Series Data Research Articles

14C GIRI SAMPLES IN AMS GOLDEN VALLEY: GRAPHITE PREPARATION USING AGE-3 AND ABSORPTION-CATALYTIC SETUP

ABSTRACT The AMS Golden Valley laboratory is equipped with two accelerator mass spectrometers: the AMS facility from the Budker Institute of Nuclear Physics (BINP) and the Mini Carbon Dating System (MICADAS-28) from Ionplus AG and two graphitization systems: the Automated Graphitization Equipment (AGE-3) from Ionplus AG and the Absorption-catalytic setup (ACS) developed at the Boreskov Institute of Catalysis (BIC). The ACS was designed for graphite preparation from labeled biomedical samples, dissolved organics, and dissolved or gaseous carbon dioxide but has proven to be suitable for the traditional dating of objects no older than 35,000 years. Here we present two series of AMS data for the samples from Glasgow International Radiocarbon Inter-comparison (GIRI), prepared using AGE-3 and ACS, and then measured on MICADAS-28. The mean value of the background F14C was 0.0024 ± 0.0009 and 0.012 ± 0.003 for AGE-3 and ACS, respectively, and both methods gave reproducible results for the OXI.

H-ACO with Consecutive Bases Pairing Constraint for Designing DNA Sequences.

DNA computing is a novel computing method that does not rely on traditional computers. The design of DNA sequences is a crucial step in DNA computing, and the quality of the sequence design directly affects the results of DNA computing. In this paper, a new constraint called the consecutive base pairing constraint is proposed to limit specific base pairings in DNA sequence design. Additionally, to improve the efficiency and capability of DNA sequence design, the Hierarchy-ant colony (H-ACO) algorithm is introduced, which combines the features of multiple algorithms and optimizes discrete numerical calculations. Experimental results show that the H-ACO algorithm performs well in DNA sequence design. Finally, this paper compares a series of constraint values and NUPACK simulation data with previous design results, and the DNA sequence set designed in this paper has more advantages.

Analysis of the Dynamics of Vegetation in the Sudan-Sahelian Zone: Case of the Moutourwa Agricultural Production Basin, Far-North Cameroon

The Moutourwa area in the Far North region of Cameroon is facing a deterioration of its climatic conditions. In this part of the Sudano-Sahelian zone, the soils are fragile and agriculture is not very productive due to various droughts. The statistical analysis study of climatic variability based on satellitic data in the area aims to analyze the dynamics of vegetation. This involves studying the spatio-temporal dynamics of vegetation using vegetation indices. The approach adopted to carry out this study is based on the descriptive statistics of the MODIS NDVI data series from the period 2010 to 2020. The study also analyzes the VCI drought indices in this study area. It appears from these analyzes that the year 2012 has the high average and the year 2015, the lowest average of our study period with 0.51 and 0.274 respectively. The years 2010, 2011, 2013, 2014, 2017, 2018 and 2019 remained around an average of 0.3. The observation of drought indices such as the VCI examines the fluctuation of their values characteristic of the heterogeneity of the vegetation state.

Microalgal assemblages response to water quality remediation in coastal waters of Perth, Australia

Nutrient reduction is an essential environmental policy for water quality remediation, but climate change can offset the ecological benefits of nutrient reduction and lead to the difficulty of environmental evaluation. Here, based on the records of three lipid microalgal biomarkers and stable isotopes of carbon and nitrogen in two sediment cores from the embayment of Perth, Australia, we reconstructed the microalgal biomasses (diatoms, dinoflagellates and coccolithophores) over the past century and evaluated the ecological effects of nutrient reduction on them, using Change Point Modeling (CPM) and redundancy analysis (RDA). The CPM result showed that total microalgal biomarkers increased by 25% and 51% in deep and shallow areas, respectively, due to nutrient enrichment caused by industrial wastewater in the 1950s and the causeway construction in the 1970s, and dinoflagellates were beneficiaries of eutrophication. The nutrient reduction policy since the 1980s had not decreased total microalgal biomass, and diatoms were beneficiaries of this period. RDA based on time series of sediment cores and water monitoring data revealed that the increase of sea-surface temperature and the decrease of rainfall since the 1980s may be important factors sustaining the high total microalgal biomass and increasing the degree of diatom dominance. The result also indicated that the variations of microalgal assemblages may better explain the effect of nutrient reduction rather than total microalgal biomass.

Research on E-Commerce Retail Demand Forecasting Based on SARIMA Model and K-means Clustering Algorithm

With the rapid development of e-commerce, precise demand forecasting and efficient inventory management have become essential for the success and profitability of retail businesses. This study focuses on demand forecasting for e-commerce retailers using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and the K-means clustering algorithm. The research utilizes a dataset containing 1996 time series of sales data from various products, merchants, and warehouses, aiming to predict demand changes for the next 15 days. The study initially evaluates three models—Linear Regression (LR), Autoregressive Integrated Moving Average (ARIMA), and SARIMA—by fitting them to historical sales data to forecast future demand. The SARIMA model is identified as the most effective through rigorous evaluation using 1-mWAPE (mean weighted absolute percentage error) and RMSE (root mean square error) metrics. To enhance homogeneity within demand categories, the K-means clustering method is applied to divide products into four distinct groups, further refining the forecasting process.The paper also addresses the challenge of integrating new sequences into the dataset by leveraging clustering results to classify sequences and using cosine similarity to identify analogous historical time series. These matched sequences serve as the basis for demand prediction using the established SARIMA model. The findings highlight the robustness of the SARIMA model in capturing trends and seasonality, providing a reliable framework for e-commerce demand forecasting that can significantly impact inventory strategies and operational efficiency.

Investigating land use changes and trends of hydro morphological indicators on the area and volume of the Ovan Lake's water zone based on the time series of Landsat data

Investigating land use changes and trends of hydro morphological indicators on the area and volume of the Ovan Lake's water zone based on the time series of Landsat data

Identification of Trends in Dam Monitoring Data Series Based on Machine Learning and Individual Conditional Expectation Curves

Dams are complex systems that involve both the structure itself and its foundation. Rheological phenomena, expansive reactions, or alterations in the geotechnical parameters of the foundation, among others, result in non-reversible and cumulative modifications in the dam response, leading to trends in the monitoring data series. The accurate identification and definition of these trends to study their evolution are key aspects of dam safety. This manuscript proposes a methodology to identify trends in dam behavioural data series by identifying the influence of the time variable on the predictions provided by the ML models. Initially, ICE curves and SHAP values are employed to extract temporal dependence, and the ICE curves are found to be more precise and efficient in terms of computational cost. The temporal dependencies found are adjusted using a GWO algorithm to different function characteristics of irreversible processes in dams. The function that provides the best fit is selected as the most plausible. The results obtained allow us to conclude that the proposed methodology is capable of obtaining estimates of the most common trends that affect movements in concrete dams with greater precision than the statistical models most commonly used to predict the behaviour of these types of variables. These results are promising for its general application to other types of dam monitoring data series, given the versatility demonstrated for the unsupervised identification of temporal dependencies.

Research on Stock Price Prediction Model Based on LSTM

Stock prices are a non-linear, long-term series of data. The research topic of this paper is to use the Long Short-Term Memory Network (LSTM) model to analyze the historical stock price data of BYD (BYD) to predict the future stock price trend, and to provide intelligent reference for investors by evaluating the model performance, aiming to improve the accuracy of stock price prediction. The accuracy and consistency of stock price prediction can be increased by using this method, which successfully takes advantage of the time characteristics in historical data. After explaining the idea and architecture of LSTM, this article creates a stock price prediction model based on it, using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as evaluation metrics. At the same time, network structures with different layers were designed to verify the effectiveness of the model, and the experimental results were shown. The advantages and limitations of the model are highlighted, and the feasibility of deep learning in the stock financial market and the credibility and advantages of strategic decision-making are demonstrated.

Forecasting the River Water Discharge by Artificial Intelligence Methods

The management of water resources must be based on accurate models of the river discharge in the context of the water flow alteration due to anthropic influences and climate change. Therefore, this article addresses the challenge of detecting the best model among three artificial intelligence techniques (AI)—backpropagation neural networks (BPNN), long short-term memory (LSTM), and extreme learning machine (ELM)—for the monthly data series discharge of the Buzău River, in Romania. The models were built for three periods: January 1955–September 2006 (S1 series), January 1955–December 1983 (S2 series), and January 1984–December 2010 (S series). In terms of mean absolute error (MAE), the best performances were those of ELM on both Training and Test sets on S2, with MAETraining = 5.02 and MAETest = 4.01. With respect to MSE, the best was LSTM on the Training set of S2 (MSE = 60.07) and ELM on the Test set of S2 (MSE = 32.21). Accounting for the R2 value, the best model was LSTM on S2 (R2Training = 99.92%, and R2Test = 99.97%). ELM was the fastest, with 0.6996 s, 0.7449 s, and 0.6467 s, on S, S1, and S2, respectively.

Optimal fusion of features from decomposed ultrasound RF data with adaptive weighted ensemble classifier to improve breast lesion classification

Early diagnosis plays a crucial role in successful treatment of breast tumors and reduced mortality. In this study, considering the complementary advantages between features and combined with the improvement of classifiers, fusion of optimal complexity and texture features from decomposed ultrasound radio frequency (RF) data is proposed to improve breast lesion classification performance. In this method, three complexity features and four texture features were extracted from the ring regions of interest for breast lesions in all decomposed RF sub-images and their combinations. Selection techniques based on analysis of feature relevance, redundancy, and interaction (FRRI) were used to determine the optimal feature sets (FS–FRRI). Finally, three classifiers with the best performance (weighted k-nearest neighbor, bagged tree, Gaussian Naive Bayes (NB)) were selected based on FS-FRRI. The three classifiers were integrated using the bagging method, and each classifier was adaptively weighted according to the genetic algorithm during the integration to classify breast lesions. The proposed method was evaluated using the Open Access Series of Breast Ultrasonic Data, with 10-fold cross-validation. The experimental results demonstrated that optimal performance was obtained by the FS–FRRI-based adaptive weighted ensemble classifier, with an accuracy of 97%, a sensitivity of 99%, a specificity of 96%, and an area under the receiver operating curve value of 0.97. Fusion of optimal complexity and texture features from decomposed ultrasound RF data with an adaptive weighted ensemble classifier can help improve breast lesion classification performance, which has great potential to assist clinicians in accurate diagnosis of breast lesions.

Gasdermin E-mediated keratinocyte pyroptosis participates in the pathogenesis of psoriasis by promoting skin inflammation.

Psoriasis is a common chronic inflammatory disease with an unclear aetiology. Keratinocytes in psoriasis are susceptible to exogenous triggers that induce inflammatory cell death. To investigate whether gasdermin E (GSDME)-mediated pyroptosis in keratinocytes contributes to the pathogenesis of psoriasis. Skin samples from patients with psoriasis and from healthy controls were collected to evaluate the expression of GSDME, cleaved caspase-3 and inflammatory factors. We then analysed the data series GSE41662 to further compare the expression of GSDME between lesional and nonlesional skin samples in those with psoriasis. In vivo, a caspase-3 inhibitor and GSDME-deficient mice (Gsdme-/-) were used to block caspase-3/GSDME activation in an imiquimod-induced psoriasis model. Skin inflammation, disease severity and pyroptosis-related proteins were analysed. In vitro, tumour necrosis factor (TNF)-α-induced caspase-3/GSDME-mediated pyroptosis in the HACAT cell line was explored. Our analysis of the GSE41662 data series found that GSDME was upregulated in psoriasis lesions vs. normal skin. High levels of inflammatory cytokines such as interleukin (IL)-1β, IL-6 and TNF-α were also found in psoriasis lesions. In mice in the Gsdme-/- and caspase-3 inhibitor groups, the severity of skin inflammation was attenuated and GSDME and cleaved caspase-3 levels decreased after imiquimod treatment. Similarly, IL-1β, IL-6 and TNF-α expression was decreased in the Gsdme-/- and caspase-3 inhibitor groups. In vitro, TNF-α induced HACAT cell pyroptosis through caspase-3/GSDME pathway activation, which was suppressed by blocking caspase-3 or silencing Gsdme. Our study provides a novel explanation of TNF-α/caspase-3/GSDME-mediated keratinocyte pyroptosis in the initiation and -acceleration of skin inflammation and the progression of psoriasis.

Evolutionary Architectures in Web Applications: A Comparative Study of Client-Server, Multi-Tier, and Service-Oriented Approaches

This manuscript provides an in-depth comparative analysis of three core architectural models in web application development: client-server, multi-tier, and service-oriented architectures (SOA). Through a series of simulations, case studies, and empirical data, the study evaluates the efficiency, scalability, and adaptability of these architectures within commercial environments. The research highlights the evolving needs of modern web applications and offers insights into choosing the appropriate architectural strategy to enhance performance and maintainability in dynamic business settings.

Are private investors overcompensated in infrastructure projects?

Private sector engagement in infrastructure procurement and ownership can have benefits for both investors and society. However, a significant challenge for authorities is determining when private sector participation provides value for money. Private investors may demand high returns on their investments even in projects with seemingly low risks. Governments with good credit ratings and access to lower cost capital may view private investors overprice in comparison to the level of risk they take. This paper examines whether the financing premiums (in addition to the cost of state financing) of four public-private partnership road projects in Finland are reasonable relative to the risks borne by private partners, using long time series of actual data. To the best of our knowledge, this is the first study to mostly use actual ex-post data on project costs over an extended period to evaluate whether the government overpays private investors. Our analysis indicates that the financing cost of the four projects is on average 201 basis points higher than the financing cost the government pays on its debt. We conclude that this premium is reasonable compensation for the risk the private investors bear in the projects. This finding has implications for selecting the most effective procurement policy for road projects.

Analisis Integrasi Dinamis Pasar Modal Indonesia terhadap Pasar Modal ASEAN Pasca Berlakunya Masyarakat Ekonomi ASEAN

This research intends to understand the dynamic integration of the Indonesian capital market with the ASEAN Capital Market before and after the implementation of the MEA (ASEAN Economic Community) or AEC. The sample used in this observation is a series of data collected in the period January 2011 to December 2015 before the MEA was implemented and January 2016 to December 2022 after the MEA was implemented, while the capital markets studied were the Indonesian Stock Exchange, Kuala Lumpur Stock Exchange, Stock Exchange Singapore, Philippine Stock Exchange and Thai Stock Exchange. This research analyzes the dynamic integration of the Indonesian capital market towards ASEAN after the implementation of the MEA using the Orthogonal Generalized Autoregressive Conditional Heteroscedasticity (OGARCH) method which can manifest the level of integration in a measurable way. The results of this research are that there is co-movement among the ASEAN capital markets studied, however, not all ASEAN capital markets are fully integrated. The results of this observation also found that the Indonesian Stock Exchange, Kuala Lumpur Stock Exchange, Thailand Stock Exchange and Singapore Stock Exchange were integrated but the Philippine Stock Exchange was not. The Philippine Stock Exchange tends to be segmented rather than integrated.

The Transaxillary Approach as a Direct Route in the Management of Upper Thoracic Spine Pathology: A Technical Note with Case Series.

This retrospective case series of prospective data aims to describe the transaxillary approach for the treatment of upper thoracic spine pathology. Various surgical techniques and approaches have been reported across the literature to address upper thoracic spine pathology, including the cervicothoracic approach, anterior transsternal approach, posterolateral approach, supraclavicular approach, and lateral parascapular approaches. These techniques are invasive. A minimally invasive, less morbid, and direct access approach to the pathology of the upper thoracic spine has not been reported in the literature. Patients with pathology affecting the first thoracic vertebra up to the sixth thoracic vertebra were classified into the upper thoracic spine group. Patients with pathology below the sixth thoracic vertebra were excluded. Patients not having a minimum follow-up of 12 months were also excluded. The study analyzed 18 patients. The mean preoperative modified Japanese Orthopedic Association score was 7.2±1.44, which improved to 10.16±1.2 (p<0.05). The majority (14/18) of the patients had an excellent outcome. Three patients had good outcomes, and one patient had a fair outcome. Five cases of intraoperative dural leak were recorded, and one patient had postoperative neurological deficit. The transaxillary approach is a safe, viable, muscle-sparing, and minimally invasive approach for ventral pathologies of the upper thoracic spine.

Warming drives phenological changes in coastal zooplankton

Climate warming has led to phenological changes over time, typically displayed as earlier emergence of various organisms in spring or summer in temperate terrestrial and marine systems alike. Similarly, warm conditions can extend seasonal occurrence. Using a time series of zooplankton data from a coastal area in the Gulf of Finland, we calculated the start, end and the length of the season for the occurrence in rotifers and for adult and juvenile stages of three calanoid copepods. We investigated whether the start and end of the season of these taxa have shifted earlier and later, respectively, and whether the season length has increased. We further investigated if potential changes are driven by climate warming. We show that both copepods and rotifers do indeed emerge earlier, but that the pattern in recent years was not conclusive, and that both temperature and ice conditions influenced the seasonal abundance patterns of some taxa. Warmer years led to earlier occurrence of Temora longicornis copepodites. Earlier ice break-up coincided with longer seasons for Acartia and earlier emergence of Eurytemora affinis. The phenological changes in zooplankton demonstrated here may have cascading effects on other trophic levels in the food web. We also demonstrate how decreased sample number influences the ability to capture intra-annual abundance patterns and discuss the implications for monitoring.

Regressing bubble cluster dynamics as a disordered many-body system

The coherent dynamics of bubble clusters are of fundamental and industrial importance, and are elusive due to the complex interactions of disordered bubble oscillations. Here we introduce and demonstrate a method for decomposition of the Lagrangian time series of bubble dynamics data by combining theory and principal component analysis. The decomposition extracts coherent features of bubble oscillations based on their energy, in a way similar to proper orthogonal decomposition of Eulerian flow field data. This method is applied to a dataset of spherical clusters under harmonic excitation at different amplitudes, with various nuclei density and polydispersity parameters. Results indicate that the underlying correlated mode of oscillations is isolated in a single dominant feature in cavitating regimes, independent of the nuclei's parameters. A systematic data analysis procedure further suggests that this feature is globally controlled by the dynamic cloud interaction parameter of Maeda and Colonius (J. Fluid Mech., vol. 862, 2019, pp. 1105–1134) that quantifies the mean-field interactions, regardless of initial polydispersity or nonlinearity. The method provides a simplified and comprehensive representation of complex bubble dynamics as well as a new path to reduced-order modelling of cavitation and nucleation.

Hydrodynamic analysis of the inner part of the Gulf of Nicoya, Costa Rica: Updating field measurements for coastal users

Although extensively studied, the inner region of the gulf of Nicoya (one of the main estuaries of Costa Rica) has a relatively low amount of research focused on its hydrodynamic. This represents a limitation for establishing hydrodynamical models and doing complex analysis of the water movement, which are vital for the proper management of the multiple economic, social, and ecological actors present at the estuary. The aim of this study is to generate and update hydrodynamic and bathymetric data of the inner region of the Gulf of Nicoya, with measurement periods and temporal resolutions that could be used for better understanding the hydrodynamic patterns and the development of future numerical models. Water level, current velocity, and bathymetry were measured in two separate field surveys, almost two weeks each, during dry and rainy seasons. Time series of each variable were collected with temporal resolutions of 20 min or less. Pressure and acoustic gauges were used for measuring the variables in representative sites of the hydrodynamics of the estuary. The measured data supports the hydrodynamic pattern described in previous studies, but shows some differences derived from the longer and finer data series. The most inner part of the estuary presents a time lag of up to 25 min and 30% amplification of the tide wave in comparison to the estuary’s entrance. Maximum currents show values of 1.3 m/s, and mostly around the change of tides, there are moments in which bottom and superficial currents flow in almost opposite direction with velocities less than 0.2 m/s. Bathymetry shows various areas where the new and historical data do not coincide. The present study contributes to update and extend the available data on hydrodynamic related variables, delivering updated time series with higher temporal resolution for future numerical models and general analysis from other coastal users.

Matrix Wasserstein distance generative adversarial network with gradient penalty for fast low-carbon economic dispatch of novel power systems

For novel power systems, access to sustainable energy sources will undoubtedly increase the uncertainty of power systems significantly. Consequently, to guarantee efficient, secure, smooth, and expedited functioning, this work proposes a matrix Wasserstein distance generative adversarial network with gradient penalty (MWGAN-GP), which considers the minimization of generation cost and carbon emission simultaneously for low-carbon economic dispatch (ED) of novel power systems. The proposed MWGAN-GP generates a series of matrix data by a trained generator, equalizes load values and the sum of unit outputs at each period by relaxation operation, and rapidly generates numerous ED solutions. The speed of ED based on MWGAN-GP is greatly faster than the speed of ED based on a traditional genetic algorithm (GA). Furthermore, the introduction of the relaxation operation decreases the average cost of ED based on MWGAN-GP by 7.94 % compared to GA according to the case with 4000 sets of load data.

Advanced forecasting of COVID-19 epidemic: Leveraging ensemble models, advanced optimization, and decomposition techniques

In the global effort to address the outbreak of the new coronavirus pneumonia (COVID-19) pandemic, accurate forecasting of epidemic patterns has become crucial for implementing successful interventions aimed at preventing and controlling the spread of the disease. The correct prediction of the course of COVID-19 outbreaks is a complex and challenging task, mainly because of the significant volatility in the data series related to COVID-19. Previous studies have been limited by the exclusive use of individual forecasting techniques in epidemic modeling, disregarding the integration of diverse prediction procedures. The lack of attention to detail in this situation can yield worse-than-ideal results. Consequently, this study introduces a novel ensemble framework that integrates three machine learning methods (kernel ridge regression (KRidge), Deep random vector functional link (dRVFL), and ridge regression) within a linear relationship (L-KRidge-dRVFL-Ridge). The optimization of this framework is accomplished through a distinctive approach, specifically adaptive differential evolution and particle swarm optimization (A-DEPSO). Moreover, an effective decomposition method, known as time-varying filter empirical mode decomposition (TVF-EMD), is employed to decompose the input variables. A feature selection technique, specifically using the light gradient boosting machine (LGBM), is also implemented to extract the most influential input variables. The daily datasets of COVID-19 collected from two countries, namely Italy and Poland, were used as the experimental examples. Additionally, all models are implemented to forecast COVID-19 at two-time horizons: 10- and 14-day ahead (t+10 and t+14). According to the results, the proposed model can yield higher correlation coefficient (R) for both case studies: Italy (t+10 = 0.965, t+14 = 0.961) and Poland (t+10 = 0.952, t+14 = 0.940) than the other models. The experimental results demonstrate that the model suggested in this paper has outstanding results in various kinds of complex epidemic prediction situations. The proposed ensemble model demonstrates exceptional accuracy and resilience, outperforming all similar models in terms of efficacy.