Analytical Model Research Articles

Optimization of resolution as a simultaneous function of solvent composition, pH and temperature in reversed-phase liquid chromatography

BackgroundThe latest chromatographic retention models are capable of accurately describe the dependencies of retention over a wide range of experimental conditions. By using a suitable conversion, these models can be transformed into equations expressing the optimization criteria as function of multiples variables. Even though that theoretical models significantly reduce the experimental requirements for optimizations, these models have been barely used. Instead, most optimizations rely on empirical exploration of the relationships between criterions and variables.There is a need for a strategy to reduce the required number of experiments in multivariated optimization of separations, and Fundamental Models offer a clear opportunity for addressing it. ResultsA Fundamental Model is used to give the simultaneous dependence of chromatographic retention of seven ionizable pesticides on the three variables: solvent composition, temperature and pH (w, T, pH). Based on few experiments, the 10 parameters required to predict the chromatographic retention of those compounds, taken as model analytes, can be obtained. Two mathematical treatments to convert retentions into resolutions between pairs are used: one considering extracolumn dispersions and other neglecting these contributions. Using the Overlapped Resolutions Maps, extended to four dimensions, two optimal conditions can be found for the two different mathematical conversions. Chromatographic conditions were empirically evaluated obtaining the best results for the optimization considering extracolumn dispersions, proving that this condition is a true optimal. It was demonstrated that any small shift in any of the variables from this true optimal leads to a loss in resolution. SignificanceFundamental Models describing chromatographic retention as a simultaneous function of multiple variables are nowadays very accurate. In this work is demonstrated that these models are useful not only to predict retentions, but also to optimize separations, even in the more challenging mode: isocratic, isothermal and iso-pH. However, the success in the optimization procedure depends also on the proper definition of the mathematical conversion of the Fundamental Models into optimization criteria.

Smart Urban Food Re-Distribution using Web Application

Smart Urban Food Re-Distribution using Web Application

Development and Impact Analysis of a Multi-Pandemic Real-Time Data Dashboard: A Comparative Analytical Approach

This research presents the development and implementation of a Multi Pandemic Real-Time Data Dashboard, designed to facilitate enhanced comparative analysis across various global pandemics. Utilizing a combination of real-time data acquisition, statistical modeling, and predictive analytics, the dashboard provides a dynamic platform for analyzing pandemic trends and outcomes. Our methodology involved integrating diverse data sources, employing mathematical models such as the SIR (Susceptible, Infected, Recovered) model and machine learning algorithms for data processing and trend prediction. Key findings indicate that the dashboard effectively identifies patterns in pandemic spread and impact, offering valuable insights for public health decision-making. The research underscores the significance of advanced data analytics in managing public health crises, highlighting the dashboard’s potential in aiding proactive pandemic response and policy formulation.

Quartz Crystal Microbalance as a Holistic Detector for Quantifying Complex Organic Matrices during Liquid Chromatography: 2. Compound-Specific Isotope Analysis.

In carbon-compound-specific isotope analysis (carbon CSIA) of environmental micropollutants, purification of samples is often required to guarantee accurate measurements of a target compound. A companion paper has brought forward an innovative approach to couple a quartz crystal microbalance (QCM) with high-performance liquid chromatography (HPLC) for the online quantification of matrices during a gradient HPLC purification. This work investigates the benefit for isotope analysis of polar micropollutants typically present in environmental samples. Here, we studied the impact of the natural organic matter (NOM) on the isotopic integrity of model analytes and the suitability of the NOM-to-analyte ratio as a proxy for the sample purity. We further investigated limitations and enhancement of HPLC purification using QCM on C18 and C8 phases for single and multiple targets. Strong isotopic shifts of up to 3.3% toward the isotopic signature of NOM were observed for samples with an NOM-to-analyte ratio ≥10. Thanks to QCM, optimization of matrix removal of up to 99.8% of NOM was possible for late-eluting compounds. The efficiency of HPLC purification deteriorated when aiming for simultaneous purification of two or three compounds, leading to up to 2.5% less NOM removal. Our results suggest that one optimized HPLC purification can be achieved through systematic screening of 3 to 5 different gradients, thereby leading to a shift of the boundaries of accurate carbon CSIA by up to 2 orders of magnitude toward lower micropollutant concentrations.

ADDRESSING PRIVACY AND ETHICAL CONSIDERATIONS IN HEALTH INFORMATION MANAGEMENT SYSTEMS (IMS)

This paper explores the crucial role of privacy and ethics in Health Information Management Systems (HIMS), addressing the inherent challenges and presenting innovative solutions through detailed case studies. We examine the significant privacy concerns, such as data breaches and unauthorized data sharing, alongside ethical issues like informed consent and equitable access to technology. Effective solutions including the Privacy-Preserving Distributed Analytics (PPDA) model and proactive bioethics committees, as demonstrated by institutions like Vanderbilt University Medical Center, illustrate successful strategies for managing these concerns. The paper emphasizes the importance of prioritizing privacy and ethics not merely as compliance requirements but as foundational elements essential to the trustworthiness and effectiveness of HIMS. It advocates for a continuous, proactive approach to address these issues as technology evolves and regulations change. Furthermore, we call for a collaborative effort among policymakers, healthcare providers, technologists, and patients to develop and refine HIMS that uphold the highest standards of privacy, ethics, and accessibility, thus enhancing the quality of care and health outcomes for all stakeholders.

Analytical Release Voltage Model of Monolithic 3-D Integrated Nanoelectromechanical Memory Switches

Analytical Release Voltage Model of Monolithic 3-D Integrated Nanoelectromechanical Memory Switches

Analytical Dynamic Energy-Carbon Flow Model and Application in Cost Allocation for Integrated Energy Systems

Analytical Dynamic Energy-Carbon Flow Model and Application in Cost Allocation for Integrated Energy Systems

A method of determining the bremsstrahlung flux-weighted average photonuclear cross section

Based on a developed analytical model, a method is proposed for measuring the photonuclear cross section averaged over bremsstrahlung flux without application of additional target-monitor of photon flux. The method involves the use of a thin isotopic target, that completely overlaps the photon beam (a photonuclear converter), as well as an algorithm for processing the data on the yield of a reaction under study in such a target. The novel technique was validated on the reactions 100Mo(γ,n)99Mo and 58Ni(γ,n)57Ni in the range of photon end-point energy of 40.7–93.9 MeV. The photon flux-weighted average cross sections of the reactions measured experimentally are in good agreement with Monte Carlo simulations and TALYS predictions on their excitation functions.

Wasserstein Robust Classification with Fairness Constraints

Problem definition: Data analytics models and machine learning algorithms are increasingly deployed to support consequential decision-making processes, from deciding which applicants will receive job offers and loans to university enrollments and medical interventions. However, recent studies show these models may unintentionally amplify human bias and yield significant unfavorable decisions to specific groups. Methodology/results: We propose a distributionally robust classification model with a fairness constraint that encourages the classifier to be fair in the equality of opportunity criterion. We use a type-[Formula: see text] Wasserstein ambiguity set centered at the empirical distribution to represent distributional uncertainty and derive a conservative reformulation for the worst-case equal opportunity unfairness measure. We show that the model is equivalent to a mixed binary conic optimization problem, which standard off-the-shelf solvers can solve. We propose a convex, hinge-loss-based model for large problem instances whose reformulation does not incur binary variables to improve scalability. Moreover, we also consider the distributionally robust learning problem with a generic ground transportation cost to hedge against the label and sensitive attribute uncertainties. We numerically examine the performance of our proposed models on five real-world data sets related to individual analysis. Compared with the state-of-the-art methods, our proposed approaches significantly improve fairness with negligible loss of predictive accuracy in the testing data set. Managerial implications: Our paper raises awareness that bias may arise when predictive models are used in service and operations. It generally comes from human bias, for example, imbalanced data collection or low sample sizes, and is further amplified by algorithms. Incorporating fairness constraints and the distributionally robust optimization (DRO) scheme is a powerful way to alleviate algorithmic biases. Funding: This work was supported by the National Science Foundation [Grants 2342505 and 2343869] and the Chinese University of Hong Kong [Grant 4055191]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0230 .

Colorimetric sensor array for versatile detection and discrimination of model analytes with environmental relevance

In the current work, a rapid, simple, low-cost, and sensitive smartphone-based colorimetric sensor array coupled with pattern-recognition methods was proposed for the determination and differentiation of some organic and inorganic bases (i.e., OH−, CO32−, PO43−, NH3, ClO−, diethanolamine, triethanolamine) as model compounds. The sensing system has been designed based on color-sensitive dyes (Fuchsine, Giemsa, Thionine, and CoCl2) which were used as sensor elements. The color changes of a sensor array were observed by the naked eye. The color patterns were recorded using digital imaging in a three-dimensional (red, green, and blue) space and quantitatively analyzed with color calibration techniques. Distinctive colorimetric patterns for target bases via linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA) were observed. The results indicated that the analytes related to each class (at the different concentration levels in the range of 0.001–1.0 mol L−1) were clustered together in the canonical discriminant plot and HCA dendrogram with high sensitivity and an overall precision of 85%. Furthermore, the first function factor of LDA correlated with the concentration of each target analyte in a correlation coefficient (R2) range of 0.864–0.996. These described procedures based on the colorimetric sensor array technique could be a promising candidate for practical applications in package technology and facile detection of pollutants.

Modification of cellulose substrate by in situ synthesis of metal-organic framework-5 for thin film microextraction of some non-steroidal anti-inflammatory drugs and their measurement by high-performance liquid chromatography-ultraviolet detector

This work, reports the successful preparation a thin film by a simple and inexpensive process for quantification of a model analytes in the urine sample using HPLC-UV. To this end, cellulose paper was employed as a substrate for the in-situ synthesis of MOF-5, to increase the resistance of the prepared film. The prepared film can be reused 26 times with no reduction in its performance. The thin film prepared by MOF-5 modified cellulose substrate was utilized in thin film microextraction (TFME) method for the extraction and preconcentration of naproxen, aspirin, tolmetin, and celecoxib. Under optimal conditions, the linear dynamic range of the target analytes was 2–500 µg L−1 with correlation coefficients (R2) ranging from 0.9961 to 0.9990. Also, the limits of detection (LODs), the limits of quantification (LOQs) and relative standard deviation (RSD%) of the proposed method for selected analytes ranged between 0.57 and 0.77 µg L−1, 1.7 to 2.3 and 3.5 % to 6.2 %, respectively. Moreover, relative recoveries varied from of 94 % to 108 %, indicating the absence of matrices effect in the proposed method. Eventually, the TFME was successfully used for the extraction of selected analytes from urine samples.

Rational design of nanozyme with integrated sample pretreatment for colorimetric biosensing

Nanozymes have been widely used in the field of biosensing owing to their high stability, low cost, adjustable catalytic activity, and convenient modification. However, achieving high selectivity and sensitivity simultaneously in nanozyme-based colorimetric sensing remains a major challenge. Nanozymes are nanomaterials with enzyme-simulating activity that are often used as solid-phase adsorbents for sample pretreatment. Our design strategy integrated sample pretreatment function into the nanozyme through separation and enrichment, thereby improving the selectivity and sensitivity of nanozyme-based colorimetric biosensing. As a proof-of-concept, glucose was used as the model analyte in this study. A phenylboric acid-modified magnetic nanozyme (Cu/Fe3O4@BA) was rationally designed and synthesized. Selectivity was enhanced by boronate-affinity specific adsorption and the elimination of interference after magnetic separation. In addition, magnetic solid-phase extraction enrichment was used to improve the sensitivity. A recovery rate of more than 80% was reached when the enrichment factor was 50. The synthesized magnetic Cu/Fe3O4@BA was recyclable at least five times. The proposed method exhibited excellent selectivity and sensitivity, simple operation, and recyclability, providing a novel and practical strategy for designing multifunctional nanozymes for biosensing.

Line Loss Calculation and Optimization in Low Voltage Lines with Photovoltaic Systems Using an Analytical Model and Quantum Genetic Algorithm

Line Loss Calculation and Optimization in Low Voltage Lines with Photovoltaic Systems Using an Analytical Model and Quantum Genetic Algorithm

STUCK PIPE MITIGATION: FINDINGS, ANALYSIS, AND MACHINE LEARNING SOLUTIONS

The occurrence of stuck pipes during drilling presents significant challenges, leading to nonproductive time (NPT) and potential damage to equipment. This paper delves into the reasons behind, effects of, and methods for addressing incidents of stuck pipes in drilling actions. Extensive literature review and analysis reveal the prevalence of stuck pipe events and their diverse underlying causes, ranging from differential sticking to mechanical jamming. Additionally, the study identifies key drilling parameters and signals indicative of potential stuck pipe incidents, emphasizing the importance of early detection and intervention to prevent adverse outcomes. Proposing the utilization of machine learning algorithms and statistical models for predictive analytics as a proactive strategy to mitigate the risk of stuck pipe incidents. The paper discusses the limitations of traditional statistical models and highlights the efficacy of machine learning methods, such as artificial neural networks (ANNs) and support vector machines (SVMs), in accurately modeling drilling processes. Furthermore, practical machine learning models using real-time drilling data are presented as effective tools for early detection and timely intervention in stuck pipe incidents. The study also describes various software tools used to assess and mitigate risks in drilling operations. Overall, this paper presents prospects for improving operational efficiency and safety of drilling operations using advanced analytical methods and software solutions. Keywords: stuck pipe, type of stuck, drillstring immobilization, nonproductive time, machine learning in drilling, risk analysis software.

Building Flexible, Data-driven Framework for Real-time Analysis

In the contemporary business landscape, the escalating demand for real-time predictive analytics is driven by the imperative for dynamic decision-making. Traditional analytics models often prove inadequate in addressing the need for agility required to respond swiftly to rapidly changing circumstances. Real-time predictive analytics, however, offers a transformative solution, empowering organizations to make informed and timely decisions in fast-paced environments. This capability proves invaluable in industries were staying ahead of emerging trends is critical, fostering a proactive approach to decision-making that can significantly impact competitiveness. The sheer volume and diversity of data require sophisticated solutions for processing and analysis. Real-time predictive analytics becomes an indispensable tool, offering the capability to promptly extract valuable insights from massive datasets. This not only enhances decision-making but also allows organizations to stay ahead by uncovering trends and patterns in real time. Scalability is a fundamental consideration for organizations on a growth trajectory. Real-time predictive analytics frameworks provide a scalable foundation, allowing businesses to seamlessly expand their analytical capabilities. This adaptability ensures that the framework can handle the increasing demands for processing power and storage, aligning with the evolving needs of a growing organization.

Evaluation of electrochemical performance of antimony modified screen-printed carbon electrodes

This study compares the electrochemical performance of screen-printed carbon electrodes (SPCEs) modified with antimony (Sb/SPCEs) under different potentiostatic pre-plating conditions. Neutral Red (NR) was employed as a novel redox probe to evaluate the electrochemical performance of Sb/SPCEs. It was demonstrated that NR in the protonated form performs quasi-reversible redox transformations at bare SPCE and Sb/SPCEs in phosphate buffer solutions (pH 5.5±0.5) in the potential range of (−0.30)–(−0.75) V, where the antimony is not electroactive. Sb/SPCEs were studied electrochemically by cyclic voltammetry (CV) / electrochemical impedance spectroscopy (EIS), and morphologically by scanning electron microscopy (SEM). Cyclic voltammetry investigations revealed the dependence of the electrochemical performance of Sb/SPCEs on the degree of coverage of the substrate with the metal. The obtained CV, EIS, and SEM data are consistent. The lowest charge transfer resistance (Rct) value (6 Ω) was obtained at Sb/SPCE with the highest degree of antimony coverage. To investigate the electroanalytical performance of Sb/SPCEs, nickel (II) ions were utilized as a model analyte. A study of roughness factors and sensitivity towards nickel (II) ions for Sb/SPCEs using two-tailed Pearson's criterion revealed a high degree of correlation between their electrochemical and electroanalytical properties. The results show that using NR as a redox probe can help controlling modification processes during the development of innovative antimony-containing sensors.

On-Chip Electrochemical Sensing with an Enhanced Detecting Signal Due to Concentration Polarization-Based Analyte Preconcentration.

Here, we integrated two key technologies within a microfluidic system, an electrokinetic preconcentration of analytes by ion Concentration Polarization (CP) and local electrochemical sensors to detect the analytes, which can synergistically act to significantly enhance the detection signal. This synergistic combination, offering both decoupled and coupled operation modes for continuous monitoring, was validated by the intensified fluorescent intensities of CP-preconcentrated analytes and the associated enhanced electrochemical response using differential pulse voltammetry and chronoamperometry. The system performance was evaluated by varying the location of the active electrochemical sensor, target analyte concentrations, and electrolyte concentration using fluorescein molecules as the model analyte and Homovanillic acid (HVA) as the target bioanalyte within both phosphate-buffered saline (PBS) and artificial sweat solution. The combination of on-chip electrochemical sensing with CP-based preconcentration renders this generic approach adaptable to various analytes. This advanced system shows remarkable promise for enhancing biosensing detection in practical applications while bridging the gap between fundamental research and practical implementation.

Effect of Bonding Type on the Failure of Reinforced Concrete Beam Strengthened with In-Situ High Performance Fibre Reinforced Concrete Layer

High performance fibre reinforced concrete (HPFRC) materials with tensile hardening behaviour can effectively be used for strengthening reinforced concrete beams. A perfect bond between the original and the reinforcing layer cannot be formed, the load-bearing capacity and ductility of the strengthened beam can significantly be affected by the interfacial bond strength between the contacting surfaces. In this paper, beam retrofitting with cast in-situ strengthening type is examined. The purpose of this experimental study is to investigate the impact of the different bond types on the load-bearing capacity, ductility, and failure mode of the strengthened beams in the case of cast in-situ strengthening. Twenty-four beam tests were performed with untreated and rough surfaced beams, with or without connecting elements. The effect of the bond type proved to be significant regarding the failure mode in the case of compression side strengthening, stronger bond resulted in higher load bearing capacity and ductility, too. When tensile side reinforcement was investigated no average increment was experienced in the maximal force and ductility due to the stronger bond. Based on the results, it can be concluded that the generally applied analytical models that assume perfect connection may lead to exaggerated results in the case of a compressed side HPC-strengthened beam. Therefore, it is necessary to develop a model that considers the effect of the imperfect bond.

Leveraging Visualization and Machine Learning Techniques in Education: A Case Study of K-12 State Assessment Data

As data-driven models gain importance in driving decisions and processes, recently, it has become increasingly important to visualize the data with both speed and accuracy. A massive volume of data is presently generated in the educational sphere from various learning platforms, tools, and institutions. The visual analytics of educational big data has the capability to improve student learning, develop strategies for personalized learning, and improve faculty productivity. However, there are limited advancements in the education domain for data-driven decision making leveraging the recent advancements in the field of machine learning. Some of the recent tools such as Tableau, Power BI, Microsoft Azure suite, Sisense, etc., leverage artificial intelligence and machine learning techniques to visualize data and generate insights from them; however, their applicability in educational advances is limited. This paper focuses on leveraging machine learning and visualization techniques to demonstrate their utility through a practical implementation using K-12 state assessment data compiled from the institutional websites of the States of Texas and Louisiana. Effective modeling and predictive analytics are the focus of the sample use case presented in this research. Our approach demonstrates the applicability of web technology in conjunction with machine learning to provide a cost-effective and timely solution to visualize and analyze big educational data. Additionally, ad hoc visualization provides contextual analysis in areas of concern for education agencies (EAs).

Simple dispersion of carbon nanofibers in a naturally occurring polyphenol in water and their electrochemical characteristics

Carbon nanofibers are hydrophobic with poor dispersion in water. They are normally functionalised using highly acidic solutions and elevated temperatures. In this communication, we show that tannic acid can be employed at room temperature using a simple sonication method to give very good dispersions of the CNFs in distilled water. Using a factorial design, the three components, the sonication period, the concentration of tannic acid and the concentration of CNFs were found to be significant, while the concentration of the tannic acid as a single component had an equally significant effect. The optimum conditions were 1.0–1.25 mg mL−1 of CNFs, and 0.85 – 1.0 mM of tannic acid with a sonication period between 50 and 60 min. The well-dispersed CNFs exhibited very good electrochemical characteristics, with efficient electron transfer in the oxidation of acetaminophen, which was used as a model analyte.