Historical Data Research Articles

IMPROVING THE ENERGY EFFICIENCY OF THERMOELECTRIC ENERGY CONVERTERS – REVIEW

The results of an analytical review of a large number of publications on the problem of improving the efficiency of thermoelectric generators (TEG) over the past decade are presented. An analysis of historical data on the thermodynamic justification of the efficiency of thermoelectric generators is presented. The following areas are analyzed: the problem of increasing the figure of merit Z through the creation of new material science technologies and new materials, the creation of multi-segment thermoelectric elements for a wide temperature range (300 ÷ 1200 K), the efficiency of heat supply to the TEG and cooling. The methods for calculating the thermodynamic efficiency of TEG are presented. It is shown that the efficiency of TEG is limited to ½ of the Carnot value for ZT values of 1-3. Modern materials allow the production of TEG with ZT values less than 1. However, the application of TEG has promising prospects with the increase in the intensity of heat supply and cooling processes. The main directions and trends (physical, chemical, technological) in the creation and improvement of TEG are identified. Some practical solutions presented in the literature, as well as the authors' solution for improving the thermodynamic efficiency of boiler units using TEG, are shown.

Increasing phosphorus loss despite widespread concentration decline in US rivers

The loss of phosphorous (P) from the land to aquatic systems has polluted waters and threatened food production worldwide. Systematic trend analysis of P, a nonrenewable resource, has been challenging, primarily due to sparse and inconsistent historical data. Here, we leveraged intensive hydrometeorological data and the recent renaissance of deep learning approaches to fill data gaps and reconstruct temporal trends. We trained a multitask long short-term memory model for total P (TP) using data from 430 rivers across the contiguous United States (CONUS). Trend analysis of reconstructed daily records (1980–2019) shows widespread decline in concentrations, with declining, increasing, and insignificantly changing trends in 60%, 28%, and 12% of the rivers, respectively. Concentrations in urban rivers have declined the most despite rising urban population in the past decades; concentrations in agricultural rivers however have mostly increased, suggesting not-as-effective controls of nonpoint sources in agriculture lands compared to point sources in cities. TP loss, calculated as fluxes by multiplying concentration and discharge, however exhibited an overall increasing rate of 6.5% per decade at the CONUS scale over the past 40 y, largely due to increasing river discharge. Results highlight the challenge of reducing TP loss that is complicated by changing river discharge in a warming climate.

A Statistical Evaluation of the Occurence of Meningitis in Takum, Taraba State, Nigeria

Meningitis remains a critical public health issue in Nigeria, particularly within the dry season when environmental factors such as low humidity and dust elevate transmission risks. Using historical incidence data from 2012 to 2021, this study utilizes the Autoregressive Integrated Moving Average (ARIMA) model estimate and predict the occurrence of meningitis occurrences. Findings frm the study revealed that the ARIMA(1,1,0) model emerged as the optimal fit, capturing the seasonal patterns and temporal trends in meningitis cases. This study recommends the integration of ARIMA-based forecasting into Nigeria’s public health strategies to strengthen early warning systems, optimize resource deployment, and enable more proactive responses during high-risk periods.

Enhancing Fraud Detection Systems through Advanced Data Engineering Techniques

Aims: Fraud remains a persistent issue in various industries, particularly in finance, e-commerce, and healthcare, where traditional rule-based systems have struggled to keep pace with the evolving complexity of fraudulent activities. This study aims to develop an enhanced fraud detection framework by addressing the limitations of traditional rule-based systems, particularly in industries where sophisticated fraud schemes prevail. Study Design: The research utilizes advanced data engineering techniques, including big data analytics, machine learning, and real-time processing, to improve the accuracy and efficiency of fraud detection systems. Place and Duration of Study: The study was conducted over two years across industries with high fraud susceptibility, including financial services, e-commerce platforms, and healthcare organizations. Methodology: The framework integrates various data sources, including transaction logs, user behavior, and external fraud indicators. These datasets were pre-processed through data cleaning, feature engineering, and integration. Supervised and unsupervised machine learning models, such as Random Forest and Gradient Boosting, were applied to detect fraud patterns. Real-time data processing enabled immediate detection and response. The system continuously learned from historical data, adapting to new fraud tactics and improving detection over time. Results: The proposed framework demonstrated a significant improvement in fraud detection accuracy, with machine learning models achieving over 90% accuracy rates. There was also a 30% reduction in false positives compared to traditional methods, and detection times were shortened by 40%, enabling faster identification and mitigation of emerging fraud schemes. Conclusion: This study concludes that integrating advanced data engineering techniques with machine learning significantly enhances fraud detection systems' accuracy, scalability, and adaptability. While promising, further improvements are needed, particularly in addressing the evolving nature of fraud schemes and ensuring the scalability of real-time data processing. These areas present opportunities for future research and development.

Research on information leakage in time series prediction based on empirical mode decomposition.

Time series analysis predicts the future based on existing historical data and has a wide range of applications in finance, economics, meteorology, biology, engineering, and other fields. Although the combination of decomposition techniques and machine learning algorithms can effectively solve the problem of predicting nonstationary sequences, this kind of decomposition-integration-prediction strategy of the prediction method has serious defects. After the decomposition of the division of the training set and the test set, the information of the test set in the process of decomposition of the information leakage ultimately shows a high accuracy of the prediction of the illusionary. This paper proposes three improvement strategies for this type of "information leakage" problem: sliding window decomposition (SW-EMD), single training and multiple decomposition (STMP-EMD), and multiple training and multiple decomposition (MTMP-EMD). They are combined with a bidirectional multiscale temporal convolutional network (MSBTCN), bidirectional long- and short-term memory network (BiLSTM), and attention mechanism (DMAttention), which introduces a dependency matrix based on cosine similarity to be applied to water quality prediction. The experimental results show that the model achieves good performance in the prediction of three water quality indicators (pH, DO and KMnO4), and the accuracies of the three models proposed in this paper are improved by 1.958% and 0.853% in terms of the RMSE and MAPE, respectively, compared with those of the mainstream LSTM models. The key contributions of this study include the following: (1) three methods are proposed to improve the class EMD decomposition, which can effectively solve the problem of "information leakage" that exists in the current models via class EMD decomposition; (2) the CEEMDAN-MSBTCN-BiLSTM-DMAttention model structure is innovated by combining improved class EMD decomposition methods; and (3) the three improved decomposition methods proposed in this paper can effectively solve the problem of "information leakage" and optimize the prediction model at the same time. This study provides an effective experimental method for water quality prediction and can effectively address the problem of "overfitting" models via class EMD decompositions during model training and testing.

Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes.

Body mass index (BMI) trajectories have been used to assess the growth of children with respect to their peers, and to anticipate future obesity and disease risk. While retrospective BMI trajectories have been actively studied, models to prospectively predict continuous BMI trajectories have not been investigated. Using longitudinal BMI measurements between birth and age 10 y from a mother-offspring cohort, we leveraged a multi-task Gaussian process approach to develop and evaluate a unified framework for modeling, clustering, and prospective prediction of BMI trajectories. We compared its sensitivity to missing values in the longitudinal follow-up of children, compared its prediction performance to cubic B-spline and multilevel Jenss-Bayley models, and used prospectively predicted BMI trajectories to assess the probability of future BMIs crossing the clinical cutoffs for obesity. MagmaClust identified 5 distinct patterns of BMI trajectories between 0 to 10 y. The method outperformed both cubic B-spline and multilevel Jenss-Bayley models in the accuracy of retrospective BMI trajectories while being more robust to missing data (up to 90%). It was also better at prospectively forecasting BMI trajectories of children for periods ranging from 2 to 8 years into the future, using historic BMI data. Given BMI data between birth and age 2 years, prediction of overweight/obesity status at age 10 years, as computed from MagmaClust's predictions exhibited high specificity (0.94), negative predictive value (0.89), and accuracy (0.86). The accuracy, sensitivity, and positive predictive value of predictions increased as BMI data from additional time points were utilized for prediction. MagmaClust provides a unified, probabilistic, non-parametric framework to model, cluster, and prospectively predict childhood BMI trajectories and overweight/obesity risk. The proposed method offers a convenient tool for clinicians to monitor BMI growth in children, allowing them to prospectively identify children with high predicted overweight/obesity risk and implement timely interventions.

Indian Ocean temperature anomalies modulate the interannual variability of springtime smoke aerosols over the Indochina Peninsula

Abstract Smoke aerosols released through frequent springtime fire activity over the Indochina Peninsula (ICP) seriously affect regional air quality, climate, and human health. However, the mechanisms driving the interannual variability of these smoke aerosols are not well understood. By analyzing multi-source historical (1980–2020) smoke aerosols and meteorological reanalysis data, we explore the response of springtime smoke aerosol changes over the ICP to the interannual variability of the Indian Ocean (IO) sea-surface temperature (SST). Our findings show a positive correlation between the variability of springtime smoke aerosol loading and the preceding winter Southeast IO (SEIO) SST anomalies. Warmer SEIO SST tends to weaken the trans-equatorial flow (TEF) and the local Hadley circulation. This weakening of the TEF impede cyclones development in the Bay of Bengal (BOB), thereby reducing southwest water vapor transport. Simultaneously, enhanced westerly winds over the northern BOB are blocked by the northwestern mountains of ICP. These winds converge and rise on the windward slopes, while descending on the leeward side with diminished humidity. Collectively, these dynamics lead to drier and hotter local meteorological conditions that favored fire-induced smoke aerosol emissions. Our findings highlight the role of the SEIO in regulating smoke aerosol variability and provide a scientific basis for developing strategies to manage smoke aerosol emissions over the ICP.

Prediction of Tempe Raw Material Needs for Home Industry Using Tsukamoto Fuzzy Logic Algorithm

The home tempeh industry is faced with the challenge of accurately predicting raw material needs. Implementing an effective prediction system can help estimate the amount of tempeh production in the future, so as to avoid stock shortages of raw materials. In addition, when tempeh producers have accurate predictions, they can manage soybean stocks better, especially when there is a stock shortage, so that production is not affected by rising soybean prices. This research aims to develop a Tsukamoto Fuzzy Logic based prediction system to overcome this problem. Research methods include collecting historical production data, designing fuzzy models, and system testing. Interim results show that the developed system is able to increase prediction accuracy by up to 95%, which in turn can reduce production costs and minimize waste of raw materials.

Forecasting High-Order Multifactor Cross Relations Using Fuzzy Time Series and Frequency Density

The forecasting technique frequently employed for decision-making, initially presented by Song and Cissom utilizing fuzzy logic principles, is Fuzzy Time Series (FTS). The FTS forecasting approach comprises fundamental steps: identifying the conversation universe, segmenting the conversation universe, fuzzification, constructing the fuzzy logic relationship (FLR), and defuzzification. This study involved the modification of the existing methodology to ascertain and categorize the conversational universe, fuzzy logic relationships (FLR), and historical data variations, each utilizing distinct frequency density partitions and cross-relationships, specifically with Indonesian rubber production data. Altering the frequency density partition algorithm to achieve partition universality, followed by fuzzification, cross-relationship analysis, and concluding with the defuzzification procedure. The degree of forecast precision uses the Average Forecasting Error Rate (AFER) to evaluate the revised outcomes against the traditional method. Simulations utilize Indonesian rubber production data from 2000 to 2023, exhibiting an AFER of 3.901%, below 10%, indicating robust forecasting criteria. The forecasting technique frequently employed for decision-making, initially presented by Song and Cissom through the principle of fuzzy logic, is Fuzzy Time Series (FTS). The FTS forecasting approach comprises fundamental steps: identifying the conversation universe, segmenting the conversation universe, fuzzification, constructing the fuzzy logic relationship (FLR), and defuzzification. This study involved the modification of the existing methodology to ascertain and categorize the conversational universe, fuzzy logic relationships, and historical data variations, each employing distinct frequency density partitions and cross-relationships, utilizing Indonesian rubber production data. Altering the frequency density partition algorithm to achieve partition universality, followed by fuzzification, cross-relationship analysis, and concluding with the defuzzification procedure. The degree of forecast precision uses the Average Forecasting Error Rate (AFER) to juxtapose the revised outcomes with the traditional methodology. Simulations utilize Indonesian rubber production data from 2000 to 2023, exhibiting an AFER of 3.901%, below 10%, indicating robust forecasting criteria.

Streaming Large-Scale Microscopy Data to a Supercomputing Facility.

Data management is a critical component of modern experimental workflows. As data generation rates increase, transferring data from acquisition servers to processing servers via conventional file-based methods is becoming increasingly impractical. The 4D Camera at the National Center for Electron Microscopy generates data at a nominal rate of 480 Gbit s-1 (87,000 frames s-1), producing a 700 GB dataset in 15 s. To address the challenges associated with storing and processing such quantities of data, we developed a streaming workflow that utilizes a high-speed network to connect the 4D Camera's data acquisition system to supercomputing nodes at the National Energy Research Scientific Computing Center, bypassing intermediate file storage entirely. In this work, we demonstrate the effectiveness of our streaming pipeline in a production setting through an hour-long experiment that generated over 10 TB of raw data, yielding high-quality datasets suitable for advanced analyses. Additionally, we compare the efficacy of this streaming workflow against the conventional file-transfer workflow by conducting a postmortem analysis on historical data from experiments performed by real users. Our findings show that the streaming workflow significantly improves data turnaround time, enables real-time decision-making, and minimizes the potential for human error by eliminating manual user interactions.

Long-term changes in the French fisheries for crustaceans in the Eastern English Channel: 1901 to 2020

Abstract Small-scale crustacean fisheries in the English Channel have existed for centuries but have never been formally assessed; hence, concerns about their ecological and socio-economic sustainability could be raised. This study explores trends in this fishery’s landings from 1901 to 2021, based on historical and recent landings data in two of the main administrative fishing divisions of the area, Cherbourg and Boulogne-sur-Mer quartiers. Crustacean fisheries were reported for both divisions from the beginning to the end of the time period considered, demonstrating that a dedicated crustacean fishery existed in the area since at least the 20th century. The species composition of the crustacean’s catch (e.g., edible crab, Cancer pagurus Linnaeus, 1758; European lobster, Homarus gammarus (Linnaeus, 1758); European spider crab, Maja brachydactyla Balss, 1922) was almost impossible to track before the 2000s. Important fluctuations were observed in catch volume reports along time that might be related to two phenomena: technological advances that would have improved the fishery performances and the decline of some previously targeted species that might have led to a transfer in fishing effort.

Long-term load forecasting for smart grid

Abstract The load forecasting problem is a complicated non-linear problem connected with the weather, economy, and other complex factors. For electrical power systems, long-term load forecasting provides valuable information for scheduling maintenance, evaluating adequacy, and managing limited energy supplies. A future generating, transmission, and distribution facility's development and planning process begins with long-term demand forecasting. The development of advanced metering infrastructure (AMI) has greatly expanded the amount of real-time data collection on large-scale electricity consumption. The load forecasting techniques have changed significantly as a result of the real-time utilization of this vast amount of smart meter data. This study suggests numerous approaches for long-term load forecasting using smart-metered data from an actual distribution system on the NIT Patna campus. Data pre-processing is the process of converting unprocessed data into a suitable format by eliminating possible errors caused by lost or interrupted communications, the presence of noise or outliers, duplicate or incorrect data, etc. The load forecasting model is trained using historical load data and significant climatic variables discovered through correlation analysis. With a minimum MAPE and RMSE for every testing scenario, the proposed artificial neural network model yields the greatest forecasting performance for the used system data. The efficacy of the proposed technique has been through a comparison of the acquired results with various alternative load forecasting methods.

THE IMPACT OF PROJECTED CANADIAN REGIONAL CLIMATE MODEL DATA ON FLEXIBLE PAVEMENT PERFORMANCE

This paper explores the impact of projected climatic loading parameters, utilizing Environment and Climate Change Canada (ECCC) data, on flexible pavement performance and design. Despite the expectation that flexible roads should endure various structural and environmental conditions throughout their design life, premature damage often occurs within the initial three to five years of service. Therefore, understanding the impact of climate change on flexible pavements in the historical, short, intermediate, and long term becomes crucial. The Pavement Mechanistic Empirical Design (PMED) was employed to assess climatic loading effects on pavement design and performance. PMED predicted rutting performances showed sensitivity when comparing historical and projected climatic loading files up to 2093. Preliminary results based on a 25-year design life for City of Windsor revealed a 72% higher rutting impact compared to historical data, shortening the pavement's design life by 28%, 56%, and 68% for short, intermediate, and long terms, respectively.

Historical evolution of the geomagnetic declination at the Royal Observatory of Madrid

Abstract. The agonic line, which represents geomagnetic declinations of 0°, recently crossed the Royal Observatory of Madrid (ROM) in December 2021, causing a shift in declination values from west to east. This event constitutes a notable milestone for this significant observatory, where the first geomagnetic observation series commenced around 1855 in Spain. In this work, taking advantage of the occurrence of this event, a detailed study has been conducted to investigate the historical evolution of the magnetic declination at the ROM to decipher prior occurrences of the agonic line crossing this place. Despite the ROM having hosted the first series of geomagnetic measurements in Spain, the present lack of geomagnetic measurements in this observatory makes it necessary to extend the declination measurements to other observatories distributed throughout the Iberian Peninsula to better define the passage of the agonic line since 1855 up to the present. For epochs prior to 1855, a bibliographic search for declination measurements conducted in the Iberian Peninsula has been carried out, complemented by historical data from the HISTMAG database. As a result, a time-continuous curve of geomagnetic declination is generated from 1590 to 2022 at the ROM coordinates. The declination curve reveals that the agonic line also crossed the ROM 400 years ago (around 1600), passing from western to eastern declination values.

Quantifying Uncertainty Costs in Renewable Energy Systems Considering Probability Function Behavior and CVaR at Low-Probability Generation Extremes using Deterministic Equations

The increase and integration of renewable energy sources in electrical power systems implies an increase in uncertainty variables, both in costs and production, of economic dispatch (ED) and currently have a significant influence on wholesale electricity markets (MEM). Uncertainty costs refer to the quantification of additional expenses or economic losses associated with the variability inherent in the generation of renewable energy, such as wind, solar, or hydroelectric. Therefore, this article presents deterministic equations related to cost overestimation and underestimation, as well as CVaR, to model and evaluate the stochasticity of risks associated with the integration of renewable sources, allowing system operators and planners to make informed decisions. To mitigate or use said risks in energy systems with high penetration of elements, mainly smart networks. In this study, a mathematical analysis is carried out using the histogram spectrum formed by the power generated by the probability density function (PDF) for solar generation, although it is possible to consider other types of functions to determine energy generation. The objective of the proposed model is to provide another tool to the system operator for energy management and planning, which relieves a little of the weight of the computational load and at the same time presents more precision in the results by being able to work with a database. Historical data if these values are available. Commonly, for this type of analysis, values are estimated using probabilistic calculations by density functions when integrating these functions, or in other recent cases by estimating them by analytical methods of the same functions. A validation of the model is presented by comparing the result with the Monte Carlo simulation, developing the total cost of uncertainty only from "low probability generation extremes". Furthermore, the results are presented through analytical uncertainty cost functions (AUCF). This analysis includes the calculation of uncertainty costs for low and high-probability energy generation, determined by the Conditional Value at Risk (CVaR), using deterministic equations.

Validation of a methodology for post-construction Energy Yield Assessment of an operational wind farm

The uncertainty associated with the prospective Energy Yield Assessment (EYA) of a wind farm may be reduced by re-estimating the energy yield after it enters normal operation. This study aims to validate a simple methodology for conducting post-construction EYA of an operational wind farm. The proposed methodology derives a linear relationship between a historical source of wind speed data and the observed wind farm production on a monthly basis.In the first stage, the impact of different data sources on the accuracy of the Long-Term energy yield estimate was assessed. Results suggest that the determination coefficient R is a reliable indicator for selecting the most adequate source of historical wind speed data to be used in the Long-Term energy yield estimate.In the second stage, the model was validated from a statistical point of view by testing the premises of the linear regression model, namely the significance of the linear correlation (ANOVA test), and the normally-distributed (Shapiro-Wilk test), non-self-correlated (Durbin-Watson), and homoscedastic (Breusch-Pagan test) residuals. Results show these premises are verified for most test cases, indicating that the model is statistically robust for most test cases.

Perancangan Model Jaringan Syaraf Tiruan untuk Memprediksi Penyakit Demam Berdarah Menggunakan Algoritma Hebb Rule

Dangeu dengue fever or what we often call dengue fever is a disease transmitted by the Aedes aegypti mosquito and caused by the dengue virus. This disease can potentially cause serious complications if it does not receive proper treatment. In this research, the author uses the application of artificial neural networks with the Hebb rule approach to predict the risk level of dengue fever. Predictions are made based on factors such as weather conditions, population density and historical case data that influence this disease. The Hebb rule is used in this research because of its ability to strengthen connections between neurons based on the input patterns they receive, so it is hoped that it can produce more accurate predictions. Test results show that this method has a fairly high level of accuracy in predicting the pattern of dengue fever cases in an area. This research indicates that the application of artificial neural networks with the Hebb rule can be an effective tool for related parties in taking preventive measures to minimize the number of dengue cases in the future.

Nash Bargaining-Based Coordinated Frequency-Constrained Dispatch for Distribution Networks and Microgrids

As the penetration of distributed renewable energy continues to increase in distribution networks, the traditional scheduling model that the inertia and primary frequency support of distribution networks are completely dependent on the transmission grid will place enormous regulatory pressure on the transmission grid and hinder the active regulation capabilities of distribution networks. To address this issue, this paper proposes a coordinated optimization method for distribution networks and microgrid clusters considering frequency constraints. First, the confidence interval of disturbances was determined based on historical forecast deviation data. On this basis, a second-order cone programming model for distribution networks with embedded frequency security constraints was established. Then, microgrids performed economic dispatch considering the reserves requirement to provide inertia and primary frequency support, and a stochastic optimization model with conditional value-at-risk was built to address uncertainties. Finally, a cooperative game between the distribution network and microgrid clusters was established, determining the reserve capacity provided by each microgrid and the corresponding prices through Nash bargaining. The model was further transformed into two sub-problems, which were solved in a distributed manner using the ADMM algorithm. The effectiveness of the proposed method in enhancing the operational security and economic efficiency of the distribution networks is validated through simulation analysis.

THE IMPORTANCE OF REMOVING THE INNER BOUNDARY MEMBRANE IN THE TREATMENT OF DIABETIC MACULAR EDEMA: A SERIES OF CLINICAL CASES

Vitreoretinal surgery (VRH) is recognized as the preferred method of treating diabetic macular edema (DMO), concomitant with additional pathology of the vitreomacular interface (VMI). Empirical data obtained as a result of numerous studies give contradictory results, therefore, the need for peeling of the internal limiting membrane (VPM) remains unresolved.Purpose. To find out the results of surgical intervention in patients with DMO as a result of epimacular fibrosis (EMF) by comparing cases with and without HPV peeling.Materials and methods. This manuscript presents a comparative assessment of three clinical cases. The patients had similar clinical manifestations and historical medical data. In all cases, patients underwent HRH for DMO combined with EMF. In two cases, the VPM was peeled, and in the third case, the VPM was preserved.Results. An analysis of the observational data and an assessment of anatomical and functional parameters revealed complications in the first and second patients at the late postoperative stage, while in the third patient the condition stabilized. Conclusion. The presented clinical cases show that anatomical and functional results are not always correlated. The increased risk of complications associated with a violation of the architecture and functionality of the retina during peeling requires a critical assessment of the clinical justification of this method.

Toward Climate Resilient Asphalt Binder Selection in Canada

Climate change and extreme weather events present a fundamental challenge to pavement engineering and planning practice, given that transportation infrastructure has traditionally been planned and designed using historical climate data under the implicit assumption that climate is stationary and future conditions will resemble past ones. Transportation professionals should consider future risks scenarios in road design, material selection and system operations. This will be challenging given the inherent uncertainties in any climate projections. However, changes might be needed since transportation professionals are expected to deliver cost-effective and climate-resilient transportation infrastructure. The material selection plays an important role in the durability of flexible roads; therefore, it is important to consider the whole road design life cycle in terms of climate change and extreme weather events impact on asphalt selection as well as to consider different traffic speeds. This led to the development of the NRC Climate Adaptation and Asphalt Selection Tool (CAAST).