Assessing the Sea-Level Rise Vulnerability in Coastal Communities: A Case Study in the Tampa Bay Region, US

Sea-level rise (SLR) has drawn unprecedented attention from coastal communities around the world. In fact, many are already being affected and, in response, SLR vulnerability assessments have increasingly emerged in the US as the local communities’ first attempt on the adaptation planning agenda. However, to date, little is known about these early planning endeavors in terms of how vulnerability is conceptualized and operationalized. By reviewing the current local SLR vulnerability assessments in the US, we find that most are only focusing on their biophysical exposure to SLR overlooking other important vulnerability factors including sensitivity and adaptative capacity. The limited number of SLR scenarios and the lack of consideration for extreme events are also considered as the major deficiencies. To fill these gaps, we propose a conceptual vulnerability assessment framework to operationalize the full concept of vulnerability and test it through a case study in the Tampa Bay region, Florida. By comparing the vulnerability results of the common practice with our proposed framework, we find large variances in the resulting findings stressing the importance of selecting the proper assessment approach. This paper finally concludes with planning implications and future research directions. Coastal planner and managers wanting to improve their understanding of the communities’ vulnerability to SLR will benefit from this study

Sea Level Rise, Homeownership, and Residential Real Estate Markets in South Florida

This article builds on a small but rapidly growing body of research that seeks to determine the impact of sea-level rise on the pricing of residential properties. Through a spatial hedonic regression analysis of real estate markets in two Florida counties (Miami–Dade and Pinellas), we assess the influence of different exposure levels on market discounts. Our article stands out in terms of its focus on two comparative case studies and its differentiation between properties that are primary homes versus nonprimary homes. We find that generally discounts are positively associated with exposure levels and overall Miami–Dade experiences higher discounts than Pinellas due to the former’s lower average elevations. We also observe different market behaviors of primary versus nonprimary home buyers and these are partially dependent on affluence. In Miami–Dade, price discounts are less for highly-priced properties purchased by nonprimary owners. We attribute this to different buying motives and risk tolerance of affluent nonprimary homeowners. We argue that nonprimary ownership, particularly in high-end waterfront residential real estate, is tempering gradual market adaptation to sea-level rise exposure risk, which could have detrimental longer-term consequences in terms of market volatility

An improved system for sentence-level novelty detection in textual streams

Novelty detection in news events has long been a difficult problem. A number of models performed well on specific data streams but certain issues are far from being solved, particularly in large data streams from the WWW where unpredictability of new terms requires adaptation in the vector space model. We present a novel event detection system based on the Incremental Term Frequency-Inverse Document Frequency (TF-IDF) weighting incorporated with Locality Sensitive Hashing (LSH). Our system could efficiently and effectively adapt to the changes within the data streams of any new terms with continual updates to the vector space model. Regarding miss probability, our proposed novelty detection framework outperforms a recognised baseline system by approximately 16% when evaluating a benchmark dataset from Google News

Compartment-specific metabolism associated with acetyl-CoA and acyl carrier protein

A characteristic feature of plant cells is the subcellular compartmentation of metabolism. Duplicated enzymes or cofactors occur in the same or distinct compartments, posing a challenge to define the complex metabolic networks that are central to biological functions. Our knowledge regarding the compartment-specific metabolism in plants has been hampered by the limitations of current analytical methods to determine the subcellular location of metabolites. In this dissertation, we integrate reverse genetic and metabolomic analyses to characterize the physiological roles of several compartment-specific enzymes and cofactors. Two distinctly localized Arabidopsis acetate-activating enzymes, the plastidic acetyl-CoA synthetase (ACS) and the peroxisomal acetate non-utilizing 1 (ACN1), are functionally redundant, but their roles in metabolism are not clear. Mutations in both ACS and ACN1 lead to abnormal phenotypes of delayed growth and infertility, which are associated with hyperaccumulation of acetate levels and decreased accumulation of acetyl-CoA-derived metabolites. Cellular acetate is generated from either the oxidation of ethanol or the non-oxidative decarboxylation of pyruvate via the common intermediate acetaldehyde. These processes are induced by hypoxia, suggesting the role of ACS and ACN1 in reducing the carbon loss in the form of ethanol after hypoxia. Using 13C-acetate as a tracer, we demonstrate that the acetate metabolized by the plastidic ACS is used for the de novo synthesis of fatty acids and leucine, whereas the acetate activated by the peroxisomal ACN1 enters the glyoxylate cycle that generates the organic acid intermediates for amino acid biosynthesis. Collectively, these studies establish the significant role of these two enzymes in protecting plant cells from the toxic accumulation of excess acetate. Typical of plants, Arabidopsis expresses two distinct Type II fatty acid synthases (FASs), one mitochondrial and the other in plastids. These two systems are supported by a small, phosphopantetheinylated protein cofactor, acyl carrier protein (ACP). The Arabidopsis genome contains eight ACP-coding genes. We demonstrate that three of these genes encode mitochondrial ACP (mtACP) isozymes, supporting the mitochondrial fatty acid synthase (mtFAS) system. Functional redundancy among the three mtACPs was dissected by a genetic strategy, which demonstrate that the simultaneous loss of all three mtACP genes is associated with an embryo-lethal phenotype. Characterization of double mutant combinations revealed unequal functional redundancy among the three mtACP isoforms, with mtACP3 being the least effective of the three in supporting the mtFAS system

The relationship between IFRS experience and audit fees in China

This study intended to provide evidence of the effect of the IFRS convergence process on audit fees in China. Moreover, the study investigates whether the integration of IFRS would increase audit costs by studying the relationship between IFRS experience and audit fees for Chinese listed companies. This study gathered data from annual reports of 30 A-share and 30 A+H shares of Chinese manufacturing firms listed in Shanghai and Shenzhen stock exchange and Hong Kong stock exchange, respectively for the period 2016 to 2018. Meanwhile, this study quantitatively studies the relationship between the IFRS experience of the auditor and IFRS experience of company and audit fees of the company. Based on an analysis of the study, when the company implement IFRS, it needs to pay auditors with IFRS expertise, higher audit fees, thus increasing the audit cost of the company. However, the study shows that a company with IFRS experience does not affect audit fee. Moreover, this study offers additional evidence for the study of the audit fees generated by the IFRS convergence in China. Therefore, this study also puts forward suggestions that Chinese firms and local audit firms must pay attention to their IFRS-related auditing skills

CRNN: a joint neural network for redundancy detection

This article proposes a novel framework for detecting redundancy in supervised sentence categorisation. Unlike traditional singleton neural network, our model incorporates character-aware convolutional neural network (Char-CNN) with character-aware recurrent neural network (Char-RNN) to form a convolutional recurrent neural network (CRNN). Our model benefits from Char-CNN in that only salient features are selected and fed into the integrated Char-RNN. Char-RNN effectively learns long sequence semantics via sophisticated update mechanism. We compare our framework against the state-of-the-art text classification algorithms on four popular benchmarking corpus. For instance, our model achieves competing precision rate, recall ratio, and F1 score on the Google-news data-set. For twenty-news-groups data stream, our algorithm obtains the optimum on precision rate, recall ratio, and F1 score. For Brown Corpus, our framework obtains the best F1 score and almost equivalent precision rate and recall ratio over the top competitor. For the question classification collection, CRNN produces the optimal recall rate and F1 score and comparable precision rate. We also analyse three different RNN hidden recurrent cells’ impact on performance and their runtime efficiency. We observe that MGU achieves the optimal runtime and comparable performance against GRU and LSTM. For TFIDF based algorithms, we experiment with word2vec, GloVe, and sent2vec embeddings and report their performance differences