National Chengchi University
Recent publications
Environmental policy has emerged as a major issue for NGOs, as they are further expected to play an influential role of enhancing cross-sector collaboration to assist governmental responsibilities in ameliorating climate change. The forest certification policy, through a civil organisational pathway of supra-government, is regarded as an approach achieving environmental sustainability. In this study, we examine NGOs' impact on forest-certification policies, which have connected actors' collaborative networks and roles across China. We employ social network analysis to test the transformation of NGOs' roles and their preferences of system-adoption. The results conclude that NGOs have taken the advantage of resources and connections via high reputation and trust collaboration, to form a triadic relationship of checks and balances with governments and associations. NGOs have not only partnered with international organisations (IOs) to function as network centres and bridgers to curb central power, but also broke through the policy stratosphere by establishing heterogeneous relationships with grass-root actors, in order to influence domestic stakeholders' preferences for forest-certification adoption.
Introduction Coronavirus disease 2019 (COVID-19), originated in late December 2019, in Wuhan, China. The World Health Organization declared a pandemic on 11 March 2020, with the rapidly rising number of cases and fatalities over hours and days all around the world. Aim To assess the number and the trend of the poisoning consultations to the Poison Unit in the Mansoura Emergency Hospital during the era of the COVID-19 pandemic in 2020. Methods We collected data from the database of the Poison Unit and the Statistics Department of the Mansoura Emergency Hospital of the cases and the calls coming to the Poison Unit in the period from January 2018 through December 2020. We compared 2020 exposures to 2018–2019 exposures by using simple logistic models to provide effect size with odds ratios. Results The Mansoura Emergency Hospital Poison Unit treated 1752 cases in 2020, compared to 2210 cases in 2018 and 2539 cases in 2019. The Poison Unit treated 26% fewer patients in 2020 than either 2018 or 2019), while calls increased nearly 50% in 2020 than in 2018 and 2019. Calls came more frequently from the general public than health professionals and more frequently in afternoon and evening than in the morning. There was a significant increase in the cases of pharmaceuticals ingestion, food poisoning, corrosives and households exposures in 2020 compared to 2018 and 2019 (p-value 0.004, 0.024, and 0.0002, respectively; odds ratio 1.224, 1.691, and 1.692, respectively). Conclusion The COVID-19 pandemic changed the pattern of poisoning exposure and use of the Poison Control Center services.
This study investigated the factors affecting turnover tendency of real estate brokers. The impact of individual-level factors (role conflict, role ambiguity, interpersonal conflict, and emotional exhaustion) and organizational-level factors (supervisor support and group trust) on turnover intentions was assessed. A hierarchical linear mediation modeling approach was used. A questionnaire was administered to real estate brokers working at real estate companies in Kaohsiung City. Ten questionnaires were administered to each of the selected 97 branch offices. Of the 970 questionnaires, 393 were recovered from 71 branches, and after omitting 43 invalid responses, there were 350 valid questionnaires from 59 branches, indicating an effective response rate of 36.1 %. The empirical results showed that interpersonal conflict mediated the impact of supervisor support on emotional exhaustion, with a full mediating effect being observed. Group trust did not mediate the impact of supervisor support on turnover tendency, which was direct, negative, and statistically significant. Job satisfaction partially mediated the impact of emotional exhaustion on turnover intentions. Our results suggest that supervisor support and emotional exhaustion, at the organizational and individual level, respectively, had the greatest impacts on turnover intentions. This demonstrates the importance of supervisor support and emotional exhaustion when researching turnover intentions.
In response to the global pandemic of the COVID-19 outbreak, Taiwan barred foreign nationals from entering into Taiwan starting from March 19th, 2020. This study aims to explore the impact of lockdown policy on the length of stay, tourism expenditure, tourist satisfaction and revisit intention based on a sample of 3987 inbound visitors collected from the 2020 Annual Survey Report on Visitor Expenditures and Trends, published by the Taiwan Tourism Bureau. The empirical results indicate that: (1) the border control policy increases the length of stay of around 33.4719 nights; (2) a drop of the total expenditure, F&B expenditure, transportation expenditure, entertainment expenditure, and shopping expenditure but an increase of accommodation expenditure is found; (3) the border control policy seems to reduce the level of tourist satisfaction but does not affect revisit intention.
Inflammation has been associated with numerous neurological disorders. Inflammatory environments trigger a series of cellular and physiological alterations in the brain. However, how inflammatory milieu affects neuronal physiology and how neuronal alterations progress in the inflammatory environments are not fully understood. In this study, we examined the effects of pro-inflammatory milieu on mitochondrial functions and neuronal activities in the hypothalamic POMC neurons. Treating mHypoA-POMC/GFP1 with the conditioned medium collected from LPS activated macrophage were employed to mimic the inflammatory milieu during hypothalamic inflammation. After a 24-h treatment, intracellular ROS/RNS levels were elevated, and the antioxidant enzymes were reduced. Mitochondrial respiration and mitochondrial functions, including basal respiratory rate, spared respiration capacity, and maximal respiration, were all significantly compromised by inflammatory milieu. Moreover, pro-inflammatory cytokines altered mitochondrial dynamics in a time-dependent manner, resulting in the elongation of mitochondria in POMC neurons after a 24-h treatment. Additionally, the increase of C-Fos and Pomc genes expression indicated that the neurons were activated upon the stimulation of inflammatory environment. This neuronal activation of were confirmed on the LPS-challenged mice. Collectively, a short-term to midterm exposure to inflammatory milieu stimulated metabolic switch and neuronal activation, whereas chronic exposure triggered the elevation of oxidative stress, the decrease of the mitochondrial respiration, and the alterations of mitochondrial dynamics.
The Sackin and Colless indices are two widely-used metrics for measuring the balance of trees and for testing evolutionary models in phylogenetics. This short paper contributes two results about the Sackin and Colless indices of trees. One result is the asymptotic analysis of the expected Sackin and Colless indices of tree shapes (which are full binary rooted unlabelled trees) under the uniform model where tree shapes are sampled with equal probability. Another is a short direct proof of the closed formula for the expected Sackin index of phylogenetic trees (which are full binary rooted trees with leaves being labelled with taxa) under the uniform model.
In this letter we focus on the cognitive science of consciousness. The general message is that, while this interdisciplinary area has made much progress in recent years, there is a tendency of downplaying conceptual issues, and therefore underestimating the difficulties of various problems. We briefly focus on a few prominent examples only, due to the space limit, but the general message should be clear: this recent tendency can be problematic for the progress of the consciousness branch of cognitive sciences.
After the Covid-19 pandemic began to wreak havoc around the world in January 2020, Taiwan managed to stay mostly Covid-free due to swift and efficient action taken by the government to contain the outbreak. However, after the country experienced its first significant wave of domestically transmitted cases in May 2021, vaccines became a highly salient issue because Taiwan did not have enough doses to immunize all its citizens. In this study, we investigate how Taiwanese appraise the government’s overall efforts to acquire vaccines. We hypothesize that, apart from a partisan divergence of opinions, some citizens would hold ambivalent attitudes toward the way the government handled the vaccine procurement process. Results from multivariate regression analysis indicate that the effect of party identification on evaluations of government is conditionally dependent on citizens’ level of ambivalence. Specifically, increased ambivalence offsets the strong effect of party affiliation on government evaluation, especially for political independents and supporters of opposition parties.
Control charts are important tools to monitor quality of products. One of useful applications is to monitor the proportion of non-conforming products. However, in practical applications, measurement error is ubiquitous and may occur due to false records or misclassification, which makes the observed proportion different from the underlying true proportion. It is also well-known that ignoring measurement error effects provides biases, and is expected that the resulting control charts may incur wrong detection. In this paper, we study this important problem and propose a valid method to correct for measurement error effects and obtain error-eliminated control chart for the proportion of non-conforming products. In addition, unlike traditional approaches, the corrected EWMA-control chart provides asymmetric control limits and is flexible to handle the data with small sample size. Numerical results are conducted to justify the validity of the corrected EWMA-control chart and verify the necessity of measurement error correction. K E Y W O R D S asymmetric control limits, average run length, error
Alzheimer's disease (AD) progresses relentlessly from the preclinical to the dementia stage. The process begins decades before the diagnosis of dementia. Therefore, it is crucial to detect early manifestations to prevent cognitive decline. Recent advances in artificial intelligence help tackle the complex high-dimensional data encountered in clinical decision-making. In total, we recruited 206 subjects, including 69 cognitively unimpaired, 40 subjective cognitive decline (SCD), 34 mild cognitive impairment (MCI), and 63 AD dementia (ADD). We included 3 demographic, 1 clinical, 18 brain-image, and 3 plasma biomarker (Aß1-42, Aß1-40, and tau protein) features. We employed the linear discriminant analysis method for feature extraction to make data more distinguishable after dimension reduction. The sequential forward selection method was used for feature selection to identify the 12 best features for machine learning classifiers. We used both random forest and support vector machine as classifiers. The area under the receiver operative curve (AUROC) was close to 0.9 between diseased (combining ADD and MCI) and the controls. AUROC was higher than 0.85 between SCD and controls, 0.90 between MCI and SCD, and above 0.85 between ADD and MCI. We can differentiate between adjacent phases of the AD spectrum with blood biomarkers and brain MR images with the help of machine learning algorithms.
Drones, or precisely quadrotors, have been increasingly used in the field of robotics and also in entertainment. Coordinated multiple drones that form visual presentations through their equipped LEDs are known as drone light shows (Waibel, M., Keays, B., Augugliaro, F.: in Drone shows: Creative potential and best practices. ETH Zurich, 2017). Such a performance offers visual enjoyment for a large audience, particularly in festivals. However, the majority of current drone light shows are manually coordinated by personnel using software. Drone light shows also have limited viewing range, thereby preventing the audience from getting a good view of the actual performance. This study proposes a method to provide multiple visual presentations in accordance with multiple viewing angles. We use visual hull to filter out the candidate areas that form input images, and takes projection error and classification values as weight for optimization. Consequently, the proposed method reduces the number of drones needed to form a multi-view structure for visual presentations. Furthermore, to meet the demands of performing the animation in multi-view structure, we implement the flight algorithm to locate the most suitable corresponding points between two different structures, and then generate the shortest flight paths without collision. Experiments conducted in our simulator provide additional insights and discussions, and each factor is visualized to provide an improved understanding of our approach for multi-view drone light shows.
People with bipolar disorder have an elevated risk of mortality. This study evaluated associations between the use of mood stabilizers and the risks of all‐cause mortality, suicide, and natural mortality in a national cohort of people with bipolar disorder. In this nationwide cohort study, we used data from January 1, 2000, to December 31, 2016, collected from Taiwan's National Health Insurance Research Database and included 25,787 patients with bipolar disorder. Of these patients, 4000 died during the study period (including 760 and 2947 from suicide and natural causes, respectively). Each standardized mortality ratio (SMR) was calculated as the ratio of observed mortality in the bipolar cohort to the number of expected deaths in the general population. Multivariable Cox proportional hazards regression with a time‐dependent model was performed to estimate the hazard ratio (HR) of each mood stabilizer with each mortality outcome. The SMRs of all‐cause mortality, suicide, and natural mortality in the bipolar disorder cohort were 5.26, 26.02, and 4.68, respectively. The use of mood stabilizers was significantly associated with decreased risks of all‐cause mortality (adjusted HR [aHR]=0.58, P < .001), suicide (aHR=0.60, P < .001), and natural mortality (aHR=0.55, P < .001) within a 5‐year follow‐up period after index admission. Among the individual mood stabilizers, lithium was associated with the lowest risks of all‐cause mortality (aHR=0.38, P < 0.001), suicide (aHR=0.39, P < 0.001), and natural mortality (aHR=0.37, P < 0.001). In addition to having protective effects against suicide and all‐cause mortality, mood stabilizers also exert a substantial protective effect against natural mortality, with lithium associated with the lowest risk of mortality. This article is protected by copyright. All rights reserved.
When people have the freedom to create and post content on the internet, particularly anonymously, they do not always respect the rules and regulations of the websites on which they post, leaving other unsuspecting users vulnerable to sexism, racism, threats, and other unacceptable content in their daily cyberspace diet. However, content moderators witness the worst of humanity on a daily basis in place of the average netizen. This takes its toll on moderators, causing stress, fatigue, and emotional distress akin to the symptomology of post-traumatic stress disorder (PTSD). The goal of the present study was to explore whether adding positive stimuli to breaktimes-images of baby animals or beautiful, aweinspiring landscapes-could help reduce the negative side-effects of being a content moderator. To test this, we had over 300 experienced content moderators read and decide whether 200 fake text-based social media posts were acceptable or not for public consumption. Although we set out to test positive emotional stimulation, however, we actually found that it is the cumulative nature of the negative emotions that likely negates most of the effects of the intervention: the longer the person had practiced content moderation, the stronger their negative experience. Connections to compassion fatigue and how best to spend work breaks as a content moderator are discussed.
Images taken in dynamic scenes may contain unwanted motion blur, which significantly degrades visual quality. Such blur causes short- and long-range region-specific smoothing artifacts that are often directional and non-uniform, which is difficult to be removed. Inspired by the current success of transformers on computer vision and image processing tasks, we develop, Stripformer, a transformer-based architecture that constructs intra- and inter-strip tokens to reweight image features in the horizontal and vertical directions to catch blurred patterns with different orientations. It stacks interlaced intra-strip and inter-strip attention layers to reveal blur magnitudes. In addition to detecting region-specific blurred patterns of various orientations and magnitudes, Stripformer is also a token-efficient and parameter-efficient transformer model, demanding much less memory usage and computation cost than the vanilla transformer but works better without relying on tremendous training data. Experimental results show that Stripformer performs favorably against state-of-the-art models in dynamic scene deblurring.
LINKED CONTENT This article is linked to Wong et al papers. To view these articles, visit https://doi.org/10.1111/apt.17120 and https://doi.org/10.1111/apt.17271
As Internet of Things (IoT) thriving over the whole world, more and more IoT devices and IoT-based protocols have been designed and proposed in order to meet people’s needs. Among those protocols, message queueing telemetry transport (MQTT) is one of the most emerging and promising protocol, which provides many-to-many message transmission based on the “publish/subscribe” mechanism. It has been widely used in industries such as the energy industry, chemical engineering, self-driving, etc. While transporting important messages, MQTT specification recommends the use of TLS protocol. However, computation cost of TLS is too heavy. Since topics in a broker are stored with a hierarchical structure, In this manuscript, we propose a novel data protection protocol for MQTT from hierarchical ID-based encryption. Our protocol adopts the intrinsic hierarchical structures of MQTT, and achieves constant-size keys, i.e. independent of the depth in hierarchical structures. Besides, the formal security model for the proposed protocol have been defined in the manuscript. The proposed protocol have been formally proven chosen-plaintext secure under the ℓ-wBDHI assumption.
Nowadays, it is convenient for people to store their data on clouds. To protect the privacy, people tend to encrypt their data before uploading them to clouds. Due to the widespread use of cloud services, public key searchable encryption is necessary for users to search the encrypted files efficiently and correctly. However, the existing public key searchable encryption schemes supporting monotonic queries suffer from either infeasibility in keyword testing or inefficiency such as heavy computing cost of testing, large size of ciphertext or trapdoor, and so on. In this work, we first propose a novel and efficient anonymous key-policy attribute-based encryption (KP-ABE). Then by applying Shen et al.’s generic construction to the proposed anonymous KP-ABE, we obtain an efficient and expressive public key searchable encryption, which to the best of our knowledge achieves the best performance in testing among the existing such schemes. Only 2 pairings are needed in testing. By applying our searchable encryption, one is able to expressively and efficiently search their encrypted data on clouds, without leaking the keyword information. Besides, we also implement our scheme and others with Python for comparing the performance. From the implementation results, our scheme owns the best performance on testing, and the size of ciphertexts and trapdoors are smaller than most of the existing schemes.
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4,773 members
Jia-Ming Chang
  • Department of Computer Science
Man-Kwan Shan
  • Department of Computer Science
Tsai-Yen Li
  • Department of Computer Science
Shu-heng Chen
  • Department of Economics
Shih-Yi Chien
  • Department of Management Information System (MIS)
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