Analytical Model Research Articles

Integrating ICAP theory in learning analytics

This paper introduces a conceptual evaluation learning analytics (LA) model developed by Keypath Education and Melbourne Business School to analyse student engagement in an online learning environment. The paper describes how a targeted LA intervention was grounded within the ICAP theory for active learning to identify, visualise and analyse student engagement metrics in a 100% online setting. The architecture of the model, including its design principles and underlying assumptions, are broadly examined against the latest research in educational, LA, and data analytics research. This includes a discussion of our initial phase testing results of captured student data alongside a discussion of key questions and desired study outcomes for the upcoming phase 2 of the data intervention. As a result, this paper provides both specific insights into how we created an evidence based and pedagogically sound evaluation model of online student engagement within a specific suite of asynchronous educational tools, as well as more general and practical insights into how other universities could develop similar approaches to enhance understanding and support of a growing online student population.

Optimizing influenza vaccine allocation: A predictive analytics approach for informed public health planning.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Excessive purchasing of influenza vaccine can lead to costly overages and waste of resources. Insufficient quantities, however, can jeopardize population health. Our project aimed to use predictive analytics to determine the influenza vaccine quantities that would be needed for the next influenza season while minimizing vaccine waste and meeting patient care demands. Several data sources were evaluated to develop a predictive analytics model to better estimate future influenza vaccine orders during upcoming influenza seasons. A retrospective analysis of influenza vaccine administrations over the last 4 influenza seasons allowed the team to develop an algorithm to predict influenza vaccine needs. Two regions within Mayo Clinic were selected to determine the validity of our ordering process. These 2 regions, identified as regions 3 and 5, ordered influenza vaccines based on the algorithm, while the other 3 regions acted as control groups, ordering though traditional methods based on purchasing data. Predictive analysis for the 2 intervention regions resulted in a savings of over $1 million when compared to traditional ordering methods. The model predicted that the quantity of vaccine ordered should be 17,574.16 and 9,164.29 quadrivalent influenza vaccines for regions 3 and 5, respectively. On the basis of actual administration data, 15,902 vaccines for region 3 and 9,016 vaccines for region 5 will be administered by the end of the season, both of which are less than the predicted amount needed, demonstrating the accuracy of the analytics. Compared to the traditional ordering method, ordering using predictive analytics allowed the team to more accurately determine future order volumes and spend, yielding significant cost savings.

Fe3O4@Pectin@Acetamide for Magnetic Solid Phase Extraction of Some Parabens in Cosmetic Samples and Their Quantification by High‐Performance Liquid Chromatography‐Ultraviolet Detection

ABSTRACTIn this project, the in‐situ formation of magnetic nanoparticles modified with pectin and acetamide was presented and used as a sorbent in the magnetic solid‐phase extraction method. The synthesis of the magnetic sorbent was studied by various characterization methods. The sorbent was employed for the microextraction of four model analytes including methyl paraben, ethyl paraben, propyl paraben, and butyl paraben. The separation and measurement of the extracted compounds were done by high‐performance liquid chromatography‐ultraviolet detection. In order to achieve the best extraction condition, factors affecting the extraction efficiency were investigated and optimized using response surface methodology. In the obtained optimized condition, the following parameters were achieved: limits of detection: 0.16–0.28 µg/L, limits of quantification: 0.49–0.92 µg/L, and intra‐ and inter‐day relative standard deviations: 1.2%–6.6% and 1.6%–4.8%, respectively. The linear dynamic ranges of the method for methyl paraben, ethyl paraben, butyl paraben, and propyl paraben were 1–200, 0.5–500, 1–500, and 1–500 µg/L, respectively. In order to investigate the effectiveness of the method, the proposed magnetic nanosorbent was used to measure the desired parabens in cosmetic samples. The results showed good accuracy and precision of the developed method for the determination of trace amounts of target analytes.

Novel sensing signal mode constructed by dual responsive silence layer combined with catalytic hydrogen evolution

For electrochemical assays, to obtain sensing signals, the redox of the modified electrochemical signal substance to provide a detectable signal is commonly required. However, during the process of assembly and sensing, the loss of signal substance caused by multiple steps of modification, incubation, and washing would lead to non-negligible fluctuation of signal and lack of reproducibility. To overcome this issue, a novel sensing signal mode without electrochemical signal substance was developed. By introducing electrochemical catalytic hydrogen evolution into the sensing scheme, and taking the overpotential as the detectable signal, the sensing system was constructed in the absence of electrochemical signal substance. As a proof of concept, the sensor was used to quantify squamous cell carcinoma antigen, a model analyte, and exhibited an excellent analytical performance with a wide linear range from 0.100 ng mL−1 to 100 ng mL−1 and a low detection limit of 52.4 fg mL−1. As envisioned, the sensor exhibited excellent long-term stability and reproducibility due to the avoidance of signal substance. This method provided a new way for the design of electrochemical sensors.

Numerical Modelling of Carrier Transport in Organic Field Effect Transistors

Background: Organic field effect transistors (OFETs), used in the fabrication of nanosensors, are one of the most promising devices in organic electronics because of their lightweight, flexibility, and low fabrication cost. However, the optimization of such OFETs is still in an early stage due to the minimal analytical and numerical models presented in the literature. Objective: This research presses to demonstrate a numerical carrier transport model based on the finite element method (FEM) to investigate the I-V characteristic of OFETs. Methods: Two various organic semiconductor materials have been included in the study, polyaniline and pentacene, where micro-scale, as well as nano-scale models have been presented. OFETs regarding channel length, dielectric thickness, and doping level impact have been studied. We nominated the threshold voltage, the on/off current ratio, the sub-threshold swing, and the field effect mobilities as the primary output evaluating parameters. Results: The numerical model has shown the criticality of the doping effect on tuning the device flowing drain current to exceed 300 μA saturation current, along with a threshold voltage of -0.1 V under a channel length of 30 nm. Conclusion: The study highlights the effectiveness of polyaniline over pentacene as nano-channel length OFET due to the boosted conductivity of polyaniline concerning pentacene.

A strategic and social analytics model for sustainable packaging in the cosmetic industry

Every day, we use cosmetic products that are not only focused on beauty but also with everything related to personal care and hygiene. The impact that these products have on sustainability cannot be overlooked. Many cosmetics contain unsustainable ingredients that can cause environmental damage and loss of biodiversity. In addition, fossil-based packaging contributes greatly to environmental pollution and increases waste in the absence of a circular supply chain. This work has a dual objective. The first is to provide a strategic analysis based on a multi-criteria approach to evaluate the most sustainable alternatives to traditional packaging that manufacturers could adopt based on the opinions of experts from different categories of stakeholders. In this study, the multi-criteria approach was employed, as it has been widely recognized in the literature for its effectiveness in evaluating and comparing alternatives across multiple, often conflicting criteria. The second is to provide a social analysis to assess consumers’ views, habits, preferences, and willingness to pay toward sustainable packaging. The results show divergence among experts who prefer refillable packaging while consumers prefer recyclable packaging. In contrast, a convergence in selling price and production costs is verified, highlighting the strategic importance of the economic dimension is for sustainable packaging, and the willingness to pay for sustainable packaging is about twice that of traditional packaging. The implications of this work suggest that circular supply chains covering the entire life cycle of products, based on a pragmatic approach, can drive the convergence of consumption and production patterns toward sustainable development.

A Privacy-Preserving Data Mining Through Comprehensive GNIPP Approach in Sensitive Data Sets

The quick growth of methods for analyzing data and the availability of easily available datasets have made it possible to build a thorough analytics model that can help with support decision-making. In the meantime, protecting personal privacy is crucial. A popular technique for medical evaluation and prediction, decision trees are easy to comprehend and interpret. However, the decision tree construction procedure may reveal personal information about an individual. By keeping the statistical properties intact and limiting the chance of privacy leaking within a reasonable bound, differential privacy offers a formal mathematical definition of privacy. To construct a boosting random forest that preserves privacy, we propose a Gaussian Noise Integrated Privacy Preservation (GNIPP) in this study. To address the issue of personal information breaches, we have designed a unique Gaussian distribution mechanism in GNIPP that enables the nodes with deeper depth to obtain more privacy during the decision tree construction process. We propose a comprehensive boosting technique based on the decision forest's prediction accuracy for assembling multiple decision trees into a forest. Furthermore, we propose an iterative technique to accelerate the assembly of decision trees. After all, we demonstrate through experimentation that the suggested GNIPP outperforms alternative algorithms on two real-world datasets.

Data-driven approach to optimizing property management strategies: spatial modeling analytics of short-term rentals

Purpose This study aims to explore the impact of locational and seasonal factors on the financial performance of short-term rental properties in Margaret River, Western Australia. It seeks to address the gap in understanding how these factors influence key financial metrics such as average daily rate (ADR) and occupancy rates, providing insights for property managers, investors and policymakers. Design/methodology/approach The research uses a mixed-method approach, integrating advanced predictive modeling techniques, such as Random Forests and Gradient Boosting, with spatial clustering algorithms like density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify the clustering structure (OPTICS). The study analyzes a comprehensive data set of short-term rental properties between 2012 and 2019. It focuses on locational attributes, seasonal variations and financial outcomes. Findings The findings reveal that properties located near tourist attractions and amenities consistently achieve higher ADRs and occupancy rates, confirming the critical role of location in driving rental demand. Seasonal analysis indicates significant fluctuations in both ADR and occupancy rates, with peaks during high tourist seasons and troughs in off-peak periods. The study underscores the importance of dynamic pricing strategies to optimize revenue and sustain occupancy across different seasons. In addition, it highlights the influence of property features, such as the number of bedrooms and bathrooms, on ADR, while noting that larger properties do not necessarily achieve higher occupancy rates. Research limitations/implications Future research could expand the scope to include different locations and explore the long-term impacts of locational and seasonal factors on property performance. Originality/value This research contributes to the literature by integrating spatial analysis with advanced predictive modeling techniques to provide a nuanced understanding of how locational and seasonal factors impact financial performance in the short-term rental market. It offers a novel application of data analytics within the context of tourism and hospitality management, bridging theoretical frameworks with practical insights.

A Reaction-Based Approach to Colorimetric Detection of Organic Analytes in Water Using a Chlorine-Containing Carbocyanine Dye and Hypochlorite

Water quality control employs techniques mostly targeting individual analytes; group detection is also practiced, but the choice of group methods is limited, which supports interest in developing such methods. We have examined the interaction of hypochlorite with a chlorine-containing heptamethine carbocyanine dye in the presence of 30 organic and inorganic model analytes that were found to induce diverse color changes in the system. The main supposed mechanisms are retardation of the dye oxidation with hypochlorite (presumably by scavenging chlorine radicals) and substitution of chlorine atom in the dye by the most nucleophilic analytes (amines, amino acids, proteins, DNA, phenol). The grass-green substitution product is more contrastingly visible against the dark-purple hypochlorite oxidation product of the dye than against the original emerald-green dye. The indicator reaction is monitored photographically for 10–40 min and the images are processed using principal component analysis (PCA) or linear discriminant analysis (LDA), allowing for data convolution for the complex color transitions. Nitrogen compounds are discriminated from the others, and more reactive analytes (tryptophan, cysteine, bovine serum albumin, and DNA) are detected in the presence of less reactive ones in natural water. The system is promising for the development of group assays for dissolved organic matter and the discrimination of water samples.

Coupling solid phase microextraction to integrated optical sensors with microfluidic open interface

Increasing number of regulations today is very demanding, so many fields need to rely to screening techniques to separate samples whose content need to be confirmed with that field's gold standard approach. Therefore, the development of simple and fast approaches is very much needed. This manuscript presents the development of a microfluidic open interface integrated with ultraviolet-visible (UV–Vis) absorption detection for rapid screening applications. The system was designed as a rapid sensor readout for compounds enriched by solid-phase microextraction devices. Two azo dyes from a liquid water enhancer solution served as model analytes. These two dyes were extracted with the solid phase microextraction fiber and introduced to the system for analysis. Experimental results demonstrated that the system could effectively detect dyes with absorbance at 403 nm, indicating its potential for quantitating analytes with UV–Vis chromophores. Although this sensing method exhibits limited selectivity, it offers a cost-effective, straightforward approach to rapid screening that can be conveniently miniaturized for on-site applications. Further advances could focus on integrating low-cost sensors with specific responses in place of the UV–Vis unit, as well as simplification and miniaturization of the system to improve on-site analysis. Additionally, incorporating autosampler systems could enable high-throughput determinations, broadening the method's applicability. The developed solid phase microextraction method reliably samples and enriches small molecules from complex systems, delivering clean extracts to the selective sensor for readout without interference from the investigated sample matrix.

Stable Halide Perovskite CsPbBr3 Nanocrystals Assisted by Covalent-Organic Frameworks for Electrochemiluminescence Analysis in an Aqueous Medium.

Lead halide perovskites have garnered attention as promising electrochemiluminescence (ECL) emitters owing to their superior photophysical characteristics. However, their poor water stability severely restricts their application in aqueous media for ECL. In this study, inorganic perovskite CsPbBr3 was assembled in situ in the imine-linked covalent-organic framework (COF-LZU1) as a novel ECL emitter. The expansive surface area and robust hydrophobic architecture of COF-LZU1 not only improved the water stability of CsPbBr3 but also guaranteed its exceptional ECL performance. The novel composite nanoluminescent material was coated onto an indium tin oxide (ITO) electrode via spin-coating and calcination processes to serve as an electrochemiluminescence (ECL) platform. A sensor was developed by combining a DNA hydrogel target-induced release system with a platform using ascorbic acid (AA) as a coreactant and T-2 toxin as the target analyte model. This method achieved a detection limit as low as 3.56 fg·mL-1 and was successfully applied to the analysis of the T-2 toxin content in corn samples. This study offers a novel path for the advancement of perovskite-based ECL emitters and their utilization in aqueous environments within the ECL field.

Effect of carbon price fluctuation on shore power system: a risk transmission analytics model

Effect of carbon price fluctuation on shore power system: a risk transmission analytics model

Ultrasensitive detection of histamine in spoiled meat employing silver nanoparticles decorated Perylene: An experimental-computational conjugation

Meat spoilage has been acquiring increasing attention recently and is directly associated with food safety and human health. Biogenic amines are the spying organic compounds fostered from the microorganism-mediated decarboxylation of amino acids during meat spoilage. Histamine, a biogenic amine acts as a model analyte and is toxic if consumed substantially. It is crucial to monitor histamine levels in meat due to its adverse effects. In this study, a simple and quick fluorescent sensor was fabricated for sensitive and selective detection of histamine. Citrate-capped silver nanoparticles (AgNP) were loaded onto Perylene (PER) to develop a sensing probe that was characterized using UV–visible, FTIR, XRD, and FESEM, and its optical behavior toward histamine was investigated. Moreover, the binding affinity between histamine and PER@AgNP was assessed using a DFT-based computer simulation. Under optimal conditions, the sensor showed linear relationships for histamine concentrations from 25 μM to 3200 μM with LOD 13.52 μM.

An Efficient Approach for Advancing Performance in Rapid Detection Based on Molybdenum Disulfide Nanoflower Supported Binary Transition Metal Oxides.

Binary transition metal oxides (BTMOs) have drawn considerable attention in recent years for their excellent catalytic properties and chemical stability in the sensing field. Regrettably, the loss of active site exposure originating from the agglomerate during preparation largely restricted their sensing applications. In this work, we report an efficient strategy for advancing the performance of BTMOs in rapid detection based on a 3D molybdenum disulfide nanoflower. The larger surface area, multiple active site exposures, and higher electrical conductivity promote the dispersion of BTMOs and the redox reaction of analytes on the surface of nanocomposites, thereby enhancing the sensitivity and widening the quantitative range. As a proof-of-method application, ferric vanadate (FeVO4) and ciprofloxacin (CIP) were chosen as model catalysts and analytes, respectively. This approach exhibits excellent sensitivity, selectivity, repeatability, and stability. The detection limit could be as low as 26.6 nM, and the linear range covered 3 orders of magnitude (from 0.1 to 500 μM). It also demonstrated good practicality in milk, honey, and drinking water with a recovery of 90.6% to 100.8%. To our knowledge, this is the first report on incorporating MoS2 into BTMOs for augmenting sensing performance in rapid detection.

Comparison of protein immobilization methods with covalent bonding on paper for paper-based enzyme-linked immunosorbent assay

In this study, two methods were examined to optimize the immobilization of antibodies on paper when conducting a paper-based enzyme-linked immunosorbent assay (P-ELISA). Human IgG, as a test-capture protein, was immobilized on paper via the formation of Schiff bases. Aldehyde groups were introduced onto the surface of the paper via two methods: NaIO4 and 3-aminopropyltriethoxysilane (APTS) with glutaraldehyde (APTS-glutaraldehyde). In the assay, horseradish peroxidase-conjugated anti-human IgG (HRP-anti-IgG) binds to the immobilized human IgG, and the colorimetric reaction of 3,3′,5,5′-tetramethylbenzyzine (TMB) produces a blue color in the presence of H2O2 and HRP-anti-IgG as a model analyte. The immobilization of human IgG, the enzymatic reaction conditions, and the reduction of the chemical bond between the paper surface and immobilized human IgG all were optimized in order to improve both the analytical performance and the stability. In addition, the thickness of the paper was examined to stabilize the analytical signal. Consequently, the APTS-glutaraldehyde method was superior to the NaIO4 method in terms of sensitivity and reproducibility. Conversely, the reduction of imine to amine with NaBH4 proved to exert only minimal influence on sensitivity and stability, although it tended to degrade reproducibility. We also found that thick paper was preferential when using P-ELISA because a rigid paper substrate prevents distortion of the paper surface that is often caused by repeated washing processes.

Performance of Predictive Models in Cervical Cancer Recurrence: A Systematic Review and Meta-Analysis of Biomarkers and Prognosis

This study investigates the use of Predictive Analytics Frameworks (PAF) for identifying biomarkers of recurrent cervical cancer and predicting prognosis. It addresses the limited comprehensive evaluations of the effectiveness of predictive models in this area, despite the growing application of machine learning in healthcare. The purpose of this systematic review and meta-analysis is to assess the performance of predictive analytics models in terms of sensitivity, accuracy, specificity, and the area under the curve (AUC-ROC) for identifying cervical cancer biomarkers and predicting prognosis. To address this research problem, a systematic review and meta-analysis was conducted, covering studies published between 2014 and 2024. A total of 1,515 studies were initially identified from the PubMed and Scopus databases, with 50 research studies meeting the inclusion criteria. Repeated measures ANOVA and meta-analysis were applied using data collected over an 8-year period to evaluate recurrence trends and the predictive power of various models. The findings suggest that predictive analytics models show significant potential for improving diagnostic accuracy in identifying cervical cancer biomarkers. However, the review also highlights several limitations, including the small number of included studies, heterogeneity across studies, and potential bias in retrospective analyses. In conclusion, while predictive analytics frameworks demonstrate promise in improving cervical cancer prognosis and biomarker identification, further research is required to validate these findings and assess their broader clinical utility. The study underscores the importance of continued exploration of predictive models to enhance decision-making in oncology.

Paper-based isotachophoretic preconcentration technique for low-cost determination of glyphosate.

Electrophoretic microfluidic paper-based analytical devices (e-µPADs) are promising for low-cost and portable technologies, but quantitative detection remains challenging. In this study, we develop a paper-based isotachophoretic preconcentration and separation method for the herbicide glyphosate as a model analyte. The device, consisting of two electrode chambers filled with leading and terminating electrolytes and a nitrocellulose strip as the separation carrier, was illuminated by a flat light source and operated with a voltage supply of 400V. Detection was accomplished using a simple camera. Colorimetric detection was optimized through competitive complexation between glyphosate, copper ions, and pyrocatechol violet as a dye. The buffer system was optimized using simulations, (i) ensuring the pH was optimal for the demetallation of the blue pyrocatechol violet-copper complex [PV] to the yellow free dye and (ii) ensuring the electrophoretic migration of glyphosate into the slower [PV] for the colorimetric reaction. A new data evaluation method is presented, analyzing the RGB channel intensities. The linear range was between 0.8 and 25µM, with a LOD of approximately 0.8µM. The ITP separation preconcentrated glyphosate by a factor of 820 in numerical simulations. The method may be applied to control glyphosate formulations, especially in developing countries where herbicide sales and applications are poorly regulated.

Ever aging AI/ML Models for Predictive Analytics in SaaS Subscription Management

The rapid evolution of Software as a Service (SaaS) has transformed how businesses manage subscriptions, necessitating innovative approaches to optimize customer retention and revenue growth. This paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) models in predictive analytics for SaaS subscription management. By leveraging advanced algorithms, organizations can analyze vast datasets to identify patterns in customer behavior, predict churn, and forecast subscription renewals. The study emphasizes the importance of feature selection and model training, highlighting how tailored predictive models can enhance decision-making processes. Furthermore, it discusses the implications of predictive analytics in personalizing customer experiences and improving service offerings, ultimately leading to increased customer satisfaction and loyalty. Through case studies and data-driven insights, this research aims to provide a comprehensive understanding of how AI/ML technologies can empower SaaS companies to navigate the complexities of subscription management and achieve sustainable growth.

Empirical investigation of a data analytics model in medical settings

This article centers around the development and analysis of machine learning and deep learning models aimed at enhancing diabetes diagnosis. In the swiftly evolving landscape of data technologies, it becomes crucial to explore the applications of these methods for accurate predictions and improved medical decision-making. Our research encompasses diverse datasets, leveraging state-of-the-art algorithms and technologies for model training and testing. The primary emphasis lies in evaluating the accuracy, sensitivity, and specificity of models within the realm of diabetes diagnosis. The study results reveal significant advancements in disease prediction, underscoring the potential of machine learning and deep learning in medical applications. This work introduces fresh perspectives on the utilization of computational methods in healthcare and serves as a foundation for prospective research in this domain.

Plasmonic-Enhanced Colorimetric Lateral Flow Immunoassays Using Bimetallic Silver-Coated Gold Nanostars.

The colorimetric lateral flow immunoassay (cLFIA) has gained widespread attention as a point-of-care testing (POCT) technique due to its low cost, short analysis time, portability, and capability of being performed by unskilled operators with minimal requirement of reagents. However, the low analytical sensitivity of conventional LFIA based on colloidal gold nanospheres limits their applications for sensitive detection of trace amounts of target analytes. In this study, we introduced a novel plasmonic-enhanced colorimetric LFIA (PE-cLFIA) platform featuring bimetallic silver-coated gold nanostars (BGNS) with exceptional optical properties, leading to ultrahigh visual color brightness. The BGNS-based PE-cLFIA was successfully applied to detect a model analyte, low-calcium response V (LcrV), a virulence protein factor found in Yersinia pestis, the causative agent of bubonic plague. The PE-cLFIA sensing using BGNS-3 composed of 45 nm silver thickness showed a high visual colorimetric sensitivity with a detection limit as low as 13.7 pg/mL, which was around 50 times more sensitive than that of a traditional gold nanoparticle-based LFIA. In addition, the antibody-conjugated BGNS-3 showed excellent stability over 6 months. To illustrate the potential for clinical applications, we demonstrated that our LFIA platform for detecting LcrV spiked in human serum without any sample preprocessing exhibited a detection limit of 22.8 pg/mL. These results open up new opportunities for developing hybrid nanoparticle systems for sensitive POCT PE-cLFIA screening for infectious disease detection.