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Electrostatic detachment of dust from the lunar surface: Microscopic fluctuations could be the key
No full textWe propose a solution to the fundamental problem and physical mechanism of dust detachment from the lunar surface. We conceptualize that the electrostatic charge fluctuation at microscopic scale could create a sufficient electric field and coulomb force to overcome the dust-surface adhesive force and detach the dust particles. Markovian process is manifested with Monte Carlo scheme to simulate the concept. The simulation establishes the random generation and annihilation of fluctuating charged microscopic spots. The results demonstrate the existence of microscopic charged spots, capable of inducing sufficient electric field and Coulomb force of the order of a few MV/m and 10s of pN, respectively, which creates favorable conditions for lunar dust detachment. This concept fits the gap and put forward a consistent mechanism describing dust dynamics and generation of dusty plasma scenario over Moon
LIPIDS: Learning-based Illumination Planning In Discretized (Light) Space for Photometric Stereo
No full textPhotometric stereo is a powerful technique for estimating per-pixel surface normals from images under varied illumination. Although several methods address photometric stereo with different image (or light) counts ranging from one to two to a hundred, very few focus on learning optimal lighting configuration. Finding an optimal configuration is challenging due to the large number of possible lighting directions. Moreover, exhaustive sampling of all possibilities is impractical due to time and resource constraints. Photometric stereo methods have demonstrated promising performance on existing datasets, which feature limited light directions sparsely sampled from the light space. Therefore, can we optimally utilize these datasets for illumination planning? In this work, we introduce LIPIDS - Learning-based Illumination Planning In Discretized light Space to achieve minimal and optimal lighting configurations for photometric stereo under arbitrary light distribution. We propose a Light Sampling Network (LSNet) that optimizes the lighting direction for a fixed number of lights by minimizing the normal loss through a normal regression network. The learned light configurations can directly estimate surface normals during inference, even using an off-the-shelf photometric stereo method. Extensive qualitative and quantitative analysis on synthetic and real-world datasets show that photometric stereo under learned lighting configurations through LIPIDS either surpasses or is nearly comparable to existing illumination planning methods across different photometric stereo backbones
Investigating the effect of TiB2-based nanostructures on the ballistic resistance of aramid fibers
No full textCharge diffusion in second-order relativistic dissipative hydrodynamics with momentum-dependent relaxation time
No full textThis article explores charge diffusion in relativistic hydrodynamics using kinetic theory with a modified collision kernel that incorporates the momentum dependence of the particle relaxation time. Starting from the Boltzmann equation within the extended relaxation time approximation (ERTA), we derive second-order evolution equations for the dissipative charge current and calculate the associated transport coefficients. The sensitivity of transport coefficients to the particle momentum dependence of the collision time scale of the microscopic interactions in the hot QCD medium is analyzed. For a conformal charge-conserved system, we compare the ERTA-modified transport coefficients for particle diffusion with exact results derived from scalar field theory. With an appropriate parameterization of the relaxation time, we demonstrate the consistency of our analysis and assess the degree of agreement of the results with the exact solutions. The relaxation times for the shear and number diffusion modes are seen to be distinct in general when the momentum dependence of the relaxation time is taken into consideration
Ion-hydration-controlled large osmotic power with arrays of angstrom scale capillaries of vermiculite
No full textIn the osmotic power generation field, reaching the industrial benchmark has been challenging because of the need for capillaries close to the sizes of ions and molecules. Here, we fabricate well-controlled �along-the-capillary� membranes of Na-vermiculite with a capillary size of ? 5 �. They exhibit 1,600 times enhanced conductivity compared with commonly studied �across-the-capillary� membranes. Interestingly, they show a very high cation selectivity of 0.83 for NaCl solutions, which results in large power densities of 9.6 W/m2 and 12.2 W/m2 at concentration gradients of 50 and 1,000, respectively, at 296 K, for a large membrane length of 100 ?m. The power density shows an exponential increase with temperature, reaching 65.1 W/m2 for a concentration gradient of 50 at 333 K. This markedly differs from the classical behavior and indicates the role of ion (de)hydration in enhancing power density, opening possibilities for exploiting such membranes for energy harvesting applications. � 2023 Elsevier B.V., All rights reserved
An active machine learning framework for automatic boxing punch recognition and classification using upper limb kinematics
No full textBoxing punch type classification and kinematic analysis are essential for coaches and athletes, providing critical insights into punch variety and effectiveness, which are vital for performance improvement. Existing methods for punch recognition and classification typically rely on wearable sensor data or video data; however, no fully automated system currently exists. While coaches prefer video-based analysis for its ability to easily visualize punch action errors and refine technique, video-based classification suffers from lower accuracy compared to sensor-based methods due to limitations such as motion blur. Current classification approaches typically employ supervised learning, requiring experts to annotate 70–80% of the data for model training. However, the high sampling frequency of sensor data makes this process time-consuming and challenging, leading to potential fatigue and an increased risk of inconsistent annotations by domain experts. This paper proposes a novel multimodal approach that integrates wearable sensor data and video data for automatic punch recognition and classification. The method also includes automatic segmentation of punch videos, which improves classification accuracy by utilizing both data sources. To reduce labeling effort, we apply a Query by Committee-based active learning technique, significantly decreasing the required labeling effort by one-sixth. Using only 15% of the typical labeling effort, our system achieves 91.41% accuracy for rear-hand punch recognition, 91.91% for lead-hand punch recognition, and 92.33% and 94.56% for punch classification, respectively. This Smart Boxer system aims to enhance punch analytics in boxing, providing valuable insights to improve training, optimize performance, and increase fan engagement with the sport
Mapping the hierarchical environmental transformations of nanoscale UiO-66 metal-organic framework
No full textHeat transfer and material flow modelling for multilayer deposition in laser-assisted directed energy deposition AM process
No full textMultiparametric stochastic model predictive control of indoor air temperature and humidity in buildings
No full textThis paper develops the multiparametric (mp) programming version of the Scenario-Based Stochastic Model Predictive Control (SB-SMPC) method to improve the control performance of MPC under disturbance. The developed mpSB-SMPC controller calculates the control law expressions offline, thereby enabling its deployment through a chip or low-cost hardware instead of a dedicated computer, as in the case of the online SB-SMPC controller. The mpSB-SMPC controller is deployed to a typical residential house system in Delhi, which consists of air conditioning (AC), heater, and air handling unit (AHU) ventilation systems. The controller aims to control the temperature, relative humidity, and occupant comfort indoors by optimally manipulating the AC, heater, and AHU ventilation actuators. Closed-loop operation under the developed mpSB-SMPC controller for different seasons shows improved tracking performance by 2.66% and 0.7% for indoor temperature and relative humidity, respectively, compared to the standard, deterministic case of mpMPC. Moreover, the operation under the developed controller shows significant improvement in peak occupant discomfort reduction by 3.9% for the whole year compared to the mpMPC base case
Things unchanged: online and offline practice of an Indian first-year composition curriculum
No full textThe shape of classroom and learning in higher education has transformed during the COVID-19 pandemic in a way of increasing learners' socialization and connectedness in the virtual setting. This study examines 4 years' work of design, development, and refinement for a two-semester-long undergraduate first-year writing (FYW) course during and after the pandemic, throughout the transition from online to offline mode in India. Undergraduate FYW courses in India have multiple functions such as learning English as a second language and academic/social integration, and they were placed in an optimal position to compensate the lack of physical interactions and socialization during the pandemic. First, we discuss three designing principles that have been constant from the establishment of the curriculum: authentic learning with digital literacy, maximizing socialization, and empowering students' voice. Second, an online survey was conducted to measure students' perception and perspective on their adapted learning environments—online only, mostly online, mostly offline, and offline only conditions. The results indicate that the offline mode was overall more satisfactory for writing practice and learning, while some functions in the online mode provided meaningful support for an interactive writing experience