Carppet – An AR Tour Guide System in Autonomous Car

Over the last decade, modern transportation systems have evolved to a considerably larger scope and higher complexity, brought by a wave of innovation in technologies and a revolutionary shift in how mobility is perceived and realized – giving rise to a strong orientation towards services. This thesis imagines a time when cars can drive fully autonomous. Drivers no longer need to drive. So, what else can we offer passengers during the time when they are sitting in the car? The purpose of this project is to introduce a concept of mainly using Augmented Reality and AI recognition to create a virtual tour guide in autonomous cars that will provide educational knowledge and immediate assistance through AR windshields and side windows. Imagine a time when you are sitting in an autonomous car, and you get to know your surroundings through AR windshield and side windows. People can now explore the world as if a real tour guide is always around

Predicting the epidemic threshold of the susceptible-infected-recovered model

Researchers have developed several theoretical methods for predicting epidemic thresholds, including the mean-field like (MFL) method, the quenched mean-field (QMF) method, and the dynamical message passing (DMP) method. When these methods are applied to predict epidemic threshold they often produce differing results and their relative levels of accuracy are still unknown. We systematically analyze these two issues---relationships among differing results and levels of accuracy---by studying the susceptible-infected-recovered (SIR) model on uncorrelated configuration networks and a group of 56 real-world networks. In uncorrelated configuration networks the MFL and DMP methods yield identical predictions that are larger and more accurate than the prediction generated by the QMF method. When compared to the 56 real-world networks, the epidemic threshold obtained by the DMP method is closer to the actual epidemic threshold because it incorporates full network topology information and some dynamical correlations. We find that in some scenarios---such as networks with positive degree-degree correlations, with an eigenvector localized on the high $k$-core nodes, or with a high level of clustering---the epidemic threshold predicted by the MFL method, which uses the degree distribution as the only input parameter, performs better than the other two methods. We also find that the performances of the three predictions are irregular versus modularity

Low energy exciton states in a nanoscopic semiconducting ring

We consider an effective mass model for an electron-hole pair in a simplified confinement potential, which is applicable to both a nanoscopic self-assembled semiconducting InAs ring and a quantum dot. The linear optical susceptibility, proportional to the absorption intensity of near-infrared transmission, is calculated as a function of the ring radius $$. Compared with the properties of the quantum dot corresponding to the model with a very small radius $R_0$, our results are in qualitative agreement with the recent experimental measurements by Pettersson {\it et al}.Comment: 4 pages, 4 figures, revised and accepted by Phys. Rev.

Bis(μ-3-nitro­phthalato-κ2 O 1:O 2)bis­[(thio­urea-κS)zinc] dihydrate

In the title complex, [Zn2(C8H3NO6)2(CH4N2S)4]·2H2O, the carboxyl­ate groups of the 3-nitro­phthalate ligands coordinate in a bis-monodentate mode to the ZnII cations. This results in the formation of a centrosymmetric dimer containing two ZnII cations with distorted tetra­hedral geometries provided by the O atoms of two different 3-nitro­phthalate dianions and the S atoms of two non-equivalent coordinated thio­urea mol­ecules. The crystal structure exhibits N—H⋯O and O—H⋯O hydrogen bonds which link the dimers into a three-dimensional network

Exploiting the Power of Human-Robot Collaboration: Coupling and Scale Effects in Bricklaying

As an important contributor to GDP growth, the construction industry is suffering from labor shortage due to population ageing, COVID-19 pandemic, and harsh environments. Considering the complexity and dynamics of construction environment, it is still challenging to develop fully automated robots. For a long time in the future, workers and robots will coexist and collaborate with each other to build or maintain a facility efficiently. As an emerging field, human-robot collaboration (HRC) still faces various open problems. To this end, this pioneer research introduces an agent-based modeling approach to investigate the coupling effect and scale effect of HRC in the bricklaying process. With multiple experiments based on simulation, the dynamic and complex nature of HRC is illustrated in two folds: 1) agents in HRC are interdependent due to human factors of workers, features of robots, and their collaboration behaviors; 2) different parameters of HRC are correlated and have significant impacts on construction productivity (CP). Accidentally and interestingly, it is discovered that HRC has a scale effect on CP, which means increasing the number of collaborated human-robot teams will lead to higher CP even if the human-robot ratio keeps unchanged. Overall, it is argued that more investigations in HRC are needed for efficient construction, occupational safety, etc.; and this research can be taken as a stepstone for developing and evaluating new robots, optimizing HRC processes, and even training future industrial workers in the construction industry

Constructing mutually unbiased bases from unextendible maximally entangled bases

We study mutually unbiased bases (MUBs) in which all the bases are unextendible maximally entangled ones. We first present a necessary and sufficient condition of constructing a pair of MUBs in $C^2 \otimes C^4$. Based on this condition, an analytical and necessary condition for constructing MUBs is given. Moreover we illustrate our approach by some detailed examples in $C^2 \otimes C^4$. The results are generalized to $C^2 \otimes C^d$ $(d\geq 3)$ and a concrete example in $C^2 \otimes C^8$ is given.Comment: 14 page

Molecular Detection of Circulating Tumor Cells With Multiple mRNA Markers by Genechip for Colorectal Cancer Early Diagnosis and Prognosis Prediction

Early detection is the hallmark of successful cancer treatment. Evidence is accumulating that primary cancers begin shedding neoplastic cells into the circulation at an early stage. To date, many different methodologies have been used for the detection of circulating tumor cells (CTCs) with variable sensitivity and specificity. In many studies, including patients with different clinical stages of colorectal cancer, the detection of CTCs in early and/or metastatic colorectal cancer has been shown to correlate with unfavorable clinical outcome. However, a high-sensitivity and high-throughput method for the detection of CTCs is currently lacking. Here, we introduce a high-sensitivity genechip analysis method from a colorimetric membrane array to a weighted enzymatic chip array in order to detect the CTC-related multiple mRNA markers derived from colorectal cancer patients as a convenient and practical tool for early diagnosis and prognosis prediction