Improved Prediction Model Research Articles

Microbial-explicit processes and refined perennial plant traits improve modeled ecosystem carbon dynamics

Globally, soils hold approximately half of ecosystem carbon and can serve as a source or sink depending on climate, vegetation, management, and disturbance regimes. Understanding how soil carbon dynamics are influenced by these factors is essential to evaluate proposed natural climate solutions and policy regarding net ecosystem carbon balance. Soil microbes play a key role in both carbon fluxes and stabilization. However, biogeochemical models often do not specifically address microbial-explicit processes. Here, we incorporated microbial-explicit processes into the DayCent biogeochemical model to better represent large perennial grasses and mechanisms of soil carbon formation and stabilization. We also take advantage of recent model improvements to better represent perennial grass structural complexity and life-history traits. Specifically, this study focuses on: 1) a plant sub-model that represents perennial phenology and more refined plant chemistry with downstream implications for soil organic matter (SOM) cycling though litter inputs, 2) live and dead soil microbe pools that influence routing of carbon to physically protected and unprotected pools, 3) Michaelis-Menten kinetics rather than first-order kinetics in the soil decomposition calculations, and 4) feedbacks between decomposition and live microbial pools. We evaluated the performance of the plant sub-model and two SOM cycling sub-models, Michaelis-Menten (MM) and first-order (FO), using observations of net ecosystem production, ecosystem respiration, soil respiration, microbial biomass, and soil carbon from long-term bioenergy research plots in the mid-western United States. The MM sub-model represented seasonal dynamics of soil carbon fluxes better than the FO sub-model which consistently overestimated winter soil respiration. While both SOM sub-models were similarly calibrated to total, physically protected, and physically unprotected soil carbon measurements, the models differed in future soil carbon response to disturbance and climate, most notably in the protected pools. Adding microbial-explicit mechanisms of soil processes to ecosystem models will improve model predictions of ecosystem carbon balances but more data and research are necessary to validate disturbance and climate change responses and soil pool allocation.

Are PPP projects poor fundability in China：Based on ensemble learning

The Public-Private Partnerships (PPP) financability is influenced by both internal and external factors of the project. Given the myriad of factors that influence the participation of social capital in PPP projects, in this study, we examine 14,038 PPP projects from the China Public‒Private Partnership Centre (CPPPC) database. We aim to construct an ensemble learning-based prediction model for the financability of PPP project by selecting 61 feature variables across four dimensions, namely, project characteristics, local government, the market environment, and macroeconomics. The experimental results demonstrate that the dragonfly algorithm effectively improves model prediction accuracy through the reduction of feature dimensionality. From a feature combination perspective, the combinations related to the project itself, local government, and macroeconomic factors exhibit superior predictive performance than other combinations. Among the relevant factors, the project's intrinsic characteristics exert the most significant impact on social capital participation in PPPs, followed by local government factors. Notably, the inclusion of market environment variables tends to decrease the level of model accuracy, except when considering the project's intrinsic characteristics in isolation. Thus, a dynamic approach is recommended to capturing instantaneous variables such as the market environment is recommended for enhancing model performance. Additionally, social capital should rely on an assessment of the financial capacity of local governments to ensure effective PPP outcomes.

Mortality and Cardiovascular End Points In Relation to the Aortic Pulse Wave Components: An Individual-Participant Meta-Analysis.

Wave separation analysis enables individualized evaluation of the aortic pulse wave components. Previous studies focused on the pressure height with overall positive but differing results. In the present analysis, we assessed the associations of the pressure of forward and backward (Pfor and Pref) pulse waves with prospective cardiovascular end points, with extended analysis for time to pressure peak (Tfor and Tref). Participants in 3 IDCARS (International Database of Central Arterial Properties for Risk Stratification) cohorts (Argentina, Belgium, and Finland) aged ≥20 years with valid pulse wave analysis and follow-up data were included. Pulse wave analysis was done using the SphygmoCor device, and pulse wave separation was done using the triangular method. The primary end points consisted of cardiovascular mortality and nonfatal cardiovascular and cerebrovascular events. Multivariable-adjusted Cox regression was used to calculate hazard ratios. A total of 2206 participants (mean age, 57.0 years; 55.0% women) were analyzed. Mean±SDs for Pfor, Pref, Tfor, and Tfor/Tref were 31.0±9.1 mm Hg, 20.8±8.4 mm Hg, 130.8±35.5, and 0.51±0.11, respectively. Over a median follow-up of 4.4 years, 146 (6.6%) participants experienced a primary end point. Every 1 SD increment in Pfor, Tfor, and Tfor/Tref was associated with 27% (95% CI, 1.07-1.49), 25% (95% CI, 1.07-1.45), and 32% (95% CI, 1.12-1.56) higher risk, respectively. Adding Tfor and Tfor/Tref to existing risk models improved model prediction (∆Uno's C, 0.020; P<0.01). Pulse wave components were predictive of composite cardiovascular end points, with Tfor/Tref showing significant improvement in risk prediction. Pending further confirmation, the ratio of time to forward and backward pressure peak may be useful to evaluate increased afterload and signify increased cardiovascular risk.

Real-time Fusion of Multi-Source Monitoring Data with Geotechnical Numerical Model Results using Data-driven and Physics-informed Sparse Dictionary Learning

Development of digital twins is emerging rapidly in geotechnical engineering, and it often requires real-time updating of numerical models (e.g., finite element model, FEM) using multiple sources of monitoring data (e.g., settlement and pore water pressure data). Conventional model updating, or calibration, often involves repeated executions of the numerical model, using monitoring data from a specific source or at limited spatial locations only. This leads to a critical research need of real-time model updating and predictions using a numerical model improved continuously by multi-source monitoring data. To address this need, a physics-informed machine learning method called multi-source sparse dictionary learning (MS-SDL) is proposed in this study. Originated from signal decomposition and compression, MS-SDL utilizes results from a suite of numerical models as basis functions, or dictionary atoms, and employs multi-source monitoring data to select a limited number of important atoms for predicting multiple, spatiotemporally varying geotechnical responses. As monitoring data are collected sequentially, no repeated evaluations of computational numerical models are needed, and an automatic and real-time model calibration is achieved for continuously improving model predictions. A real project in Hong Kong is presented to illustrate the proposed approach. Effect of monitoring data from different sources is also investigated.&#x0D;

Prediction of Sjögren’s disease diagnosis using matched electronic dental-health record data

BackgroundSjögren’s disease (SD) is an autoimmune disease that is difficult to diagnose early due to its wide spectrum of clinical symptoms and overlap with other autoimmune diseases. SD potentially presents through early oral manifestations prior to showing symptoms of clinically significant dry eyes or dry mouth. We examined the feasibility of utilizing a linked electronic dental record (EDR) and electronic health record (EHR) dataset to identify factors that could be used to improve early diagnosis prediction of SD in a matched case-control study population.MethodsEHR data, including demographics, medical diagnoses, medication history, serological test history, and clinical notes, were retrieved from the Indiana Network for Patient Care database and dental procedure data were retrieved from the Indiana University School of Dentistry EDR. We examined EHR and EDR history in the three years prior to SD diagnosis for SD cases and the corresponding period in matched non-SD controls. Two conditional logistic regression (CLR) models were built using Least Absolute Shrinkage and Selection Operator regression. One used only EHR data and the other used both EHR and EDR data. The ability of these models to predict SD diagnosis was assessed using a concordance index designed for CLR.ResultsWe identified a sample population of 129 cases and 371 controls with linked EDR-EHR data. EHR factors associated with an increased risk of SD diagnosis were the usage of lubricating throat drugs with an odds ratio (OR) of 14.97 (2.70-83.06), dry mouth (OR = 6.19, 2.14–17.89), pain in joints (OR = 2.54, 1.34–4.76), tear film insufficiency (OR = 27.04, 5.37–136.), and rheumatoid factor testing (OR = 6.97, 1.94–25.12). The addition of EDR data slightly improved model concordance compared to the EHR only model (0.834 versus 0.811). Surgical dental procedures (OR = 2.33, 1.14–4.78) were found to be associated with an increased risk of SD diagnosis while dental diagnostic procedures (OR = 0.45, 0.20–1.01) were associated with decreased risk.ConclusionUtilizing EDR data alongside EHR data has the potential to improve prediction models for SD. This could improve the early diagnosis of SD, which is beneficial to slowing or preventing complications of SD.

Predicting Cardiovascular Disease Using Simple Machine Learning Techniques

Cardiovascular diseases (CVD) become a major health concern, which needs improved prediction models for intervention. In this study, this research explores the possibilities of using simple machine learning methods, including Logistic Regression, Decision Tree, and Random Forest, for predicting cardiovascular diseases effectively. Using a large dataset (308854 instances) containing demographic, binary, and numerical information, the research applied the above machine learning algorithms to develop predictive models. This study involved data preprocessing, including feature selection and handling duplicate values. This study then divides the dataset into two subsets: training set and testing set by 7:3 and 8:2. The Logistic Regression algorithm demonstrated good predictive performance, with an accuracy rate of 92%. Decision Tree exhibited a similar accuracy of 92%, while Random Forest underperformed both slightly with an accuracy of 91%.

Novel Associations Between Mid-Pregnancy Cardiovascular Biomarkers and Preeclampsia: An Explorative Nested Case-Control Study.

Prediction of women at high risk of preeclampsia is important for prevention and increased surveillance of the disease. Current prediction models need improvement, particularly with regard to late-onset preeclampsia. Preeclampsia shares pathophysiological entities with cardiovascular disease; thus, cardiovascular biomarkers may contribute to improving prediction models. In this nested case-control study, we explored the predictive importance of mid-pregnancy cardiovascular biomarkers for subsequent preeclampsia. We included healthy women with singleton pregnancies who had donated blood in mid-pregnancy (~ 18weeks' gestation). Cases were women with subsequent preeclampsia (n = 296, 10% of whom had early-onset preeclampsia [< 34weeks]). Controls were women who had healthy pregnancies (n = 333). We collected data on maternal, pregnancy, and infant characteristics from medical records. We used the Olink cardiovascular II panel immunoassay to measure 92 biomarkers in the mid-pregnancy plasma samples. The Boruta algorithm was used to determine the predictive importance of the investigated biomarkers and first-trimester pregnancy characteristics for the development of preeclampsia. The following biomarkers had confirmed associations with early-onset preeclampsia (in descending order of importance): placental growth factor (PlGF), matrix metalloproteinase (MMP-12), lectin-like oxidized LDL receptor 1, carcinoembryonic antigen-related cell adhesion molecule 8, serine protease 27, pro-interleukin-16, and poly (ADP-ribose) polymerase 1. The biomarkers that were associated with late-onset preeclampsia were BNP, MMP-12, alpha-L-iduronidase (IDUA), PlGF, low-affinity immunoglobulin gamma Fc region receptor II-b, and T cell surface glycoprotein. Our results suggest that MMP-12 is a promising novel preeclampsia biomarker. Moreover, BNP and IDUA may be of value in enhancing prediction of late-onset preeclampsia.

UNLOCKING THE FUTURE OF MATERNAL HEALTH: PLATELET INDICES AS PREDICTORS OF PREECLAMPSIA

Objective: Preeclampsia is a dangerous pregnancy condition that puts the health of the mother and fetus at great risk. Early diagnosis and treatment of this illness depend on predictive signs. Because of their correlation with coagulation problems, platelet indices, such as platelet count (PC), platelet distribution width (PDW), mean platelet volume (MPV), and platelet crit (PCT), may be useful as biomarkers for preeclampsia. The purpose of this study is to find out how well platelet indices can predict preeclampsia.&#x0D; Methods: From Mar 1, 2021, to Feb 28, 2022, the study was carried out at the Kamla Nehru State Hospital for Mother and Child in Shimla, Himachal Pradesh. Pregnant women without pre-existing illnesses were removed, and those with normotension who had singleton pregnancies after 20 w of gestation were included. Patients were classified according to the degree and course of preeclampsia, and platelet indices were monitored during pregnancy. To ascertain the significance of fluctuations in the platelet index, statistical tests were conducted and data were analyzed using SPSS-PC-25.&#x0D; Results: According to the study, patients with preeclampsia (PE) and normotensive individuals had significantly different platelet indices. Compared to people with normotension, PE patients showed higher platelet distribution width (PDW), a lower platelet count, and an elevated mean platelet volume (MPV). Interestingly, MPV increased noticeably in PE before blood pressure rose. In severe PE patients, the platelet count drastically reduced. The potential of MPV and platelet count as predictors of PE was shown by Receiver Operating Characteristic (ROC) curve analysis. These results demonstrate the importance of platelet indices in the diagnosis of PE and the potential use of these indices as early indicators of this hypertensive condition in pregnancy.&#x0D; Conclusion: Platelet indices show potential as early indicators for identifying and tracking preeclampsia, especially platelet count, MPV, and PDW. Monitoring platelet indices in addition to blood pressure may help determine the extent and course of the illness, which might lead to better results for both the mother and the fetus. To improve clinical usefulness and improve prediction models, more research in this area is necessary.

Contributions of the vegetation index (NDVI) in water quality prediction models in a semi-arid tropical watershed

In this work, a new approach to using NDVI as a predictor of water quality parameters in arid environments is proposed. Our focus was the Araçuaí river basin, which has a predominance of native cerrado vegetation and is subject to seasonal variations in rainfall and vegetation cover. MODIS images (MOD13Q1) from 2000 to 2018 were used to calculate the NDVI of the contributing areas of the water quality monitoring stations and to analyze its relationship with fourteen water quality parameters. The NDVI showed significant seasonality, with high values in the rainy season, and temporal trends of increase in stretches related to the main river. A strong and new relationship was observed between NDVI and six water quality parameters: color, total dissolved solids, total suspended solids, total solids, nitrate and turbidity, this being stronger at the local scale, with better performance at from models that use incremental NDVI, capturing local variations in vegetation cover, instead of regional NDVI, which reflects the general state of vegetation. The results demonstrate the potential of using these indices to develop and improve prediction models of water quality parameters in river basins and to expand the spatial and temporal coverage of water quality monitoring.

Can flamelet manifolds capture the interactions of thermo-diffusive instabilities and turbulence in lean hydrogen flames?—An a-priori analysis

Flamelet-based methods are extensively used in modeling turbulent hydrocarbon flames. However, these models have yet to be established for (lean) premixed hydrogen flames. While flamelet models exist for laminar thermo-diffusively unstable hydrogen flames, for which consideration of curvature effects has resulted in improved model predictions [1], it is still unclear whether these models are directly applicable to turbulent hydrogen flames. Therefore, a detailed assessment of stretch effects on thermochemical states in a turbulent lean premixed hydrogen-air slot flame through finite-rate chemistry simulations is conducted. Strain and curvature are examined individually using a composition space model, revealing their distinct influences on thermochemical states. An a-priori analysis confirms that the previously developed tabulated manifolds fall short of capturing all turbulent flame phenomena, necessitating a novel manifold incorporating both strain and curvature variations. These results underscore the significance of these variations in developing manifold-based combustion models for turbulent lean hydrogen flames.

Improved Test Methods and Models for Localized Corrosion Assessment of Stainless Steels in Aqueous Chloride Media with Low Dissolved Oxygen

Experimental foundation has been established for improving corrosion models for stainless steels exposed to neutral aqueous chloride media with low levels of dissolved oxygen (DO). Accurate repassivation potentials were measured for UNS S31603 and UNS S32205 alloys in chloride electrolytes at room and elevated temperatures using Tsujikawa-Hisamatsu electrochemical (THE) and cyclic potentiodynamic polarization (CPP) methods. It was observed that the repassivation potential values measured using the CPP method were lower than the ones obtained from THE method for the same environmental conditions. Cyclic polarization scans in aqueous chloride media were conducted at different reverse scan rates resulting in varied repassivation potentials. Medium-term corrosion potentials were measured in chloride media with controlled dissolved oxygen levels ranging from 20 ppb to 400 ppb. These results were used as inputs for improving model prediction for repassivation and corrosion potentials. In an extension of previous modeling studies, an improved mechanistic repassivation model was developed based on a new generalized theory that explicitly accounts for the adsorption of water rather than treating it as an ever-present background. With a flexible definition of the repassivation current density, the new repassivation model reproduces the experimental repassivation potentials obtained using THE and CPP methods. The dependence of corrosion potential on DO has been modeled using a mixed-potential model. It has been demonstrated that the effect of agitation (caused by the bubbling of oxygen) needs to be accounted for to describe the corrosion potential, especially at low DO, where mass transport is important. The combined model correctly predicts the occurrence of localized corrosion when tested against exposure data from the literature.

Learning consistent representations with temporal and causal enhancement for knowledge tracing

Knowledge tracing is a crucial component of intelligent educational systems and deep learning technologies have significantly propelled its advancement. However, most existing models suffer from the incomplete data modeling problem of knowledge tracing, leading to inconsistent representations of students’ actual knowledge states. This paper proposes a new approach called Temporal- and Causal-enhanced Knowledge Tracing to improve the consistency of students’ knowledge state representations in intelligent educational systems. In particular, our method introduces a causal self-attention mechanism based on front-door adjustment theory, which improves interaction representation and reduces prediction errors caused by dataset bias. To effectively integrate the features of interval and response times into our model, we further use forget and input gates to simulate knowledge forgetting and acquisition, respectively. This maintains consistent learning behavior representations and improves model predictions. The results of our extensive experiments on the three datasets demonstrate that our method outperforms previous knowledge-tracing methods in predicting student scores.

Numerical and experimental analysis of oxygen transfer in bubble columns: Assessment of predicting the oxygen-transfer rate in clean water and with surfactant solutions

The purpose of this study was to develop a numerical model to estimate the oxygen-transfer rate for a laboratory-scale bottom aeration system at a 1.28 L reactor volume and to contribute to fundamental knowledge regarding the oxygenation of surfactant solutions. The primary goal of the study has been to develop a computational fluid dynamics (CFD) model using Euler–Euler (EE) mixture model coupled with the advection-diffusion equation to predict the oxygen-transfer rate in bubble columns containing clean water. The secondary goal has been to apply the model to water-based solutions containing the surfactant lauric acid (DDA) to identify options for further development of the model to make it applicable to surfactant solution systems. The Sauter mean diameter (SMD) was calculated to represent the average bubble diameter, based on available experimental data for different combinations of superficial velocities rate and DDA concentration. The oxygen-transfer rate in clean water fit well with experimental data at lower superficial velocities, and the differences in volumetric mass-transfer coefficients were 0.7% and 3.3% for superficial velocities of 0.24 cm/s and 0.48 cm/s, respectively. For surfactant solutions, the model overestimates the oxygen-transfer rate due to surfactant adsorption at the bubble/water interface and the consequent decrease in the mass-transfer coefficient not being modeled sufficiently. A correction factor for the mass-transfer coefficient based on a larger sample size of experimental data may need to be calculated and applied to improve model predictability.

Quantile Regression in Survival Analysis: Comparing Check-Based Modeling and the Minimum Distance Approach

Introduction: Quantile regression is a valuable alternative for survival data analysis, enabling flexible evaluations of covariate effects on survival outcomes with intuitive interpretations. It offers practical computation and reliability. However, challenges arise when applying quantile regression to censored data, particularly for upper quantiles. The minimum distance approach, utilizing dual-kernel estimation and the inverse cumulative distribution function, shows promise in addressing these challenges, especially with higher-dimensional covariates.&#x0D; Methods: This study contrasts two methods within the realm of quantile linear regression for survival analysis: check-based modeling and the minimum distance approach. Effectiveness is assessed across various scenarios through comprehensive simulation.&#x0D; Results: The simulation results showed that using the quantile regression model with the minimum distance approach reduces the percentage of root mean square error in parameter estimation compared to the quantile regression models based on the check loss function. Additionally, a larger sample size and reduced censoring percentage led to decreased root mean square error in parameter estimation.&#x0D; Conclusion: The research highlights the benefits of using the minimum distance approach for quantile regression. It reduces errors, improves model predictions, captures patterns, and optimizes parameters even with complete data. However, this approach has limitations. The accuracy of estimated quantiles can be influenced by the choice of distance metric and weighting scheme. The assumption of independence between censoring mechanism and survival time may not hold in real-world scenarios. Additionally, dealing with large datasets can be computationally complex.&#x0D; &#x0D;

Digging Deeper: The Role of Big Data Analytics in Geotechnical Investigations

This review paper explores the transformative role of big data analytics in geotechnical engineering, transferring past conventional methods to a data-driven paradigm that complements decision-making and precision in subsurface investigations. By integrating large statistics analytics with geotechnical engineering, this study demonstrates big improvements in website characterization, danger assessment, and production methodologies. The research underscores the capability of big data to revolutionize geotechnical investigations through improved prediction models, threat management, and sustainable engineering practices, highlighting the critical role of big data in addressing international warming and ozone depletion. Through the examination of numerous case studies and AI-driven methodologies, this paper sheds light at the efficiency gains and environmental benefits attainable in geotechnical engineering.

Effect of NO2 addition on the oxidation kinetics of n-pentane and natural gas blends with C1–C5n-alkanes

This study presents new ignition delay time (IDT) data for NO2 blended multi-component natural gas mixtures with methane as the major component including C2–C5 primary n-alkanes in different concentrations. The data are measured using a high-pressure shock tube and a rapid compression machine at compression pressures of 20 and 30 bar in the temperature range of 715–1480 K for stoichiometric mixtures. This paper builds upon our previous work on the development of a C1–C3/NOx kinetic mechanism and focusses on understanding the interaction chemistry of heavier n-alkane/NOx mixtures by determining the IDT characteristics of various NO2 blended n-C5H12/air combinations with 200, 400 and 1000 ppm concentrations of NO2 present at compression pressures of 10, 20 and 30 bar. The newly measured IDT results and existing experimental data from the literature are used to improve model predictions for the oxidation of n-C4H10/NOx, n-C5H12/NOx, NG2/NOx, and NG3/NOx by updating NUIGMech1.2. The improvement in the performance of GalwayMech1.0 is attributed to updated rate constants for the n-C5H12 + NO2 ↔ Ċ5H11–1, -2, -3 + HONO reactions as well as the inclusion of Ṙ + NO2 ↔ RONO reaction channels. The IDT decreased slightly when 1000 ppm of NO2 is added to n-pentane, which contrasts with propane (C3H8), where there was a significant decrease in the mixture reactivity as reported previously (A. A. E. S. Mohamed, A. B. Sahu, S. Panigrahy, M. Baigmohammadi, G. Bourque, H.J. Curran, The effect of the addition of nitrogen oxides on the oxidation of propane: An experimental and modeling study. Combust. Flame 245 (2022) 112306). This is because the competition between NO2 and O2 for 1-propyl radicals formed by H-atom abstraction from C3H8 decreases mixture reactivity, while in the case of n-C5H12, although NO2 and O2 compete for 1-pentyl radicals, the increase in reactivity is facilitated by the availability of 2-pentyl radicals in the system which also leads to chain branching when they add to O2, unlike 2-propyl (iso-propyl) radicals which ultimately do not lead to chain branching in the oxidation of propane.

GLoW SMOTE-D: Oversampling Technique to Improve Prediction Model Performance of Students Failure in Courses

GLoW SMOTE-D: Oversampling Technique to Improve Prediction Model Performance of Students Failure in Courses

Real-time prediction of wave-induced hull girder loads for a large container ship based on the recurrent neural network model and error correction strategy

Real-time acquisition of wave-induced hull girder loads of a sailing ship will help the captain make reasonable decisions, which is of great significance for improving the safety of the ship's navigation. This paper investigates the real-time prediction method of hull girder loads based on the Recurrent Neural Network (RNN) model and error correction strategy. Firstly, taking the vertical bending moment, horizontal bending moment, and torsional moment at the mid-ship position of a large container ship as examples, corresponding neural network prediction models are established through parameter influence analysis. Secondly, various sea state conditions are used to verify the feasibility of established network prediction models to predict the hull girder loads in real-time. The VBM prediction model performs better than the TM prediction model and HBM prediction model, and the errors of the TM prediction model and HBM prediction model are slightly larger in some cases. Lastly, an improved prediction model based on an error correction strategy is proposed to improve the prediction accuracy of the neural network prediction model, and the adequate performance of the error correction strategy is discussed.

An improved prediction model for COD measurements using UV-Vis spectroscopy.

In the 21st century, although water quality has been improved in the last two decades, water pollution by organic contaminants has remained a non-negligible issue in China, so Chemical-Oxygen Demand (abbreviated as COD, unit: mg L-1) is often used as the main index to measure the degree of surface water pollution. UV-Vis spectroscopy, as a sensitive and rapid analytical technique, is a green detection technology suitable for automatic online COD detection equipment. However, due to the complex composition of surface water, the interference degree of the UV-Vis spectrum caused by turbidity is strongly correlated with the size, type and color of particulate matter in the solution, which results in noise sensitivity and poor generalization of the current detection model. Therefore, the main purpose of this research is to improve the traditional detection model performance by using deep learning and a spectrum preprocessing algorithm. Firstly, we used an improved noise filter based on discrete wavelet transforms to solve the noise sensitivity. Secondly, we proposed a novel COD detection network to address poor generalization. Thirdly, we collected a total of 2259 water samples' UV-Vis absorption spectra and corresponding COD as a dataset. Then, we pipelined the improved noise removal algorithm and proposed COD detection network, as a complete COD prediction model. Finally, the experiment on the dataset shows that the COD prediction model has a good performance in terms of both noise tolerance and accuracy.

Exploring Feature Engineering Strategies for Improving Predictive Models in Data Science

A crucial step in the data science pipeline, feature engineering has a big impact on how well predictive models function. This study explores several feature engineering techniques and how they affect the robustness and accuracy of models. In order to extract useful information from unprocessed data and improve the prediction capability of machine learning models, we study a variety of techniques, from straightforward transformations to cutting-edge approaches. The study starts by investigating basic methods including data scaling, one-hot encoding, and handling missing values. Then, we go on to more complex techniques like feature selection, dimensionality reduction, and interaction term creation. We also explore the possibilities for domain-specific feature engineering, which entails designing features specifically for the issue domain and utilising additional data sources to expand the feature space. We run extensive experiments on numerous datasets including different sectors, such as healthcare, finance, and natural language processing, in order to evaluate the efficacy of these methodologies. We evaluate model performance using metrics like recall, accuracy, precision, and F1-score to get a comprehensive picture of how feature engineering affects various predictive tasks. This study also assesses the computational expense related to each feature engineering technique, taking scalability and efficiency in practical applications into account. To assist practitioners in making wise choices during feature engineering, we address the trade-offs between model complexity and performance enhancements. Our results highlight the importance of feature engineering in data science and demonstrate how it may significantly improve prediction models in a variety of fields. This study is a useful tool for data scientists because it emphasises the significance of careful feature engineering as a foundation for creating reliable and accurate prediction models.