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Social Marketing Campaign in Tobacco Control: A Comparative Analysis of Indonesia and Taiwan
No full text[[abstract]]BACKGROUND: Concern about the introduction of young adult smoking is increasing, perhaps because young adults have become a major tobacco marketing target. To solve this issue, both Bali and Taiwan have imposed local regulations on smoke-free areas. Concern about the legalization of young adult smoking is increasing, possibly because young adults have become a major marketing target for tobacco. Both Bali and Taiwan have placed local restrictions on smoke-free areas to solve this issue.
AIM: This study will analyze the social marketing strategies used to impact young adults in Indonesia and Taiwan focusing specifically on aspects of the social marketing strategy, including channel analysis, market segmentation and consumer analysis, product, price, location and promotion considerations, as well as the implementation and evaluation.
METHODS: This research employed a qualitative approach. Qualitative inquiry is a way of analysis by understanding the meaning of individuals or groups in social problems or human problems. The process comprises questions and procedures. Data collection was conducted by face-to-face interviews. The basic data for this paper came from face-to-face interviews with local government officials, local community leaders and members of the NGO community.
RESULTS: The study found that 4P (Product, Price, Place, and Promotions) with the additional +2P (Partnership and Policy) elements of social marketing are employed by social marketing in Bali and Taiwan. The product refers to the principle of a safe life, in which it is easier to avoid than to cure. Price relates to the shift in behavior and the avoidance of early smokers, from smoking to stopping. The distribution networks used in Bali concentrate more on support for families and neighborhoods.
CONCLUSION: The social marketing strategy using 4P (Product, Price, Place, and Promotion) and additional +2P (Partnership and Policy) elements is an effective model both in Taiwan and Bali. The commodity on the market is the concept of a safe life, where prevention is better than cure. The price is the shift in action, from smoking to stopping, and the prevention of early smoking. The two countries also include other parties, such as schools and community groups, to collaborate
A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles
No full text[[abstract]]In this research, we explored various state-of-the-art biomedical-specific pre-trained Bidirectional Encoder Representations from Transformers (BERT) models for the National Library of Medicine - Chemistry (NLM CHEM) and LitCovid tracks in the BioCreative VII Challenge, and propose a BERT-based ensemble learning approach to integrate the advantages of various models to improve the system's performance. The experimental results of the NLM-CHEM track demonstrate that our method can achieve remarkable performance, with F1-scores of 85% and 91.8% in strict and approximate evaluations, respectively. Moreover, the proposed Medical Subject Headings identifier (MeSH ID) normalization algorithm is effective in entity normalization, which achieved a F1-score of about 80% in both strict and approximate evaluations. For the LitCovid track, the proposed method is also effective in detecting topics in the Coronavirus disease 2019 (COVID-19) literature, which outperformed the compared methods and achieve state-of-the-art performance in the LitCovid corpus
An Efficient and Secure Identity-Based Signature System for Underwater Green Transport System
No full text[[abstract]]The smart ocean has aroused the interest of government, business, and academia because of the wealth of marine resources. It has been suggested to use underwater Internet of Things (IoT) frameworks to collect a variety of data from smart seas that can aid in the underwater green transport system, ecological sustainability, military intelligence gathering, and a variety of other operations. Because of the limited resources accessible to IoT devices regarding communication overhead, processing expenses, and battery capacity, security and privacy concerns in underwater green transport systems have lately been a critical source of worry. In this context, We presented a unique identity-based authentication mechanism for underwater green transport systems. Our suggested solution uses lightweight authentication mechanisms that prove secure communication between different elements of the green transport system
CBP2P: Cooperative Electronic Bank Payment Systems Based on Blockchain Technology
No full text[[abstract]]The most common consumer behavior was to pay in cash at a physical store in the past. However, with the rapid development and popularization of smart handheld devices and computers, merchants have had many innovative business models in recent years. For example, the digital cryptocurrency Bitcoin has the advantages of decentralization, anonymity, and the inability to tamper with data. If these benefits are combined with the current electronic payment financial operation model, they will be highly innovative. However, blockchain technologies have been applied to decentralized network architectures. As a result, the financial system will face anonymous crime, extensive data synchronization, and time-consuming transaction processes. This paper implements a P2P electronic payment system with a hierarchical structure based on the blockchain architecture, called CBP2P (Cooperative Bank Peer-to-Peer), to improve the defects and realize an accurate electronic payment system. Through the characteristics of the blockchain and cooperation between banks, we will enhance the security and availability of the system
Deep Network-Enabled Haze Visibility Enhancement for Visual IoT-Driven Intelligent Transportation Systems
No full text[[abstract]]The Internet of Things (IoT) has recently emerged as a revolutionary communication paradigm where a large number of objects and devices are closely interconnected to enable smart industrial environments. The tremendous growth of visual sensors can significantly promote the traffic situational awareness, traffic safety management, and intelligent vehicle navigation in intelligent transportation systems (ITSs). However, due to the absorption and scattering of light by the turbid medium in atmosphere, the visual IoT inevitably suffers from imaging quality degradation, e.g., contrast reduction, color distortion, etc. This negative impact can not only reduce the imaging quality, but also bring challenges for the deployment of several high-level vision tasks (e.g., object detection, tracking, recognition, etc.) in the ITS. To improve imaging quality under the hazy environment, we propose a deep network-enabled three-stage dehazing network (termed TSDNet) for promoting the visual IoT-driven ITS. In particular, the proposed TSDNet mainly contains three parts, i.e., multiscale attention module for estimating the hazy distribution in the RGB image domain, two-branch extraction module for learning the hazy features, and multifeature fusion module for integrating all characteristic information and reconstructing the haze-free image. Numerous experiments have been implemented on synthetic and real-world imaging scenarios. Dehazing results illustrated that our TSDNet remarkably outperformed several state-of-the-art methods in terms of both qualitative and quantitative evaluations. The high-accuracy object detection results have also demonstrated the superior dehazing performance of the TSDNet under hazy atmosphere conditions. The source code is available at https://github.com/gy65896/TSDNet
Emotion-Cause Pair Extraction Based on Machine Reading Comprehension Model
No full text[[abstract]]In this paper, we propose a BERT-based framework for Emotion-Cause Pair Extraction (ECPE) task. Given a passage, the ECPE task aims to jointly extract (1) emotion-related clauses and (2) cause clauses (the clause caused the emotion). Our framework is featured by the following two novel designs. First, we formulate the emotion and cause extraction task as a machine reading comprehension (MRC) task. The MRC task is to read a given text passage, and then answer questions by comprehending the article. In our formulation, we treat the ECPE passage as MRC input and pose questions like (Which clauses cause the emotions?). The idea is to leverage the power of MRC model based on recent pre-trained language model. Second, we formulate the emotion-cause pair detection as contextual relatedness detection problem, which can be also effectively addressed by pre-trained language model. The experiment results based on benchmarking datasets demonstrate the effectiveness of the proposed approach; we advance the state-of-the-art results from 61% to 65% in terms of F1 scores
Pulse-line intersection method with unboxed artificial intelligence for hesitant pulse wave classification
No full text[[abstract]]State-of-the-art artificial intelligence (AI) methods are progressively strengthened in Traditional Chinese Medicine (TCM) pulse palpation, aiding physicians to make comprehensive preliminary clinical decisions through non-invasive diagnostics. One of the well-known proven examinations i.e., hesitant pulse wave diagnosis, is a sign that the blood circulation of a person is sluggish. This examination provides a preliminary diagnosis for physiological problems. Modern AI methods such as artificial neural networks achieve better performance than traditional methods; however, the final decision of such examination lacks of interpretability. In clinical situations, patients need an easy-to-understand diagnosis to be provided for selecting appropriate clinical treatment. Therefore, this study presents feature extraction and clinical decision support systems based on Pulse-Line Intersection (PLI) and eXplainability AI (XAI) methods. The pulses were recorded from 46 patients in six different measurement points for six seconds. In addition, a comparison of several AI methods was provided to classify hesitant and normal pulse. The contribution of each feature in the classification process was analyzed by unboxing each predictive intelligence model. The results revealed that all models performed comparably, evaluated using performance matric on the testing data with average F1-score of Logistic Regression, Support Vector Machine, Random Forest, XGBoost, Multi-Layer Perceptron, and Long Short-Term Memory were 0.74, 0.74, 0.74, 0.78, 0.73, and 0.80, respectively. This work suggests that modern AI methods can provide more comprehensive explainability and higher accuracy than traditional method rankings
Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection
No full text[[abstract]]Background: Prostate cancer is the 4th most common type of cancer. To reduce the workload of medical personnel in the medical diagnosis of prostate cancer and increase the diagnostic accuracy in noisy images, a deep learning model is desired for prostate cancer detection.
Methods: A multi-scale denoising convolutional neural network (MSDCNN) model was designed for prostate cancer detection (PCD) that is capable of noise suppression in images. The model was further optimized by transfer learning, which contributes domain knowledge from the same domain (prostate cancer data) but heterogeneous datasets. Particularly, Gaussian noise was introduced in the source datasets before knowledge transfer to the target dataset.
Results: Four benchmark datasets were chosen as representative prostate cancer datasets. Ablation study and performance comparison between the proposed work and existing works were performed. Our model improved the accuracy by more than 10% compared with the existing works. Ablation studies also showed average improvements in accuracy using denoising, multi-scale scheme, and transfer learning, by 2.80%, 3.30%, and 3.13%, respectively.
Conclusions: The performance evaluation and comparison of the proposed model confirm the importance and benefits of image noise suppression and transfer of knowledge from heterogeneous datasets of the same domain
Psychometric Properties of the Death Literacy Index in Taiwan
No full text[[abstract]]台灣在2019年實施《病主法》之後，保障了病人有知情、選擇及預立醫療決定的權力，但要如何達到真正對生命善終的知情、選擇及決定，就必須提升全民的死亡識能。澳洲學者於2020年首次發表了死亡識能量表(DLI)的研究，死亡識能量表的建構具有理論及概念架構的支持，並且具有良好可靠的信效度，可被廣泛用於大眾，是目前最新、也是第一個測量死亡識能的評估工具，台灣尚無人探討過死亡識能的評估工具，若能將此量表翻譯成中文版，並進行本土化的的測試與修正，成為能具體反映台灣國民死亡識能的評估工具，將是國人追求善終的第一步。本研究目的，將澳洲死亡識能量表中文化，由五位專業領域人員進行專家效度檢測，取得IRB許可書後，以網路問卷形式，針對台灣20歲以上的民眾進行施測，將所得資料使用spss統計軟體進行分析，信度方面採取內在一致性（Cronbach’s alpha）與折半信度（皮爾森相關係數）分析檢定，效度方面採用因素分析法測量建構效度，發展出適合台灣使用之死亡識能量表。經分析檢定後，中文版內在一致性信度Cronbach’s alpha值為.928、折半係數為.962，專家效度為.91，經轉軸後因素分析其建構效度，所得KMO值為.906，總變異量為70.169%，足以表示本量表具有良好的信效度。[[abstract]]In Taiwan, after the implementation of the Patient Autonomy Act in 2019, patients’ right to be informed, to make choices, and to make advance decision are protected. However, in order to respect patient autonomy in healthcare and to safeguard their rights to a good death, it is necessary to promote the death literacy in all people. The Death Literacy Index (DLI) was first published by Australian researchers in 2020. The DLI is supported by a theoretical and conceptual framework and has good reliability and validity. It was the newest and the first tool to measure death literacy that can be widely used. No researcher in Taiwan has developed the instrument to measure the death literacy. If this instrument could be translated into Chinese and be tested, to reflect the death literacy of the Taiwanese people, the rights to a good death will be widely improved.
The purpose of this study was to translate the Death Literacy Index (DLI) and to examine psychometry of death literacy. The original DLI was translated from English to Chinese by researcher, and then was back translated from Chinese to English by experts who can speak both languages. Final, the two scale were checked by the five researchers. Then, the scale was reviewed for the validity by five experts from related fields. After the approval of the IRB, this questionnaire was delivered by internet to recruit subjects who meet the inclusive criteria, people who are over 20 years old. For reliability analysis, we used the internal consistency and split-half reliability. The constructs validity was established by factor analysis. After analyzing and validating the Chinese version, the internal consistency reliability Cronbach's alpha value was .928, the split-half reliability was .962, the expert validity was .91, and the construct validity was .906 by exploratory factor analysis, with a total variance of 70%, which is sufficient to indicate that the scale has good reliability and validity
Breakdown Behavior of a Nitrogen Implanted AlGaN/GaN HEMT Transistor with different Metal Contact Positions
No full text[[abstract]]The breakdown behavior of a calibrated depletion mode AlGaN/GaN transistor with a nitrogen-implanted gate region was simulated and studied using Sentaurus TCAD simulation in this work, with an emphasis on the metal contact design rule for a GaN-based high-electron mobility transistor (HEMT) device. Using Sentaurus simulation, the breakdown behaviors for various source/drain contact schemes were examined. Different piezoelectric effects were produced by the metal contact sites inside the source and drain, and the 2DEG was given extra polarization charges as a result (two-dimensional electron gas). The electron density has altered the way to raise the electric field distribution, which in turn increased the breakdown voltage, as a result of the variety of source/drain contact schemes.
To show how the piezoelectric effects at various metal contact sites significantly affect the breakdown voltage at various distances between drain metal contacts, the electric field distribution and 2DEG profiles were simulated. Due to the comparatively low electron density and weakly produced piezoelectric effect, the breakdown voltage of the nitrogen-implanted gated device reduced by 41% when the contact position was far from the AlGaN/GaN. For a source-drain length of 20 µm, this decrease is considerable. The breakdown simulation's minimum critical field was 4 MV/cm. Different breakdown behaviors are seen by the simulated AlGaN/GaN device at various metal contact positions in the drain