Walk Counts Research Articles

Machine learning for predicting halogen radical reactivity toward aqueous organic chemicals

Rapid advances in machine learning (ML) provide fast, accurate, and widely applicable methods for predicting free radical-mediated organic pollutant reactivity. In this study, the rate constants (logk) of four halogen radicals were predicted using Morgan fingerprint (MF) and Mordred descriptor (MD) in combination with a series of ML models. The findings highlighted that making accurate predictions for various datasets depended on an effective combination of descriptors and algorithms. To further alleviate the challenge of limited sample size, we introduced a data combination strategy that improved prediction accuracy and mitigated overfitting by combining different datasets. The Light Gradient Boosting Machine (LightGBM) with MF and Random Forest (RF) with MD models based on the unified dataset were finally selected as the optimal models. The SHapley Additive exPlanations revealed insights: the MF-LightGBM model successfully captured the influence of electron-withdrawing/donating groups, while autocorrelation, walk count and information content descriptors in the MD-RF model were identified as key features. Furthermore, the important contribution of pH was emphasized. The results of the applicability domain analysis further supported that the developed model can make reliable predictions for query compounds across a broader range. Finally, a practical web application for logk calculations was built.

Impact of urban microclimate on walking volume by street type and heat-vulnerable age groups: Seoul's IoT sensor big data

There is an emerging interest in the association between walking and meteorological conditions. Despite the growing risk of heat stress, the city-wide evaluation of heat impact on walking at the street level has largely remained unknown. This study explores the association between ambient temperatures and the number of pedestrians in Seoul, considering the street's functional and user characteristics. Using big data on the urban microclimate and walking count obtained through urban internet of things sensor, heat–walking activity relationships were investigated between different settings of urban street types (commercial/shopping, outdoor marketplace, high-rise residential, and mixed-use) and the proportion of heat-vulnerable pedestrians (aged <9 or > 65 years) on a city-wide scale. Our study results indicated that the summer ambient temperature is crucial in determining walking count while showing nonlinear quadratic relationships. Its degree of impact is significantly associated with the day of the week and time of the day, with heat sensitivity of walking count being highest in mixed-use type streets. The negative impact of temperature increased as the proportion of heat-vulnerable age group of pedestrians increased in outdoor marketplace-type streets. Our findings highlight the significance of summer heat conditions on walking count while providing empirical evidence for establishing heat mitigation policies with enhanced street vitality.

Examination Of Preoperative Walking Count, Physical Activity, And Length Of Stay

Examination Of Preoperative Walking Count, Physical Activity, And Length Of Stay

Effect of meteorological conditions on leisure walking: a time series analysis and the application of outdoor thermal comfort indexes.

Leisure walking is affected by meteorological conditions. However, it is still not clear what scales of meteorological conditions and thermal status affect the number of people who choose to leisure walk. Using a time series regression, this study examines the heat-leisure walking relationship by analyzing the effect of the seasons, weather, microclimate, and outdoor thermal comfort on walking count. Eight thermal indexes were selected to estimate the pedestrians' thermal comfort, and their predictive capacities in walking count were evaluated. Particular consideration was given to identifying heat thresholds of walking that determined the tolerance range of pedestrian heat stress. Four years of hourly daytime walking counts and publicly available ASOS meteorological data at Seoul-lo 7017, a pedestrian bridge in Seoul, were used for the analysis. Our findings indicate that walking count is correlated with seasonal climatic variations, with the highest number of pedestrians observed in fall and the lowest in summer. Moreover, air temperature played a vital role, showing that a 5.0°C rise in temperature was associated with a 1.34% rise in the square root of the walking count. Its impact becomes greater when combined with intense solar radiation and higher absolute humidity. The heat threshold for walking was between 23.8°C and 26.2°C. Empirical model indexes showed the highest predictive capacity in walking count at approximately 30.0%, which was followed by rational model indexes at 28.0%.

Logistic regression and random forest unveil key molecular descriptors of druglikeness

The identification of molecular descriptors that embody the chemical information for druglikeness will be a step forward in data-driven drug discovery and development endeavor. In this study, over 4000 Dragon-type molecular properties were generated for approximately 2000 known drugs and 2000 surrogate nondrugs. Logistic Regression (LogR) and Random Forest (RF) techniques were carried out to unveil the crucial molecular descriptors that can adequately classify a compound as drug or nondrug. Ten one-variable LogR models each demonstrated at least 70% prediction accuracy. A two-variable model consisting of HV cpx and MDDD correctly classified 85% of the test compounds. The best LogR model with 89.0% prediction accuracy identified five most influential descriptors for druglikeness: an information index HV cpx, topological index MDDD, a ring descriptor NNRS, X2A or average connectivity index of order 2, and walk and path count SRW05. The best RF model involving 10 only weakly correlated descriptors was found to be 92.5% accurate and at par with the RF and LogR models that consisted of over 200 variables. The model featured: molecular weight, MW; average molecular weight, AMW; rotatable bond fraction, RBF; percentage carbon, C%; maximal electrotopological negative variation, MAXDN; all-path Wiener index, Wap; structural information content index, neighborhood symmetry of 1 order, SIC1; number of nitrogen atoms, nN; 2D Petitjean shape index, PJI2; and self-returning walk count of order 5, SRW05. Many of these descriptors have straightforward chemical interpretability and future applicability as druglikeness filters in virtual high throughput drug discovery.

Walk counting and Nikiforov’s problem

Walk counting and Nikiforov’s problem

Graph Powering and Spectral Robustness

Spectral algorithms, such as principal component analysis and spectral clustering, typically require careful data transformations to be effective: upon observing a matrix $A$, one may look at the spectrum of $\psi(A)$ for a properly chosen $\psi$. The issue is that the spectrum of $A$ might be contaminated by non-informational top eigenvalues, e.g., due to scale` variations in the data, and the application of $\psi$ aims to remove these. Designing a good functional $\psi$ (and establishing what good means) is often challenging and model dependent. This paper proposes a simple and generic construction for sparse graphs, $$\psi(A) = \1((I+A)^r \ge1),$$ where $A$ denotes the adjacency matrix and $r$ is an integer (less than the graph diameter). This produces a graph connecting vertices from the original graph that are within distance $r$, and is referred to as graph powering. It is shown that graph powering regularizes the graph and decontaminates its spectrum in the following sense: (i) If the graph is drawn from the sparse Erd\H{o}s-R\'enyi ensemble, which has no spectral gap, it is shown that graph powering produces a `maximal' spectral gap, with the latter justified by establishing an Alon-Boppana result for powered graphs; (ii) If the graph is drawn from the sparse SBM, graph powering is shown to achieve the fundamental limit for weak recovery (the KS threshold) similarly to \cite{massoulie-STOC}, settling an open problem therein. Further, graph powering is shown to be significantly more robust to tangles and cliques than previous spectral algorithms based on self-avoiding or nonbacktracking walk counts \cite{massoulie-STOC,Mossel_SBM2,bordenave,colin3}. This is illustrated on a geometric block model that is dense in cliques.

Understanding volume and correlations of automated walk count: Predictors for necessary, optional, and social activities in Dilworth Park

In this paper, we explore the potential use of automated pedestrian walk count data in urban design research. The Center City District (CCD) research group used computer vision to collect automated pedestrian walk data from Dilworth Park, Philadelphia. By comparing the count data and participant observations of social activities in the park, we found that the frequencies of social activities in the park could be predicted by the pedestrian count when considering the outdoor thermal comfort index and the types of events taking place in Dilworth Park. By examining correlations among multiple sensors, we found that the entry–exit correlation is a useful indicator to assess how people use public space by estimating the ratio of necessary-to-optional activities.

Continuous-time quantum walks on strongly regular graphs with loops and its application to spatial search for multiple marked vertices

The continuous-time quantum walk (CTQW) on the strongly regular graph is studied in this paper, and the exact transition probability distribution between any two vertices of the graph is provided by using the method of counting the walks between these two vertices. The CTQW is also considered on the perturbed strongly regular graph (SRG) by adding multiple loops on given vertices. Spatial search using CTQW can be regarded as a special case of CTQW on the perturbed SRG by adding loops. Combined with the approach of walk counting, the proper parameter settings and search time of spatial search for both a single marked vertex and two marked vertices are provided. The results show that both kinds of spatial search can be undertaken in $$O(\sqrt{N})$$ time in a degree k SRG with N vertices, by setting jump rate of CTQW to be 1 / k.

Clustering ensembles of social networks

AbstractRecently there has been significant work in the social sciences involving ensembles of social networks, that is, multiple, independent, social networks such as students within schools or employees within organizations. There remains, however, very little methodological work on exploring these types of data structures. We present methods for clustering social networks with observed nodal class labels, based on statistics of walk counts between the nodal classes. We extend this method to consider only non-backtracking walks, and introduce a method for normalizing the counts of long walk sequences using those of shorter ones. We then present a method for clustering networks based on these statistics to explore similarities among networks. We demonstrate the utility of this method on simulated network data, as well as on advice-seeking networks in education.

The Business Case for Walking - Counting Walking to Make Walking Count in Auckland, New Zealand ( breakout presentation )

Background Auckland, New Zealand, like many western cities, went down the path of auto-dependency in the last sixty years, systematically measuring and providing for motorised vehicles while accommodating people on foot and bike. To challenge the auto-oriented paradigm, we need to count walking to make walking count. Auckland Council developed a Business Case for Walking to understand the amount and value of walking in Auckland City Centre. Aims The business case work aimed to demonstrate that: walking is the dominant mode of transportation in Auckland City Centre; that businesses make locational decisions with walkability as a key driver; that public realm is something that is both valuable and can be valued; that walking has strong economic value in its own right and that delaying pedestrians to benefit car movement comes with a big economic cost. Methods Using a range of tools from such as simple measurement; interviews with key decision-makers; measuring the value of public realm; and adapting conventional traffic tools such as the cost of delay to motorists to people on foot, to build the evidence base for walkability. Results The Business Case for Walking demonstrated that by literally putting your best foot forward by having walkability front and centre in transportation is not only good for transportation, health and social cohesion but makes total economic sense. This in turn justifies ongoing investment in enhancing walkability as a key tool to deliver liveable, vibrant, economically flourishing cities. Conclusion You have to count (and value) walkability to make walking count and demonstrate its benefits to decision-makers.

Non-backtracking walk centrality for directed networks

The theory of zeta functions provides an expression for the generating fu nction of nonbacktracking walk counts on a directed network. We show how this expression can be used to produce a centrality measure that eliminates backtracking walks at no cost. We also show that the radius of convergence of the generating function is determined by the spect rum of a three-by-three block matrix involving the original adjacency matrix. This giv es a means to choose appropriate values of the attenuation parameter. We find that three important a dditional benefits arise when we use this technique to eliminate traversals around the network that are unlikely to be of relevance. First, we obtain a larger range of choices for the attenuation para meter. Second, because the radius of convergence of the generating function is invariant under the remov al of certain types of nodes, we can gain computational efficiencies through reducing the dimension of t he resulting eigenvalue problem. Third, the dimension of the linear system defining the centrali ty measures may be reduced in the same manner. We show that the new centrality measure may be interp reted as standard Katz on a modified network, where self loops are added, and where nonreciproca l edges are augmented with negative weights. We also give a multilayer interpretation, wh ere negatively weighted walks between layers compensate for backtracking walks on the only non-emp ty layer. Studying the limit as the attenuation parameter approaches its upper bound allows us to propose an eigenvector-based nonbacktracking centrality measure in this directed network setting. We find that the two-by-two block matrix arising in previous studies focused on undirected networks must be extended to a new three-by-three block structure to allow for directed edges. We illustrat e the centrality measure on a synthetic network, where it is shown to eliminate a localization effect p resent in standard Katz centrality. Finally, we give results for real networks.

Searching for stereoisomerism in crystallographic databases: algorithm, analysis and chiral curiosities.

The automated identification of chiral centres in molecular residues is a non-trivial task. Current tools that allow the user to analyze crystallographic data entries do not identify chiral centres in some of the more complex ring structures, or lack the possibility to determine and compare the chirality of multiple structures. This article presents an approach to identify asymmetric C atoms, which is based on the atomic walk count algorithm presented by Rücker & Rücker [(1993), J. Chem. Inf. Comput. Sci. 33, 683-695]. The algorithm, which we implemented in a computer program named ChiChi, is able to compare isomeric residues based on the chiral centres that were identified. This allows for discrimination between enantiomers, diastereomers and constitutional isomers that are present in crystallographic databases. ChiChi was used to process 254 354 organic entries from the Cambridge Structural Database (CSD). A thorough analysis of stereoisomerism in the CSD is presented accompanied by a collection of chiral curiosities that illustrate the strength and versatility of this approach.

Quantitative structure-activity relationship study on the binding affinity of some aminothiazole derivatives with a dopamine receptor in brain

Quantitative structure-activity relationship (QSAR) models can be applied as a powerful tool for predicting the response in biological and chemical systems. D2 receptor subtypes such as dopamine receptors assist dopaminergic neurotransmission in brain. In this work, binding affinities of synthesized agonists with D2-like receptors in binding assays using rat brain were related to the structural properties of these agonists. The structural descriptors of these compounds are calculated. A stepwise variable selection is applied for PLS modeling. The walk and path counts, 2D autocorrelations, 3D atom pairs, RDF, 3D-MoRSE, WHIM, GETAWAY blocks are among the selected descriptors. The PLS model is built with 5 latent variables. The predictive ability of the model is evaluated on a set of 5 ligands, which are not used in modeling steps, and the acceptable results are obtained.

Walking as a Contributor to Physical Activity in Healthy Older Adults: 2 Week Longitudinal Study Using Accelerometry and the Doubly Labeled Water Method.

BackgroundPhysical activity is recommended to promote healthy aging. Defining the importance of activities such as walking in achieving higher levels of physical activity might provide indications for interventions.ObjectiveTo describe the importance of walking in achieving higher levels of physical activity in older adults.MethodsThe study included 42 healthy subjects aged between 51 and 84 years (mean body mass index 25.6 kg/m2 [SD 2.6]). Physical activity, walking, and nonwalking activity were monitored with an accelerometer for 2 weeks. Physical activity was quantified by accelerometer-derived activity counts. An algorithm based on template matching and signal power was developed to classify activity counts into nonwalking counts, short walk counts, and long walk counts. Additionally, in a subgroup of 31 subjects energy expenditure was measured using doubly labeled water to derive physical activity level (PAL).ResultsSubjects had a mean PAL of 1.84 (SD 0.19, range 1.43-2.36). About 20% of the activity time (21% [SD 8]) was spent walking, which accounted for about 40% of the total counts (43% [SD 11]). Short bouts composed 83% (SD 9) of walking time, providing 81% (SD 11) of walking counts. A stepwise regression model to predict PAL included nonwalking counts and short walk counts, explaining 58% of the variance of PAL (standard error of the estimate=0.12). Walking activities produced more counts per minute than nonwalking activities (P<.001). Long walks produced more counts per minute than short walks (P=.001). Nonwalking counts were independent of walking counts (r=−.05, P=.38).ConclusionsWalking activities are a major contributor to physical activity in older adults. Walking activities occur at higher intensities than nonwalking activities, which might prevent individuals from engaging in more walking activity. Finally, subjects who engage in more walking activities do not tend to compensate by limiting nonwalking activities.Trial RegistrationClinicalTrials.gov NCT01609764; https://clinicaltrials.gov/ct2/show/NCT01609764 (Archived by WebCite at http://www.webcitation.org/6grls0wAp)

Exploring quantitative structure–activity relationship (QSAR) models for some biologically active catechol structures using PC-LS-SVM and PC-ANFIS

Exploring predictive QSAR models for dopamine catechol structures could be used in designing more potent ligands. In this study, efforts were taken to find out the most important molecular features responsible for the biological activity of catechol structures. All 2D descriptors of Dragon including constitutional, topological, molecular walk counts, BCUT descriptors, Galvez topological, 2D autocorrelations, functional groups, atom-centred fragments, empirical descriptors and properties were calculated for the structures. Two non-linear modelling methods (PC-LS-SVM and PC-ANFIS) were used and compared in this QSAR study. The results revealed the more predictive ability of PC-LS-SVM in the QSAR analysis of the compounds with catechol substructure. The roles of topological properties and number of hydrogen bond donors group as molecular features responsible for the activity of the compounds were discussed. The obtained QSAR models can be used in future studies of drug development for human dopamine D2 receptor.

Population ecology of a California endemic: Speyeria adiaste clemencei

Effective management and recovery of threatened insect populations requires detailed ecological information. Here we combine count and mark recapture (MR) data to shed light on an understudied declining endemic butterfly species in California’s Southern Coast Ranges, Speyeria adiaste. Little is known about the number, size, and dynamics of S. adiaste populations, leaving few data on which to base conservation decisions. Our goal in this study was therefore to provide increased understanding of the population ecology of this species by studying a long-standing S. adiaste clemencei population. Our 2-year MR estimates were highly correlated with Pollard walk counts, and we observed declining population sizes from 2011 to 2014. Adult movements were well-described by a negative exponential function, indicating low probability of dispersal >5 km (probability < 1.8 × 10−7 for both sexes). Males had shorter lifespans than females. Coupled with lack of diapause in this species, the short life span and limited dispersal observed here indicate that S. adiaste clemencei does not have a strong capacity for re-colonization. Population declines in S. adiaste may lead to local extinctions, and together with low dispersal, may diminish connectivity among the apparently small and isolated remaining populations. Further research into the role of adult and larval resources for determining adult abundance, coupled with continued long-term monitoring is necessary in order to understand population dynamics in this declining endemic species.

Population Biology and Behavior of the ImperiledPhilotiella leona(Lycaenidae) in South Central Oregon

ABSTRACT. The sole known metapopulation of Philotiella leona on the Mazama Tree Farm (MTF) and adjacent areas of the Winema National Forest (WNF) in the Antelope Desert of Klamath County, Oregon was surveyed and studied during 2011–2013. The flight period extended from mid-June to mid-late July or early August (35–47 days) with higher temperatures in 2013 associated with the shortest period. Populations of P. leona based on Pollard walk counts were 4–5 X greater at 4 MTF sites than a WNF site. Populations appeared to be twice as large in 2011 than 2012 or 2013. Greatest numbers were seen in late June-early July with a gradual decline thereafter. Flight activity was meandering and low to the ground and mostly occurred after midday, as did mating and oviposition. Nectaring was observed on 9 plant species with Eriogonum umbellatum most favored. A mark, release and recapture (MRR) study was conducted at one location (∼ 0.4 ha) in the MTF with 214, 95 and 105 adults marked in 2011, 2012 and 2013, respectively....

Abstract P441: Walking Patterns on Weekdays, Weekends, and Holidays in a Large, Metropolitan U.S. Airport: The Walk to Fly Study

Objective: Many airports provide environments conducive for walking but little is known about walking patterns. The objective of this study is to characterize airport walking volume and mode choice by day of the week and around holidays. Methods: Individuals entering the transportation mall of a large, metropolitan U.S. airport may ride the train, walk, or use a moving walkway to get to their gate. Six ceiling-mounted, infrared sensors count individuals who enter the transportation mall, and those who walk or use the moving walkway. Train riders are the difference between the entry, and the walk and walkway counts. Holiday travel days occurred before and after the 2013 July 4 (July 3 and 7) and Labor Day (Aug 30 and Sept 3) holidays. Descriptive and logistic regression analyses were performed using R version 2.14.1. Results: Data collection occurred from June 15 -- Oct 2, 2013. On average, more than 46,000 individuals entered the transportation mall daily where 89.9% rode the train, 4.6% walked, and 5.5% used the moving walkway. The mean volume of individuals ranged from 34,430 on Saturdays to 48,411 on Mondays (Figure). The mean percentage of individuals walking or using the moving walkway was 10.5% on weekdays and 9.3% on weekends. The odds of walking or using the moving walkway were lower on weekends (versus weekdays) [OR 0.873 (95% CI: 0.867-0.879)] and around holidays (versus non-holidays) [OR 0.912 (0.898-0.925)]. Conclusion: In a large, metropolitan U.S. airport, walking or using the moving walkway is less common on weekends and on days surrounding holidays. Airport walking interventions may need to consider differences in walking patterns on weekdays, weekends, and holidays.

Influence of social and built environment features on children walking to school: An observational study

ObjectivesTo estimate the proportion of children living within walking distance who walk to school in Toronto, Canada and identify built and social environmental correlates of walking. MethodsObservational counts of school travel mode were done in 2011, at 118 elementary schools. Built environment data were obtained from municipal sources and school field audits and mapped onto school attendance boundaries. The influence of social and built environmental features on walking counts was analyzed using negative binomial regression. ResultsThe mean proportion observed walking was 67% (standard deviation=14.0). Child population (incidence rate ratio (IRR) 1.36), pedestrian crossover (IRR 1.32), traffic light (IRR 1.19), and intersection densities (IRR 1.03), school crossing guard (IRR 1.14) and primary language other than English (IRR 1.20) were positively correlated with walking. Crossing guard presence reduced the influence of other features on walking. ConclusionsThis is the first large observational study examining school travel mode and the environment. Walking proportions were higher than those previously reported in Toronto, with large variability. Associations between population density and several roadway design features and walking were confirmed. School crossing guards may override the influence of roadway features on walking. Results have important implications for policies regarding walking promotion.