Lowest Mean Absolute Error Research Articles

Monitoring of the trough concentration of valproic acid in pediatric epilepsy patients: a machine learning-based ensemble model

AimsFew personalized monitoring models for valproic acid (VPA) in pediatric epilepsy patients (PEPs) incorporate machine learning (ML) algorithms. This study aimed to develop an ensemble ML model for VPA monitoring to enhance clinical precision of VPA usage.MethodsA dataset comprising 366 VPA trough concentrations from 252 PEPs, along with 19 covariates and the target variable (VPA trough concentration), was refined by Spearman correlation and multicollinearity testing (366 × 11). The dataset was split into a training set (292) and testing set (74) at a ratio of 8:2. An ensemble model was formulated by Gradient Boosting Regression Trees (GBRT), Random Forest Regression (RFR), and Support Vector Regression (SVR), and assessed by SHapley Additive exPlanations (SHAP) analysis for covariate importance. The model was optimized for R2, relative accuracy, and absolute accuracy, and validated against two independent external datasets (32 in-hospital and 28 out-of-hospital dataset).ResultsUsing the R2 weight ratio of GBRT, RFR and SVR optimized at 5:2:3, the ensemble model demonstrated superior performance in terms of relative accuracy (87.8%), absolute accuracy (78.4%), and R2 (0.50), while also exhibiting a lower Mean Absolute Error (9.87) and Root Mean Squared Error (12.24), as validated by the external datasets. Platelet count (PLT) and VPA daily dose were identified as pivotal covariates.ConclusionThe proposed ensemble model effectively monitors VPA trough concentrations in PEPs. By integrating covariates across various ML algorithms, it delivers results closely aligned with clinical practice, offering substantial clinical value for the guided use of VPA.

Estimating Tetrachloroethene Sorption Coefficients Based on Soil Properties in Organic-Poor Soils

In the context of contaminated site remediation, the fate of chlorinated solvents in the subsurface and subsequent groundwater contamination is influenced by soil properties governing sorption. The solid–water distribution coefficient (Kd) is a key parameter for modeling contaminant distribution and transport, essential for risk assessment and remediation planning. This study evaluated tetrachloroethene sorption isotherms in 34 low-organic-carbon soils from the Czech Republic, assessing the influence of soil properties on Kd. Soil samples exhibited variability in organic carbon content (˂0.05–0.81%), with clay ranging from 0% to 64.9%, silt 5.1% to 71.2%, and sand 5.2% to 88.9%, specific surface area (0.41–64.39 m2 g−1), particle density (2.05–4.09 g cm−3), and porosity (43.5–67.3%). Batch experiments were conducted using standard procedures, with Kd values ranging from 0.379 to 2.272 L kg−1. Statistical analysis grouped the soils into three textural classes: sandy, clayey fine, and silty loam. The findings reveal that organic carbon content and specific surface area are the primary predictors of Kd, while clay and sand also play a significant role in shaping sorption behavior. Multivariate regression models explained 63.6% to 98.5% of Kd variability with high accuracy, as indicated by low root means square error (0.070–0.329) and mean absolute percentage error (3.8–28.8%) values. These models offer reliable predictions of sorption behavior, providing valuable tools for risk assessment and remediation strategies.

Empirical Comparison of Facebook Prophet and Traditional Models for Tomato Price Forecasting in Greece

Agricultural product prices are crucial to the income and livelihoods of millions worldwide, making accurate forecasting essential for market participants. The European Union regularly reports agricultural product prices through an online databank, ensuring up-to-date price information is widely available. This study evaluates the effectiveness of Facebook Prophet, a modern forecasting tool, in predicting tomato prices in Greece from 2013 to 2024.The results show that Facebook Prophet outperforms traditional forecasting models, providing more accurate predictions with lower Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) values. Notably, the model exhibited superior performance in forecasting tomato prices over a six-month horizon compared to conventional seasonally adjusted models. This demonstrates Facebook Prophet’s potential to significantly improve decision-making in agricultural markets, offering reliable price forecasts to stakeholders such as farmers, traders, and policymakers. Furthermore, the study incorporated feedback from market participants, which provided valuable insights into market practices and conditions. This integration of practical knowledge with advanced forecasting techniques enhanced the interpretation of the results, making them more applicable to real-world scenarios. Overall, the findings suggest that Facebook Prophet holds considerable promise for future agricultural price forecasting, with potential applications across various commodities. These insights pave the way for more precise agricultural forecasts, benefiting all market participants by supporting more informed and timely decision-making.

Advanced State-of-Health Estimation for Lithium-Ion Batteries Using Multi-Feature Fusion and KAN-LSTM Hybrid Model

Accurate assessment of battery State of Health (SOH) is crucial for the safe and efficient operation of electric vehicles (EVs), which play a significant role in reducing reliance on non-renewable energy sources. This study introduces a novel SOH estimation method combining Kolmogorov–Arnold Networks (KAN) and Long Short-Term Memory (LSTM) networks. The method is based on fully charged battery characteristics, extracting key parameters such as voltage, temperature, and charging data collected during cycles. Validation was conducted under a temperature range of 10 °C to 30 °C and different charge–discharge current rates. Notably, temperature variations were primarily caused by seasonal changes, enabling the experiments to more realistically simulate the battery’s performance in real-world applications. By enhancing dynamic modeling capabilities and capturing long-term temporal associations, experimental results demonstrate that the method achieves highly accurate SOH estimation under various charging conditions, with low mean absolute error (MAE) and root mean square error (RMSE) values and a coefficient of determination (R2) exceeding 97%, significantly improving prediction accuracy and efficiency.

Optimising Venturi flume oxygen transfer efficiency using uncertainty-aware decision trees

ABSTRACT This study optimizes standard oxygen transfer efficiency (SOTE) in Venturi flumes investigating the impact of key parameters such as discharge per unit width (q), throat width (W), throat length (F), upstream entrance width (E), and gauge readings (Ha and Hb). To achieve this, a comprehensive experimental dataset was analyzed using multiple linear regression (MLR), multiple nonlinear regression (MNLR), gradient boosting machine (GBM), extreme gradient boosting (XRT), random forest (RF), M5 (Pruned and Unpruned), random tree (RT), and reduced error pruning (REP). Model performance was evaluated based on key metrics: correlation coefficient (CC), root mean square error (RMSE), and mean absolute error (MAE). Among the proposed models, M5_Unprun emerged as the top performer, exhibiting the highest CC (0.9455), the lowest RMSE (0.1918), and the lowest MAE (0.0030). GBM followed closely with a CC value of 0.9372, an RMSE value of 0.2067, and an MAE value of 0.0006. Uncertainty analysis further solidified the superior performance of M5_Unpruned (0.7522) and GBM (0.8055), with narrower prediction bands compared to other models, including MLR, which exhibited the widest band (1.4320). One-way analysis of variance confirmed the reliability and robustness of the proposed models. Sensitivity, correlation, and SHapley Additive exPlanations analyses identified W and Hb as the most influencing factors.

Response of Topsoil Organic Carbon in the Forests of Northeast China Under Future Climate Scenarios

Soil organic carbon (SOC) serves as a highly sensitive indicator of climate change and plays a crucial role in terrestrial carbon cycles. Evaluating the impact of regional land use changes on SOC stocks is essential for assessing ecological and environmental effects. In this study, we utilized 157 soil samples and 11 environmental variables—including soil properties, topographic factors, and climatic conditions—to develop boosted regression tree (BRT) and random forest (RF) models to estimate topsoil SOC stocks for the year 2015. We used a 10-fold cross-validation approach, along with four validation metrics, to assess model performance. The BRT model demonstrated superior accuracy, with a higher R2 and Lin’s consistency correlation coefficient and a lower mean absolute error and root mean square error compared to the RF model. The key environmental factors influencing SOC stock variability in the BRT model included mean annual temperature, elevation, mean annual precipitation, the topographic wetness index (TWI), and catchment area. Based on this, we employed the space-for-time substitution approach and BRT model to forecast the spatial distribution of soil organic carbon (SOC) stocks in Northeast China’s forested regions under future climate scenarios for the 2050s and 2090s. Our findings indicate that, compared to the 2015 levels, the forecast indicates that SOC stocks will decrease by 122 Tg carbon and 123 Tg carbon under two different future scenarios, SSP245 and SSP585, respectively, by the 2050s. By the 2090s, these figures are expected to decrease further by 127 Tg C and 126 Tg C, respectively. Throughout both future periods, SOC stocks will predominantly be concentrated in the northwest region. This research highlights the necessity of thoroughly considering climatic factors in future studies of regional SOC stock dynamics. Moreover, the high-resolution maps produced in this study offer a scientific foundation for enhancing the implementation of ecological management practices in the forested regions of Northeast China, fostering environmental improvement and bolstering SOC and soil management strategies in response to future climate change.

Harnessing the Power of AI-Instructor Collaborative Grading Approach: Topic-Based Effective Grading for Semi Open-Ended Multipart Questions

Semi open-ended multipart questions consist of multiple sub questions within a single question, requiring students to provide certain factual information while allowing them to express their opinion within a defined context. Human grading of such questions can be tedious, constrained by the marking scheme and susceptible to the subjective judgement of instructors. The emergence of large language models (LLMs) such as ChatGPT has significantly advanced the prospect of automatic grading in educational settings. This paper introduces a topic-based grading approach that harnesses LLM capabilities alongside a refined marking scheme to ensure fair and explainable assessment processes. The proposed approach involves segmenting student responses according to sub questions, extracting topics utilizing LLM, and refining the marking scheme in consultation with instructors. The refined marking scheme is derived from LLM-extracted topics, validated by instructors to augment the original grading criteria. Leveraging LLM, we match student responses with refined marking scheme topics and employ a Python program to assign marks based on the matches. Various prompt versions are compared using relevant metrics to determine the most effective prompts. We evaluate LLM's grading proficiency through three approaches: zero-shot prompting, few-shot prompting, and our proposed method. Results indicate that while zero-shot and few-shot prompting methods fall short compared to human grading, the proposed approach achieves the best performance (highest percentage of exact match marks, lowest mean absolute error, highest Spearman correlation, highest Cohen’s weighted kappa) and closely mirrors the distribution observed in human grading. Specifically, the collaborative approach enhances the grading process by refining the marking scheme to student responses, improving transparency and explainability through topic-based matching, and significantly increasing the effectiveness of LLMs when combined with instructor input, rather than as standalone automated grading systems.

Applications of geographically weighted machine learning models for predicting soil heavy metal concentrations across mining sites

The accurate prediction of soil heavy metal contamination is crucial for the effective environmental management of abandoned mining areas. However, conventional machine learning models (CMLMs) often fail to account for the spatial heterogeneity of soil contamination, which limits their predictive accuracy. This study evaluated the performance of geographically weighted machine learning models (GWMLMs) in predicting soil Cd and Pb concentrations in abandoned mines in the Republic of Korea. We compared two GWMLMs (Geographically Weighted Random Forest and Geographically Weighted Extreme Gradient Boosting) with four CMLMs (Random Forest, Gradient Boosting, Light Gradient Boosting, and extreme Gradient Boosting). The data used in this study included soil samples from six abandoned mining sites with various geographical and soil input variables. The results showed that the GWMLMs consistently outperformed the CMLMs in predicting heavy metal contamination. For Cd predictions, GWMLMs exhibited on average 0.02 lower root mean square error and mean absolute error values, with a 0.26 increase in R2 values compared to CMLMs. Similarly, for Pb predictions, the GWMLMs showed 0.18 and 0.13 lower root mean square error and mean absolute error values, respectively, and a 0.17 increase in R2 relative to the CMLMs. The findings demonstrate the usefulness of GWMLMs for predicting the spatial distribution of soil heavy metals. SHapley Additive exPlanations analysis exhibited elevation and distance from abandoned mining sites as the most influential factors in predicting both Cd and Pb concentrations. This study highlights the value of GWMLMs that incorporate spatial heterogeneity into CMLMs for enhancing prediction accuracy and providing crucial insights for environmental management in mining-impacted regions.

Effective thermal conductivity of composites using homogenization

Composite materials are extensively used in engineering applications due to their customizable properties and high performance. Determining the equivalent homogenized properties of composites, such as thermal conductivity, is crucial for their effective use. Various theoretical, analytical, and experimental methods have been developed to assess these properties. This study investigates the effective thermal conductivity of composites using a deterministically based procedure for thermal analysis. This procedure accounts for the combined influences of the inclusions’ volume fractions, shapes, orientations, and locations within the matrix to determine the effective thermal conductivity of composites. The specific composite analyzed consists of a cubical PLA matrix with a single spherical or elliptical void inclusion with perfect interfaces. For that purpose, an analytical approach was developed, and MATLAB® code was created to calculate the effective thermal conductivity tensor. To benchmark the analytical results, comparisons were made against numerical finite element modeling (FEM) results conducted using ANSYS®; in addition, to previously reported analytical models from the literature. Corroboration was also obtained by comparing the results against experimental data from the literature. The accuracy of the proposed homogenization scheme was demonstrated by achieving a low mean absolute percentage error (MAPE) compared to FEM (2.88%) and to the experimental results (2.72% for void inclusion and 6.99% for filled inclusion). Additionally, a high R-squared (R2) value of 0.986 was achieved compared to FEM, and values of 0.97 and 0.998 were achieved compared to the experimental results for void and filled inclusions, respectively.

Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations

Abstract. Cloud fraction as a vital component of meteorological satellite products plays an essential role in environmental monitoring, disaster detection, climate analysis and other research areas. Random forest (RF) and multilayer perceptron (MLP) algorithms were used in this paper to retrieve the cloud fraction of AGRI (Advanced Geosynchronous Radiation Imager) on board the Fengyun-4A (FY-4A) satellite based on its full-disk level-1 radiance observation. Corrections have been made subsequently to the retrieved cloud fraction in areas where solar glint occurs using a correction curve fitted with sunglint angle as weight. The algorithm includes two steps: the cloud detection is conducted firstly for each AGRI field of view to identify whether it is clear sky, partly cloudy or overcast within the observation field. Then, the cloud fraction is retrieved for the scene identified as partly cloudy. The 2B-CLDCLASS-lidar cloud fraction product from the CloudSat and CALIPSO active remote sensing satellite is employed as the truth to assess the accuracy of the retrieval algorithm. Comparison with the operational AGRI level-2 cloud fraction product is also conducted at the same time. The results indicate that both the RF and MLP cloud detection models achieved high accuracy, surpassing that of operational products. However, both algorithms demonstrated weaker discrimination capabilities for partly cloudy conditions compared to clear-sky and overcast situations. Specifically, they tended to misclassify fields of view with low cloud fractions (e.g., cloud fraction = 0.16) as clear sky and those with higher cloud fractions (e.g., cloud fraction = 0.83) as overcast. Between the two models, RF exhibited higher overall accuracy. Both RF and MLP models performed well in cloud fraction retrieval, showing lower mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) compared to operational products. The ME for both RF and MLP cloud fraction retrieval models was close to zero, while RF had slightly lower MAE and RMSE than MLP. During daytime, the high reflectance in sunglint areas led to larger retrieval errors for both RF and MLP algorithms. However, after correction, the retrieval accuracy in these regions improved significantly. At night, the absence of visible light observations from the AGRI instrument resulted in lower classification accuracy compared to daytime, leading to higher cloud fraction retrieval errors during nighttime.

Enhancing stock market predictions via hybrid external trend and internal components analysis and long short term memory model

When it comes to financial decision-making, stock market predictability is extremely important since it offers valuable information that may guide investment strategies, risk management, and portfolio allocation overall. Traditional methods often fail to accurately predict stock prices due to their complexity and inability to handle non-linear and non-stationary patterns in market data. To address these issues, this study introduces an innovative model that combines the External Trend and Internal Components Analysis decomposition method (ETICA) with the Long Short-Term Memory (LSTM) model, aiming to enhance stock market predictions for S&P 500, NASDAQ, Dow Jones, SSE and SZSE indices. Through rigorous testing across various training data proportions and epoch settings, our findings reveal that the proposed hybrid model outperforms the single LSTM model, delivering significantly lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. This enhanced precision reduces prediction errors, underscoring the model’s robustness and reliability. The superior performance of the ETICA-LSTM model highlights its potential as a powerful financial forecasting tool, promising to transform investment strategies, optimize risk management, and enhance portfolio performance.

An investigation of the validity of the Bedek models and Cameriere-European formula used in dental age prediction in Turkish children

BackgroundDental age estimation is one of the most reliable methods for determining age, commonly used for forensic, criminal, and anthropological purposes. The purpose of this study was to assess and compare the precision of the Bedek models and the Cameriere-European Formula (CF) in determinating dental age in a sample of Turkish individuals.MethodsRetrospective panoramic radiographs of 1018 subjects (497 boys and 521 girls) aged 5–14 years were evaluated using the Bedek models and the CF. The differences between calculated dental age (DA) and chronological age (CA) for each sex and age group were analyzed using the paired sample t-test and the Wilcoxon signed-rank test. The accuracy of the methods used to estimate dental age was determined by calculating the mean absolute error (MAE) based on the proximity of the dental age to the chronological age.ResultsThe CF method overestimated CA by 0.1 year in the entire sample. Conversely, the Bedek models tended to overestimate CA, with the three-, four-, and seven-tooth models exhibiting the most significant difference. There was a statistically significant difference between boys and girls in the DA-CA calculation using the CF method in the entire sample. Among the Bedek models, the three- and four-tooth models had the lowest MAE in the entire sample, while the single-tooth model had the highest MAE.ConclusionThe CF method showed higher accuracy in predicting the age of children living in eastern Turkey compared to the Bedek Models.

A Knowledge–Data Dual‐Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes

AbstractDeveloping rechargeable batteries that operate within a wide temperature range and possess high safety has become necessary with increasing demands. Rapid and accurate assessment of the melting points (MPs), boiling points (BPs), and flash points (FPs) of electrolyte molecules is essential for expediting battery development. Herein, we introduce Knowledge‐based electrolyte Property prediction Integration (KPI), a knowledge–data dual‐driven framework for molecular property prediction of electrolytes. Initially, the KPI collects molecular structures and properties, and then automatically organizes them into structured datasets. Subsequently, interpretable machine learning further explores the structure–property relationships of molecules from a microscopic perspective. Finally, by embedding the discovered knowledge into property prediction models, the KPI achieved very low mean absolute errors of 10.4, 4.6, and 4.8 K for MP, BP, and FP predictions, respectively. The KPI reached state‐of‐the‐art results in 18 out of 20 datasets. Utilizing molecular neighbor search and high‐throughput screening, 15 and 14 promising molecules, with and without Chemical Abstracts Service Registry Number, respectively, were predicted for wide‐temperature‐range and high‐safety batteries. The KPI not only accurately predicts molecular properties and deepens the understanding of structure–property relationships but also serves as an efficient framework for integrating artificial intelligence and domain knowledge.

A Knowledge-Data Dual-Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes.

Developing rechargeable batteries that operate within a wide temperature range and possess high safety has become necessary with increasing demands. Rapid and accurate assessment of the melting points (MPs), boiling points (BPs), and flash points (FPs) of electrolyte molecules is essential for expediting battery development. Herein, we introduce Knowledge-based electrolyte Property prediction Integration (KPI), a knowledge-data dual-driven framework for molecular property prediction of electrolytes. Initially, the KPI collects molecular structures and properties, and then automatically organizes them into structured datasets. Subsequently, interpretable machine learning further explores the structure-property relationships of molecules from a microscopic perspective. Finally, by embedding the discovered knowledge into property prediction models, the KPI achieved very low mean absolute errors of 10.4, 4.6, and 4.8 K for MP, BP, and FP predictions, respectively. The KPI reached state-of-the-art results in 18 out of 20 datasets. Utilizing molecular neighbor search and high-throughput screening, 15 and 14 promising molecules, with and without Chemical Abstracts Service Registry Number, respectively, were predicted for wide-temperature-range and high-safety batteries. The KPI not only accurately predicts molecular properties and deepens the understanding of structure-property relationships but also serves as an efficient framework for integrating artificial intelligence and domain knowledge.

Research on Urban Traffic Flows Prediction Model Based on Deep Learning

Abstract. Vehicle accumulation in urban regions is a significant complication, giving rise to monetary and sustainability-related concerns. The most important procedure for effective traffic management is the precise forecasting of traffic flow. This study leverages Long Short-Term Memory (LSTM) networks to predict traffic volume on Interstate-94 (I-94) in the US, using hourly traffic and weather data from 2012 to 2018, which was normalized using Min-Max scaling, and the LSTM model was trained on 80% of the data, with the remaining 20% used for testing. Evaluation of the model was conducted using performance indicators such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Showing its strong forecasting ability, evidenced by its low MAE and RMSE, which highlight the model's high accuracy, and forecast the traffic flow under varying conditions. This study highlights the effectiveness of LSTM networks for traffic prediction, offering a significant tool for the management of the metropolitan areas. Upcoming studies might prioritize on real-time implementation and integrating additional data sources to further enhance prediction accuracy.

Research on Urban Traffic Flows Prediction Model Based on Deep Learning

Abstract. Vehicle accumulation in urban regions is a significant complication, giving rise to monetary and sustainability-related concerns. The most important procedure for effective traffic management is the precise forecasting of traffic flow. This study leverages Long Short-Term Memory (LSTM) networks to predict traffic volume on Interstate-94 (I-94) in the US, using hourly traffic and weather data from 2012 to 2018, which was normalized using Min-Max scaling, and the LSTM model was trained on 80% of the data, with the remaining 20% used for testing. Evaluation of the model was conducted using performance indicators such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Showing its strong forecasting ability, evidenced by its low MAE and RMSE, which highlight the model's high accuracy, and forecast the traffic flow under varying conditions. This study highlights the effectiveness of LSTM networks for traffic prediction, offering a significant tool for the management of the metropolitan areas. Upcoming studies might prioritize on real-time implementation and integrating additional data sources to further enhance prediction accuracy.

Rainfall Projections for the Brazilian Legal Amazon: An Artificial Neural Networks First Approach

Rainfall in the Brazilian Legal Amazon (BLA) is vital for climate and water resource management. This research uses spatial downscaling and validated rainfall data from the National Water and Sanitation Agency (ANA) to ensure accurate rain projections with artificial intelligence. To make an initial approach, Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) were employed to forecast rainfall from 2012 to 2020. The RNN model showed strong alignment with the observed patterns, accurately predicting rainfall seasonality. However, median comparisons revealed fair approximations with discrepancies. The Root Mean Square Error (RMSE) ranged from 6.7 mm to 11.2 mm, and the coefficient of determination (R2) was low in some series. Extensive analyses showed a low Wilmott agreement and high Mean Absolute Percentage Error (MAPE), highlighting limitations in projecting anomalies and days without rain. Despite challenges, this study lays a foundation for future advancements in climate modeling and water resource management in the BLA.

Advancing Geotechnical Evaluation of Wellbores: A Robust and Precise Model for Predicting Uniaxial Compressive Strength (UCS) of Rocks in Oil and Gas Wells

This study examines the efficacy of various machine learning models for predicting the uniaxial compressive strength (UCS) of rocks in oil and gas wells, which are essential for ensuring wellbore stability and optimizing drilling operations. The investigation encompasses Linear Regression, ensemble methods (including Random Forest, Gradient Boosting, XGBoost, and LightGBM), support vector machine-based regression (SVM-SVR), and multilayer perceptron artificial neural network (MLP-ANN) models. The results demonstrate that XGBoost and Gradient Boosting offer superior predictive accuracy for UCS in drillability, as indicated by low Mean Absolute Percentage Error (MAPE) values of 3.87% and 4.18%, respectively, and high R2 scores (0.8542 for XGBoost). These models emerge as optimal choices for UCS prediction focused on drillability, offering increased accuracy and reliability in practical engineering scenarios. Ensemble methods and MLP-ANN emerge as frontrunners, providing valuable tools for improving wellbore stability assessments, optimizing drilling parameter selection, and facilitating informed decision-making processes in oil and gas drilling operations. Moreover, this study lays a foundation for further research in drillability-centred predictive modelling for geotechnical parameters, advancing our understanding of rock behaviour under drilling conditions.

A Comprehensive Evaluation of a Coumarin Derivative and Its Corresponding Palladium Complex as Potential Therapeutic Agents in the Treatment of Gynecological Cancers: Synthesis, Characterization, and Cytotoxicity.

Background: The aim of this research is the synthesis and characterization of coumarin-palladium complex and the investigation of the cytotoxicity of both the ligand and the complex. Methods: The palladium( II) complex (CC) was obtained in the reaction between (E)-3-(1-((4-hydroxy-3-methoxyphenyl)amino)ethylidene)-2,4-dioxochroman-7-yl-acetate (CL) and potassium-tetrachloropalladate(II) and characterized using IR and NMR spectra, experimentally and theoretically. Cytotoxicity of CL and CC were determined for human cervical carcinoma HeLa, ovarian cancer A2780, hormone dependent breast cancer MCF7, and colorectal cancer HCT116 lines. The interaction of investigated compounds with HSA was followed by spectrofluorimetric method. The binding mechanism in the active pocket was assessed via molecular docking simulations. Results: A low mean absolute error between experimental and theoretical data proved that the optimized structure corresponded to the experimental one. Both compounds showed a satisfactory selectivity index towards neoplastic cells. The binding affinity of tested compounds to the HSA were confirmed. The molecular docking showed a much lower change in the Gibbs free energy of binding for CC compared to CL. Conclusions: The obtained results revealed that CL and CC exhibit significant effects on several cancer cell lines and good binding properties to HSA, while molecular docking discovered that CC has the most pronounced activity against alpha-fetoprotein.

Design and Equivalent Circuit Model Extraction of a Fractal Slot-Loaded 3–40 GHz Super Wideband Antenna

In this paper, we present the design and equivalent circuit model (ECM) of a fractal slot-loaded super wideband (SWB) antenna for compact and high-performance applications operating in the 3–40 GHz range. The proposed antenna features a compact dimension of 40 × 35 × 1.57 mm³, a measured bandwidth ratio of 13:1, a peak gain of 9.7 dBi, an average radiation efficiency of 94%, and a low cross-polarization level across the entire bandwidth. The presented ECM is derived using transmission line theory and incorporates the individual behavior of each constituting element of the antenna. A dual sequential optimization approach is employed to determine the optimal element values. The ECM results show good agreement with both simulated and measured results in terms of the magnitude of reflection coefficient |S11| and both real and imaginary impedances with low mean absolute percentage errors of 4.9%, 7.5%, and 7.7%, respectively, demonstrating the model’s ability to accurately predict the antenna’s performance.