Short-term traffic volume forecasting : a k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm

To enhance the performance of the k-nearest neighbors approach in forecasting short-term traffic volume, this paper proposed and tested a two-step approach with the ability of forecasting multiple steps. In selecting k-nearest neighbors, a time constraint window is introduced, and then local minima of the distances between the state vectors are ranked to avoid overlappings among candidates. Moreover, to control extreme values’ undesirable impact, a novel algorithm with attractive analytical features is developed based on the principle component. The enhanced KNN method has been evaluated using the field data, and our comparison analysis shows that it outperformed the competing algorithms in most cases.

Direct Observation of Photodissociation Products from Phenylperoxyl Radicals Isolated in the Gas Phase

Gas phase peroxyl radicals are central to our chemical understanding of combustion and atmospheric processes and are typically characterized by strong absorption in the UV (lambda(max) approximate to 240 nm). The analogous maximum absorption feature for arylperoxyl radicals is predicted to shift to the visible but has not previously been characterized nor have any photoproducts arising from this transition been identified. Here we describe the controlled synthesis and isolation in vacuo of an array of charge-substituted phenylperoxyl radicals at room temperature, including the 4-(N,N,N-trimethylammonium)methyl phenylperoxyl radical cation (4-Me3N[+]CH2-C6H4OO center dot), using linear ion-trap mass spectrometry. Photodissociation mass spectra obtained at wavelengths ranging from 310 to 500 nm reveal two major photoproduct channels corresponding to homolysis of aryl-OO and arylO-O bonds resulting in loss of O-2 and O, respectively. Combining the photodissociation yields across this spectral window produces a broad (FWHM approximate to 60 nm) but clearly resolved feature centered at lambda(max) = 403 nm (3.08 eV). The influence of the charge-tag identity and its proximity to the radical site are investigated and demonstrate no effect on the identity of the two dominant photoproduct channels. Electronic structure calculations have located the vertical (B) over tilde <- (X) over tilde transition of these substituted phenylperoxyl radicals within the experimental uncertainty and further predict the analogous transition for unsubstituted phenylperoxyl radical (C6H5OO center dot) to be 457 nm (2.71 eV), nearly 45 nm shorter than previous estimates and in good agreement with recent computational values.

Short term variability in urinary bisphenol A in Australian children

Used frequently in food contact materials, bisphenol A (BPA) has been studied extensively in recent years, and ubiquitous exposure in the general population has been demonstrated worldwide. Characterising within- and between-individual variability of BPA concentrations is important for characterising exposure in biomonitoring studies, and this has been investigated previously in adults, but not in children. The aim of this study was to characterise the short-term variability of BPA in spot urine samples in young children. Children aged ≥2-<4 years (n = 25) were recruited from an existing cohort in Queensland Australia, and donated four spot urine samples each over a two day period. Samples were analysed for total BPA using isotope dilution online solid phase extraction-liquid chromatography-tandem mass spectrometry, and concentrations ranged from 0.53–74.5 ng/ml, with geometric mean and standard deviation of 2.70 ng/ml and 2.94 ng/ml, respectively. Sex and time of sample collection were not significant predictors of BPA concentration. The between-individual variability was approximately equal to the within-individual variability (ICC = 0.51), and this ICC is somewhat higher than previously reported literature values. This may be the result of physiological or behavioural differences between children and adults or of the relatively short exposure window assessed. Using a bootstrapping methodology, a single sample resulted in correct tertile classification approximately 70% of the time. This study suggests that single spot samples obtained from young children provide a reliable characterization of absolute and relative exposure over the short time window studied, but this may not hold true over longer timeframes.

Extensions of the cube attack based on low degree annihilators

At Crypto 2008, Shamir introduced a new algebraic attack called the cube attack, which allows us to solve black-box polynomials if we are able to tweak the inputs by varying an initialization vector. In a stream cipher setting where the filter function is known, we can extend it to the cube attack with annihilators: By applying the cube attack to Boolean functions for which we can find low-degree multiples (equivalently annihilators), the attack complexity can be improved. When the size of the filter function is smaller than the LFSR, we can improve the attack complexity further by considering a sliding window version of the cube attack with annihilators. Finally, we extend the cube attack to vectorial Boolean functions by finding implicit relations with low-degree polynomials.

Breaking time-inversion invariance through decoherence : energetic consequences for attosecond neutron scattering

Nuclei and electrons in condensed matter and/or molecules are usually entangled, due to the prevailing (mainly electromagnetic) interactions. However, the "environment" of a microscopic scattering system (e.g. a proton) causes ultrafast decoherence, thus making atomic and/or nuclear entanglement e®ects not directly accessible to experiments. However, our neutron Compton scattering experiments from protons (H-atoms) in condensed systems and molecules have a characteristic collisional time about 100|1000 attoseconds. The quantum dynamics of an atom in this ultrashort, but ¯nite, time window is governed by non-unitary time evolution due to the aforementioned decoherence. Unexpectedly, recent theoretical investigations have shown that decoherence can also have the following energetic consequences. Disentangling two subsystems A and B of a quantum system AB is tantamount to erasure of quantum phase relations between A and B. This erasure is widely believed to be an innocuous process, which e.g. does not a®ect the energies of A and B. However, two independent groups proved recently that disentangling two systems, within a su±ciently short time interval, causes increase of their energies. This is also derivable by the simplest Lindblad-type master equation of one particle being subject to pure decoherence. Our neutron-proton scattering experiments with H2 molecules provide for the first time experimental evidence of this e®ect. Our results reveal that the neutron-proton collision, leading to the cleavage of the H-H bond in the attosecond timescale, is accompanied by larger energy transfer (by about 2|3%) than conventional theory predicts. Preliminary results from current investigations show qualitatively the same e®ect in the neutron-deuteron Compton scattering from D2 molecules. We interpret the experimental findings by treating the neutron-proton (or neutron-deuteron) collisional system as an entangled open quantum system being subject to fast decoherence caused by its "environment" (i.e., two electrons plus second nucleus of H2 or D2). The presented results seem to be of generic nature, and may have considerable consequences for various processes in condensed matter and molecules, e.g. in elementary chemical reactions.

Rho GTPases mediate the mechanosensitive lineage commitment of neural stem cells

Adult neural stem cells (NSCs) play important roles in learning and memory and are negatively impacted by neurological disease. It is known that biochemical and genetic factors regulate self-renewal and differentiation, and it has recently been suggested that mechanical and solid-state cues, such as extracellular matrix (ECM) stiffness, can also regulate the functions of NSCs and other stem cell types. However, relatively little is known of the molecular mechanisms through which stem cells transduce mechanical inputs into fate decisions, the extent to which mechanical inputs instruct fate decisions versus select for or against lineage-committed blast populations, or the in vivo relevance of mechanotransductive signaling molecules in native stem cell niches. Here we demonstrate that ECM-derived mechanical signals act through Rho GTPases to activate the cellular contractility machinery in a key early window during differentiation to regulate NSC lineage commitment. Furthermore, culturing NSCs on increasingly stiff ECMs enhances RhoA and Cdc42 activation, increases NSC stiffness, and suppresses neurogenesis. Likewise, inhibiting RhoA and Cdc42 or downstream regulators of cellular contractility rescues NSCs from stiff matrix- and Rho GTPase-induced neurosuppression. Importantly, Rho GTPase expression and ECM stiffness do not alter proliferation or apoptosis rates indicating that an instructive rather than selective mechanism modulates lineage distributions. Finally, in the adult brain, RhoA activation in hippocampal progenitors suppresses neurogenesis, analogous to its effect in vitro. These results establish Rho GTPase-based mechanotransduction and cellular stiffness as biophysical regulators of NSC fate in vitro and RhoA as an important regulatory protein in the hippocampal stem cell niche.

Physical activity recognition from accelerometer data using a multi-scale ensemble method

Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.

Artificial neural networks to predict activity type and energy expenditure in youth

Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. Purpose: The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. Methods: One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous intensity games or sports. During each trial, participants wore an ActiGraph GTIM on the right hip, and (V) Over dotO(2) was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square en-or (RMSE). Results: As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30-40% lower than the conventional regression-based approaches. Conclusions: ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.

Considering research enquiry into biophilic urbanism and office worker productivity

In cities, people spend a significant portion of their time indoors, much of which is in office buildings. The quality and nature of these spaces have the potential to be a strong determinant of people’s health and wellbeing. There is a body of evidence that suggests experiences of nature increase the rate of attention recovery, reduce stress, depression and anxiety, and increase cognitive abilities. Further, the presence of nature inside buildings (such as pot plants and internal green walls) can improve indoor air quality, potentially reducing illness and increasing cognitive function. Urban design that integrates nature into the built environment to provide these benefits, among others, is called ‘biophilic urbanism’ and is the subject of growing international interest and research. The potential for these benefits to increase worker productivity in office buildings is of particular interest, as this could significantly increase the financial performance of office building-based organisations. However, productivity is a complex concept that is difficult to define, and affected by a multitude of factors, which make it difficult to measure. This inability to quantify productivity increases from investments in nature- experiences in office buildings is currently a significant barrier to such investments. Within this context, this paper considers opportunities for research to explore the relationship between office-based nature experiences and productivity, by reviewing existing research in this field and reflecting on the authors’ own experiences. This review has a particular focus on the importance of quantifying this link in order to encourage private property owners to voluntarily integrate nature into buildings to provide city-wide ecosystem service benefits. The paper begins with a contextual overview of how biophilic urbanism can potentially increase worker productivity. Existing methods of measuring and evaluating the performance of biophilic urbanism within the context of office buildings are then explored, along with a discussion of issues with such methods that are currently limiting investment in biophilic urbanism to increase worker productivity and wellbeing. This includes a summary of a survey within a Perth office building to explore the impact of views of nature through a window. Drawing on these insights, the paper makes recommendations regarding opportunities for focusing future investigations to enhance understanding of how biophilic urbanism can contribute to increased wellbeing and productivity in office buildings. This paper builds on work conducted as part of the Sustainable Built Environment National Research Centre Project 1.5, Harnessing the Potential of Biophilic Urbanism in Australia, which considered the role of nature integrated into the built environment in responding to emerging challenges of climate change, resource shortages and population pressures, while providing a host of co- benefits to a range of stakeholders.

Considering research enquiry into biophilic urbanism and office worker productivity

In cities, people spend a significant portion of their time indoors, much of which is in office buildings. The quality and nature of these spaces have the potential to be a strong determinant of people’s health and wellbeing. There is a body of evidence that suggests experiences of nature increase the rate of attention recovery, reduce stress, depression and anxiety, and increase cognitive abilities. Further, the presence of nature inside buildings (such as pot plants and internal green walls) can improve indoor air quality, potentially reducing illness and increasing cognitive function. Urban design that integrates nature into the built environment to provide these benefits, among others, is called ‘biophilic urbanism’ and is the subject of growing international interest and research. The potential for these benefits to increase worker productivity in office buildings is of particular interest, as this could significantly increase the financial performance of office building-based organisations. However, productivity is a complex concept that is difficult to define, and affected by a multitude of factors, which make it difficult to measure. This inability to quantify productivity increases from investments in nature- experiences in office buildings is currently a significant barrier to such investments. Within this context, this paper considers opportunities for research to explore the relationship between office-based nature experiences and productivity, by reviewing existing research in this field and reflecting on the authors’ own experiences. This review has a particular focus on the importance of quantifying this link in order to encourage private property owners to voluntarily integrate nature into buildings to provide city-wide ecosystem service benefits. The paper begins with a contextual overview of how biophilic urbanism can potentially increase worker productivity. Existing methods of measuring and evaluating the performance of biophilic urbanism within the context of office buildings are then explored, along with a discussion of issues with such methods that are currently limiting investment in biophilic urbanism to increase worker productivity and wellbeing. This includes a summary of a survey within a Perth office building to explore the impact of views of nature through a window. Drawing on these insights, the paper makes recommendations regarding opportunities for focusing future investigations to enhance understanding of how biophilic urbanism can contribute to increased wellbeing and productivity in office buildings. This paper builds on work conducted as part of the Sustainable Built Environment National Research Centre Project 1.5, Harnessing the Potential of Biophilic Urbanism in Australia, which considered the role of nature integrated into the built environment in responding to emerging challenges of climate change, resource shortages and population pressures, while providing a host of co- benefits to a range of stakeholders.

An arteriovenous loop in a protected space generates a permanent, highly vascular, tissue-engineered construct

A major obstacle to 3-dimensional tissue engineering is incorporation of a functional vascular supply to support the expanding new tissue. This is overcome in an in vivo intrinsic vascularization model where an arteriovenous loop (AVL) is placed in a noncollapsible space protected by a polycarbonate chamber. Vascular development and hypoxia were examined from 3 days to 112 days by vascular casting, morphometric, and morphological techniques to understand the model's vascular growth and remodeling parameters for tissue engineering purposes. At 3 days a fibrin exudate surrounded the AVL, providing a scaffold to migrating inflammatory, endothelial, and mesenchymal cells. Capillaries formed between 3 and 7 days. Hypoxia and cell proliferation were maximal at 7 days, followed by a peak in percent vascular volume at 10 days (23.20±3.14% compared with 3.59±2.68% at 3 days, P<0.001). Maximal apoptosis was observed at 112 days. The protected space and spontaneous microcirculatory development in this model suggest it would be applicable for in vivo tissue engineering. A temporal window in a period of intense angiogenesis at 7 to 10 days is optimal for exogenous cell seeding and survival in the chamber, potentially enabling specific tissue outcomes to be achieved.

Chemotherapeutic compounds targeting the DNA double-strand break repair pathways : the good, the bad, and the promising

The repair of DNA double-strand breaks (DSBs) is a critical cellular mechanism that exists to ensure genomic stability. DNA DSBs are the most deleterious type of insult to a cell’s genetic material and can lead to genomic instability, apoptosis, or senescence. Incorrectly repaired DNA DSBs have the potential to produce chromosomal translocations and genomic instability, potentially leading to cancer. The prevalence of DNA DSBs in cancer due to unregulated growth and errors in repair opens up a potential therapeutic window in the treatment of cancers. The cellular response to DNA DSBs is comprised of two pathways to ensure DNA breaks are repaired: homologous recombination and non-homologous end joining. Identifying chemotherapeutic compounds targeting proteins involved in these DNA repair pathways has shown promise as a cancer therapy for patients, either as a monotherapy or in combination with genotoxic drugs. From the beginning, there have been a number of chemotherapeutic compounds that have yielded successful responses in the clinic, a number that have failed (CGK-733 and iniparib), and a number of promising targets for future studies identified. This review looks in detail at how the cell responds to these DNA DSBs and investigates the chemotherapeutic avenues that have been and are currently being explored to target this repair process.

Enriching the "I" in Bim : a BIM-Specifications (Bimspecs) approach

This paper conceptualizes a framework for bridging the BIM-Specifications divide by embedding project-specific information in BIM objects by means of a product library. We demonstrate how model information, enriched with data at various levels of development (LODs), can evolve simultaneously with design and construction using a window object embedded in a wall as life-cycle phase exemplars at different levels of granularity. The conceptual approach is informed by the need for exploring an approach that takes cognizance of the limitations of current modelling tools in enhancing the information content of BIM models. Therefore, this work attempts to answer the question, “How can the modelling of building information be enhanced throughout the life-cycle phases of buildings utilizing building specification information?”

Predicting movie ratings from audience behaviors

We propose a method of representing audience behavior through facial and body motions from a single video stream, and use these features to predict the rating for feature-length movies. This is a very challenging problem as: i) the movie viewing environment is dark and contains views of people at different scales and viewpoints; ii) the duration of feature-length movies is long (80-120 mins) so tracking people uninterrupted for this length of time is still an unsolved problem, and; iii) expressions and motions of audience members are subtle, short and sparse making labeling of activities unreliable. To circumvent these issues, we use an infrared illuminated test-bed to obtain a visually uniform input. We then utilize motion-history features which capture the subtle movements of a person within a pre-defined volume, and then form a group representation of the audience by a histogram of pair-wise correlations over a small-window of time. Using this group representation, we learn our movie rating classifier from crowd-sourced ratings collected by rottentomatoes.com and show our prediction capability on audiences from 30 movies across 250 subjects (> 50 hrs).

Simultaneous localization and planning on multiple map hypotheses

This paper presents a novel method to rank map hypotheses by the quality of localization they afford. The highest ranked hypothesis at any moment becomes the active representation that is used to guide the robot to its goal location. A single static representation is insufficient for navigation in dynamic environments where paths can be blocked periodically, a common scenario which poses significant challenges for typical planners. In our approach we simultaneously rank multiple map hypotheses by the influence that localization in each of them has on locally accurate odometry. This is done online for the current locally accurate window by formulating a factor graph of odometry relaxed by localization constraints. Comparison of the resulting perturbed odometry of each hypothesis with the original odometry yields a score that can be used to rank map hypotheses by their utility. We deploy the proposed approach on a real robot navigating a structurally noisy office environment. The configuration of the environment is physically altered outside the robots sensory horizon during navigation tasks to demonstrate the proposed approach of hypothesis selection.