Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent development in TVET steam shows very encouraging trend in term of demand of technical graduate from the industry. According to the 11th Malaysia Plan, TVET is expected to increase the enrolment by year 2020 as to align with industry

2726 Retrieval Number: C9233019320 /2020©BEIESP DOI: 10.35940/ijitee.C9233. 019320 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Abstract: The demand on high skill graduate shows increasing trend in the context of Malaysia. Current supply of higher skill graduate is in adequate to support the industry. Thus, the government expected that, the number of student enrolment in higher Technical and Vocational Education and Training (TVET) institutions to be increased. Recent studies highlighted that service quality and corporate image are two critical factors that influence student loyalty. However, the mediating effect of corporate image has given less attention in the previous research. Thus, the purpose of this research is to explore the mediating effect of corporate image on the relationship between the service quality and student loyalty. The scope of this study is final year student of higher TVET universities. 398 of completed questionnaires were analyzing using SPSS and Smart-PLS. The measurement model shows that all constructs are reliable. While the structural model result showed that there is a direct and significant effect of corporate image on the relationship between service quality and student loyalty. The mediating test indicates that there is a partial mediation of corporate image on the relationship between service quality and student satisfaction. Future research is suggested to explore in depth on other factors that could have mediate the relationship between service quality and student loyalty.
Protected Sal Forest and Livelihoods of Ethnic Minorities: Experience From Bangladesh

Protected areas are an appropriate means for managing biodiversity and have become increasingly central to conservation strategies. However, declaring a natural resource to be protected has an immense influence on the livelihoods of many local communities living in and around the protected area. This article explores the role of protected sal forests—namely, the Madhupur National Park (MNP) on the livelihoods of forest-dependent ethnic minorities, drawing empirical data from ethnic households. The results revealed that the MNP has brought changes, most of which have had negative impacts on ethnic livelihoods. Now their livelihoods are not stable, as most do not have substantial sources of securing income. In spite of this, the ethnic communities have experienced severe antagonistic relationships with park managers and faced social inequity; all these factors affect their livelihoods. Therefore, establishment of a legitimate process to constructively work with the local communities is the most feasible and just way to achieve conservation and development.
The challenge of remobilisation in plant nitrogen economy. A survey of physio-agronomic and molecular approaches.

In this article, we discuss the ways in which our understanding of the controls of nitrogen remobilisation in model species and crop plants have been increased through classical physiological studies and the use of transgenic plants or mutants with modified capacities for nitrogen or carbon assimilation and recycling. An improved understanding of the transition between nitrogen assimilation and nitrogen recycling will be vital, if improvements in crop nitrogen use efficiency are to reduce the need for excessive input of fertilisers and improve or stabilise yield. In this review, we present an overall view of past work and more recent studies on this topic, using different plants systems and models depicting the biochemical and molecular events occurring during the transition between sink leaves and source leaves. These models may provide a way to identify the nature of the metabolic or developmental signals triggering in a coordinate manner nitrogen and carbon recycling during leaf senescence. Another way of developing crop varieties with improved nitrogen use efficiency, and identifying key elements controlling the process of nitrogen remobilisation, is the use of quantitative genetics. We present and discuss recent findings on the genetic variability and basis of nitrogen use efficiency in crops in general and in maize in particular. A genetic approach using maize recombinant inbred lines was undertaken allowing the detection of Quantitative Trait Loci (QTLs) for morphological traits, grain yield and its components under high nitrogen or low nitrogen input. Co-mapping was observed between genes encoding enzymes involved in nitrogen assimilation (nitrate reductase, glutamine synthetase) and these Quantitative Trait Loci. All coincidences were consistent with the expected physiological function of the corresponding enzyme activities. This work strongly suggests that in maize, nitrogen use efficiency can be improved both by marker-assisted selection and genetic engineering.
Leaf Discs or Press Saps? A Comparison of Techniques for the Determination of Osmotic Potentials in Freeze-Thawed Leaf Material

Two standard methods for the determination of osmotic potentials in plant organs, thermocouple hydrometry of press saps and of freeze-thawed leaf discs, were compared on leaves of ten species. Strictly parallel samples invariably showed lower (more negative) osmotic potentials for leafs discs. Evidence is presented that osmotically-active solutes are not completely extracted during the preparation of press saps and the discs contain, on average, a smaller amount of apoplastic water than the saps
Comparative germination of culture-produced and plant-produced sporangia of Pythium ultimum in response to soluble seed exudates and exudate components

On synthetic media, sporangia germinated in response to certain sugars and amino acids as well as cotton seed exudate. However, when produced in association with plant tissue or on media amended with α-phosphatidyl choline, sporangia failed to germinate in response to any sugar or amino acid tested individually or in combination, despite their ability to germinate in response to cotton seed exudate
Growth and antimicrobial activity of lactic acid bacteria from rumen fluid according to energy or nitrogen source

Objetivou-se avaliar os efeitos da suplementacao com diferentes fontes de energia e de compostos nitrogenados sobre o crescimento e producao de bacteriocinas de bacterias acido-laticas in vitro. As incubacoes foram conduzidas utilizando-se fluido ruminal originado de um novilho Holandes-Zebu fistulado no rumen. O animal foi mantido em pastagem de Brachiaria decumbens recebendo 200 g/dia de proteina bruta suplementar. Os substratos e o inoculo foram acondicionados em frascos de vidro considerando-se oito tratamentos: celulose, celulose e caseina, celulose e peptona de soja, celulose e ureia, amido, amido e caseina, amido e peptona de soja e amido e ureia. Incubacoes sucessivas foram conduzidas para selecao dos microrganismos em funcao das fontes energeticas e de compostos nitrogenados. O amido favoreceu o crescimento de bacterias acido-laticas em comparacao a celulose. A suplementacao com proteina verdadeira (peptona de soja e caseina) estimulou o crescimento dessas bacterias em comparacao ao controle (sem suplementacao com compostos nitrogenados). A adicao de ureia nao estimulou o crescimento de bacterias acido-laticas. Nenhuma atividade antimicrobiana foi detectada a partir das colonias de bacterias acido-laticas isoladas. Fontes de proteina verdadeira incrementam a competicao entre bacterias fermentadoras de carboidratos estruturais e nao-estruturais.
Best-Arm Identification for Quantile Bandits with Privacy

We study the best-arm identification problem in multi-armed bandits with stochastic, potentially private rewards, when the goal is to identify the arm with the highest quantile at a fixed, prescribed level. First, we propose a (non-private) successive elimination algorithm for strictly optimal best-arm identification, we show that our algorithm is $\delta$-PAC and we characterize its sample complexity. Further, we provide a lower bound on the expected number of pulls, showing that the proposed algorithm is essentially optimal up to logarithmic factors. Both upper and lower complexity bounds depend on a special definition of the associated suboptimality gap, designed in particular for the quantile bandit problem, as we show when the gap approaches zero, best-arm identification is impossible. Second, motivated by applications where the rewards are private, we provide a differentially private successive elimination algorithm whose sample complexity is finite even for distributions with infinite support-size, and we characterize its sample complexity as well. Our algorithms do not require prior knowledge of either the suboptimality gap or other statistical information related to the bandit problem at hand.
Summer activity patterns of insectivorous bats and their Prey in Tasmania

Nightly activity patterns of seven species of bats (Eptesicus vulturnus, E. regulus, E. sagittula, Nyctophilus geoffroyi, N. timoriensis, Chalinolobus morio and FalsistreNus tasmaniensis) and their volant insect prey were studied during late spring and summer at four sites in Tasmania. Activity of bats was assessed from trapping results. Bats exhibited a biomodal pattern of foraging activity being most active in the first hours after dark, declining to low levels in the middle part of the night and rising again to a secondary peak in the three hours before dawn. These patterns of activity in bats closely reflected the patterns of insect activity. The insectivorous bats thus appear to have adjusted their nightly activities to match those of their prey, thereby maximising foraging success and energy gains.
Identification of subpopulations from connectivity matrices

Dispersal on the landscape/seascape scale may lead to complex spatial population structure with non-synchronous demography and genetic divergence. In this study we present a novel approach to identify subpopulations and dispersal barriers based on estimates of dispersal probabilities on the landscape scale. A theoretical framework is presented where the landscape connectivity matrix is analyzed for clusters as a signature of partially isolated subpopulations. Identification of subpopulations is formulated as a minimization problem with a tuneable penalty term that makes it possible to generate population subdivisions with varying degree of dispersal restrictions. We show that this approach produces superior results compared to alternative standard methods. We apply this theory to a dataset of modeled dispersal probabilities for a sessile marine invertebrate with free-swimming larvae in the Baltic Sea. For a range of critical connectivities we produce a hierarchical partitioning into subpopulations spanning dispersal probabilities that are typical for both genetic divergence and demographic independence. The mapping of subpopulations suggests that the Baltic Sea includes a fine-scale (100600 km) mosaic of invisible dispersal barriers. An analysis of the present network of marine protected areas reveal that protection is very unevenly distributed among the suggested subpopulations. Our approach can be used to assess the location and strength of dispersal barriers in the landscape, and identify conservation units when extensive genotyping is prohibitively costly to cover necessary spatial and temporal scales, e.g. in spatial management of marine populations.
Addressing the Herd Immunity Paradox Using Symmetry, Convexity Adjustments and Bond Prices

In constant parameter compartmental models an early onset of herd immunity is at odds with estimates of R values from early stage growth. This paper utilizes a result from the theory of interest rate modeling, namely a bond pricing formula of Vasicek, and an approach inspired by a foundational result in statistics, de Finetti's Theorem, to show how the modeling discrepancy can be explained. Moreover the difference between predictions of classic constant parameter epidemiological models and those with variation and stochastic evolution can be reduced to simple "convexity" formulas. A novel feature of this approach is that we do not attempt to locate a true model but only a model that is equivalent after permutations. Convexity adjustments can also be used for cross sectional comparisons and we derive easy to use rules of thumb for estimating threshold infection level in one region given knowledge of threshold infection in another.
Uncertainty quantification in classical molecular dynamics

Molecular dynamics simulation is now a widespread approach for understanding complex systems on the atomistic scale. It finds applications from physics and chemistry to engineering, life and medical science. In the last decade, the approach has begun to advance from being a computer-based means of rationalizing experimental observations to producing apparently credible predictions for a number of real-world applications within industrial sectors such as advanced materials and drug discovery. However, key aspects concerning the reproducibility of the method have not kept pace with the speed of its uptake in the scientific community. Here, we present a discussion of uncertainty quantification for molecular dynamics simulation designed to endow the method with better error estimates that will enable it to be used to report actionable results. The approach adopted is a standard one in the field of uncertainty quantification, namely using ensemble methods, in which a sufficiently large number of replicas are run concurrently, from which reliable statistics can be extracted. Indeed, because molecular dynamics is intrinsically chaotic, the need to use ensemble methods is fundamental and holds regardless of the duration of the simulations performed. We discuss the approach and illustrate it in a range of applications from materials science to ligand–protein binding free energy estimation. This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico’.
Relative effectiveness of repellents for reducing mule deer damage

We tested the repellency of chicken eggs, MGK ® Big Game Repellent (BGR), coyote (Canis latrans) urine, thiram, Hinder ®, bars of soap, and Ro.pel ® on tame mule deer (Odocoileus hemionus) in Colorado during May and June 1989. Chicken eggs, GR, and coyote urine performed better than the other repellents for deterring deer from feeding on pelleted rations. Consumption of rations treated with odor repellents increased from Day 1 through Day 4 of the trial, but consumption of thiram-treated rations decreased. The repellency of eggs, BGR, and coyote urine was reduced when apple twigs were sprayed with 7 cm of water to simulate heavy rainfall. When the deer were moderately hungry, even the best performing repellents failed to deter browsing
The entropy of Hawking radiation

In this review, we describe recent progress on the black hole information problem that involves a new understanding of how to calculate the entropy of Hawking radiation. We show how the method for computing gravitational fine-grained entropy, developed over the past 15 years, can be extended to capture the entropy of Hawking radiation. This technique reveals large corrections needed for the entropy to be consistent with unitary black hole evaporation.
Pitfalls of the Gram Loss for Neural Texture Synthesis in Light of Deep Feature Histograms

Neural texture synthesis and style transfer are both powered by the Gram matrix as a means to measure deep feature statistics. Despite its ubiquity, this second-order feature descriptor has several shortcomings resulting in visual artifacts, ill-defined interpolation, or inability to capture spatial constraints. Many previous works acknowledge these shortcomings but do not really explain why they occur. Fixing them is thus usually approached by adding new losses, which require parameter tuning and make the problem even more ill-defined, or architecturing complex and/or adversarial networks. In this paper, we propose a comprehensive study of these problems in the light of the multi-dimensional histograms of deep features. With the insights gained from our analysis, we show how to compute a well-defined and efficient textural loss based on histogram transformations. Our textural loss outperforms the Gram matrix in terms of quality, robustness, spatial control, and interpolation. It does not require additional learning or parameter tuning, and can be implemented in a few lines of code.
Forest management in Israel—The ecological alternative

Forest management (silviculture) is a long-established applied science, but also a field whose sustainability and ecological implications have been questioned. In this paper, we present the basic features of commercial forestry together with a review of novel approaches for ecologically oriented forestry. The "new forestry" advocates for multiple-species and structurally complex forests, and is directed toward a diverse array of objectives (ecosystem function, biodiversity conservation, wildlife habitats, visual quality, nutrient recycling, water retention, soil productivity, carbon sequestration, and amenity values), in addition to the provision of classic economic forestry commodities.In temperate forests, economic goals have not been abandoned. The challenge in these systems is to develop a new silvicultural approach that will keep forests sustainable and also fulfill their traditional timber production function. In Israel, the majority of forests are not economic in the traditional sense; therefore, t.
Heterogeneity-Aware Federated Learning

Federated learning (FL) is an emerging distributed machine learning paradigm that stands out with its inherent privacy-preserving advantages. Heterogeneity is one of the core challenges in FL, which resides in the diverse user behaviors and hardware capacity across devices who participate in the training. Heterogeneity inherently exerts a huge influence on the FL training process, e.g., causing device unavailability. However, existing FL literature usually ignores the impacts of heterogeneity. To fill in the knowledge gap, we build FLASH, the first heterogeneity-aware FL platform. Based on FLASH and a large-scale user trace from 136k real-world users, we demonstrate the usefulness of FLASH in anatomizing the impacts of heterogeneity in FL by exploring three previously unaddressed research questions: whether and how can heterogeneity affect FL performance; how to configure a heterogeneity-aware FL system; and what are heterogeneity's impacts on existing FL optimizations. It shows that heterogeneity causes nontrivial performance degradation in FL from various aspects, and even invalidates some typical FL optimizations.
The gastrointestinal microbiota and its role in monogastric nutrition and health with an emphasis on pigs: Current understanding, possible modulations, and new technologies for ecological studies

The gastrointestinal microbiota is an incompletely defined, dynamic community of several hundred species of primarily anaerobic bacteria. Species composition and bacterial numbers vary depending on animal age, the gastrointestinal location and a variety of nutritional and environmental factors. The microbiota positively and negatively impacts host physiology and performance in many important ways. This review will examine the establishment and composition of the normal microbiota; its beneficial and deleterious effects on the host; and methods by which to modify the microbiota. In addition, recent advances in methodology using the techniques of molecular biology to measure and describe the microbiota are discussed. Finally, recent results using the polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) technique to examine the microbiota of pigs at different ages, different intestinal sites, and after treatment with selected feed additives will be described. Key words: Microbiota, ga.
Online Sequential Extreme Learning Machines: Features Combined From Hundreds of Midlayers

In this paper, we develop an algorithm called hierarchal online sequential learning algorithm (H-OS-ELM) for single feed feedforward network with features combined from hundreds of midlayers, the algorithm can learn chunk by chunk with fixed or varying block size, we believe that the diverse selectivity of neurons in top layers which consists of encoded distributed information produced by the other neurons offers better computational advantage over inference accuracy. Thus this paper proposes a Hierarchical model framework combined with Online-Sequential learning algorithm, Firstly the model consists of subspace feature extractor which consists of subnetwork neuron, using the sub-features which is result of the feature extractor in first layer of the hierarchy we get rid of irrelevant factors which are of no use for the learning and iterate this process so that to recast the the subfeatures into the hierarchical model to be processed into more acceptable cognition. Secondly by using OS-Elm we are using non-iterative style for learning we are implementing a network which is wider and shallow which plays a important role in generalizing the overall performance which in turn boosts up the learning speed
Generalized Multi-Relational Graph Convolution Network

Graph Convolutional Networks (GCNs) have received increasing attention in recent machine learning. How to effectively leverage the rich structural information in complex graphs, such as knowledge graphs with heterogeneous types of entities and relations, is a primary open challenge in the field. Most GCN methods are either restricted to graphs with a homogeneous type of edges (e.g., citation links only), or focusing on representation learning for nodes only instead of jointly optimizing the embeddings of both nodes and edges for target-driven objectives. This paper addresses these limitations by proposing a novel framework, namely the GEneralized Multi-relational Graph Convolutional Networks (GEM-GCN), which combines the power of GCNs in graph-based belief propagation and the strengths of advanced knowledge-base embedding methods, and goes beyond. Our theoretical analysis shows that GEM-GCN offers an elegant unification of several well-known GCN methods as specific cases, with a new perspective of graph convolution. Experimental results on benchmark datasets show the advantageous performance of GEM-GCN over strong baseline methods in the tasks of knowledge graph alignment and entity classification.
Separability of the Planar 1/ρ2 Potential in Multiple Coordinate Systems

With a number of special Hamiltonians, solutions of the Schrödinger equation may be found by separation of variables in more than one coordinate system. The class of potentials involved includes a number of important examples, including the isotropic harmonic oscillator and the Coulomb potential. Multiply separable Hamiltonians exhibit a number of interesting features, including “accidental” degeneracies in their bound state spectra and often classical bound state orbits that always close. We examine another potential, for which the Schrödinger equation is separable in both cylindrical and parabolic coordinates: A z-independent V∝1/ρ2=1/(x2+y2) in three dimensions. All the persistent, bound classical orbits in this potential close, because all other orbits with negative energies fall to the center at ρ=0. When separated in parabolic coordinates, the Schrödinger equation splits into three individual equations, two of which are equivalent to the radial equation in a Coulomb potential—one equation with an attractive potential, the other with an equally strong repulsive potential.