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The SERS spectra of six bacterial biomarkers, 2,3-DHBA, 2,5-DHBA, Pyocyanin, lipoteichoic acid (LTA), Enterobactin, and β-carotene, of various concentrations, were obtained from silver nanorod array substrates, and the spectral peaks and the corresponding vibrational modes were identified to classify different spectra. The spectral variations in three different concentration regions due to various reasons have imposed a challenge to use classic calibration curve methods to quantify the concentration of biomarkers. Depending on baseline removal strategy, i.e., local or global baseline removal, the calibration curve differed significantly. With the aid of convolutional neural network (CNN), a two-step process was established to classify and quantify biomarker solutions based on SERS spectra: using a specific CNN model, a remarkable differentiation and classification accuracy of 99.99 % for all six biomarkers regardless of the concentration can be achieved. After classification, six regression CNN models were established to predict the concentration of biomarkers, with coefficient of determination R 2  > 0.97 and mean absolute error (MAE) < 0.27. The feature of important calculations indicates the high classification and quantification accuracies were due to the intrinsic spectral features in SERS spectra. This study showcases the synergistic potential of SERS and advanced machine learning algorithms and holds significant promise for bacterial infection diagnostics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Early prognostication of patient outcomes in intracerebral hemorrhage (ICH) is critical for patient care. We aim to investigate protein biomarkers' role in prognosticating outcomes in ICH patients. We assessed 22 protein biomarkers using targeted proteomics in serum samples obtained from the ICH patient dataset (N = 150). We defined poor outcomes as modified Rankin scale score of 3-6. We incorporated clinical variables and protein biomarkers in regression models and random forest-based machine learning algorithms to predict poor outcomes and mortality. We report Odds Ratio (OR) or Hazard Ratio (HR) with 95% Confidence Interval (CI). We used five-fold cross-validation and bootstrapping for internal validation of prediction models. We included 149 patients for 90-day and 144 patients with ICH for 180-day outcome analyses. In multivariable logistic regression, UCH-L1 (adjusted OR 9.23; 95%CI 2.41-35.33), alpha-2-macroglobulin (aOR 5.57; 95%CI 1.26-24.59), and Serpin-A11 (aOR 9.33; 95%CI 1.09-79.94) were independent predictors of 90-day poor outcome; MMP-2 (aOR 6.32; 95%CI 1.82-21.90) was independent predictor of 180-day poor outcome. In multivariable Cox regression models, IGFBP-3 (aHR 2.08; 95%CI 1.24-3.48) predicted 90-day and MMP-9 (aOR 1.98; 95%CI 1.19-3.32) predicted 180-day mortality. Machine learning identified additional predictors, including haptoglobin for poor outcomes and UCH-L1, APO-C1, and MMP-2 for mortality prediction. Overall, random forest models outperformed regression models for predicting 180-day poor outcomes (AUC 0.89), and 90-day (AUC 0.81) and 180-day mortality (AUC 0.81). Serum biomarkers independently predicted short-term poor outcomes and mortality after ICH. Further research utilizing a multi-omics platform and temporal profiling is needed to explore additional biomarkers and refine predictive models for ICH prognosis., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Misra et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Machine learning (ML) is able to extract patterns and develop algorithms to construct data-driven models. We use ML models to gain insight into the relative importance of variables to predict obstructive coronary artery disease (CAD) using the Coronary Computed Tomographic Angiography for Selective Cardiac Catheterization (CONSERVE) study, as well as to compare prediction of obstructive CAD to the CAD consortium clinical score (CAD2). We further perform ML analysis to gain insight into the role of imaging and clinical variables for revascularization., Methods: For prediction of obstructive CAD, the entire ICA arm of the study, comprising 719 patients was used. For revascularization, 1,028 patients were randomized to invasive coronary angiography (ICA) or coronary computed tomographic angiography (CCTA). Data was randomly split into 80% training 20% test sets for building and validation. Models used extreme gradient boosting (XGBoost)., Results: Mean age was 60.6 ± 11.5 years and 64.3% were female. For the prediction of obstructive CAD, the AUC was significantly higher for ML at 0.779 (95% CI: 0.672-0.886) than for CAD2 (0.696 [95% CI: 0.594-0.798]) (P = 0.01). BMI, age, and angina severity were the most important variables. For revascularization, the model obtained an overall area under the receiver-operation curve (AUC) of 0.958 (95% CI = 0.933-0.983). Performance did not differ whether the imaging parameters used were from ICA (AUC 0.947, 95% CI = 0.903-0.990) or CCTA (AUC 0.941, 95% CI = 0.895-0.988) (P = 0.90). The ML model obtained sensitivity and specificity of 89.2% and 92.9%, respectively. Number of vessels with ≥70% stenosis, maximum segment stenosis severity (SSS) and body mass index (BMI) were the most important variables. Exclusion of imaging variables resulted in performance deterioration, with an AUC of 0.705 (95% CI 0.614-0.795) (P <0.0001)., Conclusions: For obstructive CAD, the ML model outperformed CAD2. BMI is an important variable, although currently not included in most scores. In this ML model, imaging variables were most associated with revascularization. Imaging modality did not influence model performance. Removal of imaging variables reduced model performance., Competing Interests: The authors have read the journal's policy and the authors of this paper have the following competing interests: James K. Min is founder, paid employee, and has equity interest in Cleerly Health. However, he was an employee of the Dalio Institute of Cardiovascular Imaging at the time of the study. James K. Min also serves on the scientific advisory board of Arineta and GE Healthcare. Leslee Shaw has an equity interest in Cleerly Health. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2018. For permissions, please email: journals.permissions@oup.com.)

Introduction: Machine learning (ML) is a field in computer science that demonstrated to effectively integrate clinical and imaging data for the creation of prognostic scores. The current study investigated whether a ML score, incorporating only the 16 segment coronary tree information derived from coronary computed tomography angiography (CCTA), provides enhanced risk stratification compared with current CCTA based risk scores., Methods: From the multi-center CONFIRM registry, patients were included with complete CCTA risk score information and ≥3 year follow-up for myocardial infarction and death (primary endpoint). Patients with prior coronary artery disease were excluded. Conventional CCTA risk scores (conventional CCTA approach, segment involvement score, duke prognostic index, segment stenosis score, and the Leaman risk score) and a score created using ML were compared for the area under the receiver operating characteristic curve (AUC). Only 16 segment based coronary stenosis (0%, 1-24%, 25-49%, 50-69%, 70-99% and 100%) and composition (calcified, mixed and non-calcified plaque) were provided to the ML model. A boosted ensemble algorithm (extreme gradient boosting; XGBoost) was used and the entire data was randomly split into a training set (80%) and testing set (20%). First, tuned hyperparameters were used to generate a trained model from the training data set (80% of data). Second, the performance of this trained model was independently tested on the unseen test set (20% of data)., Results: In total, 8844 patients (mean age 58.0 ± 11.5 years, 57.7% male) were included. During a mean follow-up time of 4.6 ± 1.5 years, 609 events occurred (6.9%). No CAD was observed in 48.7% (3.5% event), non-obstructive CAD in 31.8% (6.8% event), and obstructive CAD in 19.5% (15.6% event). Discrimination of events as expressed by AUC was significantly better for the ML based approach (0.771) vs the other scores (ranging from 0.685 to 0.701), P < 0.001. Net reclassification improvement analysis showed that the improved risk stratification was the result of down-classification of risk among patients that did not experience events (non-events)., Conclusion: A risk score created by a ML based algorithm, that utilizes standard 16 coronary segment stenosis and composition information derived from detailed CCTA reading, has greater prognostic accuracy than current CCTA integrated risk scores. These findings indicate that a ML based algorithm can improve the integration of CCTA derived plaque information to improve risk stratification., (Published by Elsevier Inc.)

Arsenic (As) contamination in groundwater represents a major global health threat, potentially impacting billions of individuals. Elevated As concentrations are found in river floodplains across south and southeast Asia, as well as in the inland basins of China, despite varying sedimentological and hydrogeochemical conditions. The specific mechanisms responsible for these high As levels remain poorly understood, complicating efforts to predict and manage the contamination. Applying hydro-chemical, geological, and soil parameters as explanatory variables, this study employs multiple linear regression (MLIR) and random forest regression (RFR) models to estimate groundwater As concentrations in these regions. Additionally, random forest classification (RFC) and multivariate logistic regression (MLOR) models are applied to predict the probability of As levels exceeding 10 μg/L in the Hetao Basin (China) and Bangladesh. Model validation reveals that RFR explains 80% and 70% of spatial variability of As concentration in the Hetao Basin and Bangladesh, respectively, outperforming MLIR, which accounts for only 35% and 32%. Similarly, RFC outperforms MLOR in predicting high As probability, achieving correct classification rates of 98.70% (Hetao Basin) and 98.25% (Bangladesh) on training datasets, and 82.76% (Hetao Basin) and 91.20% (Bangladesh) on validation datasets. The performance of the MLOR model on the validation set yields accuracy rates of 81.60% and 72.18%, respectively. In the Hetao Basin, Ca2+, redox potential (Eh), Fe, pH, SO42−, and Cl− are key predictors of As contamination, while in Bangladesh, soil organic carbon (SOC), pH, and SO42− are significant predictors. This study underscores the potential of random forest (RF) models as robust tools for predicting groundwater As contamination. [ABSTRACT FROM AUTHOR]

In this study, we employ explainable machine learning (ML) models to predict daily streamflow ( Q flow ) by leveraging hydro-meteorological parameters. The predictive matrix incorporates crucial factors such as daily rainfall, temperature, relative humidity, solar radiation, wind speed, and the one-day lag value of Q flow . Notably, among these parameters, the one-day lag value of Q flow , along with rainfall, solar radiation, temperature, and relative humidity emerge as highly influential predictors. We apply various ML models, including bagging ensemble learning, boosting ensemble learning, Gaussian process regression (GPR), and automated machine learning (Auto ML). Following a rigorous evaluation, the bagging ensemble learning model stands out as the most effective with a correlation coefficient (R = 0.80) and root-mean-square error (RMSE = 218). Further, we compare the Q flow predicted using ML models with a process-based hydrological model (SWAT) that was executed using a similar set of climatic variables as the input parameters. In our case, the predictive strength of the ML model (R = 0.80; RMSE = 218) to estimate ( Q flow ) is greater than the SWAT (R = 0.82; RMSE = 281). In conclusion, by emphasizing the importance of explainable ML models and highlighting the significance of specific hydro-meteorological parameters, our study contributes to advancing the field of hydrology and water resource management. [ABSTRACT FROM AUTHOR]

We present the semi-analytical light curve modelling of 13 supernovae associated with gamma-ray bursts (GRB-SNe) along with two relativistic broad-lined (Ic-BL) SNe without GRB association (SNe 2009bb and 2012ap), considering millisecond magnetars as central-engine-based power sources for these events. The bolometric light curves of all 15 SNe in our sample are well-regenerated utilizing a χ2-minimization code, MINIM , and numerous parameters are constrained. The median values of ejecta mass (M ej), magnetar's initial spin period (P i), and magnetic field (B) for GRB-SNe are determined to be ≈5.2 M⊙, 20.5 ms, and 20.1 × 1014 G, respectively. We leverage machine learning (ML) algorithms to comprehensively compare the three-dimensional parameter space encompassing M ej, P i, and B for GRB-SNe determined herein to those of H-deficient superluminous SNe (SLSNe-I), fast blue optical transients (FBOTs), long GRBs (LGRBs), and short GRBs (SGRBs) obtained from the literature. The application of unsupervized ML clustering algorithms on the parameters M ej, P i, and B for GRB-SNe, SLSNe-I, and FBOTs yields a classification accuracy of ∼95 per cent. Extending these methods to classify GRB-SNe, SLSNe-I, LGRBs, and SGRBs based on P i and B values results in an accuracy of ∼84 per cent. Our investigations show that GRB-SNe and relativistic Ic-BL SNe presented in this study occupy different parameter spaces for M ej, P i, and B than those of SLSNe-I, FBOTs, LGRBs, and SGRBs. This indicates that magnetars with different P i and B can give birth to distinct types of transients. [ABSTRACT FROM AUTHOR]

Lesion analyses are critical for drawing insights about stroke injury and recovery, and their importance is underscored by growing efforts to collect and combine stroke neuroimaging data across research sites. However, while there are numerous processing pipelines for neuroimaging data in general, few can be smoothly applied to stroke data due to complications analyzing the lesioned region. As researchers often use their own tools or manual methods for stroke MRI analysis, this could lead to greater errors and difficulty replicating findings over time and across sites. Rigorous analysis protocols and quality control pipelines are thus urgently needed for stroke neuroimaging. To this end, we created the Pipeline for Analyzing Lesions after Stroke (PALS; DOI: https://doi.org/10.5281/zenodo.1266980), a scalable and user-friendly toolbox to facilitate and ensure quality in stroke research specifically using T1-weighted MRIs. The PALS toolbox offers four modules integrated into a single pipeline, including (1) reorientation to radiological convention, (2) lesion correction for healthy white matter voxels, (3) lesion load calculation, and (4) visual quality control. In the present paper, we discuss each module and provide validation and example cases of our toolbox using multi-site data. Importantly, we also show that lesion correction with PALS significantly improves similarity between manual lesion segmentations by different tracers (z = 3.43, p = 0.0018). PALS can be found online at https://github.com/npnl/PALS. Future work will expand the PALS capabilities to include multimodal stroke imaging. We hope PALS will be a useful tool for the stroke neuroimaging community and foster new clinical insights.

Due to the rapid increase in urbanization and anthropogenic activity in the lake catchment, nutrients like phosphorus and nitrogen are abundant in urban sewage, leading to lake eutrophication and promoting algal blooms. Chlorophyll‐a (Chl‐a) is an important indicator of algal blooms; thus, predicting concentration is vital for lake restoration and management. Literature reveals that most of the previous studies of Chl‐a prediction required multiple variables and considerable computation. Thus, it is crucial to scientifically identify the key drivers of Chl‐a in Taihu Lake for the formulation of effective environmental management measures. In this study, a long‐term dataset (1991–2015) and Daniel trend test method were employed to analyse the trend of meteorological and water quality indicators in Taihu Lake in China. The redundancy of variables was eliminated by using the rank correlation method, combined with prior knowledge that provides a solid foundation for the analysis of the driving factors of Chl‐a. Four machine learning models, named Extreme Gradient Boosting (XGBoost), random forest (RF), support vector regression (SVR) and gradient boosting decision tree (GBDT), were employed to predict the concentration of Chl‐a in different regions of Taihu Lake. The result shows that the XGBoost model outperformed the other three models in terms of R2 (>0.90) and root mean square error (RMSE) (3.69–8.40). Finally, based on the predictions of the concentration of Chl‐a using the XGBoost model, the SHapley Additive exPlanation (SHAP) framework was utilized to analyse the key factors influencing Chl‐a concentration. Finding of this study suggests that the XGBoost model performed best and could be applied to the overall stretches of Taihu Lake and other lakes in China; model prediction will be helpful in providing a zoning decision‐making approach for the rational protection of the lake environment and the prevention of algal bloom hazards. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research study was to understand the simultaneous competitive and social gains of machine learning (ML) and artificial intelligence (AI) usage in organizations. There was a knowledge hiatus regarding the contribution of the deployment of ML and AI technologies and their effects on organizations and society. Design/methodology/approach: This study was grounded on the dynamic capabilities (DC) and ML and AI automation-augmentation paradox literature. This research study examined these theoretical perspectives using the response of 239 Indian organizational chief technology officers (CTOs). Partial least square-structural equation modeling (PLS-SEM) path modeling was applied for data analysis. Findings: The results indicated that ML and AI technologies organizational usage positively influenced DC initiatives. The findings depicted that DC fully mediated ML and AI-based technologies' effects on firm performance and social performance. Research limitations/implications: This study contributed to theoretical discourse regarding the tension between organizational and social outcomes of ML and AI technologies. The study extended the role of DC as a vital strategy in achieving social benefits from ML and AI use. Furthermore, the theoretical tension of the automation-augmentation paradox was explored. Practical implications: Organizations deploying ML and AI technologies could apply this study's insights to comprehend the organizational routines to pursue simultaneous competitive benefits and social gains. Furthermore, chief technology executives of organizations could devise how ML and AI technologies usage from a DC perspective could help settle the tension of the automation-augmentation paradox. Social implications: Increased ML and AI technologies usage in organizations enhanced DC. They could lead to positive social benefits such as new job creation, increased compensation to skilled employees and greater gender participation in employment. These insights could be derived based on this research study. Originality/value: This study was among the first few empirical investigations to provide theoretical and practical insights regarding the organizational and societal benefits of ML and AI usage in organizations because of their DC. This study was also one of the first empirical investigations that addressed the automation-augmentation paradox at the enterprise level. [ABSTRACT FROM AUTHOR]

Soil Organic Carbon (SOC) is a crucial indicator of ecosystem health and soil quality. Machine learning (ML) models that predict soil quality based on environmental parameters are becoming more prevalent. However, studies have yet to examine how well each ML technique performs when predicting and mapping SOC, particularly at high spatial resolutions. Model predictors include topographic variables generated from SRTM DEM; vegetation and soil indices derived from Landsat satellite images predict SOC for the Lakhimpur district of the upper Brahmaputra Valley of Assam, India. Four ML models, Random Forest (RF), Cubist, Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM), were utilized to predict SOC for the top layer of soil (0–15 cm) at a 30 m resolution. The results showed that the descriptive statistics of the calibration and validation sets were close enough to the total set data and calibration dataset, representing the complete samples. The measured SOC content varied from 0.10 to 1.85%. The RF model's performance was optimal in the calibration and validation sets (R2c = 0.966, RMSEc = 0.159%, R2v = 0.418, RMSEv = 0.377%). The SVM model, on the other hand, had the next-lowest accuracy, explaining 47% of the variation (R2c = 0.471, RMSEc = 0.293, R2v = 0.081, RMSEv = 0.452), while the Cubist model fared the poorest in both the calibration and validation sets. The most-critical variable in the RF model for predicting SOC was elevation, followed by MAT and MRVBF. The essential variables for the Cubist model were slope, TRI, MAT, and Band4. AP and LS were the most-essential factors in the XGBoost and SVM models. The predicted OC ranged from 0.44 to 1.35%, 0.031 to 1.61%, 0.035 to 1.71%, and 0.47 to 1.36% in the RF, Cubist, XGBoost, and SVM models, respectively. Compared with different ML models, RF was optimal (high accuracy and low uncertainty) for predicting SOC in the investigated region. According to the present modeling results, SOC may be determined simply and accurately. In general, the high-resolution maps might be helpful for decision-makers, stakeholders, and applicants in sericultural management practices towards precision sericulture. [ABSTRACT FROM AUTHOR]

The article presents the discussion on science news briefs. Topics include Kullu–Rohtang Pass lies in the heart of Himachal Pradesh, in the rugged terrain near the PirPanjal Mountain range; and accumulating excess carbon and ammonium on the one hand and reducing dissolved oxygen on the other counter the environmental harm.

Electric vehicles (EVs) contribute to reducing fossil fuel dependence and environmental pollution problems. However, due to complex charging behaviors and the high demand for charging, EVs have imposed significant burdens on power systems. By providing reliable forecasts of electric vehicle charging loads to power systems, these issues can be addressed efficiently to dispatch energy. Machine learning techniques have been demonstrated to be effective in forecasting loads. This research applies six machine learning methods to predict the charging demand for EVs: RNN, LSTM, Bi-LSTM, GRU, CNN, and transformers. A dataset containing five years of charging events collected from 25 public charging stations in Boulder, Colorado, USA, is used to validate this approach. Compared to other highly applied machine learning models, the transformer method outperforms others in predicting charging demand, demonstrating its ability for time series forecasting problems. [ABSTRACT FROM AUTHOR]

Electric vehicles have been gaining attention as a cleaner means of transportation that is low-carbon and environmentally friendly and can reduce greenhouse gas emissions and air pollution. Despite EVs' many advantages, widespread adoption will negatively affect the electric grid due to their random and volatile nature. Consequently, predicting the charging demand for electric vehicles is becoming a priority to maintain a steady supply of electric energy. Time series methodologies are applied to predict the charging demand: traditional and deep learning. RNN, LSTM, and transformers represent deep learning approaches, while ARIMA and SARIMA are traditional techniques. This research represents one of the first attempts to use the Transformer model for predicting EV charging demand. Predictions for 3-time steps are considered: 7 days, 30 days, and 90 days to address both short-term and long-term forecasting of EV charging load. RMSE and MAE were used to compare the model's performance. According to the results, the Transformer outperforms the other mentioned models in terms of short-term and long-term predictions, demonstrating its ability to address time series problems, especially EV charging predictions. The proposed Transformers framework and the obtained results can be used to manage electricity grids efficiently and smoothly. [ABSTRACT FROM AUTHOR]

The emergence of algal biorefineries has garnered considerable attention to researchers owing to their potential to ensure carbon neutrality via mitigation of atmospheric greenhouse gases. Algae-derived biofuels, characterized by their carbon-neutral nature, stand poised to play a pivotal role in advancing sustainable development initiatives aimed at enhancing environmental and societal well-being. In this context, algae-based wastewater treatment systems are greatly appreciated for their efficacy in nutrient removal and simultaneous bioenergy generation. These systems leverage the growth of algae species on wastewater nutrients—including carbon, nitrogen, and phosphorus—alongside carbon dioxide, thus facilitating a multifaceted approach to pollution remediation. This review seeks to delve into the realization of carbon neutrality through algae-mediated wastewater treatment approaches. Through a comprehensive analysis, this review scrutinizes the trajectory of algae-based wastewater treatment via bibliometric analysis. It subsequently examines the case studies and empirical insights pertaining to algae cultivation, treatment performance analysis, cost and life cycle analyses, and the implementation of optimization methodologies rooted in artificial intelligence and machine learning algorithms for algae-based wastewater treatment systems. By synthesizing these diverse perspectives, this study aims to offer valuable insights for the development of future engineering applications predicated on an in-depth understanding of carbon neutrality within the framework of circular economy paradigms. • Utilization of algae in wastewater treatment helps in acheiving carbon neutrality. • Research trend of algal wastewater treatment is analyzed via bibliometric analysis. • Chlorella vulgaris is the most investigated species in wastewater treatment system. • Binary culture approach has shown significant benefits over conventional systems. • AI and ML integration in algal system has potential benefits in carbon neutrality. [ABSTRACT FROM AUTHOR]

Scanning Probe Microscopy (SPM) based techniques probe material properties over microscale regions with nanoscale resolution, ultimately resulting in investigation of mesoscale functionalities. Among SPM techniques, piezoresponse force microscopy (PFM) is a highly effective tool in exploring polarization switching in ferroelectric materials. However, its signal is also sensitive to sample‐dependent electrostatic and chemo‐electromechanical changes. Literature reports have often concentrated on the evaluation of the Off‐field piezoresponse, compared to On‐field piezoresponse, based on the latter's increased sensitivity to non‐ferroelectric contributions. Using machine learning approaches incorporating both Off‐ and On‐field piezoresponse response as well as Off‐field resonance frequency to maximize information, switching piezoresponse in a defect‐rich Pb(Zr,Ti)O3 thin film is investigated. As expected, one major contributor to the piezoresponse is mostly ferroelectric, coupled with electrostatic phenomena during On‐field measurements. A second component is electrostatic in nature, while a third component is likely due to a superposition of multiple non‐ferroelectric processes. The proposed approach will enable deeper understanding of switching phenomena in weakly ferroelectric samples and materials with large chemo‐electromechanical response. [ABSTRACT FROM AUTHOR]

The future of AI in health care could include tasks that range from simple to complex. Everything from answering the phone to case history review, population health trending and analytics, therapeutic medicine, analyzing radiology images, predicting clinical diagnoses and treatment roadmaps, and even talking to patients. The future of computer science in health care presents: ? A healthcare-oriented overview of AI (AI), tongue processing (NLP), and machine learning (ML) ? Current and future applications in health care and also the impact on patients, clinicians, and therefore the pharmaceutical industry? A observe how the longer term of AI in health care might unfold as these technologies impact the practice of medication and health care over the subsequent decade. [ABSTRACT FROM AUTHOR]

Valeriana jatamansi Jones is an aromatic herb well known for its essential oil contents, and its high medicinal and commercial values. The amount of essential oils present in it increases with maturity (age) of the plant. In this study, Hyperspectral remote sensing data recorded in the field using Analytical Spectral Devices (ASD) handheld spectroradiometer was used to discriminate the age (6, 12, 24 and 36 months) of V. jatamansi. Principal Component Analysis (PCA) was used for feature selection and 06 machine learning classifiers were used to classify the plant based on their ages, i.e., Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), Boosting Decision Tree (BDT), Decision Tree (DT) and k-Nearest Neighbourhood (kNN). For comparison, these classifiers were applied on full range of spectral reflectance data without feature selection and on feature-selected data using PCA. It was found that the accuracies of ANN, RF, BDT, SVM, DT and kNN were 91, 85, 57, 78, 35 and 42%, respectively for non-feature selected datasets. The accuracies of ANN and DT classifiers were, respectively, increased by 100% and 75% after applying PCA. The ANN classifiers resulted in 100% overall accuracy with a Kappa coefficient (K) of 1. The wavelength regions 860, 870 to 874, 876 to 885 nm in near-infrared (NIR), and 747 to 756 nm (red-edge) were identified as regions suitable for discriminate the age groups of V. jatamansi. The final trained model thus prepared was again validated on 60 plants (with different age group) grown in its natural habitat and the obtained accuracy was 88% (K = 0.84). Thus, the present study have provided a rapid technology for onsite identification of age of V. jatamansi in the field itself. The developed technology thus provides a scientific way for harvesting of this plant at its optimum age avoiding its wastage. The results of this study can also be applied to other endangered and valuable plants by way of finding its optimum growth stages for its harvesting. [ABSTRACT FROM AUTHOR]

In the past decade, advances in machine learning (ML) techniques have resulted in developing sophisticated models that are capable of modelling extremely complex multi-factorial problems like slope stability analysis. The literature review indicates that considerable works have been done in slope stability using ML, but none of them covers the analysis of residual soil slope. The present study aims to develop an artificial neural network (ANN) model that can be employed for evaluating the factor of safety of Shiwalik Slopes in the Himalayan Region. Data obtained from numerical analysis of a residual soil slope were used to develop two ANN models (ANN1 and ANN2 utilising eleven input parameters, and scaled-down number of parameters based on correlation coefficient, respectively). A four-layer, feed-forward back-propagation neural network having the optimum number of hidden neurons is developed based on trial-and-error method. The results derived from ANN models were compared with those achieved from numerical analysis. Additionally, several performance indices such as coefficient of determination (R2), root mean square error, variance account for, and residual error were employed to evaluate the predictive performance of the developed ANN models. Both the ANN models have shown good prediction performance; however, the overall performance of the ANN2 model is better than the ANN1 model. It is concluded that the ANN models are reliable, valid, and straightforward computational tools that can be employed for slope stability analysis during the preliminary stage of designing infrastructure projects in residual soil slope. [ABSTRACT FROM AUTHOR]

Current screening methods for prostate cancer (PCa) result in a large number of false positives making it difficult for clinicians to assess disease status, thus warranting advancements in screening and early detection methods. The goal of this study was to design a liquid biopsy test that uses flow cytometry–based immunophenotyping and artificial neural network (ANN) analysis to detect PCa. Numerous myeloid and lymphoid cell populations, including myeloid-derived suppressor cells, were measured from 156 patients with PCa, 123 with benign prostatic hyperplasia (BPH), and 99 male healthy donor (HD) controls. Using pattern recognition neural network (PRNN) analysis, a type of ANN, PCa detection compared against HD resulted in 96.6% sensitivity, 87.5% specificity, and an area under the curve (AUC) value of 0.97. Detecting patients with higher risk disease (⩾Gleason 7) against lower risk disease (BPH/Gleason 6) resulted in 92.0% sensitivity, 42.7% specificity, and an AUC of 0.72. This study suggests that analyzing flow cytometry immunophenotyping data with PRNNs may prove to be a useful tool to improve PCa detection and reduce the number of unnecessary prostate biopsies performed each year. [ABSTRACT FROM AUTHOR]

Globally, the increase in the climatic variability has led to adverse effects on the treeline species in the high-elevation mountain landscapes. Identifying the geographical space that supports the treeline species survival over time is essential for conservation biogeography. Increase in the global warming and snowmelt has made available the treeline species favourable niches in the higher elevations. Random Forest algorithm assuming non-parametric distribution was employed to predict the potential distribution of Betula utilis niche in the Hindu-Kush Himalayan (HKH) region. The potential distributions were simulated in the Last Inter-Glaciation (LIG), present (the year 1970–2000) and future (the year 2061–2080) environmental conditions. The actual distribution of the species in the current time was modelled and evaluated. The model sensitivity with reference to independent evaluation dataset for highly suitable B. utilis niche was 0.78. The model statistics of the current time was further applied to both the LIG and future (2061–2080) scenarios in order to get a fundamental niche of B. utilis. The treeline species, B. utilis was projected to become vulnerable to 21st century climate changes. The high suitability of B. utilis occurrence in the LIG, current and the future scenario were more likely in the elevation ranges 2601–2800 m, 3801–4000 m, and 4201–4400 m, respectively. The magnitude of advancement was relatively more along elevation and longitude, compared to the latitudinal gradient. The present study provides scientific evidence to conclude that the treeline species potential distribution in HKH is climate driven. [ABSTRACT FROM AUTHOR]

A photonic crystal fiber (PCF)-based biosensor is proposed with surface plasmon resonance (SPR) sensing approach. This sensor is used for malaria infected red blood cells (RBCs) detection as well as haemoglobin (Hb) concentration detection. A machine learning (ML) algorithm is also implemented and analyzed for the proposed sensor. For SPR mechanism, a Ti 3 C 2 T x thin film is coated over the gold coated PCF. The malaria stages in RBCs are detected by comparing the resonance wavelength of healthy and infected RBCs samples. Finite element method-based simulation is used for analyzing the sensing performance. The measured wavelength sensitivity for ring stage is 12,142 nm/RIU, for trophozoite stage is 9736 nm/RIU and for schizont stage is 8241 nm/RIU. In addition, the Hb concentration in RBCs is detected with maximum wavelength sensitivity of 53 nm/g/dl. Further, the resolution of 10−5 RIU is obtained for the proposed sensor. The low mean squared error of 0.01526 and less than 2% error has been obtained in sensitivity investigation with the ML technique. Due to the enhanced sensing performance and ML approach, the proposed sensor can be the best alternative to the existing malaria sensor. [ABSTRACT FROM AUTHOR]

This article presents a highly sensitive gold/Ti 3 C 2 T x coated photonic crystal fiber (PCF)-based surface plasmon resonance (SPR) sensor for cancer cells detection. The hybrid gold/Ti 3 C 2 T x layer is coated circularly over PCF for occurrence of SPR by coupled mode theory. The simulations and numerical analysis are done by the finite element method. The resonance wavelength shift is measured between normal and cancerous cell samples to estimate the wavelength sensitivity and resolution. In results, the maximum wavelength sensitivity for breast cancer MCF-7 cells is achieved as 10,714 nm/RIU in x-polarization mode and 13,071 nm/RIU in y-polarization mode with very low resolution of 10−6 RIU. The impact of the MXene (Ti 3 C 2 T x) thin film on sensitivity is also observed. In addition, the optimization of parametric variation is analyzed with machine learning technique with low mean squared error of 0.01525. <2% error is obtained in sensitivity analysis with multilayer perceptron regressor. Due to the advanced results and machine learning application, this sensor can be used as a fast, efficient and low-cost cancer disease detection device. [Display omitted] • A gold/Ti 3 C 2 T x coated PCF based SPR sensor is presented and numerically analyzed for detecting various cancer cells. • Vertical confinement path is used to transform the energy from core mode to SPR mode with external sensing approach. • The normal to cancerous cell is detected by measuring the corresponding resonance wavelength shift. • The Multilayer Perceptron regressor has been employed on ANN model for predicting the output of PCF-SPR sensor. [ABSTRACT FROM AUTHOR]

Domain Adaptation (DA) has been a well-known transfer learning algorithm used in Neural Machine Translation (NMT) task. Adding domain-related corpora in training data to train the model boosts the performance of NMT. Many low resource language pairs such as Hindi–Nepali and Spanish–Portuguese, for which sufficient parallel data are not available to train the model, face in-domain data scarcity issue. Non-availability of in-domain training data leads to the use of unrelated corpus in training the model, which degrades the NMT performance. This domain mismatch between training and test data results in a domain shift problem. To tackle the challenge of the domain shift problem in NMT, we propose REINFORCE-based Sentence Selection and Weighting (RSSW) method, which selects pseudo in-domain sentences from out-of-domain data and learns their weights based on Reinforcement Learning. The proposed method leverages the similarity between language pairs by encoding the source and target languages into a common encoding script for language model training. RSSW uses minimum risk training and maximum likelihood estimation as an objective function to train NMT on selected pseudo in-domain sentences. Furthermore, we employ multi-domain and fine-tuning approaches to compare proposed method on Hindi ↔ Nepali and Hindi ↔ Marathi language pairs through extensive experimental analysis. From the experimental results, we find that the proposed method outperforms the state-of-the-art and baseline approaches by ∼ 2 BLEU points in all translation directions of language pairs. • REINFORCE-based sentence selection and weighting method for domain adaptation. • Handling domain shift problem in neural machine translation. • Different languages encoded into a common script for language model training. • Maximum likelihood estimation and minimum risk training function are used. • Proposed method outperforms the existing state-of-the-art approach by ∼ 2 BLEU points. [ABSTRACT FROM AUTHOR]