Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of possible exposure to Ochratoxin A via consumption of contaminated meat products

4 Dragan Kovacevic, PhD, full professor, Faculty of Food Technology Osijek. J. J. Strossmayer University of Osijek, Franje Kuhaca 20, Osijek, Croatia Introduction Ochratoxin A is a mycotoxin, a secondary metabolite produced by the mold of Aspergillus and Penicillium species. Regarding the fact that research indicate to its nephrotoxic, neurotoxic, mutagenic, carcinogenic, teratogenic and immunosuppressive activity in people and animals and its wide prevalence as food and feed contaminant, ochratoxin A represents a health hazard worldwide (Krogh et al., 1979; Creppy, 2002; Walker and Larsen, 2005; Khoury and Atoui, 2010). Penetration of ochratoxin A in human food chain can also occur due to a possible natural contamination of cereals which are a part of feed for animals intended for meat production, but also due to a possible secondary contamination of meat products during the process of their production (Markov et al., 2013). With the goal of preventing consequences on human health, the production of food and feed should be based on principles of good agricultural and manufacturing practice, i.e., the HACCP system, hazard analysis, prevention and control of critical points and removing the source in the technological production procedure. Also, it is necessary to ensure conducting of o`cial controls to the presence of this contaminant and de'ne its maximum allowable/ recommended concentrations for di>erent categories of food of animal origin and meat products at the same time (Persi et al., 2013). Earlier research in the Republic of Croatia have indicated to a more frequent contamination of feed materials with ochratoxin A (Pepeljnjak et al., 2008; Pleadin et al., 2013). The data have shown that this toxin can also be present in meat products produced from contaminated raw materials (Madsen et al., 1992; Gareis, 1996; Jorgensen, 1998; Creppy, 1999; Gareis and Scheuer, 2000). The highest ochratoxin A concentration in earlier research was determined in o>al based meat products such as black pudding and liver sausage due to the highest accumulating exactly in kidney tissue and liver, which are edible tissues and represent a raw material for the production of this category of meat products (Petzinger and Weidenbach 2002; Persi et al., 2014). Also, the published data speak on contamination of smoked and other meat products where signi'cant levels of ochratoxin A were detected as well (Pfohl-Leszkowicz and Manderville, 2007; Dall Asta et al., 2010; Markov et al., 2013). Industrial processes of meat product production like warming, salting, drying and storage thereby haven’t shown a signi'cant in? uence to decreasing ochratoxin A concentration in end meat products (Amezequeta et al., 2009). Considering the fact that there is a long tradition in the Republic of Croatia in the production of autochthonous meat products in rural households and due to the fact that published data have indicated to a more frequent contamination with ochratoxin A precisely of the Summary
Per-flow performance analysis in network coding: An approach based on real-time calculus

Network coding provides a powerful mechanism for improving performance of communication networks. In this paper, we present an analytical approach for per-flow performance analysis in network coding. Prior work on performance analysis in network coding mainly focuses on the throughput of the overall network. Our approach aims to analyze the end-to-end performance of each flow in the network. The theoretical basis of our approach is Real-Time Calculus (RTC). Under the framework of RTC, we obtain theoretical formulations for computing the backlog and delay bounds of flows in network coding. Based on the formulations, we evaluate the per-flow performance in representative scenarios and investigate the effect of coding opportunities. Numerical results show that the coding opportunities at coding nodes can significantly affect the end-to-end performance of flows in network coding.
Enhancing web services compositions with privacy capabilities

Web service composition has been extensively studied in recent years. Although a lot of new models and mechanisms have been proposed, many issues in service composition still remain unsolved. Among them, privacy is one of the major concerns. Indeed, inheriting characteristics of Web services environments such as high dynamism and untrustworthiness often generate conflicting privacy specifications with respect to the data exchanged within a composition. Even existing technologies for managing and applying data privacy policies are unsuccessful when dealing with this kind of applications as they involve autonomous entities and continuously exchange huge amount of heterogeneous information. This made urgent to have in place effective technologies for data privacy management in service compositions. These technologies should (1) deal with the flexibility, scalability, and heterogeneity in the overall infrastructure in which data are exchanged; and (2) integrate privacy concerns into the development process of these compositions. In this context, this paper tackles the problem of modeling, managing and preserving privacy in Web service composition processes. More specifically, we propose a first step towards providing a privacy preserving Web service composition approach that enables to (i) model and specify privacy policies, preferences, and requirements both at the client and at the provider sides, (ii) enforce the privacy model and build privacy aware compositions, (iii) verify the compliance between users privacy requirements and providers privacy policies, (iv) rank the composite Web services with respect to the privacy level they offer, and (v) provide privacy aware recovery actions to deal with incompatibilities.
Vehicle Tracking based on Kalman Filter Algorithm

Received signal strength indicator (RSSI) is a difficult technique to accurately estimate the distance between two participating entities because of the obscure environmental factors that distort the signal’s strength. In this study, we demonstrate that RSSI can be used in combination with the Kalman Filter to identify the position of a node in a wireless vehicular network. By observing a series of measurements and utilizing a model of the node’s trajectory, we can filter the noisy RSSI measurements to obtain a more accurate estimation of the node’s position. In our experiment, we gathered RSSI measurements from a mobile node, and used this data in combination with the Kalman Filter to estimate the position of the mobile node within 10 feet of the true position. Throughout this report, we demonstrate our implementation of the Kalman Filter, which is conceptually two Kalman Filters condensed into a single filter. Furthermore, we present the results of our experiments that display accuracy as close as 4 ft. from the true position.
Use of super resolution algorithms for indoor positioning keeping novel designed WLAN signal structure

This paper presents the utilization of super resolution algorithms for the indoor positioning applications in order to estimate Time Difference of Arrival (TDOA) and distances using Orthogonal Frequency Division Multiplexing (OFDM) transceiver. Optimal reduction in Distance Measurement Error (DME) is achieved. We have utilized OFDM/Single Carrier-Decision Feedback Equalizer (OFDM/SC-DFE) signal structure presented in our previous works. The super resolution algorithms Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), Root Multiple Signal Classification (Root-MUSIC) and Matrix Pencil (MP) are compared for DME estimation. We have applied Minimum Descriptive Length (MDL) criterion to these algorithms, that provides optimized estimate of the length of actual Channel Impulse Response (CIR) by eliminating the noise component from the dispersive CIR. Our scheme is based on two different antennas used to transmit the pre-half-zero-carriers and post-half-zero-carriers OFDM symbols respectively, mapped to multiple carriers using Wireless Local Area Network (WLAN) system and received by the object to be positioned.
A Novel Mutation (c.200T>C) in the NAGLU Gene of a Korean Patient with Mucopolysaccharidosis IIIB

Mucopolysaccharidosis (MPS) IIIB is a lysosomal storage disorder (LSD) caused by abnormalities of the enzyme α-N-acetylglucosaminidase (NAGLU) that is required for degradation of heparan sulfate. The patient in this study was a 4-yr-old boy. He presented with normal height and weight, pectus carinatum, and multiple persistent Mongolian spots on his back. He had mild dysmorphic features with prominent speech developmental delays and, to a lesser extent, motor developmental delays. The cetylpyridinium chloride precipitation test revealed excessive mucopolysacchariduria (657.2 mg glycosaminoglycan/g creatinine; reference range, <175 mg glycosaminoglycan/g creatinine). Thin layer chromatography showed urinary heparan sulfate excretion. NAGLU enzyme activity was significantly decreased in leukocytes (not detected; reference range, 0.9-1.51 nmol/hr/mg protein) as well as in plasma (0.14 nmol/hr/mg protein; reference range, 22.3-60.9 nmol/hr/mg protein). PCR and direct sequencing analysis of the NAGLU gene showed that the patient was a compound heterozygote for 2 mutations: c.200T>C (p.L67P) and c.1444C>T (p.R482W). The c.200T>C mutation was a novel finding. This is the first report of a Korean patient with MPS IIIB who was confirmed by molecular genetic analyses and biochemical investigation.
Neighborhood Peer Effects in the Use of Preventive Health Care

Individual participation in preventive care may depend on preventive health behavior in an individual’s peer group. This paper analyzes the importance of social interactions in the context of new social policies (PROGRESA) in Mexico that aim to increase the participation in different types of preventive care. We follow the promising approach of analyzing social interactions in real world peer groups. Identification of social interactions is based on a partial-population design. Results indicate that PROGRESA succeeded in increasing preventive care usage among program eligible households. In addition, endogenous social interactions increase preventive care usage both among eligibles and non-eligibles for various types of prevention. The overall treatment effect of PROGRESA on prevention can be decomposed in a direct effect related to financial incentives and an indirect effect related to social interactions. The indirect effect accounts for 10% up to 58% of the total treatment effect.
Multi-objective optimization technique using computational intelligence

In many practical engineering design problems, the form of objective functions is not given explicitly in terms of design variables. Given the value of design variables, under this circumstance, the values of objective functions are obtained by realkomputational experiments such as structural analysis, fluid-me&anical analysis, thermodynamic analysis, and so on. Since those experiments are considerably expensive and also time consuming, thus it is actually almost impossible to find the exact solution to those problems by using conventional optimization methods. Recently, approximation methods using computational intelligence, for example, evolutionary algorjthms and neural networks have been developed remarkably. Even those algorithms need a tremendous number of experiments to obtain an approx- imate solution. Furthermore, most engineering design problems should be formulated as multi-objective optimization problems so as to meet the diversified demands of designer. It also causes that the number of experiments goes on increasing. This paper proposes a new method using computational intelligence methods, which are a machine learning algorithm and an evolutionary algorithm, in order to make the number of experiments for finding the solution of problem with multi-objective functions as few as possible. Furthermore, this paper shows that the proposed method combining a machine learning algorithm and an evolutionary algorithm can generate well approximate Pareto frontier, and a decision making with two or three objective functions can be easily performed on the basis of visualized Pareto frontiers by our method. Finally, the effectiveness of the proposed method will be illustrated through several numerical and practical examples.
The global carbon budget 1959-2011

Abstract. Accurate assessments of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the climate policy process, and project future climate change. Present-day analysis requires the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. Here we describe datasets and a methodology developed by the global carbon cycle science community to quantify all major components of the global carbon budget, including their uncertainties. We discuss changes compared to previous estimates, consistency within and among components, and methodology and data limitations. CO2 emissions from fossil fuel combustion and cement production (EFF) are based on energy statistics, while emissions from Land-Use Change (ELUC), including deforestation, are based on combined evidence from land cover change data, fire activity in regions undergoing deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. Finally, the global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms. For the last decade available (2002–2011), EFF was 8.3 p 0.4 PgC yr−1, ELUC 1.0 p 0.5 PgC yr−1, GATM 4.3 p 0.1 PgC yr−1, SOCEAN 2.5 p 0.5 PgC yr−1, and SLAND 2.6 p 0.8 PgC yr−1. For year 2011 alone, EFF was 9.5 p 0.5 PgC yr−1, 3.0 percent above 2010, reflecting a continued trend in these emissions; ELUC was 0.9 p 0.5 PgC yr−1, approximately constant throughout the decade; GATM was 3.6 p 0.2 PgC yr−1, SOCEAN was 2.7 p 0.5 PgC yr−1, and SLAND was 4.1 p 0.9 PgC yr−1. GATM was low in 2011 compared to the 2002–2011 average because of a high uptake by the land probably in response to natural climate variability associated to La Nina conditions in the Pacific Ocean. The global atmospheric CO2 concentration reached 391.31 p 0.13 ppm at the end of year 2011. We estimate that EFF will have increased by 2.6% (1.9–3.5%) in 2012 based on projections of gross world product and recent changes in the carbon intensity of the economy. All uncertainties are reported as p1 sigma (68% confidence assuming Gaussian error distributions that the real value lies within the given interval), reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. This paper is intended to provide a baseline to keep track of annual carbon budgets in the future. All data presented here can be downloaded from the Carbon Dioxide Information Analysis Center ( doi:10.3334/CDIAC/GCP_V2013 ). Global carbon budget 2013
Information Theoretic Learning with Infinitely Divisible Kernels

In this paper, we develop a framework for information theoretic learning based on infinitely divisible matrices. We formulate an entropy-like functional on positive definite matrices based on Renyi's axiomatic definition of entropy and examine some key properties of this functional that lead to the concept of infinite divisibility. The proposed formulation avoids the plug in estimation of density and brings along the representation power of reproducing kernel Hilbert spaces. As an application example, we derive a supervised metric learning algorithm using a matrix based analogue to conditional entropy achieving results comparable with the state of the art.
SyMon: Defending large structured P2P systems against Sybil attack

Sybil attack is one of the most challenging problems that plague current decentralized Peer-to-Peer systems. In Sybil attack, a single malicious user creates multiple peer identities known as sybils. These sybils are employed to target honest peers and hence subvert the system. In this paper, we propose a novel solution that enables all honest peers to protect themselves from sybils with high probability in large structured P2P systems. In our proposed sybil defense system, we associate every peer with another non-sybil peer known as SyMon. A given peer's SyMon is chosen dynamically such that the chances of both of them being sybils are very low. The chosen SyMon is entrusted with the responsibility of moderating the transactions involving the given peer and hence makes it almost impossible for sybils to compromise the system. We show the effectiveness of our proposed system in defending against Sybil attack both analytically and experimentally.
2012 International Conference on Information Management, Innovation Management and Industrial Engineering the Application of Autoregressive Conditional Intensity Model to Shanghai Stock Exchange

Ahstract-A lot of previous researchers used duration models to analyze ultra-high frequency financial data from Chinese stock market. The likelihood function of those models sometimes are hard to obtain. In fact if we turned to point process theory, the likelihood functions and intensity functions of those counting processes are intuitive simple. And the statistic inferences can be carried out easily. However those models were ignored by Chinese researchers. In this paper, we use Autoregressive Conditional Intensity(ACI) model to analyze two big cap stocks from Shanghai stock market. The empirical results supported the correctness of this model.
Hippocampal Metaplasticity Induced by Deficiency in the Extracellular Matrix Glycoprotein Tenascin-R

Predisposition of synapses to undergo plastic changes can be dynamically adjusted according to the history of synaptic activity (i.e., synapses are metaplastic). In search of a molecular mechanism underlying metaplasticity, we investigated mice deficient in the glycoprotein tenascin-R (TNR), based on the observations that this mutant exhibits elevated basal excitatory synaptic transmission and reduced perisomatic GABAergic inhibition. TNR is a major extracellular matrix glycoprotein of the CNS and carries the HNK-1 carbohydrate (human natural killer cell glycan), which has been identified as the functional epitope mediating regulation of GABAergic transmission via GABAB receptors. Here, we used patch-clamp recordings in hippocampal slices to determine the critical levels of postsynaptic neuron depolarization necessary for induction of long-term potentiation (LTP) at CA3–CA1 synapses and found that deficiency in TNR leads to a metaplastic increase in the threshold for induction of LTP. Reconstitution of slices from TNR-deficient mice with an HNK-1 glycomimetic or pharmacological treatment with either a GABAA receptor agonist, a GABAB receptor antagonist, an L-type voltage-dependent Ca2+ channel blocker, or an inhibitor of protein serine/threonine phosphatases restored LTP to the levels seen in wild-type mice. We propose that a chain of events initiated by reduced GABAergic transmission and proceeding via Ca2+ entry into cells and elevated activity of phosphatases mediates homeostatic adjustment of hippocampal plasticity in the absence of TNR. These data uncover a novel mechanism by which an extracellular matrix molecule and its associated carbohydrate provide conditions beneficial for induction of LTP in the CA1 region of the hippocampus.
Interaction of students motivation and ecological phenomena toward learning outcomes using problem-based ecopedagogy

Knowledge as a cognitive outcome and academic motivation is directly linked to the affective outcome as attitudes and behaviors. Ecopedagogy as a learning model lead students to enhance the interaction on outcomes. This research focused on the interaction of the motivation and ecological phenomenon to the cognitive and affective outcomes. Validated instruments were used to collect data on motivation, cognitive and affective. Data were analyzed using factorial design with two ways ANOVA model. The result showed the interaction between motivation and ecological phenomena to the cognitive outcome is different to the affective outcome. The different showed on high and low motivation and ecological phenomena as the climate change, the deforestation, and the using energy resources. Through an analysis of interaction, the features of such approaches based eco-pedagogy to ecological phenomena subjects are a discussed.
A framework for measuring top management support in information systems projects

Top management support (TMS) is a critical success factor (CSF) for information systems project performance (ISPP). Undesirably low ISPP reported in prior studies is attributed to the lack of understanding of the supportive role top management is expected to play. In order to improve this understanding, the study reported in this paper suggests a theme for TMS using top management roles, and further investigates the link between TMS and ISPP with the use of a conceptual framework. This framework links the top management roles that are based on Henry Mintzberg's managerial roles, with product and process based ISPP. The conceptual framework is then investigated using a series of interviews, a focus group and a questionnaire survey. The findings support a managerial role based perspective for TMS. The findings also indicate that certain top management roles are more important than others in influencing ISPP. Additionally, the findings suggest that top management needs to assume newer roles for information systems (IS) projects, which have traditionally not been recognized as important. Furthermore, the findings suggest that TMS is regarded as a key resource for IS projects and needs to be explicitly promoted by the organization.
Premo and Kansei : A Comparative Analysis *

Abstrak – Kansei Engineering is a technology that enables incorporation of human emotion in design requirements. It has in its perspective that Kansei is unique for different domain and unique for different target user group, and use mainly a verbal measurement instruments in its methodology. The technology is seen to have little shortcoming when there is a need to build universal design for universal target user. It will involve complexity when handling semantics since people do not speak the same language across the planet. Hence, a non-verbal emotion measurement tool is assumed to enhance the capability of K.E. in managing universal Kansei. This study aims to investigate the possibility of integrating PrEmo, a non-verbal self-reporting tool which were developed based on studies across culture and demographical setting into Kansei Engineering. The objectives are to analyze the similarities and differences of Kansei structure resulted by two different measurement tools, non-verbal (PrEmo) and verbal (Kansei checklist) self-reporting instrument, to provide hypothetical evidence of the feasibility of PrEmo as a tool to measure Kansei. 10 websites with significant visual design differences were used as stimuli in the evaluation procedure involving 30 respondents, who provided their Kansei responses using both instruments. The result has shown that the Kansei structure by both instruments are mostly similar, thus provide hypothetical evidence that PrEmo could be used as non-verbal selfreporting instrument to measure Kansei. The findings provide insights into future research for integration of universal Kansei.