USING THE AUTOMATED RANDOM FOREST APPROACH FOR OBTAINING THE COMPRESSIVE STRENGTH PREDICTION OF RCA

The intricate relationships and cohesiveness among numerous components make the task of designing mixture proportions for high-performance concrete (HPC) a challenging endeavour. Machine learning (ML) algorithms are indeed efficacious in mitigating this predicament. However, their lack of an explicit correlation between mixture proportions and compressive strength renders them opaque black box models. To surpass this constraint, the present research puts forward a semi-empirical methodology that involves the utilization of tactics such as non-dimensionalization and optimization. The methodology proposed exhibits a remarkable level of accuracy in predicting compressive strength across various datasets, exemplifying its all-encompassing applicability to diverse datasets.Furthermore, the exact association furnished by semi-empirical equations is a valuable asset for engineers and researchers operating in this domain, especially concerning their prognostic capabilities. The compressive strength of concrete holds significant importance in designing high-performance concrete, and achieving an optimal mixture proportion necessitates a comprehensive comprehension of the complex interplay among diverse factors, including the type and proportion of cement, water-cement ratio, size and type of aggregate, curing conditions, and admixtures. The semi-empirical approach put forth in this study presents a potential remedy to the intricate undertaking by establishing a more unequivocal correlation between mixture ratios and compressive strength

Prediction of wind turbine generator bearing failure through analysis of high frequency vibration data and the application of support vector machine algorithms

Reducing wind turbine downtime through innovations surrounding asset management has the potential to greatly influence the overall levelised cost of energy of large onshore and offshore developments. This research paper uses multiple examples of the same generator bearing failure to provide insight into how condition monitoring systems can be used in to train machine learning algorithms with the ultimate goal of predicting failure and remaining useful life. Results show that by analysing high frequency vibration data and extracting key features to train support vector machine algorithms, an accuracy of 67% can be achieved in successfully predicting failure 1-2 months before occurrence. This paper reflects on the limitations surrounding a generalised training approach, taking advantage of all available data, showing that if too many different examples are considered of different wind turbines and operating conditions the overall accuracy can be diminished

A review on Natural Language Processing Models for COVID-19 research

This survey paper reviews Natural Language Processing Models and their use in COVID-19 research in two main areas. Firstly, a range of transformer-based biomedical pretrained language models are evaluated using the BLURB benchmark. Secondly, models used in sentiment analysis surrounding COVID-19 vaccination are evaluated. We filtered literature curated from various repositories such as PubMed and Scopus and reviewed 27 papers. When evaluated using the BLURB benchmark, the novel T-BPLM BioLinkBERT gives groundbreaking results by incorporating document link knowledge and hyperlinking into its pretraining. Sentiment analysis of COVID-19 vaccination through various Twitter API tools has shown the public’s sentiment towards vaccination to be mostly positive. Finally, we outline some limitations and potential solutions to drive the research community to improve the models used for NLP tasks

Themes and Participants’ Role in Online Health Discussion: Evidence From Reddit

Health-related topics are discussed widely on different social networking sites. These discussions and their related aspects can reveal significant insights and patterns that are worth studying and understanding. In this dissertation, we explore the patterns of mandatory and voluntary vaccine online discussions including the topics discussed, the words correlated with each of them, and the sentiment expressed. Moreover, we explore the role opinion leaders play in the health discussion and their impact on participation in a particular discussion. Opinion leaders are determined, and their impact on discussion participation is differentiated based on their different characteristics such as their connections and locations in the social network, their content, and their sentiment. We apply social network analysis, topic modeling, sentiment analysis, machine learning, econometric analysis, and other techniques to analyze the collected data from Reddit. The results of our analyses show that sentiment is an important factor in health discussion, and it varies between different types of discussions. In addition, we identified the main topics discussed for each vaccine. Furthermore, the results of our study found that global opinion leaders have more influence compared to local opinion leaders in elevating the health discussion. Our study has important theoretical and practical implications

GTSO: Global Trace Synchronization and Ordering Mechanism for Wireless Sensor Network Monitoring Platforms

[EN] Monitoring is one of the best ways to evaluate the behavior of computer systems. When the monitored system is a distributed system¿such as a wireless sensor network (WSN)¿the monitoring operation must also be distributed, providing a distributed trace for further analysis. The temporal sequence of occurrence of the events registered by the distributed monitoring platform (DMP) must be correctly established to provide cause-effect relationships between them, so the logs obtained in different monitor nodes must be synchronized. Many of synchronization mechanisms applied to DMPs consist in adjusting the internal clocks of the nodes to the same value as a reference time. However, these mechanisms can create an incoherent event sequence. This article presents a new method to achieve global synchronization of the traces obtained in a DMP. It is based on periodic synchronization signals that are received by the monitor nodes and logged along with the recorded events. This mechanism processes all traces and generates a global post-synchronized trace by scaling all times registered proportionally according with the synchronization signals. It is intended to be a simple but efficient offline mechanism. Its application in a WSN-DMP demonstrates that it guarantees a correct ordering of the events, avoiding the aforementioned issues.This work was supported by the Ministerio de Economia y Competitividad by means of its project DPI2016-80303-C2-1-P. It covers the costs of publishing in open access.Navia-Mendoza, MR.; Campelo Rivadulla, JC.; Bonastre Pina, AM.; Ors Carot, R. (2018). GTSO: Global Trace Synchronization and Ordering Mechanism for Wireless Sensor Network Monitoring Platforms. Sensors. 18(1):1-22. https://doi.org/10.3390/s18010028S12218

Fault Diagnosis and Failure Prognostics of Lithium-ion Battery based on Least Squares Support Vector Machine and Memory Particle Filter Framework

123456A novel data driven approach is developed for fault diagnosis and remaining useful life (RUL) prognostics for lithium-ion batteries using Least Square Support Vector Machine (LS-SVM) and Memory-Particle Filter (M-PF). Unlike traditional data-driven models for capacity fault diagnosis and failure prognosis, which require multidimensional physical characteristics, the proposed algorithm uses only two variables: Energy Efficiency (EE), and Work Temperature. The aim of this novel framework is to improve the accuracy of incipient and abrupt faults diagnosis and failure prognosis. First, the LSSVM is used to generate residual signal based on capacity fade trends of the Li-ion batteries. Second, adaptive threshold model is developed based on several factors including input, output model error, disturbance, and drift parameter. The adaptive threshold is used to tackle the shortcoming of a fixed threshold. Third, the M-PF is proposed as the new method for failure prognostic to determine Remaining Useful Life (RUL). The M-PF is based on the assumption of the availability of real-time observation and historical data, where the historical failure data can be used instead of the physical failure model within the particle filter. The feasibility of the framework is validated using Li-ion battery prognostic data obtained from the National Aeronautic and Space Administration (NASA) Ames Prognostic Center of Excellence (PCoE). The experimental results show the following: (1) fewer data dimensions for the input data are required compared to traditional empirical models; (2) the proposed diagnostic approach provides an effective way of diagnosing Li-ion battery fault; (3) the proposed prognostic approach can predict the RUL of Li-ion batteries with small error, and has high prediction accuracy; and, (4) the proposed prognostic approach shows that historical failure data can be used instead of a physical failure model in the particle filter

Network Traffic Capturing With Application Tags

Zachytávanie sieťovej prevádzky a jej následná analýza sú užitočné v prípade, že hľadáme problémy v sieti alebo sa chceme dozvedieť viac o aplikáciach a ich sieťovej komunikácii. Táto práca sa zameriava na proces identifikácie sieťových aplikácií, ktoré sú spustené na lokálnom počítači a ich asociáci so zachytenými paketmi. Cieľom projektu je vytvorenie multi-platformového nástroja, ktorý zachytí sieťovú komunikáciu do súboru a pridá k nej aplikačné tagy, čo sú rozpoznané aplikácie a identifikácia ich paketov. Operácie, ktoré môžu byť vykonávané samostatne sú paralelizované pre zrýchlenie spracovania paketov, a teda aj zníženie ich strátovosti. Zdrojová aplikácia je zisťovaná pre všetky (prichádzajúce aj odchádzajúce) pakety. Všetky identifikované aplikácie sú uložené v aplikačnej cache spolu s informáciami o jej soketoch pre ušetrenie času nevyhľadávaním už zistených aplikácií. Je dôležité túto cache pravidelne aktualizovať, pretože komunikujúca aplikácia môže zatvoriť soket v  ľubovoľnom čase. Nakoniec sú získané informácie vložené na koniec pcap-ng súboru ako samostatný pcap-ng blok.Network traffic capture and analysis are useful in case we are looking for problems in our network, or when we want to know more about applications and their network communication. This paper aims on the process of network applications identification that run on the local host and their associating with captured packets. The goal of this project is to design a multi-platform application that captures network traffic and extends the capture file with application tags. Operations that can be done independently are parallelized to speed up packet processing and reduce packet loss. An application is being determined for every (both incoming and outgoing) packet. Records of all identified applications are stored in an application cache with information about its sockets to save time and not to search for already known applications. It's important to update the cache periodically because an application in the cache may close a connection at any time. Finally, gathered information is saved to the end of pcap-ng file as a separate pcap-ng block.

Artificial Intelligence-based Smarter Accessibility Evaluations for Comprehensive and Personalized Assessment

The research focuses on utilizing artificial intelligence (AI) and machine learning (ML) algorithms to enhance accessibility for people with disabilities (PwD) in three areas: public buildings, homes, and medical devices. The overarching goal is to improve the accuracy, reliability, and effectiveness of accessibility evaluation systems by leveraging smarter technologies. For public buildings, the challenge lies in developing an accurate and reliable accessibility evaluation system. AI can play a crucial role by analyzing data, identifying potential barriers, and assessing the accessibility of various features within buildings. By training ML algorithms on relevant data, the system can learn to make accurate predictions about the accessibility of different spaces and help policymakers and architects design more inclusive environments. For private places such as homes, it is essential to have a person-focused accessibility evaluation system. By utilizing machine learning-based intelligent systems, it becomes possible to assess the accessibility of individual homes based on specific needs and requirements. This personalized approach can help identify barriers and recommend modifications or assistive technologies that can enhance accessibility and independence for PwD within their own living spaces. The research also addresses the intelligent evaluation of healthcare devices in the home. Many PwD rely on medical devices for their daily living, and ensuring the accessibility and usability of these devices is crucial. AI can be employed to evaluate the accessibility features of medical devices, provide recommendations for improvement, and even measure their effectiveness in supporting the needs of PwD. Overall, this research aims to enhance the accuracy and reliability of accessibility evaluation systems by leveraging AI and ML technologies. By doing so, it seeks to improve the quality of life for individuals with disabilities by enabling increased independence, fostering social inclusion, and promoting better accessibility in public buildings, private homes, and medical devices

Calcul formel dans la base des polynômes unitaires de Chebyshev

We propose a set of simple and fast algorithms for evaluating and using trigonometric expressions in the form F=∑^{d}_{k=0} f_{k}cos(kπ/n), f_{k}∈ℤ where d≺n fixed. We make use of the monic Chebyshev polynomials as a basis of ℤ[x]. We can perform arithmetic operations (multiplication, division, gcd) on polynomials expressed in a Chebyshev basis (with the same bit-complexity as in the monomial basis), compute the sign of F, evaluate it numerically and compute its minimal polynomial in ℚ[x]. We propose simple and efficient algorithms for computing the minimal polynomial of 2cos(kπ/n) and also the cyclotomic polynomial Φ_n. As an application, we give a method to determine the Chebyshev knot's diagrams C(a,b,c,φ) : x=T_a(t), y=T_b(t), z=T_c(t+φ) which allows to test if a given curve is a Chebyshev knot, and point out all the possible Chebyshev knots coressponding a fixed triple (a,b,c), all of these computings can be done with a good bit complexity.Nous proposons des méthodes simples et efficaces pour manipuler des expressions trigonométriques de la forme F = Pd k=0 fk cos kπ/n, fk ∈ ℤ où d < n fixé. Nous utilisons les polynômes unitaires de Chebyshev qui forment une base de ℤ[x] avec laquelle toutes les opérations arithmétiques peuvent être exécutées aussi rapidement qu’avec le base de monômes, mais également déterminer le signe et une approximation de F, calculer le polynôme minimal de F. Dans ce cadre nous calculons efficacement le polynôme minimal de2 cos π/n et aussi le polynôme cyclotomique φn. Nous appliquons ces méthodes au calcul des diagrammes de nœuds de Chebyshev