Users' perceptions of domestic windows in Hong Kong: Challenging daylighting-based design regulations

The authors suspected that the contemporary quantified daylight control on window design is insufficient to satisfy the user expectation in Hong Kong. A survey was carried out from December 2007 to June 2008 to study the human-window interactions in high-rise residential buildings in Hong Kong. The result indicated that daylighting is not the dominant factor for domestic window design because of Hong Kong's sociocultural context; other factors such as dining habit, toilet hygiene, views from living room and privacy for bedroom proved to be more important in the users perception. This suggested that the current statutory control may not fulfill or match user expectations. Thus, the window design framework should be a qualitative approach with the understanding of space function and user behavior in the sociocultural context in order to provide for a better living environment. © 2010 Macmillan Publishers Ltd.postprin

Managerial incentives, CEO characteristics and corporate innovation in China's private sector

We use a unique World Bank survey of 1088 private manufacturing firms from 18 Chinese cities over the period 2000-2002 to empirically examine the roles of managerial incentives and CEO characteristics in a firm's innovation activities. We look at both innovation effort (R&D intensity) and innovation performance measures such as new product sales. We obtain the following main results: (1) the presence of CEO incentive schemes increases both corporate innovation effort and innovation performance; (2) sales-based performance measures in the incentive scheme, as compared with profit-based performance measure, are more conducive to firm innovation; and (3) CEO education level, professional background and political connection are positively associated with firm's innovation efforts. The main results are robust to endogeneity tests with instrumental variables. We also discuss some important policy implications. © 2010 Association for Comparative Economic Studies.postprin

D'yakonov-Perel' spin relaxation in InSb/AlInSb quantum wells

We investigate theoretically the D'yakonov-Perel' spin relaxation time by solving the eight-band Kane model and Poisson equation self-consistently. Our results show distinct behavior with the single-band model due to the anomalous spin-orbit interactions in narrow band-gap semiconductors, and agree well with the experiment values reported in recent experiment (K. L. Litvinenko, et al., New J. Phys. \textbf{8}, 49 (2006)). We find a strong resonant enhancement of the spin relaxation time appears for spin align along [$1\bar{1}0$] at a certain electron density at 4 K. This resonant peak is smeared out with increasing the temperature.Comment: 4 pages, 4 figure

Towards a Semantic Gas Source Localization under Uncertainty

Towards a Semantic Gas Source Localization under Uncertainty.Communications in Computer and Information Science book series (CCIS, volume 855), doi:10.1007/978-3-319-91479-4_42This work addresses the problem of efficiently and coherently locating a gas source in a domestic environment with a mobile robot, meaning efficiently the coverage of the shortest distance as possible and coherently the consideration of different gas sources explaining the gas presence. The main contribution is the exploitation, for the first time, of semantic relationships between the gases detected and the objects present in the environment to face this challenging issue. Our proposal also takes into account both the uncertainty inherent in the gas classification and object recognition processes. These uncertainties are combined through a probabilistic Bayesian framework to provide a priority-ordered list of (previously observed) objects to check. Moreover the proximity of the different candidates to the current robot location is also considered by a cost function, which output is used for planning the robot inspection path. We have conducted an initial demonstration of the suitability of our gas source localization approach by simulating this task within domestic environments for a variable number of objects, and comparing it with an greedy approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A study on nitrogen removal efficiency of Pseudomonas stutzeri strains isolated from an anaerobic/anoxic/oxic wastewater treatment process

In order to improve the nitrogen removal efficiency in an anaerobic/anoxic/oxic treatment plant, a strain with high nitrification and denitrification capability was isolated from a specific anaerobic/anoxic/oxic treatment process. The characteristics of isolate were experimentally analyzed. By using the nitrogen balance method, the total nitrogen loss was calculated to be 40.1% (w/w) when the carbon source was citric acid with a C/N ratio of 5. Meanwhile, the isolated strain was identified by 16S rDNA to be a Pseudomonas stutzeri with a similarity of 99%. Varying the initial TN, the C/N, the pH value and the ambient temperature in the reaction system, the efficiency of nitrogen removal was studied. The results showed that the highest efficiency occurred when the C/N was 12, the pH value was 7 and the temperature was 32°C. The results were also compared to the practically monitoring data coming with a good agreement. Consequently, it is viable to improve the nitrogen removal efficiency by varying the reaction conditions

TGFβR2 is a major target of miR-93 in nasopharyngeal carcinoma aggressiveness.

BACKGROUND: MiR-17-92 cluster and its paralogues have emerged as crucial regulators of many oncogenes and tumor suppressors. Transforming growth factor-β receptor II (TGFβR2), as an important tumor suppressor, is involved in various cancer types. However, it is in cancer that only two miRNAs of this cluster and its paralogues have been reported so far to regulate TGFβR2. MiR-93 is oncogenic, but its targetome in cancer has not been fully defined. The role of miR-93 in nasopharyngeal carcinoma (NPC) still remains largely unknown. METHODS: We firstly evaluated the clinical signature of TGFβR2 down-regulation in clinical samples, and next used a miRNA expression profiling analysis followed by multi-validations, including Luciferase reporter assay, to identify miRNAs targeting TGFβR2 in NPC. In vitro and in vivo studies were performed to further investigate the effects of miRNA-mediated TGFβR2 down-regulation on NPC aggressiveness. Finally, mechanism studies were conducted to explore the associated pathway and genes influenced by this miRNA-mediated TGFβR2 down-regulation. RESULTS: TGFβR2 was down-regulated in more than 50% of NPC patients. It is an unfavorable prognosis factor contributing to clinical NPC aggressiveness. A cluster set of 4 TGFβR2-associated miRNAs was identified; they are all from miR-17-92 cluster and its paralogues, of which miR-93 was one of the most significant miRNAs, directly targeting TGFβR2, promoting cell proliferation, invasion and metastasis in vitro and in vivo. Moreover, miR-93 resulted in the attenuation of Smad-dependent TGF-β signaling and the activation of PI3K/Akt pathway by suppressing TGFβR2, further promoting NPC cell uncontrolled growth, invasion, metastasis and EMT-like process. Impressively, the knockdown of TGFβR2 by siRNA displayed a consentaneous phenocopy with the effect of miR-93 in NPC cells, supporting TGFβR2 is a major target of miR-93. Our findings were also substantiated by investigation of the clinical signatures of miR-93 and TGFβR2 in NPC. CONCLUSION: The present study reports an involvement of miR-93-mediated TGFβR2 down-regulation in NPC aggressiveness, thus giving extended insights into molecular mechanisms underlying cancer aggressiveness. Approaches aimed at blocking miR-93 may serve as a promising therapeutic strategy for treating NPC patients

Identification of disease-causing genes using microarray data mining and gene ontology

Background: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes. Methods: We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results. Results: The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth. Conclusions: The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers

Frequency-modulated Chirp Signals for Single-photodiode Based Coherent LiDAR System

In this paper, we investigate two categories of linear frequency-modulated chirp signals suitable for single-photodiode based coherent light detection and ranging (LiDAR) systems, namely, the frequency-modulated continuous-wave (FMCW) single-sideband (SSB) signal and the amplitude-modulated double-sideband (DSB) signal, and compare their achievable receiver sensitivity performance. The DSB signal requires a simpler transmitter design, as it is real-valued and can be generated using a single-drive Mach-Zehnder modulator (MZM), while the SSB signal, which is frequency/phase modulated, requires an in-phase and quadrature modulator (IQM)-based transmitter. A theoretical analysis of direct-detection (DD) beating interference (BI) especially the local oscillator (LO) beating with itself, known as LO-LO BI, is presented. Both Monte Carlo simulations and experimental demonstrations are carried out. Good agreement between simulations and experiments is achieved. In comparison with the SSB system, the DSB signal-based system is affected by laser phase noise-induced power fluctuation, and also suffers a significant sensitivity penalty due to nonlinear LO-LO BI. A spectral guard band for mitigating LO-LO BI is necessary for the DSB signal, achieved at the expense of requiring a larger electrical bandwidth. In system tests with a delay line of 385 m, the SSB signal outperforms the DSB signal with a 10 dB better receiver sensitivity in the case with a guard band, and 25 dB better sensitivity without a guard band

Reversible Data Perturbation Techniques for Multi-level Privacy-preserving Data Publication

The amount of digital data generated in the Big Data age is increasingly rapidly. Privacy-preserving data publishing techniques based on differential privacy through data perturbation provide a safe release of datasets such that sensitive information present in the dataset cannot be inferred from the published data. Existing privacy-preserving data publishing solutions have focused on publishing a single snapshot of the data with the assumption that all users of the data share the same level of privilege and access the data with a fixed privacy level. Thus, such schemes do not directly support data release in cases when data users have different levels of access on the published data. While a straight-forward approach of releasing a separate snapshot of the data for each possible data access level can allow multi-level access, it can result in a higher storage cost requiring separate storage space for each instance of the published data. In this paper, we develop a set of reversible data perturbation techniques for large bipartite association graphs that use perturbation keys to control the sequential generation of multiple snapshots of the data to offer multi-level access based on privacy levels. The proposed schemes enable multi-level data privacy, allowing selective de-perturbation of the published data when suitable access credentials are provided. We evaluate the techniques through extensive experiments on a large real-world association graph dataset and our experiments show that the proposed techniques are efficient, scalable and effectively support multi-level data privacy on the published data