Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Relationship between Youth Leadership and Sustainability with Mediation Effects from an Indian Perspective using Path Way Analysis

Youth are agents of upcoming changes who through their knowledge,intense observation and perpetual action can face challenges that would help build a sustainable India. The revolutions that stimulate true youth leadership are nourished by being absorbed in the values and practices of a more sustainable way of living. The purpose of the paper is to study the impact of youth leadership on sustainability and the mediation effect of youth association, youth environmentalism, youth activism and youth governanceand the relation between youth leadership and sustainable India.The study is descriptive in nature and cross sectional in time dimension. Responses are gathered from NGO’s that engage youth. A theoretical model linking youth association, youth environmentalism, youth activism and youth governance and the relation between youth leadership and sustainable India is empirically tested using path analysis.The results of path analysis have revealed that youth leadership has a positive impact on sustainability.Further, youth association, youth environmentalism, youth activism and youth governance are partial mediators of the relationship between youthleadership and sustainability. This paper contributes to the existing literature by proposing a framework of the interrelationships of the factors affecting good governance and sustainability of India by the involvement of youth.
Camera Autocalibration from Kruppa's Equations Using Particle Swarm Optimization

Camera autocalibration from Kruppa’s equations must solve the problem of finding the global minimum of a cost function which has many local minima. Due tot he drawbacks of the traditional optimization algorithms in camera calibration, which include initial value sensitiveness, pool convergence and easily plunging into local minima, we study the application of the particle swarm optimization algorithm ( PSO) in camera autocalibration from Kruppa’s equations. We also give the calculation procedures of this method. The result of our experiments conducted on simulated data shows the effectiveness of the approach. Keywords-autocalibration;Kruppa’s equations;intrinct parameters;particle swarm optimization
Sialyltransferase ST3Gal-IV controls CXCR2-mediated firm leukocyte arrest during inflammation

Recent in vitro studies have suggested a role for sialylation in chemokine receptor binding to its ligand (Bannert, N., S. Craig, M. Farzan, D. Sogah, N.V. Santo, H. Choe, and J. Sodroski. 2001. J. Exp. Med. 194:1661–1673). This prompted us to investigate chemokine-induced leukocyte adhesion in inflamed cremaster muscle venules of α2,3 sialyltransferase (ST3Gal-IV)-deficient mice. We found a marked reduction in leukocyte adhesion to inflamed microvessels upon injection of the CXCR2 ligands CXCL1 (keratinocyte-derived chemokine) or CXCL8 (interleukin 8). In addition, extravasation of ST3Gal-IV−/− neutrophils into thioglycollate-pretreated peritoneal cavities was significantly decreased. In vitro assays revealed that CXCL8 binding to isolated ST3Gal-IV−/− neutrophils was markedly impaired. Furthermore, CXCL1-mediated adhesion of ST3Gal-IV−/− leukocytes at physiological flow conditions, as well as transendothelial migration of ST3Gal-IV−/− leukocytes in response to CXCL1, was significantly reduced. In human neutrophils, enzymatic desialylation decreased binding of CXCR2 ligands to the neutrophil surface and diminished neutrophil degranulation in response to these chemokines. In addition, binding of α2,3-linked sialic acid–specific Maackia amurensis lectin II to purified CXCR2 from neuraminidase-treated CXCR2-transfected HEK293 cells was markedly impaired. Collectively, we provide substantial evidence that sialylation by ST3Gal-IV significantly contributes to CXCR2-mediated leukocyte adhesion during inflammation in vivo.
Regulation and Incentives in European Aviation*

We study the effect of liberalization on costs and competition in the European airline industry. We construct and estimate a model that includes demand, capacity, and cost equations. The latter accounts for inefficiency and cost‐reducing effort. We show that failure to account for the choice of effort would lead to biased estimates of efficiency and competition in the industry. We also find that the last European Union package of deregulatory measures has led to significant efficiency improvements and has fostered competition.
Identification and characterization of genes and mutants for an N‐terminal acetyltransferase from yeast.

A gene from Saccharomyces cerevisiae has been mapped, cloned, sequenced and shown to encode a catalytic subunit of an N‐terminal acetyltransferase. Regions of this gene, NAT1, and the chloramphenicol acetyltransferase genes of bacteria have limited but significant homology. A nat1 null mutant is viable but exhibits a variety of phenotypes, including reduced acetyltransferase activity, derepression of a silent mating type locus (HML) and failure to enter G0. All these phenotypes are identical to those of a previously characterized mutant, ard1. NAT1 and ARD1 are distinct genes that encode proteins with no obvious similarity. Concomitant overexpression of both NAT1 and ARD1 in yeast causes a 20‐fold increase in acetyltransferase activity in vitro, whereas overexpression of either NAT1 or ARD1 alone does not raise activity over basal levels. A functional iso‐1‐cytochrome c protein, which is N‐terminally acetylated in a NAT1 strain, is not acetylated in an isogenic nat1 mutant. At least 20 other yeast proteins, including histone H2B, are not N‐terminally acetylated in either nat1 or ard1 mutants. These results suggest that NAT1 and ARD1 proteins function together to catalyze the N‐terminal acetylation of a subset of yeast proteins.
Bacterial Effector-Involved Temporal and Spatial Regulation by Hijack of the Host Ubiquitin Pathway

Ubiquitination is one of the most conserved post-translational modifications of proteins, and is involved in essential eukaryotic cellular processes. These include protein degradation, transcriptional regulation, cell-cycle progression, and signaling. Microbial pathogens have evolved sophisticated systems to hijack host cellular functions for their own benefit. Central to these systems are protein transport machineries; many pathogenic bacteria inject “effector proteins” to modulate host cellular processes including the ubiquitin pathway. Numerous bacterial pathogens have been found to modulate the host ubiquitin system in various ways. In this review, we focus on three examples of temporal and spatial regulation of bacterial effectors, which are mediated by the host ubiquitin system. Subversion of the host ubiquitin system must be a widespread strategy among pathogenic bacteria to accomplish successful infection.
Learning to Admit You ’ re Wrong : Statistical Tools for Evaluating Web QA

Web search engines provide specialized results to specific queries, often relying on the output of a QA system. However, targeted answers, while helpful, are embarrassing when wrong. Automated techniques are required to avoid wrong answers and improve system performance. We present the Expected Answer System, a statistical data-driven framework that analyzes the performance of a QA system with the goal of improving system accuracy. Our system is used for wrong answer prediction, missing answer discovery, and question class analysis. An empirical study of a production QA system, one of the first such evaluations presented in the literature, motivates our approach.
A QoE friendly rate adaptation method for DASH

MPEG-DASH introduced open standards into HTTP streaming, which was a huge step forward. The scope of the MPEG-DASH standard was limited to the Media Presentation Description (MPD) format of the manifest files as well as the segmentation standards for the server, but did not include the client application as part of their standards. Particularly a good representation switching algorithm at DASH client should adapt to time varying bandwidth with better QoE (Quality of Experience). In this paper, we propose a QoE friendly rate adaptation method which can achieve less switching times and more gradual quality change. To achieve these purposes, we propose a fixed-interval buffer model, which keeps the representation level unchanged whenever the media buffer size is within the preset interval. We also design a step-wise switch-up method to prevent buffer overflow and a switch-down method based on intermediate quality level to prevent buffer underflow. A quick boot algorithm is introduced to fully improve the performance of the proposed scheme. Experimental results show that the proposed algorithm can achieve more satisfactory results compared with existing ones.
Improving Noise Robustness in Subspace-Based Joint Sparse Recovery

In a multiple measurement vector problem (MMV), where multiple signals share a common sparse support and are sampled by a common sensing matrix, we can expect joint sparsity to enable a further reduction in the number of required measurements. While a diversity gain from joint sparsity had been demonstrated earlier in the case of a convex relaxation method using an l1/ l2 mixed norm penalty, only recently was it shown that similar diversity gain can be achieved by greedy algorithms if we combine greedy steps with a MUSIC-like subspace criterion. However, the main limitation of these hybrid algorithms is that they often require a large number of snapshots or a high signal-to-noise ratio (SNR) for an accurate subspace as well as partial support estimation. One of the main contributions of this work is to show that the noise robustness of these algorithms can be significantly improved by allowing sequential subspace estimation and support filtering, even when the number of snapshots is insufficient. Numerical simulations show that a novel sequential compressive MUSIC (sequential CS-MUSIC) that combines the sequential subspace estimation and support filtering steps significantly outperforms the existing greedy algorithms and is quite comparable with computationally expensive state-of-art algorithms.
Representation of New Media Art for Preservation : Interactions , Variations , and the Flow of Information

Introduction Preservation and representation of digital art presents a significant challenge for curators, archivists and artists. The most notable problem is the fundamentally interactive nature of this evolving art form. Because there are no clearly defined frameworks for the authentic representation of the variations resulting from interactions, curators and archivists are currently deciding on a case by case basis which interactions should be preserved, who should be able to interact with a given piece, and how to represent those interactions in a meaningful and objective way. Not only is this method problematic from a representational standpoint, it’s very time-consuming. My thesis will attempt to resolve some of these complexities by making connections between this new art form and an established one, namely music; assessing the representational characteristics of music’s notation system and exploring the methods by which musicians and conductors handle musical interaction.
Investigating autistic children's attitudes towards strangers with the theatrical robot - a new experimental paradigm in human-robot interaction studies

In this paper, as part of a larger study into the possible use of robots in therapy or education of children with autism, we studied the effects of two different robot appearances on autistic children's behaviour towards the robot. We used a novel experimental paradigm, the theatrical robot, which is discussed in the context of other evaluation methods used in the field of human-robot interaction (HRI). The two appearances used were a plain/robotic and an 'ordinary human' appearance. The response of children with autism towards the plain/robotic robot was notably more social and pro-active. The ordinary-human appearance resulted in the avoidance behaviour or 'aloofness', a typical behaviour that autistic children show towards strangers. Implications of these results for our work on robots and autism, as well as other HRI research are discussed.
The Analysis Of Potential Penetration & Development Of Alipay In China Using Combination Of Sor, Tam And Tpb Model

This study validates the application of the theory of planned behavior by intercepting situational (environmental) and personal factors to influence students’ entrepreneurial intentions. While TPB in entrepreneurial intentions has been widely acknowledged and attempted, but gaps still exist in terms of how the TPB variables are explained in their roles towards entrepreneurial intentions, and in particular, mediating roles of perceived behavioral control through self-efficacy and personal attitude are relatively unfound in the extant literature. Situational factors constituting of both curriculums and subjective norms are studied, while occupational challenge mindset is explored for indirect impact on entrepreneurial intention mediated through personal attitude. Sampling was drawn from the student population currently enrolled at Mae Fah Luang University, one renown public university in Chiang Rai. The university is chosen for its strategic focus in entrepreneurship and offering of wide ranges of subjects such as in Chinese medicine, cosmetics, Chinese language studies, and business majored students. Methodologically, both hierarchical multiple regression and structural equation modeling (SEM) methods would be used, which provide statistical evidences for the adapted and extended TPB framework.
2015 Ieee International Conference on Big Data (big Data) Improving Eeg Feature Learning via Synchronized Facial Video

—Morden physiological analysis begins to involve more and more types of information. Electroencephalogram (EEG) signals as a typical example is starting to be analyzed with facial expressions videos to detect emotions. Emotions play an important role in the daily life of human beings, the need and importance of automatic emotion recognition has grown with increasing role of human computer interface applications. In this paper, we concentrate on recognition of the emotions jointly from " inner " and " outer " reactions, which are electroencephalogram (EEG) signals and facial expression video. Due to the streaming nature of this problem, the data volume and velocity is very challenging. We address these challenges from the theoretic perspective and propose a real time algorithm based on EEG signals and synchronized facial video to learn feature vector jointly. Our algorithm consists of an unsupervisedly EEG dictionary component based on deep learning theorem, and a probability pooling component transforms a continuous sequential signal into an EEG " sentence " which consists of a sequence of EEG words. The EEG sentence is then jointly learned with video features into a new fixed length feature representation for emotion classification. We overcome several computational challenges on the data based on the idea of convolution and pooling, and we conduct extensive evaluation for each component of our model. We also demonstrate the state-of-the-art classification result on real-world dataset. The superior performances on the emotion recognition task indicates that 1) the natural language scenario can be applied in EEG sequences and 2) borrowing video modality can increase the overall performance.
Pengamalan kompetensi dalam kalangan pensyarah pendidikan islam Institut Pendidikan Guru di Malaysia

The efficacy in educating trainee teachers is closely related to the determination of lecturer in practising and enhancing every competency they possess while teaching. Competent lecturers are able to develop trainee teachers‟ potential holistically so that these trainees become quality teachers in schools later on. The purpose of this study was to investigate the traits of competency, practised by the Islamic Education Lecturers of Teachers‟ Training Colleges (IELTTC) in Malaysia. This study utilised full qualitative paradigm with a case study design. Participants of the study were eight Islamic Education Lecturers (IELs), sixteen of IEL colleagues, sixteen students and eight head of departments. The main source of data was verbatim interview, supported by observation and document analysis. NVivo 10 was used to analyse all three types of qualitative data so that themes and sub themes could be identified systematically. In general, the findings of this study has focused characteristics of lecturers‟ practice of competency from the following aspects: knowledge, personality, teaching and learning skills, guidance and practicum supervision, research and innovation, and service and consultation. Findings from this study produced seven IELTTC models which were Personal Quality Competency Practice Model (PQPCM-IELTTC), Expertise and Knowledge Competency Practice Model (EKCPM-IELTTC), Teaching and Learning Competency Practice Model (TLCPM-IELTTC), Guidance and Practicum Supervision Competency Practice Model (GPSCPM-IELTTC), Innovation and Research Competency Practice Model (IRCPM-IELTTC), Consultation and Service Competency Practice Model (CSCPM- IELTTC), and also Primary Competency Model (PCM- IELTTC). The implications of this study could become a guide to many parties particularly to lecturers at Malaysia Teachers‟ Training Institutes, the Ministry of Higher Education, Inspectorate of Schools, State Education Departments, schools as well as educators.
Application traffic classification in Hadoop distributed computing environment

Today, network traffic has increased because of the appearance of various applications and services. However, methods for network traffic analysis are not developed to catch up the trend of increasing usage of the network. Most methods for network traffic analysis are operated on a single server environment, which results in the limits about memory, processing speed, storage capacity. When considering the increment of network traffic, we need a method of network traffic to handle the Bigdata traffic. Hadoop system can be effectively used for analyzing Bigdata traffic. In this paper, we propose a method of application traffic classification in Hadoop distributed computing system and compare the processing time of the proposed system with a single server system to show the advantages of Hadoop.
Mentalizing in improvisational music therapy

ABSTRACT Introduction: The concept of mentalization has become increasingly widespread in the psychotherapy discourse. This article investigates three important questions in relation to music therapy and mentalization-based treatment: 1. How is mentalizing in a musical context (improvisation) different from mentalizing in a verbal context? 2. How do mentalization processes unfold in a musical context during improvisation? 3. Are there ways of responding in musical improvisation that can facilitate the ability to mentalize? Methods: We employed reflexive investigation using clinical examples to illustrate our understanding of how mentalizing unfolds in the process of music therapy practice. Results: We illustrate different ways that non-mentalizing can appear in musical improvisation and exemplify how musical improvisation can enhance the ability to mentalize. Discussion: We argue that it is possible to distinguish between mentalizing in a verbal or musical context even though there are similarities. We argue that the mind-set of the therapist is the single most important factor in facilitating mentalizing in music therapy.
Measurements of Location-Dependent Nitric Oxide Levels on Skin Surface in relation to Acupuncture Point

Location-dependent skin surface's partial nitric oxide pressure (pNO) is studied using highly sensitive amperometric NO microsensor with a small sensing area (diameter = 76 μm). The pNO level of LI4 (Hegu) acupuncture point is measured and compared with the pNO level of nonacupuncture point. In addition, the mapping of pNO is carried out over the left wrist skin area one- as well as two-dimensionally. Statistically higher pNO levels near the position of acupuncture points than non-acupuncture points are observed consistently, implying tight relationship between the level of NO release of skin and acupuncture points. The amperometric planar NO microsensor successfully monitors the heterogeneity of skin pNO distribution in high spatial resolution due to its advantageous features such as high sensitivity and small sensing dimension. The current study suggests the direct connection between NO and acupuncture points and possibly provides beneficial information to understand physiological roles and basis of the acupuncture points.
A roadmap to domain specific programming languages for environmental modeling: key requirements and concepts

The limited reuse of current environmental software can be blamed in part on the tools used to develop it; the use of generic-purpose programming languages makes it particularly hard. As environmental scientists strive to prioritize the clear statement and communication of the semantics of natural systems in favor of understanding software implementations of their models, Domain-Specific Languages may come to help, offering the option of truly declarative environment for environmental modeling. This paper discusses some key requirements and concepts for developing Domain-Specific Languages that can inform and streamline environmental modeling, and previews some use scenarios using examples from a DSL in development.
Expression analysis of extracellular proteins from Phanerochaete chrysosporium grown on different liquid and solid substrates

White-rot fungi secret a large number of hydrolytic and oxidative enzymes for degradation of lignocellulosic material. The sequencing of the genome of the white-rot fungus Phanerochaete chrysosporium has facilitated the characterization of its complete extracellular proteome. P. chrysosporium was grown on liquid medium, containing glucose, cellulose or wood chips as the carbon source, and also in solid substrate fermentation bags. For liquid-grown cultures, the extracellular protein fraction was separated by 2D gel electrophoresis. Protein spots were analysed by in-gel digestion and liquid chromatography (LC)/MS/MS. A total of 18 additional protein spots from the 2D gels yielded hits from blast searches. From solid substrate cultures in which the fungus was grown in bags, the proteins were resolved by SDS-PAGE, subjected to in-gel digestion and then identified by LC/MS/MS. An additional 16 proteins yielded hits on blast searches. Enzymes involved in cellulose, hemicellulose, lignin and protein degradation were identified. Expression patterns were very similar between cellulose-grown cultures and wood-grown cultures. In addition to enzymes which act on lignocellulosic material, proteases were also found, indicating the need of fungi to scavenge for nitrogen in wood.