Improving Protein Quantification with SERS Superspectra and Machine Learning.

Personal identification of an individual has always been a major concern in forensic science. Reconstruction of the facial profile is considered as one of the final stages in the process of identification. Nevertheless, recent advancements in artificial intelligence (AI) and machine learning (ML) have demonstrated remarkable potential in predictive modelling and forensic applications. The current study uses customised machine learning models to predict facial dimensions based on dental and jaw parameters. A sample of 422 participants (201 males and 221 females) from a North Indian population was collected and analysed. Dental casts, anthropometric facial measurements and photographs of the participants were collected with informed consent. ML models such as Support Vector Regression (SVR), Random Forest Regression (RFR), Decision Tree Regression (DTR), and Linear Regression (LR) were trained using dental and jaw measurements as input features for the models. The results show that the ML models predicted the facial dimensions with an accuracy of 90–94% and a very low prediction error of 0.1–0.9 across all facial measurements. Among the models, SVR and LR models perform well, followed by RFR, whereas DFR yielded comparatively lower accuracy. The findings demonstrate that machine learning models (SVR, RFR, DTR, and LR) can be used as novel approach to predict facial dimensions from jaw and teeth parameters. These techniques can be combined with other facial reconstruction techniques to produce more precise and accurate outcomes. The reliability and accuracy in predicting the facial dimensions indicate that the results can be applied in the practical and real situations such as personal identification, forensic investigations, disaster victim identification cases, and archaeological remains where only jaw and teeth are available for examination. Integrating ML-based predictions with traditional facial reconstruction techniques could enhance the accuracy and reliability of forensic identification methodologies.

The quantitative analysis of protein mixtures is pivotal for the understanding of variations in the proteome of living systems. Therefore, approaches have been recently devised that generally allow the relative quantitative analysis of peptides and proteins. Here we present proof of concept of the new metal-coded affinity tag (MeCAT) technique, which allowed the quantitative determination of peptides and proteins. A macrocyclic metal chelate complex (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA)) loaded with different lanthanides (metal(III) ions) was the essential part of the tag. The combination of DOTA with an affinity anchor for purification and a reactive group for reaction with amino acids constituted a reagent that allowed quantification of peptides and proteins in an absolute fashion. For the quantitative determination, the tagged peptides and proteins were analyzed using flow injection inductively coupled plasma MS, a technique that allowed detection of metals with high precision and low detection limits. The metal chelate complexes were attached to the cysteine residues, and the course of the labeling reaction was followed using SDS-PAGE and MALDI-TOF MS, ESI MS, and inductively coupled plasma MS. To limit the width in isotopic signal spread and to increase the sensitivity for ESI analysis, we used the monoisotopic lanthanide macrocycle complexes. Peptides tagged with the reagent loaded with different metals coelute in liquid chromatography. In first applications with proteins, the calculated detection limit for bovine serum albumin for example was 110 amol, and we have used MeCAT to analyze proteins of the Sus scrofa eye lens as a model system. These data showed that MeCAT allowed quantification not only of peptides but also of proteins in an absolute fashion at low concentrations and in complex mixtures.

TFETs (Tunnel Field-Effects Transistors) are assumed to be ultra-low-power devices because they have steep subthreshold swing and a more favourable scaling characteristic. Their electrical characteristics, however, cannot be adequately simulated using extensive Technology Computer-Aided Design (TCAD) simulations, which are computationally intensive and time-consuming to analyze. In an attempt to overcome this bottleneck, a proposal for a machine learning (ML)-based surrogate modeling framework is provided where the prediction of the critical TFET properties of transfer curves, Ion, Ioff, threshold voltage (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$V_{\text{th}}$</tex>), and subthreshold swing (SS) is endeavored with the help of the Random Forest Regression (RFR) model. The data collected based on TCAD simulations and literature reports was processed, trained, and tested on various ML models such as the Artificial Neural Networks (ANN), Support Vector Regression (SVR) and RFR. The calculated R2 of the proposed RFR model was better than ANN and SVR, with a much smaller MSE (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3. 2 1} \times \mathbf{1 0}^{-\mathbf{7}}$</tex>) and an RMSE (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1. 7 9} \times \mathbf{1 0}^{-\mathbf{4}}$</tex>), showing that the model is robust and exhibits better generalization within the conditions of the devices used. Also, the ranking of intrinsic feature importance offered by RFR has a better interpretation rate and complies well with the previous findings on TCAD. These findings place RFR as a sound and efficient substitute for TFET modelling, with lower computational expense and intact physical stability, and thus add another facet and a practical approach to future device design optimization, assisted by a novel and powerful ML

COVID-19 in Bangladesh: A Deeper Outlook into The Forecast with Prediction of Upcoming Per Day Cases Using Time Series.

A machine-learning-based model for predicting the effective stiffness of precast concrete columns

Improving the detection accuracy of the dual SERS aptasensor system with uncontrollable SERS “hot spot” using machine learning tools

The combination of surface enhanced Raman spectroscopy (SERS) with a handheld Raman system would lead to a powerful portable device for defense and security applications. The Thermo Scientific FirstDefender RM instrument is a 785-nm handheld Raman spectrometer intended for rapid field identification of unknown solid and liquid samples. Its sensitivity and effectiveness for SERS-based detection was initially confirmed by evaluating detection of 1,2-di(4- pyridyl)ethylene as a reporter molecule on a silver nanorod (AgNR) substrate, and the results are comparable to those from a confocal Bruker Raman system. As avian influenza A viruses (AIV) are recognized as an important emerging threat to public health, this portable handheld Raman spectrometer is used, for the first time, to detect and identify avian influenza A viruses using a multi-well AgNR SERS chip. The SERS spectra obtained had rich peaks which demonstrated that the instrument can be effectively used for SERS-based influenza virus detection. According to the SERS spectra, these different influenza viruses were distinguished from the negative control via the principal component analysis and by partial least squares-discriminate analysis. Together, these results show that the combination effective SERS substrates when combined with a portable Raman spectrometer provides a powerful field device for chemical and biological sensing.

Leaf area index (LAI) is one of the key biophysical indicators for characterizing the growth and status of vegetation and is also used in modeling earth system processes. Machine learning algorithms (MLAs) such as random forest regression (RFR), artificial neural network regression (ANNR) and support vector regression (SVR) based on satellite data have been widely used for the estimation of LAI. However, the selection of input variables has a great impact on the estimation performance of MLAs. In this study, we aimed to improve the LAI inversion model of Inner Mongolia grassland based on MLAs incorporating empirical knowledge. Firstly, we used the ANNR, SVR and RFR approaches, respectively, to rank the input variables including vegetation indices, climate factors, soil factors and topography factors and found that Normalized Difference Phenology Index (NDPI) contributed the most to LAI estimation. Secondly, we selected four sets of input variables, namely, all variables—A, model selected variables—B, overlapping variables—C and self-defined variables—D, respectively. Subsequently, we built twelve LAI estimation models (RFR-A, RFR-B, RFR-C, etc.) based on three MLAs and four sets of input variables. The evaluation of them showed the RFR produced higher prediction accuracy, followed by ANNR and SVR. Furthermore, the RFR-D presented the highest accuracy in predicting LAI (R2 = 0.55, RMSE = 0.37 m2/m2, MAE = 0.29 m2/m2). Finally, we compared our results with MODIS LAI and GEOV2 LAI products and found that all of them showed a similar spatial distribution of grassland LAI in the four sub-regions covering all grassland types, but our model exhibited larger LAI values in the desert steppe and smaller LAI values in the others. These findings demonstrated that MLAs incorporating empirical knowledge could improve the accuracy of modelling LAI and further study is necessary to reduce the uncertainty in LAI mapping in grassland.

Soil salinization affects crop production and food security. Mapping spatial distribution and severity of salinity is essential for agricultural management and development. This study was aimed to test the effectiveness of machine learning algorithms for soil salinity mapping taking the Mussaib area in Central Mesopotamia as an example. A combined dataset consisting of Landsat 5 Thematic Mapper (TM) and ALOS L‐band radar data acquired at the same time was used for fulfilling the task. Relevant biophysical indicators were derived from the TM images, and the soil component was retrieved by removing the vegetation contribution from the L‐band radar backscattering coefficients. Field‐measured salinity at the three corner plots of triangles were averaged to represent the salinity of these triangular areas. These averaged plots were converted into raster by either direct rasterization or buffering‐based rasterization into different cell size to create the training set (TS). One of the three triangle corners was randomly selected to constitute a validation set (VS). Using this TS, the support vector regression (SVR) and random forest regression (RFR) algorithms were then applied to the combined dataset for salinity prediction. Results revealed that RFR performed better than SVR with higher accuracy (93.4–94.2% vs. 85.2–89.4%) and less normalized root mean square error (NRMSE; 6.10–7.69% vs. 10.29–10.52%) when calibrated with both TS and VS. In comparison, prediction by multivariate linear regression (MLR) achieved in our previous study using the same datasets also showed less NRMSE than SVR. Hence, both RFR and MLR are recommended for soil salinity mapping.

The Moderate Resolution Imaging Spectroradiometer (MODIS) level-3 chlorophyll-a (Chl-a) product is one of the widely used ocean colour products that is often used for water quality monitoring of marine ecosystems. However, this product includes a large amount of missing data due to high surface reflectance and cloudy conditions that inevitably affect its suitability for spatiotemporal analysis of water quality. The objective of this study was to compare four Machine Learning (ML) techniques including K-nearest neighbour (KNN), Support Vector Regression (SVR), Random Forest Regression (RFR), and Artificial Neural Network (ANN) with well-known Data Interpolation Empirical Orthogonal Function (DINEOF) method for spatiotemporal missing imputation of MODIS Chl-a. The Southern Caspian Sea, which has a high Chl-a concentration, was selected as the case study. A cross-validation approach ranging missing data ratio from 0.1 to 0.8 was implemented to investigate the optimal parameters of the models and compare their performance for missing imputation. The results indicated that all ML models, except KNN, outperformed the DINEOF method for missing imputation of MODIS Chl-a. The SVR with the highest accuracy and the lowest variability of errors had the best performance among the five competing models, while the KNN showed the worst performance. The main reason for the better accuracy of the SVR than the other models is its structural risk minimization procedure that leads to the better generalization of the SVR model. The current results showed that the ML techniques used in the current study, the SVR in particular, are able to produce reliable imputations of the MODIS Chl-a missing data and can be a useful tool in water quality monitoring of marine ecosystems.

We have fabricated silver nanorod arrays by electrodepositing the nanorods evenly in the shallow pores of porous anodic aluminum oxide (AAO) templates. The diameter and length were 28 and 44 nm, respectively. The maxima of the transverse and longitudinal modes of the surface plasmon were near 417 and 511 nm, respectively. A good surface‐enhanced Raman scattering (SERS) spectrum was observed by excitation with the 514.5‐nm laser line. The SERS intensity increased almost linearly upon malachite green isothiocyanate adsorption on the tips of the silver nanorods as the concentration of the mother solutions increased. Our results show that silver nanorods fabricated on AAO templates could be used as an SERS substrate for quantitative analyses. Copyright © 2009 John Wiley &amp; Sons, Ltd.

Human intervention cannot halt natural disasters like earthquakes, but machine learning application expertise can be utilized to detect patterns in data and increase understanding and predictive power. Recent development of machine learning models has increasingly developed interest in forecasting and predicting the magnitude of earthquakes. In this work, Random Forest Regressor (RFR), Multi-Layer Perceptron Regressor(MLPR), and Support Vector Regression (SVR) models were employed to predict the magnitude of greater than 6 mb earthquakes that occurred in the year 2015 in the central Himalaya. We noticed RFR method had been able to predict the magnitude of the Gorkha earthquake (6.9mb), and the Kodariearthquake (6.7 mb) in comparison with the other two models. We also checked the performance of these models by three parameters Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) and noticed the better performance of RFR model. The findings illustrate that RFR is achieving better performance than the other two algorithms, as the predicted magnitudes are close to the actual magnitudes.

In this study, machine learning (ML) techniques are employed to predict used car prices. Several features are used to calculate the price of used cars, but in this paper, we find efficient ways to find the most precise car prices. Despite the fact that there are websites offering this service, they could not employ the most precise prediction system. It is also possible to predict a used car's true market value using a variety of models and techniques. It's important to understand their genuine market value before buying or selling. Both buyers and sellers will be benefitted from these accurate predictions. Support Vector regression, Random Forest regression, and CatBoost regression techniques are used in the proposed system. In the existing method [13], mean absolute error for decision tree regression was 0.6711, which was the least among other algorithms like Linear regression, Lasso regression, Ridge regression, Bayesian Ridge regression, and etc., they used. In the proposed system, mean absolute error (MAE) for Support Vector regression, CatBoost regression and Random Forest regression techniques are 0.1459, 0.1371 and 0.1284 respectively. The prices of second hand/used cars are predicted using the CatBoost regression, Support Vector regression, and Random Forest regression techniques. The accuracy of these algorithms are 86.28%, 85.40% and 87.16%. Among these three algorithms, Random Forest regression gives the least MAE of 0.1317 and the highest accuracy of 87.16%.

This study explores reliability engineering in e-commerce systems by analyzing critical user journey stages – browsing, cart, and checkout – using advanced machine learning models. The research uses random forest regression (RFR) and support vector regression (SVR) to assess and predict system performance, stability, and potential failure points. Data was obtained from simulated e-commerce transactions that included key parameters such as response time, server load, transaction success rate, and error frequency. The models were evaluated using statistical indicators including coefficient of determination (R²), mean square error (MSE), and mean absolute error (MAE) to measure predictive accuracy. The results indicated that both models achieved high reliability and accuracy, with SVR showing slightly higher generalization ability. The findings highlight that predictive modeling can effectively identify performance bottlenecks, reduce downtime, and improve the overall robustness of e-commerce operations. By integrating reliability engineering principles with machine learning techniques, this study contributes to improving user experience, enhancing system resilience, and ensuring scalable and stable online transaction environments. This hybrid analytics approach provides a foundation for developing proactive monitoring strategies in modern e-commerce infrastructures. Keywords: Reliability Engineering, E-Commerce, Random Forest Regression, Support Vector Regression, Performance Optimization Objective: Improve the reliability and efficiency of the browsing, cart, and checkout processes, ensuring a seamless and consistent e-commerce user experience

This work evaluates machine learning (ML) and deep learning (DL) models for Photovoltaic (PV) power output prediction based on two real-world datasets. Eight models – Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Linear Regression (LR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Decision Tree (DT), one Dimensional Convolutional Neural Network (1DCNN) and Artificial Neural Network (ANN) – were evaluated using Root Mean Squared Error (RMSE), R-squared (R²), Mean Absolute Error (MAE), and Mean Squared Error (MSE). For Dataset1 (region of Cacak) 1DCNN achieved the optimal performance, with the reduced errors and the largest R² values at three spatial locations, and it was closely followed by RFR and GBR. Likewise, in Dataset2 (region of Kraljevo), 1DCNN, ANN, and RFR give the best results. Conventional models, such as LR and DT, performed poorly in both data tribes. The results highlight the ability of state-of-the-art ensemble and DL models in learning nonlinear patterns in solar energy data and thus, the importance of choosing the right prediction tools to provide accurate and reliable PV power forecasts. &nbsp;