Miracle-dz/UniFAR-FTUs huggingface dataset:
Better constraints on sources of carbonaceous aerosols using a combined 14 C – macro tracer analysis in a European rural background site

Abstract. The source contributions to carbonaceous PM 2.5 aerosol were investigated at a European background site at the edge of the Po Valley, in Northern Italy, during the period January–December 2007. Carbonaceous aerosol was described as the sum of 8 source components: primary (1) and secondary (2) biomass burning organic carbon, biomass burning elemental carbon (3), primary (4) and secondary (5) fossil organic carbon, fossil fuel burning elemental carbon (6), primary (7) and secondary (8) biogenic organic carbon. The mass concentration of each component was quantified using a set of macro tracers (organic carbon OC, elemental carbon EC, and levoglucosan), micro tracers (arabitol and mannitol), and 14 C measurements. This was the first time that 14 C measurements covered a full annual cycle with daily resolution. This set of 6 tracers, together with assumed uncertainty ranges of the ratios of OC-to-EC, and the reference fraction of modern carbon in the 8 source categories, provides strong constraints to the source contributions to carbonaceous aerosol. The uncertainty of contributions was assessed with a Quasi-Monte Carlo (QMC) method accounting for the variability of OC and EC emission factors, the uncertainty of reference fractions of modern carbon, and the measurement uncertainty. During winter, biomass burning composed 64 % (±15 %) of the total carbon (TC) concentration, while in summer secondary biogenic OC accounted for 50 % (±16 %) of TC. The contribution of primary biogenic aerosol particles was negligible during the entire year. Moreover, aerosol associated with fossil sources represented 27 % (±16 %) and 41 % (±26 %) of TC in winter and summer, respectively. The contribution of secondary organic aerosol (SOA) to the organic mass (OM) was significant during the entire year. SOA accounted for 30 % (±16 %) and 85 % (±12 %) of OM during winter and summer, respectively. While the summer SOA was dominated by biogenic sources, winter SOA was mainly due to biomass burning and fossil sources. This indicates that the oxidation of semi-volatile and intermediate volatility organic compounds co-emitted with primary organics is a significant source of SOA, as suggested by recent model results and Aerosol Mass Spectrometer measurements. Comparison with previous global model simulations, indicates a strong underestimate of wintertime primary aerosol emissions in this region. The comparison of source apportionment results in different urban and rural areas showed that the sampling site was mainly affected by local aerosol sources during winter and regional air masses from the nearby Po Valley in summer. This observation was further confirmed by back-trajectory analysis applying the Potential Source Contribution Function method to identify potential source regions.
A Clustering Approach for Data and Structural Anonymity in Social Networks

advent of social network sites in the last few years seems to be a trend that will likely continue in the years to come. Online social interaction has become very popular around the globe and most sociologists agree that this will not fade away. Such a development is possible due to the advancements in computer power, technologies, and the spread of the World Wide Web. What many naive technology users may not always realize is that the information they provide online is stored in massive data repositories and may be used for various purposes. Researchers have pointed out for some time the privacy implications of massive data gathering, and a lot of effort has been made to protect the data from unauthorized disclosure. However, most of the data privacy research has been focused on more traditional data models such as microdata (data stored as one relational table, where each row represents an individual entity). More recently, social network data has begun to be analyzed from a different, specific privacy perspective. Since the individual entities in social networks, besides the attribute values that characterize them, also have relationships with other entities, the possibility of privacy breaches increases. Our main contributions in this paper are the development of a greedy privacy algorithm for anonymizing a social network and the introduction of a structural information loss measure that quantifies the amount of information lost due to edge generalization in the anonymization process.
Recognizing Polarity and Attitude of Words in Text

In this work, we present a problem of Sentiment Classification in texts. Sentiment Classification is an Opinion Mining and Sentiment Analysis task that has opened several problems, including a significant variety of applications. We propose a strategy to distinguish words that convey evaluation of an item from the rest, as well as to classify the evaluation polarity (positive or negative). In addition, relying on Appraisal Theory, we intend to classify the evaluation words in affect, judgment and appreciation. Both, polarity and attitude are recognized using a corpus-based approach. We have the purpose of applying this task in Spanish texts; thereby, we have created a corpus of movie reviews in this language that was manually processed. In the experiments, we noticed that our strategy has a good performance, achieving 76.78% and 80.16% of accuracy classifying polarity and attitude, respectively.
Shortest Path Based Potential Common Friend Recommendation in Social Networks

Friend recommendation is a very popular application in online social network(OSN) services to help users make new friends and expand their social circle. Much of the existing research is based on either topological structure of OSN or user profiles to recommend new friends, while research based on both topological structure and user profiles to recommend potentially common friends between two undirectly connected users is still lacking. Recommending potentially common friends is useful in practical applications such as finding potential partners between two business groups in commercial social network in order to expand their business range or to reach potential customers, finding common bus stops between two specified bus stops on traffic network etc. In this paper we propose a novel shortest path based common friends recommendation approach in OSN to find and recommend potential common friends between two users. Our approach consists of the improved Floyd_Warshall algorithm and the Extended Longest Common Subsequence(ELCS) algorithm on both the topological structures of OSN and the partial profiles of users. The experiment results show that our approach can help users find the potential common friends efficiently and effectively.
Automatic gazetteer enrichment with user-geocoded data

Geographical knowledge resources or gazetteers that are enriched with local information have the potential to add geographic precision to information retrieval. We have identified sources of novel local gazetteer entries in crowd-sourced OpenStreetMap and Wikimapia geotags that include geo-coordinates. We created a fuzzy match algorithm using machine learning (SVM) that checks both for approximate spelling and approximate geocoding in order to find duplicates between the crowd-sourced tags and the gazetteer in effort to absorb those tags that are novel. For each crowd-sourced tag, our algorithm generates candidate matches from the gazetteer and then ranks those candidates based on word form or geographical relations between each tag and gazetteer candidate. We compared a baseline of edit distance for candidate ranking to an SVM-trained candidate ranking model on a city level location tag match task. Experiment results show that the SVM greatly outperforms the baseline.
Public Key Timed-Release Searchable Encryption

This paper introduces and explores a new concept of timed-release searchable encryption (TRSE) which can be used to solve the time-sensitive cipher text retrieval problem. In this paper, we preliminarily focus on the PKTRSE which is a kind of public key TRSE. In our PKTRSE model, the sender encrypts a message so that only the intended receiver can search target cipher text containing specified keywords after a pre-set release time in the future. We begin by explaining what is PKTRSE and introducing the application scenario of PKTRSE. Then, we show the preconditions of timed-release public key encryption combining other asymmetric crypto-graphic mechanisms. Afterwards, we formalize the notion of PKTRSE and its security game model. Finally, we give two construction schemes of PKTRSE: A generic scheme and a concrete scheme which are secure under the BDH assumption in the random oracle model.
Probabilistic Modelling of Cognitive Processes in the Remember-Know Paradigm

Research with the remember-know paradigm into the subjective experience that accompanies retrieving information from memory has been conducted with the aim of explaining the phenomenon of retrieval experience per se, or with the aim of explaining recognition memory performance, assuming that retrieval experience allows for separating different memory systems. In both lines of research, conclusions are typically based on the rates of remember and know responses to target items. However, there are reasons to question the suitability of these measures for answering either research question. The present thesis evaluates alternative measurement models for both purposes. A meta-analysis of published and two new experiments showed that the task-oriented model, which was designed to provide pure measures of retrieval experience per se, is a valid measurement model. In two experiments, it was used to test the distinctiveness-fluency framework of retrieval experience. Although both experiments yielded the same result in terms of rememberknow response rates, the model-based analysis showed that only one manipulation affected the critical valid measure of retrieval experience. Besides casting doubt on the distinctiveness-fluency framework, this finding demonstrates empirically that remember-know response rates may lead to erroneous conclusions.
An Empirical Exploration of How Process Standardization Reduces Outsourcing Risks

Are standardized business processes less risky to outsource? Despite the importance, of both areas, neither the outsourcing nor standardization literature has so far offered a conclusive picture of the value of standards to outsourcing. We aim to provide an exploratory first step by suggesting that process standards have a positive impact on business process outsourcing (BPO) risk. Theoretically drawing from perceived risk theory and the theory of reasoned action we develop a model of BPO risk and empirically show that indeed risk perception is higher for less standardized processes. Using data from 126 German banks, it turns out that financial and performance risks significantly differ between high- and low-standardized processes and are consistently higher for low-standardized processes. While this work is very explanatory and the findings are quite preliminary, they are very encouraging as they indicate promising further research into the value of standards for business and outsourcing
Inhibition of angiogenesis and tumour growth by VEGF121–toxin conjugate: differential effect on proliferating endothelial cells

Vascular endothelial growth factor (VEGF) plays an important role in tumour angiogenesis. VEGF binds to tyrosine kinase receptors, which are expressed almost exclusively on tumour endothelium. Therefore, VEGF can be used to target toxin molecules to tumour vessels for anti-angiogenic therapy. However, recent evidence suggests that VEGF can also bind in an isoform-specific fashion to a newly identified neuropilin-1 (NP-1) receptor. NP-1 is widely expressed in normal tissue and presents a potential target for unwanted toxicity. As a consequence, we investigated whether the VEGF121 isoform, which lacks the NP-1 binding domain, could be used to target toxin polypeptides to tumour vasculature. Treatment of endothelial cells with a VEGF121–diphtheria toxin (DT385) conjugate selectively inhibited proliferating endothelial cells, whereas confluent cultures were completely resistant to the construct. In addition, VEGF121–DT385 conjugate treatment completely prevented tumour cell induced angiogenesis in vivo. Most importantly, the conjugate inhibited tumour growth in athymic mice and induced tumour-specific vascular damage. There was also no apparent toxicity associated with the treatment. Our results suggest that proliferating endothelial cells are highly sensitive to VEGF121–toxin conjugates and that the binding to NP-1 receptors is not necessary for efficient inhibition of tumour growth. © 2000 Cancer Research Campaign
Tuning an artificial neural network to increase the efficiency of a fingerprint matching algorithm

In this paper we presented a simplified solution of an artificial neural network which matches the patterns of fingerprints. As universal approximator, the artificial neural networks (ANNs) are able to bridge the non linear outcome of the superposition of elementary failures. We built up a multilayer feedforward network, and used the back-propagation on a medium size sample, to reach better accuracy. This implementation needs a significantly shorter processing time and less complex IT background. We were focusing on the testing of the training's outcome, because it highly depends on the starting conditions.
Reduced buffering solution for multi-hop HaRTES switched Ethernet networks

In the context of switched Ethernet networks, multi-hop communication is essential as the networks in industrial applications comprise a high amount of nodes, that is far beyond the capability of a single switch. In this paper, we focus on multi-hop communication using HaRTES switches. The HaRTES switch is a modified Ethernet switch that provides real-time traffic scheduling, dynamic Quality-of-Service and temporal isolation between real-time and non-real-time traffic. Herein, we propose a method, called Reduced Buffering Scheme, to conduct the traffic through multiple HaRTES switches in a multi-hop HaRTES architecture. In order to enable the new scheduling method we propose to modify the HaRTES switch structure. Moreover, we develop a response time analysis for the new method. We also compare the proposed method with a method previously proposed, called Distributed Global Scheduling, based on their traffic response times. We show that, the new method forwards all types of traffic including the highest, the medium and the lowest priority, faster than the previous method in most of the cases. Furthermore, we show that the new method performs even better for larger networks compared with the previous one.
Non-Negative Multiple Matrix Factorization

Non-negative Matrix Factorization (NMF) is a traditional unsupervised machine learning technique for decomposing a matrix into a set of bases and coefficients under the non-negative constraint. NMF with sparse constraints is also known for extracting reasonable components from noisy data. However, NMF tends to give undesired results in the case of highly sparse data, because the information included in the data is insufficient to decompose. Our key idea is that we can ease this problem if complementary data are available that we could integrate into the estimation of the bases and coefficients. In this paper, we propose a novel matrix factorization method called Non-negative Multiple Matrix Factorization (NMMF), which utilizes complementary data as auxiliary matrices that share the row or column indices of the target matrix. The data sparseness is improved by decomposing the target and auxiliary matrices simultaneously, since auxiliary matrices provide information about the bases and coefficients. We formulate NMMF as a generalization of NMF, and then present a parameter estimation procedure derived from the multiplicative update rule. We examined NMMF in both synthetic and real data experiments. The effect of the auxiliary matrices appeared in the improved NMMF performance. We also confirmed that the bases that NMMF obtained from the real data were intuitive and reasonable thanks to the non-negative constraint.
Metabotropic Glutamate Receptors Increase Amyloid Precursor Protein Processing in Astrocytes: Inhibition by Cyclic AMP

Abstract: Neurotransmitter receptors that increase phosphatidylinositol hydrolysis generate second messengers that activate protein kinase C. Here, we used metabotropic glutamate receptor agonists to increase both phosphatidylinositol hydrolysis and secretion of the soluble extracellular fragment of amyloid precursor protein (APPs) from cortical astrocyte cultures. The increase in APPs secretion was mimicked by direct activation of protein kinase C with phorbol ester and was suppressed by the metabotropic glutamate receptor antagonist l‐(+)‐2‐amino‐3‐phosphonopropionic acid or by the protein kinase C inhibitor GF109203X. Ionotropic glutamate agonists did not increase APPs secretion. Forskolin or dibutyryl cyclic AMP inhibited the increase in APPs secretion caused by metabotropic glutamate receptor agonists or by phorbol ester treatment but did not affect basal APPs levels. Therefore, glutamatergic agonists that increase protein kinase C activation or decrease cyclic AMP formation may enhance the conversion of full‐length APP to nonamyloidogenic APPs in Alzheimer's disease.
Simulation Teaching in 3D Augmented Reality Environment

Traditional 3D virtual environments give people an isolated feeling in their interactive experience. This paper proposes a simulation instruction mode based on 3D Augmented Reality (AR) environments which can improve interactive experience. The camera detects the presetting markers which will later generate 3D virtual objects and shoots teachers' and students' real experimental operations at the same time, interposing the virtual objects on the real scene to produce a blended environment. With that technology, we construct both the local AR-based environments and the distant AR-based environments and devise several simulation instruction cases, such as Newton's First Law of Motion, Newton's Second Law of Motion, Simple Pendulum and Ants Meeting Algorithm, etc. According to the experimental analysis of students' learning interests and learning results, we conclude that in an AR-based learning environment, students adopt a natural interactive method and enjoy the same experience as in real environments due to the abandonment of mouse and keyboard devices. Furthermore, that blended AR-based learning environment is able to interpose objects which are inaccessible in real life due to limitation of expenses and safety or some other factors on the real scene, presenting an innovative and fascinating learning mode which eliminates the isolated feeling in learning.
Cortical Activation in the Human Brain induced by Transcranial Direct Current Stimulation

【Purpose: Recently, neurostimulation studies involving manipulation of cortical excitability of the human brain have been increasingly attempted. We investigated whether transcranial direct current stimulation (tDCS) applied to the underlying cerebral cortex, directly induces cortical activation during fMRI scanning. Methods: We recently recruited five healthy subjects without a neurological or psychiatric history and who were right-handed, as verified by the modified Edinburg Handedness Inventory. fMRI was done while constant anodal tDCS was delivered to the underlying SM1 area? immediately after the pre-stimulation for eighteen minutes. Results: Group analysis yielded an averaged map that showed that the SM1 area and the superior parietal cortex in the ipsilateral hemisphere were activated. The voxel size and peak intensity were, respectively, 82 and 5.22 in the SM1, and 85 and 5.77 in the superior parietal cortex. Conclusion: Cortical activation can be induced by constant anodal tDCS of the underlying motor cortex. This suggests that tDCS may be an effective therapeutic device for enhancing? physical motor function by modulating neural excitability of the motor cortex. 】
A fully-synthesizable single-cycle interconnection network for Shared-L1 processor clusters

Shared L1 memory is an interesting architectural option for building tightly-coupled multi-core processor clusters. We designed a parametric, fully combinational Mesh-of-Trees (MoT) interconnection network to support high-performance, single-cycle communication between processors and memories in L1-coupled processor clusters. Our interconnect IP is described in synthesizable RTL and it is coupled with a design automation strategy mixing advanced synthesis and physical optimization to achieve optimal delay, power, area (DPA) under a wide range of design constraints. We explore DPA for a large set of network configurations in 65nm technology. Post placement&routing delay is 38FO4 for a configuration with 8 processors and 16 32-bit memories (8×16); when the number of both processors and memories is increased by a factor of 4, the delay increases almost logarithmically, to 84FO4, confirming scalability across a significant range of configurations. DPA tradeoff flexibility is also promising: in comparison to the maxperformance 16×32 configuration, there is potential to save power and area by 45% and 12 % respectively, at the expense of 30% performance degradation.
Hubungan antara gaya kepimpinan pengetua dengan motivasi dalam kalangan guru sekolah menengah kebangsaan harian daerah Kluang, Johor

The aim of this study was to identify the relationship between principals’ leadership style and their motivation at the National Secondary Schools in Kluang, Johor. This study involved 181 teachers from Secondary Schools in Kluang. Leadership style were measured using “The Leadership Behavior Description Questionnaire (LBDQ)” instruments developed by the University of Ohio (Halpin 1966) and has been translated and modified by Rahimah Ahmad (1991). While the teachers’ motivation was measured using “A Questionnaire Measure of Individual Differences in Achieving Tendency (QMAT)” instruments developed by Mehrabian and Bank (1978). Descriptive statistics and inferential statistics in term of t test and Pearson Correlation Coefficient were used to test hypotheses at the significance level . 05. The finding of this research showed that the most dominant for principals' leadership style at the National Secondary School in Kluang was the supportive leadership style (70.2%), while the teachers’ motivation was moderate (54.1%). There were significant differences in terms of principals' leadership style based on teachers' perceptions by demographic factors such as teaching experience and age except gender factor, while there were no differences in terms of teachers’ motivation based on gender, teaching experience and age factors. The study also showed that there was a significant relationship between the principals' leadership style based on the perception of teachers and teacher's motivation. In fact, each dimension of leadership style was found to have a significant relationship with the of teachers’ motivation.
Cloning and Heterologous Expression of Hematin-Dependent Catalase Produced by Lactobacillus plantarum CNRZ 1228

ABSTRACT Lactobacillus plantarum CNRZ 1228 exhibited heme-dependent catalase activity under environmental conditions similar to those encountered during sausage fermentation. The 1,455-bp catalase gene (katL) was cloned and encoded a protein of 484 amino acids. Expression of katL in a heterologous host showed that katL encodes a functional catalase. PCR screening of selected strains of lactic acid bacteria for katL indicated the presence of similar genes in other strains of lactobacilli.