Nanyang Technological University

About: Nanyang Technological University is a education organization based out in Singapore, Singapore. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 48003 authors who have published 112815 publications receiving 3294199 citations. The organization is also known as: NTU & Universiti Teknologi Nanyang.

Selection models in accounting research

Abstract: This study explains the challenges associated with the Heckman (1979) procedure to control for selection bias, assesses the quality of its application in accounting research, and offers guidance for better implementation of selection models. A survey of 75 recent accounting articles in leading journals reveals that many researchers implement the technique in a mechanical way with relatively little appreciation of important econometric issues and problems surrounding its use. Using empirical examples motivated by prior research, we illustrate that selection models are fragile and can yield quite literally any possible outcome in response to fairly minor changes in model specification. We conclude with guidance on how researchers can better implement selection models that will provide more convincing evidence on potential selection bias, including the need to justify model specifications and careful sensitivity analyses with respect to robustness and multicollinearity. Data Availability: Data used...

Synthesis of Few-Layer MoS2 Nanosheet-Coated TiO2 Nanobelt Heterostructures for Enhanced Photocatalytic Activities

Abstract: MoS(2) nanosheet-coated TiO(2) nanobelt heterostructures--referred to as TiO(2)@MoS(2)--with a 3D hierarchical configuration are prepared via a hydrothermal reaction. The TiO(2) nanobelts used as a synthetic template inhibit the growth of MoS(2) crystals along the c-axis, resulting in a few-layer MoS(2) nanosheet coating on the TiO(2) nanobelts. The as-prepared TiO(2)@MoS(2) heterostructure shows a high photocatalytic hydrogen production even without the Pt co-catalyst. Importantly, the TiO(2)@MoS(2) heterostructure with 50 wt% of MoS(2) exhibits the highest hydrogen production rate of 1.6 mmol h(-1) g(-1). Moreover, such a heterostructure possesses a strong adsorption ability towards organic dyes and shows high performance in photocatalytic degradation of the dye molecules.

Extreme Learning Machine for Multilayer Perceptron

Abstract: Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number of hidden nodes. To address this issue, in this paper, a new ELM-based hierarchical learning framework is proposed for multilayer perceptron. The proposed architecture is divided into two main components: 1) self-taught feature extraction followed by supervised feature classification and 2) they are bridged by random initialized hidden weights. The novelties of this paper are as follows: 1) unsupervised multilayer encoding is conducted for feature extraction, and an ELM-based sparse autoencoder is developed via $\ell _{1}$ constraint. By doing so, it achieves more compact and meaningful feature representations than the original ELM; 2) by exploiting the advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (DL), the hidden layers of the proposed framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. Therefore, it has much better learning efficiency than the DL. Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods. Furthermore, multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme.

Array of nanosheets render ultrafast and high-capacity Na-ion storage by tunable pseudocapacitance

Abstract: Sodium-ion batteries are a potentially low-cost and safe alternative to the prevailing lithium-ion battery technology. However, it is a great challenge to achieve fast charging and high power density for most sodium-ion electrodes because of the sluggish sodiation kinetics. Here we demonstrate a high-capacity and high-rate sodium-ion anode based on ultrathin layered tin(II) sulfide nanostructures, in which a maximized extrinsic pseudocapacitance contribution is identified and verified by kinetics analysis. The graphene foam supported tin(II) sulfide nanoarray anode delivers a high reversible capacity of ∼1,100 mAh g(-1) at 30 mA g(-1) and ∼420 mAh g(-1) at 30 A g(-1), which even outperforms its lithium-ion storage performance. The surface-dominated redox reaction rendered by our tailored ultrathin tin(II) sulfide nanostructures may also work in other layered materials for high-performance sodium-ion storage.