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國小教師壓力源對身心健康之影響：休閒調適策略的中介效果
No full text[[abstract]]目前國小教師普遍缺少休閒活動的參與，讓教師壓力無法獲得有效地調適，導致影響身心健康。本研究旨在探討國小教師壓力源、休閒調適與身心健康之現況，並分析影響效果。以「國小教師壓力源、身心健康與休閒調適量表」為研究工具，採便利取樣方式抽取樣本，共回收459份問卷，所得資料經描述性統計及結構方程模式進行資料分析，結果如下：一、個人背景變項部分：以女性、41－50歲、已婚、擔任級任教師（未兼任行政工作）、21年以上服務年資及市區學校之教師居多數。二、壓力源透過休閒調適策略之中介效果，會產生緩衝的功能，降低壓力源對身心健康造成的負面影響，間接維繫身心健康。最後根據研究結果，提出實務策略及未來研究之建議。[[abstract]]Since teachers of primary school are few to participate in leisure, their pressures cannot be relieved effectively. It will inevitably affect physical and mental health. This research aims to investigate the status quo of the elementary school teachers' stressors, leisure coping, physical and mental health as well, analyze the influential effects. The study was conducted by means of questionnaire surveys. The researcher compiled the questionnaire based on relevant literature. ＂The Scale for Elementary School Teachers' Stressors, physical and mental health and Leisure Coping＂ was used as a research tool for the study. This study was based on the convenience sampling. Among the mailed questionnaires, 459 were retrieved. The statistical procedures used in this research were descriptive statistics and Structural Equation Modeling(SEM).The research obtained conclusions as follows: 1.Personal background variables: Most of the teachers are female, at the age of 41-50 ,married and are the homeroom teachers(not worked the administrative jobs at the same time),over 21 years of seniority. 2. The stressors via the mediation effect of leisure coping strategies, as a buffer, will reduce the negative influences to the physical and mental health and maintain the physical and mental health indirectly. Finally, based on the outcomes and the conclusions of this study, the propositions are provided for practical strategies and the researches in the future
Acute Effects of 160-Degree V-Shape Whole-Body Periodic Acceleration (WBPA) on Blood Pressure and Cardiovascular Hemodynamics
No full text[[abstract]]Whole-Body Periodic Acceleration (WBPA) has been reported to induce endothelial nitric
oxide and cause vasodilation. However, the effects of WBPA on blood pressure and cardiovascular
hemodynamics are still unclear and controversial. The objective of this study was to determine
whether a single session of 160-degree V-shape Whole-Body Periodic Acceleration (WBPA-V-160),
i.e., periodic motion of the supine body headward to footward, improved blood pressure and cardiovascular parameters. A pre-evaluation and post-evaluation of blood pressure and cardiovascular
hemodynamics via DynaPulse Noninvasive and Quantitative Hemodynamic Profile Analysis were
performed after a single 30 min trial of WBPA-V-160 with a moving distance, headward to footward,
of 2 mm, at a constant frequency of 4 Hz. Systolic BP, diastolic BP, heart rate, end systolic pressure, end
diastolic pressure, mean arterial BP, and pulse pressure at post-evaluation were significantly lower
than at pre-evaluation after WBPA-V-160, whereas systemic vascular compliance and brachial artery
distensibility at post-evaluation were significantly higher than at pre-evaluation. The WBPA-V-160,
performed for 30 min, did improve blood pressure and cardiovascular hemodynamics by lowing the
BP parameters and enhancing systemic vascular compliance
Antioxidant and Anti-α-Glucosidase Activities of Various Solvent Extracts and Major Bioactive Components from the Fruits of Crataegus pinnatifida
No full text[[abstract]]Crataegus pinnatifida is used to treat various diseases, including indigestion, congestive heart failure, hypertension, atherosclerosis, and myocardial dysfunction. We evaluated antioxidant and anti-α-glucosidase activities of various solvent extracts and major bioactive components from the fruit of C. pinnatifida. Ethyl acetate extracts showed potent antioxidant activities with IC50 values of 23.26 ± 1.97 and 50.73 ± 8.03 μg/mL, respectively, in DPPH and ABTS radical scavenging assays. Acetone extract exhibited significant anti-α-glucosidase activity with IC50 values of 42.35 ± 2.48 μg/mL. HPLC analysis was used to examine and compare the content of active components in various solvent extracts. We isolated four active compounds and evaluated their antioxidant and anti-α-glucosidase properties. Among the isolated compounds, chlorogenic acid and hyperoside showed potential antioxidant activities in ABTS and superoxide radical scavenging assays. Moreover, hyperoside also displayed stronger anti-α-glucosidase activity than other isolates. The molecular docking model and the hydrophilic interactive mode of anti-α-glucosidase assay revealed that hyperoside might have a higher antagonistic effect than positive control acarbose. The present study suggests that C. pinnatifida and its active extracts and components are worth further investigation and might be expectantly developed as the candidates for the treatment or prevention of oxidative stress-related diseases and hyperglycemia
Validation of the traditional Chinese version of the Sinus and Nasal Quality of Life Survey (SN-5) for children
No full text[[abstract]]Background: Persistent sinonasal symptoms are common in children with chronic rhinosinusitis. The Sinus and Nasal Quality of Life (QoL) Survey (SN-5) was the first validated questionnaire measuring sinonasal-related QoL in populations aged 2-12 years. No norm has been established for Chinese-speaking countries. We translated the SN-5 into traditional Chinese and evaluated validity and reliability.
Methods: From December 2016 to December 2017, healthy volunteers and children with persistent sinonasal symptoms were enrolled. Guardians of the participants completed the SN-5, a visual analog scale (VAS) of nasal symptoms, and the Obstructive Sleep Apnea-18 (OSA-18); the responses were used to assess internal consistency, discriminant validity, and treatment responsiveness. A nontreatment group was administered the SN-5 1 week later to assess test-retest reliability.
Results: We recruited 31 healthy volunteers and 85 children with rhinosinusitis, 50 and 35 in the treatment and nontreatment groups, respectively. The SN-5 demonstrated good internal consistency (Cronbach's α = 0.86) and test-retest reliability (0.74, p < 0.01). It exhibited good discriminant validity between the healthy and rhinosinusitis groups (p < 0.001). The SN-5 scores were correlated with the VAS scores (0.63, p < 0.001). The effect size of the SN-5 scores was 0.51. The total SN-5 and OSA-18 scores changed significantly after 4-week treatment (p < 0.05) and demonstrated good responsiveness. The SN-5 and OSA-18 scores were significantly and positively correlated (r2 = 0.53, p < 0.001).
Conclusion: Our traditional Chinese version of the SN-5 is reliable and valid for measuring sinonasal-related QoL in children in Chinese-speaking countries.
Trial registration number: NCT04836403
A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system
No full text[[abstract]]The Internet of Things (IoT) is a relatively new technology that has piqued academics’ and business information systems’ attention in recent years. The Internet of Things establishes a network that enables smart devices in an organisational information system to connect to one another and exchange data with the central storage. Android apps are placed on Android apps to enhance the user-friendliness of IoT devices in business information systems, making them more interactive and user-friendly. However, the usage of Android apps makes IoT devices susceptible to all forms of malware attacks, including those that attempt to hack into IoT devices and get access to sensitive information stored in the corporate information system. The researchers offered a variety of attack mitigation approaches for detecting harmful malware embedded in an Android application operating on an IoT device. In this context, machine learning offered the most promising strategies to detect malware attacks in IoT-based enterprise information systems because of its better accuracy and precision. Its capacity to adapt to new forms of malware attacks is a result of its learning capabilities. Therefore, we conduct a detailed survey, which discusses emerging machine learning algorithms for detecting malware in business information systems powered by the Internet of Things. This article reviews all available research on malware detection, including static malware detection, dynamic malware detection, promoted malware detection and hybrid malware detection
AI-enabled digital forgery analysis and crucial interactions monitoring in smart communities
No full text[[abstract]]Digital forgery has become one of the attractive research fields in today’s technology. There are several types of forgery in digital media transmission, especially digital image transmission. A common type of forgery is copy-move forgery (CMF). The CMF may be encountered in streets, railway stations, underground stations, or festivals. This type of forgery may lead to hugger-mugger in some cases. Therefore, there is a need to find a sufficient countermeasure mechanism to detect image forgeries. This paper presents a new CMFD approach that depends on deep learning for IoT based smart cities. Two well-known deep learning models, namely CNN and ConvLSTM, are adopted for CMFD. The proposed models are tested on MICC-220, MICC-600 and MICC 2000 datasets for validation. Several tests are performed to verify the effectiveness of the proposed models. The simulation results reveal that the testing accuracy reaches 95%, 73%, and 94% for MICC-F220, MICC-F600 and MICC-F2000 datasets. In addition, the proposed approach achieves an accuracy of 85% for a combined set of all datasets
Generating Multi-Issued Session Key by Using Semi Quantum Key Distribution with Time-Constraint
No full text[[abstract]]Information security refers to protect the information from unauthorized access or modification. Quantum Key Distribution (QKD) is a way to generate a key preventing those malicious activities. One of QKD protocol, namely Semi-quantum key distribution (SQKD) protocol, is designed to allow two users to establish a secure secret key when either of them is limited to performing certain “classical” operations. It is proven to be secure from any type of attack. However, it will be a problem in the multi-session communication since the SQKD activities follow the number of the session. In this paper, we propose two modified SQKDs with time-constraint approach. Time-constraint is beneficial in QKD activity since it could generate session key between two parties within a certain time-constraint. By setting the number of session key and its time-constraint before QKD activities, many scheduled communications would be prepared well. Furthermore, BAN Logic analysis is applied to analyze the goal of the protocol, the considered assumptions, wasted phase, and the demand for data encryption. Finally, the performance analysis of the protocols is presented, and it shows a better performance compared with other certain QKDs
IoT Network Traffic Classification using Machine Learning Algorithms: An Experimental Analysis
No full text[[abstract]]Internet of Things (IoT) refers to a wide variety of embedded devices connected to the Internet, enabling them to transmit and share information in smart environments with each other. The regular monitoring of IoT network traffic generated from IoT devices is important for their proper functioning and detection of malicious activities. One such crucial activity is the classification of IoT devices in the network traffic. It enables the administrator to monitor the activities of IoT devices which can be useful for proper implementation of Quality of Service, detect malicious IoT devices, etc. In the literature, various methods are proposed for IoT traffic classification using various machine learning algorithms. However, the accuracy of these machine learning algorithms depends on the data generated from various IoT devices, features extracted from network traffic, site at which IoT is deployed, etc. Moreover, the selection of features and machine learning algorithms are manual operations that are prone to error. Therefore, it is important to study the network traffic characteristics as well as suitable machine learning algorithms for accurate and optimized IoT traffic classification. In this article, we perform an in-depth comparative analysis of various popular machine learning algorithms using different effective features extracted from IoT network traffic. We utilize a public data set having 20 days of network traces generated from 20 popular IoT devices. Network traces are first processed to extract the significant features. We then selected state-of-the-art machine learning algorithms based on the recent survey papers for the IoT traffic classification. We then comparatively evaluated the performance of those machine learning algorithms on the basis of classification accuracy, speed, training time, etc. Finally, we provided a few suggestions for selecting the machine learning algorithm for different use cases based on the obtained results
Monthly Revenue Forecast of Exchange Listed Companies in Taiwan: Taking the Yuanta/P-shares Taiwan Top 50 ETF Components as Examples
No full text[[abstract]]股票上市公司的基本面是影響股票市場長期發展趨勢的主因，以長期投資股票市場的角度，上市公司的獲利與盈餘終將反應其市場價值，而營業收入（以下簡稱營收）正是公司獲利的主要來源。營收具有資訊內涵，因此，上市公司未來月營收的預測，能給予投資者提早取得有意義的資訊內涵。本研究為了進一步提升預測的準確性，對傳統ARIMA模型進行改良，並實施幾種不同演算法的預測效能比較，以平均絕對百分比誤差（MAPE）評估各方法之預測準確度。最後以最佳預測方法預測上市公司未來12個月之月營收，提供股票市場長期投資者事先了解該公司未來月營收可能的成長趨勢及其投資上的參考。[[abstract]]The fundamentals of listed stock companies are the main factors affecting the long-term development trend of the stock market. From the perspective of long-term investment in the stock market, the profits and earnings of listed companies will eventually reflect their market value. Operating revenue has information connotation and is the main source of company profits. Therefore, the forecast of future monthly revenue of listed companies can give investors early access to meaningful information. In order to further improve the accuracy of forecasting, this study improves the traditional ARIMA model and implements the comparison of the prediction performance of several different algorithms. The average absolute percentage error (MAPE) is used to evaluate the forecast accuracy of each algorithm. Finally, the best algorithms are used to forecast the monthly revenue of the listed company in Taiwan in the next 12 months. The results provide long-term investors in the stock market with a prior understanding of the company's future monthly revenue growth trend and investment reference