Miracle-dz/UniFAR-FTUs huggingface dataset:
Centralized Fog Computing Security Platform for IoT and Cloud in Healthcare System

This chapter proposes an efficient centralized secure architecture for end to end integration of IoT based healthcare system deployed in Cloud environment. The proposed platform uses Fog Computing environment to run the framework. In this chapter, health data is collected from sensors and collected sensor data are securely sent to the near edge devices. Finally, devices transfer the data to the cloud for seamless access by healthcare professionals. Security and privacy for patients’ medical data are crucial for the acceptance and ubiquitous use of IoT in healthcare. The main focus of this work is to secure Authentication and Authorization of all the devices, Identifying and Tracking the devices deployed in the system, Locating and tracking of mobile devices, new things deployment and connection to existing system, Communication among the devices and data transfer between remote healthcare systems. The proposed system uses asynchronous communication between the applications and data servers deployed in the cloud environment.
Reply to Spreng et al.: Multiecho fMRI denoising does not remove global motion-associated respiratory signals

In 2 human functional magnetic resonance imaging (fMRI) datasets (89 “ME” subjects; 12 “NA” subjects), we used signal decay properties to separate 2 kinds of signals: S0 artifacts, which were spatially specific, and T2* modulations, which occurred over the whole brain (1). We established that whole-brain (global) fMRI signals were nearly unchanged before and after removal of S0 signals. Hence, most global signals are T2* signals, compatible with neural activity or with respiratory-related pCO2 changes. In a dataset with paired respiratory records (NA data), we illustrated that changes in respiratory traces were temporally accompanied by prominent global signal modulations, an association visible in “gray plots” of single scans (2). Across scans, variance in global signals correlated with variance in respiratory measures. Spreng et al. (3) critique our paper, stating that “there is no definitive evidence . that respiration effects . even substantively contribute . to residual global signal [following removal of S0 … [↵][1]1To whom correspondence may be addressed. Email: jdp9009{at}nyp.org. [1]: #xref-corresp-1-1
ELECTRIC PROPULSION SYTEM TRADE-OFF ANALYSIS BASED ON ALTERNATIVE PROPELLANT SELECTION

Recent mission analyses have shown a need for higher power electric propulsion (EP) systems for both Earth orbit raising and deep space applications. Therefore, the development of high power EP systems is being emphasized as the necessary step towards possible future missions of large satellites. Traditionally, xenon has always been the propellant of choice for EP applications due to the optimal compromise between performance and ease of handling it can provide. Although xenon has several technical advantages, its high price suffers of a remarkable fluctuation, posing serious budget concerns. To reduce the propellant cost in high power electric propulsion applications, a more economical alternative to xenon needs to be identified, while retaining thruster performance levels. For a proper propellant selection, system level considerations will be a crucial aspect. Funded by the European Space Agency under the ARTES 5.1 programme element, an experimental campaign was performed at Sitael to test a 5 kW class Hall-effect thruster, operated with a 75% Kr25% Xe blend, that allowed to characterise thruster performances as well as the erosion evolution of the channel walls. The results of the test campaign were processed and exploited to validate a reduced order performance and erosion model. The effect of the selected alternative propellant on thruster lifetime and performance were extrapolated as well, along with the consequences at system level of its implementation. This paper presents the modellization and model validation work and provides a trade-off analysis to identify the order of magnitude of potential improvement at system level through the use of different krypton-xenon mixtures. 1. THRUSTER UNDER ANALYSIS
Do fluid inclusions in high-grade metamorphic terranes preserve peak metamorphic density during retrograde decompression?

Abstract It is often assumed that fluid inclusions in quartz do not maintain their density if they experience internal pressures 2 kbar in excess of confining pressure. However, results of a long-term (180 d) laboratory experiment simulating a nearly isothermal decompression PT path showed that two of 160 inclusions recorded the original density. These two inclusions thus maintained about 2 kbar internal overpressure for the duration of the experiment without modification. Comparison with results reported in the literature reveals that a small percentage of fluid inclusions in quartz from high-grade metamorphic rocks maintain peak densities during retrograde uplift. These inclusions represent outliers in homogenization temperature histograms and are often ignored during data interpretation.
Exploring Fama-French Five-Factor Model on Chinese A-Share Stock Market

Motivated by the valuation theory and recent empirical findings on the strong profitability and investment effects in asset returns. Fama and French propose a five-factor model contains the market factor and factors related to size, book-to-market equity ratio, profitability and investment, which outperforms the Fama-French Three-Factor Model in their paper 2014. This paper explores Fama-French Five-Factor Model on Chinese A-Share Stock Market and the empirical results show that the explanatory power of profitability and investment factors differs among different sets of portfolios. In comparison with Fama-French Three-Factor Model, the presence of profitability and investment factors RMW and CMA seem not capture more variations of expected stock returns than the original three-factor model at least for Size-B/P portfolios; there is no significant evidence that Fama-French Five-Factor Model performs better than Fama-French Five-Factor Model on Chinese A-share stock market over the research period.
Comparison of Metaheuristic and Linear Programming Models for the Purpose of Optimising Building Energy Supply Operation Schedule

Increasing complexity of building energy systems has led to a wide range of methods to minimise cost of meeting demand for all types of energy. Metaheuristics and mixed integer linear programmes (MILP) are the two most prevalent optimisation methods in the field, with relative advantages which have not previously been compared under common criteria. The principle objective of this paper is to scrutinise these two optimisation methods when applied to a problem of finding the optimal operational schedule of an energy system serving a hotel. 11 technologies are modelled by both methods, but all exhibit nonlinear characteristics which must be linearised for use in MILP. Comparison of the two models results in variation between objective function below 1%, where piecewise linearised MILP gives the most optimal solution. The time to solution varies by orders of magnitude between models: 0.08s for simple linear MILP, 1.64s for piecewise linear MILP and 274s for metaheuristic. System designers, or controllers, must decide between solution time and realistic representation of technologies when choosing an optimisation method, a compromise which may be balanced by piecewise optimisation. Further, Proposed operation schedules vary slightly between methods, allowing some subjectivity in exact operation schedule, without compromising objective function. Metaheuristics favours qualitative subjectivity, while MILP favours quantitative subjectivity.
IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks

The Fifth Generation of Mobile Communications (5G) will lead to the growth of use cases demanding higher capacity and a enhanced data rate, a lower latency, and a more flexible and scalable network able to offer better user Quality of Experience (QoE). The Internet of Things (IoT) is one of these use cases. It has been spreading in the recent past few years, and it covers a wider range of possible application scenarios, such as smart city, smart factory, and smart agriculture, among many others. However, the limitations of the terrestrial network hinder the deployment of IoT devices and services. Besides, the existence of a plethora of different solutions (short vs. long range, commercialized vs. standardized, etc.), each of them based on different communication protocols and, in some cases, on different access infrastructures, makes the integration among them and with the upcoming 5G infrastructure more difficult. This paper discusses the huge set of IoT solutions available or still under standardization that will need to be integrated in the 5G framework. UAVs and satellites will be proposed as possible solutions to ease this integration, overcoming the limitations of the terrestrial infrastructure, such as the limited covered areas and the densification of the number of IoT devices per square kilometer.
International Perspectives on the Implications of Cannabis Legalization: A Systematic Review & Thematic Analysis

The legality, recreational and medical use of cannabis varies widely by country and region but remains largely prohibited internationally. In October 2018, Canada legalized the recreational use of cannabis—a move many viewed as controversial. Proponents of legalization have emphasized the potential to eradicate the marijuana black market, improve quality and safety control, increase tax revenues, improve the availability of medical cannabis, and lower gang-related drug violence. Conversely, opponents of legalization have stressed concerns about cannabis’ addictive potential, second-hand cannabis exposure, potential exacerbation of underlying and established mental illnesses, as well as alterations in perception that affect safety, particularly driving. This systematic review synthesizes recent international literature on the clinical and public health implications of cannabis legalization.
Penalized nonnegative representations for speech separation

In this work we address the problem of supervised audio source separation within a reverberant environment. We make use of a nonnegative representation in order to model the mixture along with reverberation. This kind of models often pose the problem that the number of variables to learn is large with respect to the data, which is to say there are many possible choices of the elements that result in the same approximation of the mixture. We use a probabilistic approach in order to derive a penalized cost function that aims to overcome this issue by inducing a certain structure over the representation elements. Preliminary results account for a considerable improvement in restoration quality with the introduction of penalizers.
A Multi-Modal Approach to Frontal and Non Frontal Facial Feature Detection

by Chris Boehnen Biometrics are measurable characteristics specific to an individual. They can be used to identify individuals. The use of biometrics for identification has the potential to make our lives easier, and the world we live in a safer place. While numerous different types of exploitable biometrics exist, facial identification is highly pursued because the data can be captured at a distance without requiring the subject s active cooperation. While traditionally 2D images of faces have been used, 3D scans that contain both 3D data and registered color are becoming easier to acquire. Before 3D face images can be used to identify an individual, they require some form of initial alignment information, typically based on facial feature locations. This thesis proposes and analyzes a multimodal approach to automatic facial feature detection. After beginning with a discussion on biometrics and the role of automatic facial feature detection, we provide a comparative evaluation of 3D sensors. We follow this by a discussion of the algorithm s performance when constrained to frontal images and an analysis of its performance on a more complex dataset with significant head pose variation.
A prokaryotic-eukaryotic hybrid viral vector for delivery of large cargos of genes and proteins into human cells

An engineered viral vector of bacteriophage T4 linked to AAV delivers large payloads of genes and proteins into human cells. Development of safe and efficient nanoscale vehicles that can deliver large molecular cargos into human cells could transform future human therapies and personalized medicine. Here, we design a hybrid viral vector composed of a prokaryotic virus (bacteriophage T4) and a eukaryotic virus [adeno-associated virus (AAV)]. The small 25-nm AAV is attached to the large 120 nm × 86 nm T4 head through avidin-biotin cross-bridges using the phage decoration proteins Soc and Hoc. AAV “piggy-backed” on T4 capsid, by virtue of its natural ability to enter human cells acted as an efficient “driver,” delivering the largest payloads of foreign DNA (up to 170 kb) and protein (up to 1025 molecules) reported to date, and elicited robust immune responses in mice against flu and plague pathogens and conferred complete protection against lethal pneumonic plague challenge. The T4-AAV represents a unique platform for assembly of natural building blocks into potential therapeutics against genetic and infectious diseases.
Analysis of a coupled fluid‐structure interaction model of the left atrium and mitral valve

We present a coupled left atrium‐mitral valve model based on computed tomography scans with fibre‐reinforced hyperelastic materials. Fluid‐structure interaction is realised by using an immersed boundary‐finite element framework. Effects of pathological conditions, eg, mitral valve regurgitation and atrial fibrillation, and geometric and structural variations, namely, uniform vs non‐uniform atrial wall thickness and rule‐based vs atlas‐based fibre architectures, on the system are investigated. We show that in the case of atrial fibrillation, pulmonary venous flow reversal at late diastole disappears, and the filling waves at the left atrial appendage orifice during systole have reduced magnitude. In the case of mitral regurgitation, a higher atrial pressure and disturbed flows are seen, especially during systole, when a large regurgitant jet can be found with the suppressed pulmonary venous flow. We also show that both the rule‐based and atlas‐based fibre defining methods lead to similar flow fields and atrial wall deformations. However, the changes in wall thickness from non‐uniform to uniform tend to underestimate the atrial deformation. Using a uniform but thickened wall also lowers the overall strain level. The flow velocity within the left atrial appendage, which is important in terms of appendage thrombosis, increases with the thickness of the left atrial wall. Energy analysis shows that the kinetic and dissipation energies of the flow within the left atrium are altered differently by atrial fibrillation and mitral valve regurgitation, providing a useful indication of the atrial performance in pathological situations.
Internally generated revenue , per capita income , economic performance , government , Nigeria

Economic performance in every nation is enhanced through adequate revenue generation at all levels of the government. In Nigeria there are three tiers of government charged with the responsibility to generate revenue internally to supplement the allocation from the federation account. The statutory allocation from the federation account in Nigeria is not sufficient to achieve the overall economic performance targeted in the nation at large, therefore, it is the responsibility of government at all levels to devise strategies to exploit revenue opportunities in their jurisdiction. The inability of many state governments in the country to generate sufficient revenue internally has been a big obstacle to their economic development. The misuse of the IGR for political settlements is also a big challenge in Nigerian States. For this period of 36 years under review, statistics have it that the IGR accruing to the Federal, States and LGAs are N2,927.80, N6,827.69 billion and N405.22 billion respectively (CBN Statistical Bulletin, 2016). Apart from Lagos and Rivers States that have huge IGR, many other states depend so much on the federation account. This undue dependence on federal allocation has hindered economic development in various States in Nigeria. In the light of these problems, the study seeks to determine the impact of internally generated revenue (IGR) of the three tier of the government on the economic performance of the country. The study examines the impact of internally generated revenue (IGR) of the three tiers of government oneconomic performance in Nigeria. Inability of the various levels of the government to exploit IGR sources and over dependence on allocations from the federation account has affected economic performance in Nigeria. The specific objectives of the study are basically to establish the influence of internally generated revenue of the federal, state and local governments on per capita income in Nigeria. Therefore, the study made use of annual time series data which spanned from 1981-2016 and were obtained from CBN Statistical Bulletin, CBN Annual Reports and World Bank website. The data were collected on Per Capita Income(PCI), Federal Government Independent Revenue (FGIR), State Government Internally Generated Revenue (SIGR) and Local government Internally Generated Revenue (LIGR). Ordinary Least Squares (OLS) method was employed to carry out the multi-regression analysis with the aid of e-views version 9. The result indicated that FGIR has insignificant positive impact on PCI while SGIR and LGIR have significant positive impact on PCI. Based on these findings, the study suggests that government at all levels should put more efforts in taking advantage of all IGR sources within their domain in order to achieve the desired economic goals in the country. ABSTRACT INTERNALLY GENERATED REVENUE AND NIGERIA'S ECONOMIC PERFORMANCE
Probiotic Supplementation is Associated with Increased Antioxidant Capacity and Copper Chelation in C. difficile-Infected Fecal Water

Probiotic supplementation plays a key role in maintaining intestinal homeostasis due to its ability to modulate gut microbiota. Although their potential as potent antioxidants have previously been explored, their ability to affect the redox status in the gut lumen of healthy subjects or those with gastrointestinal (GI) disorders remains unclear. In our study, we assessed the ability of single strain and multispecies probiotic supplementation to cause a change in the redox status of normal fecal water and in Clostridium (C.) difficile-infected fecal water using a simulated gastrointestinal model. Changes in redox status were assessed by ferric-reducing antioxidant power (FRAP), 2’,2’-diphenyl-1-picrylhydrazyl (DPPH), and iron and copper chelation assays. The findings from our study showed that in normal fecal water, probiotic supplements, apart from Lactobacillus (L.) rhamnosus R0011, showed a significant increase in iron chelation (p < 0.05), which was associated with lower FRAP and copper chelation. In C. difficile-infected fecal water, all probiotic supplements showed a significant increase in FRAP (p < 0.05) and were associated with increased copper chelation. The DPPH assay showed no treatment effect in either fecal water. These findings suggest that C. difficile mediates dysregulation of redox status, which is counteracted by probiotics through ferric-reducing ability and copper chelation.
Insights into Early Recovery from Influenza Pneumonia by Spatial and Temporal Quantification of Putative Lung Regenerating Cells and by Lung Proteomics

During influenza pneumonia, the alveolar epithelial cells of the lungs are targeted by the influenza virus. The distal airway stem cells (DASCs) and proliferating alveolar type II (AT2) cells are reported to be putative lung repair cells. However, their relative spatial and temporal distribution is still unknown during influenza-induced acute lung injury. Here, we investigated the distribution of these cells, and concurrently performed global proteomic analysis of the infected lungs to elucidate and link the cellular and molecular events during influenza pneumonia recovery. BALB/c mice were infected with a sub-lethal dose of influenza H1N1 virus. From 5 to 25 days post-infection (dpi), mouse lungs were subjected to histopathologic and immunofluorescence analysis to probe for global distribution of lung repair cells (using P63 and KRT5 markers for DASCs; SPC and PCNA markers for AT2 cells). At 7 and 15 dpi, infected mouse lungs were also subjected to protein mass spectrometry for relative protein quantification. DASCs appeared only in the damaged area of the lung from 7 dpi onwards, reaching a peak at 21 dpi, and persisted until 25 dpi. However, no differentiation of DASCs to AT2 cells was observed by 25 dpi. In contrast, AT2 cells began proliferating from 7 dpi to replenish their population, especially within the boundary area between damaged and undamaged areas of the infected lungs. Mass spectrometry and gene ontology analysis revealed prominent innate immune responses at 7 dpi, which shifted towards adaptive immune responses by 15 dpi. Hence, proliferating AT2 cells but not DASCs contribute to AT2 cell regeneration following transition from innate to adaptive immune responses during the early phase of recovery from influenza pneumonia up to 25 dpi.
Syntax-mediated semantic parsing

Querying a database to retrieve an answer, telling a robot to perform an action, or teaching a computer to play a game are tasks requiring communication with machines in a language interpretable by them. Semantic parsing is the task of converting human language to a machine interpretable language. While human languages are sequential in nature with latent structures, machine interpretable languages are formal with explicit structures. The computational linguistics community have created several treebanks to understand the formal syntactic structures of human languages. In this thesis, we use these to obtain formal meaning representations of languages, and learn computational models to convert these meaning representations to the target machine representation. Our goal is to evaluate if existing treebank syntactic representations are useful for semantic parsing. Existing semantic parsing methods mainly learn domain-specific grammars which can parse human languages to machine representation directly. We deviate from this trend and make use of general-purpose syntactic grammar to help in semantic parsing. We use two syntactic representations: Combinatory Categorial Grammar (CCG) and dependency syntax. CCG has a well established theory on deriving meaning representations from its syntactic derivations. But there are no CCG treebanks for many languages since these are difficult to annotate. In contrast, dependencies are easy to annotate and have many treebanks. However, dependencies do not have a well established theory for deriving meaning representations. In this thesis, we propose novel theories for deriving meaning representations from dependencies. Our evaluation task is question answering on a knowledge base. Given a question, our goal is to answer it on the knowledge base by converting the question to an executable query. We use Freebase, the knowledge source behind Google’s search engine, as our knowledge base. Freebase contains millions of real world facts represented in a graphical format. Inspired from the Freebase structure, we formulate semantic parsing as a graph matching problem, i.e., given a natural language sentence, we convert it into a graph structure from the meaning representation obtained from syntax, and find the subgraph of Freebase that best matches the natural language graph. Our experiments on Free917, WebQuestions and GraphQuestions semantic parsing datasets conclude that general-purpose syntax is more useful for semantic parsing than induced task-specific syntax and syntax-agnostic representations.