On the Emergence and Evolution of Mark-up Middlemen: An Inframarginal Model

This paper is aimed to provide an economic interpretation on the emergence and evolution of the specialised middlemen whose duty is to facilitate the transactions of goods and services in an economy. In a general equilibrium framework, the emergence and evolution of the specialised middlemen conforms to Adam Smith’s insight of deepening specialisation and the division of labour with the improvement in institutions and/or transaction technologies. Consequently, the emergence and the growth of the intermediation sector in both absolute and relative terms, the expansion of the network which provides transaction services, the evolution of market structure from autarky towards division of labour, the improvement in productivity, the reduction in wholesaling-retailing price dispersion, will be realised in concurrencymiddlemen, transaction efficiency, inframarginal economics

Compatibility And Degradability Of Kenaf-Filled Linear Low Density Polyethylene Polyvinyl Alcohol Composites

The research on natural fibres polymer composites are rapidly growing due to an increasing demand on environmentally friendly polymer products with reasonable price. The on-going efforts are focused on the improvement in overall properties of these composites. In this research work, linear low-density polyethylene (LLDPE)/poly (vinyl alcohol) (PVOH) blend were utilized as polymer matrices with a fixed composition at 60/40 (wt. %), whereas kenaf bast fiber (KNF) was used as filler. The effect of filler loading, as well as various chemical treatments on the natural filler towards the processing characteristic, tensile, structural, morphological, thermal and biodegradability properties of LLDPE/PVOH/KNF composites were explored. LLDPE/PVOH/KNF composites containing different KNF loadings (i.e. 0, 10, 20, 30 and 40 phr) were prepared by means of melt-mixing and compression moulding. It was found that with increasing KNF loading, the processing torque, tensile modulus, thermal stability and water absorption of composites were increased. Nevertheless, tensile strength and elongation at break of composites were found declined. This indicated weak interfacial adhesion between LLDPE/PVOH matrices and KNF, as revealed by SEM studies. Natural weathering and soil burial has affected the properties of LLDPE/PVOH/KNF composites, as displayed by the deterioration in tensile properties, damage of exposed surfaces, and higher percentage of weight loss. Results from FTIR spectra further confirmed the occurrence of degradation with appearance of intense carbonyl peaks. The existence of chemical treatments of KNF has enhanced the tensile, morphological and thermal properties, as well as reduced the water absorption of LLDPE/PVOH/KNF composites. The chemical treatment of KNF was further confirmed by FTIR spectroscopy. Based on the results, it was found that addition of 3- (trimethoxysilyl)propyl methacrylate (TMS) treated KNF into LLDPE/PVOH matrices has increased the processing torque, tensile strength, tensile modulus, thermal stability and reduced the water absorption of the composites. This was evidenced by the enhanced interfacial adhesion between TMS-treated KNF and LLDPE/PVOH matrices in SEM analysis. Addition of treated KNF with eco-friendy coupling agent (EFCA), chromium (III) sulfate and lysine into LLDPE/PVOH matrices were found respectively increased the processing torque, tensile properties, thermal stability and reduced the water absorption of composites. Results from SEM analysis revealed an improvement in the interfacial adhesion between treated KNF and LLDPE/PVOH matrices. FTIR results also confirmed that chemical bonds were formed between coupling agents and KNF, subsequently provide linkages between KNF and LLDPE/PVOH matrices

Preparation, Characterization And Properties Of Polypropylene/Waste Tyre Dust/Kenaf Powder Composites

Thermoplastic elastomer composites of polypropylene (PP)/waste tyre dust (WTD)/kenaf powder (KNFp) were prepared with a fix thermoplastic elastomer blend composition of PP/WTD at 70/30 (wt./wt.%). The amount of KNFp used in this research was 0, 5, 10, 15 and 20 phr. All composites were prepared using a Thermo Haake Rheomix Polydrive R600/610 internal mixer at temperature of 180°C and rotor speed of 50 rpm for a mixing time of 10 minutes. The results showed that the stabilization torque, tensile modulus, water uptake and thermal stability of composites increased with increasing KNFp loading. However, the tensile strength and elongation at break was decreased. By substituting KNFp with kenaf short fiber (KNFs), the tensile strength and tensile modulus of the composites were higher. However, the increasing in processing torque causes a difficulty during preparation of the PP/WTD/KNFs composites. Addition of 3-aminopropyltriethoxysilane (APTES) to the composites has resulted in higher stabilization torque, tensile strength and tensile modulus, whereas the elongation at break and water uptake were lower. APTES was found to be effective to act as coupling agent due to the enhancement in interfacial adhesion of the composites, as shown by the SEM micrographs. For PP/WTD/KNFp composites with the addition of phthalic anhydride (PA), tensile strength, tensile modulus and water uptake were increased, but the elongation at break and processing torque were decreased

Learning study: Helping teachers to use theory, develop professionally, and produce new knowledge to be shared

The lesson study approach is a systematic process for producing professional knowledge about teaching by teachers, and has spread rapidly and extensively in the United States. The learning study approach is essentially a kind of lesson study with an explicit learning theory-the variation theory of learning. In this paper, we argue that having an explicit learning theory adds value to lesson study, as the variation theory of learning serves as a source of guiding principles for the teachers when they engage in pedagogical design, lesson analysis and evaluation. Through the use of two Hong Kong learning study cases, one from each of the two major ways of conducting learning study, we demonstrate the power of variation theory in explaining and predicting the relationship between what has taken place in the classroom and what the learners learn, and subsequently identifying ways to improve student learning through promoting teacher professional learning in a learning study setting. © 2011 The Author(s).published_or_final_versionSpringer Open Choice, 21 Feb 201

Most robust and fragile two-qubit entangled states under depolarizing channels

For a two-qubit system under local depolarizing channels, the most robust and most fragile states are derived for a given concurrence or negativity. For the one-sided channel, the pure states are proved to be the most robust ones, with the aid of the evolution equation for entanglement given by Konrad et al. [Nat. Phys. 4, 99 (2008)]. Based on a generalization of the evolution equation for entanglement, we classify the ansatz states in our investigation by the amount of robustness, and consequently derive the most fragile states. For the two-sided channel, the pure states are the most robust for a fixed concurrence. Under the uniform channel, the most fragile states have the minimal negativity when the concurrence is given in the region [1/2,1]. For a given negativity, the most robust states are the ones with the maximal concurrence, and the most fragile ones are the pure states with minimum of concurrence. When the entanglement approaches zero, the most fragile states under general nonuniform channels tend to the ones in the uniform channel. Influences on robustness by entanglement, degree of mixture, and asymmetry between the two qubits are discussed through numerical calculations. It turns out that the concurrence and negativity are major factors for the robustness. When they are fixed, the impact of the mixedness becomes obvious. In the nonuniform channels, the most fragile states are closely correlated with the asymmetry, while the most robust ones with the degree of mixture.Comment: 10 pages, 9 figs. to appear in Quantum Information & Computation (QIC

Homophily Outlier Detection in Non-IID Categorical Data

Most of existing outlier detection methods assume that the outlier factors (i.e., outlierness scoring measures) of data entities (e.g., feature values and data objects) are Independent and Identically Distributed (IID). This assumption does not hold in real-world applications where the outlierness of different entities is dependent on each other and/or taken from different probability distributions (non-IID). This may lead to the failure of detecting important outliers that are too subtle to be identified without considering the non-IID nature. The issue is even intensified in more challenging contexts, e.g., high-dimensional data with many noisy features. This work introduces a novel outlier detection framework and its two instances to identify outliers in categorical data by capturing non-IID outlier factors. Our approach first defines and incorporates distribution-sensitive outlier factors and their interdependence into a value-value graph-based representation. It then models an outlierness propagation process in the value graph to learn the outlierness of feature values. The learned value outlierness allows for either direct outlier detection or outlying feature selection. The graph representation and mining approach is employed here to well capture the rich non-IID characteristics. Our empirical results on 15 real-world data sets with different levels of data complexities show that (i) the proposed outlier detection methods significantly outperform five state-of-the-art methods at the 95%/99% confidence level, achieving 10%-28% AUC improvement on the 10 most complex data sets; and (ii) the proposed feature selection methods significantly outperform three competing methods in enabling subsequent outlier detection of two different existing detectors.Comment: To appear in Data Ming and Knowledge Discovery Journa

Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning

Hard negative mining has shown effective in enhancing self-supervised contrastive learning (CL) on diverse data types, including graph CL (GCL). The existing hardness-aware CL methods typically treat negative instances that are most similar to the anchor instance as hard negatives, which helps improve the CL performance, especially on image data. However, this approach often fails to identify the hard negatives but leads to many false negatives on graph data. This is mainly due to that the learned graph representations are not sufficiently discriminative due to oversmooth representations and/or non-independent and identically distributed (non-i.i.d.) issues in graph data. To tackle this problem, this article proposes a novel approach that builds a discriminative model on collective affinity information (i.e., two sets of pairwise affinities between the negative instances and the anchor instance) to mine hard negatives in GCL. In particular, the proposed approach evaluates how confident/uncertain the discriminative model is about the affinity of each negative instance to an anchor instance to determine its hardness weight relative to the anchor instance. This uncertainty information is then incorporated into the existing GCL loss functions via a weighting term to enhance their performance. The enhanced GCL is theoretically grounded that the resulting GCL loss is equivalent to a triplet loss with an adaptive margin being exponentially proportional to the learned uncertainty of each negative instance. Extensive experiments on ten graph datasets show that our approach does the following: 1) consistently enhances different state-of-the-art (SOTA) GCL methods in both graph and node classification tasks and 2) significantly improves their robustness against adversarial attacks. Code is available at https://github.com/mala-lab/AUGCL.Comment: Accepted to TNNL