Raman Spectroscopyfor Nitrate Detection in Water:A Review of the Current State of Art

The presence of inorganic nitrogen species in water can be unsuitable for drinking and detrimental to the environment. In this study, a surface-enhanced Raman spectroscopy (SERS) method coupled with a commercially available gold nanosubstrate (a gold-coated silicon material) was evaluated for the detection of nitrate and nitrite in water and wastewater. Applications of SERS coupled with gold nanosubstrates resulted in an enhancement of Raman signals by a factor of ∼10(4) compared to that from Raman spectroscopy. The new method was able to detect nitrate with linear ranges of 1-10,000 mg NO3(-)/L (R(2)= 0.978) and 1-100 mg NO3(-)/L (R(2)= 0.919) for water and wastewater samples, respectively. Among the common anions, phosphate appeared to be the major interfering anion affecting nitrate measurement. Nevertheless, the percentage error of nitrate measurement in wastewater by the proposed SERS method was comparable to that by ion chromatography. The nitrate detection limits in water and wastewater samples were about 0.5 mg/L. The SERS method could simultaneously detect sulfate, which may serve as a reference standard in water. These results suggested that the SERS coupled with nanosubstrates is a promising method to determine nitrate concentrations in water and wastewater.

Nitrate is widely distributed in various water environments, and its potential toxicity poses a great threat to human health and environment. It is of great significance to realize rapid detection of nitrate in water. Raman spectroscopy, as a molecular spectrum, has been widely used in the detection of ionic concentration in liquid samples. However, fluorescence background interference and spectral peak overlap are still a challenge for the detection of trace targets in practical applications. In this study, we proposed a rapid detection method for nitrate at low concentration in drinking water based on Raman spectra combined with adsorption materials., The difference between characteristic spectrum of nitrate adsorption at low concentration and background spectrum of adsorption material was emphatically analyzed after verifying the effectiveness of nitrate adsorbent. The peak decomposition method of nitrate characteristic spectrum was established to achieve highly sensitive detection of nitrate concentration of 5-10 mg/L. The calibration curve of NO₃ - -N concentration in 5-100 mg/L was established according to the normalized spectral intensity. The correlation coefficient R² of the established regression model reached 0.98. The root mean square error (RMSE) was 3.56 mg/L. This study provides a rapid detection method for nitrate in water, which can provide a low-cost assessment method for daily household drinking water quality and water quality purification combined with portable Raman spectrometer. At the same time, this method is also expected to achieve fast on-site detection for early warning response of surface water pollution.

This dissertation focuses on the investigation and development of an optical biosensor based on fiber-enhanced Raman spectroscopy (FERS) that provides chemical selective and sensitive label-free detection of biomolecules. FERS has been achieved by using various types of liquid core optical fibers, which guide the light within the liquid sample and increase the interaction length with the analyte molecules. The first part of this dissertation explains the FERS technique in detail and describes the current state of research of FERS. Several essential characteristics, such as fiber length, attenuation, material and refractive index, are thoroughly discussed in considerations of Raman intensity enhancement. Liquid-core fibers formed with hollow-core photonic-crystal fibers (HC-PCFs) and polymer fibers are introduced and discussed, as they are the most important breakthroughs. The objective of this research is to develop a robust optical fiber platform based on Raman spectroscopy that shows potential for use in bio-analytical and clinical applications. In this work, I demonstrate a combination of UV-resonance Raman spectroscopy (UV-RRS) and liquid-core fibers, to increases the sensitivity for the detection of low-concentrated pharmaceuticals tremendously. This combined enhancement technique was applied for the detection of bile pigments for monitoring of diseases related to hyperbilirubinemia and hyperbiliverdinemia. Their poor optical quality strongly limits the performances of the polymer-based liquid-core fibers. Therefore, the implementation of HC-PCFs was explored in two different types of optical guiding. Waveguiding in the visible range is achieved for the first time in both kinds of liquid-filled HC-PCFs, and therefore the Raman scattering wavelengths are not anymore limited to the insensitive NIR range. In order to achieve easy-to-use and stable FERS devices for further development, the performance of HC-PCFs in the aspect of light-confinement was studied with the help of a specially designed multi-channel Raman chemical imaging. The optimal fiber length, spatial filtering, and optical coupling were thoroughly analyzed, and an automatic coupling system was developed. With the development of optical fibers, FERS shows increasing potential as a robust, fast, chemical selective and sensitive tool for the detection of biomolecules in clinical, pharmaceutical, and biological applications.

Train dispatching (TD) is at the forefront of all rail operations that transport passengers or goods. Recent technological advances and the explosion of digital data have introduced data-driven methods (DDMs) in rail operations. In this study, DDMs on the TD problem are briefly explored, focusing on relevant studies on delay distribution, delay propagation, and timetable rescheduling. Data-driven TD methods, including statistical methods (SM), graphical models (GM), and machine learning (ML) methods are reviewed. Then, key issues in establishing different data-driven models for the TD problem are addressed. Subsequently, ML methods are considered to be among the most promising DDMs that lead to innovative TD methods, relying on rich data obtained from train operations. This study emphasizes the potentials for designing new alternatives in the three key fields of interest and provides directions for further research on TD. Future research, including the ML-driven TD and intelligent TD, were discussed in this study.

Prediction of chronic kidney disease (CKD) has emerged as a useful technique for early detection of at-risk persons and the introduction of appropriate management strategies. Machine learning and data-driven methods have been used in predictive modeling to examine massive databases of patient demographics, medical histories, test findings, and genetic information. These cutting-edge methods allow for the profiling of high-risk patients and the tailoring of healthcare administration approaches. Patient outcomes, complication rates, and healthcare system efficiency may all benefit greatly from CKD screening and prediction. Responsible use of CKD prediction algorithms, however, requires resolving issues with data availability, integration, and ethics. The area of medicine has benefited greatly from the use of Machine Learning (ML) methods, which have played an increasingly central role in illness prediction. In this study, we use a strategy that makes use of ML methods to construct effective tools for predicting the development of CKD. Multiple ML models are trained, and their results are compared using a variety of criteria. We applied five ML methods such as logistic regression (LR), Decision tree (DT), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). The LR and KNN have the highest accuracy with 99%.

Application of serum SERS technology based on thermally annealed silver nanoparticle composite substrate in breast cancer.

<div class="section abstract"><div class="htmlview paragraph">This paper discusses an emerging area of applying machine learning (ML) methods to augment traditional Computational Fluid Dynamics (CFD) simulations of road vehicle aerodynamics. ML methods have the potential to both reduce the computational effort to predict a new geometry or car condition and to explore a greater number of design parameters with the same computational budget. Similar to traditional CFD methods, there exists a broad range of approaches. In particular, the accuracy and computational efficiency of a CFD simulation vary greatly depending on the choice of turbulence model (DNS, LES, RANS) and the underlying spatial and temporal numerical discretizations. Similarly, the end-user must select the correct ML method depending on the use-case, the available input data, and the trade-off between accuracy and computational cost. In this paper, we showcase several case studies using various data-driven ML methods to highlight the promise of these approaches. Whilst these case studies are not comprehensive investigations of the underlying methods and do not include all possible ML approaches (i.e., physics-driven), they highlight the ability of these models to in general predict new designs in near real-time (i.e., less than 5 seconds), after typically less than 1 hour of training on a single GPU. There still exists a need for high quality training data from traditional CFD methods and high-fidelity CFD simulations to validate the ML predictions. Thus, ML approaches should be seen as tools to augment traditional CFD methods rather than to replace them. While this work focuses on preliminary studies, future work will look at more comprehensive real-world/industrial-size calculations for the more promising technologies identified here.</div></div>

Simple SummaryRadiogenomics enables prediction of the status and prognosis of patients using non-invasively obtained imaging data. Current machine learning (ML) methods used in radiogenomics require huge datasets, which involve the handling of large heterogeneous datasets from multiple cohorts/hospitals. In this study, two different glioma datasets were used to test various ML and image pre-processing methods to confirm whether the models trained on one dataset are universally applicable to other datasets. Our result suggested that the ML method that yielded the highest accuracy in a single dataset was likely to be overfitted. We demonstrated that implementation of standardization and dimension reduction procedures prior to classification, enabled the development of ML methods that are less affected by the multiple cohort difference. We advocate using caution in interpreting the results of radiogenomic studies of the training and testing datasets that are small or mixed, with a view to implementing practical ML methods in radiogenomics.Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results: we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohorts.

ABSTRACTThe accuracy of detection of nitrate in water for quality monitoring is a significant yet challenging task. To address this, the present work proposes an ensemble machine learning–based chemometric framework for the optical detection of nitrate in water. It incorporates an absorbance‐based reagent‐less detection of nitrate in water to support the robustness of the model. The absorption spectra were recorded using a portable set‐up in the presence and absence of interfering ions. Different interfering ions, namely, nitrite (NO2−), calcium (Ca2+), magnesium (Mg2+), carbonate (CO32−), bromide (Br−), chloride (Cl−) and phosphate (PO43−), in all possible combinations (binary, ternary, quaternary, quinary, senary and septenary mixtures) are added to target analyte to validate the real‐time application of the proposed algorithm. Under the multiview framework, two models, MVNPM‐I and MVNPM‐II, i.e., multiview nitrate prediction models, are proposed. MVNPM‐I is based on an ensemble of regressors' results, and MVNPM‐II uses multiple views of the dataset followed by an ensemble of their results. The performance of the models is assessed using a hold‐out validation scheme with 10 repetitions and measured using R2 score and mean squared error (MSE). The best results of R2 score 0.9978 with a standard deviation 0.0014 and MSE of 1.1799 with a standard deviation of 0.8639 are obtained using the MVNPM‐II model. Further, the performance measures of the proposed models show that they can handle the presence of interfering ions. The algorithm was also tested using real‐world samples with an R2 score and MSE of 0.9998 and 0.696, respectively. The promising results strengthen the applicability of the proposed method in real‐world scenarios.

Maize lodging is a prevalent stress that can significantly diminish corn yield and quality. Unmanned aerial vehicles (UAVs) remote sensing is a practical means to quickly obtain lodging information at field scale, such as area, severity, and distribution. However, existing studies primarily use machine learning (ML) methods to qualitatively analyze maize lodging (lodging and non-lodging) or estimate the maize lodging percentage, while there is less research using deep learning (DL) to quantitatively estimate maize lodging parameters (type, severity, and direction). This study aims to introduce advanced DL algorithms into the maize lodging classification task using UAV-multispectral images and investigate the advantages of DL compared with traditional ML methods. This study collected a UAV-multispectral dataset containing non-lodging maize and lodging maize with different lodging types, severities, and directions. Additionally, 22 vegetation indices (VIs) were extracted from multispectral data, followed by spatial aggregation and image cropping. Five ML classifiers and three DL models were trained to classify the maize lodging parameters. Finally, we compared the performance of ML and DL models in evaluating maize lodging parameters. The results indicate that the Random Forest (RF) model outperforms the other four ML algorithms, achieving an overall accuracy (OA) of 89.29% and a Kappa coefficient of 0.8852. However, the maize lodging classification performance of DL models is significantly better than that of ML methods. Specifically, Swin-T performs better than ResNet-50 and ConvNeXt-T, with an OA reaching 96.02% and a Kappa coefficient of 0.9574. This can be attributed to the fact that Swin-T can more effectively extract detailed information that accurately characterizes maize lodging traits from UAV-multispectral data. This study demonstrates that combining DL with UAV-multispectral data enables a more comprehensive understanding of maize lodging type, severity, and direction, which is essential for post-disaster rescue operations and agricultural insurance claims.

Context- and Physiology-aware Machine Learning for Upper-Limb Myocontrol

Purpose: This research examines and contributes to the behavioural lit­erature on voluntary tax compliance. It focuses on the use and potential of machine-learning (ML) methods and models to predict individual tax morale across Europe, and it identifies the factors that influence predic­tive accuracy.Design/Methodology/Approach: Using data from the fifth wave (2017– 2020) of the European Values Survey (EVS), a data-driven, systematic approach employing six ML methods is applied to predict individual tax morale across Europe. The importance of formal, informal and socio-de­mographic factors is assessed, and the study tests whether incorporating the Corruption Perception Index (CPI) improves predictive accuracy.Findings: The results indicate that ML methods and models can enhance understanding and prediction of individual tax morale in Europe. Among the deployed models, artificial neural networks (ANNs) achieved the highest accuracy. Accuracy increased across all ML methods when the CPI was included. Attitudes towards bribery, perceptions of immigrants’ im­pact on the national welfare system, and gender emerged as significant formal, informal and socio-demographic factors.Academic contribution to the field: The study offers a novel application of data-driven ML methods to the prediction of individual tax morale. Given the scarcity of empirical ML research in the social sciences, the find­ings provide valuable insights in a European context and may serve as a basis for further global research.Practical Implications: The conclusions are particularly relevant for gov­ernments and tax administrations seeking to improve tax compliance and revenue collection. In the European context, the results confirm the virtu­ous circle linking effective government performance, high tax morale and voluntary tax compliance—insights that are crucial for decision-makers, regulators, European institutions and tax-policy makers.Originality/Value: The findings confirm that, when ML methods are ap­plied, individual tax morale can be viewed as an outcome of interactions between formal and informal institutions. They also show that predictive accuracy is higher in countries with lower corruption, as indicated by a higher CPI.

Reservoir identification and production prediction are two of the most important tasks in petroleum exploration and development. Machine learning (ML) methods are used for petroleum-related studies, but have not been applied to reservoir identification and production prediction based on reservoir identification. Production forecasting studies are typically based on overall reservoir thickness and lack accuracy when reservoirs contain a water or dry layer without oil production. In this paper, a systematic ML method was developed using classification models for reservoir identification, and regression models for production prediction. The production models are based on the reservoir identification results. To realize the reservoir identification, seven optimized ML methods were used: four typical single ML methods and three ensemble ML methods. These methods classify the reservoir into five types of layers: water, dry and three levels of oil (Ⅰ oil layer, Ⅱ oil layer, Ⅲ oil layer). The validation and test results of these seven optimized ML methods suggest the three ensemble methods perform better than the four single ML methods in reservoir identification. The XGBoost produced the model with the highest accuracy; up to 99%. The effective thickness of Ⅰ and Ⅱ oil layers determined during the reservoir identification was fed into the models for predicting production. Effective thickness considers the distribution of the water and the oil resulting in a more reasonable production prediction compared to predictions based on the overall reservoir thickness. To validate the superiority of the ML methods, reference models using overall reservoir thickness were built for comparison. The models based on effective thickness outperformed the reference models in every evaluation metric. The prediction accuracy of the ML models using effective thickness were 10% higher than that of reference model. Without the personal error or data distortion existing in traditional methods, this novel system realizes rapid analysis of data while reducing the time required to resolve reservoir classification and production prediction challenges. The ML models using the effective thickness obtained from reservoir identification were more accurate when predicting oil production compared to previous studies which use overall reservoir thickness.

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then, the field has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildfire science and management. Our overall objective is to improve awareness of ML methods among wildfire researchers and managers, as well as illustrate the diverse and challenging range of problems in wildfire science available to ML data scientists. To that end, we first present an overview of popular ML approaches used in wildfire science to date and then review the use of ML in wildfire science as broadly categorized into six problem domains, including (i) fuels characterization, fire detection, and mapping; (ii) fire weather and climate change; (iii) fire occurrence, susceptibility, and risk; (iv) fire behavior prediction; (v) fire effects; and (vi) fire management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildfires within a data science context. In total, to the end of 2019, we identified 300 relevant publications in which the most frequently used ML methods across problem domains included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent-based learning — in the wildfire sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods such as deep learning requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildfire research and management communities play an active role in providing relevant, high-quality, and freely available wildfire data for use by practitioners of ML methods.

Fiber enhanced Raman gas spectroscopy