Edutainment System for Children with Autism Spectrum Disorder

I have always believed that a qualified designer should design products that can help society; therefore, I have kept my eye on core social issues. My graduate thesis is definitely a great opportunity for me to help minority groups in society through a designer\u27s perspective. As we all have noticed, the number of families that have a child with autism spectrum disorder (ASD) is increasing year by year, and these families are all struggling with difficult issues. That\u27s why I chose ASD as my thesis topic. I want to help children with ASD through design. Because ASD is a broad subject, my project started with plenty of research from books, online articles, and community activities, in order to narrow down the main topic and to decide on a target group. The design process included background research, research of children with ASD and their families, the creation of a problem definition, ideation, design advancement, and specifications. I hope my final design will not only help children with ASD with their physical performance, but also help them with their social and communication skills. All in all, I think my thesis design shows how a product designer can care about individuals with unique needs in the society and originate a new product system that can help resolve issues for them. In the future, I also would like to develop this project into a universal design that can help all children, whether they are challenged or not

NMR longitudinal surface relaxation phenomena of metal oxide nanoparticles in porous media

1H Nuclear Magnetic Resonance (NMR) has long been applied in downhole logging and laboratory analyses to investigate pore size distributions of rocks through correlation with measured relaxation time distributions. However, due to the inherent chemical heterogeneity of pore surfaces in rock, the pore surface relaxivity, which links relaxation time and pore size, varies throughout the pore system. I seek to modify and control the surface relaxivity in natural porous media through coating of paramagnetic nanoparticles so that NMR measurements can be used to compute pore sizes directly. I chose zirconia nanoparticle dispersions with opposite surface charge but similar size. The absence of surface coating on zirconia nanoparticles simplified the calculation of nanoparticle surface relaxivity and interactions between nanoparticles and pore walls. Glass bead packs and Boise sandstone cores were saturated with positively charged zirconia nanoparticle dispersions in which nanoparticles can be electrostatically adsorbed onto pore surfaces, while negatively charged zirconia nanoparticle dispersions were employed as a control group to provide the baseline of nanoparticle retention due to non-electrostatic attraction. When 1.114 vol. % positively charged zirconia nanoparticles dispersion was used to saturate a glass bead pack, 11.6% of the nanoparticles were adsorbed to the bead surfaces and modified the glass bead surface relaxivity. I performed core flushing with DI water, pure acid and alkali, and compared properties of zirconia nanoparticles before and after exposure to Boise sandstone. After 2 pore volumes of core flooding, there was around 3% of negatively charged nanoparticles trapped in Boise sandstone core while around 30% to 40% of positively charged nanoparticles were retained in Boise sandstone cores. The results indicated that besides van der Waals attraction, electrostatic attraction is the driving force for retention of nanoparticles with positive surface charge in sandstone cores. Full coverage of nanoparticles onto sandstone surface was not achieved. The attachment of nanoparticles onto sandstone surface changed the mineral surface relaxivity. After contact with Boise sandstone, nanoparticles themselves exhibited increased relaxivity due to interactions between nanofluids and mineral surface under different pH conditions. The complicated interactions between nanofluids and pore surfaces make it difficult to predict sandstone surface relaxivity with attached nanoparticles. Since adsorption of nanoparticles changed the pore surface relaxivity, it is crucial to know nanoparticle relaxivity and factors that may affect the relaxivity of nanoparticles. T1 values of zirconia nanoparticle dispersions before and after mixing with various Fe(III) solutions were measured and compared. Adsorption of iron onto zirconia nanoparticles was confirmed based on measurements of aqueous Fe remaining in supernatants. Adsorbed iron increases zirconia nanoparticles’ surface relaxivity, as the relaxation rate of zirconia nanoparticles increased with the amount of adsorbed Fe(III). Besides adsorbed paramagnetic species, surface coatings also play a role in changing nanoparticle surface relaxivity. Since organic surface coatings usually give a small value of relaxivity, it is better to use a nanoparticle core with high relaxivity as to investigate the effect of organic surface coatings. I examined the relaxation properties of (3-Aminopropyl)triethoxysilane (APTES) coated Fe3O4 nanoparticles in mixtures with different D2O volume fractions. Fe3O4 nanoparticles exhibited decreased relaxivity with more APTES coating. The presence of D2O affects proton-proton relaxation but not electron-proton relaxation. Comparison of relaxivity of APTES coated Fe3O4 nanoparticles with different coating amount and D2O volume fractions indicated that at relatively high Fe concentration, when electron-proton interaction dominates surface relaxation, hydrogen atoms in the APTES did not significantly alter the surface relaxation mechanism of nanoparticles. At lower Fe3O4 concentration, proton-proton relaxation brought by APTES also played a role in the overall relaxation mechanism on nanoparticle surfaces, as more APTES coating showed lower apparent surface relaxivities with higher D2O volume fractions in the mixture.Petroleum and Geosystems Engineerin

Interference-aware coordinated power allocation in autonomous Wi-Fi environment

Self-managed access points (APs) with growing intelligence can optimize their own performances but pose potential negative impacts on others without energy ef ciency. In this paper, we focus on modeling the coordinated interaction among interest-independent and self-con gured APs, and conduct the power allocation case study in the autonomous Wi-Fi scenario. Speci cally, we build a `coordination Wi-Fi platform (CWP), a public platform for APs interacting with each other. OpenWrt-based APs in the physical world are mapped to virtual agents (VAs) in CWP, which communicate with each other through a standard request-reply process de ned as AP talk protocol (ATP).With ATP, an active interference measurement methodology is proposed re ecting both in-range interference and hidden terminal interference, and the Nash bargaining-based power control is further formulated for interference reductions. CWP is deployed in a real of ce environment, where coordination interactions between VAs can bring a maximum 40-Mb/s throughput improvement with the Nash bargaining-based power control in the multi-AP experiments

Network association strategies for an energy harvesting aided super-wifi network relying on measured solar activity

The super-WiFi network concept has been proposed for nationwide Internet access in the United States. However, the traditional mains power supply is not necessarily ubiquitous in this large-scale wireless network. Furthermore, the non-uniform geographic distribution of both the based-stations and the tele-traffic requires carefully considered user association. Relying on the rapidly developing energy harvesting techniques, we focus our attention on the sophisticated access point (AP) selection strategies conceived for the energy harvesting aided super-WiFi network. Explicitly, we propose a solar radiation model relying on the historical solar activity observation data provided by the University of Queensland, followed by a beneficial radiation parameter estimation method. Furthermore, we formulate both a Markov decision process (MDP) as well as a partially observable MDP (POMDP) for supporting the users’ decisions on beneficially selecting APs. Moreover, we conceive iterative algorithms for implementing our MDP and POMDP-based AP-selection, respectively. Finally, our performance results are benchmarked against a range of traditional decision-making algorithms

Automated Prompting for Non-overlapping Cross-domain Sequential Recommendation

Cross-domain Recommendation (CR) has been extensively studied in recent years to alleviate the data sparsity issue in recommender systems by utilizing different domain information. In this work, we focus on the more general Non-overlapping Cross-domain Sequential Recommendation (NCSR) scenario. NCSR is challenging because there are no overlapped entities (e.g., users and items) between domains, and there is only users' implicit feedback and no content information. Previous CR methods cannot solve NCSR well, since (1) they either need extra content to align domains or need explicit domain alignment constraints to reduce the domain discrepancy from domain-invariant features, (2) they pay more attention to users' explicit feedback (i.e., users' rating data) and cannot well capture their sequential interaction patterns, (3) they usually do a single-target cross-domain recommendation task and seldom investigate the dual-target ones. Considering the above challenges, we propose Prompt Learning-based Cross-domain Recommender (PLCR), an automated prompting-based recommendation framework for the NCSR task. Specifically, to address the challenge (1), PLCR resorts to learning domain-invariant and domain-specific representations via its prompt learning component, where the domain alignment constraint is discarded. For challenges (2) and (3), PLCR introduces a pre-trained sequence encoder to learn users' sequential interaction patterns, and conducts a dual-learning target with a separation constraint to enhance recommendations in both domains. Our empirical study on two sub-collections of Amazon demonstrates the advance of PLCR compared with some related SOTA methods

Topology, Vorticity and Limit Cycle in a Stabilized Kuramoto-Sivashinsky Equation

A noisy stabilized Kuramoto-Sivashinsky equation is analyzed by stochastic decomposition. For values of control parameter for which periodic stationary patterns exist, the dynamics can be decomposed into diffusive and transverse parts which act on a stochastic potential. The relative positions of stationary states in the stochastic global potential landscape can be obtained from the topology spanned by the low-lying eigenmodes which inter-connect them. Numerical simulations confirm the predicted landscape. The transverse component also predicts a universal class of vortex like circulations around fixed points. These drive nonlinear drifting and limit cycle motion of the underlying periodic structure in certain regions of parameter space. Our findings might be relevant in studies of other nonlinear systems such as deep learning neural networks.Comment: Main body: 16 pages, 3 figures; Supplementary: 14 pages, 6 figure

Information-sharing outage-probability analysis of vehicular networks

In vehicular networks, information dissemination/sharing among vehicles is of salient importance. Although diverse mechanisms have been proposed in the existing literature, the related information credibility issues have not been investigated. Against this background, in this paper, we propose a credible information-sharing mechanism capable of ensuring that the vehicles do share genuine road traffic information (RTI). We commence with the outage-probability analysis of information sharing in vehicular networks under both a general scenario and a specific highway scenario. Closed-form expressions are derived for both scenarios, given the specific channel settings. Based on the outage-probability expressions, we formulate the utility of RTI sharing and design an algorithm for promoting the sharing of genuine RTI. To verify our theoretical analysis and the proposed mechanism, we invoke a real-world dataset containing the locations of Beijing taxis to conduct our simulations. Explicitly, our simulation results show that the spatial distribution of the vehicles obeys a Poisson point process (PPP), and our proposed credible RTI sharing mechanism is capable of ensuring that all vehicles indeed do share genuine RTI with each other

Coverage Optimization Strategy for WSN based on Energy-aware

In order to optimize the wireless sensor network coverage, this paper designs a coverage optimization strategy for wireless sensor network (EACS) based on energy-aware. Under the assumption that the geographic positions of sensor nodes are available, the proposed strategy consists of energy-aware and network coverage adjustment. It is restricted to conditions such as path loss, residual capacity and monitored area and according to awareness ability of sensors, it would adjust the monitored area, repair network hole and kick out the redundant coverage. The purpose is to balance the energy distribution of working nodes, reduce the number of “dead” nodes and balance network energy consumption. As a result, the network lifetime is expanded. Simulation results show that: EACS effectively reduces the number of working nodes, improves network coverage, lowers network energy consumption while ensuring the wireless sensor network coverage and connectivity, so as to balance network energy consumption