Temporal Plots Research Articles

A sensitivity analysis of cellular automata and heterogeneous topology networks: partially-local cellular automata and homogeneous homogeneous random boolean networks

ABSTRACT Elementary Cellular Automata (ECA) are well-studied computational universes capable of impressive computational variety, but harnessing their potential has been challenging. When combined with Reservoir Computing (RC), harnessing this computation becomes feasible, and furthermore enables energy efficient AI. This study compares ECA reservoirs to topological heterogeneous and more biological plausible counterparts of Partially-Local CA (PLCA) and Homogeneous Homogeneous Random Boolean Networks (HHRBN). Using the 5-bit memory benchmark, Temporal Derrida plots and collapse rate, finding are that more disordered topology does not equate to more disordered computation and moreover the evidence suggest this heterogeneity shrinks the critical range.

Spatiotemporal investigation of near-surface CH4 and factors influencing CH4 over South, East, and Southeast Asia

Methane (CH4) is the second most abundant greenhouse gas after CO2, which plays the most important role in global and regional climate change. To explore the long-term spatiotemporal variations of near-surface CH4, datasets were extracted from Greenhouse gases Observing SATellite (GOSAT), and the Copernicus Atmospheric Monitoring Service (CAMS) reanalyzed datasets from June 2009 to September 2020 over South, East, and Southeast Asia. The accuracy of near-surface CH4 from GOSAT and CAMS was verified against surface observatory stations available in the study region to confirm both dataset applicability and results showed significant correlations. Temporal plots revealed continuous inflation in the near-surface CH4 with a significant seasonal and monthly variation in the study region. To explore the factors affecting near-surface CH4 distribution, near-surface CH4 relationship with anthropogenic emission, NDVI data, wind speed, temperature, precipitation, soil moisture, and relative humidity were investigated. The results showed a significant contribution of anthropogenic emissions with near-surface CH4. Regression and correlation analysis showed a significant positive correlation between NDVI data and near-surface CH4 from GOSAT and CAMS, while a significant negative correlation was found between wind and near-surface CH4. In the case of temperature, soil moisture, and near-surface CH4 from GOSAT and CAMS over high CH4 regions of the study area showed a significant positive correlation. However significant negative correlations were found between precipitation and relative humidity with GOSAT and CAMS datasets over high CH4 regions in South, East, and Southeast Asia. Moreover, these climatic factors showed no significant correlation within the low near-surface CH4 areas in our study region. Our study results showed that anthropogenic emissions, NDVI data, wind speed, temperature, precipitation, soil moisture, and humidity could significantly affect the near-surface CH4 over South, East, and Southeast Asia.

Profiling targets and potential target pairs of CAR-T cell therapy in clinical trials

Since the approval of the first chimeric antigen receptor (CAR)-T product in 2017, the number of new CAR-T clinical trials worldwide exceeds 100 per year. 1649 clinical studies have been conducted to explore possible future clinical applications of targets or target pairs through different biotechnologies. In this study, we aim to take a data-driven analytical approach to explore potential dual-target pairs based on clinical trial information. We screened 1283 non-withdrawal interventional CAR-T clinical trials spanning 96 different targets and 74 target pairs from clinicaltrials.gov. Through the Circos plot and temporal network plots, the information between targets and indications was visualized. Based on the assumption that two targets of a target pair must target the same indication, five new target pairs were inferred, including CD19/CD7, CD19/CD5, CD19/CD37, and CD19/BAFFR and validated by expression pattern, literature and patent information. This study provides novel support for target profiling of CAR-T from the perspective of clinical trials and also provides a reference for researchers and developers to select new targets or target pairs of CAR-T cell therapy.

Elastic Field Evolution in Al–X (X = Sc, Zr, Er) Alloy During Heat Treatment, Insights From 3D-Multiphase Field Study

Abstract Microstructural evolution and resulting stress, strain, and concentration field distribution during Al3X (X = Sc, Zr, Er) precipitation in Al matrix are investigated in this work using the 3D-multiphase field method. Depending on the heat treatment, modulus mismatch, lattice parameter mismatch, and interfacial free energy, precipitate developed to rhombicuboctahedron, and near cuboidal morphologies. The composition distribution and Al–Al3X transformation driving force map identified a difference in precipitation kinetics for each alloy. The precipitation mechanism in the three systems is analyzed in detail with temporal evolution plots of energy components during phase transformation. Al3Er precipitate exhibits the highest growth rate due to Er's high diffusivity and significant lattice parameter mismatch in the Al–Er system. The system has a high chemical and elastic driving force for particle growth, thus attaining quasi-static equilibrium at a relatively lower temperature and time. Therefore, this system observes high magnitude stress, strain, and strain energy field around the Al matrix. The theoretical simulation results obtained from the present study will benefit aluminum multicomponent alloy design for high-strength applications.

The PaleoJump database for abrupt transitions in past climates

Tipping points (TPs) in Earth’s climate system have been the subject of increasing interest and concern in recent years, given the risk that anthropogenic forcing could cause abrupt, potentially irreversible, climate transitions. Paleoclimate records are essential for identifying past TPs and for gaining a thorough understanding of the underlying nonlinearities and bifurcation mechanisms. However, the quality, resolution, and reliability of these records can vary, making it important to carefully select the ones that provide the most accurate representation of past climates. Moreover, as paleoclimate time series vary in their origin, time spans, and periodicities, an objective, automated methodology is crucial for identifying and comparing TPs. To address these challenges, we introduce the open-source PaleoJump database, which contains a collection of carefully selected, high-resolution records originating in ice cores, marine sediments, speleothems, terrestrial records, and lake sediments. These records describe climate variability on centennial, millennial and longer time scales and cover all the continents and ocean basins. We provide an overview of their spatial distribution and discuss the gaps in coverage. Our statistical methodology includes an augmented Kolmogorov–Smirnov test and Recurrence Quantification Analysis; it is applied here, for illustration purposes, to selected records in which abrupt transitions are automatically detected and the presence of potential tipping elements is investigated. These transitions are shown in the PaleoJump database along with other essential information about the records, including location, temporal scale and resolution, as well as temporal plots. This open-source database represents, therefore, a valuable resource for researchers investigating TPs in past climates.

Height-diameter relationships and stem volume equations in young and middle-aged forest stands of Ukraine

Height-diameter (h-d) relationships in forest stands are commonly used in various scientific and practical forestry applications. Accurate h-d models combined with tree stem volume equations are recognised to be effective in growing stock volume estimation. The purpose of the study was threefold: 1) development of a set of mathematical models of the h-d relationship in young and middle-aged forest stands for ten forest-forming species in Ukraine; 2) modelling stem volume in above mentioned forest stands; 3) comparison of established mathematical models with corresponding ones for premature, mature, and overmature forest stands. The study was based on permanent and temporal sample plots data (about 600) established in forest stands during 1950s-2020s within the most forested regions of Ukraine (Polissia, Forest-Steppe, Carpathians). In total, about 10,000 sample trees were measured on the sample plots to accurately estimate their stem volume outside bark. The h-d models demonstrated very similar relationships between stem heights and diameters for most of our species except for spruce and fir in mountain Carpathian forests where the steeper h-d curves were obtained. The study revealed that birch and hornbeam tree stems had the lowest volumes among surveyed species. The results also indicated that tree species tend to have higher volumes (up to 7% for coniferous, and up 10% for aspen and birch forests) in young and middle-aged forest sands than in older ones. For the other species, a statistically significant difference between stem volumes of trees of different ages was not observed. The developed mathematical models can complement the corresponding models for older groups of forest stands since they revealed an important aspect of relationships between the key tree stem parameters. These models are also applicable for a more precise stem volume estimation during thinning operations in the young and middle-aged forests in Ukraine

Predictors of dominance rank and agonistic interactions in captive Livingstone's fruit bats.

Male dominance hierarchies have been studied in many animals but rarely in bats (Chiroptera). The dominance rank of social animals may dictate access to resources and mates; therefore, it has important implications for an individual's fitness and is crucial for successful captive management. Between January and December 2018, at both Bristol Zoo Gardens (Bristol, UK) and Jersey Zoo (Jersey, British Isles), we observed 19 male Livingstone's fruit bats Pteropus livingstonii using focal follows for 345 h overall, noting the outcome of all agonistic interactions. We recorded instigators of interactions, along with winners and losers, and analyzed these data using the R-package "EloRating" to create Elo-rating temporal plots of dominance ranks. We used generalized linear mixed models and multiple linear regression to analyze interaction data and test hypotheses regarding predictors of dominance rank, frequency of agonistic interaction, and choice of interaction partner. Age was positively correlated with dominance rank up to around year 9, when an asymptote was attained. Highly ranked bats instigated the most agonistic interactions, and largely directed these interactions at bats with much lower rankings than themselves. Hierarchies were extremely stable throughout the data collection period at both sites. We conclude that Livingstone's fruit bats have a stable linear dominance hierarchy, with high-ranking, typically older males instigating the most interactions with lowest ranking males to secure dominance rank. This study adds to the limited discourse on Pteropus social behaviors, indicating that some bat species may have social systems similar in complexity to some nonhuman primates.

Multisource and temporal variability in Portuguese hospital administrative datasets: Data quality implications

BackgroundUnexpected variability across healthcare datasets may indicate data quality issues and thereby affect the credibility of these data for reutilization. No gold-standard reference dataset or methods for variability assessment are usually available for these datasets. In this study, we aim to describe the process of discovering data quality implications by applying a set of methods for assessing variability between sources and over time in a large hospital database. MethodsWe described and applied a set of multisource and temporal variability assessment methods in a large Portuguese hospitalization database, in which variation in condition-specific hospitalization ratios derived from clinically coded data were assessed between hospitals (sources) and over time. We identified condition-specific admissions using the Clinical Classification Software (CCS), developed by the Agency of Health Care Research and Quality. A Statistical Process Control (SPC) approach based on funnel plots of condition-specific standardized hospitalization ratios (SHR) was used to assess multisource variability, whereas temporal heat maps and Information-Geometric Temporal (IGT) plots were used to assess temporal variability by displaying temporal abrupt changes in data distributions. Results were presented for the 15 most common inpatient conditions (CCS) in Portugal. Main findingsFunnel plot assessment allowed the detection of several outlying hospitals whose SHRs were much lower or higher than expected. Adjusting SHR for hospital characteristics, beyond age and sex, considerably affected the degree of multisource variability for most diseases. Overall, probability distributions changed over time for most diseases, although heterogeneously. Abrupt temporal changes in data distributions for acute myocardial infarction and congestive heart failure coincided with the periods comprising the transition to the International Classification of Diseases, 10th revision, Clinical Modification, whereas changes in the Diagnosis-Related Groups software seem to have driven changes in data distributions for both acute myocardial infarction and liveborn admissions. The analysis of heat maps also allowed the detection of several discontinuities at hospital level over time, in some cases also coinciding with the aforementioned factors. ConclusionsThis paper described the successful application of a set of reproducible, generalizable and systematic methods for variability assessment, including visualization tools that can be useful for detecting abnormal patterns in healthcare data, also addressing some limitations of common approaches. The presented method for multisource variability assessment is based on SPC, which is an advantage considering the lack of gold standard for such process. Properly controlling for hospital characteristics and differences in case-mix for estimating SHR is critical for isolating data quality-related variability among data sources. The use of IGT plots provides an advantage over common methods for temporal variability assessment due its suitability for multitype and multimodal data, which are common characteristics of healthcare data. The novelty of this work is the use of a set of methods to discover new data quality insights in healthcare data.

Reduction of emissions by avoided deforestation in andean high-land tropical forests

Deforestation and forest degradation, mainly in areas with high carbon density, is one of the most important source of greenhouse gases (GHG). The impact of deforestation on carbon storage in total biomass and its CO2 emissions is analyzed in four land covers in the Santuario de Fauna y Flora Iguaque (SFFI), Boyacá, Colombia. A total of 32 temporal sampling plots (TSP) of 250m2 was established to measure trees with diameter at breast height (dbh) ³ 10 cm whereas 17 TSP of 36m2 were established to measure total and stipe height of all frailejones (Espeletia boyacensis Cuatrec, E. tunjana Cuatrec and E. cf. Incana). Above and belowground biomass was estimated with allometric models, whereas carbon was calculated using the 0.47 fraction. The sampling area was proportional to the area of each land cover: open heathlands and moorlands (OMH), dense heathlands and moorlands (DMH), broad-leaved forest with continuous canopy, not on mire (BFCC), natural grassland prevailingly without trees and shrubs (NSWT). BFCC and DMH showed higher carbon storage in biomass (55 and 27Mg C/ha, respectively). SFFI stored around 135.9Gg C, from which 25-38Gg CO2e could be emitted to the atmosphere in the 20 next years if the deforestation rates continue. BFCC and OMH are the covers with the highest potential of CO2 emissions to the atmosphere. Therefore, prioritizing the preservation of these ecosystems by Reducing Emissions from Deforestation and Degradation (REDD+) programs, is a key to counter the effects of climate change and ensure the supply of ecosystemic services that support local communities’ livelihoods.

Unsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data

Global crop mapping and monitoring requires high-resolution spatio-temporal information. In this regard, dual polarimetric Synthetic Aperture Radar (SAR) sensors provide high temporal and high spatial resolutions with large swath width. Generally, crop phenological development studies utilized SAR backscatter intensity-based descriptors. However, these descriptors are derived either from the covariance matrix elements or from the eigendecomposition. Therefore, this approach fails to utilize the complete polarization information of the scattered wave. In this study, we propose a target characterization parameter, θxP that utilizes the 2D Barakat degree of polarization and the elements of the covariance matrix. We also propose an unsupervised clustering scheme using θxP and the scattering entropy, HxP. We utilize time-series Sentinel-1 data of canola and wheat fields over a Canadian test site to show the sensitivity of θxP to the development of crop morphology at different phenological stages. During the initial growth stages, θxP values are low due to the low vegetation density. In contrast, at advanced phenological stages, we observe decreased values of θxP due to the appearance of complex canopy structure. Similarly, the effectiveness of the unsupervised HxP/θxP clustering plane is also evident from the temporal clustering plots. This innovative clustering framework is beneficial for the operational use of Sentinel-1 SAR data for agricultural applications.

Carbon capture in three land use systems in the Colombian Amazonia

The main strategies to combat climate change are reducing greenhouse gas (GHG) emissions and increasing carbon sinks in terrestrial ecosystems such as forests, forest plantations, and agroforestry systems. Deforestation and land use changes in the Amazonia bear great responsibility both for the fixation and emission of GHG. The aim of this research was to estimate the carbon stored in above-ground biomass of forests, rubber plantations (Hevea brasiliensis Muell Arg.), and trees in pastures in the Colombian Amazonia piedmont. Data was collected in 40 farms located in the rural area of the municipality of Belén de Los Andaquíes (Colombia). A total of 174 temporal sampling plots of 250 m2 each were established (80 in forests, 40 in rubber plantations and 54 in pastures with trees). In these plots, the diameter at breast height (dbh) was measured in trees with dbh ≥ 10 cm, and the above-ground biomass was estimated with allometric models for the Colombian Amazon. The carbon stored was 154.1 Mg ha-1 in forests, 1.4 Mg ha-1 in pastures with trees and 138.9 Mg ha-1 in rubber plantations. Positive changes for mitigation of climate change could be achieved through the conversion of agricultural areas, mainly pastures, to forests (+560 Mg CO2 ha-1). Likewise, if deforestation stops in the area, the estimated emissions reduction would be 0.16 Tg CO2 year-1.

Assessing the Spatial and Temporal Relationship between Coastal Runoff and Chlorophyll-a in the Philippines Using Gridded Datasets

Custado, M.J.G. and David, C.P.C., 2021. Assessing the spatial and temporal relationship between coastal runoff and chlorophyll-a in the Philippines using gridded datasets. Journal of Coastal Research, 37(4), 726–736. Coconut Creek (Florida), ISSN 0749-0208.Publicly available gridded datasets were used in examining the relationship between long-term temporal patterns in runoff rates and chlorophyll-a within the coastal areas of the Philippines. Temporal plots generated from remotely sensed chlorophyll-a data and runoff from the Global Runoff Reconstruction dataset illustrated variabilities in seasonal patterns across coastal segments and climate types. The linear correlation analysis performed on the datasets revealed a characteristic distribution of negative coefficients within the internal seas of the archipelago and positive coefficients in open coasts and embayments. Riverine-influenced sites with positive correlation coefficients (r = 0.33–0.62, p < 0.00001) were mostly associated with delivery of excess nutrients from land-use activities. The distribution of these coefficients was demonstrated to be affected by the physical nature of the coast. Moreover, seasonal changes in total suspended solids adjacent to a river discharge site were discussed, demonstrating how high sediment load can dampen productivity because of limited light penetration in marine waters. The analysis of publicly available datasets in this study provided assessments that can be used to complement coastal management efforts, particularly in the context of increasing anthropogenic stress and climate change.

Development and validation of a novel nomogram to predict cancer-specific survival in patients with uterine cervical adenocarcinoma.

BackgroundThe treatment strategies and prognostic factors for uterine cervical adenocarcinoma (UAC) primarily refer to that for squamous cell carcinoma (SCC). However, the biological behavior, treatment outcomes of UAC differ from that of SCC. This study aimed to develop and validate a prognostic nomogram for predicting the probability of 3- and 5-year cancer-specific survival (CSS) in patients with UAC.MethodsA total of 8,991 UAC patients from the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. Patients diagnosed between 1988 and 2010 (n=5,655) were enrolled for model development and internal validation, and those diagnosed between 2011 and 2016 (n=3,336) were used for temporal validation. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to select predictors of CSS. Cox hazard regression analysis was used to construct the model, which was presented as a static nomogram and web-based dynamic nomogram. The nomogram was internally validated using the bootstrap resampling method and underwent temporal validation.ResultsTumor grade, stage T, stage N, stage M, tumor size, and surgery of the primary site were identified as independent prognostic factors for CSS and subsequently incorporated into construction of the nomogram. The nomogram could accurately predict 3- and 5-year CSS with an optimism adjusted c-statistic of 0.90 [95% confidence intervals (CI): 0.89–0.91] and 0.89 (95% CI: 0.88–0.91) after internal validation, respectively; while, after temporal validation, the statistics were 0.89 (95% CI: 0.87–0.91) and 0.88 (95% CI: 0.83–0.94), respectively. The internal and temporal calibration plots demonstrated good consistency between the predicted and observed values of CSS. Based on the model, the cases were stratified into high- and low-risk groups. The Kaplan-Meier plot showed that high-risk patients exhibited significantly poorer survival than those at low risk (P<0.0001). The prediction model exhibited a good discriminative ability and an optimal accuracy.ConclusionsIn the form of a static nomogram or an online calculator, an effective and convenient nomogram was developed and validated to help clinicians quantify the risk of mortality, make personalized survival assessments, and create optimal treatment plans for UAC patients.

Magnetic Hyperthermia and Photocatalytic Properties of MnFe2O4 Nanoparticles Synthesized by Solvothermal Reflux Method

Development of new superparamagnetic materials with narrow size distribution is crucial for biomedical and environmental applications. Hence, we report the synthesis of narrow size distributed single grain MnFe2O4 nanoparticles of average particle size 9 nm by solvothermal reflux method. Synthesized compound crystallized in face centered cubic spinel structure and is confirmed by X-ray diffraction profiles. Transmission electron micrograph shows narrow size distributed particles with an average particle size of 9 nm and is equal to crystallite diameter estimated from Scherrer equation. The spinel crystal structure is further confirmed by electron diffraction profiles, Fourier transformed infrared spectrum, and Raman spectrum at room temperature. Magnetic properties of the sample show superparamagnetic nature at room temperature with moderate saturated magnetization of 56.4 emug−1. Magnetic heating properties of nanoparticles dispersion show the attainment of hyperthermia temperature (43 °C) in a short span of time of 1.6 min for 2 mg/mL and 2.6 min for 1 mg/mL concentrations. Estimated specific heat generation rate or specific power absorption rate, from temporal temperature plots, is 145.78 Wg−1 and is useful for magnetic hyperthermia application in cancer therapy. Photocatalysis properties of sample show 96% of rhodamine B dye degradation in little less than 6 h under UV light irradiation and are useful for photocatalytic applications in wastewater treatment in industries.

EHRtemporalVariability: delineating temporal data-set shifts in electronic health records.

BackgroundTemporal variability in health-care processes or protocols is intrinsic to medicine. Such variability can potentially introduce dataset shifts, a data quality issue when reusing electronic health records (EHRs) for secondary purposes. Temporal data-set shifts can present as trends, as well as abrupt or seasonal changes in the statistical distributions of data over time. The latter are particularly complicated to address in multimodal and highly coded data. These changes, if not delineated, can harm population and data-driven research, such as machine learning. Given that biomedical research repositories are increasingly being populated with large sets of historical data from EHRs, there is a need for specific software methods to help delineate temporal data-set shifts to ensure reliable data reuse.ResultsEHRtemporalVariability is an open-source R package and Shiny app designed to explore and identify temporal data-set shifts. EHRtemporalVariability estimates the statistical distributions of coded and numerical data over time; projects their temporal evolution through non-parametric information geometric temporal plots; and enables the exploration of changes in variables through data temporal heat maps. We demonstrate the capability of EHRtemporalVariability to delineate data-set shifts in three impact case studies, one of which is available for reproducibility.ConclusionsEHRtemporalVariability enables the exploration and identification of data-set shifts, contributing to the broad examination and repurposing of large, longitudinal data sets. Our goal is to help ensure reliable data reuse for a wide range of biomedical data users. EHRtemporalVariability is designed for technical users who are programmatically utilizing the R package, as well as users who are not familiar with programming via the Shiny user interface.Availability: https://github.com/hms-dbmi/EHRtemporalVariability/Reproducible vignette: https://cran.r-project.org/web/packages/EHRtemporalVariability/vignettes/EHRtemporalVariability.htmlOnline demo: http://ehrtemporalvariability.upv.es/

Temporal Pass Plots: An intuitive method for visualising activity patterns of bats and other vocalising animals

Abstract Bats perform a number of ecosystem services but face numerous threats. Improved monitoring strategies are essential to better target conservation efforts, and passive acoustic monitoring (PAM) is being increasingly used to record bat activity. Although temporal patterns of bat activity can provide useful information about behaviour and habitat use, such assessments are rarely performed. Here, we describe the Temporal Pass Plot (TPP) that utilises audio file metadata to create highly detailed visualisations of bat activity, enabling direct comparisons of temporal activity patterns between different species and habitats. The example workflow and accompanying suite of utilities facilitate the production of TPPs with R or Excel. TPPs created using bat pass data revealed inter- and intra-specific differences in temporal activity that would normally be missed by typical assessments, which tend to measure bat activity using the total, or average, number of bat passes recorded on each night. This analysis illustrates how TPPs are capable of revealing useful insights into bat behaviour and expanding the scope of information acquired by PAM. TPPs provide an intuitive means of visualising patterns of bat activity over prolonged periods of time. With further study, activity patterns within TPPs could be related to specific behaviours such as roosting and commuting, enabling more detailed assessments of habitat use. Although bats are ideal candidates for TPPs, this technique could also be adapted for other species that can be monitored using time-stamped acoustic data.

Retention time prediction of hydrocarbons in cryogenically modulated comprehensive two-dimensional gas chromatography: A method development and translation application

Thermodynamic modeling of GC × GC separations provides a tool for rapid method evaluation and optimization. Separations of 95 hydrocarbons on two cryogenically modulated GC × GC systems (atmospheric outlet and vacuum outlet) are modeled, displaying average second dimension retention time modeling absolute errors of 0.17 s and 0.12 s respectively, and generating modeled chromatograms which sufficiently represent experimental data. A web-based GC × GC modeling routine is presented which allows users to model separations, currently focused on hydrocarbons, with full control over all system parameters. The method translation capabilities of the application are further demonstrated by replicating Piotrowski et al.’s GC × GC-HRT temporal distribution plots of hydraulic fracturing flowback fluid hydrocarbons [28].

Spatial-temporal variability of throughfall in a subtropical deciduous forest from the hilly regions of eastern China

Throughfall variability plays a crucial role in regulating hydrological and biogeochemical processes in forest ecosystems. However, throughfall variability and its potential influencing factors remain unclear in the subtropical deciduous forest because of its complex canopy and meteorological conditions. Here, the spatial variability and temporal stability of throughfall were investigated from October 2016 to December 2017 within a deciduous forest in the subtropical hilly regions of eastern China, and the effects of meteorological variables and distance from nearest tree trunk on throughfall variability were systematically evaluated. Throughfall variability during the leafed period was slightly higher than that during the leafless period inferred from the coefficient of variation of throughfall amounts (CVTF), with 13.2%–40.9% and 18.7%–31.9%, respectively. The multiple regression model analysis suggested that the controlling factors of throughfall variability were different in studied periods: Maximum 10-min rainfall intensity, wind speed and air temperature were the dominant influencing factors on throughfall variability during the leafed period, with the relative contribution ratio (RCR) of 25.9%, 18.7% and 8.9%, respectively. By contrast, throughfall variability was affected mainly by the mean rainfall intensity (RCR=40.8%) during the leafless period. The temporal stability plots and geostatistical analysis indicated that spatial patterns of throughfall were stable and similar among rainfall events. Our findings highlight the important role of various meteorological factors in throughfall variability and are expected to contribute to the accurate assessment of throughfall, soil water and runoff within the subtropical forests.

A Method for the Development of Dynamic Site Index Models Using Height–Age Data from Temporal Sample Plots

Knowledge of the potential productivity of forest sites is fundamental for making strategic decisions in forest management. Site productivity is usually evaluated using the site index, and therefore the development of site index models is one of the crucial tasks in forest research and forest management. This research aims to develop an effective method for building top-growth and site index models using data from temporary sample plots (TSP). Exploiting the advantages of the generalised algebraic difference approach (GADA), the proposed method overcomes the limitations of the guide curve method that has been to date used in site index modelling using TSPs data and allows to obtain only a set of anamorphic site index curves. The proposed approach enables the construction of dynamic site index models with polymorphism and variable asymptotes. Such models better reflect local, site-specific height growth trajectories and therefore allow more appropriate site index estimation. We tested the proposed method using data collected from 5105 temporary sample plots in Poland. Our results indicate that growth trend estimates using height–age measurements of TSPs may be valuable data for modelling top height growth. For these reasons, the proposed method can be very useful in forest management.

Comparing Sequential with Combined Spatiotemporal Clustering of Passenger Trips in the Public Transit Network Using Smart Card Data

Smart card datasets in the public transit network provide opportunities to analyse the behaviour of passengers as individuals or as groups. Studying passenger behaviour in both spatial and temporal space is important because it helps to find the pattern of mobility in the network. Also, clustering passengers based on their trips regarding both spatial and temporal similarity measures can improve group-based transit services such as Demand-Responsive Transit (DRT). Clustering passengers based on their trips can be carried out by different methods, which are investigated in this paper. This paper sheds light on differences between sequential and combined spatial and temporal clustering alternatives in the public transit network. Firstly, the spatial and temporal similarity measures between passengers are defined. Secondly, the passengers are clustered using a hierarchical agglomerative algorithm by three different methods including sequential two-step spatial-temporal (S-T), sequential two-step temporal-spatial (T-S), and combined one-step spatiotemporal (ST) clustering. Thirdly, the characteristics of the resultant clusters are described and compared using maps, numerical and statistical values, cross correlation techniques, and temporal density plots. Furthermore, some passengers are selected to show how differently the three methods put the passengers in groups. Four days of smart card data comprising 80,000 passengers in Brisbane, Australia, are selected to compare these methods. The analyses show that while the sequential methods (S-T and T-S) discover more diverse spatial and temporal patterns in the network, the ST method entails more robust groups (higher spatial and temporal similarity values inside the groups).