Fine Temporal Scales Research Articles

A Deep Learning Method Based on Multi-Modality EEG for Automatic Depression Screening

Signal variability is linked to irregularities in time series caused by intrinsic nonlinearities of the neural system and can be measured on variable temporal scales over short time series. By measuring refined complex multiscale permutation entropy (RCMPE) from resting-state electroencephalography (EEG) data, we investigated the presence of a specific range of time scales characterizing major depressive disorder (MDD).We used an EEG dataset acquired from 22 MDD patients and 22 healthy controls in the eyes-closed (EC) and eyes-open (EO) states available on the PRED + CT website. Signal variability in both the EC and EO states was compared between the two groups, and their relationship to depressive symptom severity was examined.In the EC state, the RCMPE was higher in the MDD group than in the control group on a coarse temporal scale, approximately 20–32 ms, at almost all sensors. It also showed a negative correlation with depressive symptom severity on a fine temporal scale, approximately 2–26 ms, in the frontal, right temporal, and left parietal sensor areas in MDD. The EO state revealed a group difference but no relationship with depressive symptom severity.Our results suggested that the diagnosis of MDD as a trait and the severity of depressive symptoms as a state are linked to EEG signal variability on the coarse temporal scale and the fine scale in the resting state, respectively.Signal variability reflects different characteristics of depression depending on the temporal scale.

Seasonal and Age-Related Variation in Daily Travel Distances of California Condors

Despite a dramatic recovery from the brink of extinction, California Condors (Gymnogyps californianus) still face significant anthropogenic threats. Although condor movement patterns across large temporal scales are understood, less is known about their movements on a fine temporal scale. We used a trajectory-based analysis of GPS telemetry data gathered from condors during 2013 to 2018 to investigate the relationship between the distances condors travel in a day, demographic characteristics (e.g., age and sex), and time of year. Most (>71.4%) daily travel distances by condors were <100 km, and, on average, condors traveled 70.1 ± 60.9 km/d (x̄ ± SD). On two occasions one condor traveled >400 km in a single day (477 km one day and 415 km the following day). The tendency for condors to travel long distances increased with age, and condors traveled longer distances during the summer and when nesting. Traveling such long distances likely exposes birds to threats across a greater variety of landscapes than would be expected for birds that moved shorter distances. Given anticipated condor range expansion and population increase, this work highlights the importance of coordinating condor conservation across the broad spatial scales at which they move.

Hay meadow vibroscape and interactions within insect vibrational community

SummaryOur experiences shape our knowledge and understanding of the world around us. The natural vibrational environment (vibroscape) is hidden to human senses but is nevertheless perceived and exploited by the majority of animals. Here, we show that the vibroscape recorded on plants in a temperate hay meadow is a dynamic low-frequency world, rich in species-specific vibrational signals. The overall vibroscape composition changed throughout the season and also depended on the plant species, as well as on the spatial position of individual plants within the meadow. Within the studied community, vibrationally signaling species sharing this communication channel avoided interference primarily by partitioning vibrational space on a fine temporal scale. The vibroscape is a reliable source of information in the environment and expands our understanding of ecological and evolutionary processes.

Assessing changes in clusters of wildlife road mortalities after the construction of wildlife mitigation structures.

Collisions with vehicles can be a major threat to wildlife populations, so wildlife mitigation structures, including exclusionary fencing and wildlife crossings, are often constructed. To assess mitigation structure effectiveness, it is useful to compare wildlife road mortalities (WRMs) before, during, and after mitigation structure construction; however, differences in survey methodologies may make comparisons of counts impractical. Location‐based cluster analyses provide a means to assess how WRM spatial patterns have changed over time. We collected WRM data between 2015 and 2019 on State Highway 100 in Texas, USA. Five wildlife crossings and exclusionary fencing were installed in this area between September 2016 and May 2018 for the endangered ocelot (Leopardus pardalis) and other similarly sized mammals. Roads intersecting State Highway 100 were mitigated by gates, wildlife guards, and wing walls. However, these structures may have provided wildlife access to the highway. We combined local hot spot analysis and time series analysis to assess how WRM cluster intensity changed after mitigation structure construction at fine spatial and temporal scales and generalized linear regression to assess how gaps in fencing and land cover were related to WRM cluster intensity in the before, during, and after construction periods. Overall, WRMs/survey day decreased after mitigation structure construction and most hot spots occurred where there were more fence gaps, and, while cluster intensity increased in a few locations, these were not at fence gaps. Cluster intensity of WRMs increased when nearer to fence gaps in naturally vegetated areas, especially forested areas, and decreased nearer to fence gaps in areas with less natural vegetation. We recommend that if fence gaps are necessary in forested areas, less permeable mitigation structures, such as gates, should be used. Local hot spot analysis, coupled with time series and regression techniques, can effectively assess how WRM clustering changes over time.

Projected effects of climate change on shallow landslides, North Shore Mountains, Vancouver, Canada

Previous research in southwest British Columbia (BC) suggested that projected climate change would result in in increases in the frequency of debris flows on the order of a 10% by the end of the century compared to the last approximately 30 years (Jakob and Lambert, 2009). Advances in climate modeling now allow higher resolution simulation of precipitation compared to the work completed in 2009. Regional climate models have become better able to resolve convective activity at a finer temporal scale. This paper quantifies and projects changes in the frequency and magnitude of shallow landslides and resulting debris flows because of the impacts of climate change for the North Shore Mountains of Vancouver, BC. Analysis of a landslide database curated by Metro Vancouver shows a doubling in the frequency of landslides since 1981, prior to which recording was sporadic. How much of the increase since 1981 is attributable to recording improvements cannot be quantified which implies that it is too early to attribute frequency changes unambiguously to climate change. Prior to 2004, the average volume of shallow landslides was 1800 m3, whereas after 2004, this volume increased 7-fold to 13,000 m3. In particular, landslides with volumes exceeding 10,000 m3 appear to have increased abruptly since in the early 2000s. Using a Poisson model, we project an increase of up to 300% in the frequency of shallow landslides until the end of this century. A multivariate regression analysis suggests that this increase in landslide frequency will be accompanied by a potential increase in the average landslide volume of up to 50% for Relative Concentration Path (RCP) 8.5. Such changes may challenge watershed management attributable to increases in sediment transport rates, widening channels and in some cases may transition from single thread to braiding or anastomosing. The likelihood of landslides impacting and impounding water courses is projected to increase as is the turbidity in reservoirs because of the influx of fine-grained sediment. Outside of the Vancouver watersheds, infrastructures will increasingly be tested by more frequent and sometimes higher magnitude landslides.

Associations between minute-level smartphone GPS-derived exposure to greenness and consumer wearable-derived physical activity in the Nurses’ Health Study 3

BACKGROUND AND AIM: Increased exposure to greenness has been linked to increased physical activity and improved health outcomes, including better mental health and lower cardiovascular disease and mortality rates. However, most studies measure greenness exposure surrounding residential addresses, and do not account for each study participants’ movements. We examine minute-level smartphone GPS-derived exposure to greenness and consumer wearable-derived physical activity. METHODS: We assessed daily GPS-derived greenness exposure using wearable accelerometer data from participants in the US-based prospective Nurses’ Health Study 3 cohort mHealth Substudy (2018-2020). The mHealth substudy consisted of 348 female participants (mean age 35.7) who undertook 7-day sampling periods, four times across a year, to capture seasonal variability in greenness and physical activity. Normalized Difference Vegetation Index data were derived from 30m resolution Landsat imagery, which was spatially joined to the GPS points that were recorded every 10 minutes during sampling periods. Fitbit proprietary algorithms provided physical activity summarized as mean number of steps per minute for each 10-minute period. Generalized linear and additive mixed models were utilized to examine near momentary associations between greenness and physical activity adjusting for socioeconomic status, season, age, and smoking. RESULTS:Across study periods mean 10-minute step count ranged from 0-111.57 steps and 10-minute NDVI ranged from 0 to 0.71. Summer months had higher values and large ranges due to seasonal and individual-level mobility changes. Contrary to our hypotheses, higher non-linear greenness exposure was associated with lower mean steps per minute. Participants in the top greenness quintiles took 2.25 (95% CI 2.42, 2.08) fewer steps per 10-minute average compared to those with the lowest greenness quintile, after adjusting for confounders. CONCLUSIONS:We utilized objective data at fine temporal and spatial scales to present novel estimates on the real time association between greenness and physical activity. Increases in greenness were associated with small decreases in steps per minute. KEYWORDS: Activity Space, Green Space, Physical Activity, Mixed Models

The dual benefits of synchronized mating signals in a Japanese treefrog: attracting mates and manipulating predators.

In dense mating aggregations, such as leks and choruses, acoustic signals produced by competing male conspecifics often overlap in time. When signals overlap at a fine temporal scale the ability of females to discriminate between individual signals is reduced. Yet, despite this cost, males of some species deliberately overlap their signals with those of conspecifics, synchronizing signal production in the chorus. Here, we investigate two hypotheses of synchronized mating signals in a Japanese treefrog (Buergeria japonica): (1) increased female attraction to the chorus (the beacon effect hypothesis) and (2) reduced attraction of eavesdropping predators (the eavesdropper avoidance hypothesis). Our results from playback experiments on female frogs and eavesdropping micropredators (midges and mosquitoes) support both hypotheses. Signal transmission and female phonotaxis experiments suggest that away from the chorus, synchronized calls are more attractive to females than unsynchronized calls. At the chorus, however, eavesdroppers are less attracted to calls that closely follow an initial call, while female attraction to individual signals is not affected. Therefore, synchronized signalling likely benefits male B. japonica by both increasing attraction of females to the chorus and reducing eavesdropper attacks. These findings highlight how multiple selective pressures likely promoted the evolution and maintenance of this behaviour. This article is part of the theme issue 'Synchrony and rhythm interaction: from the brain to behavioural ecology'.

Crowdsourcing the perceived urban built environment via social media: The case of underutilized land

Crowdsourcing the public’s perceptions of the built environment in real time enables more responsive and agile infrastructure and land use planning. Social media has emerged to be an effective platform for citizens, engineers, and planners to communicate opinions and feelings transparently. However, a comprehensive terminological resource of the perceived built environment (BE) for consistent data collection and a specified analytical framework are still lacking, particularly for different underutilized land issues. To fill this knowledge gap, we demonstrate a BE-specific term construction and expansion method specifically for collecting Twitter data and propose a Geo-Topic-Sentiment analytical framework for retrieving and analyzing relevant tweets. We conduct a demonstrative study on un(der)utilized land-related BE terms across ten metropolitan statistical areas in the U.S. Findings reveal spatial variations in contents and sentiments about underutilized land environments, and more localized efforts may be required to address specific land use issues across different urban contexts. The research demonstrates Twitter as a useful platform in crowdsourcing perceived BE and sentiments at fine temporal and spatial scales in a timely manner. It contributes to engineering informatics by investigating the role of social media in environmental planning and proposing integrated domain-specific data analytic approaches for engineering practices.

Alpha and broadband high-frequency activity track task dynamics and predict performance in controlled decision-making.

Intracranial recordings in human subjects provide a unique, fine-grained temporal and spatial resolution inaccessible to conventional non-invasive methods. A prominent signal in these recordings is broadband high-frequency activity (approx. 70-150Hz), generally considered to reflect neuronal excitation. Here we explored the use of this broadband signal to track, on a single-trial basis, the temporal and spatial distribution of task-engaged areas involved in decision-making. We additionally focused on the alpha rhythm (8-14Hz), thought to regulate the (dis)engagement of neuronal populations based on task demands. Using these signals, we characterized activity across cortex using intracranial recordings in patients with intractable epilepsy performing the Multi-Source Interference Task, a Stroop-like decision-making paradigm. We analyzed recordings both from grid electrodes placed over cortical areas including frontotemporal and parietal cortex, and depth electrodes in prefrontal regions, including cingulate cortex. We found a widespread negative relationship between alpha power and broadband activity, substantiating the gating role of alpha in regions beyond sensory/motor cortex. Combined, these signals reflect the spatio-temporal pattern of task-engagement, with alpha decrease signifying task-involved regions and broadband increase temporally locking to specific task aspects, distributed over cortical sites. We report sites that only respond to stimulus presentation or to the decision report and, interestingly, sites that reflect the time-on-task. The latter predict the subject's reaction times on a trial-by-trial basis. A smaller subset of sites showed modulation with task condition. Taken together, alpha and broadband signals allow tracking of neuronal population dynamics across cortex on a fine temporal and spatial scale.

COVID-19 disruption reveals mass-tourism pressure on nearshore sea turtle distributions and access to optimal breeding habitat.

Quantifying the extent to which animals detect and respond to human presence allows us to identify pressure (disturbance) and inform conservation management objectively; however, obtaining baselines against which to compare human impact is hindered in areas where human activities are already well established. For example, Zakynthos Island (Greece, Mediterranean) receives around 850,000 visitors each summer, while supporting an important loggerhead sea turtle rookery (~300 individuals/season). The coronavirus (COVID‐19)‐driven absence of tourism in May–June 2020 provided an opportunity to evaluate the distribution dynamics of this population in the absence (2020) vs. presence (2018 and 2019) of visitors using programmed unmanned aerial system (UAS) surveys. Ambient sea temperature transitioned from suboptimal for breeding in May to optimal in late June, with turtle distribution appearing to shift from shallow (to benefit from waters 3–5°C above ambient) to deeper waters in 2018 and 2019, but not 2020. The 2020 data set demonstrated that increased tourism pressure, not temperature, drives turtles offshore. Specifically, >50% of turtles remained within 100 m of shore at densities of 25–50 visitors/km, even when sea temperature rose, with 2018 and 2019 data supporting this trend. Reduced access to warmer, nearshore waters by tourism could delay the onset of nesting and increase the length of the egg maturation period between nesting events (internesting interval) at this site. A coastal refuge zone could be delimited in May–June where touristic infrastructure is minimal, but also where turtles frequently aggregate. In conclusion, sea turtles appear capable of perceiving changes in the level of human pressure at fine spatial and temporal scales and adjusting their distribution accordingly.

Quantifying the trade-off between percentage of renewable supply and affordability in Pacific island countries: Case study of Samoa

Pacific island countries are particularly susceptible to sea level rise as a result of climate change and also face high costs and energy security issues from imported fossil fuels. As a result many Pacific island countries are perusing 100% renewable energy targets. Due to challenges with variable renewable energy sources, detailed studies that balance renewable energy supply and demand at a fine temporal scale have been carried out for many countries to inform policy directions. Despite having adopted renewable energy targets, most Pacific island countries have not been subject to the same level of detailed analysis in the academic literature. In addition, the results from other countries are not directly applicable given the particular local resources, climatic conditions and economic situation of Pacific island countries. In this paper, focusing on the case of Samoa, we use an approach based on historical electricity demand and generation time-series data to investigate future scenarios that achieve very high percentages of renewable electricity. The results show that scenarios of high proportions (above 90%) of renewable energy generation coupled with storage (28%–37% of direct solar, 17%–30% of stored solar and 25%–40% of hydro) are economically viable (Net Present Value >0) but, there is a significant trade-off between percentage of renewable supply and affordability. These results have important implications for energy policy directions for Samoa and are directly applicable to many other countries in the Pacific.

Inclement weather forces stopovers and prevents migratory progress for obligate soaring migrants

BackgroundMigrating birds experience weather conditions that change with time, which affect their decision to stop or resume migration. Soaring migrants are especially sensitive to changing weather conditions because they rely on the availability of environmental updrafts to subsidize flight. The timescale that local weather conditions change over is on the order of hours, while stopovers are studied at the daily scale, creating a temporal mismatch.MethodsWe used GPS satellite tracking data from four migratory Turkey Vulture (Cathartes aura) populations, paired with local weather data, to determine if the decision to stopover by migrating Turkey Vultures was in response to changing local weather conditions. We analyzed 174 migrations of 34 individuals from 2006 to 2019 and identified 589 stopovers based on variance of first passage times. We also investigated if the extent of movement activity correlated with average weather conditions experienced during a stopover, and report general patterns of stopover use by Turkey Vultures between seasons and across populations.ResultsStopover duration ranged from 2 h to more than 11 days, with 51 % of stopovers lasting < 24 h. Turkey Vultures began stopovers immediately in response to changes in weather variables that did not favor thermal soaring (e.g., increasing precipitation fraction and decreasing thermal updraft velocity) and their departure from stopovers was associated with improvements in weather that favored thermal development. During stopovers, proportion of activity was negatively associated with precipitation but was positively associated with temperature and thermal updraft velocity.ConclusionsThe rapid response of migrating Turkey Vultures to changing weather conditions indicates weather-avoidance is one of the major functions of their stopover use. During stopovers, however, the positive relationship between proportion of movement activity and conditions that promote thermal development suggests not all stopovers are used for weather-avoidance. Our results show that birds are capable of responding rapidly to their environment; therefore, for studies interested in external drivers of weather-related stopovers, it is essential that stopovers be identified at fine temporal scales.

An application of GRACE mission datasets for streamflow and baseflow estimation in the Conterminous United States basins

Runoff and baseflow estimates provide critical information on surface and groundwater flow dynamics, water resource availability, and water usage patterns for effective water resource planning and management. Developing a physical-based model at large spatial scales such as continental/global scales require extensive datasets, complex parameterization, and higher computational capacity. Recent advances in satellite datasets related to water cycle process modeling, on the other hand, involve less parameterization and more effective computation over larger spatial and finer temporal scales. Evapotranspiration (ET) is a critical flux in the water cycle that can be estimated with reasonable accuracy from satellite-based information by using complementary relationship (CR) concepts. Similarly, the Gravity Recovery and Climate Experiment (GRACE) mission satellite datasets provide useful estimates of total water storage change over large spatial extents. GRACE datasets have been extensively used in groundwater storage change and groundwater depletion studies. However, the application of GRACE data products for estimating runoff and baseflow is limited. Therefore, in this study, we used a CR approach to estimate ET and a water balance method for estimating runoff and baseflow over the Conterminous United States (CONUS). We evaluated the CR-based water balance model (CR-GRACE) with United States Geological Survey (USGS) runoff datasets based on hydrologic unit code level-2 (HUC-2) basin boundaries. The CR-GRACE model estimated annual runoff values agreed with USGS runoff values with root mean square error (RMSE) and correlation coefficient values of 14.7 mm/month and 0.81, respectively. We also evaluated the CR-GRACE model with two well-known Land Surface Models (LSM), Noah-LSM and Variable Infiltration Capacity LSM (VIC-LSM), from the Global Land Data Assimilation System (GLDAS) database. The comparison results for baseflow estimates from the CR-GRACE model have improved over Noah-LSM and VIC-LSM model estimates with RMSE values of 62.16, 101.00, and 207.32 mm/year, respectively. Furthermore, this study demonstrates the feasibility of using the CR-GRACE model to estimate runoff and baseflows with limited parameterization and datasets.

Aberrant EEG signal variability at a specific temporal scale in major depressive disorder

ObjectiveSignal variability is linked to irregularities in time series caused by intrinsic nonlinearities of the neural system and can be measured on variable temporal scales over short time series. By measuring refined complex multiscale permutation entropy (RCMPE) from resting-state electroencephalography (EEG) data, we investigated the presence of a specific range of time scales characterizing major depressive disorder (MDD). MethodWe used an EEG dataset acquired from 22 MDD patients and 22 healthy controls in the eyes-closed (EC) and eyes-open (EO) states available on the PRED + CT website. Signal variability in both the EC and EO states was compared between the two groups, and their relationship to depressive symptom severity was examined. ResultsIn the EC state, the RCMPE was higher in the MDD group than in the control group on a coarse temporal scale, approximately 20–32 ms, at almost all sensors. It also showed a negative correlation with depressive symptom severity on a fine temporal scale, approximately 2–26 ms, in the frontal, right temporal, and left parietal sensor areas in MDD. The EO state revealed a group difference but no relationship with depressive symptom severity. ConclusionOur results suggested that the diagnosis of MDD as a trait and the severity of depressive symptoms as a state are linked to EEG signal variability on the coarse temporal scale and the fine scale in the resting state, respectively. SignificanceSignal variability reflects different characteristics of depression depending on the temporal scale.

Reconstruction and Dynamics of the Human Intestinal Microbiome Observed In Situ

The human gut microbiome has primarily been studied through the use of fecal samples, a practice that has generated vital knowledge on the composition and functional capacities of gastrointestinal microbial communities. However, this reliance on fecal materials limits the investigation of microbial dynamics in other locations along the gastrointestinal tract ( in situ ), and the infrequent availability of fecal samples prevents analysis at finer temporal scales (e.g., hours). In our study, we utilized colonic transendoscopic enteral tubing, a technology originally developed for fecal microbiota transplantation, to sample the ileocecal microbiome twice daily; metagenomic and metatranscriptomic analyses were then conducted on these samples. A total of 43 ileocecal and 28 urine and fecal samples were collected from five healthy volunteers. The ileocecal and fecal microbiomes, as profiled in the five volunteers, were found to be similar in metagenomic profiling, yet their active genes (metatranscriptome) were found to be highly distinct. Both microbiomes were perturbed after laxative exposure; over time, they exhibited reduced dissimilarity to their pre-treatment state, thereby demonstrating resilience as an innate property of the gut microbiome, although they did not fully recover within our observation time window. Sampling of the ileocecal microbiome during the day and at night revealed the existence of diurnal rhythms in a series of bacterial species and functional pathways, particularly those related to short-chain fatty acid production, such as Propionibacterium acnes and coenzyme A biosynthesis II . Autocorrelation analysis and fluctuations decomposition further indicated the significant periodicity of the diurnal oscillations. Metabolomic profiling in the fecal and urine samples mirrored the perturbance and recovery in the gut microbiome, indicating the crucial contribution of the gut microbiome to many key metabolites involved in host health. This study provides novel insights into the human gut microbiome and its inner resilience and diurnal rhythms, as well as the potential consequences of these to the host.

A Simple Solution for Refining Lake Water Temperature Profiles Data Arrayed from High-Frequency Monitoring Sensors

The revolutionized aquatic monitoring sensors are essential in capturing environmental patterns that traditional discrete samplings might not be able to. They allow scientists to further synthesize and better conclude processes in aquatic ecosystems. These sensors produce high-frequency data that provide information on a fine temporal scale, even near real-time. The massive quantities of the streamed data, however, create challenges for scientists to grasp the concrete information. Filtering data quality, on the other hand, is another problem scientists might have encountered as sensor accuracy and precision may drift along the line. Hence, quality assurance and quality control might be quite labouring owing to the size of datasets to handle. This paper proposed a semi-mechanistic algorithm to improved false water temperature data. Using “theoretical” thermal stratification as a reference, this algorithm fixed sensors error readings. A 5-month dataset of water temperature profiles of Lake Maninjau, West Sumatra, captured every 10 minutes from a set of sensors in thermistor chain was applied. We found that most data fit to the theoretical temperature profile, R&lt;sup&gt;2&lt;/sup&gt; = 0.962, RMSE = 0.081&lt;sup&gt;o&lt;/sup&gt;C. A number of errors, however, were observed in the upper layer of the lake (&amp;lt;20 m), the most dynamic layer in terms of its thermal variation. Sensor drifts in this active upper mixed layer can be related to the generated errors. Through this simple solution, not only improving the quality of the observed water temperature data, but was also able to identify the most probable source of errors

Seasonal Space Use and Movement of a Grey Wolf (Canis lupus) in a Protected Archipelago in Lake Superior, Ontario

Data on movement of grey wolves (Canis lupus) in island systems is largely restricted to coastal environments and little is known about their space use and movement in freshwater archipelagos. We used data from a GPS-collared wolf in a protected archipelago in Lake Superior, Ontario to examine broad seasonal patterns in space use, movement, and activity. Over approximately 1 y of monitoring, the wolf made 190 crossings between islands and showed more extensive use of the archipelago during the nonwinter season. When ice was present in the archipelago, the mean weekly inter-island crossing rate of the wolf (± se) was 6.08 ± 1.31, with crossings largely restricted to the interior islands bounded by ice, compared to 2.85 ± 0.45 during the open water season. Mean wolf activity was highest in the nonwinter season, but movement rates were comparable across seasons. Our study is the first to document wolf movements in a freshwater archipelago with seasonal ice cover and supports data collection at fine temporal scales to better understand trends in wolf space use and movement at small spatial scales.

Accelerating the Generation of Static Coupling Injection Maps Using a Data-Driven Emulator

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Accurate modeling of the internal flow and spray characteristics in fuel injectors is a critical aspect of direct injection engine design. However, such high-fidelity computational fluid dynamics (CFD) models are often computationally expensive due to the requirement of resolving fine temporal and spatial scales. This paper addresses the computational bottleneck issue by proposing a machine learning-based emulator framework, which learns efficient surrogate models for spatiotemporal flow distributions relevant for static coupling injection maps, namely total void fraction, velocity, and mass, within a design space of interest. Different design points involving variations of needle lift, fuel viscosity, and level of non-condensable gas in the fuel were explored in this study. An interpretable Bayesian learning strategy was employed to understand the effect of the design parameters on the void fraction fields at the exit of the injector orifice. The results show a strong influence of the amount of non-condensable gas on the level of cavitation as well as the overall shape of the gas-phase structures at the orifice exit. The emulator framework involves the construction of deep autoencoders for efficient dimensionality reduction of the flowfields. Deep artificial neural networks were then employed for prediction of the flowfields for unknown operating conditions. The emulated flowfields were then tested by evaluating spray and combustion predictions from one-way coupling spray simulations. The analysis of the spray predictions from CFD-generated and emulator-predicted injections maps revealed that the emulation framework is capable of reproducing spray predictions with similar level of accuracy, yet at a fraction of the computational cost. The maximum achievable speed-up using the emulator framework is up to 2 million times over the traditional CFD approach for generating static coupling injection maps. The emulation framework provides an efficient pathway for integrating detailed injector simulations into spray and engine simulations.&lt;/div&gt;&lt;/div&gt;

Original Article: Decline of notified dengue infections in Indonesia in 2017: Discussion of the possible determinants.

This study was conducted to quantify the trend in dengue notifications in the country in 2017 and to explore the possible determinants. Annual nation-wide dengue notification data were obtained from the National Disease Surveillance of Ministry of Health of Indonesia. Annual incidence rate (IR) and case fatality rate (CFR) in 2017 and the previous years were quantified and compared. Correlations between annual larva free index (LFI), implementation coverage of integrated vector management (IVM), El Niño Southern Oscillation (Niño3.4), Dipole Mode Index (DMI), Zika virus seropositivity and the percent change in IR and CFR of dengue were examined. The change of dengue IR and CFRs were mapped. In 2017, dengue IR was declined by 71% (22.55 per 100,000 population) compared to 2016 (77.96 per 100,000 population) while the CFR was slightly reduced from 0.79% to 0.75%. Reduction in IR and CFR occurred in 94.1% and 70.1% out of 34 provinces, respectively. The trend of dengue IR seems to be influenced by Niño3.4 but there is no clear evidence that Niño3.4 is the main reason for dengue reduction in 2017. It is difficult to elucidate that the reduction of dengue in 2017 was associated with previous Zika outbreaks. In conclusion, there was a significant reduction on dengue notifications in Indonesia in 2017. Further investigation is needed to look at the role of climate on the decline of dengue IR at finer temporal scale. In addition, study on the role of cross-protective immunity generated by Zika infection on dengue incidence is also warranted.

Behavior of skipjack (Katsuwonus pelamis), yellowfin (Thunnus albacares), and bigeye (T. obsesus) tunas associated with drifting fish aggregating devices (dFADs) in the Indian Ocean, assessed through acoustic telemetry

AbstractWe investigated the associative behavior of skipjack (Katsuwonus pelamis), yellowfin (Thunnus albacares), and bigeye (T. obsesus) tuna within multi‐species aggregations associated with drifting fish aggregating devices (dFADs) in two different regions of the western Indian Ocean: the Mozambique Channel and the Seychelles, using acoustic telemetry. We documented the residence and absence times of tunas at two temporal scales (coarse and fine scale) and made comparisons between regions. A total of 56 tunas were tagged and released at 7 different dFADs (4 in the Mozambique Channel and 3 in the Seychelles) during four research cruises. We recorded the first observations of skipjack tuna making excursions of more than 24 hours away from dFADs before returning and confirmed findings of other studies showing that yellowfin tuna can make long excursions (4.07 days) before returning to their home dFADs. Combining both studied regions, average residence times were 7.59 days (min 0.03; max 16.49), 6.64 days (min 0.01; max 26.72), and 4.58 days (min 0.09; max 18.33) for bigeye, yellowfin, and skipjack tuna, respectively. Exponential models best fitted the residence times for all three tuna species, indicating time‐independent probabilities of departure from dFADs. For yellowfin tuna, at a coarse temporal scale, no regional differences were observed in the residence times. However, at a fine temporal scale, regional differences were apparent in both residence and absence times. This study provides new information on the associative behavior of tunas at dFADs in the Indian Ocean which is key to improving the science‐based management of dFADs.