In this investigation, we explored the corrosive effects of date palm seed extracted from natural sources and biomass residues on mild steel in a solution of 0.5 M hydrochloric acid (HCl), employing a combination of e...
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Phenylalanine is an aromatic essential amino acid that exhibits the tendency to self-aggregate into fibrillar structures in its enantiomerically pure form. This observation was indicated as the underlying mechanism of...
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Today Artificial Intelligence (AI) supports difficult decisions about policy, health, and our personal lives. The AI algorithms we develop and deploy to make sense of information, are informed by data, and based on mo...
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Today Artificial Intelligence (AI) supports difficult decisions about policy, health, and our personal lives. The AI algorithms we develop and deploy to make sense of information, are informed by data, and based on models that capture and use pertinent details of the population or phenomenon being analyzed. For any application area, more importantly in precision medicine which directly impacts human lives, the data upon which algorithms are run must be procured, cleaned, and organized well to assure reliable and interpretable results, and to assure that they do not perpetrate or amplify human prejudices. This must be done without violating basic assumptions of the algorithms in use. Algorithmic results need to be clearly communicated to stakeholders and domain experts to enable sound conclusions. Our position is that AI holds great promise for supporting precision medicine, but we need to move forward with great care, with consideration for possible ethical implications. We make the case that a no-boundary or convergent approach is essential to support sound and ethical decisions. No-boundary thinking supports problem definition and solving with teams of experts possessing diverse perspectives. When dealing with AI and the data needed to use AI, there is a spectrum of activities that needs the attention of a no-boundary team. This is necessary if we are to draw viable conclusions and develop actions and policies based on the AI, the data, and the scientific foundations of the domain in question.
The development of the internet is getting faster, participating in encouraging the emergence of new and innovative information. In filtering the various information that appears, we need a recommended system to perfo...
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The development of the internet is getting faster, participating in encouraging the emergence of new and innovative information. In filtering the various information that appears, we need a recommended system to perform well for users in today's internet era. A well-performing recommendation system in question is a reliable recommendation algorithm. This algorithm is fundamental to analyzing various information, such as responses on social media based on user behavior data related to the topic of COVID. This data is crawled from tweets on social media Twitter. The data analysis algorithm obtained uses Python, which is then visualized in the form of a diagram. The processed data is user comments on Twitter, and the text data is analyzed using Python, using more than 60000 data sets taken to form visualizations and conclusions. From sentiment analysis, polarity and subjectivity data are obtained to be analyzed, which are negative, neutral, or positive. The result is show positive tweets with 29.2%, negative tweets is 13%, and 57.8% neutral tweets. Lastly, sentiment analysis can help people effectively infer vast and complex data from social media like Twitter.
In this research, we have been developed teaching materials for electromagnetics that demonstrate theories using real-world actual applications (capacitors, coils, piezoelectric elements, Wireless Power Transfer(WPT),...
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Flow matching in the continuous simplex has emerged as a promising strategy for DNA sequence design, but struggles to scale to higher simplex dimensions required for peptide and protein generation. We introduce Gumbel...
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Research has been carried out to monitor vehicle tires before they are used and can reduce damage, including overcoming vehicle fuel waste because air pressure is continuously monitored. This research aims to utilize ...
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ISBN:
(纸本)9781665453905
Research has been carried out to monitor vehicle tires before they are used and can reduce damage, including overcoming vehicle fuel waste because air pressure is continuously monitored. This research aims to utilize the MPX5500DP sensor as an air pressure device, the LM35 sensor as a temperature reader, and a buzzer based on IoT to build a tire pressure monitoring system (TPMS). The MPX5500DP and LM35 sensor inputs to the Arduino Uno microcontroller are distributed by the NodeMCU, fitted with a Wi-Fi module. The Blynk application sends and displays the data on a smartphone using the IoT-based. Based on this research, data on the percentage of errors in monitoring air pressure and tire temperature on vehicles were obtained by comparing the data to the pressure gauge and thermometer: 1—the results of the average reading of the sensor error value. MPX5500DP air pressure against pressure gauge is 5.3%. 2—the average reading of the LM35 sensor error value on the temperature thermometer is 6.8%. With this research, the air pressure and temperature in the tires can be monitored in real-time via a smartphone using the IoT-based.
Sb 2 Se 3 is used to switch between broadband transparency and enhanced index contrast in two device types leveraging Bragg gratings for tunable stop-and pass-band functionalities. Experimental results highlight fabr...
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ISBN:
(纸本)9798350369311
Sb
2
Se
3
is used to switch between broadband transparency and enhanced index contrast in two device types leveraging Bragg gratings for tunable stop-and pass-band functionalities. Experimental results highlight fabrication challenges and efficacy of the designs.
Attacks on web applications are constantly growing in both frequency and severity. Abundant data on the internet stimulates hackers to attempt different types of cyberattacks. Attack detection using conventional appro...
Attacks on web applications are constantly growing in both frequency and severity. Abundant data on the internet stimulates hackers to attempt different types of cyberattacks. Attack detection using conventional approaches and outdated data processing techniques has become outmoded as a result of this development. The purpose of this study is to investigate IoT attacks and discuss the efficient ML technique implementation strategies for restricting security risks. Among different security techniques, Machine learning (ML) systems demonstrated commendable feasibility in improving network and device security for the Internet of Things. The study with contextual research recognises and comprehends that modified “Support Vector Machine (SVM)” as well as “Random Forest (RF)” ML techniques showed optimal performance in IoT attack detection and prevention.
In this paper we consider the modeling of measurement error for fund returns data. In particular, given access to a time-series of discretely observed log-returns and the associated maximum over the observation period...
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