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• Machine Learning to classify watermarked Italian paper. • XRF and FTIR spectroscopies to study late middle-aged Italian watermarked paper. • A new spatial-temporal approach to trace the local recipes and the geographical paper mills' production. • Bergamo and Bologna stand out as marked watermarked Italian paper mills. Manuscripts, illuminated codex, books, documents and letters are composite materials, traces of the past starting from the invention of the writing. In this context, dating is one of the most important information for document attribution, and watermarked papeItaly'smarkers for studying their time-spatial distribution. In the Late Middle Ages, Italy's most important centre of paper mill was located in the town of Fabriano (Marche region, Italy). Here, a selection of ten Italian Late Middle Ages watermarked papers belonging to the Corpus Chartarum Italicarum (Corpus of Italian papers) is characterised by elemental and molecular spectroscopies and collected data are analysed by Machine Learning (ML) to trace the local fabrication recipes and the geographical paper mills production. Data from portable X-ray fluorescence and Fourier transform infrared spectroscopy were analysed through Singular Vector Machine, Soft Independent Modeling of Class Analogy and Moving Blocks methods. This innovative ML spatial-temporal approach based on keeping the temporal variable fixed is used to find elemental benchmarks for classifying the watermarked Italian notarial catalogue of the Late Middle Ages finding differences in the local recipes and studying the homogeneity in the paper mills' production. Results show that watermarked paper from Northern Italy, from the town of Strozza, Piacenza and Bergamo , as well as Bologna, present a high elemental and molecular homogeneity, which indicates that the hand-made processing technique could be the same helped by the proximity of the three cities, starting point for technologies exchange or influence. No heavy metals are found in the watermarked paper and K, Ca, Fe and Zn are identified as elemental benchmarks. Finally, Ca, Ti, Mn, Cr and Fe are particularly present on the edges of the watermarked papers. [Display omitted] [ABSTRACT FROM AUTHOR]

The development of an advanced analytical platform with regard to SARS-CoV-2 is crucial for public health. Herein, we present a machine learning platform based on paper-assisted ratiometric fluorescent sensors for highly sensitive detection of the SARS-CoV-2 RdRp gene. The assay involves target-induced rolling circle amplification to generate magnetic DNAzyme, which is then detectable using the paper-assisted ratiometric fluorescent sensor. This sensor detects the SARS-CoV-2 RdRp gene with a visible-fluorescence color response. Moreover, leveraging different fluorescence responses, the ResNet algorithm of machine learning assists in accurately identifying fluorescence images and differentiating the concentration of the SARS-CoV-2 RdRp gene with over 99% recognition accuracy. The machine learning platform exhibits exceptional sensitivity and color responsiveness, achieving a limit of detection of 30 fM for the SARS-CoV-2 RdRp gene. The integration of intelligent artificial vision with the paper-assisted ratiometric fluorescent sensor presents a novel approach for the on-site detection of COVID-19 and holds potential for broader use in disease diagnostics in the future. [ABSTRACT FROM AUTHOR]

Biohazards, which may occur at all supply chain stages, pose significant threats to food safety and public health. Addressing these concerns and enhancing food safety necessitates a nondestructive pathogen surveillance approach capable of continuously and simultaneously detecting multiple pathogens. Detecting and differentiating low concentrations of pathogenic bacteria amid high background microflora levels in foods is challenging, requiring technology with high sensitivity and robust discriminatory capability. This study introduces an artificial neural network-driven paper chromogenic array sensor (ANN-PCA) technique developed for the nondestructive, continuous, and simultaneous detection of Salmonella Enteritidis (SE) and Escherichia coli O157:H7 (Ec) from a high background microflora in shredded cheddar cheese. This method enables accurate detection of SE and Ec in monoculture and cocktail culture while distinguishing them from a high level of background microflora (∼7.5 log CFU/g), with accuracies ranging from 72 ± 11% to 92 ± 3%. In addition, SE and Ec were successfully identified at concentrations as low as 1 log CFU/g within one day, with an accuracy of 72 ± 11%. This approach exhibits promising potential for integration into a digitalized, smart, and resilient nondestructive surveillance system for real-time pathogen detection in foods throughout the supply chain without enrichment, incubation, or other sample preparation steps. [Display omitted] • Development of a machine learning-driven paper chromogenic array sensor approach. • Non-destructive, continuous, and simultaneous detection of multiple pathogens. • Detection of pathogenic bacteria from a high level of background microflora. • The approach provided potential detection for pathogens throughout the food supply chain. [ABSTRACT FROM AUTHOR]

Present report demonstrates the application of Sb 0.3 Sn 0.7 Se 2 single crystal as a paper based flexible photodetector. Direct vapour transport grown bulk crystals of Sb 0.3 Sn 0.7 Se 2 has been converted to nanosheets by chemical assisted exfoliation process. The paper-based photodetector is fabricated and the switching action is studied. Tuning of photodetector has been carried out by 670 nm laser illumination for different bias voltages. Temporal photo-response is also studied under polychromatic light with different intensities in vacuum and open environment. The low temperature stability of photodetector has been studied for temperature range 300 K–180 K. Experimental results are obtained in terms of time resolved photocurrent under different illumination and atmospheric conditions. The flexibility and stability are also examined in detail for fabricated detector. Overall, the results suggest the application of Sb 0.3 Sn 0.7 Se 2 as a versatile flexible photodetector. Additionally, the machine learning (ML) model is trained and tested using an experimental photocurrent dataset that has a complex material design with variations in time, bias voltage, intensity, and temperature. The k-nearest neighbor algorithm exhibited outstanding performance, achieving the highest R2 value of 0.9986 when applied to a temperature dataset, with a test size of 0.4. Performance metrics such as mean absolute error and root mean squared error of various test sizes ranging from 0.4 to 0.6 are used to assess the model's accuracy and robustness in changing conditions. This comprehensive analysis not only establishes a platform for future experimental optimization of photodetector materials but also underscores the efficacy of ML regression techniques in developing high-performance photodetectors. [Display omitted] • Chemical assisted exfoliation process were used for the synthesis of SbSnSe2 nanosheets. • Synthesized nanosheets were decorated on paper for the fabrication of flexible photo-detector. • The photodetection application was demonstrated for different atmospheric conditions and different illuminations. • Low temperature stability of the detector was also studied. • KNN Machine learning model were applied to the experimental data for the prediction of photodetection behavior. [ABSTRACT FROM AUTHOR]

• Five Machine Learning (ML) models compared for wave height time series prediction. • Complex ML models do not outperform simple AR in wave height time series prediction. • Comment to related papers: signal decomposition in test set series is WRONG. Significant Wave Height (SWH) is crucial in many aspect of ocean engineering. The accurate prediction of SWH has therefore been of immense practical value. Recently, Artificial Intelligence (AI) time series prediction methods have been widely used for single-point short-term SWH time-series forecasting, resulting in many AI-based models claiming to achieve good results. However, the extent to which these complex AI models can outperform traditional methods has largely been overlooked. This study compared five different models - AutoRegressive (AR), eXtreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and WaveNet - for their performance on SWH time series prediction at 16 buoy locations. Surprisingly, the results suggest that the differences of performance among different models are negligible, indicating that all these AI models have only "learned" the linear auto-regression from the data. Additionally, we noticed that many recent studies used signal decomposition method for such time series prediction, and most of them decomposed the test sets, which is WRONG. [ABSTRACT FROM AUTHOR]

• Qualitative and quantitative detection of amine gases could be achieved by CSA. • A visible detection of beef freshness using the amine-responsive CSA was proposed. • ResNet34 had the best performance for beef freshness detection based on CSA. • T-SNE could further visualize and understand the classification process of DL. This study developed an innovative approach that combines a colourimetric sensor array (CSA) composed of twelve pH-response dyes with advanced algorithms, aiming to detect amine gases and assess the freshness of chilled beef. With the assistance of multivariate statistical analysis, the sensor array can effectively distinguish five amine gases and enable rapid quantification of trimethylamine vapour with a limit of detection (LOD) of 8.02 ppb and visually monitor the fresh levels of chilled beef. Moreover, the utilization of deep learning models (ResNet34, VGG16, and GoogleNet) for chilled beef freshness evaluation achieved an overall accuracy of 98.0 %. Furthermore, t -distributed stochastic neighbour embedding (t -SNE) visualized the feature extraction process and provided explanations to understand the classification process of deep learning. The results demonstrated that applying deep learning techniques in the process of pattern recognition of CSA can help in realizing the rapid, robust, and accurate assessment of chilled beef freshness. [ABSTRACT FROM AUTHOR]

Global food systems can benefit significantly from continuous monitoring of microbial food safety, a task for which tedious operations, destructive sampling, and the inability to monitor multiple pathogens remain challenging. This study reports significant improvements to a paper chromogenic array sensor - machine learning (PCA-ML) methodology sensing concentrations of volatile organic compounds (VOCs) emitted on a species-specific basis by pathogens by streamlining dye selection, sensor fabrication, database construction, and machine learning and validation. This approach enables noncontact, time-dependent, simultaneous monitoring of multiple pathogens (Listeria monocytogenes , Salmonella , and E. coli O157:H7) at levels as low as 1 log CFU/g with over 90% accuracy. The report provides theoretical and practical frameworks demonstrating that chromogenic response, including limits of detection, depends on time integrals of VOC concentrations. The paper also discusses the potential for implementing PCA-ML in the food supply chain for different food matrices and pathogens, with species- and strain-specific identification. [ABSTRACT FROM AUTHOR]

• A machine learning approach was used to investigate moisture penetration in paper materials. • The study evaluated the capabilities of both the Feedforward Neural Network (FNN) and the Recurrent Neural Network (RNN). • Numerically generated data was employed for network training. • A continuum model incorporating anisotropic properties, creep behavior, viscoelasticity, and moisture dependency was used for simulations. • The RNN demonstrated superior predictive capability compared to the FNN. In this work, we predicted the gradient of the deformational moisture dynamics in a sized commercial paper by observing the curl deformation in response to the one-sided water application. The deformational moisture is a part of the applied liquid which ends up in the fibers causing swelling and subsequent mechanical response of the entire fiber network structure. The adapted approach combines traditional experimental procedures, advanced machine learning techniques and continuum modeling to provide insights into the complex phenomenon relevant to ink-jet digital printing in which the sized and coated paper is often used, meaning that not all the applied moisture will reach the fibers. Key material properties including elasticity, plastic parameters, viscoelasticity, creep, moisture dependent behavior, along with hygroexpansion coefficients are identified through extensive testing, providing vital data for subsequent simulation using a continuum model. Two machine learning models, a Feedforward Neural Network (FNN) and a Recurrent Neural Network (RNN), are probed in this study. Both models are trained using exclusively numerically generated moisture profile histories, showcasing the value of such data in contexts where experimental data acquisition is challenging. These two models are subsequently utilized to predict moisture profile history based on curl experimental measurements, with the RNN demonstrating superior accuracy due to its ability to account for temporal dependencies. The predicted moisture profiles are used as inputs for the continuum model to simulate the associated curl response comparing it to the experiment representing "never seen" data. The result of comparison shows highly predictive capability of the RNN. This study melds traditional experimental methods and innovative machine learning techniques, providing a robust technique for predicting moisture gradient dynamics that can be used for both optimizing the ink solution and paper structure to achieve desirable printing quality with lowest curl propensities during printing. [ABSTRACT FROM AUTHOR]

Efficient control loop performance is pivotal in process industries to ensure optimal production, maintain product quality, and adhere to regulatory standards. Poorly tuned controllers can disrupt these objectives, necessitating accurate detection methods. This paper introduces a novel approach for detecting poor controller tuning through advanced techniques: the Gramian Angular Field (GAF) and Stack Auto-Encoder (SAE). Unlike manual methods, this automated system promptly identifies poorly tuned controllers, offering real-time monitoring and timely alerts to operators. The proposed methodology is substantiated through two case studies: the ISDB dataset and the pulp and paper dataset. The outcomes illustrate that the proposed approach correctly determines the appropriate outcome for the majority of the analyzed control loops across diverse industries. • New method detects poorly tuned controllers via Gramian angular field and SAE. • PV and OP images help SAE distinguish poor tuning from other oscillation causes. • Transfer learning has been used to improve methodology's effectiveness. • Tested on benchmark control loops, yielding accurate verdicts for most cases. [ABSTRACT FROM AUTHOR]

[Display omitted] • 3D-ACM involves two rounds of Hilbert transform and tensor product operations. • It significantly enhances the equivalent frequency points and sample numbers. • The R-squared values for PLS-LR, KNN, RF and CNN models approach or equal 1. • These supervised models are comparable to unsupervised models such as PCA-LR. We have developed a novel 3D asynchronous correlation method (3D-ACM) designed for the classification and identification of Chinese handmade paper samples using Raman spectra and machine learning. The 3D-ACM approach involves two rounds of tensor product and Hilbert transform operations. In the tensor product process, the outer product of the spectral data from different samples within the same category is computed, establishing inner connections among all samples within that category. The Hilbert transform introduces a 90-degree phase shift, resulting in a true three-dimensional spectral data structure. This expansion significantly increases the number of equivalent frequency points and samples within each category. This enhancement substantially boosts spectral resolution and reveals more hidden information within the spectral data. To maximize the potential of 3D-ACM, we employed six machine learning models: principal component analysis (PCA) with linear regression (LR), support vector machine (SVM) with LR, k-Nearest Neighbors (KNN), random forest (RF), and convolutional neural network (CNN). When applied to the 3D-ACM data preprocessing method, R-squared values of PLS-LR, KNN, RF and CNN supervised models, approached or equaled 1. This indicates exceptional performance comparable to unsupervised models like PCA. 3D-ACM stands as a versatile mathematical technique not confined to spectral data. It also eliminates the necessity for additional experimental setups or external control conditions, distinct from traditional two-dimensional correlation spectroscopy. Moreover, it preserves the original experimental data, setting it apart from conventional data preprocessing methods. This positions 3D-ACM as a promising tool for future material classification and identification in conjunction with machine learning. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel analytical technique for colorimetric glucose determination on µPADs. • Non-invasive sweat glucose detection independent of sample volume. • Seamless operation: no extra gadgets nor smartphone camera is required. • Computationally inexpensive readout on portable Arm platforms demonstrated. The colorimetric method is widely exploited for glucose measurement on microfluidic paper-based and microfluidic thread/paper-based analytical devices, μPADs and μTPADs, respectively. However, two significant challenges still hinder the real-world applications: the variation of the generated color based on sample volume variation, and the consistent dependency on an imaging camera - usually a smartphone camera - for color readout. The latter also suffers from multiple limitations, such as the variable ambient light conditions and imaging variation among different smartphone brands and models. This manuscript suggests a new device demonstrating a novel viewpoint to the colorimetric method to address both aforementioned challenges. The presented device, Volume-Independent Autonomous box (VIA box), continuously monitors the transient color of the µPAD upon the introduction of the sample till color stabilization, in contrast to similar studies where merely the final stabilized color is read. The interpretation of the transient color profile with respect to time results in the measurement of the glucose level independent of sample volume. In addition, the VIA box monitors µPAD's color using an ordinary RGB sensor instead of an imaging camera in similar studies. Since merely RGB values are recorded in any measurement instance, the computational cost is extremely low compared to the relevant literature where image processing techniques are used. VIA box exploits a simple low-end microcontroller to continuously monitor the transient color, interpret the color profile, and show the glucose level. The samples used in this manuscript are made of artificial sweat samples, including the known glucose range in real sweat samples. [ABSTRACT FROM AUTHOR]

Antibiotic residues accumulation in the environment endangers ecosystems and human health. There is an urgent need for a facile and efficient strategy to detect antibiotics. Here, we report a photoluminescent sensor array based on protein-stabilized gold nanoclusters (AuNCs) for the detection of two families of antibiotics, tetracyclines and quinolones. The nanoclusters were synthesized with bovine serum albumin (BSA) and ovalbumin (OVA), respectively. They had different interactions with seven kinds of antibiotics and exhibited diverse photoluminescence (PL) responses, which were analyzed by linear discriminant analysis and ExtraTrees algorithms. The sensor array performed well in both classification and quantification of seven antibiotics. And the quantitative results of all antibiotics obtained R2 of no less than 0.99 at 0–100 μM when using suitable regression models. Additionally, the sensor array was able to distinguish antibiotic mixtures and multiple interfering substances, and it also kept 100% classification accuracy in river water samples. Moreover, test paper assisted by a smartphone was applied for quick detection of antibiotics, with good performance in both HEPES buffer and river water. These studies reveal great potential for the point-of-use analysis of antibiotics in environmental monitoring. [Display omitted] • The first nanoclusters-based photoluminescent sensor array for quinolones is developed. • Seven antibiotics of tetracyclines and quinolones are successfully identified. • ExtraTrees algorithm is first employed for antibiotics detection. • Test paper assisted by a smartphone is used to identify antibiotics in river water. [ABSTRACT FROM AUTHOR]

This study demonstrates how a colorimetric biosensor based on microfluidic paper can swiftly diagnose a disease and predict its prognosis to triage patients effectively. This was the first biosensor to quantify the gold standard cardiac troponin (cTnI) and lipid biomarkers, including high-density lipoprotein (HDL) and low-density lipoprotein (LDL) simultaneously. Prior research encountered obstacles or limitations, such as measuring a single biomarker or total cholesterol, which cannot distinguish between LDL and HDL. CatBoost, an advanced machine learning (ML) technique used for diagnosis that combines the predictive power of ML algorithms obtained an impressive area under the receiver operating curves (AUROC) of 0.97 ± 0.018 for all possible classification thresholds. CatBoost is a brand-new ensemble framework based on the interaction of multiple health parameters that can generate AUROC values of 0.897 ± 0.047 for the accurate prognosis of recurrent acute myocardial infarction (AMI), demonstrating remarkably accurate AMI diagnosis and prognosis. In addition, this paper-based analytical device (µPAD) biosensor employs an electrophoretic method to overcome the challenges posed by non-specific adsorption. This is accomplished by isolating non-specific biomolecules based on differences in their isoelectric points and removing non-specifically adsorbing colorimetric markers. The limits of detection (LoD) for cTnI in AMI were lower than their respective clinical cutoff values. This study also demonstrated that the proposed ML framework produced significantly better results than conventional statistical analysis. High correlation filtering and (t-SNE) dimensionality reduction were utilized for a limited number of data points. The respectable accuracy and AUROC of this method were also validated using cross-validation. [Display omitted] • First ML-integrated colorimetric biosensing for accurate AMI diagnosis and prognosis. • Cardiac and lipid biomarkers simultaneous detection improves diagnostic reliability. • The electrophoretic method omits the washing steps to allow rapid diagnosis. • The new ML framework significantly enhances outcomes compared to statistical analysis. [ABSTRACT FROM AUTHOR]

• Explore qualitative aspects of citations to measure the influence of a research article. • Apply a feature representation technique in combination with machine learning models to find the sentiment of citation. • Determine the sentiment of citation instances into positive, negative, or neutral. • Analyze the efficacy of SMOTE in balancing the citation sentiment dataset. Ascertaining the impact of research is significant for the research community and academia of all disciplines. The only prevalent measure associated with the quantification of research quality is the citation-count. Although a number of citations play a significant role in academic research, sometimes citations can be biased or made to discuss only the weaknesses and shortcomings of the research. By considering the sentiment of citations and recognizing patterns in text can aid in understanding the opinion of the peer research community and will also help in quantifying the quality of research articles. Efficient feature representation combined with machine learning classifiers has yielded significant improvement in text classification. However, the effectiveness of such combinations has not been analyzed for citation sentiment analysis. This study aims to investigate pattern recognition using machine learning models in combination with frequency-based and prediction-based feature representation techniques with and without using Synthetic Minority Oversampling Technique (SMOTE) on publicly available citation sentiment dataset. Sentiment of citation instances are classified into positive, negative or neutral. Results indicate that the Extra tree classifier in combination with Term Frequency-Inverse Document Frequency achieved 98.26% accuracy on the SMOTE-balanced dataset. [ABSTRACT FROM AUTHOR]

[Display omitted] • The 2-D correlation methods do not require external perturbation variables. • They are pure mathematical methods that utilize the tensor product of spectral data. • The R2 values of KNN and RF for TDACM are close to 1, indicating nearly 100% improvement. The paper introduces two new methods, namely the cross correlation method (CCM) and two-dimensional correlation method (TDCM), for preprocessing Raman spectroscopy data for analyzing Chinese handmade paper samples. CCM expands the spectral dimension from 1 × N to 1 × 2 N - 1 by taking cross-correlation between two spectral data of the same category. TDCM includes two-dimensional synchronous correlation method (TDSCM) and two-dimensional asynchronous correlation method (TDACM), which expand the spectral dimension from 1 × N to N × N by taking tensor products between two spectral data and between one spectral data and the Hilbert transformation of the other spectral data of the same category, respectively. The experimental data were preprocessed using baseline removal, CCM, TDSCM, and TDACM methods. Four machine learning models were employed to evaluate the effects of these methods: principal component analysis (PCA) combined with linear regression (LR), support vector machine (SVM) combined with LR, k-Nearest Neighbors (KNN), and random forest (RF). The results show that the R-squared values for the PCA model were nearly 1 for all types of data, indicating high accuracy. However, for SVM-LR, KNN, and RF models, the R-squared values were sorted in the order of raw data, baseline removal data, CCM, TDSCM, and TDACM preprocessed data. The R-squared values of KNN and RF machine learning models for TDACM preprocessed data were approaching 1, indicating that the accuracy of machine learning was significantly improved by nearly 100%. This has led to a remarkable improvement in the accuracy of supervised models such as KNN and RF, bringing them closer to the level of unsupervised models such as PCA. [ABSTRACT FROM AUTHOR]

A label-free detection method for noninvasive biofluids enables rapid on-site disease screening and early-stage cancer diagnosis by analyzing metabolic alterations. Herein, we develop three-dimensional plasmonic hexaplex nanostructures coated on a paper substrate (3D-PHP). This flexible and highly absorptive 3D-PHP sensor is integrated with commercial saliva collection tube to create an efficient on-site sensing platform for lung cancer screening via surface-enhanced Raman scattering (SERS) measurement of human saliva. The multispike hexaplex-shaped gold nanostructure enhances contact with saliva viscosity, enabling effective sampling and SERS enhancement. Through testing patient salivary samples, the 3D-PHP sensor demonstrates successful lung cancer detection and diagnosis. A logistic regression-based machine learning model successfully classifies benign and malignant patients, exhibiting high clinical sensitivity and specificity. Additionally, important Raman peak positions related to different lung cancer stages are investigated, suggesting insights for early-stage cancer diagnosis. Integrating 3D-PHP senor with the conventional saliva collection tube platform is expected to offer promising practicality for rapid on-site disease screening and diagnosis, and significant advancements in cancer detection and patient care. [Display omitted] [ABSTRACT FROM AUTHOR]

• 252 chemicals were identified by SNTS in groundwater from four regions with diverse contamination histories. • A novel and robust systematic machine learning-based workflow for predicting chemical indicators was proposed. • The proposed workflow showed good predictive ability (Q2) of 0.897. • 51 chemical indicators for tracking groundwater contamination sources were suggested. In this study, the strong analytical power of gas chromatography coupled to a high resolution mass spectrometry (GC-HRMS) in suspect and non-target screening (SNTS) of organic micropollutants was combined with machine learning tools for proposing a novel and robust systematic environmental forensics workflow, focusing on groundwater contamination. Groundwater samples were collected from four different regions with diverse contamination histories (namely oil [OC], agricultural [AGR], industrial [IND], and landfill [LF]), and a total of 252 organic micropollutants were identified, including pharmaceuticals, personal care products, pesticides, polycyclic aromatic hydrocarbons, plasticizers, phenols, organophosphate flame retardants, transformation products, and others, with detection frequencies ranging from 3 % to 100 %. Amongst the SNTS identified compounds, a total of 51 chemical indicators (i.e., OC: 13, LF: 12, AGR: 19, IND: 7) which included level 1 and 2 SNTS identified chemicals were pinpointed across all sampling regions by integrating a bootstrapped feature selection method involving the bootfs algorithm and a partial least squares discriminant analysis (PLS-DA) model to determine potential prevalent contamination sources. The proposed workflow showed good predictive ability (Q2) of 0.897, and the suggested contamination sources were gasoline, diesel, and/or other light petroleum products for the OC region, anthropogenic activities for the LF region, agricultural and human activities for the AGR region, and industrial/human activities for the IND region. These results suggest that the proposed workflow can select a subset of the most diagnostic features in the chemical space that can best distinguish a specific contamination source class. [Display omitted] [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Hyper spectral imaging was used to get many spectra in a short time. • The identification of eight paper types was done by using machine learning. • Even papers with very similar spectra could be distinguished with high accuracy. • For 68,400 spectra per sample, the accuracy was 87.8%. Abstract Paper is non-uniform; thus, it is difficult to ascertain whether the slight differences in near-infrared absorption spectra are caused by the difference in paper type or the paper's non-uniformity. We used near-infrared hyperspectral imaging to measure the spectra of many different points quickly for eight different paper samples. Next, we analysed the data using machine learning and constructed a model to ascertain whether the differences in the spectra are due to the difference in paper type or the paper's non-uniformity. Even papers with very similar spectral shapes were distinguished with high accuracy with this method. As the number of spectra used to construct the classification model increased, the accuracy of the classification model increased. With 6840 spectra per sample, the accuracy exceeded 85%, and with 68,400 spectra per sample, the accuracy for identification was 87.8%. [ABSTRACT FROM AUTHOR]

Stationary hydrogen energy-based power plants generating electricity to supply high-powered plug-in electric vehicles (PEVs) have recently become popular in renewable energy-based power plants. Besides, in a PEV charging station, various types of powered charge devices can be established such as DC fast chargers or 3.7 kW, 7.4 kW, 11 kW, and 22 kW AC chargers. This paper introduces a demand-side management-oriented optimal charging strategy that includes two stages for PEVs in a hydrogen energy-based microgrid. The paper focuses on two stages to execute an optimal charging of PEVs in compliance with their users' requests and satisfaction and considering the power system loading. It is assumed that there are three types of chargers in the PEV charging station and the users. In the first stage randomly created requests are classified by an ensemble learning classifier method that performs higher performance classification by combining the results from multiple classifiers in a machine learning classification. The second stage schedules the PEVs according to the classification results and users' requests. To test the proposed system, first random requests are created then they are sent to the classifier, and the results of classifiers are scheduled in each other. The demand-side management-oriented charge scheduling and managing strategy which includes the proposed two stages has been compared with non-managed cases. Case study results reveal that the proposed approach provides 52.1% peak load reduction and 72.3% valley filling improvement by the SOS algorithm. The results highlight the advantages of the proposed system in terms of peak reduction and valley filling. [Display omitted] • A new intelligent decision-maker method is proposed for DSM in a microgrid. • Proposed method is developed for a stationary hydrogen energy-based power plant. • The optimal DSM of EVs is investigated with a hybrid ML method. • RUSBoost and SOS Algorithms-based Hybrid Intelligent Decision Maker is developed. • It provides 52.1% peak load reduction and 72.3% valley filling improvement. [ABSTRACT FROM AUTHOR]

Sleep stages can provide valuable insights into an individual's sleep quality. By leveraging movement and heart rate data collected by modern smartwatches, it is possible to enable the sleep staging feature and enhance users' understanding about their sleep and health conditions. In this paper, we present and validate a recurrent neural network based model with 23 input features extracted from accelerometer and photoplethysmography sensors data for both healthy and sleep apnea populations. We designed a lightweight and fast solution to enable the prediction of sleep stages for each 30-s epoch. This solution was developed using a large dataset of 1522 night recordings collected from a highly heterogeneous population and different versions of Samsung smartwatch. In the classification of four sleep stages (wake, light, deep, and rapid eye movements sleep), the proposed solution achieved 71.6 % of balanced accuracy and a Cohen's kappa of 0.56 in a test set with 586 recordings. The results presented in this paper validate our proposal as a competitive wearable solution for sleep staging. Additionally, the use of a large and diverse data set contributes to the robustness of our solution, and corroborates the validation of algorithm's performance. Some additional analysis performed for healthy and sleep apnea population demonstrated that algorithm's performance has low correlation with demographic variables. • Proposal and evaluation of a smartwatch-based solution for sleep staging. • Experimental evaluation on a large and diverse dataset of 1522 sleep recordings. • Solution based on neural networks trained on accelerometer and PPG sensor data. • 71.6 % of balanced accuracy for classification of 4 sleep stages compared with PSG. • Low correlation of performance with demographic variables and sleep apnea disorder. [ABSTRACT FROM AUTHOR]

General physicians misclassify depression in more than half of the cases. Researchers have explored the feasibility of leveraging passively collected data points, also called digital biomarkers, to provide more granular understanding of depression phenotypes as well as a more objective assessment of disease. This paper provides a systematic review following the PRISMA guidelines (Page et al., 2021) to understand which digital biomarkers might be relevant for passive screening of depression. Pubmed and PsycInfo were systematically searched for studies published from 2019 to early 2024, resulting in 161 records assessed for eligibility. Excluded were intervention studies, studies focusing on a different disease or those with a lack of passive data collection. 74 studies remained for a quality assessment, after which 27 studies were included. The review shows that depressed participants' real-life behavior such as reduced communication with others can be tracked by passive data. Machine learning models for the classification of depression have shown accuracies up to 0.98, surpassing the quality of many standardized assessment methods. Inconsistency of outcome reporting of current studies does not allow for drawing statistical conclusions regarding effectiveness of individual included features. The Covid-19 pandemic might have impacted the ongoing studies between 2020 and 2022. While digital biomarkers allow real-life tracking of participant's behavior and symptoms, further work is required to align the feature engineering of digital biomarkers. With shown high accuracies of assessments, connecting digital biomarkers with clinical practice can be a promising method of detecting symptoms of depression automatically. • This paper provides a PRISMA-guided systematic review of studies (2019-2024), showing model accuracy ranges of 0.52-0.98. • The review summarises constructs related to mood, location, smartphone use, communication, sleep quality and heart rate. • To be integrated into clinical practice, captured data needs to be reduced to the most relevant features. [ABSTRACT FROM AUTHOR]

It is critical to have an accurate prediction of surface roughness (Sa) in order to improve grinding productivity, reduce costs, and minimize the period of time required for trials and testing. Although many prediction methods have been developed, fewer studies have been conducted on the prediction of surface roughness in longitudinal ultrasonic vibration-assisted grinding (LUVAG). In this paper, a surface roughness prediction model algorithm based on ensemble learning of support vector machines (ELSVM) is proposed that can be used for surface roughness prediction of LUVAG alumina ceramics. This paper first details the development of the ELSVM surface roughness prediction model, which consists of four modules: the prepossessing module, the multi-algorithm regression module, the support vector machine algorithm (SVM) module, and the ensemble module. In addition, ELSVM was compared with four other machine learning methods based on experimental results for surface roughness prediction modeling. The error of ELSVM model was reduced by 6.3%, 7.9%, 8.9%, and 7.5%, respectively, compared to the individual prediction models such as I-AISPSO, I-AIS, SPSO, and KBaNN. Comparison results show that the ELSVM model has the lowest average ratio of Mean Absolute Error (MAE) for surface roughness prediction in LUVAG. • The artificial immune particle swarm optimization is improved. • The adaptive particle swarm optimization algorithm and the manual free algorithm are combined in parallel. • A surface roughness prediction model of support vector machine based on ensemble learning algorithm is proposed. • Longitudinal ultrasonic assisted grinding can reduce surface roughness. [ABSTRACT FROM AUTHOR]

• A model to explore the multiphysics characteristic of semiconductors is presented. • The model determines the thermal and mechanical properties of complex patterns. • Results show a strong correlation between thermal and mechanical properties. • Dielectric bands asymmetrically affect thermal and mechanical behavior. • Machine learning can predict one property from another. As semiconductor performance improves through advanced package designs, it becomes important to consider both thermal and mechanical properties. Better understanding their relationship enhances system design and optimization. This study has introduced a numerical method that takes into account both thermal and mechanical fluxes to construct thermal and mechanical property maps for practical application in semiconductor engineering. Furthermore, this paper investigated the relationship between the two properties using the constructed property maps in commercially available semiconductor packages. Owing to the complexity of packaging design patterns, a coupled isoparametric mapping and machine learning (ML) method was introduced to investigate their effects. The model then determines and investigates the anisotropic equivalent thermal and mechanical properties of the commercialized semiconductor packages with complex pattern designs, according to the package materials, volume fractions of each material, and design patterns. This approach pursues more sophistication and practicality compared to simplified composite structure property evaluation models. Additionally, all processes in the algorithm are automated based on ML methods, making them practically applicable in the semiconductor industry. The study shows that there is a strong correlation between the thermal and mechanical characteristics in the complex package patterns. This relationship was able to form a fairly clear category based on the shape of the dielectric band because the dielectric band had significantly different effects on the thermal and mechanical fluxes. The study discussed the physical implications of the similarities and differences between the two behaviors and validated the results of the equivalent properties through finite element (FE) analysis. Finally, the study cross-validates the relationship by showing that information from one flux behavior can be used to predict the other based on the ML method. Based on the results, this paper can provide a better understanding of thermal and mechanical fluxes in semiconductor package patterns for package design and optimization. [ABSTRACT FROM AUTHOR]

This paper uses machine learning approaches to predict the association between traffic volume, air pollution, and meteorological conditions. A key focus is on the interaction between these factors. The paper does this using hourly traffic volume, N O x , P M 2.5 , and weather data for Oslo, Norway. I considered a total of six datasets of the 2019 whole-year data to verify the prediction accuracy of the models. I find that the autoregressive integrated moving average model with exogenous input variables, and the autoregressive moving average dynamic linear model outperform the machine learning models in predicting air pollution. At the same time, I also explored the effect of sampling weather subsets on prediction accuracy. Finally, my study makes optimal policy recommendations for reducing air pollution from traffic volume, after considering the interaction and lagged effects of meteorology, time variables, traffic, and air pollution. [ABSTRACT FROM AUTHOR]

We conceived a novel approach to screen oil types on a wax-printed paper-based microfluidic platform. Various oil samples spontaneously flowed through a micrometer-scale channel via capillary action while their components were filtered and partitioned. The resulting capillary flow velocity profile fluctuated during the flow, which was used to screen oil types. Raspberry Pi camera captured the video clips, and a custom Python code analyzed them to obtain the capillary flow velocity profiles. 106 velocity profiles (each with 125 frames for 5 s) were recorded from various oil samples to build a training database. Principal component analysis (PCA), support vector machine (SVM), and linear discriminant analysis (LDA) were used to classify the oil types into heavy-to-medium crude, light crude, marine fuel, lubricant, and diesel oils. The second-order polynomial SVM model with PCA as a pre-processing step showed the highest accuracy: 90% in classifying crude oils and 81% in classifying non-crude oils. The assay took less than 30 s from the sample to answer, with 5 s of the capillary action-driven flow. This simple and effective assay will allow rapid preliminary screening of oil types, enable early tracking, and reduce the number of suspect samples to be analyzed by laboratory fingerprinting analysis. [Display omitted] • A novel approach to screen oil types on a paper microfluidic platform. • Raspberry Pi camera acquired capillary flow velocity profiles of diverse oil samples. • Various machine learning based classifications were tested, including PCA, SVM, and LDA. • 90% accuracy in classifying crude oil samples and 81% in non-crude oil samples. • < 30 s from the sample to answer without the need for laboratory equipment. [ABSTRACT FROM AUTHOR]

Smartphone has long been considered as one excellent platform for disease screening and diagnosis, especially when combined with microfluidic paper-based analytical devices (μPADs) that feature low cost, ease of use, and pump-free operations. In this paper, we report a deep learning-assisted smartphone platform for ultra-accurate testing of paper-based microfluidic colorimetric enzyme-linked immunosorbent assay (c-ELISA). Different from existing smartphone-based μPAD platforms, whose sensing reliability is suffered from uncontrolled ambient lighting conditions, our platform is able to eliminate those random lighting influences for enhanced sensing accuracy. We first constructed a dataset that contains c-ELISA results (n = 2048) of rabbit IgG as the model target on μPADs under eight controlled lighting conditions. Those images are then used to train four different mainstream deep learning algorithms. By training with these images, the deep learning algorithms can well eliminate the influences of lighting conditions. Among them, the GoogLeNet algorithm gives the highest accuracy (>97%) in quantitative rabbit IgG concentration classification/prediction, which also provides 4% higher area under curve (AUC) value than that of the traditional curve fitting results analysis method. In addition, we fully automate the whole sensing process and achieve the "image in, answer out" to maximize the convenience of the smartphone. A simple and user-friendly smartphone application has been developed that controls the whole process. This newly developed platform further enhances the sensing performance of μPADs for use by laypersons in low-resource areas and can be facilely adapted to the real disease protein biomarkers detection by c-ELISA on μPADs. [Display omitted] • This deep learning-assisted smartphone platform is unaffected by ambient lighting. • A fully automated "image in, answer out" operation fashion. • A 2048 custom image dataset is used to test 4 mainstream deep learning algorithms. • GoogLeNet provides >97% accuracy in quantitative rabbit IgG testing. • The area under the curve (AUC) is 4% higher than that of conventional methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • Micro-NIR spectrometer was proposed to identify the paper types. • Four machine learning algorithms were discussed in both Micro-NIR and ATR-FTIR. • The performance of Micro-NIR model was better than ATR-FTIR. Quick identification of paper types for customs is extremely crucial. Although there are a variety of researches focus on the discrimination of paper, these techniques either require complex preprocessing or large-scale instruments, which are not suitable for customs environments. In this study, we predicted the type of customs paper by using a Micro-NIR spectrometer, and compared the results with Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR). Four different classification algorithms, including linear and non-linear classifiers: K-nearest neighbor (KNN), soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA), and least squares-support vector machine (LS-SVM) were employed to classify the type of paper. 20 groups of datasets were selected by Monte Carlo sampling. For Micro-NIR data, the performances of KNN and LS-SVM were outstanding than SIMCA and PLS-DA, with the average accuracies 96.06% and 98.91%, respectively. The outcome of SIMCA and PLS-DA were similar, with the average accuracies 93.00% and 93.97%. Based on the standard derivation, the best stability of models was LS-SVM (1.06%), followed by PLS-DA (1.12%), KNN (1.22%) and SIMCA (3.07%). Compared with ATR-FTIR, the effects of Micro-NIR were better, which were embodies in the better KNN and SIMCA models, and the comparable LS-SVM model. The result demonstrated that the Micro-NIR combined with machine learning algorithms was an effective method to classify the type of customs paper efficiently and quickly, even better than ATR-FTIR. [ABSTRACT FROM AUTHOR]

[Display omitted] • Paper chromogenic array (PCA) integrated with machine learning (ML) was developed. • PCA exhibit distinguishable pattern shifts when reacting volatile metabolites. • PCA pattern recognition achieved using a cross-validated neural network. • PCA accurately identify multiplexed pathogens from indigenous microflora. • The nondestructive PCA-ML holds great potential as a smart food safety system. Non-destructive detection of human foodborne pathogens is critical to ensuring food safety and public health. Here, we report a new method using a paper chromogenic array coupled with a machine learning neural network (PCA-NN) to detect viable pathogens in the presence of background microflora and spoilage microbe in seafood via volatile organic compounds sensing. Morganella morganii and Shewanella putrefaciens were used as the model pathogen and spoilage bacteria. The study evaluated microbial detection in monoculture and cocktail multiplex detection. The accuracy of PCA-NN detection was first assessed on standard media and later validated on cod and salmon as real seafood models with pathogenic and spoilage bacteria, as well as background microflora. In this study PCA-NN method successfully identified pathogenic microorganisms from microflora with or without the prevalent spoilage microbe, Shewanella putrefaciens in seafood, with accuracies ranging from 90% to 99%. This approach has the potential to advance smart packaging by achieving nondestructive pathogen surveillance on food without enrichment, incubation, or other sample preparation. [ABSTRACT FROM AUTHOR]

Advances in artificial intelligence (AI) and automated robotics will profoundly influence space operations. By utilising machine learning and deep learning approaches, AI-enabled systems may accomplish tasks as well as improve their own performance. These capabilities are useful in the often-remote settings of outer space and will grow in value as automated space operations become more widespread. As AI extends throughout the space domain, automated algorithms will take on many of the roles that have historically been handled by humans. Artificial intelligence is progressing from theory to implementation in the space environment by exposing new satellites and orbital autonomous vehicles to new data. Even though all initial computational parameters are provided, such systems' outputs can be very unpredictable, putting people, property, and the environment at risk. This paper investigates the application of United Nations space treaties, selected regional AI regulations, and various 'soft-law' instruments and industry initiatives focusing on responsible AI system development to space-based AI systems. Following that, reforms are proposed to clarify the practical relationship between AI systems and the international legal regime that governs space, as well as a 'bottom-up' regulatory approach to better facilitate the future development of regulation governing the use of AI by the global space sector. While this work does not purport to provide a conclusive resolution to these multifaceted matters, its objective is to underscore significant obstacles that arise at the convergence of space law and AI, serving as a preliminary foundation for subsequent discussions on this issue. • Advances in AI and automated robotics will have a profound impact on space operations. Automated algorithms will take on roles traditionally handled by humans as AI becomes more widespread in space. • However, the unpredictable outputs of AI systems can put people, property, and the environment at risk, raising questions about liability. • The paper investigates the application of UN space treaties, regional AI regulations, and industry initiatives to space-based AI systems. • Reforms are proposed to clarify the relationship between AI systems and the international legal regime governing space. • A 'bottom-up' regulatory approach is suggested to facilitate future regulation of AI in the global space sector. [ABSTRACT FROM AUTHOR]

With the rapid advancement of 3D sensors, there is an increasing demand for 3D scene understanding and an increasing number of 3D deep learning algorithms have been proposed. However, a large-scale and richly annotated 3D point cloud dataset is critical to understanding complicated road and urban scenes. Motivated by the need to bridge the gap between the rising demand for 3D urban scene understanding and limited LiDAR point cloud datasets, this paper proposes a richly annotated WHU-Urban3D dataset and an effective method for semantic instance segmentation. WHU-Urban3D stands out from existing datasets due to its distinctive features: (1) extensive coverage of both Airborne Laser Scanning and Mobile Laser Scanning point clouds, along with panoramic images; (2) containing large-scale road and urban scenes in different cities (over 3. 2 × 1 0 6 m 2 area), with richly point-wise semantic instance labels (over 200 million points); (3) inclusion of particular attributes (e.g., reflected intensity, number of returns) in addition to 3D coordinates. This paper also provides the performance of several representative baseline methods and outlines potential future works and challenges for fully exploiting this dataset. The WHU-Urban3D dataset is publicly accessible at https://whu3d.com/. [ABSTRACT FROM AUTHOR]

A pre-treatment of graphene with bleach is considered one of the possible purification methods after liquid-phase exfoliation. However, the effect of this treatment on the mechanical reinforcement strategy for polymer film is yet to be investigated to date. In this full work, the influence of the C/O ratio, I D /I G, and volume of graphene after combination with cellulose tissue on the resulting stiffness of polyvinyl alcohol (PVA) composite film has been extensively studied. It is noticed that the incorporation of 30 ml graphene that had been pre-treated for 3 h into PVA had produced the best increment in elastic modulus (1.6 GPa against 0.4 GPa) while a shorter pre-treatment duration of graphene (1 h) would require more graphene volume (40 ml) to match the previous stiffness improvement level. By using the collected experimental data (90 samples), we further modeled the effect of tissue and PVA mass, C/O ratio, I D /I G , and graphene volume on modulus using machine learning (ML) algorithms. [Display omitted] • Combination of cellulose tissue and graphene as filler hybrid to combat poor dispersibility of bleach-treated graphene • Mechanical reinforcement effect was observed for graphene treated at 3 h due to the well-balanced C/O and I D /I G. • Addition of more tissue/graphene mass is required for graphene with a lower C/O to enhance the stiffness. • Machine learning study shows k-nearest neighbours with k = 1 is the best prediction model for composite stiffness. [ABSTRACT FROM AUTHOR]

Nowadays, there has been a rapidly increasing number of scientific submissions in multiple research domains. A large number of journals have various acceptance rates, impact factors, and rankings in different publishers. It becomes time-consuming for many researchers to select the most suitable journal to submit their work with the highest acceptance rate. A paper submission recommendation system is more critical for the research community and publishers as it gives scientists another support to complete their submission conveniently. This paper investigates the submission recommendation system for two main research topics: computer science and applied mathematics. Unlike the previous works (Wang et al., 2018; Son et al., 2020) that extract TF–IDF and statistical features as well as utilize numerous machine learning algorithms (logistics regression and multiple perceptrons) for building the recommendation engine, we present an efficient paper submission recommendation algorithm by using different bidirectional transformer encoders and the Mixture of Transformer Encoders technique. We compare the performance between our methodology and other approaches by one dataset from Wang et al. (2018) with 14012 papers in computer science and another dataset collected by us with 223,782 articles in 178 Springer applied mathematics journals in terms of top K accuracy (K = 1 , 3 , 5 , 10). The experimental results show that our proposed method extensively outperforms other state-of-the-art techniques with a significant margin in all top K accuracy for both two datasets. We publish all datasets collected and our implementation codes for further references. 1 1 https://github.com/BinhMisfit/PSRMTE. • Bidirectional transformer encoders can improve the performance of the paper submission recommendation system. • The Mixture of Transformer Encoders framework shows the efficiency in the paper submission recommendation problem. • Proposed techniques can surpass other recent techniques on two datasets related. [ABSTRACT FROM AUTHOR]

• Proposed a Multi Criteria Decision Making based Reverse Vaccinology model for predicting Bacterial Protective Antigens. • The proposed model tested on extracted physicochemical features from bacterial protein sequence. • Applied four strategies of Synthetic Minority Oversampling Technique and Edited Nearest Neighbour to handle the data imbalance problem. • Consider MCDM-based TOPSIS and CRITIC methods for order preference with soft and hard ranking model. Reverse vaccinology (RV) is a significant step in sensible vaccine design. In recent years, many machine learning (ML) methods have been used to improve RV prediction accuracy. However, there are still issues with prediction accuracy and programme accessibility in ML-based RV. This paper presents a supervised ML-based method to classify bacterial protective antigens (BPAgs) and identify the model(s) that consistently perform well for the training dataset. Six ML classifiers are used for testing with physiochemical features extracted from a comprehensive training dataset. Selecting the best performing model from different performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) has not been easy, because all the metrics has the same importance to predict BPAgs. To fix this issue, we propose a soft and hard ranking model based on multi-criteria decision-making (MCDM) approach for selecting the best performing ML method that classifies BPAgs. First, our proposed model uses homologous proteins (positive and negative samples) from Protegen and Uniprot databases. Second, we applied four strategies of Synthetic Minority Oversampling Technique and Edited Nearest Neighbour (SMOTE-ENN) to handle the data imbalance problem and train the model using ML methods. Third, we consider MCDM-based technique for order preference by similarity to the ideal solution (TOPSIS) method integrated with soft and hard ranking model. The entropy is used to obtain weighted evaluation criteria for ranking the models. Our experimental evaluations show that the proposed method with best performing models (Random Forest and Extreme Gradient Boosting) outperforms compared to existing open-source RV methods using benchmark datasets. [ABSTRACT FROM AUTHOR]

In the numerical simulations of multi-phase flow using Volume-of-Fluid (VOF) method, the calculation of the interface normal is a crucial point. In this paper, a machine learning method is used to develop an artificial neural network (ANN) model to make more accurate prediction of the local normal vector from neighboring volume fractions. Spherical surfaces with different radii are intersected with a structural background grid to generate 84328 groups of data: 3×3×3 neighboring volume fractions are used as input, and normal vector as output. Using 90% data as training dataset, the ANN model is well trained by optimizing the number of hidden layers and the number of neurons on each layer. Using the remaining 10% data, normal predictions are made using ANN-VOF and the most used YOUNG and HEIGHT-FUNCTION methods. The RMSE of the ANN-VOF/YOUNG/ HEIGHT-FUNCTION methods are 0.008/0.022/0.045 respectively. In the reconstruction of a sinusoidal surface, the MSE of the ANN-VOF/YOUNG/ HEIGHT-FUNCTION methods are 0.008/0.018/0.041. It is demonstrated that the ANN-VOF method has better performance for interface normal prediction. The proposed method has a simple computational logic and does not need to deal with complex geometric topology, which lays the foundation for application in other more complex grids. [ABSTRACT FROM AUTHOR]

The collection of SuperDARN ionospheric echo data to make ionospheric convection maps is of great significance for Space Weather research. The ionospheric echoes for SuperDARN are generally mixed with scatter from the ground or sea surface, thus the clustering analysis of SuperDARN backscatter echoes is important. In this paper, the first implementation of deep clustering based on the graph automatic encoder network is efficiently realized for classifying the SuperDARN data. In addition, the model is compared with the traditional algorithm and machine learning clustering algorithms. Application of the model to sample data reveals that the deep clustering algorithm can capture the structural characteristics of the echoes and improve the accuracy of echo clustering and classifying. [ABSTRACT FROM AUTHOR]

Developments in vision-based systems and human pose estimation algorithms have the potential to detect, monitor and intervene early on neurodegenerative diseases through gait analysis. However, the gap between the technology available and actual clinical practice is evident as most clinicians still rely on subjective observational gait analysis or objective marker-based analysis that is time-consuming. This paper aims to examine the main developments of vision-based motion capture and how such advances may be integrated into clinical practice. The literature review was conducted in six online databases using Boolean search terms. A commercial system search was also included. A predetermined methodological criterion was then used to assess the quality of the selected articles. A total of seventeen studies were evaluated, with thirteen studies focusing on gait classification systems and four studies on gait measurement systems. Of the gait classification systems, nine studies utilized artificial intelligence-assisted techniques, while four studies employed statistical techniques. The results revealed high correlations of gait features identified by classifier models with existing clinical rating scales. These systems demonstrated generally high classification accuracies and were effective in diagnosing disease severity levels. Gait measurement systems that extract spatiotemporal and kinematic joint information from video data generally found accurate measurements of gait parameters with low mean absolute errors, high intra- and inter-rater reliability. Low cost, portable vision-based systems can provide proof of concept for the quantification of gait, expansion of gait assessment tools, remote gait analysis of neurodegenerative diseases and a point of care system for orthotic evaluation. However, certain challenges, including small sample sizes, occlusion risks, and selection bias in training models, need to be addressed. Nevertheless, these systems can serve as complementary tools, equipping clinicians with essential gait information to objectively assess disease severity and tailor personalized treatment for enhanced patient care. • Low-cost vision-based systems may enable more objective quantification of gait. • Vision-based systems allow early detection and severity monitoring of diseases. • Gait classification systems show high correlations with clinical rating scales. • Gait measurement systems effectively extract spatiotemporal and kinematic data. • A point of care system enabling remote gait analysis and orthotic evaluation. [ABSTRACT FROM AUTHOR]

Hydrogen is crucial in the forthcoming low-carbon energy systems offering the potential to reduce CO 2 emissions and its versatility to be utilized across diverse energy sectors. Precise assessment of hydrogen thermo-physical characteristics is vital for the effective design and execution of numerous processes encompassing hydrogen production, transportation, storage, and utilization. In this paper, various distinct machine learning (ML) approaches, namely extreme gradient boosting (XGBoost), Random Forest (RF), Adaptive boosting (AdaBoost), and Light gradient boosting machine (LightGBM) were implemented to estimate thermo-physical properties, namely thermal conductivity, density, viscosity, and diffusivity in water based on pressure and temperature variables. For this purpose, comprehensive experimental data points covering a broad range of pressures and temperatures were acquired from the literature. The results demonstrated a strong agreement between the predictions generated by all proposed techniques and the experimental data. Furthermore, XGBoost yielded a root mean square error (RMSE) of 0.0085, 2.1548, 0.3343, and 1.5308 for the estimation of thermal conductivity, density, viscosity, and diffusivity, respectively, demonstrating superior accuracy in predicting all outcomes. In the sensitivity evaluation, it was observed that temperature, with absolute relevancy factor 0.88, 0.54, 0.246, and 0.92 had the most significant influence on the hydrogen thermo-physical properties in the order mentioned above. Furthermore, the trend analysis of the model indicated that all thermo-physical properties of hydrogen experience positive effects from temperature and pressure, except for density, which decreases as temperature increases. The leverage technique confirmed the statistical validity of both the experimental dataset used for modeling and the model's development. This study's results provide the efficient design and secure operation of hydrogen storage, refueling stations, transportation infrastructure, and production facilities. [Display omitted] • Hydrogen holds great importance in the forthcoming low-carbon energy. • XGBoost, RF, AdaBoost, and LightGBM are utilized for modeling the thermo-physical properties of hydrogen. • XGBoost demonstrates its superior accuracy in predicting thermal conductivity, density, viscosity, and diffusivity. • Temperature has the most significant influence on the hydrogen thermo-physical properties. [ABSTRACT FROM AUTHOR]

Due to the coupling of multiple physical fields inside proton exchange membrane fuel cell (PEMFC) and the complex and changeable external application scenarios, the faults of PEMFC system greatly affects its engineering application. The paper proposes an ensemble learning-based fault diagnosis method for PEMFC systems, utilizing five distinct algorithms to develop an integrated ensemble classifier for fault detection. The original sample dataset collected from fuel cells undergoes a process of deep feature extraction to obtain a composite feature sample dataset, which is then inputted into an ensemble classifier to establish a fault diagnosis model. The experimental data of four kinds of faults of 80 W fuel cell test bench, EC fuel cell system and 50 W water-cooled fuel cell test bench are analyzed by using this classification model. The results show that the accuracy and generalization of the ensemble learning algorithm for different fault diagnosis of PEMFC system in different application environments are verified. • Adopt multiple classification algorithms for fault modeling and diagnosis. • Good applicability to different faults is shown in different test scenarios. • Compared with a single algorithm, it has higher fault identification accuracy. [ABSTRACT FROM AUTHOR]

Airborne hyperspectral images can be used to map the land cover in large urban areas, thanks to their very high spatial and spectral resolutions on a wide spectral domain. While the spectral dimension of hyperspectral images is highly informative of the chemical composition of the land surface, the use of state-of-the-art machine learning algorithms to map the land cover has been dramatically limited by the availability of training data. To cope with the scarcity of annotations, semi-supervised and self-supervised techniques have lately raised a lot of interest in the community. Yet, the publicly available hyperspectral data sets commonly used to benchmark machine learning models are not totally suited to evaluate their generalization performances due to one or several of the following properties: a limited geographical coverage (which does not reflect the spectral diversity in metropolitan areas), a small number of land cover classes and a lack of appropriate standard train/test splits for semi-supervised and self-supervised learning. Therefore, we release in this paper the Toulouse Hyperspectral Data Set that stands out from other data sets in the above-mentioned respects in order to meet key issues in spectral representation learning and classification over large-scale hyperspectral images with very few labeled pixels. Besides, we discuss and experiment self-supervised techniques for spectral representation learning, including the Masked Autoencoder (He et al., 2022), and establish a baseline for pixel-wise classification achieving 85% overall accuracy and 77% F1 score. The Toulouse Hyperspectral Data Set and our code are publicly available at www.toulouse-hyperspectral-data-set.com and www.github.com/Romain3Ch216/tlse-experiments/ , respectively. [ABSTRACT FROM AUTHOR]

Risk assessment studies of local asteroid hazards traditionally simulate the physics of meteors with engineering models tailored to analyze tens-of-millions of scenarios. However, these simplified approaches still need to solve time-dependent ODEs to model the entry process and the resulting ground damage. With a computational cost of O(0.01 CPU.s) per scenario, simulating these large numbers of potential entry conditions can take several days on local computers. To improve computational efficiency, we propose in this paper an orthogonal approach based on machine learning models to predict the size of damaged areas given a list of entry parameters. We train 5 machine learning methods and compare the predictions to the outputs of NASA's state-of-the-art Probabilistic Asteroid Impact Risk (PAIR) model. PAIR's remarkable capability to handle a broad spectrum of entry conditions and a high volume of scenarios establishes it as our reference model, with PAIR estimates assumed ground truth and used to train, test, and validate the machine learning models. The ML models, initially trained with PAIR's primitive input variables, are then further enhanced with engineered features to improve the performance of interpretable models. We find that complex models like neural networks are well-suited to estimate blast hazards, while simpler, interpretable linear models can accurately assess thermal damage. For both types of hazards, the radii of damaged areas can be predicted with less than 10% average relative errors for radii greater than 100 km. The CPU time is decreased by a factor O(1 0 3 ) compared to the PAIR model, enabling the simulation of millions of scenarios within minutes on a local computer. Beyond serving as surrogate models, we incorporate the machine learning models into a comprehensive Shapley sensitivity analysis, yielding a ranking of entry parameters based on their contributions to ground damages. These results offer valuable insights for prioritizing observation missions. • Machine learning models enable accurate and rapid simulations of asteroid hazards. • Data-driven methods can assess the radius of damaged areas with 10% average errors. • Models provide insights on the relative importance of entry parameters. • Risk studies can be performed on local machines instead of supercomputers. [ABSTRACT FROM AUTHOR]

Detecting action units is an important task in face analysis, especially in facial expression recognition. This is due, in part, to the idea that expressions can be decomposed into multiple action units. To evaluate systems that detect action units, F1-binary score is often used as the evaluation metric. In this paper, we argue that F1-binary score does not reliably evaluate these models due largely to class imbalance. Because of this, F1-binary score should be retired and a suitable replacement should be used. We justify this argument through a detailed evaluation of the negative influence of class imbalance on action unit detection. This includes an investigation into the influence of class imbalance in train and test sets and in new data (i.e., generalizability). We empirically show that F1-micro should be used as the replacement for F1-binary. • We show that AU base rates have a large influence on detection across different architectures. • We show how different evaluation metrics are impacted by AU base rates. • We argue that F1-binary should not be used, for AU detection, and that F1-micro should replace it. [ABSTRACT FROM AUTHOR]

Although current semantic segmentation approaches have achieved impressive performance, their ability to incrementally learn new classes is limited. Moreover, pixel-by-pixel annotations are costly and time-consuming. Therefore, a new field called Weakly-supervised Incremental Learning for Semantic Segmentation (WILSS) has emerged, which learns new classes using image-level labels. However, image-level labels do not provide sufficient detail, and we discover that the state-of-the-art of WILSS suffers from confusion between old knowledge and new knowledge. To address this issue, we propose W eakly-supervised I ncremental learning for S emantic segmentation with Class H ierarchy (WISH), a method that considers the hierarchical structure of each class when determining which knowledge to trust in cases of confusion between old and new knowledge. Our method has achieved new state-of-the-art performances in all settings compared to the previous methods on the Pascal VOC and MS COCO datasets. • Our paper introduces WISH, utilizing hierarchy and image labels for class incremental learning in semantic segmentation. • We efficiently utilize hierarchy, boosting segmentation performances without added costs. • Our method outperforms existing methods in all configurations, showcasing hierarchical integration's effectiveness. [ABSTRACT FROM AUTHOR]

In many high-impact applications, it is important to ensure the quality of the output of a machine learning algorithm as well as its reliability in comparison to the complexity of the algorithm used. In this paper, we have initiated a mathematically rigorous theory to decide which models (algorithms applied on data sets) are close to each other in terms of certain metrics, such as performance and the complexity level of the algorithm. This involves creating a grid on the hypothetical spaces of data sets and algorithms so as to identify a finite set of probability distributions from which the data sets are sampled and a finite set of algorithms. A given threshold metric acting on this grid will express the nearness (or statistical distance) of each algorithm and data set of interest to any given application. A technically difficult part of this project is to estimate the so-called metric entropy of a compact subset of functions of infinitely many variables that arise in the definition of these spaces. [ABSTRACT FROM AUTHOR]

Condensation on surfaces in the presence of non-condensable gas (NCG) is a ubiquitous and critical phenomenon in many industrial fields. However, current empirical correlations for predicting condensation heat transfer coefficients in the presence of NCG may exhibit significant deviations from reality. Machine learning algorithms are now being dedicated to this field, but the models developed can only predict the condensation heat transfer coefficients of steam or vapor of a non-aqueous working fluid with similar physical properties to water in the presence of NCG. In the present study, a comprehensive theoretical analysis was conducted to investigate the total condensation heat transfer coefficients with NCG, and 16 dimensionless numbers were identified as input variables for machine learning models. Based on a filtered database consisting of 4377 data points extracted from 37 papers, the Spearman correlation coefficients were calculated to evaluate the relationship between the total heat transfer coefficients and the input variables, indicating the magnitude of the impact of the 16 dimensionless variables. Four machine learning models, namely Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost), Random Forest Regression (RFR), and Multilayer Perceptron (MLP), were developed to predict the total heat transfer coefficients for water and non-aqueous working fluids. The mean absolute percentage errors for the four models were 1.38%, 1.63%, 3.00%, and 4.42%, respectively, with the GBR model exhibiting the highest degree of accuracy. The determination of the application scope of these models was conducted by analyzing the value ranges for each dimensionless parameter and its corresponding frequency distribution. • Four machine-learning models were developed to predict condensation heat transfer. • Sixteen dimensionless variables were determined as input parameters for these models. • These models can predict condensation heat transfer under different working conditions. • Conditions were diverse in types of wall geometry, convection, working fluid and NCG. [ABSTRACT FROM AUTHOR]

• A SMOTE approach was proposed to handle imbalanced data of hazards levels. • SMOTE was integrated with Gaussian process for the prediction of hazards levels. • A real-world example is used to illustrate and validate the proposed approach. • Results from different data generation approaches are compared with that of SMOTE. • Results from different machine learning methods are compared with that of GP. Cultural soil heritages (CSHs) are artifacts with historical, artistic, and scientific significance; however, they are vulnerable to various hazards, such as weathering, fractures, hollowing, collapses, and gullies. This is especially true for those CSHs exposed to the outdoors. Due to the large number of CSHs sites within China, managing and protecting these heritages with the aid of detailed on-site investigations is time-consuming and expensive. Consequently, evaluating the spatial distribution and degree of hazards developed in all these heritages becomes impractical. To address this issue, this paper developed a Gaussian process classification (GPC) method to predict the spatial distribution of typical hazards (i.e., weathering, fractures, hollowing, collapses, and gullies) and the development level of each hazard from eight environmental factors (e.g., annual relative humidity and annual sunshine time) and a limited number of investigation data. As the number of investigation data for different levels of each hazard is usually imbalanced and sparse, this study proposed a synthetic minority oversampling technique (SMOTE) with GPC to form the SMOTE-GPC method. A real-world example is used to illustrate this approach. Results from real-world data demonstrated that the proposed method achieved an F 1 score, precision, recall, and Cohen's kappa with values greater than 0.93 in both the training and testing datasets, indicating its good performance. [ABSTRACT FROM AUTHOR]

Efficient detection of refrigerant leakage is of utmost importance for industrial refrigeration systems due to its potential to cause substantial impacts on system performance and the environment. Existing research on fault detection and diagnosis in refrigeration systems primarily revolves around solutions based on experimental or laboratory data. However, in the industrial use case, achieving accurate and early detection poses significant challenges. This paper reports on the development of a novel refrigerant leak detection method for industrial vapor compression refrigeration systems. Our method leverages real-world data obtained from operational installations, enabling us to assess its reliability and applicability. The proposed data-driven approach involves predicting the fault-free liquid level in the installation receiver and comparing the actual and predicted levels. In this work, we place emphasis on features and model selection. Dedicated metrics combined with a model comparison method are proposed to evaluate and compare the performance of commonly used regression models with two sets of features to determine the most effective one. Furthermore, we provide insights into the results obtained from the deployment of the proposed method in real-world industrial installations. • Refrigerant leak is detected through the receiver liquid level in the installation. • A regression model can be used to predict the fault-free liquid level. • System performance indicators were proven to be effective in long-term predictions. • Extremely randomized forest emerged as the top performer. [ABSTRACT FROM AUTHOR]

The present study employs density functional theory-based first principle calculation to investigate the electron transport properties of polyaniline following exposure to acidic and alkaline pH. In-situ deposited polyaniline-based paper device maintains emeraldine salt form while it is exposed to acidic pH and converts to emeraldine base when it is subjected to alkaline pH solutions. These structural changes at acidic and alkaline pH are validated experimentally by Raman spectra. Furthermore, the Raman spectra computed from density functional theory are validated with the experimental spectra. The changes in the theoretical energy band gap of polyaniline obtained from first principle calculations were correlated with the changes in the experimental impedimetric response of the sensor after exposure to acidic and alkaline solutions. Finally, the impedimetric responses were used to predict urine pH through a machine learning based smart and interactive web application. Different machine learning based regression models were implemented to acquire the best possible outcome. Gradient Boosting Regressor with least square loss model was selected as it showed lowest mean square, mean absolute, and root mean square error than other models. The smart sensing platform successfully predicts the unknown pH of urine samples with an average accuracy of more than 98%. The locally deployed smart web app can be accessed within a local area network by the end-user, which holds promise towards effective detection of urinary pH. [Display omitted] [ABSTRACT FROM AUTHOR]

With the rapidly evolving geo-political landscape and unceasing advancements in technology, sustainable energy security is a very important topic. Research indicates that electrochemical energy systems are quite promising to solve many of energy conversion, storage, and conservation challenges while offering high efficiencies and low pollution. The paper provides an overview of electrochemical energy devices and the various optimization techniques used to evaluate them. The optimization techniques include linear regression, factorial design, the Taguchi method, artificial neural networks, filters, and a combination of such methods to improve these systems. To support the growing interest in research, the bulk of this study focuses on a review of the most promising and highly researched electrochemical energy devices, such as fuel cells, batteries, and supercapacitors. The paper also provides modest commentary on hydrogen production technologies, electrochemical reactors, and membrane separation technologies, amongst other technologies. Building on a previous paper by the authors of this paper on artificial intelligence in hybrid renewable energy systems with fuel cells, this work provides a comparative review of optimization techniques for supercapacitors by highlighting key findings based on model accuracy. A summary of the advantages and disadvantages of the different major optimization techniques is presented. The paper concludes that a combination of optimization techniques is used to overcome the drawbacks of individual techniques, with adaptive filters being the most widely studied. This paper presents studies on the Design of Experiments (DoE) with the goal of building a better understanding of the relationships that exist between different operating variables in various electrochemical devices. • The electrochemical systems can be optimized for better performance with high accuracy. • The optimization techniques used are either model-based or data-driven. • Linear regression and artificial neural networks are the most common techniques studied. • Hybrid models could be applied to enhance systems' accuracy and minimize errors. [ABSTRACT FROM AUTHOR]