Series Data Research Articles

The ∼170 kyr astronomical cycle in the Early Permian Lucaogou Formation of the Junggar Basin

According to Milankovitch’s theory, periodic climate change in Earth’s history is controlled by the periodic changes in the Earth’s orbit and axis of rotation. Milankovitch cycle include eccentricity, obliquity, and precession cycles. In addition to them, there are also some amplitude modulation (AM) cycles that affect the climate system through a series of “nonlinear” feedback processes, such as the 173 kyr obliquity AM cycle. Previous studies have demonstrated that the ∼170 kyr cycle modulate the paleoclimate and carbon cycle at mid-high latitude regions in the Meso-Cenozoic. However, due to the limitation of astronomical solutions and the lack of high-resolution geological records, the ∼170 kyr cycle has been less reported in the Paleozoic Era. In this study, cyclostratigraphic analysis of natural gamma ray (GR) logging data from four wells (Ji30, Ji31, Ji32, and Ji174) and total organic carbon (TOC) data from well Ji174 penetrating the Early Permian Lucaogou Formation in Jimusar Sag, Junggar Basin suggests preservation of eccentricity, obliquity, and precession cycles, and the ∼170 kyr AM cycle. Through the astronomical tuning of GR logging data obtained from four wells to eccentricity target cycles, we established the floating astronomical time scale (ATS). The results indicate an average sedimentation rate ranging from 7.4 to 9.5 cm/kyr and a duration from 2.8 to 3.2 million years (Myr) for the Lucaogou Formation. The differences in sedimentation rate and duration among these four wells may result from different well locations. Moreover, the ∼170 kyr cycle signal has been identified in the detrended GR logging and TOC data series, and its obliquity AM series. This signal might be attributed to the obliquity AM cycles originated from the interaction between s3 and s6 (s3 and s6 represent the precession of nodes of Earth and Saturn), which was recorded in the GR logging and TOC data time series due to nonlinear responses within the depositional system.

Spatial-Based Landslide Vulnerability Index Assessment in Bogor Area, Indonesia

Within the concept of disaster risk, vulnerability is one of the key determinants. It acknowledges the degree of unsafe conditions in a susceptible zone so that mitigation measures and disaster resilience can be enforced. Bogor area is the most susceptible region to landslides with more than 485 landslide events since 2012. However, there is still inadequate information on its vulnerability to landslides, as the risk reduction challenge per se, is a long-run task. Correspondingly, this study aims to measure the degree of vulnerability to landslides in Bogor area from four focal points (i.e.: social, physical, economic, and environmental dimensions), through the proxy of Vulnerability Index (VI). This study employs a GIS-based spatial analysis on a sub-district level. The result shows that although having high records on landslide events, Bogor area mainly retrieves low VI. In general, Bogor area has low VI [0.347–0.454] on 26 sub-districts. Moreover, 13 sub-districts attain medium VI (0.454–0.562], and 7 sub-districts achieve high VI (0.562–0.670]. Sub-districts that have high VI are: Bogor Tengah, Bogor Barat, Bogor Selatan, Cibinong, Bojonggede, Ciomas, and Bogor Utara; which are mainly promoted by the high indexes on social and physical vulnerabilities. Nevertheless, further study is still needed to extend the knowledge of relationship between landslide susceptibility and this vulnerability result, by using a more extensive and longer data series. That is especially in accord with taking the appropriate mitigation measures in spatial planning and landslide risk management.

On the Use of Weather Generators for the Estimation of Low-Frequency Floods under a Changing Climate

The present work presents a methodology based on the use of stochastic weather generators (WGs) for the estimation of high-return-period floods under climate change scenarios. Applying the proposed methodology in a case study, Rambla de la Viuda (Spain), satisfactory results were obtained through the regionalization of the bias-corrected EUROCORDEX climate projections and the integration of this information into the parameterization of the WG. The generated synthetic data series fed a fully distributed hydrological model to obtain the future flood quantiles. The results obtained show a clear increase in the precipitation extreme quantiles for the two analyzed projections. Although slightly reducing the annual amount of precipitation, variations between 4.3% for a return period of 5 years in the mid-term projection and 19.7% for a return period of 100 years in the long-term projection have been observed. In terms of temperatures, the results point to clear increases in the maximum and minimum temperatures for both projections (up to 3.6 °C), these increases being greater for the long-term projection, where the heat waves intensify significantly in both magnitude and frequency. Finally, although rivers may present, in general, with lower flows during the year, flood quantiles experience an increase of 53–58% for high return periods, which reach values of up to 145% when we move to smaller catchments. All this combined translates into substantial shifts in the river flow regimes, increasing the frequency and magnitude of extreme flood events.

Evolutionary adaptation to steady or changing environments affects competitive outcomes in marine phytoplankton

AbstractThe interplay of phytoplankton competition and adaptation affects how phytoplankton, and ultimately marine ecosystems, respond to global warming. However, current ecosystem models that are run under global warming scenarios do not include both processes simultaneously. To fill this gap, we developed an innovative ecosystem model for the Baltic Sea that simulates competition between three phytoplankton functional groups and allows for adaptation to changing temperatures. As adaptation can be affected by the resuspension of dormant resting cells from the sediment, we explicitly implemented this mechanism. We found that resuspension tends to slow down adaptation, and that competition and adaptation influence each other. The outcome of the competition‐adaptation interplay depends on environmental conditions. In a steady environment, competition drives adaptation to individual temperature niches to reduce competition pressure. In a changing environment, adaptation allows inferior competitors to mitigate the dominance of preadapted superior competitors. Our results demonstrate that by neglecting adaptation, models can systematically overestimate warming‐related changes in taxa dominance. Ecosystem models should include both competition and adaptation to accurately simulate phytoplankton responses to global warming. Our model is ideally suited to integrate emerging evolutionary data based on long‐term data series (e.g., from sediment archives) to further improve projections of future ecosystem change.

Weight Optimization for missing data prediction of Landslide Susceptibility Mapping in Remote sensing Analysis

To keep track of changes to the Earth's surface, extensive time series of data from remote sensing using image processing is required. This research is motivated by the effectiveness of computational modelling techniques; however, the problem of missing data is multifaceted. When data at numerous a-periodic timestamps are absent during multi-temporal analysis, the issue becomes increasingly problematic. To make remote sensing time series analysis easier, weight optimised machine learning is used in this study to rebuild lost data. Keeping the causality restriction in mind, this method makes use of data from previous and subsequent timestamps. The architecture is based on an ensemble of numerous forecasting modules, built on the observed data in the time-series order. Dummy data is used to connect the forecasting modules, which were previously linked by the earlier half of the sequence. After that, iterative improvements are made to the dummy data to make it better fit the next segment of the sequence. On the basis of Landsat-7 TM-5 satellite imagery, the work has been proven to be accurate in forecasting missing images in normalised difference vegetation index time series. In a performance evaluation, the proposed forecasting model was shown to be effective.

Application of the SMAP monthly model to the Piauitinga river basin located in the State of Sergipe, Brazil

The generation of flow data allows the assessment of the capacity to meet water demands, predict floods, and estimate the potential for hydraulic exploitation for electrical energy generation. In Brazil, precipitation data series, due to their ease of measurement, are more extensive compared to flow data series, enabling the use of hydrological models called rainfall-runoff models capable of estimating flows from precipitation data. Therefore, utilizing the Brazil Gridded Meteorological Data (BR-DWGD) database, this study aims to generate, calibrate, and validate flow data for the Piauitinga river basin located in the state of Sergipe, Brazil, using the monthly rainfall-runoff SMAP model. The soil parameters considered in the validation for the studied region showed a good fit to the observed data, achieving a Nash-Sutcliffe (NS) of 84% and log-Nash-Sutcliffe (NSLog) of 85% in calibration, and Nash-Sutcliffe (NS) of 70% and a log-Nash-Sutcliffe (NSLog) of 80% in validation. Therefore, since the rainfall-runoff model used exhibited good performance for the studied hydrographic basin, it becomes feasible to use the generated synthetic series to fill possible gaps in the historical series of monthly average flows.

ALSTNet: Autoencoder fused long‐ and short‐term time‐series network for the prediction of tunnel structure

AbstractIt is crucial to predict future mechanical behaviors for the prevention of structural disasters. Especially for underground construction, the structural mechanical behaviors are affected by multiple internal and external factors due to the complex conditions. Given that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models, this study proposed an improved prediction model through the autoencoder fused long‐ and short‐term time‐series network driven by the mass number of monitoring data. Then, the proposed model was formalized on multiple time series of strain monitoring data. Also, the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction model. As the results indicate, the proposed model shows obvious superiority in predicting the future mechanical behaviors of structures. As a case study, the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.

Assessment of Bayesian Changepoint Detection Methods for Soil Layering Identification Using Cone Penetration Test Data

This paper assesses the effectiveness of different unsupervised Bayesian changepoint detection (BCPD) methods for identifying soil layers, using data from cone penetration tests (CPT). It compares four types of BCPD methods: a previously utilised offline univariate method for detecting clay layers through undrained shear strength data, a newly developed online univariate method, and an offline and an online multivariate method designed to simultaneously analyse multiple data series from CPT. The performance of these BCPD methods was tested using real CPT data from a study area with layers of sandy and clayey soil, and the results were verified against ground-truth data from adjacent borehole investigations. The findings suggest that some BCPD methods are more suitable than others in providing a robust, quick, and automated approach for the unsupervised detection of soil layering, which is critical for geotechnical engineering design.

Development of Land Cover and Carbon Storage in Plawangan Hill of Gunung Merapi National Park, Yogyakarta, Using Landsat Data Series 2009, 2013, 2017, and 2023

Globally, habitat loss, deforestation, and climate change are mostly caused by land cover changes (LCC). The amount of land covered by trees has had a major impact on global warming and climate change. Increasing the amount of land cover helps to mitigate climate change and global warming. This study aims to investigate the changes in land cover and carbon storage in Plawangan Hill, Indonesia, over four years (2009, 2013, 2017, and 2023). The study site was defined as a conservation area that has been periodically impacted both directly and indirectly by volcanic eruptions. Images from Landsat 7 and 8 were used to collect data. Additionally, land cover changes were assessed using the forest canopy density (FCD Mapper) model, which was then utilized to quantify the carbon storage of the research site. The findings demonstrated fluctuations in land cover changes between 2009 and 2023. Additionally, changes in land cover have a direct impact on changes in carbon storage. The age of the trees, type of vegetation, succession stage, and history of eruptions were the variables that were apparent to be the main causes of these changes.

Students' Perceptions in Developing Authentic Assessment with Website Integrated to Measure Physics Learning Outcomes: A Need Analysis

Assessment is one of the crucial roles in measuring student abilities to face challenges of the 21st century. This study examines how college students’ ambitions influence their perceptions and desires for authentic assessment, based on website development. Data series via interviews and closed query questionnaires were given to 131 high school students within the metropolis of Surakarta. Facts analysis used a qualitative method with descriptive strategies. The findings of this look suggest that 64% of 130 senior high school students find it tough to clear up physics questions. Students experience that they consider physics issues harder to resolve than the equations that have been memorized. Students want within the evaluation, they have experienced those actual troubles within the learning system so that the ability to understand principles, abilities, and attitudes can be higher. Therefore, it is far essential to expand real assessment as an incorporated device between getting to know and evaluation of getting to know to degree physics learning outcomes.
 Keywords: authentic assessment, website integrated, physics learning outcomes

Fluctuations and disparity in broiler and carcass price before during and after covid-19 pandemic in Indonesia

This study aims to analyze fluctuations and disparity in broiler and carcass prices before, during, and after COVID-19 pandemic in five broiler-producing regions in Indonesia, including North Sumatra, West Java, Central Java, East Java, and South Sulawesi. Weekly data series were used to analyze fluctuations and disparity before (2017–February 2020), during (March 2020–2022), and after (January–June 2023) COVID-19 pandemic. Furthermore, Coefficient of variation (CV) and convergence models were also used during the analysis. The results showed that broiler price fluctuations in the five study areas were categorized as “medium” before and during pandemic. After pandemic, the majority of the areas were in the “low” category, except for South Sulawesi province, where CV was 10.02%. Carcass price fluctuations were categorized as “low” and “moderate” before and during COVID-19 pandemic. After the viral outbreak, all the provinces investigated in this study were classified as “low”. This indicated that the variability in carcass price decreased across all provinces after pandemic. The coefficient value of β1 was less than 1 indicating that broiler and carcass price did not show significant disparity between regions at all periods. The primary drivers of fluctuations and disparity were related to production and availability factors, and the government played a role in maintaining stability in producing areas.

The Reef Visual Census: a review of an essential long- term data source for reef-fish management in Florida

The Reef Visual Census is a collaborative effort that conducts standardized reef fish surveys on coral reefs and hardbottom habitats across southern Florida. The combined efforts of multiple agencies and organizations have resulted in a program with a strong sampling methodology, broad spatial coverage, the ability to maintain a long time series of data, and the versatility to be used in a variety of ways. The Reef Visual Census has provided an essential data set for reef fish management in Florida since 2003. We present the importance of this data set using case studies that cover stock assessments, marine protected areas, and emerging management uses. This review highlights the utility of the Reef Visual Census, demonstrates the benefits of a long-term collaborative partnership, outlines appropriate applications for the data, and suggests future uses.

Optimizing the role of rice central market in Java, Indonesia: the effect of toll roads construction to the rice market integration

Rice is considered as the primary staple food that Indonesian people mostly consume on daily basis. The need of rice in Indonesia is always increasing. However, the production of rice in Indonesia has not meet its demand. One of the factors resulting the number of paddy field decreasing is the construction of toll roads. The government of Indonesia has invested intensely in the development of a toll road network. The length of toll road operation has increased from around 750 km in 2014 to over 2,500 km in 2022. The construction encroaching paddy fields and resulting the production of rice decreasing every year. The paper objective is to investigate the implications of toll road investment to the rice market integration by focusing on Java as the central rice-producing region in Indonesia. We used the Vector Error Correction Model (VECM) approach to analyze the long-ranging daily rice price data series. The result of the study found that Cipinang Central Market in Jakarta has not the significant spatial market integration with rice markets in Java. Despite the fact that West Java, East Java, and Central Java with Cipinang Central Market have a major business tie, prices are not increasing. This shows a severe lack of market integration. The phrase “optimal market integration” refers to a more efficient market, especially when it comes to establishing rice market prices.

Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits

The primary objective of the study was development of a machine learning (ML)-based workflow for fracture hit (“frac hit”) detection and monitoring using shale oil-field data such as drilling surveys, production history (oil and produced water), pressure, and fracking start time and duration records. The ML method takes advantage of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks to identify the frac hits due to hydraulic communication between the fracking child well(s) and the producing parent well(s) within the same pad (intra-pad interaction) and/or on different pads (inter-pad interaction). It utilizes time series of pressure and production data from within a pad and from adjacent pads. The workflow can capture time variable features of frac hits when the model architecture is deep and wide enough, with enough trainable parameters for deep learning and feature extraction, as demonstrated in this paper by using training and testing subsets of the field data from selected neighboring pads with over a couple of hundred wells. The study was focused on frac-hit interaction among paired wells and demonstrated that the ML model, once trained, can predict the frac-hit probability.

SeRL: Style-embedding representation learning for unsupervised CT images synthesis from unpaired MR images

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide, and the fastest-growing cause of cancer deaths in the United States. Computed tomography (CT) and magnetic resonance (MR) imaging are the key image-based examination methods for HCC patients. Patients may alternatively undergo CT and MR exams given a radiation dose of the former and the cost of the latter. Due to organ representation differences between CT and MR, it’s a great challenge to detect the tumors from a time series of data consisting of both CT and MR by a single model. The annotations for these time series data will consume much time and labor for the physicians. Thus, we propose our style-embedding representation learning (SeRL) for unsupervised and unpaired abdomen CT and MR translation. Different from current medical image translation models, the style-representation information from real CT and real MR images has been embedded in the translation process to bypass some local minima during the convergence process and improve the synthesis results. Our patch-corrosion augmentation method enhances the style-embedding representation learning by bringing more diversity to the training data. Combined with the self-attention module, SeRL eliminates the noise caused by low grayscale pixel values during translation. Results on the unpaired HCC patient's CT and MR images show that our proposed SeRL is able to generate high quality CT images from MR ones. Evaluations such as Frechet Inception Distance (FID), Sliced Wasserstein Distance (SWD), and liver segmentation dice score are utilized to demonstrate our advantages over other state-of-the-art unsupervised methods.

Econometric analysis of the development of labor potential of agro-industrial complex of Kazakhstan

The goal is to reveal the basis for the formation of labor potential in agriculture, its condition and role in socio-economic development of rural areas. Consider theoretical foundations of economic and mathematical modeling of dynamics of the formation of human resources in agro-industrial complex of Kazakhstan. Methods - application of methodology for carrying out forecast calculations based on econometric model in the form of a system of joint equations, tracing the connection of labor market with changes in demographic structure of the country's rural population. The study used statistical data series provided by the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan. An overview of government support measures aimed at increasing labor reserves in rural areas is presented. As a result of scientific research, calculation forecast models were obtained, on the basis of which the values of main statistical indicators characterizing the dynamics of labor activity in rural areas of the republic for the long-term period were determined. Results - ways to overcome negative trends that slow down labor activity and form effective labor potential in rural areas are proposed: ensuring real growth in rural employment in all areas of activity; creation of additional high-performance jobs; improvement of social infrastructure. Conclusions - based on forecast calculations of indicators reflecting the directions of development of human capital in agricultural sector, opportunities for increasing GDP of products (services) of agriculture, forestry and fisheries have been identified. Studying the problems of rural labor economics makes it possible to note imbalances in the supply and demand of qualified specialists. In this regard, there is a need for active labor policy, stimulation of job search, training in professional skills, acquisition of a new profession.

Modelling Floodplain Vegetation Response to Climate Change, Using the Soil and Water Assessment Tool (SWAT) Model Simulated LAI, Applying Different GCM’s Future Climate Data and MODIS LAI Data

Scientists widely agree that anthropogenically driven climate change significantly impacts vegetation growth, particularly in floodplain areas, by altering river flow and flood regimes. This impact will accelerate in the future, according to climate change projections. For example, in Australia, climate change has been attributed to a decrease in winter precipitation in the range of 56% to 72.9% and an increase in summer from 11% to 27%, according to different climate scenarios. This research attempts to understand vegetation responses to climate change variability at the floodplain level. Further, this study is an effort to enlighten our understanding of temporal climate change impacts under different climate scenarios. To achieve these aims, a semi-distributed hydrological model was applied at a sub-catchment level to simulate the Leaf Area Index (LAI). The model was simulated against future time series of climate data according to Global Climate Model (GCM) projections. The time series data underwent a non-parametric Mann–Kendall test to detect trends and assess the magnitude of change. To quantify the model’s performance, calibration and validation were conducted against the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI. The calibration and validation results show Nash–Sutcliffe efficiency (NSE) values of 0.85 and 0.78, respectively, suggesting the model’s performance is very good. The modeling results reveal that the rainfall pattern fluctuates under climate projections within the study site, in which vegetation tends to be more vibrant during the warmer seasons. Moreover, the modeling results highlighted increases in the average projected future winter temperatures, which can help vegetation growth during winter. The results of this study may be employed for sustainable floodplain management, restoration, land-use planning, and policymaking, and help floodplain communities better prepare for and respond to changing flood patterns and related challenges under a future changing climate.

Tipping of the double-diffusive regime in the southern Adriatic Pit in 2017 in connection with record high-salinity values

Abstract. In double-diffusive mixing, whenever salinity and temperature decrease with depth, the water column is either unstable or predisposed to a state called salt fingering (SF), which exhibits increased vertical mixing. Analysis of a high-frequency time series of thermohaline data measured at the EMSO-E2M3A regional facility in the southern Adriatic Pit (SAP) from 2014 to 2019 reveals that in the south Adriatic, SF is the dominant regime. The same time series shows the presence of a very saline core of the Levantine Intermediate Water that penetrated with unprecedented strength during the winter of 2016/17 at around 550 dbar and even higher-salinity water above. The effect of strong heat loss at the surface during that winter allowed deep convection to transport this high-salinity water from the intermediate to the deep layers within the pit. This resulted in an increased predisposition to SF throughout the water column. In the subsurface layer (350 to 550 dbar) the increase is from 27 % to 72 % of observations. We observe an alteration of vertical stratification throughout the water column during the winter of 2016/17 from a stratified water column to an almost homogeneous water column down to 700 dbar, with no return in the following years.

Multivariate regression studies for the investigation of the COD, BOD, and TOC concentrations in the water of the Styr River within the zone of influence of the power plant discharge

Abstract The discharge of cooling water into surface waters containing residual heat energy is a pressing issue in the environmental impact of nuclear power. Cooling water flow can cause negative changes in the water and ecological processes of natural waters, in particular an imbalance of organic matter, in combination with a complex of natural factors. The purpose of the article was to perform a statistical analysis of the dynamics of organic matter content in the water of the Styr River in the Rivne Nuclear Power Plant (Rivne NPP) impact zone to assess the manifestation of the environmental impact of the discharge of cooling water with residual heat. The monitoring data series consisted of the results of monthly monitoring of the rate of chemical oxygen demand, the rate of biochemical oxygen demand in 5 days, and total organic carbon in the Styr River sections before and after the discharge of Rivne NPP cooling water in 2018–2022.

SpatialcoGCN: deconvolution and spatial information-aware simulation of spatial transcriptomics data via deep graph co-embedding.

Spatial transcriptomics (ST) data have emerged as a pivotal approach to comprehending the function and interplay of cells within intricate tissues. Nonetheless, analyses of ST data are restricted by the low spatial resolution and limited number of ribonucleic acid transcripts that can be detected with several popular ST techniques. In this study, we propose that both of the above issues can be significantly improved by introducing a deep graph co-embedding framework. First, we establish a self-supervised, co-graph convolution network-based deep learning model termed SpatialcoGCN, which leverages single-cell data to deconvolve the cell mixtures in spatial data. Evaluations of SpatialcoGCN on a series of simulated ST data and real ST datasets from human ductal carcinoma in situ, developing human heart and mouse brain suggest that SpatialcoGCN could outperform other state-of-the-art cell type deconvolution methods in estimating per-spot cell composition. Moreover, with competitive accuracy, SpatialcoGCN could also recover the spatial distribution of transcripts that are not detected by raw ST data. With a similar co-embedding framework, we further established a spatial information-aware ST data simulation method, SpatialcoGCN-Sim. SpatialcoGCN-Sim could generate simulated ST data with high similarity to real datasets. Together, our approaches provide efficient tools for studying the spatial organization of heterogeneous cells within complex tissues.