Temporal Component Research Articles

Multivariate Bayesian Semiparametric Regression Model for Forecasting and Mapping HIV and TB Risks in West Java, Indonesia

Multivariate “Bayesian” regression via a shared component model has gained popularity in recent years, particularly in modeling and mapping the risks associated with multiple diseases. This method integrates joint outcomes, fixed effects of covariates, and random effects involving spatial and temporal components and their interactions. A shared spatial–temporal component considers correlations between the joint outcomes. Notably, due to spatial–temporal variations, certain covariates may exhibit nonlinear effects, necessitating the use of semiparametric regression models. Sometimes, choropleth maps based on regional data that is aggregated by administrative regions do not adequately depict infectious disease transmission. To counteract this, we combine the area-to-point geostatistical model with inverse distance weighted (IDW) interpolation for high-resolution mapping based on areal data. Additionally, to develop an effective and efficient early warning system for controlling disease transmission, it is crucial to forecast disease risk for a future time. Our study focuses on developing a novel multivariate Bayesian semiparametric regression model for forecasting and mapping HIV and TB risk in West Java, Indonesia, at fine-scale resolution. This novel approach combines multivariate Bayesian semiparametric regression with geostatistical interpolation, utilizing population density and the Human Development Index (HDI) as risk factors. According to an examination of annual data from 2017 to 2021, HIV and TB consistently exhibit recognizable spatial patterns, validating the suitability of multivariate modeling. The multivariate Bayesian semiparametric model indicates significant linear effects of higher population density on elevating HIV and TB risks, whereas the impact of the HDI varies over time and space. Mapping of HIV and TB risks in 2022 using isopleth maps shows a clear HIV and TB transmission pattern in West Java, Indonesia.

A comparison of spatial and temporal drivers of stream metabolism

Abstract Stream metabolism provides insight into the functional processes that regulate trophic status, biomass, and nutrient cycling in streams. Comparative (spatial) analyses of streams generally do not account for shifting at‐site controls on stream metabolism over time and, thus, may not identify the primary controls on cross‐site differences in trophic status, biomass, and nutrient cycling over longer time scales. The spatial component included assessing environmental factors controlling stream metabolism (daily values) during a summer low‐flow period (August) at 17 wadable stream sites in four regions of the United States. Explanatory variables for the spatial analysis included discrete nutrients and physical parameters. The temporal component included assessing 12 of the 17 sites with more than 90 days of stream metabolism values, and continuous nitrate, photosynthetically active radiation (PAR), turbidity, maximum water temperature (°C) and stream stage (water surface elevation). Spatial analysis at the 17 sites during August (31 days) found that average gross primary production (GPP) ranged from 0.1 to 4 g O2 m−2 day−1 and ecosystem respiration (ER) from 0.37 to 6.4 g O2 m−2 day−1. Sites were primarily heterotrophic; however, some sites varied from autotrophic to heterotrophic daily. Multiple regression indicated that GPP was a function of orthophosphate, percent canopy cover, and the number of days since a high flow (R2 = 0.51), whereas ER was primarily a function of GPP (R2 = 0.40). Temporal patterns in daily stream metabolism were evaluated using at‐site autoregressive models at 12 streams with relatively complete, continuous records. PAR, nitrate and maximum water temperature were the dominant variables influencing GPP, and water depth and nitrate were the most common explanatory variables for ER models. The autoregressive components (i.e., GPP or ER for the previous day) had a strong influence on both GPP and ER except after spates, indicating that biomass may be the dominant control on metabolism rather than variability in environmental factors during stable‐flow periods. We found two dominant temporal metabolism regimes: a seasonal pattern with elevated spring metabolism followed by low stable summer metabolism and a temporally disturbed pattern which tended to occur in streams with frequent spates. These patterns were not indicated by differences in metabolism among streams during stable low‐flow conditions. Because streams are generally heterotrophic with only limited periods when production may be high, comparative analyses must estimate stream metabolism over a time scale representing at‐site spatial variability and account for controls on periods of high production to adequately characterise differences between streams.

Sea level variability and coastal inundation over the northeastern Mediterranean Sea

ABSTRACT Sea level is a key element of global-scale climatic changes with significant coastal impacts. The northeastern Mediterranean Sea (Aegean, Ionian, and Cretan Seas: AICS) consists of extended low-lying coastal zones exposed to coastal inundation due to seawater level increases. The variability of the Sea Level Anomaly (SLA) in the AICS has been evaluated with the use of satellite-derived observations from 1993 to 2021. The general SLA trend over the AICS domain was 3.6 mm/year, which is higher than that of the overall Mediterranean Sea. The coastal inundation variability along the AICS coastline, specifically in 20 characteristic study areas, was investigated based on the long-term satellite-derived sea level observations, high-resolution land elevation data, and numerical simulations, conducted with a coastal inundation (CoastFLOOD) model. The coastal zones were categorized based on the frequency of occurrence and percentage coverage of flooding incidents during the 29-year period, while the interannual trends and the seasonality of inundated littoral and inland areas also showed intense spatial variation. High flooding levels occurred at coastal areas of either extended low-lying areas and/or high sea level elevations. The coastal zones were categorized based on a heuristic Coastal Inundation Hazard Index (CIHI: 1–5 ranking) that was determined by the occurrence frequency of severe floods (temporal component) and their impact area (spatial component). Our study findings provide vital information about the coastal inundation conditions and the respective hazard level of the AICS, useful to support enhanced coastal zone management for the improvement of coastal protection and decisions about coastal hazards’ mitigation.

Detecting Malicious Social Bots Based on Clickstream Sequences

The crucial part development in amount, pace, and range of user data (such Information created by users) web-based social networks has led to attempts to develop new techniques for acquiring and reviewing such massive data. For instance, social bots till now used to offer users higher-quality customer service while carrying out automated analytical tasks. Harmful social bots, such as fake news, have circulated false information, which has had an effect on the real world. Consequently, it is crucial to identify and get rid of risky social bots from online social networks.Based on their characteristics, the bulk of currently employed detection methods for risky social robots focus concerning the numeral aspects. Low analytical accuracy is caused by the ease with which social bots can mimic these characteristics. This article provides a word of art feature method based to the shifting likelihood of clickstream sequences and semi-supervised clustering for the detection of harmful social bots. This method considers both the transition probability of stream of click by the user analysis and the temporal component of activity. The detection accuracy for various types of malicious social bots based on transition probabilities of clickstreams increases by an average of 12.8%, according to the results of our tests on actual platforms for online social networks. This is in contrast to detection methods based on examination of user behavior by employing stats.

High hopes before the fall: Otto Bauer and Oszkár Jászi on nationality and Habsburg rule in the dual monarchy of Austria-Hungary, 1907–18

ABSTRACT This study offers an overview of ‘the nationalities question’ in the Habsburg Empire, with special focus on its treatment by the Austrian social democrat, Otto Bauer, and the Hungarian ‘radical’ or ‘liberal socialist’, Oszkár Jászi. Analysing and comparing the writings of these intellectuals published between 1907 and 1918, this article shows how the contrasting legal and political contexts in Austria (Cisleithenia) and in Hungary (Transleithenia) led these authors to create contrasting alternative solutions to the problems posed by the multi-ethnic composition of the Habsburg Empire. It is argued that these thinkers’ positions were fundamentally defined by Austria’s status as a ‘nationalities state’, Hungary’s identity as a nation-state, and the authors’ own affiliations with the dominant nationalities within their respective states. The article underscores the important temporal component at work in their respective pre-1918 assessments of the dual monarchy of Austria-Hungary and its effect on their stances on the reign of the Habsburgs. Whereas Bauer saw the Hungarian king and Austrian emperor Franz Joseph as a possible strong ally for the implementation of his ideas, Jászi viewed the acts of the Habsburg dynasty as alien to Hungarian interests and therefore as a source of potential danger.

Air quality changes in NE Romania during the first Covid 19 pandemic wave

This study analyzes for the first time uniformly and causally the level of pollution and air quality for the NE-Romania Region, one of the poorest region in the European Union. Knowing the level of pollution and air quality in this region, which can be taken as a benchmark due to its positional and economic-geographical attributes, responds to current scientific and practical needs. The study uses an hourly database (for five pollutants and five climate elements), from 2009 to 2020, from 19 air quality monitoring stations in northeastern Romania. Pollutant levels were statistically and graphically/cartographically modeled for the entire 2009–2020 interval on the distributive-spatial and regime, temporal component. Inter-station differences and similarities were analyzed causally. Taking advantage of the emergency measures between March 16 and May 14, 2020, we observed the impact of the event on the regional air quality in northeastern Romania. During the emergency period, the metropolitan area of Suceava (with over 100,000 inhabitants) was quarantined, which allowed us to analyze the impact of the quarantine period on the local air quality. We found that, in this region, air quality falls into class I (for NO2, SO2 and CO), II for O3 and III for PM10. During the lockdown periods NO2 and SO2 decreased for the entire region by 8.6 and 14.3%, respectively, and in Suceava by 13.9 and 40.1%, respectively. The causes of the reduction were anthropogenic in nature.

COVID-19 and Access to Medical Professional Careers: Does Gender Matter?

To know to what extent home confinement resulting from the COVID-19 pandemic has affected the results of the Medical Intern Resident Program (MIR) exam and whether or not a gender gap has occurred as a consequence. Econometric modeling of the final result obtained in the MIR exam and identification of the explanatory factors that determine it and its relevance, effect and meaning. From the results obtained in the MIR test of the 2019, 2020 and 2021 calls, it can be seen that examinations and academic records together with demographic and calendar factors are determinants to explain the observed behavior of the final result. In relation to the gender factor, the existence of a differential fixed effect in favor of women is shown, although the interaction with the exam shows the opposite result. The nationality variable allows us to visualize a scenario of academic homogeneity. The effect of the calendar directly linked to the COVID-19 pandemic makes it possible to quantify the negative impact exerted on the final result. (1) The work reflects the impact of factors such as sex, nationality or the COVID-19 pandemic on access to specialized health training in Spain. (2) In contrast to previous studies, we found a significant difference in behavior between men and women, favorably linked to the female sex. However, the so-called sprint effect associated with the male sex was detected. (3) The negative effects of the COVID-19 pandemic on the final score are visualized. The existing differential with respect to the control category is quantified and the dominance of the hierarchical position of the temporal component within the set of explanatory factors is visualized.

Isotropic compact stellar model in Rastall's gravitational theory

Is Rastall's theory of gravity equivalent to Einstein's general relativity theory? This question has sparked a significant debate, prompting researchers to delve into the topic. To investigate further, we apply Rastall's theory's field equation to a spacetime characterized by spherical symmetry. This leads us to encounter a system of non-linear differential equations that is overdetermined. To address this, we make assumptions about the specific form of the metric potential's temporal component, denoted as gtt. Additionally, we impose constraints to eliminate the anisotropic condition, resulting in a vanishing effect. These steps allow us to determine the form of grr and ultimately achieve an isotropic spacetime. Furthermore, our investigation focuses on the potential of obtaining a set of parameters that align with the observed behavior of pulsars. To achieve this, we employ junction conditions to match the interior spacetime with the exterior Schwarzschild configuration, thereby constraining the model's relevant constants. Subsequently, we employ the pulsar SAXJ1748.9−2021, characterized by a measured mass of M=1.81±0.31,M⊙ and a radius of R=11.7±1.7 km, to numerically explore the physical properties of the model. Stability is assessed using the Tolman-Oppenheimer-Volkoff equation and the adiabatic index. Our findings suggest that Rastall's parameter, a key distinction of Rastall's theory from Einstein's general relativity, can play a crucial role in forming a realistic, compact object consistent with observational data. Furthermore, we verify the model's validity by comparing it with various observed masses and radii of different pulsars, ensuring a satisfactory fit between the model proposed in this study and the observed data.

Extreme learning machine for soil temperature prediction using only air temperature as input.

Soil temperature (TS) is a crucial parameter in many fields, especially agriculture. In developing countries like Algeria, the soil temperatures (TS) and the meteorological data are limited. This study investigates the use of Extreme Learning Machine (ELM) for the accurate prediction of daily ST at three different depths (30cm, 60cm, and 100cm) using a minimal number of climatic inputs. The inputs used in this study include maximum and minimum air temperatures, relative humidity, and day of the year (DOY) as a representative of the temporal component. Five different combinations of inputs were used to develop ELM models and determine the best set of input variables. The ELM models were then compared with traditional methods such as multiple linear regression, artificial neural networks, and adaptive neuro-fuzzy inference system. Based on evaluation metrics such as R, RMSE, and MAPE, the ELM models with air temperatures and DOY as inputs (ELM-M0 and ELM-M3) demonstrated superior performance at all depths when compared to the other techniques. The most accurate predictions were found at a depth of 100cm using the ELM-M3 model, which employed inputs of minimum and maximum air temperatures and DOY, with R value of 0.98, RMSE of 0.68°C, and MAPE of 3.4%. The results demonstrate that the inclusion of DOY in the climatic dataset significantly enhances the performance and accuracy of machine learning models for ST prediction. The ELM was found to be a fast, simple, effective, and useful tool for TS prediction.

Patterns of fish occupancy of artificial habitats in the eastern Mediterranean shallow littoral

Coastal ecosystems act as spawning grounds, settlement areas or nurseries for a diversity of fish species. Today, these ecosystems are under various anthropogenic stressors. One of these disturbances is the structural modification of the shallow littoral by coastal infrastructure such as ports, piers, marinas and roads. Here, we investigate whether there are differences between modified (artificial) and non-modified (natural) habitats in central Greece in terms of the fish assemblage structure, species diversity and richness, fish abundance and the proportion of juveniles. We carried out underwater visual census from September 2020 to August 2021 across two habitats (natural and artificial) in three locations of central Greece. The fish community and Shannon diversity index did not vary across habitat type; however, both species richness and total abundance were significantly higher in artificial habitats. The abundance of five (out of ten examined in total) taxa was significantly affected by habitat type. Higher abundance in the artificial habitat in the warmer or more productive months was found, possibly relevant to food availability. The significance of habitat interactions with location and the temporal component in community composition and species abundance models indicated intense heterogeneity in species-specific occupancy and may be linked to the type and characteristics of artificial habitats. Modeling of the percentage of small-sized individuals indicated patterns of juvenile occupancy requiring further investigation about the potential role of artificial habitats for some species. Future research should examine the impact of specific artificial habitat types on population dynamics and investigate the implications of artificial habitat construction for conserving fish diversity and maintaining coastal ecosystem functioning.

4D monitoring of mountain areas using the UAV-PPK workflow

The mapping and three-dimensional modeling of mountain areas and the study of land cover change dynamics are current tasks in preserving and maintaining protected natural parks and forests. In this context, recent developments in digital photogrammetry using the SfM-MVS method to process captured imagery and the development of unmanned aerial systems (UAS) allow for reducing the costs, time, and the use of human resources and obtaining and repeatable 3D topographic data for moun-tainous regions. We will call this acquired 3D high-resolution topographic data (HRTD) 4D data in the context of an additional temporal component. The main objective of this study is to evaluate the applicability of PPK (Post-Processing Kinematic) direct georeferencing of images captured by UAVs and processed through the SfM-MVS method to obtain HRTDs for 4D land cover analysis. We analyze a 3D HRTD with an acquisition interval of two years for a mountain test area in Plana Mountain near Sofia. The test area has a diverse vegetation cover, including coniferous forest, grassland, hay meadows, shrubs, and single deciduous trees. We conducted multiple surveys of the test area with a budget PPK-UAV configuration (DJI Phantom 4 Pro with a single-frequency PPK-GNSS kit installed) from March 2020 to October 2022. Two autumn surveys from September 2020 and October 2022 were se-lected, which possess the most-good performance on numerical data accuracy. We performed 3D data analysis on 1) Assessment of the accuracy of PPK-SfM-MVS photogrammetry generated topographic data (3D clouds and DSM); 2) Investigation of the errors in the individual specific surfaces (for the individual isolated sections) using the M3C2 tool for comparing and evaluating dense point clouds; 3) Determining land cover changes in the demarcated areas using a surface of differences (DoD). Accuracy analysis showed that the PPK solution provides comparable accuracy (about RMSE3D = 0.067 m for the 2020 data, georeferencing (PPK+1GCP) and RMSE3D about 0.13 m for the 2022 data, georeferencing (PPK only)) like the GCP method. The multi-temporal topographic reconstructions based on UAV- PPK-SfM allowed us to quantify and qualitatively determine the land cover changes that occurred. The UAV-PPK-SfM workflow in the context of 4D land surface monitoring and the results suggested that even low-cost UAV-PPK systems can provide data suitable for measuring geomorphic change at the scale of the acquired data. The multi-temporal topographic reconstructions based on UAV- PPK-SfM allowed us to quantify and qualitatively determine the land cover changes that occurred. The UAV-PPK-SfM workflow in the context of 4D land surface monitoring and the results suggested that even low-cost UAV-PPK systems can provide data suitable for measuring geomorphic change at the scale of the acquired data.

The Variability of Pan Evaporation over China during 1961–2020

Abstract The most basic features of climatological normals and variability are useful for describing observed or likely future climate fluctuations. Pan evaporation (Epan) is an important indicator of climate change; however, current research on Epan has focused on its change in mean rather than its variability. The variability of monthly Epan from 1961 to 2020 at 969 stations in China was analyzed using a theoretical framework that can distinguish changes in Epan variance between space and time. The Epan variance was decomposed into spatial and temporal components, and the temporal component was further decomposed into interannual and intra-annual components. The results show that the variance in Epan was mainly controlled by the temporal component. The time variance was mainly controlled by intra-annual variance, decreasing continuously in the first 30 years, and slightly increasing after the 1990s. This is mainly due to the fact that the decrease of wind speed and the increase of water vapor pressure deficit with the temperature increase offset each other and inhibit the variability of Epan. The variance decreased more in the northern region, whereas it exhibited a small decrease or slight increase in the southern region. The reduction in seasonality was dominated by spring, followed by summer. The differences in Epan variability in space and season were mainly caused by the differing rates of change in evaporation driving forces, such as a greater reduction in wind speed in the northern region and spring. Significance Statement The purpose of this study is to better understand how the variability of evaporation changes rather than in mean under climate change. This is important because the variability is useful to describe the observed or likely future fluctuations, and a small fluctuation may have large impacts on water practices, such as agricultural production. Our findings showed that the temporal and spatial variability of evaporation decreased due to its drivers offsetting each other. However, because the drivers are numerous and continuously changing under climate change, it is necessary to pay attention to its mean and variability for serving water resources practice.

Forecasting Stock Market Indices Using Gated Recurrent Unit (GRU) Based Ensemble Models: LSTM-GRU

A "time sequence analysis" is a particular method for looking at a group of data points gathered over a long period of time. Instead of merely randomly or infrequently, time series analyzers gather information from data points over a predetermined length of time at scheduled times. But this kind of research requires more than just accumulating data over time. Data in time series may be analyzed to illustrate how variables change over time, which makes them different from other types of data. To put it another way, time is a crucial element since it demonstrates how the data changes over the period of the information and the outcomes. It offers a predetermined architecture of data dependencies as well as an extra data source. Time Series forecasting is a crucial field in deep learning because many forecasting issues have a temporal component. A time series is a collection of observations that are made sequentially across time. In this study, we examine distinct machine learning, deep learning and ensemble model algorithms to predict Nike stock price. We are going to use the Nike stock price data from January 2006 to January 2018 and make predictions accordingly. The outcome demonstrates that the hybrid LSTM-GRU model outperformed the other models in terms of performance.

Counterfactual Dynamics Forecasting – a New Setting of Quantitative Reasoning

Rethinking and introspection are important elements of human intelligence. To mimic these capabilities, counterfactual reasoning has attracted attention of AI researchers recently, which aims to forecast the alternative outcomes for hypothetical scenarios (“what-if”). However, most existing approaches focused on qualitative reasoning (e.g., casual-effect relationship). It lacks a well-defined description of the differences between counterfactuals and facts, as well as how these differences evolve over time. This paper defines a new problem formulation - counterfactual dynamics forecasting - which is described in middle-level abstraction under the structural causal models (SCM) framework and derived as ordinary differential equations (ODEs) as low-level quantitative computation. Based on it, we propose a method to infer counterfactual dynamics considering the factual dynamics as demonstration. Moreover, the evolution of differences between facts and counterfactuals are modelled by an explicit temporal component. The experimental results on two dynamical systems demonstrate the effectiveness of the proposed method.

1287-P: Identifying When Incident Diabetes Was Diagnosed in Children and Young Adults, Using Natural Language Processing of Clinical Notes

Identifying when an incident diabetes (DM) diagnosis was made is complicated using retrospective, structured electronic health record (EHR) data alone. Unstructured clinical notes have been underused but contain valuable information that could complement traditional methods. However, manually reviewing clinical notes is time-consuming. We developed and validated a simple rule-based Natural Language Processing (NLP) method to extract incident DM timing from clinical notes. In a single center we used structured EHR data to identify a cohort (age <45 as of 12/31/19) with likely type 1 (T1D) or type 2 (T2D) DM based on 2016-2019 records: (≥1 T1D ICD-10 code and insulin and no other DM medication) or ([≥2 T2D and no T1D codes] or [≥1 T2D code and a DM medication besides insulin or metformin]). This cohort had 2,654 patients (548,316 clinical notes, 2003-present). We randomly selected 58,450 clinical notes (1,465 patients) as a training set to look for relevant text patterns. We handcrafted the rules into our NLP tool. We required 3 distinct concepts at the sentence level to determine an incident DM diagnosis: DM (not, e.g., epilepsy), an onset attribute (e.g., “diagnosed in”), and a temporal component (e.g., 8/2008). We pre-defined all related keywords and date formats for these concepts in our training notes. We then tested the NLP algorithm against manual review in an independent set of 100 randomly selected patients from the cohort. Analysis was at the patient level (true+: ≥1 true+ note per patient). NLP in the training set found 1,268 patients with at least 1 of the 3 concepts and 826 patients with all 3. In the test set, we excluded 4 patients without substantive notes. NLP correctly detected incident DM timing in 73 of 96 patients. The NLP had recall 88%, specificity 77%, precision (PPV) 96%, and NPV 50%. NLP was helpful in finding incident DM timing and may complement structured EHR queries for identifying incident DM. Refinement of our NLP algorithm is ongoing. Disclosure A.Wong: None. V.W.Zhong: None. M.Rosenman: None. Funding Centers for Disease Control and Prevention (1U18DP006693-01-00)

Shifting Paradigms in Coffee-Based Cultivation: Cultural Management Practices and Technology Utilization in Upland, Cavite, Philippines

Purpose: The aim of this study is to analyze the changes in the cultural management practices and technology used in farming that have taken place with the coffee-based farming systems in an upland area in Cavite, Philippines from 2001-2020. Theoretical framework: The Triple Bottom Line (TBL) paradigm is a sustainable development strategy considering social, environmental, and economic factors. Once agricultural systems are tested, TBL might help promote productive and sustainable farming practices that benefit producers, communities, and the environment. By adopting a complete sustainability strategy, the TBL framework may help develop socially, ecologically, and economically sustainable agricultural systems and contribute significantly to long-term adaptation and prosperity. Design/methodology/approach: The study is qualitative research design and used several data collection methods, including in-depth interviews, key informant interviews, focus group discussion, document analysis, field notes, and farm visits and observations. Findings: The results show that in farmers' different cultural management practices, significant changes have occurred in several methods over the past two decades. Regarding the application and use of new technologies, farmers applied newly introduced technologies by individuals and other institutions. Research, Practical & Social implications: The study sheds light on the current state of coffee-based cultivation and practices and make recommendations to further improve farmers' cultural management practices and sustain the local coffee-based farming system. In addition, access to new and relevant technology and knowledge on how to apply it to improve farming productivity among coffee-based farmers This study also provides valuable guidance for extension experts and workers, policymakers, coffee-based growers, and researchers who want to improve coffee-based farming systems' sustainability and economic viability at the same time empower farming communities. Originality/value: This study is a qualitative examination of how technology utilizations and cultural management practices interact with coffee-based agricultural systems in Upland Cavite, Philippines. This research provides much-needed in-depth knowledge of how traditional agricultural systems adjust to technology improvements during the global crisis since there have been few qualitative studies on this topic, particularly at such a trying time. It provides a localized perspective that acknowledges regional variations and the influences of geographical and socio-cultural nuances as it examines the socio-economic and cultural impacts of technology adoption in the specific sector of coffee-based farming, which is vital to the Philippine economy. Moreover, the study is noteworthy in providing a temporal component that describes how technology has changed practices through time. The findings of this research, which were obtained during an unprecedented period, should help stakeholders create more effective and culturally-sensitive strategies for adopting technology and sustainability in coffee-based cultivation. Combining these distinctive elements highlights the research's significant uniqueness and worth.

Combining seeded region growing and k-nearest neighbours for the segmentation of routinely acquired spatio-temporal image data

PurposeThe acquisition conditions of medical imaging are often precisely defined, leading to a high homogeneity among different data sets. Nonetheless, outliers or artefacts still appear and need to be reliably detected to ensure a reliable diagnosis. Thus, the algorithms need to handle small sample sizes especially, when working with domain specific imaging modalities.MethodsIn this work, we suggest a pipeline for the detection and segmentation of light pollution in near-infrared fluorescence optical imaging (NIR-FOI), based on a small sample size. NIR-FOI produces spatio-temporal data with two spatial and one temporal dimension. To calculate a two-dimensional light pollution map for the entire image stack, we combine region growing and k-nearest neighbours (kNN), which classifies pixels into fore- and background by its entire temporal component. Thus, decision-making on reduced data is omitted.ResultsWe achieved a F_1 score of 0.99 for classifying a data set as light polluted or pollution-free. Additionally, we reached a total F_1 score of 0.90 for detecting regions of interest within the polluted data sets. Finally, an average Dice’s coefficient measuring the segmentation performance over all polluted data sets of 0.80 was accomplished.ConclusionsA Dice’s coefficient of 0.80 for the area segmentation does not seem perfect. However, there are two main factors, besides true prediction errors, lowering the score: Segmentation mistakes on small areas lead to a rapid decrease in the score and labelling errors due to complex data. However, in combination with the light-polluted data set and pollution area detection, these results can be considered successful and play a key role in our general goal: Exploiting NIR-FOI for the early detection of arthritis within hand joints.

State‐of‐the‐art in Large‐Scale Volume Visualization Beyond Structured Data

AbstractVolume data these days is usually massive in terms of its topology, multiple fields, or temporal component. With the gap between compute and memory performance widening, the memory subsystem becomes the primary bottleneck for scientific volume visualization. Simple, structured, regular representations are often infeasible because the buses and interconnects involved need to accommodate the data required for interactive rendering. In this state‐of‐the‐art report, we review works focusing on large‐scale volume rendering beyond those typical structured and regular grid representations. We focus primarily on hierarchical and adaptive mesh refinement representations, unstructured meshes, and compressed representations that gained recent popularity. We review works that approach this kind of data using strategies such as out‐of‐core rendering, massive parallelism, and other strategies to cope with the sheer size of the ever‐increasing volume of data produced by today's supercomputers and acquisition devices. We emphasize the data management side of large‐scale volume rendering systems and also include a review of tools that support the various volume data types discussed.

Laser-induced fluorescence measurements of spatially resolved OH densities in a helium plasma jet interacting with three different targets in the open air

The presented research work is devoted to the measurements of the absolute densities of ground state OH radicals in an atmospheric pressure helium plasma jet launched in free air onto three different targets. The OH density is measured by using laser induced fluorescence (LIF). The plasma is generated inside a glass tube by a dielectric barrier discharge powered by a mono-polar pulsed high voltage. Estimation of the absolute density is based on a quantitative analysis of the spectral and temporal component of the LIF signal and does not require any assumption on the densities of colliding species. Based on this analysis, the spatially-resolved density distributions of OH radicals are determined in front of three different targets (metallic, dielectric, and ultra-pure water) placed in the open air at 2 cm from the exit of the glass tube. In order to also investigate the temporal behaviour of the OH densities, and the influence of the gas flow rate and of the amplitude of the voltage-pulse, planar LIF has been performed investigating the fluorescence signal (i) during the intervals of the discharge pulses, (ii) for various gas flow rates, and (iii) as a function of the amplitude of the voltage pulse. In the free jet configuration without a target, the OH densities were determined to be in the order of , whereas they increased up to a factor of 10 in contact with a target. The spatial distributions were also influenced by the dielectric properties of the target: in the case of the metallic target, the maximum of the OH density was found to be in the center between the exit of the glass tube and the target, whereas in the case of the dielectric target, the maximum was found to be close to the surface of the target. In the case of ultra-pure water, the densities were equally distributed between the exit of the glass tube and the target. With this work, the influence of a target on the production of OH radicals was demonstrated, which enables the optimisation of the plasma parameters for an efficient production of OH radicals in front of a target with various dielectric properties.

Research on online monitoring of grid current transformer based on transformers and BiGRU

Transformers play a crucial role in ensuring the safety of power grids. It is of great value to diagnose faults using the large amount of power data generated by the grid. It is possible to detect internal latent faults in transformers in advance of their occurrence if the normal operating condition of the transformer is detected in a timely manner. To perform online fault diagnosis of grid current transformers, we combine the Transformer and BiGRU methods. There is a temporal component to the fault input sample sequences. By using Transformer’s multi-headed attention mechanism to extract deep features from fault input sample sequences, the temporal association between latent variables can be fully exploited. As a result of the extraction of features, BiGRU is used to generate fault category coding as an output. The experimental results indicate that using the proposed algorithm achieves better results than using a single model, which is useful for the study and application of fault diagnosis in power grids for current transformers.