Miracle-dz/UniFAR-FTUs huggingface dataset:
NEW EMBEDDED COMPUTING ARCHITECTURE USING HETEROGENEOUS PROCESSORS FOR FAST PROCESSING AND LOWER COMPLEXITY

When it comes to communicating between processors in a Multi-Computing system, there are many choices. Exotic architectures are available with tens to hundreds of cores. This allows plentiful processing power to be utilized on problems. However, how to retain the cores feed with data? Core is a wasted resource when idle. Both of bandwidth between resources and latency incurred in transfers are performance concern. That can significantly affect the bandwidth for small transfers and can also make the system-transfer function exceed its design requirements for real-time operation. The solution to this problem lies in multi-level of computing architecture. A new architecture at SoC and chip level is designed and implemented to come up with high-performance embedded multi-computing architecture for AUV. It will be based on FPGA technology that will be as computing unit and router for other processing units as well. The designed architecture provides a concurrent environment for programming and testing. Furthermore, numerous FPGA mega core modules are provided to easily verify the targeted problem, address processing issues and balance data movement with processing power. The outputs and deliverables from the designed architecture are manifold in terms of performance and throughput accuracy; covering a sensing area of over 95%. The designed architecture seeks to provide dynamic reconfigurable platform with the knowledge and tools needed to improve in today's academic, research, industry environment and realistic applications.
The Impact of Health and Nutrition on Education

Child health and nutrition are strongly associated with educational achievement. But associations do not necessarily indicate causality; estimates generally are likely to be biased in one direction or the other. As a result analysts and policymakers should have much less confidence in findings about the effect of health on schooling success than has been claimed in previous surveys. The evidence is more nuanced and qualified than is often recognized but may still support the conclusion that health may have considerable effects on postschooling productivity. Policy implications point toward providing public subsidies for the collection, analysis, and dissemination of information about the links between health and education; and toward providing services to improve the health of poor children. Copyright 1996 by Oxford University Press.
Problems Found in the Adaptation of a Fluid Cracking Catalytic Pilot Plant for Studing second Generation Biofuel Production by Pyrolysis

As a means of reducing dependence on oil and mitigate climate change, the use of biomass as energy source has proven to be a promising option. The fuels produced from biomass are classified as first and second generation. The first-generation biofuels are mainly produced from sugar, starch and vegetable oil using conventional technologies, the most important in this segment are ethanol and biodiesel. Ethanol is produced from biomass such as sugarcane, beet sugar and starch crops (maize and wheat). The second-generation biofuels are produced from lignocellulosic biomass mainly consisting of agricultural waste, forestry and industrial. With the purpose of studying routes for the processing of lignocellulosic biomass was performed a survey of the main technology routes are opting for a pilot plant scale for assessing the conditions of processing via fast pyrolysis. The aim of this paper is to present the main operational problems and its alternatives taken to resolve them, recorded during the tests and pre-conditioning operation of the pilot. The test conditions were based on data identified in literature, namely, temperature: 450 – 550 °C, residence time: 0.5 to 5 s heating rate is too high. Preliminarily found that the pilot unit respond well to temperature changes but noted the need to systematically study the load power, addressed this point, there was a drop in efficiency associated with the formation of coke. Additional tests in progress aims to adjust the operating conditions, assess the difficulties to achieve the overall mass balance of the plant and initiate studies on the process itself.
Fully automated web harvesting using a combination of new and existing heuristics

Several techniques exist for extracting useful content from web pages. However, the definition of ‘useful’ is very broad and context dependant. In this research, several techniques – existing ones and new ones – are evaluated and combined in order to extract object data in a fully automatic way. The data source used for this, are mostly web shops, sites that promote housing, and vacancy sites. The data to be extracted from these pages, are respectively items, houses and vacancies. Three kinds of approaches are combined and evaluated: clustering algorithms, algorithms that compare pages, and algorithms that look at the structure of single pages. Clustering is done in order to differentiate between pages that contain data and pages that do not. The algorithms that extract the actual data are then executed on the cluster that is expected to contain the most useful data. The quality measure used to assess the performance of the applied techniques are precision and recall per page. It can be seen that without proper clustering, the algorithms that extract the actual data perform very bad. Whether or not clustering performs acceptable heavily depends on the web site. For some sites, URL based clustering outstands (for example: nationalevacaturebank.nl and funda.nl) with precisions of around 33% and recalls of around 85%. URL based clustering is therefore the most promising clustering method reviewed by this research. Of the extraction methods, the existing methods perform better than the alterations proposed by this research. Algorithms that look at the structure (intra page document structure) perform best of all four methods that are compared with an average recall between 30% to 50%, and an average precision ranging from very low (around 2%) to quite low (around 33%). Template induction, an algorithm that compares between pages, performs relatively well as well, however, it is more dependent on the quality of the clusters. The conclusion of this research is that it is not possible yet using a combination of the methods that are discussed and proposed to fully automatically extract data from websites.
A Bayesian quantification of consistency in correlated datasets

We present three tiers of Bayesian consistency tests for the general case of $correlated$ datasets. Building on duplicates of the model parameters assigned to each dataset, these tests range from Bayesian evidence ratios as a global summary statistic, to posterior distributions of model parameter differences, to consistency tests in the data domain derived from posterior predictive distributions. For each test we motivate meaningful threshold criteria for the internal consistency of datasets. Without loss of generality we focus on mutually exclusive, correlated subsets of the same dataset in this work. As an application, we revisit the consistency analysis of the two-point weak lensing shear correlation functions measured from KiDS-450 data. We split this dataset according to large vs. small angular scales, tomographic redshift bin combinations, and estimator type. We do not find any evidence for significant internal tension in the KiDS-450 data, with significances below $3\, \sigma$ in all cases. Software and data used in this analysis can be found at this http URL
Detection of Blackhole Nodes for Securing Wireless Sensor Network Using MATLAB

Wireless Sensor Networks consists of hundreds and thousands of micro sensor nodes that monitor a remote environment by data aggregation from individual nodes and transmitting this data to the base station for further processing and inference. The energy of the battery operated nodes is the most vulnerable resource of the WSN, which is depleted at a high rate when information is transmitted, because transmission energy is dependent on the distance of transmission. In a clustering approach, the Cluster Head node loses a significant amount of energy during transmission to base station. So the selection of Cluster Head is very critical. An effective selection protocol should choose Cluster Heads based on the geographical location of node and its remaining energy. In this work a centralized protocol for Cluster Head selection in WSN is discussed, which is run at the base station, thus reducing the nodes energy consumption and increasing their life-time. The primary idea is implemented using a trust model based selection of Cluster Head from among the nodes of network, which is concluded depending on two parameters, the current energy of the node and the distance of the node from the base station. The protocol is named TR-LEACH based on Energy and Distance, and is run periodically at the base station where a new set of cluster heads are selected at every round, thus distributing the energy load in the network and increasing the network lifetime. The simulation results show that the proposed approach is more effective than the existing AODV protocol.
Temperature-dependent changes in proteolytic activities and protein composition in the psychrotrophic bacterium Arthrobacter globiformis S155

SUMMARY: Proteolytic activities, active against casein and insulin are present in cell-free extracts of the psychrotrophic bacterium Arthrobacter globiformis S155. These activities were compared (i) following growth of the bacterium at different temperatures (10, 20 and 32°C) and (ii) after a temperature shift from 10 to 32°C. Both activities (measured at 20°C) were greater in cells grown at 32°C and after a temperature shift. Chloramphenicol prevented the increases in activities. The proteolysis of casein was stimulated by ATP; the stimulation was greater following a temperature shift. In addition, proteolysis of casein (measured at 20°C) was activated by pre-incubation at temperatures between 45 and 60°C; the proportion of proteolytic activity activated by such treatment also increased with growth temperature. Comparison by two-dimensional electrophoresis of the polypeptides synthesized at 10 and 32°C suggests that A. globiformis S155 produces proteins specific to both temperatures (13 species were either exclusive to or present in increased amounts during growth at 10°C, while at least 21 polypeptides were preferentially synthesized at 32°C). A temperature shift from 10 to 32°C promoted the synthesis of at least 16 polypeptides, many with M r values similar to those of heat shock proteins synthesized at higher temperatures in mesophiles. A temperature up-shift of A. globiformis S155 from 10 to 20°C did not provoke the synthesis of new proteins; heat shock proteins were produced only at 28°C and higher. The similarity in kinetics of the appearance of heat shock proteins and the increase in proteolytic activities suggest that in A. globiformis S155 some heat shock proteins might also possess protein catabolic function.
Poly(allylamine) magnetomicelles for image guided drug delivery.

This work was funded by the Institute of Science and Technology in Medicine and the School of Pharmacy, Keele University. The NMR and ICP-OES analysis was carried out in the Lennard-Jones Laboratories in the School of Physical and Geographical Sciences. The magnetometer used in this research was obtained through the Science City Advanced Materials project: Creating and Characterizing Next Generation Advanced Materials project, with support from Advantage West Midlands (AWM) and part funded by the European Regional Development Fund (ERDF).
5 J un 2 01 8 Opportunities in Machine Learning for Healthcare

Healthcare is a natural arena for the application of machine learning, especially as modern electronic health records (EHRs) provide increasingly large amounts of data to answer clinically meaningful questions. However, clinical data and practice present unique challenges that complicate the use of common methodologies. This article serves as a primer on addressing these challenges and highlights opportunities for members of the machine learning and data science communities to contribute to this growing domain.
Parametric-based mobility for providing opportunistic geocasting in spatially separated wireless sensor networks

Uneven deployment and environment constraint leads to the formation of a spatially separated wireless sensor network (SS-WSN). Most of the traditional WSN architectures consist of static nodes that are densely deployed over a sensing area and are fully connected. SS-WSN is a group of disjoint subnetworks where connectivity is generally maintained by energy rich mobile nodes (ERMN), which roam about from one subnetwork to another. We propose an algorithm whereby the mobility of ERMN helps in distributing the geo message opportunisticall in the region of interest. The routing consists of two phases: (1) transmission of the geo message from the source node to the ERMN based on a proactive mobility predictive scheme (PMPS) within a subnetwork (intra-routing); (2) relaying the geo message from the source subnetwork to the destination subnetwork (inter-routing). This is achieved with the help of an ERMN that traverses on a parametric curve, visiting each subnetwork. The key issue addressed here is to minimize latency and improve reliability, thereby keeping the network virtually connected at variable period of time. Extensive simulation shows that the proposed solution performed better than random path-based mobility routing.
Underlying Appendicitis Leading to Chorioamnionitis in Preterm Rupture of Membranes

Background : PPROM complicates 3% of pregnancies, the most commonly identified etiology is infection. Appendicitis is a well-known cause of peritonitis and systemic illness, complicating approximately 1/1700 pregnancies. Case : A healthy 26 year old primagravida female at 24 weeks gestation presented with PPROM. She was managed expectantly and delivered at 26 weeks gestation due to suspected chorioamnionitis, manifested by abdominal pain and tenderness. Postpartum the patient complained of mild abdominal pain and nausea that was deemed appropriate for her post-operative state, and she was discharged home on post-operative day 3. The following day she presented to our emergency department with worsening abdominal pain. Imaging was suggestive of appendicitis, and the patient subsequently underwent surgery. Intra-operative findings were significant for an inflamed appendix matted to the posterior surface of the uterus and diffuse erythema of the uterine serosa. Final pathology reports confirmed acute appendicitis, chorioamnionitis and funisitis. Conclusion : It is possible that an underlying appendicitis lead to intrauterine infection and subsequent preterm delivery in our patient.
The Influence Of Firm’s Fair Value System On Earnings Quality Under IFRS

This paper analyzes the influence of firms’ fair value system on earnings quality under IFRS. Korean firms are required to adopt IFRS in 2011. IFRS adoption was expected to increase value relevance of book value of equity and benefit information users’ decision making. However, prior Korean studies report that value relevance of book value of equity is indifferent between under K-GAAP and IFRS. We consider that the indifference in value relevance of book value of equity after IFRS adoption is due to different level of fair value system among firms. We investigate whether the different level of fair value system among firms lead to the difference in earnings quality. Furthermore, we examine how each firm’s fair value system affect earnings quality under IFRS. This study finds following results. First, firms with weak fair value system smooth income more frequently. Second, firms with weak fair value system experience small amount of positive profit and slight increase in net income compared to prior period more frequently. Third, firms with weak fair value system make less timely loss recognition. Lastly, book value of equity and goodwill has low relative value relevance for weak fair value systemic firms, while both book value of equity and goodwill have incremental value relevance for firms with strong fair value evaluation system.
Studies of Discriminant Analysis and Logistic Regression Model Application in Credit Risk for China’s Listed Companies

With the appearance of listed companies’ credit issues and frequent credit crisis, investors are increasingly concerned about credit risk analysis for listed companies. In view of the current development methods of credit risk analysis and the importance of identifying corporate financial risk, this paper designed an effective indicator system and established the credit evaluation models of China’s listed companies by taking advantage of their 2009 financial data. Combined with the reality of China's listed companies, we use the established models to discriminate and analyze. The result of empirical research on the credit risk analysis for listed companies is that Logistic regression model is superior to discriminant analysis model. Key words: Credit Risk; Discriminant Analysis; Logistic Regression Model; Principal Component Analysis
Citizen participation in public planning: A literature review

The aim of this work is to present an overview of participatory practices through the evolutionary synthesis of so-called "ladders of public partici pation", created in the last 50 years. The paper shows how, through them, the role of public participation in planning addressed the redistribution of power between those who govern and the governed. The definition of participatory democracy, the motivation of all actors, ICT and the conceptualization of the planning and decision-making framework are all factors for participation offered to the public. Thus, the paper contributes to considering a possible new ladder of participation.
OPTIMIZATION OF PROCESS PARAMETERS FOR THE PRODUCTION OF ANTIBACTERIAL COMPOUND BY A NEWLY ISOLATED NOCARDIA CYRIACIGEORGICA, KD-15 STRAIN FROM KAZIRANGA NATIONAL PARK OF NORTH EAST INDIA

Objective: An attempt has been made to evaluate the optimal cultural conditions for obtaining high yields of bioactive metabolites. Methods: A strain of actinobacteria was isolated from the soil sample of Kaziranga National Park of Assam. Strain was identified on the basis of biochemical test as well as 16SrDNA sequence . The strain was tested for antibacterial activity using agar well diffusion method and to enhance its growth and metabolite production, the strain was cultured at different carbon and nitrogen sources and at different pH, salinity and temperature. Results: The strain was identified as Nocardia cyriacigeorgica. The secondary metabolites exhibited excellent antimicrobial activity against dreaded human pathogenic bacteria Staphylococcus aureus (MTCC 96), Bacillus subtilis (MTCC 441), Escherichia coli (MTCC 739) and Pseudomonas aeruginosa (MTCC 2453). The strain utilized glucose as good carbon source for growth and starch for metabolite production. Soybean meal and beef extract were the nitrogen sources for the elaboration of both growth and bioactive metabolites. The optimum temperature, salinity and pH for growth and bioactive metabolite production of the strain were recorded as 28±2 oC, 1.5% and 7.5 respectively. Conclusion: As our results showed the potency of N. cyriacigeorgica as an antibacterial agent under these optimal conditions, so further study can be carried out in this regard. This is the first report of production of antibacterial compound from this emerging pathogenic strain N. cyriacigeorgica .
Relationship between emotional intelligence and religiosity with dark triad personality of corruption prisoner

This study aims to determine the relationship between emotional intelligence and religiosity with dark triad of personality of corruption prisoners. The data were obtained from 62 prisoners of Corruption in Kedungpane Semarang Prison selected by quota sampling. The research instruments used are Schutte Emotional Intelligence Scale (SEIS), The New Indices of Religious Orientation Revised (NIROR), and Short Dark Triad (SD3) to measure emotional intelligence, religiosity, and dark triad of personality. The results showed: (1) a relationship between emotional intelligence and religiosity together with dark triad of personality of corruption prisoners, (2) a positive correlation between emotional intelligence and dark triad of personality of corruption prisoners, (3) a negative correlation between intrinsic religious orientation and psychopathy, (4) a positive correlation between extrinsic religious orientation and dark triad of personality of corruption prisoners, and (5) a positive correlation between quest religious orientation and dark triad of personality of corruption prisoners.
Relative importance of phototrophic, heterotrophic, and mixotrophic nanoflagellates in the microbial food web of a river-influenced coastal upwelling area

The contribution of phototrophic nanoflagellates (PNF) as primary producers and het- erotrophic (HNF) and mixotrophic (MNF) nanoflagellates as major grazers of bacterioplankton was assessed during a 3 yr study in a highly productive, river-influenced coastal upwelling area under contrasting seasons (winter/non-upwelling vs. spring/upwelling). Sampling was conducted at 2 stations — around a river plume and at an intensive seasonal upwelling site — with contrasting en- vironmental gradients. The MNF were evaluated functionally for the possession of photo- pigments and experimentally for the ability to take up prey, specifically fluorescently labeled bacteria (FLB). During the short-term experiments, we estimated grazing rates over FLB for 2 size categories of HNF and MNF. Both bacterial production (BP) and nanophytoplankton primary production (PP) were higher in the river plume area. PNF abundance ranged from 6 × 10 9 to 411 × 10 9 cells m �2 , whereas HNF abundance fluctuated between 27 × 10 9 and 267 × 10 9 cells m �2 . In contrast, the abundance of MNF was usually low, with a maximum of ~7 × 10 9 cells m �2 . For MNF, ingestion rates were between 7.3 and 30.7 bacteria per flagellate and hour (bact flag �1 h �1 ), whereas HNF in- gestion ranged from 2 to 7.5 bact flag �1 h �1 . However, since HNF dominated in terms of abundance, they were the dominant grazers on bacterioplankton populations. Estimates of grazing pressure for the microbial food web showed that MNF were capable of removing 1 to 51% BP d �1 , whereas HNF could control BP, eliminating from 24% BP d �1 up to more than 100% BP d �1 . Given the area's relatively high nutrient condition, the elevated MNF biomass in the river plume and the greater bacterivory impact from MNF in winter, it seems that light and, thus, the energy/carbon limitation could be the main trigger for mixotrophy in this river-influenced coastal upwelling area.
so cph ] 2 0 M ay 2 01 8 Load Dependence of Power Outage Statistics

The size distributions of power outages are shown to depend on the stress, or the proximity of the load of an electrical grid to complete breakdown. Using the data for the U.S. between 2002-2017, we show that the outage statistics are dependent on the usage levels during different hours of the day and months of the year. At higher load, not only are more failures likely, but the distribution of failure sizes shifts, to favor larger events. At a finer spatial scale, different regions within the U.S. can be shown to respond differently in terms of the outage statistics to variations in the usage (load). The response, in turn, corresponds to the respective bias towards larger or smaller failures in those regions. We provide a simple model, using realistic grid topologies, which can nonetheless demonstrate biases as a function of the applied load, as in the data. Given sufficient data of small scale events, the method can be used to identify vulnerable regions in power grids prior to major blackouts.