Simulation of Slightly Degraded Reinforced Concrete Shaft Lining in Thick Topsoil

This paper simulates the degradation of a 20 m-tall shaft lining in thick topsoil at the vertical depth of 500 m. The simulation was carried out under the similarity theory. Three reduced scale models were prepared from the original structure. The first model was subjected to circumferential load and then cured naturally, the second model was subjected to circumferential load and then soaked in corrosive solution, and the third model was subjected to circumferential and vertical loads and then soaked in corrosive solution. Without changing the circumferential load, vertical load was applied to the three models until they failed. The three models were found to have similar failure patterns: the uniform cracks first appeared at the top and bottom of the outer lining; the concrete of the inner lining cracked, and the upper part of the shaft lining was crushed, showing a diagonal shear failure. Model 1 had greater cracking load and ultimate load than model 2 and model 3. This is because the coupling between vertical and circumferential loads induces micro-cracks between the inner and outer linings, and thus accelerates the corrosion of the RC shaft lining in the corrosive solution

Investigating Skin Temperature-Based Overheating in mmWave Smartphones Power and Thermal Models for Optimal Non-Throttling Performance

5G mmWave, as a revolutionary cellular technology, holds monumental potential for innovations in many academic and industrial areas. However, widespread adoption of this technology is hindered by the severe overheating issues experienced by current Commercial Off-The-Shelf (COTS) mmWave smartphones. This study aims to identify the root causes of device skin temperature related throttling during 5G transmission, and to quantify power reduction required to prevent such throttling in a given ambient temperature. The key insight of our paper is leveraging the power model and thermal model of mmWave smartphone to acquire the quantitative relationship among power consumption, ambient temperature and device skin temperature. This approach allows us to determine the extent of power reduction required to prevent throttling under specific ambient temperature conditions

Hyperbolic Deep Neural Networks: A Survey

Recently, there has been a rising surge of momentum for deep representation learning in hyperbolic spaces due to theirhigh capacity of modeling data like knowledge graphs or synonym hierarchies, possessing hierarchical structure. We refer to the model as hyperbolic deep neural network in this paper. Such a hyperbolic neural architecture potentially leads to drastically compact model withmuch more physical interpretability than its counterpart in Euclidean space. To stimulate future research, this paper presents acoherent and comprehensive review of the literature around the neural components in the construction of hyperbolic deep neuralnetworks, as well as the generalization of the leading deep approaches to the Hyperbolic space. It also presents current applicationsaround various machine learning tasks on several publicly available datasets, together with insightful observations and identifying openquestions and promising future directions

Study on Deterioration Mechanism and Prevention and Curing Techniques of an RC Bunker

In the coal mining industrial environment, the materials of RC bunkers suffer from serious aging problems. The cracking and deterioration of concrete and the corrosion of steel reinforcement lead to a degeneration of structural performance and a decline of structural reliability. In this paper, based on on-site detection and coal mining ground industrial environment, the deterioration characteristics of RC bunkers were tested. The investigation involved the apparent characteristics, carbonized depth, compression strength of concrete, reinforcement distribution and cover thickness, corrosion rate and mechanical properties of reinforcement, decline degree of bunkers, and so on. Then a design review check was done. Combined with the above information, the cause and mechanisms of cracking and damage of the structure were studied; finally, the problems of RC bunkers were targeted and improved. The work provides a reference method for repairing a deteriorated RC bunker in an aggressive service environment

Six-degrees-of-freedom test mass readout via optical phase-locking heterodyne interferometry

Accurate position and posture measurements of the freely-falling test mass are crucial for the success of spaceborne gravitational wave detection missions. This paper presents a novel laboratory-developed test mass motion readout that utilizes quadrant photodetectors to measure the translation and tilt of a test mass. Departing from conventional methods like Zeeman effect or AOM frequency shift modulation, the readout system employs the phase locking of two lasers to generate the dual-frequency heterodyne source. Notably, the out-of-loop sensitivity of the phase locking reaches below 30 pm/Hz1/2 within the frequency band of 1 mHz and 10 Hz. The system comprises three measurement interferometers and one reference interferometer, featuring a symmetric design that enables measurements of up to six degrees of freedom based on polarization-multiplexing and differential wavefront sensing. Ground-simulated experimental results demonstrate that the proposed system has achieved a measurement sensitivity of 4 pm/Hz1/2 and 2 nrad/Hz1/2 at 1 Hz, a resolution of 5 nm and 0.1 urad, a range of 200 um and 600 urad, respectively. These findings showcase the system's potential as an alternative method for precisely monitoring the motion of test masses in spaceborne gravitational wave detection missions and other applications requiring accurate positioning and multi-degrees-of-freedom sensing.Comment: 7 pages, 10 figures. arXiv admin note: substantial text overlap with arXiv:2207.0642

A Single Visualization Technique for Displaying Multiple Metabolite-Phenotype Associations.

To assist with management and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity, we need better visualization tools. Using a dataset of several hundred metabolite measures profiled in a cohort of ~1500 individuals sampled from a population-based community study, we performed association analyses with eight demographic and clinical traits and outcomes. We compared frequently used existing graphical approaches with a novel 'rain plot' approach to display the results of these analyses. The 'rain plot' combines features of a raindrop plot and a conventional heatmap to convey results of multiple association analyses. A rain plot can simultaneously indicate effect size, directionality, and statistical significance of associations between metabolites and several traits. This approach enables visual comparison features of all metabolites examined with a given trait. The rain plot extends prior approaches and offers complementary information for data interpretation. Additional work is needed in data visualizations for metabolomics to assist investigators in the process of understanding and convey large-scale analysis results effectively, feasibly, and practically

Study on Deterioration Mechanism and Prevention and Curing Techniques of an RC Bunker

In the coal mining industrial environment, the materials of RC bunkers suffer from serious aging problems. The cracking and deterioration of concrete and the corrosion of steel reinforcement lead to a degeneration of structural performance and a decline of structural reliability. In this paper, based on on-site detection and coal mining ground industrial environment, the deterioration characteristics of RC bunkers were tested. The investigation involved the apparent characteristics, carbonized depth, compression strength of concrete, reinforcement distribution and cover thickness, corrosion rate and mechanical properties of reinforcement, decline degree of bunkers, and so on. Then a design review check was done. Combined with the above information, the cause and mechanisms of cracking and damage of the structure were studied; finally, the problems of RC bunkers were targeted and improved. The work provides a reference method for repairing a deteriorated RC bunker in an aggressive service environment

Statistical Workflow for Feature Selection in Human Metabolomics Data.

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations