Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Workplace Protection Factors for Different Biological Contaminants

This study compared workplace protection factors (WPFs) for five different contaminants (endotoxin, fungal spores, (1→3)-β-D-glucan, total particle mass, and total particle number) provided by an N95 elastomeric respirator (ER) and an N95 filtering facepiece respirator (FFR). We previously reported size-selective WPFs for total particle numbers for the ER and FFR, whereas the current article is focused on WPFs for bioaerosols and total particle mass. Farm workers (n = 25) wore the ER and FFR while performing activities at eight locations representing horse farms, pig barns, and grain handling facilities. For the determination of WPFs, particles were collected on filters simultaneously inside and outside the respirator during the first and last 15 min of a 60-min experiment. One field blank per subject was collected without actual sampling. A reporting limit (RL) was established for each contaminant based on geometric means (GMs) of the field blanks as the lowest possible measurable values. Depending on the contaminant type, 38–48% of data points were below the RL. Therefore, a censored regression model was used to estimate WPFs (WPFcensored). The WPFcensored provided by the two types of respirators were not significantly different. In contrast, significant differences were found in the WPFcensored for different types of contaminants. GMs WPFscensored for the two types of respirators combined were 154, 29, 18, 19, and 176 for endotoxin, fungal spore count, (1→3)-β-D-glucan, total particle mass, and total particle number, respectively. The WPFcensored was more strongly associated with concentrations measured outside the respirator for endotoxin, fungal spores, and total particle mass except for total particle number. However, when only data points with outside concentrations higher than 176×RL were included, the WPFs increased, and the association between the outside concentrations and the WPFs became weaker. Results indicate that difference in WPFs observed between different contaminants may be attributed to differences in the sensitivity of analytical methods to detect low inside concentrations, rather than the nature of particles (biological or non-biological).
Feature Selection in Extrusion Beltline Moulding Process Using Particle Swarm Optimization

Optimization is necessary for the control of any process to achieve better product quality, high productivity with low cost. The beltline moulding process is difficult task due to its low defects, making the material sensitive to reject. The efficient beltline moulding process involves the optimal selection of operating parameters to maximize the number of production while maintaining the required quality limiting beltline surface damage. In this research, objective is to obtain optimum process parameters, which satisfies given limit, minimizes number of defects and maximizes the productivity at the same time. A recently developed optimization algorithm called particle swarm optimization is used to find optimum process parameters. Accordingly, the results indicate that a system where multilayer perceptron is used to model and predict process outputs and particle swarm optimization is used to obtain optimum process parameters can be successfully applied to beltline moulding process through Particle Swarm Optimization (PSO). Results obtained are superior in comparison with Genetic Algorithm (GA) approach.
A defect-tolerant multiplexer using differential logic for FPGAs

As the dimensions of CMOS devices scale down to the nanometers, manufacturing defects are becoming a challenging concern in current and future technologies. This work aims at improving defect tolerance in FPGAs which are certainly affected by technology downsizing. Since the cornerstone of the FPGA logic and interconnect resources is the multiplexer, we propose a defect-tolerant multiplexer architecture based on differential logic. This architecture proved to be more resilient to single defects (opens and bridges) than its single-ended standard counterpart and more compact than existing hardened architectures. The architectures were studied under single defect injection by a tool that models several possible defects for a given design according to its extracted netlist. Eventually, the robustness gain using differential logic was assessed for different sizes of FPGA look-up tables.
Measuring Large-Scale Dynamic Graph Similarity by RICom: RWR with Intergraph Compression

By how much is a large-scale graph transformed over time or by a significant event? ' or 'how structurally similar are two large-scale graphs? ' are the two questions that this paper attempts to address. The proposed method efficiently calculates and accurately produces graph similarity. Our approach is based on the well-known random walk with restart (RWR) algorithm, which quantifies relevance between nodes to express the structural and connection characteristics of graphs. Intergraph compression, which is inspired by interframe compression, merges two input graphs and reorders their nodes contributing to improved process-data storage efficiency and processing convenience. This is a boon to the RWR algorithm for large-scale graphs. The representation of a graph transformed via intergraph compression can be used to accurately show similarity because sub-matrix blocks are reordered to concentrate nonzero elements. In performing the RWR algorithm, which quantifies inter-node relevance, transformed representation of graph with Intergraph compression is efficient in space requirement and produces results more quickly and accurately over conventional graph transformation schemes. We demonstrate the validity of our method through experiments and apply it to the usage data of public transportation SmartCard in a large metropolitan area to suggest usefulness of the proposed algorithm.
SCUOLA project: The “hub of smart services” for cities and communities

To evolve existing networks present in a city (electric, gas, water, public lighting and transport) may allow a rational use of natural resources (wind and sun), a reduction of consumption (energy efficiency) as well as the evolution of the energy vectors, obtained using ICT (Information and Communications Technology) as enabling technology. The city, seen as a collection of interconnected networks, can make possible the interaction among the different networks, and between citizens and the city, with the aim of making the city more suitable to the needs of the citizens; the citizens are committed in the creation of new sustainable cities through the development of communication technologies. The aim of SCUOLA project is to test an advanced system that can integrate and coordinate the development of smart grids, RES (Renewable Energy Sources), energy efficiency (from the point of view of both heat and electricity) and services to the citizen.
How well do the automatic stabilisers work

In this paper we review the empirical literature on automatic stabilisers by summarising estimates on automatic stabilisers and total fiscal multipliers and by critically reviewing some key assumptions that are not always as prominently displayed as they should be. Following Darby and Melitz (2008), we estimate automatic stabilisers in a single equation regression framework for the following countries: the US, Japan, the UK, France, Germany, Italy and Spain. We take some steps to investigate symmetry and time invariance of automatic stabilisers. Both the literature review and our estimates indicate that the estimates of automatic stabilisers are not generally robust and should be interpreted with care. We also find some evidence of parameters changing over time and some indication of asymmetries over the cycle. Therefore it may be misleading to use older estimates of automatic stabilisers and we argue that for policy purposes the automatic stabilisers should be viewed in a broader context of which shocks are occurring and also how strong the other forces in the economy may be.
AdvAnced PrActices council Driving Competitive Strategy Through Thought Leadership

The Society for Information Management's (SIM) Advanced Practices Council (APC) is an exclusive forum for senior IT executives who value directing and applying pragmatic research; exploring emerging IT issues in-depth; and hearing different, global perspectives from colleagues in other industries. Executive Summary With rising economic pressures and frustration with existing information systems, Software as a Service (SaaS) is gaining acceptance. It's no longer only a point solution for a small business, but is now being embraced in the largest organizations. CIOs considering SaaS adoption must weigh the short term benefits it can provide, such as quicker and less expensive implementations, with concerns related to having data and applications under a third-party vendor's control (security, privacy, vendor sustainability, etc.) Research shows that SaaS is being adopted by functional areas, regardless of the IT department's standards. Therefore, even if CIOs would prefer to keep all applications in house, it is likely they will be faced with the challenge of integrating SaaS applications in the near term. There are several integration options and, based on the historical trends, we recommend consideration be given to both SaaS-delivered process-based integration tools and Platform as a Service providers.
Looking back or looking out? A reevaluation of economic voting

Economic voting theory explains electoral outcomes with economic performance during the rule of an incumbent. Voters are assumed to evaluate politicians based on Bayesian calculations about the quality of the politician. In this paper we enrich this framework with the possibility of inter-country comparison. From a simple model of information externality, we derive that voters in fact find it useful to use such comparisons. The predictions of the model are tested using panel on data elections in 16 European countries from 1950 to 2007. Our results support the hypothesis that inter-country comparisons were relevant factors in at least our sample of electoral outcomes.
Teacher Responses to Pay-for-Performance Policies: Survey Results from Four High-Poverty, Urban School Districts.

Policymakers are increasingly adopting “pay-for-performance” policies in which teachers are compensated based on their performance as measured by classroom evaluations and/or student achievement test results. Prior research has produced largely inconclusive findings concerning support among teachers for these policies and their effects on teachers. This paper analyzes teacher survey data drawn from a larger study of four high-poverty, urban school districts as they implemented a pay-for-performance initiative. The paper reports on attitudes about compensation reform in general, support for and perceived benefits and drawbacks of the policy in question, as well as on perceived effects the policy on teachers’ working conditions, specifically in the areas of collaboration. Findings indicate high levels of support for pay-for-performance in general but somewhat less support for specific components of the program, such as using student performance to determine teacher pay. Responses were largely mixed regarding the effects of the policy on working conditions such as collaboration. A quasi-experimental design was used to allow for the comparison of responses on a select set of survey items, and teachers’ participation in the program generally had no effect on their attitudes about compensation when compared to teachers in similar schools who had not participated in the program. Differences in survey responses based on teacherlevel characteristics are also addressed, but these did not explain variations in teachers’ responses. Despite teachers’ generally favorable perceptions of the pay-for-performance program, their knowledge of the program’s details was limited throughout the period of implementation, thus raising serious questions about the program’s ability to achieve its goal of bringing about change in teacher behavior.
Education and Business as a key topics at the Instagram posts in the area of Gamification

The aim of this paper is to examine the communication content of Instagram social network users, on the basis of the hashtags they use relating to gamification and to define communities within the network in the context of education. The results are based on the analysis of Instagram's worldwide social network. Primary data were collected using a script to capture communication on the social network Instagram. The analysis included Instagram photos selected on the basis of hashtag #gamification (17,994 contributions). The results identify that the most commonly associated expressions with hashtags #gamification are hashtags associated with education and business, especially where startup and innovation are concerned. On the basis of an analysis visually isolated communities with average modularity of 0.506 were identified, which relate to the communication of the gamification on the social network Instagram: 1) Education, 2) Entrepreneurship, 3) Gamification in general, 4) Social and 5) Enjoyment. The benefit of analysis for the education area is to identify the university‘s links between Education and Entrepreneurship and the Teacher and Trust between education and enjoyment.
Neural Networks for Time Series Processing

This paper provides an overview over the most common neural network types for time series processing i e pattern recognition and forecasting in spatio temporal patterns Emphasis is put on the relationships between neural network models and more classical approaches to time series processing in particular forecasting The paper begins with an introduction of the basics of time series processing and discusses feedforward as well as recurrent neural networks with respect to their ability to model non linear dependencies in spatio temporal patterns
Molecular characterization of Escherichia coli O157:H7 strains isolated from different sources and geographic regions

Escherichia (E.) coli serotype O157:H7 is a globally distributed human enteropathogen and is comprised of microorganisms with closely related genotypes. The main reservoir for this group is bovine bowels, and infection mainly occurs after ingestion of contaminated water and food. Virulence genetic markers of 28 O157:H7 strains were investigated and multilocus enzyme electrophoresis (MLEE) was used to evaluate the clonal structure. O157:H7 strains from several countries were isolated from food, human and bovine feces. According to MLEE, O157:H7 strains clustered into two main clonal groups designated A and B. Subcluster A1 included 82% of the O157:H7 strains exhibiting identical MLEE pattern. Most enterohemorrhagic E. coli (EHEC) O157:H7 strains from Brazil and Argentina were in the same MLEE subgroup. Bovine and food strains carried virulence genes associated with EHEC pathogenicity in humans.
Hot pixel generation in active pixel sensors: dosimetric and microdosimetric response

The dosimetric and microdosimetric response of an active pixel sensor is analyzed. The shift in dark current before and after irradiation is the metric under study. Heavy ions are seen to damage the pixel in much the same way as proton radiation, i.e., displacement damage is the dominant mechanism. A rate of singularly highly dosed cells, i.e. hot pixels, is calculated from the distribution of dosed cells. The probability of a bot pixel is seen to exhibit behavior that is not typical of other microdose effects. The cross section of hot pixels is seen to increase with increasing NIEL.
Secrecy Throughput Maximization for MISO Multi-Eavesdropper Wiretap Channels

In this paper, the secure transmission strategy for a multi-input-single-output multi-eavesdropper system with coexistence of a secure user (Bob) and a normal user (NU) is investigated. The NU and Bob require normal and secure data transmissions, respectively, and thus, the stream for the NU can be exploited to confuse the eavesdroppers. To guarantee the security of Bob, artificial noise (AN) is also deliberately injected into the null space of Bob and NU. The power allocation among Bob, NU, and AN, as well as the wiretap code rates, is jointly optimized to maximize the effective secrecy throughput (EST), under the average throughput constraint of the NU and statistical channel state information (CSI) of eavesdroppers. Both non-adaptive and adaptive transmission schemes are proposed, based on the statistical and instantaneous CSI of the legitimate channels, respectively. An alternative optimization algorithm is proposed to obtain the optimal parameters. It is proved that the EST is a quasi-concave function of the secrecy rate and the power allocated to Bob, and for fixed wiretap code rates, the optimal power allocation is derived in a closed-form expression. Numerical results show that the EST increases with the increase in transmitting power and the number of transmit antennas, and decreases with the increasing throughput constraint of the NU. Improved EST can be achieved through injecting AN and concurrent transmission of Bob and NU.
Minimum Volume Embedding

Minimum Volume Embedding (MVE) is an algorithm for non-linear dimensionality reduction that uses semidefinite programming (SDP) and matrix factorization to find a low-dimensional embedding that preserves local distances between points while representing the dataset in many fewer dimensions. MVE follows an approach similar to algorithms such as Semidefinite Embedding (SDE), in that it learns a kernel matrix using an SDP before applying Kernel Principal Component Analysis (KPCA). However, the objective function for MVE directly optimizes the eigenspectrum of the data to preserve as much of its energy as possible within the few dimensions available to the embedding. Simultaneously, remaining eigenspectrum energy is minimized in directions orthogonal to the embedding thereby keeping data in a so-called minimum volume manifold. We show how MVE improves upon SDE in terms of the volume of the preserved embedding and the resulting eigenspectrum, producing better visualizations for a variety of synthetic and real-world datasets, including simple toy examples, face images, handwritten digits, phylogenetic trees, and social networks.
Reflex assisted walking for a hexapod robot

This paper describes the performance of a hexapod robot that uses low level leg reflexes to aid in walking over uneven terrain, and is currently being developed at the University of Johannesburg. The goal of this research is a robot able to deploy in both the inspection and search and rescue roles, within an underground mine environment. The robot has six legs with three degrees of freedom per leg, and is equipped with a two degree of freedom arm with a sensor payload attached to a pan-tilt system. Throughout the development of this new robot the Design Science Research Methodology was used to guide the decision making process. This paper presents an overview of the robot, including the control architecture, and the testing conducted to verify the robot's performance when walking over a laboratory test field.
Recent Developments in Polymer MEMS

Polymer materials, including elastomers, plastics and fibers, are being actively used for MEMS sensors and actuators. Polymer materials provide many advantages in terms of cost, mechanical properties, and ease of processing. In addition, polymers are being investigated for displays, memory, and circuitry. However, the incorporation of polymers for structural or functional purposes in MEMS systems raises new issues and challenges. This article provides a comprehensive review of the recent state of the art of polymer based MEMS—including materials, fabrication processes, and representative devices, including microfluidic valves and tactile sensors. Frequently used materials are reviewed, including polydimethylsiloxane (PDMS), parylene, nanocomposite elastomers, and SU‐8 epoxy.
Objects co-segmentation: Propagated from simpler images

Recent works on image co-segmentation aim to segment common objects among image sets. These methods can co-segment simple images well, but their performance may degrade significantly on more cluttered images. In order to co-segment both simple and complex images well, this paper proposes a novel paradigm to rank images and to propagate the segmentation results from the simple images to more and more complex ones. In the experiments, the proposed paradigm demonstrates its effectiveness in segmenting large image sets with a wide variety in object appearance, sizes, orientations, poses, and multiple objects in one image. It outperformed the current state-of-the-art algorithms significantly, especially in difficult images.
A social identity analysis of disordered eating behaviour

Across the developed world, rates of disordered eating are increasing. Formal eating disorders, unhealthy dieting and obesity have all been escalating over the last forty years. Various theoretical models have been proposed to explain this increase. Sociocultural models have drawn attention to features of the social environment, such as the cultural value placed on thinness for women (causing body dissatisfaction, and subsequently, weight-loss attempts), or the hyperavailability of energy dense foods and energy-saving technologies (causing obesity). Individualistic models have identified a variety of genetic and personality factors, such as perfectionism, low self-esteem and thin-ideal internalisation, that increase vulnerability to disordered eating. However, these two approaches to disordered eating are currently a) incompatible, and b) unable to account for the evidence of social influence in eating behaviour. In this thesis, l propose a social identity analysis of disordered eating behaviour. This conceptualisation is able to parsimoniously incorporate previous findings by attending to the mechanism through which sociocultural phenomena are represented psychologically (via selfcategorisation). Across 10 studies and 5 empirical chapters, evidence is presented for this social identity analysis. Firstly, a Dieting Intentions Scale is developed and validated in four studies, such that future dieting behaviour may be adequately measured as a dependent variable in the research. Secondly, two experiments demonstrate that the perception of shared psychological group membership is a necessary condition for social influence in eating behaviour. Thirdly, three studies show that the predictors of dieting intentions are context-dependent, and are determined by an individual ' s sa lient self-categorisations. Fourthly and finally, one experiment tests the applicabili ty of the social identity analysis of social influence in a clinical population. The thesis has implications for social-psychological theory, in extending and testing the social identity approach, particularly in the health domain. However, it has greater
Aliasing Reduction for Modified Discrete Cosine Transform Domain Filtering and its Application to Speech Enhancement

Efficient combinations of coding and manipulation of audio signals in the spectral domain are often desirable in communication systems. The modified discrete cosine transform (MDCT) represents a popular spectral transform in audio coding as it leads to compact signal representations. However, as the MDCT corresponds to a critically sampled filter bank, it is in general not appropriate to directly apply it to filtering tasks. In this paper we present a method to compensate for aliasing terms that arise from such direct MDCT domain filtering. The discussion is thereby based on a rigorous matrix representation of critically sampled filter banks which also leads to corresponding efficient realizations. As an application showcase, noise reduction for MDCT based speech coding is considered in simulations.