Guangdong University of Foreign Studies

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.

This study examines the effect of online reviews on new product sales for consumer electronics and video games. Analyses of panel data of 332 new products from Amazon.com over nine months reveal that the valence of reviews and the volume of page views have a stronger effect on search products, whereas the volume of reviews is more important for experience products. The results also show that the volume of reviews has a significant effect on new product sales in the early period and such effect decreases over time. Moreover, the percentage of negative reviews has a greater effect than that of positive reviews, confirming the negativity bias. Thus, marketers need to consider the distinctive influences of various aspects of online reviews when launching new products and devising e-marketing strategies.

Abstract In the modern era of the wave of globalization, financial development is leading toward a higher rate of economic expansion and promoting energy innovation around the globe. Nevertheless, environmental impact of financial development has preoccupied government officials to circumvent adverse impact on environmental quality. Thus, this paper examines the nexus between financial development, economic growth, energy innovation, and environmental pollution for the period of 1990–2017 for the panel of Organization for Economic Cooperation and Development (OECD) countries. To obtain robust and unbiased results, this study utilizes Pooled Mean Group Autoregressive Distributed Lag (PMG/ARDL) estimator that counters the issue of heterogeneity and cross‐sectional dependence. Empirical evidence suggests that financial development promotes energy innovation and improves environmental quality. Globalization also has a long‐term relationship with energy innovation and reduces greenhouse gas (GHG) emissions. Moreover, findings validate the environmental Kuznets curve for OECD countries in the significance of financial development, globalization, and energy innovation.

A sensor is a tool used to directly measure the test compound (analyte) in a sample. Ideally, such a device is capable of continuous and reversible response and should not damage the sample. Nanosensor refers to a system in which at least one of the nanostructures is used to detect gases, chemicals, biological agents, electric fields, light, heat, etc. in its construction. The use of nanomaterials significantly increases the sensitivity of the system. In biosensors, the part of the system used to attach to the analyte and specifically detect it is a biological element (such as a DNA strand, antibody, enzyme, whole cell). The “Nano Biosensors” series reviews various types of biosensors and biochips (including an array of biosensors), emphasizing the role of nanostructures, developed for medical and biological applications. Nano Biosensors Electrochemical sensors are sensors that use the biological element as a diagnostic component and the electrode as a transducer. The use of nanostructures in these systems is usually done to fill the gap between the converter and the bioreceptor, which is at the nanoscale. Given the nature of the biomaterial detection process, electrochemical biosensors are divided into catalytic and propulsion. Common electrochemical techniques common in sensors include potentiometric, chronometry, voltammetry, impedance measurement, and field effect transistor (FET). Simultaneous use of the advantages of nanostructures and electrochemical techniques has led to the emergence of sensors with high sensitivity and decomposition power. The use of nanostructures in these sensors is usually done to fill the gap between the converter and the bioreceptor, which is at the nanoscale. Various types of nanostructures including nanoparticles, nanotubes and nanowires, nanopores, self-adhesive monolayers and nanocomposites can be used to improve the performance and efficiency of sensors in their structure. Simultaneous use of the advantages of nanostructures and electrochemical techniques has led to the emergence of sensors with high sensitivity and decomposition power.

This paper addresses the finite-time tracking issue for nonlinear quantized systems with unmeasurable states. Compared with the existing researches, the finite-time quantized feedback control is considered for the first time. By proposing a new finite-time stability criterion and designing a state observer, a novel adaptive neural output-feedback control strategy is raised by backstepping technique. Under the presented control scheme, the finite-time quantized feedback control problem is coped with without limiting assumption for nonlinear functions.

We propose an attention-injective deformable convolutional network called ADCrowdNet for crowd understanding that can address the accuracy degradation problem of highly congested noisy scenes. ADCrowdNet contains two concatenated networks. An attention-aware network called Attention Map Generator (AMG) first detects crowd regions in images and computes the congestion degree of these regions. Based on detected crowd regions and congestion priors, a multi-scale deformable network called Density Map Estimator (DME) then generates high-quality density maps. With the attention-aware training scheme and multi-scale deformable convolutional scheme, the proposed ADCrowdNet achieves the capability of being more effective to capture the crowd features and more resistant to various noises. We have evaluated our method on four popular crowd counting datasets (ShanghaiTech, UCF_CC_50, WorldEXPO'10, and UCSD) and an extra vehicle counting dataset TRANCOS, and our approach beats existing state-of-the-art approaches on all of these datasets.

Identification of intrinsic brain activity differences and similarities between major depression (MDD) and bipolar disorder (BD) is necessary. However, results have not yet yielded consistent conclusions. A meta-analysis of whole-brain resting-state functional MRI (rs-fMRI) studies that explored differences in the amplitude of low-frequency fluctuation (ALFF) between patients (including MDD and BD) and healthy controls (HCs) was conducted using seed-based d mapping software. Systematic literature search identified 50 studies comparing 1399 MDD patients and 1332 HCs, and 15 studies comparing 494 BD patients and 593 HCs. MDD patients displayed increased ALFF in the right superior frontal gyrus (SFG) (including the medial orbitofrontal cortex, medial prefrontal cortex [mPFC], anterior cingulate cortex [ACC]), bilateral insula extending into the striatum and left supramarginal gyrus and decreased ALFF in the bilateral cerebellum, bilateral precuneus, and left occipital cortex compared with HCs. BD showed increased ALFF in the bilateral inferior frontal gyrus, bilateral insula extending into the striatum, right SFG, and right superior temporal gyrus (STG) and decreased ALFF in the bilateral precuneus, left cerebellum (extending to the occipital cortex), left ACC, and left STG. In addition, MDD displayed increased ALFF in the left lingual gyrus, left ACC, bilateral precuneus/posterior cingulate gyrus, and left STG and decreased ALFF in the right insula, right mPFC, right fusiform gyrus, and bilateral striatum relative to BD patients. Conjunction analysis showed increased ALFF in the bilateral insula, mPFC, and decreased ALFF in the left cerebellum in both disorders. Our comprehensive meta-analysis suggests that MDD and BD show a common pattern of aberrant regional intrinsic brain activity which predominantly includes the insula, mPFC, and cerebellum, while the limbic system and occipital cortex may be associated with spatially distinct patterns of brain function, which provide useful insights for understanding the underlying pathophysiology of brain dysfunction in affective disorders, and developing more targeted and efficacious treatment and intervention strategies.

In the recent era of globalisation, the tourism sector is growing rapidly and stimulates economic growth across the world, however, the inevitable environmental consequences of tourism cannot be ignored. For sustainable tourism, it is necessary to understand the interrelationship between economic growth, tourism, and environmental quality. Hence, the objective of the current research is to investigate the dynamic relationship between tourism, economic growth, and CO2 emissions from 1995 to 2014 in the context of BRICS economies. A group of econometric tests robust to heterogeneity and cross-sectional dependence is applied to achieve accurate and unbiased results. Empirical findings propose that tourism sector significantly encourages economic growth; however, tourism degrades the quality of the environment. Also, globalisation has a long-term relationship with economic growth but an insignificant relationship with CO2 emissions. The long-term elasticities further recommend that investment stimulate economic growth and mitigate CO2 emissions. Moreover, environmental Kuznets curve (EKC) holds in BRICS countries in its significance to tourism and globalisation. Finally, a heterogeneous panel non-causality test detects bi-directional causality between tourism receipts and CO2 emissions. Moreover, tourism and investment in tourism Granger cause each other. Empirical findings direct towards important policy implications.

BACKGROUND: The outbreak of the coronavirus disease-19 (COVID-19) has caused enormous stress among the public in China. Intellectual input from various aspects is needed to fight against COVID-19, including understanding of the public's emotion and behaviour and their antecedents from the psychological perspectives. Drawing upon the cognitive appraisal theory, this study examined three cognitive appraisals (i.e., perceived severity, perceived controllability, and knowledge of COVID-19) and their associations with a wide range of emotional and behavioural outcomes among the Chinese public. METHODS: Participants were 4607 citizens (age range: 17-90 years, Mage = 23.71 years) from 31 provinces in China and they took part in a cross-sectional survey online. RESULTS: The results showed that the public's emotional and behavioural reactions were slightly affected by the outbreak of COVID-19. Moreover, the public had limited participation in the events regarding COVID-19 but actively engaged in precautionary behaviour. In addition, results of structural equation model with latent variables revealed that the three appraisals were differentially related to the outcome variables (i.e., negative emotion, positive emotion, sleep problems, aggression, substance use, mobile phone use, social participation, and precautionary behaviour). CONCLUSIONS: The findings highlight the utility of cognitive appraisal, as a core process of coping stress, in explaining the public's emotion and behaviour in the encounter of public health concern. Practically, the findings facilitate the government and practitioners to design and deliver targeted intervention programs to the public.

Abstract Brand managers use celebrity microbloggers to endorse their products on microblogs. Previous studies on celebrity endorsement mechanisms concentrated on source factors such as celebrity's characteristics and celebrity—product congruence. This study introduces a new audience factor: the fan–celebrity parasocial interaction (PSI) to explore the celebrity endorsement mechanism within a microblog context. The study hypothesizes that PSI and source factors (credibility, attractiveness, and congruence) significantly influence endorsement effectiveness. The results of an online survey ( N = 862) indicate that PSI and celebrity–product congruence are salient antecedents of endorsement effectiveness. PSI serves as a mediator of the effect of source attractiveness on endorsement effectiveness. Source credibility and celebrity–product congruence are mediators between PSI and endorsement effectiveness. The study develops and tests a conceptual model to illustrate the influential mechanism of celebrity endorsement on microblog platforms.

Abstract Proper use and efficient management of natural resources are critical to shaping a sustainable future in many resource‐rich countries in Africa. It is also well‐known that globalization creates a great awareness for sustainable resource extraction and provides cleaner production technology transfers to underdeveloped countries and enables them to establish a sustainable development pattern. However, evidence on the role of globalization in reducing the environmental impacts of natural resources in resource‐based economies is relatively scant. This study investigates sustainable future strategies by examining the role of natural resources, globalization, human capital, and urbanization in shaping the ecological footprint that is a broader indicator of environmental sustainability. To this end, Sub‐Saharan African countries—endowed with a rich natural resource base ranging from arable land, forest, freshwater, marine resources, oil, natural gas, minerals, and wildlife—are analyzed through advanced estimation techniques. Empirical results show that both resource dependence and abundance complicate to design a sustainable future by increasing the pressure on the environment. Similarly, urbanization deteriorates ecological conditions in Sub‐Saharan African countries. However, globalization and human capital seem the main sources of a cleaner and sustainable environment. The findings of the study shed new light on the main role of globalization in providing cleaner practices to reverse the negative influence of natural resource dependence and/or abundance on environmental quality.

This study set out to investigate the relationship between L2 vocabulary knowledge (VK) and second-language (L2) reading/listening comprehension. More than 100 individual studies were included in this meta-analysis, which generated 276 effect sizes from a sample of almost 21,000 learners. The current meta-analysis had several major findings. First, the overall correlation between VK and L2 reading comprehension was .57 ( p < .01) and that between VK and L2 listening was .56 ( p < .01). If the attenuation effect due to reliability of measures was taken into consideration, the ‘true’ correlation between VK and L2 reading/listening comprehension may likely fall within the range of .56–.67, accounting for 31%–45% variance in L2 comprehension. Second, all three mastery levels of form–meaning knowledge (meaning recognition, meaning recall, form recall) had moderate to high correlations with L2 reading and L2 listening. However, meaning recall knowledge had the strongest correlation with L2 reading comprehension and form recall had the strongest correlation with L2 listening comprehension, suggesting that different mastery levels of VK may contribute differently to L2 comprehension in different modalities. Third, both word association knowledge and morphological awareness (two aspects of vocabulary depth knowledge) had significant correlations with L2 reading and L2 listening. Fourth, the modality of VK measure was found to have a significant moderating effect on the correlation between VK and L2 text comprehension: orthographical VK measures had stronger correlations with L2 reading comprehension as compared to auditory VK measures. Auditory VK measures, however, were better predictors of L2 listening comprehension. Fifth, studies with a shorter script distance between L1 and L2 yielded higher correlations between VK and L2 reading. Sixth, the number of items in vocabulary depth measures had a positive predictive power on the correlation between VK and L2 comprehension. Finally, correlations between VK and L2 reading/listening comprehension was found to be associated with two types of publication factors: year-of-publication and publication type. Implications of the findings were discussed.

Abstract In an environment with economic policy uncertainty, the policies of energy efficiency are probably compromised due to weak regulation measures. Inconsistent economic policies may lead to higher energy consumption, which damages the environment. However, works of literature are silent on the role of economic policy uncertainty in carbon emission and energy nexus. Hence, the study is an attempt to investigate the role of economic policy uncertainty in the relationship between energy intensity and carbon dioxide (CO 2 ) emissions in the United States by using annual data spanning from 1985 to 2017. A recent time series method proposed by Jordan and Philips (2018) is employed to get robust and consistent estimation results in the study. Empirical results reveal that (a) higher energy intensity contributes to pollution, (b) economic policy uncertainty adversely affects environmental quality, (c) economic policy uncertainty strengthens the detrimental effect of energy intensity on CO 2 emissions.

Abstract The rapid developments of globalization promote interaction among countries and people around the globe through the fast mode of information and telecommunication technology (ICT). ICT development also contributes to economic growth through various channels, but it may influence the environment on the other hand. Considering this concern, the present study focuses on examining the relationship between ICT developments and carbon emissions through the globalization channel. The study employs robust panel data estimation methods, continuously updated fully modified, and continuously updated bias corrected estimators, for the data set of Brazil, Russia, India, China, and South Africa (BRICS) economies spanning from 1990 to 2015. Findings of the study are robust against heteroskedasticity, endogeneity, and cross‐sectional dependence issues. The robust panel data estimators reveal that ICT has a favourable effect on carbon emissions in BRICS countries. Also, globalization leads to environmental pollution by contributing to an increase in CO 2 emissions. These findings provide new insights to the policymakers in combatting environmental challenges.

The transition toward clean energy is an issue of great importance with growing debate in climate change mitigation. The complex nature of nuclear energy-CO2 emissions nexus makes it difficult to predict whether or not nuclear acts as a clean energy source. Hence, we examined the relationship between nuclear energy consumption and CO2 emissions in the context of the IPAT and Environmental Kuznets Curve (EKC) framework. Dynamic Auto-regressive Distributive Lag (DARDL), a newly modified econometric tool, is employed for estimation of long- and short-run dynamics by using yearly data spanning from 1971 to 2018. The empirical findings of the study revealed an instantaneous increase in nuclear energy reduces environmental pollution, which highlights that more nuclear energy power in the Indian energy system would be beneficial for climate change mitigation. The results further demonstrate that the overarching effect of population density in the IPAT equation stimulates carbon emissions. Finally, nuclear energy and population density contribute to form the EKC curve. To achieving a cleaner environment, results point out governmental policies toward the transition of nuclear energy that favours environmental sustainability.

Abstract This study highlights the importance of fiscal decentralization in promoting a sustainable environment. The literature on the importance of fiscal decentralization in affecting environmental quality is scant, and thus, this study attempts to fill the gap by incorporating the linear and nonlinear terms of fiscal decentralization as possible determinants for CO 2 emissions. Particularly, we utilize data from seven highly fiscally decentralized countries, that is, Australia, Austria, Belgium, Canada, Germany, Spain, and Switzerland, over the period 1990–2018. For empirical analysis, advanced panel data econometric tools are used that can deal with both heterogeneous coefficients and dependence of cross‐sections. The findings confirm that linear and nonlinear terms of fiscal decentralization improve the environment by reducing CO 2 emissions. Moreover, gross domestic product (GDP) increases, while eco‐innovation and renewable energy usage reduce CO 2 emissions. This study recommends that any policy that targets green growth will affect CO 2 emissions. Moreover, policies targeting fiscal decentralization, GDP, eco‐innovation, and renewable energy will play the role in more than 1 year, namely in the long run.

Abstract The income and pollution relationship is widely debated in the literature, but income alone cannot control emissions, and that requires environmental regulation measures. Even though weak environmental regulations may offset market failures caused by negative externality of pollution, investigation of their role in the control of environmental pollution is of prime importance. To better understand the role of environmental regulations in carbon emissions mitigation, this study takes a step to offers a new perspective on the role of environmental regulations in pollution reduction for Brazil, Russia, India, China, and South Africa. To this end, the study employs panel data econometric tools for data spanning from 1995 to 2016. Besides, the study conducts fully modified ordinary least squares estimator to get country‐wise results. Empirical results confirm the positive role of environmental regulations in carbon emission mitigation, and the current environmental control measures are successful in achieving pollution reduction targets in the sample countries. Environmental regulations help in establishing the inverted U‐shaped relationship between income and pollution. Climate change mitigation in BRICS (Brazil, Russia, India, China, and South Africa) countries is not associated with economic development only but driven by effective environmental regulations as well.

Abstract The essence of mobile learning is learners’ agentic use of mobile devices to create learning experiences across time and space. Thus, understanding learners’ perceptions and preferred use of mobile devices for learning are critical to realizing the educational potentials of mobile learning. This study explored language learners’ self-directed use of mobile devices beyond the classroom through a survey and interview study with foreign language learners at a university in Hong Kong. A total of 256 learners were surveyed and 18 were interviewed to understand the nature of mobile language learning experiences that these learners engaged in autonomously beyond the classroom. Exploratory factor analysis yielded three dimensions of self-directed out-of-class mobile learning experience. Among the three dimensions, learners were found to use mobile devices more for facilitating the personalization of learning than for enhancing the authenticity and social connection in learning. This study further revealed that selective use was an outcome of the interaction between learner-defined affordances of the devices, their culturally informed and habitual use of the devices, their perceptions of the nature of the learning tasks, and the tempo-spatial circumstances of task implementation. The findings suggest that these factors need to be considered when designing mobile learning activities and educational interventions that promote mobile learning beyond the classroom.

Coronavirus disease 2019 (COVID-19) has caused thousands of deaths in China. Prior research suggests that individuals’ perceived severity of COVID-19 is related to a range of negative emotional and behavioral reactions among the Chinese public. However, scant research has examined the underlying mechanisms. Drawing upon the risk-resilience model, this study proposes that self-control, as a resilient factor, would potentially moderate the association between perceived severity of COVID-19 and mental health problems. Data from a national survey was used to examine this idea. Participants were 4607 citizens from 31 regions in China (Mage = 23.71 years, 72.5% female) who completed a national survey at the beginning of February 2020. Results of hierarchical regression showed that after controlling for a number of demographic variables, perceived severity of COVID-19 and self-control were positively and negatively related to mental health problems, respectively. More importantly, self-control moderated the “perceived severity of COVID-19–mental health problems” association, with this link attenuating as the levels of self-control increased. These findings suggest that compared to those with high self-control, individuals with low self-control are more vulnerable and are more in need of psychological aids to maintain mental health in the encounter of the COVID-19 outbreak. Practically, enhancing individuals’ self-control ability might be a promising way to improve individuals’ mental health during the early period of the COVID-19 outbreak.

This article uses a multi-country global general equilibrium (GE) model to numerically simulate the effects of possible China–US trade wars. We introduce an endogenous trade imbalance structure with trade cost into the model which helps to explore both tariff and non-tariff trade war effects. Our simulation results show that China will be significantly hurt by the China–US trade war, but negative impacts are affordable. The US can gain under unilateral sanction measures to China, but will lose if China takes retaliation measures. Comparing the effects under mutual trade war, China will lose more than the US. Introducing non-tariff barrier trade wars will intensify the negative effects, and comparatively negative effects to China are larger than to the US. Mexico’s involvement in trade war with the US will strengthen the negative effects and comparatively hurt the US more. Under non-cooperative and cooperative Nash bargaining equilibrium, the US can gain more than China in trade war negotiation, which means the US has stronger bargaining power than China. Additionally, trade wars between China and the US will hurt most countries and the world especially in GDP and manufacturing employment, but benefit their welfare and trade.