined as an embedded device based on a micro controller that can transmit and receive information. These devices are extremely low in power, memory, and resources. Therefore, the research community has recognized the importance of IoT device operating systems (OSs). An adequate OS with a kernel, networking, real-time capability, and more can make these devices flexible. This review provides a detailed comparison of the OSs designed for IoT devices on the basis of their architecture, scheduling methods, networking technologies, programming models, power and memory management methods, together with other features required for IoT applications. In addition, various applications, challenges, and case studies in the field of IoT research is discussed.
Hybrid Modulation Strategy for Asymmetrical Cascade H-Bridge Multilevel Inverters

In this paper, a modulation strategy proposal for asymmetrical cascade converters is presented. This proposal consists of a combination of staircase and PWM switching strategies. The simplicity of the implementation makes this proposal attractive, and it is intended for any number of cells and it works for asymmetrical binary and trinary converters. Furthermore, the main feature of this proposal is that it permits reduction of the AC voltage distortion, as shown through simulation and experimental results.
Longitudinal evaluation of markers of inflammation in HIV‐positive and HIV‐negative Rwandan women

African women are disproportionately affected by HIV infection and may experience non‐AIDS‐related complications associated with inflammation. High‐sensitivity C‐reactive protein (hsCRP), d‐dimer and transthyretin have been examined as inflammatory markers elsewhere, but it is unclear how they change over time in HIV‐negative or HIV‐positive African women with or without antiretroviral therapy (ART) initiation.
CANDLE: The critical analysis of the nocturnal distribution of light exposure – A prospective pilot study quantifying the nocturnal light intensity on a critical care unit

Patients with critical illness have disrupted circadian rhythms, which can lead to increased morbidity, mortality and length of intensive care unit stay. Light intensity within the intensive care unit influences the circadian rhythm and may therefore impact on patient outcome. We performed an observational single-centre pilot study monitoring nocturnal light exposure of intensive care unit patients between November and December 2016. As there are currently no medical guidance on recommended light levels, we audited our findings against building regulation standards. The median light intensity was 1.5 lux, which is below the 20 lux standards; however, there were significant outliers. There was positive correlation between patient illness severity based on SOFA score and maximum lux (R = 0.45, P = 0.026); however, there was no relationship between patient illness severity and median lux exposure (R = 0.23, P = 0.28). As illness severity increased so did the time spent greater than 20 lux (R = 0.59, P = 0.0021), and the individual occasions where lux breached the 20 lux limit (R = 0.52, P = 0.009). There was no relationship between illness severity of neighbouring patients and maximum lux (R = −0.11, P = 0.69) or neighbouring illness severity and median lux (R = −0.04, P = 0.87). This preliminary work will form the basis of future projects, including national guidance and evaluating the impact of environmental light on patient-centred outcomes.
Phonocardiogram Signal Denoising Based on Nonnegative Matrix Factorization and Adaptive Contour Representation Computation

This letter introduces a new technique for phonocardiogram (PCG) signal denoising based on nonnegative matrix factorization (NMF) of its spectrogram and adaptive contour representation computation (ACRC) of its short-time Fourier transform (STFT). More precisely, NMFs on PCG and synchronous electrocardiogram spectrograms are first used to filter out high-energy noises from PCG. Then, ACRC is performed on a low-pass filtered version of the STFT of the resulting signal to identify relevant time–frequency components that are subsequently used for signal retrieval. Numerical experiments conducted on a real database of noisy PCG signals, Signal Separation Evaluation Campaign (SiSEC2016), illustrate the superiority of the proposed method over state-of-the-art techniques.
Stroke in China and India

Having more than one-third of the world's 7.6 billion population, China and India experience heavy personal, social, and financial costs of stroke and other vascular disease, in the context of having a high prevalence of background vascular risk and rapidly evolving demography (aging, urbanization), lifestyles (sedentary, diet), and economies. However, their differing health care systems and insurance schemes may influence the patterns of care and recovery from stroke, but to date, no one has directly quantified this. The article by Li et al.1 in this issue of Neurology® is therefore a welcome contribution to defining the comparative patient profile, as well as the management and outcomes, for acute ischemic stroke (AIS) between these 2 countries.
An Automatic Test Data Generation Tool using Machine Learning

This paper discusses an open source tool that is capable to assist users in generating automatic test data for multiple programs under test. The tool works by clustering inputs data from a corpus folder and producing generative models for each of the clusters. The models have a recurrent neural network structure and their training and sampling are parallelized with Tensorflow. As features, the tool supports online updating of the corpus folder and the already trained models, and supports any kind of program under test or input file example. There is no manual effort for users, other than customizing per cluster parameters for optimizations or using function hooks that they could use through a data structure, which acts as an expert system. The evaluation section shows the efficiency of both learning and code coverage using some concrete programs and new tests sampling methods.
Multi-Reference Training with Pseudo-References for Neural Translation and Text Generation

Neural text generation, including neural machine translation, image captioning, and summarization, has been quite successful recently. However, during training time, typically only one reference is considered for each example, even though there are often multiple references available, e.g., 4 references in NIST MT evaluations, and 5 references in image captioning data. We first investigate several different ways of utilizing multiple human references during training. But more importantly, we then propose an algorithm to generate exponentially many pseudo-references by first compressing existing human references into lattices and then traversing them to generate new pseudo-references. These approaches lead to substantial improvements over strong baselines in both machine translation (+1.5 BLEU) and image captioning (+3.1 BLEU / +11.7 CIDEr).
Frequency Support From a DC-Grid Offshore Wind Farm Connected Through an HVDC Link: A Communication-Free Approach

DC collection grid and high-voltage direct-current (HVDC) transmission lines provide promising solutions for an offshore wind farm (OWF). However, due to the decoupling effect of the dc collection grid and an HVDC link, an HVDC-connected dc-grid OWF cannot directly participate in the frequency regulation of the onshore ac grid. To address this issue, this paper presents a communication-free control scheme, which allows an HVDC-connected all-dc OWF to contribute to the onshore ac grid frequency regulation. This scheme consists of making the offshore dc collection grid voltage and onshore frequency deviations proportional. It relies on the HVDC voltage control at the onshore converter, together with the dc collection grid voltage control at the offshore converter. Furthermore, by continuously adjusting the droop of each wind generator in response to its local wind speed, this scheme takes into account the wind speed difference among wind generators, thereby improving the frequency nadir/peak and guaranteeing the wind generator stability. Simulation results verify the effectiveness of the proposed scheme.
Evidence of induced muscle regeneration persists for years in the mouse

Introduction: Efficient repositioning of centralized nuclei after injury has long been assumed, with centralized nuclei frequently cited as indicators of ongoing regeneration. However, reports of centralized nuclei that persist after full recovery of fiber area and muscle force production call into question the time course of nuclear repositioning. Methods: We evaluated regeneration after cardiotoxin‐induced damage in 10‐week‐old mice by quantifying intracellular and extracellular pathology at 2 and 94 weeks post‐injection. Results: Centrally nucleated fibers were still prevalent at 94 weeks post‐injection, representing > 25% of muscle fibers. Areas with > 90% centrally nucleated fibers could still be identified. Extra‐myocellular indicators of regeneration (e.g., fibrosis and fatty infiltration) also remained significantly elevated at the 94‐week time‐point. Discussion: These findings indicate that not all nuclei are repositioned at the conclusion of induced muscle regeneration. Muscle Nerve 58:858–862, 2018
The effects of male harm vary with female quality and environmental complexity in Drosophila melanogaster

Mate competition provides the opportunity for sexual selection which often acts strongly on males, but also the opportunity for sexual conflict that can alter natural selection on females. Recent attention has focused on the potential of sexual conflict to weaken selection on females if male sexual attention, and hence harm, is disproportionately directed towards high- over low-quality females, thereby reducing the fitness difference between these females. However, sexual conflict could instead strengthen selection on females if low-quality females are more sensitive to male harm than high-quality females, thereby magnifying fitness differences between them. We quantify the effects of male exposure on low- versus high-quality females in Drosophila melanogaster in each of two environments (‘simple’ and ‘complex’) that are known to alter behavioural interactions. We show that the effects of male harm are greater for low- compared to high-quality females in the complex but not the simple environment, consistent with mate competition strengthening selection on females in the former but not in the latter environment.
Linked Data Approach to Mutually Enrich Traditional Education Resources with Global Open Education

Education is one of the fields that appreciate values such as interoperability, openness, shareability and reusability due to its deep impact on the whole education's evolution. However, most of the educational organizations suffer from lack of interoperability and being heterogeneous within their internal resources schemas and with the external education organizations and movements. This hinders the mutual benefits exploitation among these organizations. Examples of such significant benefits are the mutual enrichment between the traditional education organizations resources and open educational movements such as open education resources (OER). These benefits could lead to important leap in education evolution. To achieve such purpose and reach to more connected open educational resources, we propose in this paper an interoperable architecture to produce and exploit education resources as linked open data that is well aligned in education organizations internally and externally. That is due to the capabilities of linked data that offer common ontologies and vocabularies to make common understanding and interoperable access of education resources. In this architecture we suggest ‘LD-wrapper’ components to produce ‘linked open data layer’ of integrated educational data within the education organization. We also propose, in this architecture, performing two levels of alignment internally and externally to enrich data with OER, external linked education cloud and the global Linked Open Data (LOD) cloud. This leads to providing the education organization with SPARQL end point to also enable others to exploit its linked data.
Towards a Cloud-based System for Software Protection and Licensing

In this paper we report on the commercial background as well as resulting high-level architecture and design of a cloud-based system for cryptographic software protection and licensing. This is based on the experiences and insights gained in the context of a real-world commercial R&D project at Wibu-Systems AG, a company that specialises in software encryption and licensing solutions.
A Distributed Access Control Scheme based on Risk and Trust for Fog-cloud Environments

Fog computing is a technology, which benefits from both Cloud Computing and Internet of Things paradigms. Instead of centralizing the information stored in the cloud, the idea in the fog environment is to use devices located at the edge of the network to prevent congestion during data exchanges. Since its development, the fog computing was integrated in numerous applications, including, connected vehicles (v2x), and smart cities, among others. Furthermore, the fog environment is highly variable within the resources as it varies dynamically. Therefore, a careful management of resources is essential for maximizing the efficiency of this distributed environment. That is why, the access control to resources is one of the challenging issues to be taken into account to enhance security in fog environments. In this paper, we develop a novel distributed fogcloud computing mechanism for access control based on trust and risk estimation. We evaluate the performance of the novel approach through appropriate simulations, which improves system performance by minimizing the time spent to have permissions to access services and optimizing the overall system resource
Comparison of Different Bio-based Polymers for Electronic Substrates

Year by year the amount of bio-based material is rising. Caused by the lack of petrochemical resources in future years, it will be more and more important to research new alternatives for standard material. Two different bio-based polymer composites were produced in lab scale as a possible alternative for printed circuit boards (PCB). The materials are compared. The paper at hand presents results of present studies like characteristic properties, thermal process compatibility and the realization of lead structures on the substrates. The suitability of technical conceptions, the use for the electronics assembly, and possible applications are examined. Results are showcasing the technological usability. The material properties and further modifications of these substrates will be discussed.
From VQA to Multimodal CQA: Adapting Visual QA Models for Community QA Tasks

In this work, we present novel methods to adapt visual QA models for community QA tasks of practical significance - automated question category classification and finding experts for question answering - on questions containing both text and image. To the best of our knowledge, this is the first work to tackle the multimodality challenge in CQA, and is an enabling step towards basic question-answering on image-based CQA. First, we analyze the differences between visual QA and community QA datasets, discussing the limitations of applying VQA models directly to CQA tasks, and then we propose novel augmentations to VQA-based models to best address those limitations. Our model, with the augmentations of an image-text combination method tailored for CQA and use of auxiliary tasks for learning better grounding features, significantly outperforms the text-only and VQA model baselines for both tasks on real-world CQA data from Yahoo! Chiebukuro, a Japanese counterpart of Yahoo! Answers.
RNA-Binding Proteins in Amyotrophic Lateral Sclerosis

Significant research efforts are ongoing to elucidate the complex molecular mechanisms underlying amyotrophic lateral sclerosis (ALS), which may in turn pinpoint potential therapeutic targets for treatment. The ALS research field has evolved with recent discoveries of numerous genetic mutations in ALS patients, many of which are in genes encoding RNA binding proteins (RBPs), including TDP-43, FUS, ATXN2, TAF15, EWSR1, hnRNPA1, hnRNPA2/B1, MATR3 and TIA1. Accumulating evidence from studies on these ALS-linked RBPs suggests that dysregulation of RNA metabolism, cytoplasmic mislocalization of RBPs, dysfunction in stress granule dynamics of RBPs and increased propensity of mutant RBPs to aggregate may lead to ALS pathogenesis. Here, we review current knowledge of the biological function of these RBPs and the contributions of ALS-linked mutations to disease pathogenesis.