The Seven Sins as told in verses by Edmund Spencer

https://digitalcommons.risd.edu/specialcollections_bookcontest9th2023/1002/thumbnail.jp

Ionospheric clutter models for high frequency surface wave radar

High frequency surface wave radar (HFSWR), operating at frequencies between 3 and 30 MHz, has long been employed as an important ocean remote sensing device. These high frequency (HF) radars can provide accurate and real-time information for sea state monitoring and hard-target detection, which is greatly beneficial for planning and executing oceanographic projects, search and rescue events, and other commercial marine activities. Ideally, in HFSWR operation, the radio waves may be coupled with ocean waves and propagate along the curvature of the ocean surface with ranges well beyond 200 km. However, during transmission, a portion of the radar radiation may travel upwards to the ionosphere from the transmitting antenna. This may be partially reflected back to the receiving antennas directly (vertical propagation) or via the ocean surface (mixed-path propagation). This ionospheric clutter may significantly impact the performance of HFSWR. Furthermore, the high intensity and random behaviour of the ionospheric spectral contamination of radar echoes make the suppression of this kind of clutter challenging. In this thesis, comprehensive theoretical models of the ionospheric clutter are investigated. The physical influences of the ionospheric electron density on HF radar Doppler spectra are taken into account in the ionospheric reflection coefficient. Next, based on previous modeling involving the scattering of HF electromagnetic radiation from the ocean surface and a first-order mixed-path propagation theory, the second-order received electric field for mixed-path propagation is derived for a monostatic radar configuration. This is done by considering the reflection from the ionosphere and scattering on the ocean surface with second-order sea waves. Then, the field integrals are taken to the time domain, with the source field being that of a vertically polarized pulsed dipole antenna. Subsequently, the second-order received power model is developed by assuming that the ocean surface and the ionosphere may be modeled as stochastic processes. The ionospheric clutter model including a pulsed radar source is further investigated for the case of vertical propagation for a monostatic configuration and mixed-path propagation for a bistatic configuration. Next, a theoreticalmixed-path propagationmodel is developed by involving a frequencymodulated continuous waveform (FMCW) radar source. In order to investigate the power spectrum of the resulting ionospheric clutter and its relative intensity to that of the first-order ocean clutter, the normalized ionospheric clutter power is simulated. Numerical simulation results are provided to indicate the performance of the ionospheric clutter under a variety of radar operating parameters, ionospheric conditions and sea states

Use of Structural Equation Modelling and Neural Network to Analyse Shared Parking Choice Behaviour

The shared parking mode represents a feasible solution to the persistent problem of parking scarcity in urban areas. This paper aims to examine the shared parking choice behaviours using a combination of structural equation modelling (SEM) and neural network, taking into account both the parking location characteristics and the travellers’ characteristics. Data were collected from a commercial district in Nanjing, China, through an online questionnaire survey covering 11 factors affecting shared parking choice. The method involved two steps: firstly, SEM was applied to examine the influence of these factors on shared parking choice. Following this, the seven factors with the strongest correlation to shared parking choice were used to train a neural network model for shared parking prediction. This SEM-informed model was found to outperform a neural network model trained on all eleven factors across precision, recall, accuracy, F1 and AUC metrics. The research concluded that the selected factors significantly influence shared parking choice, reinforcing the hypothesis regarding the importance of parking location and traveller characteristics. These findings provide valuable insights to support the effective implementation and promotion of shared parking

A Book of Happiness

https://digitalcommons.risd.edu/bookcontest_supplementalcontent/1023/thumbnail.jp

Adaptive state observer event-triggered consensus control for multi-agent systems with actuator failures

An adaptive neural network event-triggered consensus control method incorporating a state observer was proposed for a class of uncertain nonlinear multi-agent systems (MASs) with actuator failures. To begin, a state observer was constructed in an adaptive backstepping framework to estimate the MASs' unmeasurable states, and a radial basis function neural network (RBFNN) was employed to approximate the unknown nonlinear function of MASs. Meanwhile, to reduce the impact of actuator failure on the performance of MASs, the adaptive event-triggered mechanism (ETM) was designed to dynamically compensate for actuator failures, which alleviated the communication burden among individual agents by decreasing the update frequency of the control signals. Furthermore, all followers can track the leader's output signal with the synchronization errors converging to zero. Finally, simulation examples were used to verify the effectiveness of the proposed control strategy

Enhancing Deep Knowledge Tracing with Auxiliary Tasks

Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recent studies have applied multiple types of deep neural networks to solve the KT problem. However, there are two important factors in real-world educational data that are not well represented. First, most existing works augment input representations with the co-occurrence matrix of questions and knowledge components\footnote{\label{ft:kc}A KC is a generalization of everyday terms like concept, principle, fact, or skill.} (KCs) but fail to explicitly integrate such intrinsic relations into the final response prediction task. Second, the individualized historical performance of students has not been well captured. In this paper, we proposed \emph{AT-DKT} to improve the prediction performance of the original deep knowledge tracing model with two auxiliary learning tasks, i.e., \emph{question tagging (QT) prediction task} and \emph{individualized prior knowledge (IK) prediction task}. Specifically, the QT task helps learn better question representations by predicting whether questions contain specific KCs. The IK task captures students' global historical performance by progressively predicting student-level prior knowledge that is hidden in students' historical learning interactions. We conduct comprehensive experiments on three real-world educational datasets and compare the proposed approach to both deep sequential KT models and non-sequential models. Experimental results show that \emph{AT-DKT} outperforms all sequential models with more than 0.9\% improvements of AUC for all datasets, and is almost the second best compared to non-sequential models. Furthermore, we conduct both ablation studies and quantitative analysis to show the effectiveness of auxiliary tasks and the superior prediction outcomes of \emph{AT-DKT}.Comment: Accepted at WWW'23: The 2023 ACM Web Conference, 202

Pipeline for precise insoluble matrisome coverage in tissue extracellular matrices

The extracellular matrix (ECM) is assembled by hundreds of proteins orchestrating tissue patterning and surrounding cell fates via the mechanical–biochemical feedback loop. Aberrant ECM protein production or assembly usually creates pathological niches eliciting lesions that mainly involve fibrogenesis and carcinogenesis. Yet, our current knowledge about the pathophysiological ECM compositions and alterations in healthy or diseased tissues is limited since the methodology for precise insoluble matrisome coverage in the ECM is a “bottleneck.” Our current study proposes an enhanced sodium dodecyl sulfonate (E-SDS) workflow for thorough tissue decellularization and an intact pipeline for the accurate identification and quantification of highly insoluble ECM matrisome proteins. We tested this pipeline in nine mouse organs and highlighted the full landscape of insoluble matrisome proteins in the decellularized ECM (dECM) scaffolds. Typical experimental validations and mass spectrometry (MS) analysis confirmed very little contamination of cellular debris remaining in the dECM scaffolds. Our current study will provide a low-cost, simple, reliable, and effective pipeline for tissue insoluble matrisome analysis in the quest to comprehend ECM discovery proteomic studies

Prioritizing protein complexes implicated in human diseases by network optimization.

BACKGROUND: The detection of associations between protein complexes and human inherited diseases is of great importance in understanding mechanisms of diseases. Dysfunctions of a protein complex are usually defined by its member disturbance and consequently result in certain diseases. Although individual disease proteins have been widely predicted, computational methods are still absent for systematically investigating disease-related protein complexes. RESULTS: We propose a method, MAXCOM, for the prioritization of candidate protein complexes. MAXCOM performs a maximum information flow algorithm to optimize relationships between a query disease and candidate protein complexes through a heterogeneous network that is constructed by combining protein-protein interactions and disease phenotypic similarities. Cross-validation experiments on 539 protein complexes show that MAXCOM can rank 382 (70.87%) protein complexes at the top against protein complexes constructed at random. Permutation experiments further confirm that MAXCOM is robust to the network structure and parameters involved. We further analyze protein complexes ranked among top ten for breast cancer and demonstrate that the SWI/SNF complex is potentially associated with breast cancer. CONCLUSIONS: MAXCOM is an effective method for the discovery of disease-related protein complexes based on network optimization. The high performance and robustness of this approach can facilitate not only pathologic studies of diseases, but also the design of drugs targeting on multiple proteins