Analytical Model Research Articles

Preparation and study of a capillary electrochromatographic column prepared by conjugating β-CD COFs and gold-poly glycidyl methacrylate nanoparticles.

A new enantioselective open-tubular capillary electrochromatography (OT-CEC) was developed employing β-cyclodextrin covalent organic frameworks (β-CD COFs) conjugated gold-poly glycidyl methacrylate nanoparticles (Au-PGMA NPs) as a stationary phase. The resulting coating layer on the inner wall of the fabricated capillary column was characterized by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), energy dispersive spectroscopy (EDS), and electroosmotic flow (EOF) experiments. The performance of the fabricated capillary column was evaluated by CEC using enantiomers of seven model analytes, including two proton pump inhibitors (PPIs, omeprazole and tenatoprazole), three amino acids (AAs, tyrosine, phenylalanine, and tryptophan), and two fluoroquinolones (FQs, gatifloxacin and sparfloxacin). The influences of coating time, buffer concentration, buffer pH, and applied voltage on enantioseparation were investigated to obtain satisfactory enantioselectivity. In the optimum conditions, the enantiomers of seven analytes were fully resolved within 10min with high resolutions of 3.03 to 5.25. The inter- to intra-day and column-to-column repeatabilities of the fabricated capillary column were lower than 4.26%RSD. Furthermore, molecular docking studies were performed based on the chiral fabricated column and as ligand isomers of analytes using Auto Dock Tools. The binding energies and interactions acquired from docking results of analytes supported the experimental data.

The Transformative Role of Microsoft Azure AI in Healthcare

This paper explores the transformative impact of Azure AI technologies on healthcare, focusing on diagnostics, predictive analytics, operational efficiency, and drug discovery. Azure AI tools are reshaping healthcare delivery by harnessing data-driven insights and advanced machine learning algorithms to enhance patient outcomes significantly. Azure AI enables more precise diagnostics through advanced image recognition and pattern analysis, improving the speed and accuracy of medical assessments. It supports predictive analytics models that personalize treatment plans based on individual patient data, thereby optimizing care and reducing risks. In hospital settings, Azure AI enhances operational efficiencies by predicting maintenance needs and optimizing resource allocation, streamlining workflows and improving overall service delivery. Furthermore, Azure AI accelerates drug discovery processes by analyzing extensive datasets to identify potential drug candidates swiftly. These advancements not only improve the efficiency and effectiveness of healthcare services but also hold promise for addressing healthcare disparities and promoting equitable access to quality care globally.

The Transformative Role of Microsoft Azure AI in Healthcare

This paper explores the transformative impact of Azure AI technologies on healthcare, focusing on diagnostics, predictive analytics, operational efficiency, and drug discovery. Azure AI tools are reshaping healthcare delivery by harnessing data-driven insights and advanced machine learning algorithms to enhance patient outcomes significantly. Azure AI enables more precise diagnostics through advanced image recognition and pattern analysis, improving the speed and accuracy of medical assessments. It supports predictive analytics models that personalize treatment plans based on individual patient data, thereby optimizing care and reducing risks. In hospital settings, Azure AI enhances operational efficiencies by predicting maintenance needs and optimizing resource allocation, streamlining workflows and improving overall service delivery. Furthermore, Azure AI accelerates drug discovery processes by analyzing extensive datasets to identify potential drug candidates swiftly. These advancements not only improve the efficiency and effectiveness of healthcare services but also hold promise for addressing healthcare disparities and promoting equitable access to quality care globally.

Idiographic artificial intelligence to explain students' self-regulation: Toward precision education

Existing predictive learning analytics models have exclusively relied on aggregate data which not only have obfuscated individual differences but also made replicability and generalizability difficult. This study takes a radical departure and uses a person-specific approach to predicting and explaining students' self-regulation (SRL). A person-specific approach entails developing a predictive model for each individual using their own data (i.e., idiographic, single-subject or N = 1). We also use explainable and interpretable artificial intelligence (AI) models that allow us to identify the variables that explain students' SRL and guide data-informed decisions. Our study has shown that idiographic single-subject models are tenable, informative, and can accurately capture the individualized students' SRL process. Predictions varied vastly across students regarding accuracy and predictors. The traditional average model did not match any student regarding the predictors' order. These findings are a testament that the “average” is rare and often does not represent any individual student. The variability in our study has shown that no single model can accurately and reliably capture all students. To account for the unique learning processes of individual students, idiographic methods could provide a solution. Educational relevance statementIndividualized artificial intelligence is feasible and reliable and can help understand each person using their own data. Using idiographic models, we can deliver solutions that are precise, accurate and interventions that are more likely to work.

ProBee: A Provenance-based Design for an Educational Game Analytics Model

ProBee: A Provenance-based Design for an Educational Game Analytics Model

Ultrasensitive self-powered biosensor with facile chemical signal amplification strategy using hydrogen peroxide-triggered silver oxidation reaction

The amplification strategies used for self-powered biosensor based on biofuel cell (BFC-SPB) need to be further developed. Because the currently developed strategies utilized the complicated hybridization of DNA or poorly readable current signal of capacitors for amplification, which limits the practical application in public health emergencies. Here, we present a facile chemical amplification strategy for BFC-SPB. The 5-minute amplification was triggered by simply adding H2O2 solution dropwise to the sensing cathode after the formation of the immune sandwich. The Ag NP of immunoprobe were oxidized to Ag(I), which can be served as the electron acceptor of the cathode. The amount of immunoprobe was positively correlated with that of the antigen, resulting in corresponding and high concentration of Ag(I) after the amplification, which enhanced the ability of the cathode as the electron acceptor. Meanwhile the glucose oxidation reaction (GOR) was performed on the bioanode modified with glucose oxidase (GOx). After assembling the bioanode and sensing cathode, the open circuit voltage of the BFC-SPB, measured by digital multimeter, distinctly rises with the elevated concentration of the antigen. To demonstrate the proof of concept, immunoglobulin G (IgG), selecting as a model analyte, was sensitively detected using this method. Result indicated that the limit of detection was 4.4 fg mL-1 (0.03 amol mL-1) in the linear range of 1 pg mL-1-10 μg mL-1. This work initiates a brand-new way of chemical amplification strategy for BFC-SPB, and offers a promising platform for practical applications.

DNA tweezer-assembled, regenerated nanosensor for one-step and sustainable detection of viral RNA and Hg2+

Transcription-polymerase chain reaction (RT-qPCR) is the golden standard to detect viral RNA. However, it suffers from several inherent defects such as high false-negative rates, time-consuming and expensive. To exploit better analytical methods for future global pandemics, it is vital to develop rapid, sustainable, and environmentally-friendly sensing methods. Here, a one-step, regenerated nanosensor assembled by DNA tweezers is developed for rapid and sustainable detection of viral RNA. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA is selected as model analyte. The DNA tweezer is designed with a central strand and two arm strands containing overhangs complementary to SARS-CoV-2 RNA. The SARS-CoV-2 RNA hybridizes with the two arm overhangs and strains the DNA tweezer to closed conformation, resulting in the close proximity of the donor and acceptor fluorophores. With the addition of an anti-strand complementary to the SARS-CoV-2 RNA, the closed DNA tweezer is relaxed to open conformation through a strand displacement process, leading to the separation of the dual fluorophores. Thus, the DNA tweezer is revived for fresh SARS-CoV-2 RNA. The proposed nanosensor can be regenerated for three cycles with the same performance. Moreover, the viral RNA can be detected within 30 min with a detection limit of 0.04 nM. By altering the sequences of the DNA tweezer, rapid, sensitive, and sustainable detection of Hg2+ is achieved. The proposed nanosensor provides a novel approach for rapid, flexible, and sustainable detection of viral RNA and Hg2+, exhibiting great potential for routine preliminary coronavirus disease tests, epidemic control, and environmental monitoring.

Smartphone-Based Colourimetric Detection of Methyl Red, Co(II), Uric Acid, and Topotecan after Pre-concentration onto a Hectorite Clay-Hydroxyethylcellulose Hybrid

Objective:: This paper describes a new, digital image colourimetry-based format for the quantification of analytes in an aqueous solution. Method:: The proposed method is based on analyte pre-concentration by adsorption onto Bentone LT. Bentone LT pellet isolation comes after adsorption, followed by in-situ application of an analyteselective chromogenic reaction. The resulting pellet colouration is captured by the phone’s integrated camera and assessed using the free open-source image processing software, ImageJ. Responses are calibrated and quantified. Results:: We tested the applicability of the proposed methodology for the quantification of specific model analytes which are of concern in environmental matrices (methyl red, Co(II), uric acid, topotecan). The smartphone-based assay was proven reliable in quantifying the model analytes (standard recovery of 82-116%), alone or in mixture, from dilute aqueous solutions and was found to depict accurately the adsorption behaviour followed photometrically in solution. Lower limit of linearity was calculated at 0.05, 0.11, 0.85 and 0.20 μg/mL for methyl red, Co(II), uric acid, and topotecan, respectively. The proposed format was found superior when compared to alternative published photometric/ colourimetric assays in terms of the lower limit of linearity. In the presence of possible adsorption interferents, the lower limit of linear response was shifted to slightly higher concentrations for topotecan i.e. from 0.2 μg/mL to 0.5 μg/mL. Conclusion:: We here demonstrate the extended applicability of the proposed methodology for the smartphone-based quantification of the specific model analytes. The applicability of this analysis format likely extends to other analytes, where analyte-specific colour formation is feasible.

Optimizing waste management strategies through artificial intelligence and machine learning - An economic and environmental impact study

Applying artificial intelligence (AI) and machine learning (ML) techniques to optimize waste management strategies, focusing on enhancing economic efficiency and reducing environmental impact, is vital. The study utilized ML models to analyze and forecast waste generation trends, assess the viability of various waste management methods, and develop optimization models for resource allocation and operational efficiency. The research employs the World Bank’s comprehensive waste management dataset. After rigorous data preprocessing, including cleaning and feature selection, a variety of ML techniques, such as regression models, classification algorithms like Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and optimization algorithms, including linear programming, are applied. Unlike other research, this study achieved 85 % accuracy on predictive analytics models for forecasting waste generation trends, primarily attributed to integrating more diverse data sets, including socio-economic factors. Also, the optimization resource allocation achieved a 15 % increase in operational efficiency. These findings provide significant insights for policymakers and urban planners, suggesting that integrating ML in waste management can lead to more sustainable and cost-effective practices. This paper demonstrates the transformative potential of ML in optimizing waste management strategies, offering a pathway towards more sustainable and economically viable waste management solutions globally.

A Novel Analytical Model for On-Load Performance Prediction of Delta-Type IPM Motors Based on Rotor Simplification

A Novel Analytical Model for On-Load Performance Prediction of Delta-Type IPM Motors Based on Rotor Simplification

Ultra-stable Pickering liquid crystal-in-water emulsions decorated with thermoresponsive soft microgels and their response to aqueous analytes

This work presents an extensive investigation of the Pickering stabilization of liquid crystal (LC)-in-water emulsions using soft nanometer-sized colloidal poly(N-isopropylacrylamide) (PNIPAM) microgel particles and the responsivity of the PNIPAM microgel-stabilized Pickering LC droplet-based assembly towards amphiphilic analytes. The evolution of size and stability of LC droplets with microgel concentration are found to be dependent on the emulsifier-regime. Despite microgel coating, the LC-water interface remains accessible to model analytes such as anionic sodium dodecyl sulfate (SDS) and cationic dodecyltrimethylammonium bromide (DTAB). The analytes can pass through the interfacial pores and/or meshes of porous microgels to induce a bipolar-to-radial ordering transition within the droplets. The dose-response behaviour is largely influenced by the initial microgel concentration, type of analytes and interfacial layer (charge), and the number of droplets exposed to the analytes. The PNIPAM microgel-coated LC droplets notably exhibited enhanced sensitivity under conditions promoting electrostatic attractive interactions between the interfacial microgel layer and analytes. Overall, the results highlight the potential of Pickering LC-in-water emulsions for reliable and quantitative analysis of aqueous analytes, thus opening up new avenues toward the development of droplet-based optical sensors.

Predictive interpretable analytics models for forecasting healthcare costs using open healthcare data

Healthcare expenditure, a considerable proportion of national budgets, has risen rapidly. Consequently, considerable research is devoted to controlling healthcare costs. Many efforts are underway to improve medical price transparency. Price transparency will help patients become better informed, allowing them to shop for care they can afford, eventually leading to efficiency in healthcare markets. This first requires medical pricing data to be made available publicly. Since the raw pricing data can be large and cover multiple conditions, it is necessary to provide an engine to process the data to facilitate its usage and understanding. We recommend creating computational models that predict healthcare costs for various patient conditions and demographics. Patients and providers can interrogate the underlying data to understand the variation of healthcare costs concerning medical conditions and demographic variables of interest, including age. We demonstrate our approach by creating predictive models using recent machine learning techniques. We analyzed anonymous patient data from the New York State Statewide Planning and Research Cooperative System, consisting of 2.34 million records from 2019. We built models to predict costs from over two dozen patient variables, including diagnosis codes, severity of illness, age, and other demographic variables. We investigated three models: regression, decision trees, and random forests. These models are explainable. We analyzed features to determine those that were predictive of total costs. We determined that the diagnosis code, severity of illness, and length of stay were good predictors of total costs, whereas race and gender are not useful in predicting total costs. We obtained the best performance using a catboost regressor, which yielded an R2 score of 0.85, better than the values reported in the literature.

Layer-By-Layer Designed Spark-Type AuCuPt Alloy with Robust Broadband Absorption to Enhance Sensitivity in Flexible Detection of Estriol by a Lateral Flow Immunoassay.

Excessive intake of estrogen poses significant health risks to the human body; hence, there is a necessity to develop rapid detection methods to monitor its levels of addition. Gold nanoparticles (AuNPs), commonly utilized as colorimetric signal labels, find extensive application in lateral flow immunoassay (LFIA). However, the detection sensitivity of traditional AuNPs-LFIA is typically constrained by low molar extinction coefficients and reliance on a single signal. Herein, in this work, unique spark-type AuCuPt nanoflowers modified with tannic acid (AuCuPt@TA) were precisely designed by reasonable layer-by-layer element composition and green modification. The obtained AuCuPt displays robust broadband absorption spanning the visible to near-infrared spectrum, showcasing a notable molar extinction coefficient of 2.38 × 1012 M-1 cm-1 and a photothermal conversion efficiency of 48.5%. Based on this, selecting estriol (E3) as a model analyte, colorimetric/photothermal dual-signal LFIA (CLFIA and PLFIA) was developed. Limits of detection (LOD) of the CLFIA and PLFIA were achieved at 0.033 ng mL-1 and 0.021 ng mL-1, respectively, which represent a 9.3- and 14.6-fold improvement compared to the visual LOD of AuNPs-LFIA. Moreover, the application feasibility of the immunoassay was further evaluated in the milk and pork with satisfactory recoveries ranging from 86.21% to 117.91%. Thus, this work has enhanced the performance of LFIA for E3 detection and exhibited enormous potential for other sensing platform construction.

A universal electrochemical sensor for detection of nucleic acids and protein based on host-guest recognition of β-cyclodextrin polymer

A novel electrochemical sensing approach was developed to detect DNA, RNA, and protein targets, utilizing the host–guest interaction between methylene blue (MB)-labeled probe and β-cyclodextrin-based nanocomposite (rGO/β-CDP). During the assay, the MB-labeled probe interacted with the corresponding target to form a probe/target complex. Due to steric hindrance, the formation of this complex reduced the amount of MB-labeled probes that can be captured by the β-cyclodextrin-based nanocomposite on the electrode surface, resulting in a measurable decrease in the electrochemical signal. Upon optimization, the sensing platform exhibited satisfactory analytical performance for three cancer-related model analytes: p53 DNA, microRNA-21 RNA, and thrombin protein, achieving low detection limits of 3.4 nM, 4.4 nM, and 5.7 pM, respectively. In this work, the electrochemical assay separates the detection process into two stages: target recognition in solution and signal transduction on the electrode surface. This fabrication effectively circumvents the issues associated with nonspecific conformational changes and the complicated probe installation procedures on electrode surfaces that are common in traditional sensing platforms. Integrating host–guest recognition with electrochemical assay, the developed approach not only advances the understanding of host–guest chemistry in sensor applications but also establishes a foundation for future innovations in the detection of biomarkers.

Understanding self-regulation strategies in problem-based learning through dispositional learning analytics

In the ongoing discussion about how learning analytics can effectively support self-regulated student learning and which types of data are most suitable for this purpose, this empirical study aligns with the framework who advocated the inclusion of both behavioral trace data and survey data in learning analytics studies. By incorporating learning dispositions in our learning analytics modeling, this research aims to investigate and understand how students engage with learning tasks, tools, and materials in their academic endeavors. This is achieved by analyzing trace data, which captures digital footprints of students’ interactions with digital tools, along with survey responses from the Study of Learning Questionnaire (SLQ), to comprehensively examine their preferred learning strategies. Additionally, the study explores the relationship between these strategies and students’ learning dispositions measured at the start of the course. An innovative aspect of this investigation lies in its emphasis on understanding how learning dispositions act as antecedents and potentially predict the utilization of specific learning strategies. The data is scrutinized to identify patterns and clusters of such patterns between students’ learning disposition and their preferred strategies. Data is gathered from two cohorts of students, comprising 2,400 first year students. This analytical approach aims to uncover predictive insights, offering potential indicators to predict and understand students’ learning strategy preferences, which holds value for teachers, educational scientists, and educational designers. Understanding students’ regulation of their own learning process holds promise to recognize students with less beneficial learning strategies and target interventions aimed to improve these. A crucial takeaway from our research underscores the significance of flexibility, which entails the ability to adjust preferred learning strategies according to the learning environment. While it is imperative to instruct our students in deep learning strategies and encourage autonomous regulation of learning, this should not come at the expense of acknowledging situations where surface strategies and controlled regulation may prove to be more effective.

A type-2 neutrosophic entropy-based group decision analytics model for sustainable aquaculture engineering

Fish cages are crucial for the establishment of sustainable fish farms. The selection of fish cages is an essential subject to be examined for ensuring sustainability. This research introduces an advanced decision support model for the selection of fish cage types used in fish farming in reservoirs (R-FFs). The decision support model is based on multi-criteria decision-making (MCDM) approach and utilizes type-2 neutrosophic numbers (T2NNs). The importance levels of criteria are determined using the T2NN-Entropy method, and the ranking of fish cage types is achieved through the alternative ranking order method accounting for two-step normalization (AROMAN). The developed model is referred to as the T2NN-Entropy-AROMAN hybrid method and relies on expert opinions. Two advanced aggregation operators based on Yager t-norm and t-conorm operations, named T2NN Yager weighted arithmetic mean and T2NN Yager weighted geometric mean, are developed for aggregating T2NNs. The validity of these two aggregation operators is demonstrated. A real-life case study is developed to confirm the applicability of the T2NN-Entropy-AROMAN hybrid method. This case study focuses on the selection of fish cage types for the Artvin-Borçka R–FF in Artvin province, Turkey. The research results indicate that floating cages are the best alternative type for sustainable fish farming. Sensitivity analysis scenarios confirm the robustness of the research model and findings. The research findings, coupled with the developed model, offer valuable insights and guidance to decision-makers, researchers, and practitioners within the sector.

Analytical Modeling and Throughput Computation of Blockchain Sharding

Analytical Modeling and Throughput Computation of Blockchain Sharding

Multiplexed electrochemical nucleic acid sensor based on visible light-mediated metal-free thiol-yne click reaction for simultaneous detection of different nucleic acid targets

Simultaneous detection of multiple tumor biomarkers with a simple and low-cost assay is crucial for early cancer detection and diagnosis. Herein, we presented a low-cost and simple assay for multiplexed detection of tumor biomarkers using a spatially separated electrodes strategy. The sensor is fabricated based on a metal-free thiol-yne click reaction, which is mediated by visible light, on commercially available indium tin oxide (ITO) electrodes. Four biomarkers, including p53 DNA, Brca2 DNA, K-ras DNA, and MicroRNA-204 RNA, were used as model analytes, and the corresponding oligonucleotide probes were modified on the desired electrode units sequentially with 530 nm irradiation light in the presence of photosensitizer Eosin Y. By this visible light-mediated coupling reaction, oligonucleotide probe densities of up to 9.2 ± 0.7 × 1010 molecules/cm2 were readily obtained on the ITO electrode surface. The proposed multiplexed E-NA sensor could detect four different nucleic acid targets concurrently without crosstalk among adjacent electrodes and was also successfully applied for detecting targets in a 20% fetal calf serum sample. The detection limits for p53 DNA, Brca2 DNA, K-ras DNA, and MicroRNA-204 RNA were 0.72 nM, 0.97 nM, 2.15 nM, and 1.73 nM, respectively. The developed approach not only has a great potential for developing cost-effective biosensors on affordable substrates for nucleic acid target detection, but also be easily extended to detect other targets by modifying the specific oligonucleotide probes anchored on the electrode.

Car Price Prediction Using Machine Learning

Abstract: The objective of the "Car Price Prediction Using Machine Learning" project is to anticipate car prices based on pertinent variables by utilizing predictive modelling and advanced data analytics. The rising need for precise and active pricing systems in the automobile sector is addressed by this initiative. The system will evaluate past vehicle data, including brand, model, manufacturing year, the mileage, type of fuel, and other details, by utilizing machine learning techniques. In order to produce specific predictions, the suggested model will be trained on an extensive dataset, noticing patterns and parallels within the data. In order to create a reliable prediction model, the research focuses on using regression techniques, such as ensemble approaches or linear regression. System of measurement like as absolute mean error and R-squared coefficient will be used to evaluate the predicted accuracy of the system in order to regulate its usefulness. If this resourcefulness is employed successfully, it will have a big impact on the automobile business for clients and venders alike by giving them a tool to evaluate fair market prices and helping them make decisions. This study enhances to the body of knowledge in price analysis using machine learning and lays the footing for future improvements in the prognostication of dynamic market trends in the automobile sector.

A covalent organic framework nanosheet-nanochannel composite with signal amplification strategy for electrochemical enantioselective recognition

Recognition and separation of chiral isomers are of great importance in both industrial and biological applications. However, owing to identical molecular formulas and chemical properties of enantiomers, signal transduction and amplification are still two major challenges in chiral sensing. In this study, we developed an enantioselective device by integrating chiral covalent organic framework nanosheets (CONs) with nanochannels for sensitive identification and quantification of enantiomers. Using 3,4-dihydroxyphenylalanine (DOPA) as the model analyte, the as-prepared chiral nanofluidic device exhibits a remarkable chiral recognition ability to l-DOPA than d-DOPA. More importantly, due to the chelation of DOPA with Fe3+ ions, it can efficiently block the ion transport through channel and shield the channel surface charge, which will amplify the difference in the electrochemical response of l-DOPA and d-DOPA. Therefore, a sensitive chiral recognition can be achieved using the present nanofluidic device coupled using electrochemical amplification strategy. Notably, using this method, an ultra-low concentration of l-DOPA (as low as 0.21 pM) can be facilely and successfully detected with a linear range of 1 pM–10 μM. This study provides a reliable and sensitive approach for achieving highly selective detection of chiral molecules.