LIFECYCLE ENERGY CONSUMPTION PREDICTION OF RESIDENTIAL BUILDINGS BY INCORPORATING LONGITUDINAL UNCERTAINTIES

Accurate prediction of buildings’ lifecycle energy consumption is a critical part in lifecycle assessment of residential buildings. Longitudinal variations in building conditions, weather conditions and building’s service life can cause significant deviation of the prediction from the real lifecycle energy consumption. The objective is to improve the accuracy of lifecycle energy consumption prediction by properly modelling the longitudinal variations in residential energy consumption model using Markov chain based stochastic approach. A stochastic Markov model considering longitudinal uncertainties in building condition, degree days, and service life is developed: 1) Building’s service life is estimated through Markov deterioration curve derived from actual building condition data; 2) Neural Network is used to project periodic energy consumption distribution for each joint energy state of building condition and temperature state; 3) Lifecycle energy consumption is aggregated based on Markov process and the state probability. A case study on predicting lifecycle energy consumption of a residential building is presented using the proposed model and the result is compared to that of a traditional deterministic model and three years’ measured annual energy consumptions. It shows that the former model generates much narrower distribution than the latter model when compared to the measured data, which indicates improved result

Data De-Duplication with Adaptive Chunking and Accelerated Modification Identifying

The data de-duplication system not only pursues the high de-duplication rate, which refers to the aggregate reduction in storage requirements gained from de-duplication, but also the de-duplication speed. To solve the problem of random parameter-setting brought by Content Defined Chunking (CDC), a self-adaptive data chunking algorithm is proposed. The algorithm improves the de-duplication rate by conducting pre-processing de-duplication to the samples of the classified files and then selecting the appropriate algorithm parameters. Meanwhile, FastCDC, a kind of content-based fast data chunking algorithm, is adopted to solve the problem of low de-duplication speed of CDC. By introducing de-duplication factor and acceleration factor, FastCDC can significantly boost de-duplication speed while not sacrificing the de-duplication rate through adjusting these two parameters. The experimental results demonstrate that our proposed method can improve the de-duplication rate by about 5 %, while FastCDC can obtain the increase of de-duplication speed by 50 % to 200 % only at the expense of less than 3 % de-duplication rate loss

Comments on the identity of Neoseiulus californicus sensu lato (Acari: Phytoseiidae) with a redescription of this species from southern China

Abstract The identity of Neoseiulus californicus sensu lato is reviewed and its polymorphic nature in published descriptions is discussed. Some mistakes in previous redescriptions of this species are clarified by studying the voucher specimens. A new strain of this species was discovered from Eriobotrya japonica in Dinghushan National Nature Reserve, Zhaoqing, Guangdong Province, southern China, and both adult male and female of this population are redescribed. Previous records of N. californicus and N. fallacis in China are reviewed. Preanal glands are described for the first time for a phytoseiid species. World distribution records for N. californicus sensu lato are reviewed, with extension of its range to southern China and Australia/Oceania

Probing NaCl hydrate formation from aqueous solutions by Terahertz Time-Domain Spectroscopy

The cooling-induced formation of hydrate in aqueous NaCl solutions was probed using terahertz time-domain spectroscopy (THz-TDS). It was found that the NaCl hydrate formation is accompanied with emergence of four new absorption peaks at 1.60, 2.43, 3.34 and 3.78 THz. Combining the X-ray diffraction measurement with the solid-state based density functional theory (DFT) calculations, we assign the observed terahertz absorption peaks to the vibrational modes of the formed NaCl⋅2H2O hydrate during cooling. This work dedicates THz-TDS based analysis great potential in studying ionic hydrate and the newly revealed collective vibrational modes could be the sensitive indicators to achieve quantitative analysis in phase transitions and lattice dynamics

A novel therapeutic approach: Blocking Glioblastoma cells’ interaction with their microenvironment

Abstract Due to the highly invasive nature of Glioblastoma (GB), complete surgical resection is not feasible, while motile tumour cells are often associated with several specific brain structures that enhance treatment-resistance. Here, we investigate the therapeutic potential of Disulfiram and Carbenoxolone, that inhibit two distinct interactions between GB and the brain tissue microenvironment: stress-induced cell-matrix adhesion and gap junction mediated cell-cell communication, respectively. Increase in cell numbers of tumour-initiating cells, which are cultured in suspension as cell clusters, and adherent differentiated cells can be blocked to a similar extent by Carbenoxolone, as both cell populations form gap junctions, but the adherent differentiated cells are much more sensitive to Disulfiram treatment, which – via modulation of NF-κB signalling – interferes with cell-substrate adhesion. Interestingly, inducing adhesion in tumour-initiating cells without differentiating them does not sensitize for Disulfiram. Importantly, combining Disulfiram, Carbenoxolone and the standard chemotherapeutic drug Temozolomide reduces tumour size in an orthotopic mouse model. Isolating GB cells from their direct environment within the brain represents an important addition to current therapeutic approaches. The blockage of cellular interactions via the clinically relevant substances Disulfiram and Carbenoxolone, has distinct effects on different cell populations within a tumour, potentially reducing motility and/or resistance to apoptosis

Incorporating Uncertainties into Building Life Cycle Assessment(LCA) by Using Stochastic Approach

Life Cycle Assessment (LCA) has been extensively used in the building sector for assessing the environmental performance of construction materials, products or a whole building. Although numerous building LCAs have been performed, most of them took the deterministic way which assumed deterministic point values and simplified linear quantification model to compile life cycle inventory, which may lead to inaccuracy of LCA results and further influence the decision making. This research aimed to improve the reliability of traditional deterministic building LCA, by incorporating both data uncertainties and model uncertainties through a stochastic approach, which combined Monte Carlo simulation and Markov Chain modeling with the assistance of data quality indicators (DQI) and classical statistics. The case study showed that deterministic LCA may generate outcomes with low probabilities, which could lead to biased conclusions. The proposed stochastic LCA will provide distributions of output rather than deterministic point value. Thus, it will provide more information with additional confidence for decision makers and allow more objective decision making

Addressing uncertainties in residential energy performance benchmarking and projecting through data mining approach

The goal of this research is to develop a framework for improving the reliability of life cycle energy assessment of residential buildings. The proposed research method is primarily focused on developing improved building envelope performance benchmarking model and utilizing stochastic models to achieve improved life cycle operation energy consumption prediction. The proposed framework was applied to houses in a Midwest residential community, for which historical energy consumption records of the houses are available. The results of the models were validated through infrared thermal inspections. The results show the benchmarking model can generate comparable results to infrared thermal inspections. The stochastic life cycle energy consumption projection model can produce more practical results compared to that of traditional deterministic models and present more uncertainty information for decision-making. In the newly developed benchmarking model, the advantages of existing benchmarking approaches for addressing spatial uncertainties (i.e. multiple regression, principal component analysis and data envelopment analysis) are combined and lead to an improved approach for objectively evaluating the building envelope thermal performance for realizing the effective energy retrofitting. In the developed stochastic model for predicting life cycle energy consumptions of residential buildings, temporal variations of building energy consumption are considered by using Markov chain and neural network models. Major limitations of the developed models are identified as the follows: First, the impacts of spatial variability (uncertainty) of external noise factors that lead to the variation of the residential energy performance (REP) have not been fully considered. Second, occupants\u27 behavior related temporal variability of the residential energy consumption factors is not accurately quantified in the model. These identified limitations are subject to further research contingent upon data availability

A Building LCA Case Study Using Autodesk Ecotect and BIM Model

The main objective of this study is to evaluate the potential of utilizing building information model (BIM) to perform whole building Life Cycle Analysis (LCA). The research question addressed was how life cycle performance of a building was affected quantitatively by design configurations. Life cycle energy consumption and CO2 emission of a university building in the Midwest was calculated using Autodesk® Ecotect and BIM model. The study compared life cycle performance, i.e., CO2 emissions and energy consumptions, among different design configurations, as well as their distributions in the stages of the building’s life time. Sensitivity analysis was performed by changing several alternative parameters, to identify which parameter has more impacts on building performance. Preliminary results indicated that whole building life cycle performance is affected by several design parameters, with different degree of sensitivity. The conclusions of the study are: 1) The combination of Ecotect and BIM model provides a convenient tool to conduct whole building LCA through the easier data flow from the BIM model to Ecotect. The data entry workload for whole building LCA can be reduced significantly. 2) Energy consumption in the operating stage dominates the lifecycle energy consumption of the building. 3) Sensitivity analysis of impact of design change can be conducted using the combination of Ecotect and BIM model