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As the world has entered the ‘era of limits’, it is quintessential to prioritize and emphasize the importance of limits to growth. Principles of system thinking along with the system dynamics methodology help delve into the roots of complex system behavior to better predict them. A system is an interconnection of independent but inter-related parts where a positive change in one part of the system may prove to be calamitous for some other part and thereby for the system as a whole.
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Abstract—This study proposes a multiclass model to classify the severity of knee osteoarthritis (KOA) using bioimpedance measurements. The experimental setup considered three types of measurements using eight electrodes: global impedance with adjacent pattern, global impedance with opposite pattern, and direct impedance measurement, which were taken using an electronic device proposed by authors and based on the Analog Devices AD5933 impedance converter. The study comprised 37 participants, 25 with healthy knees and 13 with three different degrees of KOA.
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This dataset is a subset from the Oxford University Our World in Data Covid 19 Dataset. This dataset contains data points collected on an ongoing basis from Johns Hopkins University, Center for Systems Science and Engineering COVID-19 data, OXFORD COVID-19 Government Response Tracker, and European Centre for Disease Control, from January 2020 to present.
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The demand for poultry products will keep rising as the world's population rises. One of the most significant and rapidly expanding economic sectors of India's agriculture sector is poultry. To meet this demand, increasing housing and managing more chicken birds is one potential technique to boost productivity. It will become more challenging for producers to keep track of the health, production, and welfare conditions of all of their birds as a result of this technique, labour shortages, and escalating biosecurity measures.
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This article provides an introduction to the field of datasets, including their types, characteristics, and applications. Datasets refer to collections of data that have been organized for specific purposes. They can come in various forms, including structured data, unstructured data, and semi-structured data. Each type of dataset has its own unique characteristics and uses. For example, structured data typically includes datasets that have been organized into tables and rows, such as spreadsheets or databases, while unstructured data typically includes text, images, and videos.
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Iman Sharafaldin et al. generated the real time network traffic and these are made available at the Canadian Institute of Cyber security Institute website. The team of researchers published the network traffic data and has made the dataset publicly available in both PCAP and CSV formats. The network traffic data is generated during two days. Training Day was on January 12th, 2018 and Testing Day was on March 11th, 2018.
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This Named Entities dataset is implemented by employing the widely used Large Language Model (LLM), BERT, on the CORD-19 biomedical literature corpus. By fine-tuning the pre-trained BERT on the CORD-NER dataset, the model gains the ability to comprehend the context and semantics of biomedical named entities. The refined model is then utilized on the CORD-19 to extract more contextually relevant and updated named entities. However, fine-tuning large datasets with LLMs poses a challenge. To counter this, two distinct sampling methodologies are utilized.
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Experimental measurement data was obtained utilizing RCbenchmark 1780 with full-range PWM signals. Measurements were made for two series of setups.
First series is related to low-voltage setups using the following T-MOTOR components: - motors: MN4014 400Kv, MN5212 340Kv, MN501-S 360Kv, U7 280Kv, MN6007 320Kv, P60 340Kv, MN701-S 280Kv; - ESC: Air 40A, Flame 40A, Flame 70A, Alpha 60A, Flame 100A; - propellers: P17×5.8, P18×6.1, P20×6, P22×6.6, P24×7.2, G26×8.5; - battery: 6-cell (6S) Lithium polymer (LiPo).
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This data provides the traffic data transmission and reception at Wikipedia's six data centers (Eqiad, Codfw, Esams, Ulsfo, Eqsin, and Drmrs) in Wikitech.
- Eqiad : Data center located in Ashburn, USA
- Codfw : Data Center in Carrollton, Texas, USA
- Esams : Data center located in Amsterdam, The Netherlands
- Ulsfo : Data Center located in San Francisco
- Eqsin : Data Center located in Singapore
- Drmrs : Data Center located in Marseille, France.
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The training data consists of data from various faults from five individual configurations, while the testing data is blind and is from one individual configuration of the rock drill. A final validation data set will be from two individual configurations from the rock drill and the labels are blind.
The training data set contains data from 11 different fault classification categories, in which 10 are different failure modes and one class is from the healthy/no fault condition.
Each file follows the naming convention of data_{sensor}{individual impact cycle number}.csv.
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