Hong Kong Polytechnic University

About: Hong Kong Polytechnic University is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Tourism & Population. The organization has 29633 authors who have published 72136 publications receiving 1956312 citations. The organization is also known as: HKPU & PolyU.

Plant phenology and global climate change: Current progresses and challenges

Abstract: Plant phenology, the annually recurring sequence of plant developmental stages, is important for plant functioning and ecosystem services and their biophysical and biogeochemical feedbacks to the climate system. Plant phenology depends on temperature, and the current rapid climate change has revived interest in understanding and modeling the responses of plant phenology to the warming trend and the consequences thereof for ecosystems. Here, we review recent progresses in plant phenology and its interactions with climate change. Focusing on the start (leaf unfolding) and end (leaf coloring) of plant growing seasons, we show that the recent rapid expansion in ground- and remote sensing- based phenology data acquisition has been highly beneficial and has supported major advances in plant phenology research. Studies using multiple data sources and methods generally agree on the trends of advanced leaf unfolding and delayed leaf coloring due to climate change, yet these trends appear to have decelerated or even reversed in recent years. Our understanding of the mechanisms underlying the plant phenology responses to climate warming is still limited. The interactions between multiple drivers complicate the modeling and prediction of plant phenology changes. Furthermore, changes in plant phenology have important implications for ecosystem carbon cycles and ecosystem feedbacks to climate, yet the quantification of such impacts remains challenging. We suggest that future studies should primarily focus on using new observation tools to improve the understanding of tropical plant phenology, on improving process-based phenology modeling, and on the scaling of phenology from species to landscape-level.

Toward Convolutional Blind Denoising of Real Photographs

Abstract: While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs. The main reason is that their learned models are easy to overfit on the simplified AWGN model which deviates severely from the complicated real-world noise model. In order to improve the generalization ability of deep CNN denoisers, we suggest training a convolutional blind denoising network (CBDNet) with more realistic noise model and real-world noisy-clean image pairs. On the one hand, both signal-dependent noise and in-camera signal processing pipeline is considered to synthesize realistic noisy images. On the other hand, real-world noisy photographs and their nearly noise-free counterparts are also included to train our CBDNet. To further provide an interactive strategy to rectify denoising result conveniently, a noise estimation subnetwork with asymmetric learning to suppress under-estimation of noise level is embedded into CBDNet. Extensive experimental results on three datasets of real-world noisy pho- tographs clearly demonstrate the superior performance of CBDNet over state-of-the-arts in terms of quantitative met- rics and visual quality. The code has been made available at https://github.com/GuoShi28/CBDNet.

Survey of Green Vehicle Routing Problem: Past and future trends

Abstract: Green Logistics has emerged as the new agenda item in supply chain management. The traditional objective of distribution management has been upgraded to minimizing system-wide costs related to economic and environmental issues. Reflecting the environmental sensitivity of vehicle routing problems (VRP), an extensive literature review of Green Vehicle Routing Problems (GVRP) is presented. We provide a classification of GVRP that categorizes GVRP into Green-VRP, Pollution Routing Problem, VRP in Reverse Logistics, and suggest research gaps between its state and richer models describing the complexity in real-world cases. The purpose is to review the most up-to-date state-of-the-art of GVRP, discuss how the traditional VRP variants can interact with GVRP and offer an insight into the next wave of research into GVRP. It is hoped that OR/MS researchers together with logistics practitioners can be inspired and cooperate to contribute to a sustainable industry.

The effects of entrepreneurial personality, background and network activities on venture growth*

Abstract: This study investigates the effects of entrepreneurial personality traits, background and networking activities on venture growth among 168 Chinese entrepreneurs in small and medium sized businesses in Singapore. Personality traits include need for achievement, internal locus of control, self-reliance and extroversion; background comprises education and experience; networking activities consist of size and frequency of communication networks. A structural equation modelling technique – partial least squares (PLS) – is used to estimate a path model with latent variables. The results indicate that experience, networking activities, and number of partners as well as internal locus of control and need for achievement all have positive impact on venture growth. Two other personality traits, self-reliance and extroversion have negative impact on number of partners and positive impact on networking activities, respectively. The impact of education on venture growth, however, is moderated by firm size, positive for larger firms and negative for smaller firms. Our findings indicate that among all the factors that we have considered, an entrepreneur’s industrial and managerial experience is the dominating factor affecting venture growth.