Data Processing Research Articles

Using the R Package, nsink, to assess landscape N removal in coastal catchments

Background Excess nitrogen (N) loading to coastal ecosystems impairs estuarine water quality. Land management decisions made within estuarine watersheds have a direct impact on downstream N delivery. Natural features within watersheds can act as landscape sinks for N, such as wetlands, streams and ponds that transform dissolved N into gaseous N, effectively removing it from the aquatic system. Identifying and evaluating these landscape sinks and their spatial relationship to N sources can help managers understand the effects of alternative decisions on downstream resources. Methods The N-Sink approach uses widely available GIS data to identify landscape sinks within HUC-12 (or larger) catchments, estimate their N removal potential and map the effect of those sinks on N movement through the catchment. Static maps are produced to visualize N removal efficiency, transport and delivery, the latter in the form of an index. The R package nsink was developed to facilitate data acquisition, processing and visualization. Results nsink creates static maps for a specific HUC-12, or users can visit the University of Connecticut website to explore previously mapped areas. Users can investigate specific flowpaths interactively by clicking on any location within the catchment. A flowpath is generated with a table describing N removal along each segment. We describe the motivation behind developing nsink, discuss implementation in R, and present two use case examples. nsink is available from https://github.com/USEPA/nsink. Conclusions N-Sink is a decision support tool created for local decision-makers and NGOs to facilitate better understanding of the relationship between land use and downstream N delivery. Local decision-makers that have prioritized N mitigation in their long-term planning can use nsink to better understand the potential impact of proposed development projects, zoning variances, and land acquisition or restoration. nsink also allows resource economists to investigate the tradeoffs among different, often costly, N reduction strategies.

Operative Procedures and Postoperative Complications in Patients with Gastric Outlet Obstruction

Background: Gastric outlet obstruction denotes complete or partial obstruction of the distal stomach, pylorus, or proximal duodenum. This can happen as a result of an obstructing lesion, external compression, or obstruction caused by acute edema, chronic scarring and fibrosis, or a combination of the two. The most common causes of gastric outlet obstruction are gastric cancer and pyloric stenosis secondary to peptic ulceration. We have very limited research-based information regarding postoperative complications in patients with gastric outlet obstruction. Aim of the study: This study was carried out to determine the operative procedures and postoperative complications in patients with gastric outlet obstruction. Methods: Between September 2012 to February 2013, this prospective observational study was carried out in the surgery department of Rangpur Medical College Hospital, Rangpur, Bangladesh. A total of 50 patients presenting with the features of gastric outlet obstruction were included in this study. A convenient purposive sampling technic was used in sample selection. In 27 patients, surgery was performed. Registration schedules, editing computerization, preparing dummy tables, and analyzing and matching data were all part of the data processing work. Results: In the cases of benign gastric outlet obstruction, bilateral truncal vagotomy and retro colic isoperistaltic gastrojejunostomy were preferred. Distal partial gastrectomy with lymph node dissection was done in resectable antral growth of the stomach. Palliative gastrojejunostomy was done in advanced gastric and pancreatic malignancy. Gastrojejunostomy with biopsy from the duodenum and lymph node was taken from one pre-operatively diagnosed case of duodenal tuberculosis, which was made by endoscopic biopsy. Complication occurred mostly in the malignant cases; 7 (43.75%) patients developed wound infection, of which 5 (31.25%) patients developed wound dehiscence. There was a duodenal stump-blow out in ...

Advanced Applications of Polymer Hydrogels in Electronics and Signal Processing

The current state of affairs in the field of using polymer hydrogels for the creation of innovative systems for signal and image processing, of which computing is a special case, is analyzed. Both of these specific examples of systems capable of forming an alternative to the existing semiconductor-based computing technology, but assuming preservation of the used algorithmic basis, and non-trivial signal converters, the nature of which requires transition to fundamentally different algorithms of data processing, are considered. It is shown that the variability of currently developed information processing systems based on the use of polymers, including polymer hydrogels, leads to the need to search for complementary algorithms. Moreover, the well-known thesis that modern polymer science allows for the realization of functional materials with predetermined properties, at the present stage, receives a new sounding: it is acceptable to raise the question of creating systems built on a quasi-biological basis and realizing predetermined algorithms of information or image processing. Specific examples that meet this thesis are considered, in particular, promising information protection systems for UAV groups, as well as systems based on the coupling of neural networks with holograms that solve various applied problems. These and other case studies demonstrate the importance of interdisciplinary cooperation for solving problems arising from the need for further modernization of signal processing systems.

Optimization of ANN Models Using Metaheuristic Algorithms for Prediction of Tailpipe Emissions in Biodiesel Engine

ABSTRACTMachine learning techniques are gaining momentum in the present‐day context in most engineering applications due to their versatility and accuracy. They facilitate faster data processing coupled with a high degree of accuracy. They are extensively used in understanding and modeling engine combustion and emissions. Engine emissions significantly contribute to environmental degradation. In the current study, an effort has been made to compare the emissions recorded from a four‐stroke single‐cylinder biodiesel engine with those obtained using artificial neural network (ANN) models, where the hyperparameters have been optimized using nature‐inspired metaheuristic optimization algorithms like JAYA, WOA, ROA, and WaOA. The study was conducted using diesel and cardanol‐methanol‐diesel blends of B10M10, B20M10, and B30M10, by varying the fuel injection pressure from 180 bar (standard injection timing) to 220 bar with an interval of 20 bar. Furthermore, experiments were conducted with oxygen enrichment at concentrations of 3%, 5%, and 7% w/w on the standard oxygen concentration of air. The study showed a remarkable reduction of 59% in CO emissions at 220 bar fuel injection pressure with 7% w/w oxygen enrichment for the B30M10 blend as compared to 180 bar without oxygen enrichment. A similar reduction of 32.6% and 16.6% were observed for HC emissions and smoke opacity for the same operating conditions. However, a rising trend of 50% was observed for NOx emissions for the same blend and operating conditions. The findings indicate that the data recorded conforms with that obtained by using the ANN model optimized through these metaheuristic algorithms.

Edge computing-based ensemble learning model for health care decision systems

A growing number of humans have suffered severe chronic illnesses, which has caused a boost in the requirement for diagnostic and medical treatment procedures that are both accurate and fast. Improved patient conditions and enhanced Decision-Making Systems (DMS) for healthcare professionals are the primary objectives of the Clinical Decision Support System (CDSS) recommended in this research article. The main drawback of traditional Machine Learning (ML) techniques is their failure to predict reliably. To solve this problem, the proposed model creates an Ensemble Extreme Learning Machine (EN-ELM) algorithm that combines predictors trained on several different data sets. This lowers the chance of overfitting. The suggested CDSS uses many different data processing methods, including Adaptive Synthetic (ADASYN) and isolation Forest (iForest), which fix problems like outliers and class imbalance. This approach significantly enhances the framework’s classification performance. Also, the CDSS is compatible with an EC model, which enables real-time computation while minimizing the requirement for integrated systems. The recommended CDSS applies iForest and ADASYN to execute large-scale trials validating high standards of accuracy across numerous datasets. Researchers concluded that a suitable ELM classification threshold of 85% is the most effective, which substantially boosts the accuracy of the predictive model. When applied to various medical datasets, such as Hepatocellular Carcinoma (HCC), Cervical Cancer, Chronic Kidney Disease (CKD), Heart Disease, and Arrhythmia, the EN-ELM achieved accuracy rates of 99.36%, 98.15%, 97.85%, 97.06%, and 96.72%, respectively. By measuring this progress, the CDSS could dramatically improve the accuracy of chronic illness diagnosis and treatment, which similarly affects clinicians.

A Comprehensive Approach to Fault Classification of Helicopter Engines with Adaboost Ensemble Model

This work is based on the PHM North America 2024 Conference Data Challenge’s datasets of Helicopter turbine engine performance measurements. These datasets were large and moderately imbalanced. For dealing with these challenges, we demonstrate significant tools covering feature engineering, augmentation and selection, model exploration, visualizations, model explainability and confidence margin estimation. This work was performed in its entirety using MATLAB. All these tools will be generally applicable to data-driven modeling and prediction of health to real life applications. Initially, we explored the 742k observations in the training set, noting a 60-40 split between healthy and faulty labels, and identified two major operational clusters within the data. We enhanced the dataset by removing duplicates and engineered new features based on domain knowledge, expanding the feature set to 242 dimensions. However for the torque margin estimation, we trained a regression model on a limited subset (18 features), which includes engineered features using domain knowledge, quadratic terms and linear interaction between all the terms. For the final submission, we utilized a stepwise linear regression model to optimize feature selection. This approach achieved a perfect regression score on test data, validated by a consistent torque margin residual range of +/- 0.5%. The model's RMSE and MAE metrics were optimal for employing a normal distribution probability density function. For , we reduced the feature set to 58 using dimensionality reduction techniques and balanced the data with upsampling and down-weighing the minority class. We employed ASHA (Asynchronous Successive Halving Algorithm) in conjunction with AutoML to efficiently determine the most suitable model family, significantly saving compute time. Subsequently, we trained ensemble models, including bagged tree and AdaBoost (Adaptive Boosting), which minimized false negatives and positives, achieving robust classification performance. This was particularly critical given the high penalty for false negatives in the data challenge. The MathWorks team score on Testing Data was 0.9686 at the close of competition. This was further improved to 0.9867. Our approach demonstrates the effectiveness of combining strategic data processing, feature engineering, and model selection to enhance predictive accuracy in complex operational datasets.

Improvement of Optimal Human Resource Allocation Algorithm Based on Deep Latent Semantic Model

With the advances of technology, the banking industry faces the challenge of processing large-scale, heterogeneous human resource data. Traditional methods have difficulties in providing efficient and accurate analysis and prediction. This paper proposes an optimized human resource allocation algorithm based on deep latent Semantic Model (DLSM), which improves the accuracy and efficiency of data analysis by integrating deep neural network (DNN) and transfer learning technology. The experimental results show that the DLSM model performs well in text classification and information retrieval tasks, with the accuracy, accuracy, recall and F1 scores improving by 7.5%, 5.5%, 6.0% and 5.8%, respectively. This study confirms the excellent performance of DLSM model in HR data processing, provides an efficient and scientifically based solution for recruitment optimization in the banking industry, and enhances the competitiveness of enterprises.

Design Science Approach Comparing Ensemble Learning and Artificial Neural Networks for Uncertainty-Aware Helicopter Turbine Engines Health Monitoring

This work presents the development of an uncertainty-aware health monitoring system for helicopter turbine engines, focusing on improving operational availability and reducing maintenance costs. We address the critical issue of uncertainty quantification in data-driven fault detection and prognostics, essential for increasing system reliability. The project follows an iterative development cycle, incorporating multiple techniques for data processing, such as polynomial feature generation and data cleansing, and model development, including ensemble learning and artificial neural networks. Evaluation is performed using K-fold and group-fold cross-validation. The final solution consists of a cascaded ensemble learning model combining bagged linear regression built on polynomial features and random forest. This model demonstrates robust performance, achieving a test score of 0.955719 and a validation score of 0.886953, showcasing the effectiveness of uncertainty-aware machine learning methods in health monitoring systems.

Using media accessibility as a stimulus for teaching audio-visual translation: a Singapore case study

ABSTRACT Despite attempts to integrate media accessibility components into audio-visual translation courses, consensus is lacking on the content, aims and orientation of the paradigmatic implementation of the curriculum, with inadequate attention given to the empirical assessment of pedagogical effectiveness. This article describes an exploratory endeavour in curriculum design that aimed to enhance beginner students’ awareness of accessibility by using technology-enabled multimodal translation as teaching materials (i.e. subtitling, dubbing, audio description, live subtitling and social media and website translation). We adopted a tripartite model to evaluate pedagogical efficacy, conducting a pre-course psychosocial assessment of learner-specific affective variables and personal propensities, evaluating process data based on the trainees’ progressive and summarised assessments and triangulating the results with students’ post-course feedback from questionnaire surveys and semi-structured interviews. This article advocates for teaching audio-visual translation with a strong accessibility-centred and technology-driven learning orientation, which is challenging but viable in general translator training.

Multiyear high-temporal-resolution measurements of submicron aerosols at 13 French urban sites: data processing and chemical composition

Abstract. This paper presents a first comprehensive analysis of long-term measurements of atmospheric aerosol components from aerosol chemical speciation monitor (ACSM) and multiwavelength Aethalometer (AE33) instruments collected between 2015 and 2021 at 13 (sub)urban sites as part of the French CARA (Chemical Characterization of Particles) program. The datasets contain the mass concentrations of major chemical species within submicron aerosols (PM1), namely organic aerosols (OAs), nitrate (NO3-), ammonium (NH4+), sulfate (SO42-), non-sea-salt chloride (Cl−), and equivalent black carbon (eBC). Rigorous quality control, technical validation, and environmental evaluation processes were applied, adhering to both guidance from the French Reference Laboratory for Air Quality Monitoring (LCSQA) and the Aerosol, Clouds, and Trace Gases Research Infrastructure (ACTRIS) standard operating procedures. Key findings include geographical differences in the aerosol chemical composition, seasonal variations, and diel patterns, which are influenced by meteorological conditions, anthropogenic activities, and proximity to emission sources. Overall, OA dominates PM1 at each site (43 %–60 % of total mass), showing distinct seasonality with higher concentrations (i) in winter, due to enhanced residential heating emissions, and (ii) in summer, due to increased photochemistry favoring secondary aerosol formation. NO3 is the second most important contributor to PM1 (15 %–30 %), peaking in late winter and early spring, especially in northern France, and playing a significant role during pollution episodes. SO4 (8 %–14 %) and eBC (5 %–11 %) complement the major fine-aerosol species, with their relative contributions strongly influenced by the origin of air masses and the stability of meteorological conditions, respectively. A comparison with the 3D chemical transport model (CTM) CHIMERE shows high correlations between simulations and measurements, albeit with an OA concentration underestimation of 46 %–76 %. Regional discrepancies in NO3 concentration levels emphasize the importance of these datasets with respect to validating air quality models and tailoring air pollution mitigation strategies. The datasets can be found at https://doi.org/10.5281/zenodo.13318298 (Chebaicheb et al., 2024).

Relationship between Gadget Use, Sleep Patterns, Stress and the Incidence of Central Obesity in Adolescents in Jambi City

Obesity is a condition in which the body has excess body weight as measured by several measurements. The accumulation of fat in the body's subcutaneous tissue, or under the skin, around organs, and sometimes even in the tissues of those organs, is a sign of obesity. In the last five years, the prevalence of adolescent obesity in Indonesia has almost doubled. The purpose of this study was to determine the factors associated with the incidence of central obesity in adolescents in Jambi City. This descriptive quantitative research used a cross-sectional study. In this study, the Solvin formula was used to calculate the sample size, which consisted of 96 adolescents living in the working area of ??the Putri Ayu health center in 2024. Data collection used a questionnaire. Data processing was univariate and bivariate analysis. The analysis used was chi-square. The results showed that there was no relationship between the use of gadgets (p=0.219) with the incidence of central obesity in adolescents there was a relationship between sleep patterns (p = 0.000), stress (p = 0.001) with the incidence of central obesity in adolescents in Jambi City. Factors that can cause obesity in adolescents include unhealthy eating habits, it is important to maintain a balanced diet, rich in fiber, and frequently consume fruits and vegetables

Improved EMAT Sensor Design for Enhanced Ultrasonic Signal Detection in Steel Wire Ropes

This study is focused on optimizing electromagnetic acoustic transducer (EMAT) sensors for enhanced ultrasonic guided wave signal generation in steel cables using CAD and modern manufacturing to enable contactless ultrasonic signal transmission and reception. A lab test rig with advanced measurement and data processing was set up to test the sensors’ ability to detect cable damage, like wire breaks and abrasion, while also examining the effect of potential disruptors such as rope soiling. Machine learning algorithms were applied to improve the damage detection accuracy, leading to significant advancements in magnetostrictive measurement methods and providing a new standard for future development in this area. The use of the Vision Transformer Masked Autoencoder Architecture (ViTMAE) and generative pre-training has shown that reliable damage detection is possible despite the considerable signal fluctuations caused by rope movement.

Application of Big Data in Digital Currency and Blockchain Finance

With the rise of emerging economic models, the cryptocurrency system, centered on the internet and electronization, is gradually returning to its core functions — storage, data processing, and related services. The deep value of massive data lies in rapid mining to uncover its inherent patterns, which is not only crucial for the operation, regulation, and monetary policy implementation of cryptocurrencies but also serves as an essential support for the transition from paper money to digital currencies. Given this, this research plans to conduct a detailed exploration of the application of big data in the field of cryptocurrency and blockchain finance, aiming to contribute to the continuous progress of China’s financial industry.

Pengaruh Realisasi Pajak Kendaraan Bermotor, Bea Balik Nama Kendaraan Bermotor, Dan Retribusi Daerah Terhadap Pendapatan Asli Daerah Kota Bandar Lampung Tahun 2020-2023

This study aims to determine the effect of the Realization of Motor Vehicle Tax (PKB), Motor Vehicle Name Return Duty (BBNKB), and Regional Levy on Regional Original Revenue (PAD) of Bandar Lampung City. The independent variable contains PKB, BBNKB, and Regional Levy while the bound variable is PAD. This study uses a Quantitative Method by processing data. The sample in this study is the number of PKB, BBNKB and Regional Levy receipts from 2020 - 2023 (monthly data) as many as 48 samples. The analysis in this study is multiple linear regression analysis, classical assumption test, and hypothesis test with a significance level of 5%. From the results of the research that has been carried out, it shows that PKB, BBNKB and Regional Levy together (Simultaneous) have a significant effect of 86.4% while the remaining 23.6% are affected by other variables outside this study.

Thermal Runaway Warning of Lithium Battery Based on Electronic Nose and Machine Learning Algorithms

Characteristic gas detection can be an efficient way to predict the degree of thermal runaway of a lithium battery. In this work, a sensor array consisting of three commercial MOS sensors was employed to discriminate between three target gases, CO, H2 and a mixture of the two, which are characteristic gases released during the thermal runaway of lithium batteries. In this work, an integrated model that makes the classification stage results one of the feature inputs for the concentration regression stage was employed, successfully reducing the RMSE of the concentration regression results. In addition, we also explored the influence of the selection of the response time length on the classification and regression tasks, achieving the best results in a short time through the optimum algorithm. To assess the impact of time duration sensor data on the results, we selected four time windows of different length and extracted the corresponding sensor response data for subsequent processing. Initially, principal component analysis (PCA) was used to visualise the clustering of the three target gas samples at room temperature, providing a preliminary data analysis. For the classification phase, we chose three classification algorithms—MLP (Multilayer Perceptron), ELM (Extreme Learning Machine), and SVM (Support Vector Machine)—and performed a comprehensive comparison of their classification and generalisation abilities using grid search for hyperparameter optimisation and five-fold cross-validation. The results demonstrated that MLP achieved 99.23% classification accuracy during the 20 s response period. In the concentration regression phase, we combined the classification results with the raw features to create a new feature set, which was then input into a multi-output MLP regression model. The root mean square error (RMSE) employing the new feature set was used to measure the prediction error. Ultimately, the findings showed that the input of combined features significantly reduced the regression error for the mixed gas.

Predicting Portfolios Using Fuzzy Logic and Optimization Techniques: A Case Study of BSE-IT Listed Companies

Stock price trends were considered in this study, the companies in the BSE-IT index, with a view to optimizing portfolio allocation based on risk tolerance. The GARCH model has been applied here to allow an investigation into the volatility of the stock returns and to set up different clusters using K-Means clustering that group together companies owning the same attributes related to volatility for strategic classification. Data collection and processing occur between April 1, 2014, and March 31, 2024, for reasons of reliability and relevance. Descriptive statistics report on the variance of stock performance across companies; different risk-return profiles are thus exposed. The GARCH model measures each stock's long-term variance, along with sensitivity to past shocks, and overall volatility persistence, making this an excellent inventory for risk assessment. K-Means clustering groups companies based on similar volatility patterns, thereby informing portfolio construction. Findings present how clustering with econometric modeling may assist in the production of optimal portfolios that depend on specified risk capacities, thus efficiently managing and implementing an IT investment strategy.

Template Synthesis of the Iron(III) Complex with the Ligands Based on Acylpyrazolonepyridines

The reaction of new bidentate ligand, 1-(5-hydroxy-1-methyl-3-(pyridin-2-yl)-1Н-pyrazol-4-yl)ethan-1-one (L), with iron(III) chloride affords the mononuclear iron(III) complex FeL₂Cl₃, which is characterized by XRD (CIF file CCDC no. 2309481). The intramolecular hydrogen bond between the protonated pyridyl and acetyl groups in ligand L, which exists in the crystal as a zwitterion, provides the formation of rarely met iron complexes in which the β-diketonate fragment coordinates via the η1 mode. A similar coordination mode along with a possibility of a more favorable η2 coordination provides new possibilities for the design of heteropolynuclear compounds of various structures used in the fabrication of molecular devices of data storage and processing.

Evaluating Advanced Machine Learning Models for Histopathological Diagnosis of Hansen Disease.

Leprosy is a neglected infectious disease caused by Mycobacterium leprae and Mycobacterium lepromatosis and remains a public health challenge in tropical regions. Therefore, the development of technological tools such as machine learning (ML) offers an opportunity to innovate strategies for improving the diagnosis of this complex disease. To validate the utility of different ML models for the histopathological diagnosis of Hansen disease. An observational study was conducted where 55 H&E-stained tissue slides from leprosy patients and 51 healthy skin controls were analyzed using microphotographs captured at various magnifications. These images were categorized based on histopathological findings and processed using the Cross-Industry Standard Process for Data Mining methodology for designing and training ML models. Five types of ML models were evaluated using standard metrics such as accuracy, sensitivity, and specificity, emphasizing data normalization as a fundamental step in optimizing model performance. The artificial neural network (ANN) model demonstrated an accuracy of 70%, sensitivity of 74%, and specificity of 65%, demonstrating its ability to identify leprosy cases with moderate precision. The receiver operating characteristic curve of the ANN model showed an area under the curve of 0.71. Conversely, models such as decision trees, logistic regression, and random forests showed similar accuracy results but with slightly lower sensitivity, potentially indicating a higher risk of false negatives in detecting leprosy-positive cases. The ANN model emerges as a promising alternative for leprosy detection. However, further refinement of these models is necessary to enhance their adaptability across different clinical settings and participation in patient care.

The Effect Of Work Discipline and Work Environment On Employee Loyalty Of The Post Office Operations Division Main Branch Surabaya 60000

The goal of this study is to determine how employee loyalty is affected by work environment and discipline in the operations division of the Surabaya Main Branch Post Office 60000. Primary data from questionnaires given to respondents served as the basis for this study. Structural Equation Model-Partial Least Square (SEM-PLS) is a statistical analysis tool used in research data processing that use partial least squares analysis methodologies. According to the study’s findings, employee loyalty is positively impacted by work environment and discipline.

Review of Digital Twins enabled applications for demand response

Digital Twins (DT), as one of the emerging trends in digitalisation, has attracted a large amount of research in the past few years due to its ability to provide more accurate models and better predictions for various energy-related applications. In the context of demand response (DR), the real-time bidirectional communication characteristic of DT allows for functions such as real-time energy forecast and decision making aid, which provided high potential in DR applications due to the nature of DR being highly time sensitive. The objective of this paper is to explore the concept and application of DT in the domain of DR, as there are currently limited numbers of review on this topic. It was identified that most work in this area shows promising results but is still exploratory. The main application of DT in the domain of DR is using DT as an enabling tool by leveraging the real-time and data processing function of DT for monitoring and decision-making, and providing a medium for better visualisation. Future studies can experiment with applying DT frameworks to larger case studies, and apply more elaborate DT with higher maturity levels.