This study introduces an innovative approach to enhance the utilization of carbon fiber thermosetting composites in advanced structural engineering by addressing the challenges of high manufacturing costs and limited ...
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ISBN:
(纸本)9781510673175;9781510673168
This study introduces an innovative approach to enhance the utilization of carbon fiber thermosetting composites in advanced structural engineering by addressing the challenges of high manufacturing costs and limited production rates. We develop, deploy and test an ML pipeline utilizing PIC-based sensors (SOI technology, 220 nm thick, fabricated at IMEC's MPW). They are based on a Bragg structure, packaged using ball lenses and suitable for operating at 180 degrees Celsius and 5 bar pressure. The focus is on accurately predicting two crucial parameters: Cure time and Temperature Overshoot, vital for determining the process duration and part quality. Using advanced tools and sensors, this study achieves a high prediction accuracy of 98% in millisecond scale while effectively handling the outliers. The ML pipeline allows the real-time process optimization of manufacturing process, minimizing the cost, and providing insights into the quality of the composite part through the in-depth monitoring of the process.
Mobile crowdsensing (MCS) is a new paradigm of data collection with large-scale sensing. A group of mobile users are recruited as workers to move around in a specific region and carry out sensing tasks. A challenging ...
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Mobile crowdsensing (MCS) is a new paradigm of data collection with large-scale sensing. A group of mobile users are recruited as workers to move around in a specific region and carry out sensing tasks. A challenging problem of MCS is task allocation, especially when the MCS platform needs to assign tasks to selected workers among a large user pool and consider mixed spatial and temporal features, including locations and time windows of tasks, and trajectories and arrival time of workers. In this paper, we take into account these features and study the task allocation problem that assigns tasks to workers over time and guarantees the tasks are accomplished before their deadlines. We consider an offline scenario where the MCS platform is informed of all the information of tasks and workers in advance, and an online scenario where the platform does not know the information of workers before they enter the system. For the offline scenario, we provide a cooperative ant colony algorithm with swarm intelligence to approximate the optimal solution in large-scale cases. For the online scenario with incomplete information, we propose several online algorithms, among which the predictive online algorithm exploits historical records of workers and performs the best. Finally, we conduct simulations and evaluate the differences among the online solutions and offline solutions. The results show that the proposed online solutions can approach the offline optimal solution in small-scale cases, and its approximation obtained by the cooperative offline solution in large-scale cases.
Recent technological developments, such as automatic broadcasting systems and computer vision, are enabling cost-effective data gathering, bringing big data analytics to youth sports. In a recent insightful article, B...
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Predicting case outcomes is useful for legal professionals to understand case law, file a lawsuit, raise a defense, or lodge appeals, for instance. However, it is very hard to predict legal decisions since this requir...
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Predicting case outcomes is useful for legal professionals to understand case law, file a lawsuit, raise a defense, or lodge appeals, for instance. However, it is very hard to predict legal decisions since this requires extracting valuable information from myriads of cases and other documents. Moreover, legal system complexity along with a huge volume of litigation make this problem even harder. This paper introduces an approach to predicting Brazilian court decisions, including whether they will be unanimous. Our methodology uses various machine learning algorithms, including classifiers and state-of-the-art Deep Learning models. We developed a working prototype whose F1-score performance is similar to 80.2% by using 4,043 cases from a Brazilian court. To our knowledge, this is the first study to present methods for predicting Brazilian court decision outcomes.
This research explores the application of machine learning techniques to predict Bitcoin price dynamics. Bitcoin, as a decentralized digital currency, has garnered significant attention due to its high volatility and ...
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Objective: Child undernutrition is a global public health problem with serious implications. In this study, we estimate predictive algorithms for the determinants of childhood stunting by using various machine learnin...
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Objective: Child undernutrition is a global public health problem with serious implications. In this study, we estimate predictive algorithms for the determinants of childhood stunting by using various machine learning (ML) algorithms. Design: This study draws on data from the Ethiopian Demographic and Health Survey of 2016. Five ML algorithms including eXtreme gradient boosting, k-nearest neighbours (k-NN), random forest, neural network and the generalised linear models were considered to predict the socio-demographic risk factors for undernutrition in Ethiopia. Setting: Households in Ethiopia. Participants: A total of 9471 children below 5 years of age participated in this study. Results: The descriptive results show substantial regional variations in child stunting, wasting and underweight in Ethiopia. Also, among the five ML algorithms, xgbTree algorithm shows a better prediction ability than the generalised linear mixed algorithm. The best predicting algorithm (xgbTree) shows diverse important predictors of undernutrition across the three outcomes which include time to water source, anaemia history, child age greater than 30 months, small birth size and maternal underweight, among others. Conclusions: The xgbTree algorithm was a reasonably superior ML algorithm for predicting childhood undernutrition in Ethiopia compared to other ML algorithms considered in this study. The findings support improvement in access to water supply, food security and fertility regulation, among others, in the quest to considerably improve childhood nutrition in Ethiopia.
Currently, the constant population growth in Peru leads to the generation of large amounts of solid waste, most of these originate from domestic sources and are collected by municipalities. The inefficiency in solid w...
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The paper begins by analyzing the components of events whose variations have a nature similar to the variations of stock market prices. In particular, it is shown in the paper that such variations can, in general, be ...
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Background: Current practice frequently fails to provide care consistent with the preferences of decisionally-incapacitated patients. It also imposes significant emotional burden on their surrogates. Algorithmic-based...
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Background: Current practice frequently fails to provide care consistent with the preferences of decisionally-incapacitated patients. It also imposes significant emotional burden on their surrogates. Algorithmic-based patient preference predictors (PPPs) have been proposed as a possible way to address these two concerns. While previous research found that patients strongly support the use of PPPs, the views of surrogates are unknown. The present study thus assessed the views of experienced surrogates regarding the possible use of PPPs as a means to help make treatment decisions for decisionally-incapacitated patients. Methods: This qualitative study used semi-structured interviews to determine the views of experienced surrogates [n = 26] who were identified from two academic medical centers and two community hospitals. The primary outcomes were respondents’ overall level of support for the idea of using PPPs and the themes related to their views on how a PPP should be used, if at all, in practice. Results: Overall, 21 participants supported the idea of using PPPs. The remaining five indicated that they would not use a PPP because they made decisions based on the patient’s best interests, not based on substituted judgment. Major themes which emerged were that surrogates, not the patient’s preferences, should determine how treatment decisions are made, and concern that PPPs might be used to deny expensive care or be biased against minority groups. Conclusions: Surrogates, like patients, strongly support the idea of using PPPs to help make treatment decisions for decisionally-incapacitated patients. These findings provide support for developing a PPP and assessing it in practice. At the same time, patients and surrogates disagree over whose preferences should determine how treatment decisions are made, including whether to use a PPP. These findings reveal a fundamental disagreement regarding the guiding principles for surrogate decision-making. Future research is neede
The need of electrical energy saving policies is becoming increasingly important nowadays, specially after the latest electricity market tensions in Europe. The energy consumption impacts directly in our electricity b...
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ISBN:
(纸本)9781665416399
The need of electrical energy saving policies is becoming increasingly important nowadays, specially after the latest electricity market tensions in Europe. The energy consumption impacts directly in our electricity bills, thus optimization strategies are required to decrease its negative consequences on the economy and encouraging the consumption of renewable energies and reducing the carbon footprint. For this reason, non-intrusive load monitoring is gaining more attention as a modern strategy to assist in controlling electricity consumption. Several techniques have been proposed using basic information from current or voltage waveforms. However, the use of harmonic features is not widespread due to the need for a device capable of measuring power signals at a high sampling rate and for the high complexity of the disaggregation problem. The use of new high-end IoT devices like openZmeter, can help on this task. In this paper, several sets of harmonic features (current, active power, and custom combination) used along with a particle filter algorithm are compared. The results show that increasing the number of odd harmonics increases the accuracy of the result provided by the algorithm.
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