Optimization of cool roof and night ventilation in office buildings: A case study in Xiamen, China

Increasing roof albedo (using a “cool” roof) and night ventilation are passive cooling technologies that can reduce the cooling loads in buildings, but existing studies have not comprehensively explored the potential benefits of integrating these two technologies. This study combines an experiment in the summer and transition seasons with an annual simulation so as to evaluate the thermal performance, energy savings and thermal comfort improvement that could be obtained by coupling a cool roof with night ventilation. A holistic approach integrating sensitivity analysis and multi-objective optimization is developed to explore key design parameters (roof albedo, night ventilation air change rate, roof insulation level and internal thermal mass level) and optimal design options for the combined application of the cool roof and night ventilation. The proposed approach is validated and demonstrated through studies on a six-storey office building in Xiamen, a cooling-dominated city in southeast China. Simulations show that combining a cool roof with night ventilation can significantly decrease the annual cooling energy consumption by 27% compared to using a black roof without night ventilation and by 13% compared to using a cool roof without night ventilation. Roof albedo is the most influential parameter for both building energy performance and indoor thermal comfort. Optimal use of the cool roof and night ventilation can reduce the annual cooling energy use by 28% during occupied hours when air-conditioners are on and reduce the uncomfortable time slightly during occupied hours when air-conditioners are off

Gait Recognition from Motion Capture Data

Gait recognition from motion capture data, as a pattern classification discipline, can be improved by the use of machine learning. This paper contributes to the state-of-the-art with a statistical approach for extracting robust gait features directly from raw data by a modification of Linear Discriminant Analysis with Maximum Margin Criterion. Experiments on the CMU MoCap database show that the suggested method outperforms thirteen relevant methods based on geometric features and a method to learn the features by a combination of Principal Component Analysis and Linear Discriminant Analysis. The methods are evaluated in terms of the distribution of biometric templates in respective feature spaces expressed in a number of class separability coefficients and classification metrics. Results also indicate a high portability of learned features, that means, we can learn what aspects of walk people generally differ in and extract those as general gait features. Recognizing people without needing group-specific features is convenient as particular people might not always provide annotated learning data. As a contribution to reproducible research, our evaluation framework and database have been made publicly available. This research makes motion capture technology directly applicable for human recognition.Comment: Preprint. Full paper accepted at the ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), special issue on Representation, Analysis and Recognition of 3D Humans. 18 pages. arXiv admin note: substantial text overlap with arXiv:1701.00995, arXiv:1609.04392, arXiv:1609.0693

Cluster analysis of multiplex ligation-dependent probe amplification data in choroidal melanoma.

PurposeTo determine underlying correlations in multiplex ligation-dependent probe amplification (MLPA) data and their significance regarding survival following treatment of choroidal melanoma (CM).MethodsMLPA data were available for 31 loci across four chromosomes (1p, 3, 6, and 8) in tumor material obtained from 602 patients with CM treated at the Liverpool Ocular Oncology Center (LOOC) between 1993 and 2012. Data representing chromosomes 3 and 8q were analyzed in depth since their association with CM patient survival is well-known. Unsupervised k-means cluster analysis was performed to detect latent structure in the data set. Principal component analysis (PCA) was also performed to determine the intrinsic dimensionality of the data. Survival analyses of the identified clusters were performed using Kaplan-Meier (KM) and log-rank statistical tests. Correlation with largest basal tumor diameter (LTD) was investigated.ResultsChromosome 3: A two-cluster (bimodal) solution was found in chromosome 3, characterized by centroids at unilaterally normal probe values and unilateral deletion. There was a large, significant difference in the survival characteristics of the two clusters (log-rank, p<0.001; 5-year survival: 80% versus 40%). Both clusters had a broad distribution in LTD, although larger tumors were characteristically in the poorer outcome group (Mann-Whitney, p<0.001). Threshold values of 0.85 for deletion and 1.15 for gain optimized the classification of the clusters. PCA showed that the first principal component (PC1) contained more than 80% of the data set variance and all of the bimodality, with uniform coefficients (0.28±0.03). Chromosome 8q: No clusters were found in chromosome 8q. Using a conventional threshold-based definition of 8q gain, and in conjunction with the chromosome 3 clusters, three prognostic groups were identified: chromosomes 3 and 8q both normal, either chromosome 3 or 8q abnormal, and both chromosomes 3 and 8q abnormal. KM analysis showed 5-year survival figures of approximately 97%, 80%, and 30% for these prognostic groups, respectively (log-rank, p<0.001). All MLPA probes within both chromosomes were significantly correlated with each other (Spearman, p<0.001).ConclusionsWithin chromosome 3, the strong correlation between the MLPA variables and the uniform coefficients from the PCA indicates a lack of evidence for a signature gene that might account for the bimodality we observed. We hypothesize that the two clusters we found correspond to binary underlying states of complete monosomy or disomy 3 and that these states are sampled by the complete ensemble of probes. Consequently, we would expect a similar pattern to emerge in higher-resolution MLPA data sets. LTD may be a significant confounding factor. Considering chromosome 8q, we found that chromosome 3 cluster membership and 8q gain as traditionally defined have an indistinguishable impact on patient outcome

DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity.

An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple ([Formula: see text]40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples

A survey on scheduling and mapping techniques in 3D Network-on-chip

Network-on-Chips (NoCs) have been widely employed in the design of multiprocessor system-on-chips (MPSoCs) as a scalable communication solution. NoCs enable communications between on-chip Intellectual Property (IP) cores and allow those cores to achieve higher performance by outsourcing their communication tasks. Mapping and Scheduling methodologies are key elements in assigning application tasks, allocating the tasks to the IPs, and organising communication among them to achieve some specified objectives. The goal of this paper is to present a detailed state-of-the-art of research in the field of mapping and scheduling of applications on 3D NoC, classifying the works based on several dimensions and giving some potential research directions