Optimal integration of a hybrid solar-battery power source into smart home nanogrid with plug-in electric vehicle

Hybrid solar-battery power source is essential in the nexus of plug-in electric vehicle (PEV), renewables, and smart building. This paper devises an optimization framework for efficient energy management and components sizing of a single smart home with home battery, PEV, and potovoltatic (PV) arrays. We seek to maximize the home economy, while satisfying home power demand and PEV driving. Based on the structure and system models of the smart home nanogrid, a convex programming (CP) problem is formulated to rapidly and efficiently optimize both the control decision and parameters of the home battery energy storage system (BESS). Considering different time horizons of optimization, home BESS prices, types and control modes of PEVs, the parameters of home BESS and electric cost are systematically investigated. Based on the developed CP control law in home to vehicle (H2V) mode and vehicle to home (V2H) mode, the home with BESS does not buy electric energy from the grid during the electric price's peak periods

Solving the comfort-retrofit conundrum through post-occupancy evaluation and multi-objective optimisation

Developing appropriate building retrofit strategies is a challenging task. This case study presents a multi-criteria decision-supporting method that suggests optimal solutions and alternative design references with a range of diversity at the early exploration stage in building retrofit. This method employs a practical two-step method to identify critical comfort and energy issues and generate optimised design options with multi-objective optimisation based on a genetic algorithm. The first step is based on a post-occupancy evaluation, which cross-refers benchmarking and correlation and integrates them with non-linear satisfaction theory to extract critical comfort factors. The second step parameterises previous outputs as objectives to conduct building simulation practice. The case study is a typical post-war highly glazed open-plan office in London. The post-occupancy evaluation result identifies direct sunlight glare, indoor temperature, and noise from other occupants as critical comfort factors. The simulation and optimisation extract the optimal retrofit strategies by analysing 480 generated Pareto fronts. The proposed method provides retrofit solutions with a criteria-based filtering method and considers the trade-off between the energy and comfort objectives. The method can be transformed into a design-supporting tool to identify the key comfort factors for built environment optimisation and create sustainability in building retrofit. Practical application : This study suggested that statistical analysis could be integrated with parametric design tools and multi-objective optimisation. It directly links users’ subjective opinions to the final design solutions, suggesting a new method for data-driven generative design. As a quantitative process, the proposed framework could be automated with a program, reducing the human effort in the optimisation process and reducing the reliance on human experience in the design question defining and analysis process. It might also avoid human mistakes, e.g. overlooking some critical factors. During the multi-objective optimisation process, large numbers of design options are generated, and many of them are optimised at the Pareto front. Exploring these options could be a less human effort-intensive process than designing completely new options, especially in the early design exploration phase. Overall, this might be a potential direction for future study in generative design, which greatly reduce the technical obstacle of sustainable design for high building performance.</p

Extracting double-quantum coherence in two-dimensional electronic spectroscopy under pump-probe geometry

Two-dimensional electronic spectroscopy (2DES) can be implemented with different geometries, e.g., BOXCARS, collinear and pump-probe geometries. The pump-probe geometry has its advantage of overlapping only two beams and reducing phase cycling steps. However, its applications are typically limited to observe the dynamics with single-quantum coherence and population, leaving the challenge to measure the dynamics of the double-quantum (2Q) coherence, which reflects the many-body interactions. We propose an experimental technique in 2DES under pump-probe geometry with a designed pulse sequence and the signal processing method to extract 2Q coherence. In the designed pulse sequence with the probe pulse arriving earlier than pump pulses, our measured signal includes the 2Q signal as well as the zero-quantum (0Q) signal. With phase cycling and the data processing using causality enforcement, we extract the 2Q signal. The proposal is demonstrated with the rubidium atoms. And we observe the collective resonances of two-body dipole-dipole interactions of both $D_{1}$ and $D_{2}$ lines.Comment: 7 pages, 5 figure

Information scrambling and entanglement in quantum approximate optimization algorithm circuits

Variational quantum algorithms, which consist of optimal parameterized quantum circuits, are promising for demonstrating quantum advantages in the noisy intermediate-scale quantum (NISQ) era. Apart from classical computational resources, different kinds of quantum resources have their contributions in the process of computing, such as information scrambling and entanglement. Characterizing the relation between complexity of specific problems and quantum resources consumed by solving these problems is helpful for us to understand the structure of VQAs in the context of quantum information processing. In this work, we focus on the quantum approximate optimization algorithm (QAOA), which aims to solve combinatorial optimization problems. We study information scrambling and entanglement in QAOA circuits respectively, and discover that for a harder problem, more quantum resource is required for the QAOA circuit to obtain the solution. We note that in the future, our results can be used to benchmark complexity of quantum many-body problems by information scrambling or entanglement accumulation in the computing process.Comment: 11 pages, 9 figures, Some minor correction

Scalable wavelength-multiplexing photonic reservoir computing

Photonic reservoir computing (PRC) is a special hardware recurrent neural network, which is featured with fast training speed and low training cost. This work shows a wavelength-multiplexing PRC architecture, taking advantage of the numerous longitudinal modes in a Fabry-Perot semiconductor laser. These modes construct connected physical neurons in parallel, while an optical feedback loop provides interactive virtual neurons in series. We experimentally demonstrate a four-channel wavelength-multiplexing PRC, which runs four times faster than the single-channel case. It is proved that the multiplexing PRC exhibits superior performance on the task of signal equalization in an optical fiber communication link. Particularly, this scheme is highly scalable owing to the rich mode resources in Fabry-Perot lasers

Computational inference of mRNA stability from histone modification and transcriptome profiles

Histone modifications play important roles in regulating eukaryotic gene expression and have been used to model expression levels. Here, we present a regression model to systematically infer mRNA stability by comparing transcriptome profiles with ChIP-seq of H3K4me3, H3K27me3 and H3K36me3. The results from multiple human and mouse cell lines show that the inferred unstable mRNAs have significantly longer 3′Untranslated Regions (UTRs) and more microRNA binding sites within 3′UTR than the inferred stable mRNAs. Regression residuals derived from RNA-seq, but not from GRO-seq, are highly correlated with the half-lives measured by pulse-labeling experiments, supporting the rationale of our inference. Whereas, the functions enriched in the inferred stable and unstable mRNAs are consistent with those from pulse-labeling experiments, we found the unstable mRNAs have higher cell-type specificity under functional constraint. We conclude that the systematical use of histone modifications can differentiate non-expressed mRNAs from unstable mRNAs, and distinguish stable mRNAs from highly expressed ones. In summary, we represent the first computational model of mRNA stability inference that compares transcriptome and epigenome profiles, and provides an alternative strategy for directing experimental measurements

On second-order combinatorial algebraic time-delay interferometry

Inspired by the combinatorial algebraic approach proposed by Dhurandhar {\it et al.}, we propose two novel classes of second-generation time-delay interferometry (TDI) solutions and their further generalization. The primary strategy of the algorithm is to enumerate specific types of residual laser frequency noise associated with second-order commutators in products of time-displacement operators. The derivations are based on analyzing the delay time residual when expanded in time derivatives of the armlengths order by order. It is observed that the solutions obtained by such a scheme are primarily captured by the geometric TDI approach and therefore possess an intuitive interpretation. Nonetheless, the fully-symmetric Sagnac and Sagnac-inspired combinations inherit the properties from the original algebraic approach, and subsequently lie outside of the scope of geometric TDI. We explicitly show that novel solutions, distinct from existing ones in terms of both algebraic structure and sensitivity curve, are encountered. Moreover, at its lowest order, the solution is furnished by commutators of relatively compact form. Besides the original Michelson-type solution, we elaborate on other types of solutions such as the Monitor, Beacon, Relay, Sagnac, fully-symmetric Sagnac, and Sagnac-inspired ones. The average response functions, residual noise power spectral density, and sensitivity curves are evaluated for the obtained solutions. Also, the relations between the present scheme and other existing algorithms are discussed.Comment: 22 pages, 4 figure

Multi-atlas based representations for Alzheimer's disease diagnosis: Multi-Atlas Based Alzheimer's Disease Diagnosis

Brain morphometry based classification from magnetic resonance (MR) acquisitions has been widely investigated in the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). In the literature, a morphometric representation of brain structures is obtained by spatial normalization of each image into a common space (i.e., a pre-defined atlas) via non-linear registration, thus the corresponding regions in different brains can be compared. However, representations generated from one single atlas may not be sufficient to reveal the underlying anatomical differences between the groups of disease-affected patients and normal controls (NC). In this article, we propose a different methodology, namely the multi-atlas based morphometry, which measures morphometric representations of the same image in different spaces of multiple atlases. Representations generated from different atlases can thus provide the complementary information to discriminate different groups, and also reduce the negative impacts from registration errors. Specifically, each studied subject is registered to multiple atlases, where adaptive regional features are extracted. Then, all features from different atlases are jointly selected by a correlation and relevance based scheme, followed by final classification with the support vector machine (SVM). We have evaluated the proposed method on 459 subjects (97 AD, 117 progressive-MCI (p-MCI), 117 stable-MCI (s-MCI), and 128 NC) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and achieved 91.64% for AD/NC classification and 72.41% for p-MCI/s-MCI classification. Our results clearly demonstrate that the proposed multi-atlas based method can significantly outperform the previous single-atlas based methods