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比較新型與傳統型神經肌肉逆轉劑於老年膝關節置換術患者麻醉安全之回溯調查
No full text[[abstract]]背景：全球老年人口逐年增加，高齡手術麻醉及甦醒成為重要議題。新型逆轉劑Sugammadex能加速逆轉神經肌肉阻斷並促使一般患者盡早脫離呼吸器，對於老年患者麻醉安全指標缺乏相關研究。
目的：比較使用新型與傳統型神經肌肉逆轉劑對於老年膝關節置換術患者麻醉恢復成效調查。
方法：病歷回溯法自2018年1月1日至2018年8月31日期間，採立意取樣65歲以上行膝關節置換術且接受神經肌肉逆轉劑老年患者，自擬結構式問卷「神經肌肉逆轉劑麻醉恢復病歷回溯表」進行登錄，共收案214例。
結果：不論新型或傳統組皆以女性居多(p<0.05)，其他基本性質未達顯著差異。麻醉照護指標方面，新型組術中接受較多次神經肌肉阻斷劑(Rocuronium)(p<0.05)，但在拔管前吐氣二氧化碳監測(ETCO2)兩組雖達顯著差異但皆於正常範圍內(p<0.05)。新型組不論在麻醉與手術時間 (p<0.05)、給藥至拔管時間(p=0.02)，皆有低於傳統組的趨勢，且新型組在拔管後平均神經肌肉阻斷程度(TOF)為98%，達拔管安全指標。恢復期照護指標方面，新型組意識恢復優於傳統組(p<0.05)。在恢復期合併症方面兩組無論在延遲拔管、低血氧、高血壓、頭暈、嘔吐及喉嚨痛，皆未達顯著差異；新型組在術後整體滿意度得分高於傳統組，呈顯著差異(p<0.05)。
結論：接受新型阻斷劑老年病患能縮短手術麻醉時間、意識恢復及自主呼吸，達到麻醉安全指標。恢復期合併症及術後品質分析與傳統組相近，本研究結果可提供醫護人員在麻醉安全及提升照護品質。
關鍵字：麻醉恢復、神經肌肉逆轉劑、Sugammadex、麻醉安全、老人[[abstract]]Background: The pace of population ageing is much faster than in the past and aging increases the probability of a person to undergo surgery. Elderly patients take more time to recover from general anesthesia therefore generation of muscle reversal agent is important in facilitating anesthesia safety issue.
Objective: To compare the effects of new and traditional neuromuscular reversal agents on anesthesia recovery in elderly patients undergoing knee arthroplasty.
Methods: From January 1, 2018 to August 31, 2018, the medical records were retrospectively sampled for elderly patients over 65 years old who underwent knee arthroplasty and received neuromuscular reversal. Self-made structural questionnaire "neuromuscular reversal" The agent's anesthesia recovery medical record retrospective table was registered, and a total of 214 cases were received.
Results: Population of the new or traditional reversal agents were predominantly female (p < 0.05), and there were no significant differences in the baseline characteristics. In terms of anesthesia care indicators, the new reversal agents received more than one neuromuscular blocker (Rocuronium) (p<0.05), but the two groups of exhaled carbon dioxide monitoring (ETCO2) before extubation showed significant differences but were within the normal range ( p<0.05). The new reversal agents had a lower trend than the traditional group in the time of anesthesia and surgery (p<0.05), and the time of administration to extubation (p=0.02), and the average neuromuscular blockade after extubation in the new reversal agents (TOF) is 98%. In terms of recovery period care indicators, the recovery of new reversal agents consciousness was better than that of the traditional group (p<0.05). There were no significant differences between the two groups in terms of delayed complication, delayed extubation, hypoxemia, hypertension, dizziness, vomiting, and sore throat.. The overall satisfaction score of the new group was higher than that of the traditional group (p<0.05).
Conclusion: Elderly patients receiving new reversal agents can recover consciousness and spontaneous breathing in a short time, and achieve anesthesia safety indicators
Anti-Inflammatory Effects of Rhamnetin on Bradykinin-Induced Matrix Metalloproteinase-9 Expression and Cell Migration in Rat Brain Astrocytes
No full text[[abstract]]Bradykinin (BK) has been shown to induce matrix metalloproteinase (MMP)-9 expression and participate in neuroinflammation. The BK/MMP-9 axis can be a target for managing neuroinflammation. Our previous reports have indicated that reactive oxygen species (ROS)-mediated nuclear factor-kappaB (NF-κB) activity is involved in BK-induced MMP-9 expression in rat brain astrocytes (RBA-1). Rhamnetin (RNT), a flavonoid compound, possesses antioxidant and anti-inflammatory effects. Thus, we proposed RNT could attenuate BK-induced response in RBA-1. This study aims to approach mechanisms underlying RNT regulating BK-stimulated MMP-9 expression, especially ROS and NF-κB. We used pharmacological inhibitors and siRNAs to dissect molecular mechanisms. Western blotting and gelatin zymography were used to evaluate protein and MMP-9 expression. Real-time PCR was used for gene expression. Wound healing assay was applied for cell migration. 2',7'-dichlorodihydrofluorescein diacetate (H2DCF-DA) and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX) were used for ROS generation and NOX activity, respectively. Promoter luciferase assay and chromatin immunoprecipitation (ChIP) assay were applied to detect gene transcription. Our results showed that RNT inhibits BK-induced MMP-9 protein and mRNA expression, promoter activity, and cell migration in RBA-1 cells. Besides, the levels of phospho-PKCδ, NOX activity, ROS, phospho-ERK1/2, phospho-p65, and NF-κB p65 binding to MMP-9 promoter were attenuated by RNT. In summary, RNT attenuates BK-enhanced MMP-9 upregulation through inhibiting PKCδ/NOX/ROS/ERK1/2-dependent NF-κB activity in RBA-1
A New Approach for Power Signal Disturbances Classification using Deep Convolutional Neural Networks
No full text[[abstract]]This paper proposes a new approach for power signal
disturbances (PSDs) classification using a two-dimension
(2D) deep convolutional neural network (CNN). The data
preprocessing stage introduces a conversion method from
signal to the 2D grayscale image. Firstly, the signal is
divided into multiple cycles. The zero-crossing rate is
adopted to specify a cycle’s start and endpoints. Then,
the cycles are transformed into matrices. Next, the matrices are merged into a new form matrix. Lastly, the
matrix is converted into the 2D image grayscale. The obtained 2D image preserves information and waveform the
sinusoidal of the signal. The experiment was carried out
on datasets containing 14 different disturbance categories
with the same model learning structure. The results show
that the 2D deep CNN performs better than the onedimension (1D) deep CNN. According to this result, the
2D deep CNN can improve the PSDs classification effectiveness. Furthermore, the proposed method outperforms
the conversion method used in previous studies
A novel approach for DDoS attacks detection in COVID-19 scenario for small entrepreneurs
No full text[[abstract]]The current COVID-19 issue has altered the way of doing business. Now that most customers prefer to do business online, many companies are shifting their business models, which attracts cyber attackers to launch several kinds of cyberattacks against commercial companies simultaneously. The most common and lethal DDoS attack disables the victim’s online resources. While large businesses can afford defensive measures against DDoS assaults, the situation is different for new entrepreneurs. Their lack of security resources restricts their ability to ward off DDoS attacks. Here, we aim to highlight the problems that prospective entrepreneurs should be aware of before joining the business, followed by a filtering mechanism that efficiently identifies DDoS assaults in the COVID-19 scenario, which is the subject of our research. The suggested approach employs statistical and machine learning techniques to discriminate between DDoS attack data and regular communication. Our suggested framework is cost-effective and identifies DDoS attack traffic with a 92.8% accuracy rate
A Robust Authentication System With Application Anonymity in Multiple Identity Smart Cards
No full text[[abstract]]User authentication plays a crucial role in smart card-based systems. Multi-application smart cards are easy to use as a single smart card supports more than one application. These cards are broadly divided into single identity cards and multi-identity cards. In this paper, the authors have tried to provide a secure multi-identity multi-application smart card authentication scheme. Security is provided to user data by using dynamic tokens as verifiers and nested cryptography. A new token is generated after every successful authentication for the next iteration. Anonymity is also provided to data servers which provides security against availability attacks. An alternate approach to store data on servers is explored, which further enhances the security of the underlying system
Context Aware Recommender Systems: A Novel Approach Based on Matrix Factorization and Contextual Bias
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Open AccessFeature PaperArticle
Context Aware Recommender Systems: A Novel Approach Based on Matrix Factorization and Contextual Bias
by Mario Casillo 1,Brij B. Gupta 2,3,4ORCID,Marco Lombardi 1,*ORCID,Angelo Lorusso 1ORCID,Domenico Santaniello 1ORCID andCarmine Valentino 1,5ORCID
1
Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy
2
Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan
3
Research and Innovation Department, Skyline University College, Sharjah P.O. Box 1797, United Arab Emirates
4
School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
5
Department of Mathematics, University of Salerno, 84084 Fisciano, Italy
*
Author to whom correspondence should be addressed.
Electronics 2022, 11(7), 1003; https://doi.org/10.3390/electronics11071003
Received: 22 February 2022 / Revised: 17 March 2022 / Accepted: 18 March 2022 / Published: 24 March 2022
(This article belongs to the Special Issue Context-Aware Computing and Smart Recommender Systems in the IoT)
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Abstract
In the world of Big Data, a tool capable of filtering data and providing choice support is crucial. Recommender Systems have this aim. These have evolved further through the use of information that would improve the ability to suggest. Among the possible exploited information, the context is widely used in literature and leads to the definition of the Context-Aware Recommender System. This paper proposes a Context-Aware Recommender System based on the concept of embedded context. This technique has been tested on different datasets to evaluate its accuracy. In particular, the use of multiple datasets allows a deep analysis of the advantages and disadvantages of the proposed approach. The numerical results obtained are promising
Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications
No full text[[abstract]]In this paper, novel convolutional neural network (CNN) and convolutional long short-term (ConvLSTM) deep learning models (DLMs) are presented for automatic detection of arrhythmia for IoT applications. The input ECG signals are represented in 2D format, and then the obtained images are fed into the proposed DLMs for classification. This helps to overcome most of the problems of the previous machine and deep learning models such as overfitting, and working on more than one lead of ECG signals. We use several publicly available datasets from PhysioNet such as MIT-BIH, PhysioNet 2016 and PhysioNet 2018 for model assessment. Overall accuracies of 97%, 98 %, 94 % and 91 % are obtained on spectrograms of MIT-BIH dataset, compressed MIT-BIH dataset, PhysioNet 2016 dataset, and PhysioNet 2018 dataset, respectively. Compared to the previous works, the proposed framework is more robust and efficient, especially in the case of noisy data
Orchestration of APT malware evasive manoeuvers employed for eluding anti-virus and sandbox defense
No full text[[abstract]]The modern day cyber attacks are highly targeted and incorporate advanced tactics, techniques and procedures for greater stealth, impact and success. These attacks are also known as Advanced Persistent Threats(APT) because of their evasive and stealth nature along with longer foothold on the victim’s digital infrastructure. The malware involved in APT attacks are sophisticated and developed with the intention of sabotaging the victim’s digital infrastructure or performing espionage. They are capable of targeting multiple operating environments starting from desktop and server operating systems (Windows, Linux and MacOS), Mobile platforms (Android, iOS), Embedded platforms (IoT Devices), to Industrial control systems (ICS/SCADA Devices). The evolution of evasive tactics and techniques employed in such advanced malware leads to extensive research efforts to develop mechanisms that can counter these evasion techniques. The research primarily aims to demonstrate that evasive manoeuvers are currently over-weighing the security countermeasures deployed by the prevalent security solutions. This paper will first explain the evasion mechanism in a systematic manner employed in modern APT malware and aims to implement a novel Evasive Manoeuvers Re-Engineering Framework(EMRF).EMRF aims to establish and demonstrate combinations of evasive manoeuvers with much known APT malware samples to elude security solutions. The payload variants, i.e., executable, dynamic link library, and shell-code, were experimented through a research-based framework EMRF to demonstrate 36% to 96% of evasive behavior countering the majority of defender engines. The EMRF system with its dynamic user defined evasion manoeuvers is able to transform non-zero-day payloads more potent by evading majority of the modern security solutions. This research clearly demonstrates the attacker’s ability to deliver non-zero-day payloads easily rather than investing resources and time in discovering zero-day exploits and developing zero-day payloads. This important observation can potentially disrupt the Advanced Persistent Threat Defenses incorporated in modern day security solution where focus is mainly on to detect zero-day payloads and exploits. Exhibiting the threat landscape poised due to APT, the paper utilizes a dataset of 4403 APT malware samples to extract and orchestrate the prevalence of evasive manoeuvers like stealth, covert communication, and anti-analysis mechanisms. This paper will contribute towards advanced malware analysis as an avenue to analyzing intrusion, evasion, and deception to prevent detection and verification, an association of responsibility, and determination of intent
Smart defense against distributed Denial of service attack in IoT networks using supervised learning classifiers
No full text[[abstract]]From smart home to industrial automation to smart power grid, IoT- based solutions penetrate into every working field. These devices expand the attack surface and turned out to be an easy target for the attacker as resource constraint nature hinders the integration of heavy security solutions. Because IoT devices are less secured and operate mostly in unattended scenario, they perfectly justify the requirements of attacker to form botnet army to trigger Denial of Service attack on massive scale. Therefore, this paper presents a Machine Learning-based attack detection approach to identify the attack traffic in Consumer IoT (CIoT). This approach operates on local IoT network-specific attributes to empower low-cost machine learning classifiers to detect attack, at the local router. The experimental outcomes unveiled that the proposed approach achieved the highest accuracy of 0.99 which confirms that it is robust and reliable in IoT networks
Application of artificial intelligence for Speech Synthesis
No full text[[abstract]]人類在工業革命之後逐漸開始掌握“技術”這一概念。架構能夠記錄的儀器，讓機械進行模仿，接下來就是人類的糾正，最後架構自主學習的道路。人工智慧在前進的道路上不斷地被應用在醫療、軍事、管理、教育等領域。技術改變生活，當人類滿足了物質生活所需要的一切時，精神生活便成爲了下一個目標，應用在娛樂成爲了新時代最新的趨勢。民國一百一十年VITS技術被提出，為TTS（語音合成系統）提供了一種全新的語音背景，傳統深度學習合成皆是採用機械裝置模擬，電子合成器，共振峰合成器，選擇拼接合成，基於HMM參數合成以及深度學習等六個階段，這樣的多階段拼接不僅導致學習消耗資源十分巨大，同時由於各自獨立學習相互拼接導致合成語音效果不夠優秀。VITS將合成訓練階段壓縮為一個階段，同時對情緒與音色進行訓練，大幅度降低訓練所需資料，同時也成功提高了訓練成果。本文的研究目的在於探討VITS系統訓練出來的成果對於公衆是否有娛樂產業上的價值，并且是否會對現今配音員的工作產生市場衝擊。對於其他娛樂產業是否有更高的商業價值。[[abstract]]The word of technology has been focused on various respects since the industrial revolution happen in UK in the 18th century. Recently, the fresh issue of artificial intelligence has also forwarded and applied commonly on medical, military, educational and industrial fields for its generally mature development. VITS(Variational Inference with adversarial learning for end-to-end Text-to-Speech) was presented last year and provided as a mythology for application on speech synthesis system to offer vocal background simulation. In general, the vocal synthesis method of the traditional deep learning is based on the combination of mechanical simulation, electronic mixture and peak speech compounder. Additionally, the accomplishment of vocal synthesis by association with HMM (Hidden Markov Models) parameters and deep learning is not only ineffective but also tedious due to multiage connections and learning independently. While VITS furnishes technique by compressing the synthesis training into just one stage and giving the complete training of emotional and vocal speech at the same time, so that it can reduce the needed data and enhance the training result simultaneously. This study was to emphasize on VITS employed for speech synthesis on entertainment industry value and assess its impact on of dubber career