Miracle-dz/UniFAR-FTUs huggingface dataset:
Delta-4: an open architecture for dependable systems

The Delta-4 project, carried out within the framework of the European Strategic Programme for Research in Information Technology (ESPRIT), has defined an open, fault-tolerant distributed computing architecture for use in application areas such as computer-integrated manufacturing, process control and office automation. The project has demonstrated its architecture via Unix-based implementations running demonstration applications at Credit Agricole (a credit card authorisation system) and Renault (a process control application within an integrated manufacturing cell). The Delta-4 approach to fault-tolerance is based upon the replication of software components on distinct host computers using a range of different replication strategies. The author presents an overview of the Delta-4 architecture, and of the facilities which it provides for supporting the requirements of real-time systems with respect to throughput and response. The problems of replica divergence are discussed, and a solution based on message selection and pre-emption synchronisation messages is proposed.
Platelet-Rich Plasma and Its Utilities in Alopecia: A Systematic Review.

BACKGROUND The use of platelet-rich plasma is becoming more prevalent in the field of dermatology. Variable preparation techniques and treatment methods have been described with reported success in alopecia. OBJECTIVE To consolidate the available evidence of platelet-rich plasma and its utility in the treatment of alopecia for the practicing dermatologist. METHODS Evaluating the available evidence up to May 31, 2018, a search was conducted in the PubMed database for "platelet rich plasma" or "platelet releasate" or "platelet gel" or "PRP" and "dermatology" or "skin" or "hair" or "cutaneous. " RESULTS Nineteen articles met the inclusion criteria for analysis including 3 alopecia areata studies with a total of 71 patients and 16 androgenetic alopecia studies with a total of 389 patients. Although the heterogeneity of the studies prevented direct comparisons and subsequent statistical analysis, the majority demonstrated that platelet-rich plasma produced successful hair growth in androgenetic alopecia and alopecia areata. CONCLUSION This review advocates for the use of platelet-rich plasma in 3 to 4 monthly sessions for the treatment of alopecia. Future studies should include a detailed description of the platelet-rich plasma isolation process to allow for comparison among studies, provide reproducibility, and generate a standardized treatment protocol.
MSH3 modifies somatic instability and disease severity in Huntington’s and myotonic dystrophy type 1

A genome-wide association study recently identified an MSH3/DHFR locus associated with Huntington’s disease progression. Flower, Lomeikaite et al. identify tandem repeat variants at this locus, and show that a three-repeat allele is associated with reduced somatic expansion, delayed onset and slower progression in Huntington’s disease and myotonic dystrophy type 1.
A review on parenteral delivery of peptides and proteins

Abstract The recent advances in pharmaceutical industry in the area of drug delivery and development have resulted into wide variety of biomolecules, particularly peptides and proteins, to palliate the treatment for severe diseases. The rampant development of peptides and proteins in industry mandate the understanding of their characteristic nature and developability. Despite their therapeutic potential, peptides and proteins pose complex challenges in drug delivery and require scientist to contemplate to achieve suitable delivery system. As most of the commercial products for proteins and peptides are available as a parenteral delivery and freeze drying process is one commonly used technique, it is imperative to understand the complex steps of development for better and faster product development. This review provides an overview of the stability and formulation development for peptides and proteins. The most common route for protein delivery, parenteral, has been focused on parenteral solution and lyophilization as formulation strategy. Additionally, new drug delivery and half-life extension approaches will further the reach of this unique class of molecules. Efforts are underway to explore the area with new technologies and development.
Impact of severe maternal morbidity on adverse perinatal outcomes in high-income countries: systematic review and meta-analysis protocol

Introduction Severe maternal morbidity (SMM) includes conditions that are on a continuum of maternal morbidity to maternal death. Rates of SMM are increasing both in high-income countries (HICs) as well as in low/middle-income countries (LMICs). There is evidence that analysis of SMM trends and detailed investigation of factors implicated in these cases may reflect the standard of maternal healthcare both in HICs and LMICs. SMM is also associated with poorer perinatal outcomes. The aim of this protocol is to describe the proposed methodology for the synthesis and analyses of the data describing the relationship between SMM and adverse perinatal outcomes in a systematic review and meta-analysis. Methods This systematic review and meta-analysis will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and will be registered with the International Prospective Register of Systematic Reviews (PROSPERO). Original peer-reviewed epidemiologic/clinical studies of observational (cross-sectional, cohort, case-control) and randomised controlled trial studies conducted in high-income countries will be included. An electronic search of PubMed, Embase, CINAHL and Scopus databases will be performed without restricting publication date/year. Two authors will independently screen the titles, review abstracts and perform data extraction. Where possible, meta-analyses will be done to calculate pooled estimates. Ethics and dissemination As this is a protocol for systematic review and meta-analysis of published data, ethics review and approval are not required. The findings will be published in peer-reviewed journals and disseminated at scientific conferences. PROSPERO registration number CRD42019130933.
Fog Computing Model to Orchestrate the Consumption and Production of Energy in Microgrids

Energy advancement and innovation have generated several challenges for large modernized cities, such as the increase in energy demand, causing the appearance of the small power grid with a local source of supply, called the Microgrid. A Microgrid operates either connected to the national centralized power grid or singly, as a power island mode. Microgrids address these challenges using sensing technologies and Fog-Cloudcomputing infrastructures for building smart electrical grids. A smart Microgrid can be used to minimize the power demand problem, but this solution needs to be implemented correctly so as not to increase the amount of data being generated. Thus, this paper proposes the use of Fog computing to help control power demand and manage power production by eliminating the high volume of data being passed to the Cloud and decreasing the requests’ response time. The GridLab-d simulator was used to create a Microgrid, where it is possible to exchange information between consumers and generators. Thus, to understand the potential of the Fog in this scenario, a performance evaluation is performed to verify how factors such as residence number, optimization algorithms, appliance shifting, and energy sources may influence the response time and resource usage.
A Review of The Lesser-Studied Microemulsion-Based Synthesis Methodologies Used for Preparing Nanoparticle Systems of The Noble Metals, Os, Re, Ir and Rh

This review focuses on the recent advances in the lesser-studied microemulsion synthesis methodologies of the following noble metal colloid systems (i.e., Os, Re, Ir, and Rh) using either a normal or reverse micelle templating system. The aim is to demonstrate the utility and potential of using this microemulsion-based approach to synthesize these noble metal nanoparticle systems. Firstly, some fundamentals and important factors of the microemulsion synthesis methodology are introduced. Afterward, a review of the investigations on the microemulsion syntheses of Os, Re, Ir, and Rh nanoparticle (NP) systems (in all forms, viz., metallic, oxide, mixed-metal, and discrete molecular complexes) is presented for work published in the last ten years. The chosen noble metals are traditionally very reactive in nanosized dimensions and have a strong tendency to aggregate when prepared via other methods. Also, the particle size and particle size distribution of these colloids can have a significant impact on their catalytic performance. It is shown that the microemulsion approach has the capability to better stabilize these metal colloids and can control the size of the synthesized NPs. This generally leads to smaller particles and higher catalytic activity when they are tested in applications.
Tautomerism in Azo and Azomethyne Dyes: When and If Theory Meets Experiment

The performance of 26 hybrid density functionals was tested against a tautomeric dataset (TautData), containing experimental information for the keto-enol tautomeric equilibrium in 16 tautomeric azodyes and Schiff bases in cyclohexane, carbon tetrachloride and acetonitrile. The results have shown that MN12-SX, BHandH and M06-2X can be used to describe the tautomeric state of the core structures in the frame of ~0.5 kcal/mol error and correctly predict the tautomeric state in respect of dominating tautomeric form. Among them MN12-SX is the best performer, although it fails to describe the nonplanarity of some of the enol tautomers. The same experimental dataset was used to develop and test a special DFT functional (TautLYP) aimed at describing the tautomeric state in azo- and azomethyne compounds in solution when nonspecific solvents are used.
Impact of quercetin on systemic levels of inflammation: a meta-analysis of randomised controlled human trials

Abstract Knowledge regarding the anti-inflammatory capability of quercetin remains inconclusive and controversial due to the heterogeneous methods and inconsistent results of RCTs. We performed a series of meta-analyses of RCTs to evaluate the impact of quercetin supplementation on inflammatory biomarkers. Three cytokines (CRP, IL-6, TNF-α) with enough eligible studies (n = 6, 5 and 4, respectively) were selected for further meta-analyses. Data from these RCTs were pooled, and both overall effect and stratified subgroup analyses were performed. No relevant overall effects on peripheral CRP, IL-6 and TNF-α were observed. Subgroup analyses revealed a significant reduction in circulating CRP in participants with diagnosed diseases (SMD: –0.24, 95% CI: –0.49, 0.00) and IL-6 in females (SMD: –1.37, 95% CI: –1.93, –0.81), subjects with diagnosed diseases (SMD: –1.37, 95% CI: –1.93, –0.81) and with high-dose interventions (SMD: –0.69, 95% CI: –1.10, –0.38). In conclusion, consumption of quercetin is a promising therapeutic strategy for chronic disease management.
Mental Health Help-Seeking Experiences of Hispanic Women in the United States: Results from a Qualitative Interpretive Meta-Synthesis

ABSTRACT The purpose of this study was to conduct a qualitative interpretive meta-synthesis to examine the experiences and perceptions of mental health help-seeking behaviors by Hispanic women in the United States. Synthesis of five studies resulted in one major theme of determinants of mental health help-seeking organized into three subthemes: (1) societal determinants, (2) health services system, and (3) individual determinants. These results provide a more holistic understanding of the mental health help-seeking experiences of Hispanic women, an important consideration when developing interventions to address the disparities in access to and quality of mental health treatment experienced by this unique population.
Reduction Rules of Graphical Representation in a Workflow Process Model

In this paper, we propose a set of reduction rules of graphical representation in a workflow process model called RTWD net. The concept of synchronization processes is introduced for reducing net structure, and an algorithm of abstracting synchronization processes is presented. We can clarify the main structure of RTWD net model through reduction rules and abstracting its synchronization processes. Finally, the feasibility of the method is illustrated by a case study.
Non-coherent differential encoded multi-carrier SS modulation schemes using low-rate orthogonal convolutional coding in frequency selective Rayleigh fading

This paper investigates the performance benefits of applying convolutional coding and interleaving to a non-coherent DS-CDMA system using OFDM to combat the bit-error rate degradation suffered in a frequency selective Rayleigh faded channel. The channel model is typical of the mobile radio environment with Doppler effects and frequency selective Rayleigh fading. The paper also compares the performance of the coded OFDM-CDMA system with a conventional single-carrier DS-CDMA system which uses a RAKE receiver to combat fading. Finally, a comparison is made between the performance of ordinary convolutional codes and low-rate orthogonal convolutional codes (LROCC). It is found that the LROCC-OFDM system outperforms both the OFDM-CDMA and DS-CDMA RAKE systems quite substantially when interleaving is used.
Interlinking e-Learning Resources and the Web of Data for Improving Student Experience

The Web provides not only several data sources with useful and relevant information with e-Learning purposes, but also information that is not easy to retrieve. The web of linked data is a repository based on semantic technologies. Several researchers have been oriented to this kind of interoperable e-Learning repositories and establish that the Linked Data approach has the potential to fulfill the e-Learning vision of Web-scale interoperability of e-Learning resources as well as highly personalized and adaptive e-Learning applications. The paper presents an automatic concept extraction system used to improve personalized searching framework. It could be considered as one possible instance of a more general concept concerning the transition from the Document Web to the Document/Data Web and the consequent managing of these immense volumes of data.
Data Mining Tools for Malware Detection

Although the use of data mining for security and malware detection is quickly on the rise, most books on the subject provide high-level theoretical discussions to the near exclusion of the practical aspects. Breaking the mold, Data Mining Tools for Malware Detection provides a step-by-step breakdown of how to develop data mining tools for malware detection. Integrating theory with practical techniques and experimental results, it focuses on malware detection applications for email worms, malicious code, remote exploits, and botnets. The authors describe the systems they have designed and developed: email worm detection using data mining, a scalable multi-level feature extraction technique to detect malicious executables, detecting remote exploits using data mining, and flow-based identification of botnet traffic by mining multiple log files. For each of these tools, they detail the system architecture, algorithms, performance results, and limitations. Discusses data mining for emerging applications, including adaptable malware detection, insider threat detection, firewall policy analysis, and real-time data mining Includes four appendices that provide a firm foundation in data management, secure systems, and the semantic web Describes the authors tools for stream data mining From algorithms to experimental results, this is one of the few books that will be equally valuable to those in industry, government, and academia. It will help technologists decide which tools to select for specific applications, managers will learn how to determine whether or not to proceed with a data mining project, and developers will find innovative alternative designs for a range of applications.
Single trial VEP extraction using digital filter

We describe a method to extract single trial visual evoked potential (VEP) buried in ongoing electroencephalogram (EEG) activity. The common method for separating VEP from EEG is to use signal averaging. But we use digital filters to extract VEP assuming that VEP spectra are in the gamma band. As an application, a fuzzy ARTMAP (FA) neural network classifier with voting strategy is used with this extracted VEP to discriminate alcoholics from normal subjects. The VEP is extracted from subjects while seeing visuals of the Snodgrass and Vanderwart (1980) picture set. The high FA classification of 96.5% shows the validity of the proposed method to successfully remove EEG contamination.
An interconnection network and a routing scheme for a massively parallel message-passing multicomputer

The communication system of a massively parallel architecture called MEGA is presented. The implications of massive parallelism for routing strategies and communication models are discussed. A routing strategy, called forced routing, is proposed. It minimizes contention by making full use of all the shortest paths in the network. Its performance has been studied by simulation, and the results are presented. The mixed communication model used allows processes with mutual reference to have direct information exchanges. The routing strategy can be implemented within a restricted chip area in CMOS technology. <<ETX>>
Soil moisture simulation by JULES in New Zealand: verification and sensitivity tests

Hourly observations of soil moisture volumetric content (at depths of 0–0.4 m) have been conducted for a few years at 55 climate stations with diverse soil types and annual rainfall in New Zealand. Thirty two of these stations also have surface meteorological observations. In this study, these observations were used for the first time to validate a land surface model (LSM) for soil moisture simulation, the Joint UK Land Environment Simulator (JULES). At the 32 stations, the yearly mean absolute differences (MADs) of the simulated soil moisture volumetric ratio were about 5% or smaller, except at four South Island stations, where larger MADs of 8–12% were found. Further analysis of rainfall and soil moisture observations at these four stations showed that the observed soil moisture includes a contribution from lateral soil water flow, which is not considered in the one‐dimensional version of JULES used in this study. For the remaining 28 stations, the relatively small yearly MADs (∼5%) were mainly due to biases. To understand the major factors contributing to the errors/biases, three sensitivity tests were conducted. These tests showed that errors in the soil texture data were the most likely contributor to the errors in simulated soil moisture. This study demonstrates that for a better simulation of soil moisture in New Zealand, especially in the lower South Island, a LSM needs to include lateral soil moisture/water flow, and to use more accurate prescriptions of the hydraulic and thermal properties of the soil.
Efficient memory-based learning for robot control

This dissertation is about the application of machine learning to robot control. A system which has no initial model of the robot/world dynamics should be able to construct such a model using data received through its sensors|an approach which is formalized here as the SAB (State-ActionBehaviour) control cycle. A method of learning is presented in which all the experiences in the lifetime of the robot are explicitly remembered. The experiences are stored in a manner which permits fast recall of the closest previous experience to any new situation, thus permitting very quick predictions of the e ects of proposed actions and, given a goal behaviour, permitting fast generation of a candidate action. The learning can take place in high-dimensional non-linear control spaces with real-valued ranges of variables. Furthermore, the method avoids a number of shortcomings of earlier learning methods in which the controller can become trapped in inadequate performance which does not improve. Also considered is how the system is made resistant to noisy inputs and how it adapts to environmental changes. A well founded mechanism for choosing actions is introduced which solves the experiment/perform dilemma for this domain with adequate computational e ciency, and with fast convergence to the goal behaviour. The dissertation explains in detail how the SAB control cycle can be integrated into both low and high complexity tasks. The methods and algorithms are evaluated with numerous experiments using both real and simulated robot domains. The nal experiment also illustrates how a compound learning task can be structured into a hierarchy of simple learning tasks.
Naturalness versus stringy naturalness with implications for collider and dark matter searches

The notion of stringy naturalness-- that an observable O_2 is more natural than O_1 if more (phenomenologically acceptable) vacua solutions lead to O_2 rather than O_1-- is examined within the context of the Standard Model (SM) and various SUSY extensions: CMSSM/mSUGRA, high-scale SUSY and radiatively-driven natural SUSY (RNS). Rather general arguments from string theory suggest a (possibly mild) statistical draw towards vacua with large soft SUSY breaking terms. These vacua must be tempered by an anthropic veto of non-standard vacua or vacua with too large a value of the weak scale m(weak). We argue that the SM, the CMSSM and the various high-scale SUSY models are all expected to be relatively rare occurances within the string theory landscape of vacua. In contrast, models with TeV-scale soft terms but with m(weak)~100 GeV and consequent light higgsinos (SUSY with radiatively-driven naturalness) should be much more common on the landscape. These latter models have a statistical preference for m_h~ 125 GeV and strongly interacting sparticles beyond current LHC reach. Thus, while conventional naturalness favors sparticles close to the weak scale, stringy naturalness favors sparticles so heavy that electroweak symmetry is barely broken and one is living dangerously close to vacua with charge-or-color breaking minima, no electroweak breaking or pocket universe weak scale values too far from our measured value. Expectations for how landscape SUSY would manifest itself at collider and dark matter search experiments are then modified compared to usual notions.
Growth and Duplication of Public Source Code over Time: Provenance Tracking at Scale

We study the evolution of the largest known corpus of publicly available source code, i.e., the Software Heritage archive (4B unique source code files, 1B commits capturing their development histories across 50M software projects). On such corpus we quantify the growth rate of original, never-seen-before source code files and commits. We find the growth rates to be exponential over a period of more than 40 years. We then estimate the multiplication factor, i.e., how much the same artifacts (e.g., files or commits) appear in different contexts (e.g., commits or source code distribution places). We observe a combinatorial explosion in the multiplication of identical source code files across different commits. We discuss the implication of these findings for the problem of tracking the provenance of source code artifacts (e.g., where and when a given source code file or commit has been observed in the wild) for the entire body of publicly available source code. To that end we benchmark different data models for capturing software provenance information at this scale and growth rate. We identify a viable solution that is deployable on commodity hardware and appears to be maintainable for the foreseeable future.