Derivation of Electroweak Chiral Lagrangian from One Family Technicolor Model

Based on previous studies deriving the chiral Lagrangian for pseudo scalar mesons from the first principle of QCD in the path integral formalism, we derive the electroweak chiral Lagrangian and dynamically compute all its coefficients from the one family technicolor model. The numerical results of the $p^4$ order coefficients obtained in this paper are proportional to the technicolor number $N_{\rm TC}$ and the technifermion number $N_{\rm TF}$, which agrees with the arguments in previous works, and which confirms the reliability of this dynamical computation.Comment: 6 page

First-principles study of native point defects in Bi2Se3

Using first-principles method within the framework of the density functional theory, we study the influence of native point defect on the structural and electronic properties of Bi$_2$Se$_3$. Se vacancy in Bi$_2$Se$_3$ is a double donor, and Bi vacancy is a triple acceptor. Se antisite (Se$_{Bi}$) is always an active donor in the system because its donor level ($\varepsilon$(+1/0)) enters into the conduction band. Interestingly, Bi antisite(Bi$_{Se1}$) in Bi$_2$Se$_3$ is an amphoteric dopant, acting as a donor when $\mu$$_e$$<$0.119eV (the material is typical p-type) and as an acceptor when $\mu$$_e$$>$0.251eV (the material is typical n-type). The formation energies under different growth environments (such as Bi-rich or Se-rich) indicate that under Se-rich condition, Se$_{Bi}$ is the most stable native defect independent of electron chemical potential $\mu$$_e$. Under Bi-rich condition, Se vacancy is the most stable native defect except for under the growth window as $\mu$$_e$$>$0.262eV (the material is typical n-type) and $\Delta$$\mu$$_{Se}$$<$-0.459eV(Bi-rich), under such growth windows one negative charged Bi$_{Se1}$ is the most stable one.Comment: 7 pages, 4 figure

Testing mechanisms of compensatory fitness of dioecy in a cosexual world

Questions: All else being equal, populations of dioecious species with a 50:50 sex ratio have only half the effective reproductive population size of bisexual species of equal abundance. Consequently, there is a need to explain how dioecious and bisexual species coexist. Increased mean individual seed mass, fecundity, and population density have all been proposed as attributes of unisexual individuals or populations that may contribute to the persistence or resilience of dioecious species. To date, no studies have compared sympatric dioecious and cosexual species with respect to all three components of fitness. In this study, we sought evidence for these compensatory advantages (higher seed mass, greater seed production per unit basal area, and higher population density) in dioecious species. Location: Five 20–25&nbsp;ha forest dynamic plots spanning a latitudinal gradient in China, including two temperate, two subtropical, and one tropical forest. Methods: We used a phylogenetically corrected generalized linear modelling approach to assess the phylogenetic dependence and joint evolution of sexual system, seed mass and production, and ecological abundances among 48–333 species and 32,568–136,237 individuals per forest. Results: Across all five forests, we detected no consistent advantage for dioecious relative to sympatric cosexual species with respect to mean individual seed mass, seed production or the density of stems in any size class. Conclusions: Our study suggests that seed traits may provide compensatory mechanisms in some forests, but most often the coexistence of sexual systems cannot be explained by advantages of dioecy related to seed quality and demographic parameters. Future investigations of the factors that promote coexistence may increase our understanding by expanding the search to include attributes such as lifespan and tolerance or resistance to herbivores

Resolving and Tuning Mechanical Anisotropy in Black Phosphorus via Nanomechanical Multimode Resonance Spectromicroscopy

Black phosphorus (P) has emerged as a layered semiconductor with a unique crystal structure featuring corrugated atomic layers and strong in-plane anisotropy in its physical properties. Here, we demonstrate that the crystal orientation and mechanical anisotropy in free-standing black P thin layers can be precisely determined by spatially resolved multimode nanomechanical resonances. This offers a new means for resolving important crystal orientation and anisotropy in black P device platforms in situ beyond conventional optical and electrical calibration techniques. Furthermore, we show that electrostatic-gating-induced straining can continuously tune the mechanical anisotropic effects on multimode resonances in black P electromechanical devices. Combined with finite element modeling (FEM), we also determine the Young's moduli of multilayer black P to be 116.1 and 46.5 GPa in the zigzag and armchair directions, respectively.Comment: Main Text: 13 Pages, 4 Figures; Supplementary Information: 5 Pages, 2 Figures, 2 Table

The Effect of Classroom Environment on Satisfaction and Performance: Towards IoT-Sustainable Space

The physical classroom environment includes the overall design and layout facilities that are provided in a classroom. Classroom facilities should be organised to maximise the satisfaction and performance of students. With the increased demand of well-equipped classrooms, upgrades in new high-technology need to be adopted to enable the optimisation of the students’ perceptions and behaviours. A number of studies have investigated the impact of classrooms in high schools. However, few studies have investigated the impact of the physical classroom environment in university settings. This paper examines the impact of the physical classroom environment on students’ satisfaction and performance in a university setting. A total of 173 responses from students were obtained regarding their perceptions of five physical classroom environment factors, namely, classroom layout, noise, temperature, lighting and colour. The questionnaire results showed that students have different demands for the physical classroom environment. Using the guidance of the person-environment fit theory, a smart IoT-enabled classroom has been proposed. The results of this study could be used by managers who make capital decisions on classroom construction upgrades and facility managers who aim to improve the satisfaction and performance of students in higher education institutions

Cost-aware compressive sensing for networked sensing systems

Compressive Sensing is a technique that can help reduce the sampling rate of sensing tasks. In mobile crowdsensing applications or wireless sensor networks, the resource burden of collecting samples is often a major concern. Therefore, compressive sensing is a promising approach in such scenarios. An implicit assumption underlying compressive sensing - both in theory and its applications - is that every sample has the same cost: its goal is to simply reduce the number of samples while achieving a good recovery accuracy. In many networked sensing systems, however, the cost of obtaining a specific sample may depend highly on the location, time, condition of the device, and many other factors of the sample. In this paper, we study compressive sensing in situations where different samples have different costs, and we seek to find a good trade-off between minimizing the total sample cost and the resulting recovery accuracy. We design CostAware Compressive Sensing (CACS), which incorporates the cost-diversity of samples into the compressive sensing framework, and we apply CACS in networked sensing systems. Technically, we use regularized column sum (RCS) as a predictive metric for recovery accuracy, and use this metric to design an optimization algorithm for finding a least cost randomized sampling scheme with provable recovery bounds. We also show how CACS can be applied in a distributed context. Using traffic monitoring and air pollution as concrete application examples, we evaluate CACS based on large-scale real-life traces. Our results show that CACS achieves significant cost savings, outperforming natural baselines (greedy and random sampling) by up to 4x

More with less: Lowering user burden in mobile crowdsourcing through compressive sensing

Mobile crowdsourcing is a powerful tool for collecting data of various types. The primary bottleneck in such systems is the high burden placed on the user who must manually collect sensor data or respond in-situ to simple queries (e.g., experience sampling studies). In this work, we present Compressive CrowdSensing (CCS) - a framework that enables compressive sensing techniques to be applied to mobile crowdsourcing scenarios. CCS enables each user to provide significantly reduced amounts of manually collected data, while still maintaining acceptable levels of overall accuracy for the target crowd-based system. Näive applications of compressive sensing do not work well for common types of crowdsourcing data (e.g., user survey responses) because the necessary correlations that are exploited by a sparsifying base are hidden and non-Trivial to identify. CCS comprises a series of novel techniques that enable such challenges to be overcome. We evaluate CCS with four representative large-scale datasets and find that it is able to outperform standard uses of compressive sensing, as well as conventional approaches to lowering the quantity of user data needed by crowd systems