Two centuries for an almost complete community turnover from native to non-native species in a riverine ecosystem

Non-native species introductions affect freshwater communities by changing community compositions, functional roles, trait occurrences and ecological niche spaces. Reconstructing such changes over long periods is difficult due to limited data availability. We collected information spanning 215 years on fish and selected macroinvertebrate groups (Mollusca and Crustacea) in the inner-Florentine stretch of the Arno River (Italy) and associated water grid, to investigate temporal changes. We identified an almost complete turnover from native to non-native fish (1800: 92% native; 2015: 94% non-native species) and macroinvertebrate species (1800: 100% native; 2015: 70% non-native species). Non-native fish species were observed ~50 years earlier compared to macroinvertebrate species, indicating phased invasion processes. In contrast, α-diversity of both communities increased significantly following a linear pattern. Separate analyses of changes in α-diversities for native and non-native species of both fish and macroinvertebrates were nonlinear. Functional richness and divergence of fish and macroinvertebrate communities decreased non-significantly, as the loss of native species was compensated by non-native species. Introductions of non-native fish and macroinvertebrate species occurred outside the niche space of native species. Native and non-native fish species exhibited greater overlap in niche space over time (62%-68%) and non-native species eventually replaced native species. Native and non-native macroinvertebrate niches overlapped to a lesser extent (15%-30%), with non-natives occupying mostly unoccupied niche space. These temporal changes in niche spaces of both biotic groups are a direct response to the observed changes in α-diversity and species turnover. These changes are potentially driven by deteriorations in hydromorphology as indicated by alterations in trait modalities. Additionally, we identified that angling played a considerable role for fish introductions. Our results support previous findings that the community turnover from native to non-native species can be facilitated by, for example, deteriorating environmental conditions and that variations in communities are multifaceted requiring more indicators than single metrics.

Predicting the detection of leprosy in a hyperendemic area of Brazil: Using time series analysis

BACKGROUND

Brazil has the second highest prevalence of leprosy worldwide. Autoregressive integrated moving average models are useful tools in surveillance systems because they provide reliable forecasts from epidemiological time series.

AIM

To evaluate the temporal patterns of leprosy detection from 2001 to 2015 and forecast for 2020 in a hyperendemic area in northeastern Brazil.

METHODS

A cross-sectional study was conducted using monthly leprosy detection from the Brazil information system for notifiable diseases. The Box-Jenkins method was applied to fit a seasonal autoregressive integrated moving average model. Forecasting models (95% prediction interval) were developed to predict leprosy detection for 2020.

RESULTS

A total of 44,578 cases were registered with a mean of 247.7 cases per month. The best-fitted model to make forecasts was the seasonal autoregressive integrated moving average ((1,1,1); (1,1,1)). It was predicted 0.32 cases/100,000 inhabitants to January of 2016 and 0.38 cases/100,000 inhabitants to December of 2020.

LIMITATIONS

This study used secondary data from Brazil information system for notifiable diseases; hence, leprosy data may be underreported.

CONCLUSION

The forecast for leprosy detection rate for December 2020 was < 1 case/100,000 inhabitants. Seasonal autoregressive integrated moving average model has been shown to be appropriate and could be used to forecast leprosy detection rates. Thus, this strategy can be used to facilitate prevention and elimination programmes.

Are networks of trophic interactions sufficient for understanding the dynamics of multi-trophic communities? Analysis of a tri-trophic insect food-web time-series

Resource-consumer interactions are considered a major driving force of population and community dynamics. However, species also interact in many non-trophic and indirect ways and it is currently not known to what extent the dynamic coupling of species corresponds to the distribution of trophic links. Here, using a 10-year data set of monthly observations of a 40-species tri-trophic insect community and nonlinear time series analysis, we compare the occurrence and strengths of both the trophic and dynamic interactions in the insect community. The matching between observed trophic and dynamic interactions provides evidence that population dynamic interactions reflect resource-consumer interactions in the many-species community. However, the presence of a trophic interaction does not always correspond to a detectable dynamic interaction especially for top-down effects. Moreover a considerable proportion of dynamic interactions are not attributable to direct trophic interactions, suggesting the unignorable role of non-trophic and indirect interactions as co-drivers of community dynamics.

A Novel Measure Inspired by Lyapunov Exponents for the Characterization of Dynamics in State-Transition Networks

The combination of network sciences, nonlinear dynamics and time series analysis provides novel insights and analogies between the different approaches to complex systems. By combining the considerations behind the Lyapunov exponent of dynamical systems and the average entropy of transition probabilities for Markov chains, we introduce a network measure for characterizing the dynamics on state-transition networks with special focus on differentiating between chaotic and cyclic modes. One important property of this Lyapunov measure consists of its non-monotonous dependence on the cylicity of the dynamics. Motivated by providing proper use cases for studying the new measure, we also lay out a method for mapping time series to state transition networks by phase space coarse graining. Using both discrete time and continuous time dynamical systems the Lyapunov measure extracted from the corresponding state-transition networks exhibits similar behavior to that of the Lyapunov exponent. In addition, it demonstrates a strong sensitivity to boundary crisis suggesting applicability in predicting the collapse of chaos.

Spatio-temporal and time series analysis of bluetongue outbreaks with environmental factors extracted from Google Earth Engine (GEE) in Andhra Pradesh, India

This study describes the spatial and temporal patterns of bluetongue (BT) outbreaks with environmental factors in undivided Andhra Pradesh, India. Descriptive analysis of the reported BT outbreaks (n = 2,697) in the study period (2000-2017) revealed a higher frequency of outbreaks during monsoon and post-monsoon months. Correlation analysis of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), rainfall and relative humidity (RH) displayed a significant positive correlation with BT outbreaks (p < .05). Retrospective unadjusted space-time, adjusted temporal and spatial analysis detected two, five and two statistically significant (p < .05) clusters, respectively. Time series distribution lag analysis examined the temporal patterns of BT outbreaks with environmental, biophysical factors and estimated that a decrease in 1 unit of rainfall (mm) was associated with 0.2% increase in the outbreak at lag 12 months. Similarly, a 1°C increase in land surface temperature (LST) was associated with 6.54% increase in the outbreaks at lag 12 months. However, an increase in 1 unit of wind speed (m/s) was associated with a 16% decrease in the outbreak at lag 10 months. The predictive model indicated that the peak of BT outbreaks were from October to December, the post-monsoon season in Andhra Pradesh region. The findings suggest that environmental factors influence BT outbreaks, and due to changes in climatic conditions, we may notice higher numbers of BT outbreaks in the coming years. The knowledge of spatial and temporal clustering of BT outbreaks may assist in adopting proper measures to prevent and control the BT spread.

Case Report: Dynamic Interdependencies Between Complementary and Alternative Medicine (CAM) Practice, Urinary Interleukin-6 Levels, and Fatigue in a Breast Cancer Survivor

Background: This study investigated the influence of complementary and alternative medicine (CAM) techniques (i.e., Jin Shin Jyutsu, music, physiotherapy, Tai Chi, and energy healing) on urinary interleukin-6 (IL-6) levels and fatigue in a 49-year-old breast cancer survivor suffering from cancer-related fatigue and depression. Data were sampled under conditions of "life as it is lived." Methods: For 28 days, a female breast cancer survivor collected her full urine output in 12-h intervals from about 8 a.m. to 8 p.m. and from about 8 p.m. to 8 a.m. These urine samples were used to determine urinary IL-6 levels through ELISA and creatinine concentrations via HPLC. In 12-h intervals (every morning and evening), the patient completed the DIARI, which included fatigue measurement and notes on incidents and activities such as CAM practice. In addition, the patient was interviewed weekly to identify meaningful everyday incidents. In this context, CAM practice was also discussed. Time series analysis consisted of ARIMA modeling and cross-correlational analyses (p < 0.05). Results: When each CAM technique was considered separately in time series analysis, CAM was consistently associated with increases in urinary IL-6 release and decreases in fatigue. Furthermore, when all CAM techniques experienced as positive were included in one time series, a biphasic urinary IL-6 response pattern was found in which CAM practice was first preceded by decreases in IL-6 by 12-0 h and then followed by increases in IL-6 after 108-120 h. Finally, cross-correlations between IL-6 and fatigue showed that increases in IL-6 were followed by decreases in fatigue intensity after 48-60 h and, conversely, that decreases in fatigue intensity were followed by decreases in IL-6 after 24-36 h and 48-60 h. Conclusion: IL-6 increases and fatigue decreases highlight potential health-promoting effects of CAM practice. Moreover, a cyclic IL-6 pattern in response to all CAM activities experienced as positive underscores that CAM was meaningful to the patient. Additionally, a negative feedback circuit between IL-6 and fatigue intensity was detected. Taken together, this study confirms the necessity of integrating subjective meaning and dynamic complexity into biopsychosocial research in order to understand human functioning under real-life conditions.

Ecological forecasts reveal limitations of common model selection methods: predicting changes in beaver colony densities

Over the past two decades, there have been numerous calls to make ecology a more predictive science through direct empirical assessments of ecological models and predictions. While the widespread use of model selection using information criteria has pushed ecology toward placing a higher emphasis on prediction, few attempts have been made to validate the ability of information criteria to correctly identify the most parsimonious model with the greatest predictive accuracy. Here, we used an ecological forecasting framework to test the ability of information criteria to accurately predict the relative contribution of density dependence and density-independent factors (forage availability, harvest, weather, wolf [Canis lupus] density) on inter-annual fluctuations in beaver (Castor canadensis) colony densities. We modeled changes in colony densities using a discrete-time Gompertz model, and assessed the performance of four models using information criteria values: density-independent models with (1) and without (2) environmental covariates; and density-dependent models with (3) and without (4) environmental covariates. We then evaluated the forecasting accuracy of each model by withholding the final one-third of observations from each population and compared observed vs. predicted densities. Information criteria and our forecasting accuracy metrics both provided strong evidence of compensatory density dependence in the annual dynamics of beaver colony densities. However, despite strong within-sample performance by the most complex model (density-dependent with covariates) as determined using information criteria, hindcasts of colony densities revealed that the much simpler density-dependent model without covariates performed nearly as well predicting out-of-sample colony densities. The hindcast results indicated that the complex model over-fit our data, suggesting that parameters identified by information criteria as important predictor variables are only marginally valuable for predicting landscape-scale beaver colony dynamics. Our study demonstrates the importance of evaluating ecological models and predictions with long-term data and revealed how a known limitation of information criteria (over-fitting of complex models) can affect our interpretation of ecological dynamics. While incorporating knowledge of the factors that influence animal population dynamics can improve population forecasts, we suggest that comparing forecast performance metrics can likewise improve our knowledge of the factors driving population dynamics.

A Tutorial on Estimating Time-Varying Vector Autoregressive Models

Time series of individual subjects have become a common data type in psychological research. These data allow one to estimate models of within-subject dynamics, and thereby avoid the notorious problem of making within-subjects inferences from between-subjects data, and naturally address heterogeneity between subjects. A popular model for these data is the Vector Autoregressive (VAR) model, in which each variable is predicted by a linear function of all variables at previous time points. A key assumption of this model is that its parameters are constant (or stationary) across time. However, in many areas of psychological research time-varying parameters are plausible or even the subject of study. In this tutorial paper, we introduce methods to estimate time-varying VAR models based on splines and kernel-smoothing with/without regularization. We use simulations to evaluate the relative performance of all methods in scenarios typical in applied research, and discuss their strengths and weaknesses. Finally, we provide a step-by-step tutorial showing how to apply the discussed methods to an openly available time series of mood-related measurements.

Variations in Constitutive Properties of the Fluid Elicit Divergent Vibrational and Pressure Response Under Shock Wave Loading

We performed a characterization of the shock wave loading on the response of the specimen representing a simplified head model. A polycarbonate cylinder (2-in. outer diameter, wall thickness: 0.06 or 0.12 in.) was filled with two fluids: pure de-ionized water and 40% glycerol in water, which differ only slightly in their constitutive material properties. These two fluids were selected to represent the cerebrospinal fluid and cerebral blood, using their high strain rate viscosity as a primary selection criterion. The model specimen was exposed to a single shock wave with two nominal intensities: 70 and 130 kPa overpressure. The response of the model was measured using three strain gauges and three pressure sensors, one mounted on the front face of the cylinder and two embedded in the cylinder to measure the pressure inside of the fluid. We noted several discriminant characteristics in the collected data, which indicate that the type of fluid is strongly influencing the response. The vibrations of the cylinder walls are strongly correlated with the fluid kind. The similarity analysis via the Pearson coefficient indicated that the pressure waveforms in the fluid are only moderately correlated, and these results were further corroborated by Euclidean distance analysis. Continuous wavelet transform of pressure waveforms revealed that the frequency response is strongly correlated with the properties of the fluid. The observed differences in strain and pressure modalities stem from relatively small differences in the properties of the fluids used in this study.

Temporal analysis of visceral leishmaniasis between 2000 and 2019 in Ardabil Province, Iran: A time-series study using ARIMA model

Background:

Visceral leishmaniasis in human (VLH) also known as kala-azar is a neglected disease of humans that mainly occurs in more than 50 countries mostly located in the Eastern Mediterranean and the Northern America.

Objective:

The purpose of this study was to determine the temporal patterns and predict of occurrence of VL in Ardabil Province, in northwestern Iran using autoregressive integrated moving average (ARIMA) models.

Methods:

This descriptive study employed yearly and monthly data of 602 cases of VLH in the province between January 2000 to December 2019, which was provided by the leishmaniasis national surveillance system. The monthly occurrences case constructed the ARIMA model of time-series model. The insignificance of the correlation in the lags of 12, 24 and 36 months, and Chi-square test showed the occurrence of VLH does not have a seasonal pattern. Eleven potential ARIMA models were examined for VLH cases. Finally, the best model was selected with the lower Akaike Information Criteria (AIC) and Bayesian information criterion (BIC) value. Then, the selected model was used to forecast frequency of monthly occurrences case. The forecasting precision was estimated by mean absolute percentage error (MAPE). Data analysis was performed using Stata14 and its package time series analysis.

Results:

ARIMA (5, 0, 1) model with AIC (25.7) and BIC (43.35) was selected. The MAPE value was 26.89% and the portmanteau test for white noise was (Q = 23.02, P = 0.98) for the residuals of the selected model showed that the data were fully modelled. The total cumulative VLH cases in the next 24 months’ in Ardabil province predicted 14 cases (95% CI: 4-54 case).

Conclusion:

The ARIMA (5, 0, 1) model can be a useful tool to predict VLH cases as early warning system and the results are helpful for policy makers and primary care physicians in the readiness of public health problems before the outbreak of the disease.

A Comprehensive Framework for Uncovering Non-Linearity and Chaos in Financial Markets: Empirical Evidence for Four Major Stock Market Indices

The presence of chaos in the financial markets has been the subject of a great number of studies, but the results have been contradictory and inconclusive. This research tests for the existence of nonlinear patterns and chaotic nature in four major stock market indices: namely Dow Jones Industrial Average, Ibex 35, Nasdaq-100 and Nikkei 225. To this end, a comprehensive framework has been adopted encompassing a wide range of techniques and the most suitable methods for the analysis of noisy time series. By using daily closing values from January 1992 to July 2013, this study employs twelve techniques and tools of which five are specific to detecting chaos. The findings show no clear evidence of chaos, suggesting that the behavior of financial markets is nonlinear and stochastic.

Time Series Analysis of Climate and Air Pollution Factors Associated with Atmospheric Nitrogen Dioxide Concentration in Japan

Nitrogen dioxide (NO₂) is an air pollutant discharged from combustion of human activities. Nitrous acid (HONO), measured as NO₂, is thought to impact respiratory function more than NO₂. HONO and NO₂ have an equilibrium relationship, and their reaction is affected by climate conditions. This study was conducted to discuss the extent of HONO contained in NO₂, depending on the level of urbanization. Whether climate conditions that promote HONO production enhanced the level of NO₂ measured was investigated using time series analysis. Climate and outdoor air pollution data measured in April 2009-March 2017 in urban (Tokyo, Osaka, and Aichi) and rural (Yamanashi) areas in Japan were used for the analysis. Air temperature had a trend of negative associations with NO₂, which might indicate the decomposition of HONO in the equilibrium between HONO and NO₂. The associations of relative humidity with NO₂ did not have consistent trends by prefecture: humidity only in Yamanashi was positively associated with NO₂. In high relative humidity conditions, the equilibrium goes towards HONO production, which was observed in Yamanashi, suggesting the proportion of HONO in NO₂ might be low/high in urban/rural areas.

Spatial and Temporal Analysis of Plasmodium knowlesi Infection in Peninsular Malaysia, 2011 to 2018

The life-threatening zoonotic malaria cases caused by Plasmodium knowlesi in Malaysia has recently been reported to be the highest among all malaria cases; however, previous studies have mainly focused on the transmission of P. knowlesi in Malaysian Borneo (East Malaysia). This study aimed to describe the transmission patterns of P. knowlesi infection in Peninsular Malaysia (West Malaysia). The spatial distribution of P. knowlesi was mapped across Peninsular Malaysia using Geographic Information System techniques. Local indicators of spatial associations were used to evaluate spatial patterns of P. knowlesi incidence. Seasonal autoregressive integrated moving average models were utilized to analyze the monthly incidence of knowlesi malaria in the hotspot region from 2012 to 2017 and to forecast subsequent incidence in 2018. Spatial analysis revealed that hotspots were clustered in the central-northern region of Peninsular Malaysia. Time series analysis revealed the strong seasonality of transmission from January to March. This study provides fundamental information on the spatial distribution and temporal dynamic of P. knowlesi in Peninsular Malaysia from 2011 to 2018. Current control policy should consider different strategies to prevent the transmission of both human and zoonotic malaria, particularly in the hotspot region, to ensure a successful elimination of malaria in the future.

Dynamics of dengue outbreaks in gangetic West Bengal: A trend and time series analysis

Background and Aims:

Dengue is a vector-borne viral disease which is one of the major causes of public health problem in India, and its control is often the major challenges of municipal bodies in the country, especially in West Bengal. The previous outbreaks of the disease can be used to forecast the future occurrence and burden, so that authorities may optimize the available resources in order to contain and minimize the impact.

Materials and Methods:

Weekly disease outbreak data were extracted from Integrated Disease Surveillance Programme website and arranged as monthly data. Mann-Kendall test was used to determine the significance of the disease trends in various districts of Gangetic West Bengal. Time series analysis was done by using Seasonal ARIMA method to predict the number of Dengue outbreak cases for the year 2020.

Results:

Murshidabad was the only district of Gangetic West Bengal that had a significant upward Dengue cases outbreak trend. Nadia had a downward trend but it was not statistically significant. Model SARIMA (1,0,0) (1,0,0) ₁₂ was chosen to forecast the Dengue outbreak cases which showed that the cases might start from the month of June, peak in August and wane off in October 2020. However, this prediction was not significant.

Conclusion:

Gangetic West Bengal might experience similar dengue cases as the previous year, but their numbers would be low. Only the district of Murshidabad would have upward trend. Knowledge in advance about periods of disease occurrence may enable health authorities to initiate control measures during the start of the outbreak season.

Global Forecasting Confirmed and Fatal Cases of COVID-19 Outbreak Using Autoregressive Integrated Moving Average Model

The world health organization (WHO) formally proclaimed the novel coronavirus, called COVID-19, a worldwide pandemic on March 11 2020. In December 2019, COVID-19 was first identified in Wuhan city, China, and now coronavirus has spread across various nations infecting more than 198 countries. As the cities around China started getting contaminated, the number of cases increased exponentially. As of March 18 2020, the number of confirmed cases worldwide was more than 250,000, and Asia alone had more than 81,000 cases. The proposed model uses time series analysis to forecast the outbreak of COVID-19 around the world in the upcoming days by using an autoregressive integrated moving average (ARIMA). We analyze data from February 1 2020 to April 1 2020. The result shows that 120,000 confirmed fatal cases are forecasted using ARIMA by April 1 2020. Moreover, we have also evaluated the total confirmed cases, the total fatal cases, autocorrelation function, and white noise time-series for both confirmed cases and fatalities in the COVID-19 outbreak.

Analysis of Emergency Department Visits and Hospital Activity during Influenza Season, COVID-19 Epidemic, and Lockdown Periods in View of Managing a Future Disaster Risk: A Multicenter Observational Study

ED-visits and through-ED admissions to medical/surgical wards (MSW) and intensive care unit (ICU) during influenza, COVID-19 and lockdown periods were evaluated in a four-hospital prospective observational study from November 2018 to March 2020. ED visit characteristics and main diagnostic categories were assessed. Analysis of 368,262 ED-visits highlighted a significantly increasing trend in ED-visits during influenza followed by a significantly decreasing trend after lockdown. For MSW-admissions, a pattern of growth during influenza was followed by a fall that began during COVID-19 pandemic and intensified during the lockdown. For ICU-admissions, a significant rise during the COVID-19 pandemic was followed by diminution during the lockdown period. During lockdown, significantly diminishing trends were shown for all diagnostic categories (between -40.8% and -73.6%), except influenza-like illness/COVID cases (+31.6%), Pulmonary embolism/deep vein thrombosis (+33.5%) and frequent users (+188.0%). The present study confirms an increase in demand during the influenza epidemic and during the initial phase of the COVID-19 epidemic, but a drop in activity during the lockdown, mainly related to non-COVID conditions. Syndromic surveillance of ILI cases in ED is a tool for monitoring influenza and COVID-19, and it can predict ED activity and the need for MSW and ICU beds.

Quantifying and Numerically Representing Recharge and Flow Components in a Karstified Carbonate Aquifer

Karstified carbonate aquifers are highly heterogeneous systems characterized by multiple recharge, flow, and discharge components. The quantification of the relative contribution of these components, as well as their numerical representation, remains a challenge. This paper identifies three recharge components in the time and frequency domain. While the analysis in the time domain follows traditional approaches, the analysis of the power spectrum allows frequencies associated with specific spectral coefficients and noise types to be distinguished more objectively. The analysis follows the presented hypothesis that the different frequency-noise components are the result of aquifer heterogeneity transforming the random rainfall input into a sequence of non-Gaussian signals. The distinct signals are then numerically represented in the context of a semidistributed pipe network model in order to simulate recharge, flow, and discharge of an Irish karst catchment more realistically. By linking the power spectra of the modeled recharge components with the spectra of the spring discharge, the information usually gained by classical performance indicators is significantly widened. The modeled spring discharge is well matched in the time and frequency domain, yet the different recharge dynamics explain the signal of the aquifer outlet in different noise domains across the spectrum. This study demonstrates the conjunctive use of frequency analysis in conceptualization of a hydrological system together with modeling and evaluation.

Estimating the power to detect a change caused by a vaccine from time series data

When evaluating the effects of vaccination programs, it is common to estimate changes in rates of disease before and after vaccine introduction. There are a number of related approaches that attempt to adjust for trends unrelated to the vaccine and to detect changes that coincide with introduction. However, characteristics of the data can influence the ability to estimate such a change. These include, but are not limited to, the number of years of available data prior to vaccine introduction, the expected strength of the effect of the intervention, the strength of underlying secular trends, and the amount of unexplained variability in the data. Sources of unexplained variability include model misspecification, epidemics due to unidentified pathogens, and changes in ascertainment or coding practice among others. In this study, we present a simple simulation framework for estimating the power to detect a decline and the precision of these estimates. We use real-world data from a pre-vaccine period to generate simulated time series where the vaccine effect is specified a priori. We present an interactive web-based tool to implement this approach. We also demonstrate the use of this approach using observed data on pneumonia hospitalization from the states in Brazil from a period prior to introduction of pneumococcal vaccines to generate the simulated time series. We relate the power of the hypothesis tests to the number of cases per year and the amount of unexplained variability in the data and demonstrate how fewer years of data influence the results.

Digitized Autism Observation Diagnostic Schedule: Social Interactions beyond the Limits of the Naked Eye

The complexity and non-linear dynamics of socio-motor phenomena underlying social interactions are often missed by observation methods that attempt to capture, describe, and rate the exchange in real time. Unknowingly to the rater, socio-motor behaviors of a dyad exert mutual influence over each other through subliminal mirroring and shared cohesiveness that escape the naked eye. Implicit in these ratings nonetheless is the assumption that the other participant of the social dyad has an identical nervous system as that of the interlocutor, and that sensory-motor information is processed similarly by both agents’ brains. What happens when this is not the case? We here use the Autism Diagnostic Observation Schedule (ADOS) to formally study social dyadic interactions, at the macro- and micro-level of behaviors, by combining observation with digital data from wearables. We find that integrating subjective and objective data reveals fundamentally new ways to improve standard clinical tools, even to differentiate females from males using the digital version of the test. More generally, this work offers a way to turn a traditional, gold-standard clinical instrument into an objective outcome measure of human social behaviors and treatment effectiveness.

A Quantitative Study on Crucial Food Supplies after the 2011 Tohoku Earthquake Based on Time Series Analysis

Awareness of the requested quantity and characteristics of emergency supplies is crucial for facilitating an efficient relief operation. With the aim of focusing on the quantitative study of immediate food supplies, this article estimates the numerical autoregressive integrative moving average (ARIMA) model based on the actual data of 14 key commodities in the Sendai City of Japan during the 2011 Tohoku earthquake. Although the temporal patterns of key food commodity groups are qualitatively similar, the results show that they follow different ARIMA processes, with different autoregressive moving averages and difference order patterns. A key finding is that 3 of the 14 items are significantly related to the number of temporary residents in shelters, revealing that the relatively low number of different items makes it easier to deploy these key supplies or develop regional purchase agreements so as to promptly obtain them from distributors.

Associations between monthly rainfall and mortality in cattle due to East Coast fever, anaplasmosis and babesiosis

Weather conditions can impact infectious disease transmission, causing mortalities in humans, wild and domestic animals. Although rainfall in dry tropical regions is highly variable over the year, rainfall is thought to play an important role in the transmission of tick-borne diseases. Whether variation in rainfall affects disease-induced mortalities, is, however, poorly understood. Here, we use long-term data on monthly rainfall and Boran cattle mortality (1998-2017) to investigate associations between within-year variation in rainfall and cattle mortalities due to East Coast fever (ECF), anaplasmosis and babesiosis in Laikipia, Kenya, using ARIMAX modelling. Results show a negative correlation between monthly rainfall and cattle mortality for ECF and anaplasmosis, with a lag effect of 2 and 6 months, respectively. There was no association between babesiosis-induced mortalities and monthly rainfall. The results of this study suggest that control of the tick-borne diseases ECF and anaplasmosis to reduce mortalities should be intensified during rainy periods after the respective estimated time lags following dry periods.

Humidity is a consistent climatic factor contributing to SARS-CoV-2 transmission

There is growing evidence that climatic factors could influence the evolution of the current COVID‐19 pandemic. Here, we build on this evidence base, focusing on the southern hemisphere summer and autumn period. The relationship between climatic factors and COVID‐19 cases in New South Wales, Australia was investigated during both the exponential and declining phases of the epidemic in 2020, and in different regions. Increased relative humidity was associated with decreased cases in both epidemic phases, and a consistent negative relationship was found between relative humidity and cases. Overall, a decrease in relative humidity of 1% was associated with an increase in cases of 7–8%. Overall, we found no relationship with between cases and temperature, rainfall or wind speed. Information generated in this study confirms humidity as a driver of SARS‐CoV‐2 transmission.

Finding Structure in Time: Visualizing and Analyzing Behavioral Time Series

The temporal structure of behavior contains a rich source of information about its dynamic organization, origins, and development. Today, advances in sensing and data storage allow researchers to collect multiple dimensions of behavioral data at a fine temporal scale both in and out of the laboratory, leading to the curation of massive multimodal corpora of behavior. However, along with these new opportunities come new challenges. Theories are often underspecified as to the exact nature of these unfolding interactions, and psychologists have limited ready-to-use methods and training for quantifying structures and patterns in behavioral time series. In this paper, we will introduce four techniques to interpret and analyze high-density multi-modal behavior data, namely, to: (1) visualize the raw time series, (2) describe the overall distributional structure of temporal events (Burstiness calculation), (3) characterize the non-linear dynamics over multiple timescales with Chromatic and Anisotropic Cross-Recurrence Quantification Analysis (CRQA), (4) and quantify the directional relations among a set of interdependent multimodal behavioral variables with Granger Causality. Each technique is introduced in a module with conceptual background, sample data drawn from empirical studies and ready-to-use Matlab scripts. The code modules showcase each technique's application with detailed documentation to allow more advanced users to adapt them to their own datasets. Additionally, to make our modules more accessible to beginner programmers, we provide a "Programming Basics" module that introduces common functions for working with behavioral timeseries data in Matlab. Together, the materials provide a practical introduction to a range of analyses that psychologists can use to discover temporal structure in high-density behavioral data.

Unsupervised KPIs-Based Clustering of Jobs in HPC Data Centers

Performance analysis is an essential task in high-performance computing (HPC) systems, and it is applied for different purposes, such as anomaly detection, optimal resource allocation, and budget planning. HPC monitoring tasks generate a huge number of key performance indicators (KPIs) to supervise the status of the jobs running in these systems. KPIs give data about CPU usage, memory usage, network (interface) traffic, or other sensors that monitor the hardware. Analyzing this data, it is possible to obtain insightful information about running jobs, such as their characteristics, performance, and failures. The main contribution in this paper was to identify which metric/s (KPIs) is/are the most appropriate to identify/classify different types of jobs according to their behavior in the HPC system. With this aim, we had applied different clustering techniques (partition and hierarchical clustering algorithms) using a real dataset from the Galician computation center (CESGA). We concluded that (i) those metrics (KPIs) related to the network (interface) traffic monitoring provided the best cohesion and separation to cluster HPC jobs, and (ii) hierarchical clustering algorithms were the most suitable for this task. Our approach was validated using a different real dataset from the same HPC center.

Are Strategies Favoring Pattern Matching a Viable Way to Improve Complexity Estimation Based on Sample Entropy?

It has been suggested that a viable strategy to improve complexity estimation based on the assessment of pattern similarity is to increase the pattern matching rate without enlarging the series length. We tested this hypothesis over short simulations of nonlinear deterministic and linear stochastic dynamics affected by various noise amounts. Several transformations featuring a different ability to increase the pattern matching rate were tested and compared to the usual strategy adopted in sample entropy (SampEn) computation. The approaches were applied to evaluate the complexity of short-term cardiac and vascular controls from the beat-to-beat variability of heart period (HP) and systolic arterial pressure (SAP) in 12 Parkinson disease patients and 12 age- and gender-matched healthy subjects at supine resting and during head-up tilt. Over simulations, the strategies estimated a larger complexity over nonlinear deterministic signals and a greater regularity over linear stochastic series or deterministic dynamics importantly contaminated by noise. Over short HP and SAP series the techniques did not produce any practical advantage, with an unvaried ability to discriminate groups and experimental conditions compared to the traditional SampEn. Procedures designed to artificially increase the number of matches are of no methodological and practical value when applied to assess complexity indexes.