In recent years, molecular representation learning has emerged as a key area of focus in various chemical tasks. However, many existing models fail to fully consider the geometric information of molecular structures, ...
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This study investigates residential electricity and gas demand in Iraq using smart meter data from 15,000 households between 2019 and 2023. The primary objective is to analyze temporal energy consumption patterns, foc...
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This study investigates residential electricity and gas demand in Iraq using smart meter data from 15,000 households between 2019 and 2023. The primary objective is to analyze temporal energy consumption patterns, focusing on the impact of environmental, behavioral, and household-specific factors. The results show that electricity consumption peaks during the afternoon, particularly between 12:00 and 14:00, with average usage reaching 2.3 to 2.6 kWh, while gas consumption increases in the winter months, especially in the early morning hours, driven by heating and cooking activities. The analysis of weekend versus weekday consumption reveals a 6.4% increase in electricity and a 3.1% rise in gas usage on weekends, indicating shifts in behavioral energy usage patterns. The study finds that predicted values closely match observed data, with a deviation of only 5% for electricity and 7% for gas, showcasing high model accuracy. The concept of "variability" is clarified as fluctuations in demand across different times of the day, which were reduced in households with efficient appliances, as identified from household energy audits linked to smart meters. The dataset also provides insights into the use of energy-efficient appliances, collected from utility-linked surveys and integrated household registration data. The findings offer valuable insights into demand-side energy management and are particularly relevant to urban Iraqi households but may also inform demand forecasting in similar regions with comparable climates and energy use behaviors.
Conformable robotic systems are attractive for applications in which they can be used to actuate structures with large surface areas, to provide forces through wearable garments, or to realize autonomous robotic syste...
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—A key challenge in robotics is to create efficient methods for grasping objects with diverse shapes, sizes, poses, and properties. Grasping with hand-like end effectors often requires careful selection of hand orien...
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There is a growing demand for large-scale Synthetic Aperture Sonar (SAS) datasets. This demand stems from data-driven applications such as Automatic Target Recognition (ATR) [1]-[3], segmentation [4] and oceanographic...
There is a growing demand for large-scale Synthetic Aperture Sonar (SAS) datasets. This demand stems from data-driven applications such as Automatic Target Recognition (ATR) [1]-[3], segmentation [4] and oceanographic research of the seafloor, simulation for sensor prototype development and calibration [5], and even potential higher level tasks such as motion estimation [6] and micronavigation [7]. Unfortunately, the acquisition of SAS data is bottlenecked by the costly deployment of SAS imaging systems, and even when data acquisition is possible, the data is often skewed towards containing barren seafloor rather than objects of interest. This skew introduces a data imbalance problem wherein a dataset can have as much as a 1000-to-1 ratio of seafloor background to object-of-interest SAS image chips.
We report the realization of room-temperature, stimulated-emission in Er-doped-GaN multiple-quantum-wells at the 1.5-µm. Structures were grown by MOCVD and lasing was confirmed by threshold-behaviors of emission-...
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During haptic interaction with touched objects, contact with the skin elicits mechanical signals that propagate rapidly to distances removed from the location of contact. Prior research has shown that these touch elic...
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