Analytical Applications Research Articles

Artificial Intelligence (AI) in working capital management: Practices and future potential

This study delves into how artificial intelligence (AI) transforms working capital management by addressing the limitations of traditional methods. The focus is to critically review research publications, case studies and industry reports using qualitative research methodology to examine how AI improves operational efficiency and decision-making in this area. The study demonstrates the practical application of advanced machine learning algorithms and big data analytics in optimizing inventory management, enhancing demand forecasting and improving cash flow predictions. A thorough review of recent research and case studies reveals additional benefits, including automated reconciliations, debtor risk analysis, accelerated cash inflows, invoice processing and proactive working capital management. Despite challenges in integrating AI with legacy systems, the potential for substantial improvements in financial health and operational efficiency is significant. The study also suggests future research directions, such as developing comprehensive AI-driven applications for broader working capital considerations, creating empirical validation frameworks for model performance and addressing ethical considerations to fully harness AI's potential in optimizing working capital management.

Determination of pesticide residues in five different corn-based products using a single and simple solid–liquid extraction without clean-up steps followed by comprehensive two-dimensional liquid chromatography coupled to tandem mass spectrometry

Pesticide residues in food products are a critical issue concerning food safety. Recent studies have increasingly focused on developing selective, rapid, innovative, and environmentally friendly analytical methods for detecting pesticides in foodstuffs. This study presents a novel analytical method utilizing comprehensive two-dimensional liquid chromatography-tandem mass spectrometry for the simultaneous determination of one hundred and thirteen multiclass pesticides in corn products. The method simplifies the process with a single-step solid–liquid extraction and benefits from two-dimensional separation to enhance resolution and minimize matrix effects. The methodology was validated according to European Commission DG-SANTE (SANTE/11312/2021) and European Committee for Standardization (EN 15662, 2018) guidelines, obtaining acceptable analytical parameters. Limits of detection ranged from 0.2 µg/L to 30.2 µg/L and limits of quantification from 0.3 to 91.6 μg/L, all bellow regulatory limits. Accuracy, ranged from 60 to 120 % and precision, estimated by relative standard deviation, was consistently below 15.1 % in all cases. The applicability of the method was demonstrated by analyzing pesticides in five representative corn products (tortilla, corn flakes, corn cake, starch, and polenta). Sixteen pesticides were detected, all within the regulatory residue limits. Three metric tools were applied to assess the analytical process. Environmental sustainability was evaluated using the AGREE metric, while analytical performance and method applicability were assessed with the Red-Green-Blue and Blue Applicability Grade Index models, respectively, yielding promising scores (0.5, ≥ 0.55, and 0.65). These findings and the comparison to previous methods indicated that this approach represents a promising, alternative and user-friendly strategy for pesticide determination, offering effective productivity and reliable analytical performance.

A scalable and transparent data pipeline for AI-enabled health data ecosystems.

Transparency and traceability are essential for establishing trustworthy artificial intelligence (AI). The lack of transparency in the data preparation process is a significant obstacle in developing reliable AI systems which can lead to issues related to reproducibility, debugging AI models, bias and fairness, and compliance and regulation. We introduce a formal data preparation pipeline specification to improve upon the manual and error-prone data extraction processes used in AI and data analytics applications, with a focus on traceability. We propose a declarative language to define the extraction of AI-ready datasets from health data adhering to a common data model, particularly those conforming to HL7 Fast Healthcare Interoperability Resources (FHIR). We utilize the FHIR profiling to develop a common data model tailored to an AI use case to enable the explicit declaration of the needed information such as phenotype and AI feature definitions. In our pipeline model, we convert complex, high-dimensional electronic health records data represented with irregular time series sampling to a flat structure by defining a target population, feature groups and final datasets. Our design considers the requirements of various AI use cases from different projects which lead to implementation of many feature types exhibiting intricate temporal relations. We implement a scalable and high-performant feature repository to execute the data preparation pipeline definitions. This software not only ensures reliable, fault-tolerant distributed processing to produce AI-ready datasets and their metadata including many statistics alongside, but also serve as a pluggable component of a decision support application based on a trained AI model during online prediction to automatically prepare feature values of individual entities. We deployed and tested the proposed methodology and the implementation in three different research projects. We present the developed FHIR profiles as a common data model, feature group definitions and feature definitions within a data preparation pipeline while training an AI model for "predicting complications after cardiac surgeries". Through the implementation across various pilot use cases, it has been demonstrated that our framework possesses the necessary breadth and flexibility to define a diverse array of features, each tailored to specific temporal and contextual criteria.

Gum Arabic-assisted green synthesis of gold nanoparticles as fluorescence modulator for potential analytical applications

Aim: To demonstrate a simple, eco-friendly, and cost-effective green method to synthesize gold nanoparticles (AuNPs) using the aqueous extract of gum Arabic (GA) as a reducing and stabilizing agent. Methods: Green synthesis of nanoparticles, characterization by absorption, infra-red and fluorescence spectroscopy. Results: The absorption spectrum (UV-Vis) showed an absorption peak ~522 nm corresponding to the surface plasmon resonance (SPR) absorption peak of AuNPs. Transmission electron microscopy (TEM) images revealed spherical-shaped nanoparticles with an average size of 15 nm. Fourier transform infrared (FTIR) analysis showed that the nanoparticles are coated with organic compounds that are present in GA. The fluorescence quenching properties of the AuNPs were assessed by monitoring their effects on fluorescence intensity of coumarin 153 (C153) dye. The fluorescence of the dye decreased with an increase in concentration of the nanoparticles. Upon addition of the protein bovine serum albumin (BSA) to the mixture the fluorescence increased (recovery) again. Conclusions: The fluorescence quenching and recovery (turn-on/off system) is a valuable method for protein detection in solution. By observing the effect of BSA on the quenched fluorescence, this nanoparticle system shows promise in biomedicine, drug delivery and environmental monitoring.

Harnessing Data Analytics for Enhanced Understanding and Management of Depression Disorders in Mental Health Care

Depression is a widespread and incapacitating mental health condition that affects millions of people around the world.The integration of data analytics into mental health care presents a transformative opportunity to enhance the understanding, diagnosis, and management of depression. This paper explores the application of data analytics in identifying patterns and trends from diverse data sources such as electronic health records (EHRs) and social media. Through advanced techniques including machine learning, natural language processing (NLP), and predictive modeling, data analytics facilitates early detection of depressive symptoms, the development of personalized treatment plans, and continuous patient monitoring. Additionally, the aggregation and analysis of large- scale data provide valuable insights for public health strategies aimed at reducing the prevalence and impact of depression. This paper highlights the critical role of interdisciplinary collaboration in leveraging data analytics to improve mental health outcomes and underscores the importance of robust safeguards to ensure patient confidentiality and trust. Keywords Data Analytics, Depression, Mental Health, Predictive Modeling, Machine Learning, Natural Language Processing, Electronic Health Records

Self-Powered Electrochemiluminescence for Imaging the Corrosion of Protective Coating of Metal and Quantitative Analysis.

In almost all electrochemical systems for electrochemiluminescence (ECL) analysis, electrodes are connected with an external power source, either directly or via wireless energy transfer circuit. That is inconvenient and makes some applications impossible. Herein, we both use galvanized iron with two different metals as both power source and electrodes to achieve a self-powered ECL and exploit ECL for the imaging of the corrosion of protective coating of widely used metal (e.g. galvanized iron) for the first time. The self-powered ECL enables the visualization of the deterioration of galvanic coating on iron using a smartphone and the detection of ascorbic acid with a linear range of 0.5-100 μM and a limit of detection of 0.31 μM. The devices based on self-powered approach do not require external power supply, thus effectively reducing their volume and cost. The self-powered ECL holds great promise for metal corrosion imaging and analytical applications.

Shopping marketplace analysis based on customer insights using social media analytics

Using the recent advances in data analytics, Companies can leverage sentiment analysis to identify trends and areas for improvement of their market strategy and operation planning. This analysis allows them to understand the sentiments expressed by customers and accurately predict customer behavior. Social media platforms have fundamentally altered the field of digital marketing. The findings of this research try to provide:•Potential implications for businesses aiming to optimize their product offerings and enhance customer satisfaction within specific cultural contexts.•A case study has been designed for understanding customers' perceptions and satisfaction levels toward various shops, including local ethnic stores and big chain stores.The paper has conducted a literature review around main pillars like application of data analytics on social media, and sales strategies for establishing a marketplace using data analytics. Exploring the utilization of social media and customer feedback, the paper has proposed a conceptual model for creating insights for extraction of shopping experiences and factors used for customer purchasing decision making.

AI-POWERED PREDICTIVE ANALYTICS FOR INTELLECTUAL PROPERTY RISK MANAGEMENT IN SUPPLY CHAIN OPERATIONS: A BIG DATA APPROACH

The rapid advancement of technology and the increasing complexity of global supply chains have heightened the need for robust intellectual property (IP) risk management strategies. This study explores the application of artificial intelligence (AI) and big data analytics in enhancing IP risk management within supply chains. A comprehensive literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, identifying 578 records through database searches and an additional 90 records through other sources. After removing duplicates, 568 records were screened, with 196 full-text articles assessed for eligibility. Ultimately, 135 articles were included in the final synthesis. The findings reveal that AI-driven predictive analytics significantly enhance the detection and mitigation of IP risks by analyzing large volumes of data from various sources, such as patent filings, market trends, and social media. Big data analytics tools like Hadoop and Spark facilitate real-time monitoring and early identification of potential IP threats, providing a comprehensive view of the supply chain landscape. Several successful case studies across different industries, including pharmaceuticals, electronics, and fashion, demonstrate the practical applications of these technologies in addressing IP risks. However, the review also highlights several challenges, including data quality, scalability, model interpretability, data privacy, and integration with legacy systems. Despite these challenges, the benefits of AI and big data analytics in IP risk management are substantial, enabling organizations to protect their intellectual assets more effectively. The study underscores the need for future research to address these challenges and explore innovative solutions to maximize the potential of AI and big data analytics in IP risk management. By investing in the necessary infrastructure and expertise, organizations can enhance their resilience and maintain a competitive edge in the global market.

High-Power Impulse Magnetron Sputter Deposition of Ag on Self-Assembled Au Nanoparticle Arrays at Low-Temperature Dewetting Conditions.

Plasmons have facilitated diverse analytical applications due to the boosting signal detectability by hot spots. In practical applications, it is crucial to fabricate straightforward, large-scale, and reproducible plasmonic substrates. Dewetting treatment, via applying direct thermal annealing of metal films, has been used as a straightforward method in the fabrication of such plasmonic nanostructures. However, tailoring the evolution of the dewetting process of metal films poses considerable experimental complexities, mainly due to nanoscale structure formation. Here, we use grazing-incidence small- and wide-angle X-ray scattering for the in situ investigation of the high-power impulse magnetron sputter deposition of Ag on self-assembled Au nanoparticle arrays at low-temperature dewetting conditions. This approach allows us to examine both the direct formation of binary Au/Ag nanostructure and the consequential impact of the dewetting process on the spatial arrangement of the bimetallic nanoparticles. It is observed that the dewetting at 100 °C is sufficient to favor the establishment of a homogenized structural configuration of bimetallic nanostructures, which is beneficial for localized surface plasmon resonances (LSPRs). The fabricated metal nanostructures show potential application for the surface-enhanced Raman scattering (SERS) detection of rhodamine 6G molecules. As SERS platform, bimetallic nanostructures formed with dewetting conditions turn out to be superior to those without dewetting conditions. The method in this work is envisioned as a facile strategy for the fabrication of plasmonic nanostructures.

Methods for Determining Losses and Parameters of Cylindrical-Rotor Medium-Power Synchronous Generators

This paper presents the analytical and numerical application of a method to determine the parameters and power losses in the core of two medium-power synchronous generators. These generators are used as emergency power sources powered by diesel engines, gas engines, and gas turbines. They cover peak electricity demand but can also be used in traction drives. This article presents a new numerical method for determining losses in the generator core based on the use of a time-stepping solution using the FEM method and calculating these losses using analytical formulas. In calculating the losses for the FEM method, approximations of the loss characteristics of the sheet were used with a wide range of induction values and frequencies. This method is specific to the solution used and was adapted from the authors’ previous work on losses in induction machines. A one-phase winding with alternating voltage was supplied to determine the basic parameters in the form of synchronous reactance. Also, an important novelty is the introduction of a new method of determining the saturation state of the magnetic circuit, which significantly affects the machine parameters. The obtained results were used in analytical calculations and implemented in a computer program that allows for the calculation of electromagnetic parameters, operating characteristics, and core losses, taking into account additional losses, total losses, and efficiency, as well as machine parameters in unsteady operating states and the current characteristics of a three-phase symmetrical short circuit at the machine terminals. The calculations obtained were verified experimentally by measurements of real machines.

A Bayesian and Analytic Hierarchy Process-Based Multilevel Community Resilience Evaluation Method and Application Study

Cities are complex systems influenced by a multitude of factors, encompassing society, economy, culture, and environment. These factors make urban development highly vulnerable to various disturbances. Communities work as the fundamental building blocks of a city and directly impact both its social structure and spatial layout. Moreover, urban planning and policies play a crucial role in shaping the development trajectory of communities and the living environment for residents. This study aims to develop a Bayesian and analytic hierarchy process (BAHP)-based multilevel community resilience evaluation method to assess the ability of the community system to withstand disturbances and recover from them. First, the proposed method establishes a comprehensive assessment index system that can evaluate social and environmental resilience as well as institutional and managerial resilience at multiple levels. This system serves as a quantitative decision-making tool to elucidate the impact of various factors on community resilience. Furthermore, the “relative demand coefficient” (RDC) is proposed to compare different communities’ resilience by using Bayesian inference to determine its most probable value (MPV). To validate the applicability of the proposed method, an empirical study was conducted in the Dafapu community located in the Longgang District of Shenzhen. Meanwhile, a simulated virtual community is employed for comparison with the Dafapu community as an illustrative example showcasing the proposed method’s superior performance after integrating the RDC. The empirical study demonstrates that the proposed BAHP-based method can effectively and quantitatively highlight the recovery capabilities and limitations for different communities in various dimensions while providing a clear direction for enhancing urban community resilience. This research contributes new insights to the theory, provides a practical tool to quantify community resilience, and offers a viable path for the actual enhancement of community resilience.

Strategi Pengembangan Pemasaran dalam Menarik Minat Konsumen Pengguna Jasa di Kinipagi Wedding Planner Cirebon

The development of an increasingly advanced era has a great influence on the creative mindset of the Indonesian people and the world in all respects. The event organizer or Event Organizer (EO) service business has prospects or developments that are increasingly needed by the community, not only needed in urban areas but also in rural areas. Kinipagi Wedding Planner is one of the Wedding Organizer businesses in Cirebon which was established in 2019, precisely before the COVID-19 Pandemic in Indonesia. The data collection technique in this study uses Nonprobability Sampling technique which in this study does not provide equal opportunities or opportunities for each element of population members to be selected as sample members. The sampling technique in this study uses Purposive Sampling, which is a sampling technique with certain considerations. The research design used in this research is descriptive qualitative research design because researchers want to know how marketing development strategies and how to maintain existence to attract consumer interest in Kinipagi Wedding Planner service users. The data collection methods used in this research are observation, interviews, and documentation. results that Kinipag Wedding planner has already implemented marketing mix and has also performed SWOT analysis. The application of SWOT analytics and marketing mix on Kinipagi Wedding Planer helped have gone well especially the price factor offered is relatively cheaper compared to other wedding organizers in Cirebon. However, there were some shortcomings such as hard-to-reach locations, human resources that need to be improved, and promotional media that still have to be broadcast.

Advanced Method Optimization for Sampling and Analysis Instrumentation.

This work presents a generalized approach for analytical method optimization that branches the gap between techniques historically employed and accurate modern optimization techniques suitable for various applications. The novelty of the described strategy is the utilization of multivariate, multiobjective optimization with Karush-Kuhn-Tucker conditions to bound the optimization space to solutions within the physical limitations of instrumentation. Briefly, the basic steps outlined in this paper are to (1) determine the objective(s) that should be maximized or minimized based on the goals of the analytical application, (2) conduct a screening experiment, (3) perform ANOVA to determine the parameters which have a statistically significant effect on the objective, (4) conduct an experiment (e.g., Box-Behnken design) to collect data for fitting the objective equation, and (5) determine the physical constraints of the parameters and solve the Lagrangian to determine the optimal method parameters. A broad approach to optimization target selection allows for robust method tuning to develop improved data sets amenable for chemometrics and machine learning algorithm development. Gas chromatography-mass spectrometry was selected as a use case due to its broad use across scientific fields and time-consuming method development involving numerous parameters. This strategy can reduce the cost of research, improve data quality, and enable the rapid development of new analytical techniques.

Enlarged Plasma Ablations In Microwave-Enhanced Laser-Induced Breakdown Spectroscopy With Coaxial Multifiber Arrays

This study comprises a comprehensive investigation of the enhancement of laser-induced breakdown spectroscopy (LIBS) using multifiber array configurations and microwave (MW) enhancement. We demonstrated a significant increase in both the emission intensity and Signal-to-Noise Ratio (SNR) through the integration of a six-multifiber setup, resulting in a six-fold increase in broadband emissions. The introduction of MW further augmented this enhancement, exhibiting a two to three orders of magnitude increase in emissions. Remarkably, the combined approach of multifiber LIBS and MW enhancement resulted in a dramatic 2000-time improvement in emission signals. Concurrently, the SNR was enhanced by two to three orders of magnitude. These advancements have opened new avenues for expanding the limits of detection in analytical applications, which is the focus of our ongoing investigation. Our findings have significant implications for various industries including environmental monitoring, materials science, and biomedical research.

Scaffolding Feedback Literacy: Designing a Feedback Analytics Tool with Students

Feedback is essential in learning. The emerging concept of feedback literacy underscores the skills students require for effective use of feedback. This highlights students’ responsibilities in the feedback process. Yet, there is currently a lack of mechanisms to understand how students make sense of feedback and whether they act on it. This gap makes it hard to effectively support students in feedback literacy development and improve the quality of feedback. As a specific application of learning analytics, feedback analytics (analytics on learner engagement with feedback) can offer insights into students’ learning engagement and progression, which can in turn be used to scaffold student feedback literacy. This study proposes a feedback analytics tool, designed with students, aimed at aiding students to synthesize feedback received from multiple sources, scaffold the sense-making process, and prompt deeper reflections or actions on feedback based on data about students’ interactions with feedback. We held focus group discussions with 38 students to learn about their feedback experiences and identified tool features. Based on identified user requirements, a prototype was developed and validated with 16 students via individual interviews. Based on the findings, we envision a feedback analytics tool with the aim of scaffolding student feedback literacy.

An Investigation in to Sports Analytics and Related Tools

This study explores the application of sports analytics and the utilization of various tools in the sports industry. Sports analytics, also known as sports data analytics, is the application of data analysis and statistical approaches to extract important insights. Additionally, it aids in making well-informed judgments and enhancing performance in the realm of sports. Sports data analytics include a diverse array of methods and approaches, such as statistical analysis, machine learning, data visualization, predictive modeling, and optimization algorithms. The objective of sports business analytics is to get practical insights from a dataset in order to enhance decision-making and foster innovation in the sports industry

A self-catalyzing strategy for co-immobilization of two distinct proteins at equimolar ratio: A case study of 3A and 2C to develop a chromatographic method for finding prospective dual-target compoundsfrom complex matrices

BackgroundImmobilized proteins hold promise as the basic units that have enabled a broad range of analytical applications within chemical measurement science. As yet, the co-immobilization of diverse proteins at precise ratio and whether they give rise to improved analytical performance remain challengeable. Herein, we utilized a circularly permuted HaloTag (cpHaloTag) to achieve the co-immobilization of two proteins at precise ratio, which was applied in developing a chromatographic method with improved specificity for pursuing dual-target compounds. ResultsThe methodology involved the fusion 3A and 2C at N- and C-terminuses of cpHaloTag, the immobilization of the fusion protein onto silica gel through bioorthogonal reaction, the morphological and functional characterization, the application in finding dual-target compounds. Expression of the fusion protein in E. coli system showed a yield of milligram level with the presence of 3A and 2C domains. Immobilization of the protein was achieved in 10 min with a reaction efficiency more than 88.5 %. Immobilized 3A-cpHalo-2C exhibited higher specificity and better retentions of canonical compounds of the two enzymes in comparison with the column containing immobilized 3A or 2C alone. In real sample application, screening analysis found that hyperoside, cymaroside, and baicalin were dual-target compounds in concert with 3A and 2C in Shuanghuanglian extract. SignificanceTaking 3A and 2C as probe, we proposed a simple method for direct co-immobilization of diverse proteins from cell lysates and demonstrated an affinity chromatographic-based dual-target compound screening platform. The implications of these methodology are possible to insight the de novo design of multi-target surface for fabricating new bioanalytical methods with improved performance.

Customizing an all‐in‐one polydimethylsiloxane‐based electroanalytical platform using a 3D‐pen templated channel

AbstractThe combination of 3D‐printing technology and microfluidics has garnered widespread interest for creating new designs and has opened a large window for creativity. However, some laboratories do not have the necessary technology to explore this potential advantage. In this paper, we demonstrate how to explore target technologies to fabricate a single version of a polydimethylsiloxane (PDMS)‐based electroanalytical platform using a low‐cost protocol and disposable materials. This protocol is cost‐effective in terms of equipment and materials, allowing for the creation of a circular channel, a T‐junction system, and two designs of electrochemical detectors customized onto a single platform. The interesting new device has some important characteristics, such as fabrication following green and sustainable protocols. Additionally, it can address the limitations associated with flow‐based analysis, for which the classical three‐electrode configuration is inadequate. It has also demonstrated the feasibility of using the device in analytical applications for the determination of salicylic acid in commercial dermocosmetic samples, including aqueous samples.

Programmable Lanthanide Metal-Organic Framework for Ultra-Efficient Nucleic Acids Extraction and Interaction Analysis.

Efficient, mild, and reversible adsorption of nucleic acids onto nanomaterials represents a promising analytical approach for medical diagnosis. However, there is a scarcity of efficient and reversible nucleic acid adsorption nanomaterials. Additionally, the lack of comprehension of the molecular mechanisms governing their interactions poses significant challenges. These issues hinder the rational design and analytical applications of the nanomaterials. Herein, we propose an ultra-efficient nucleic acid affinity nanomaterial based on programmable lanthanide metal-organic frameworks (Ln-MOFs). Through experiments and density functional theory calculations, a rational design guideline for nucleic acid affinity of Ln-MOF was proposed, and a modular and flexible preparation scheme was provided. Then, Er-TPA (terephthalic acid) MOF emerged as the optimal candidate due to its pore size-independent adsorption and desorption capabilities for nucleic acids, enabling ultra-efficient adsorption (about 150% mass ratio) within 1 min. Furthermore, we elucidate the molecular-level mechanisms underlying the Ln-MOF adsorption of single- and double-stranded DNA and G4 structures. The affinity nanomaterial based on Ln-MOF exhibits robust nucleic acid extraction capability (4-fold higher than commercial reagent kits) and enables mild and reversible CRISPR/Cas9 functional regulation. This method holds significant promise for broad application in DNA/RNA liquid biopsy and gene editing, facilitating breakthroughs in analytical chemistry, pharmacy, and medical research.

Review on the Use of Data Analysis for Customer Segmentation and Personalization in Marketing

Marketing is a part of business that requires considering countless variables and making accurate decisions. With the development of computer science and algorithms, the advancement of data analysis methods can be applied in this field to bring great convenience. Despite a large number of data analysis models being constructed, there is a lack of review articles that need to summarize algorithms created by previous generations. This paper starts with a lookback to the basic descriptive and predictive data analysis methods following with the introduction and further explanation of customer segmentation and personalization. Combined with the basic marketing methods, we studied the application of data analytics to business industry. Moreover, this paper presents case study that highlight the application of data analysis in different areas: online retail, e-commerce and transportation. Finally we use each case to find utilization of data analytics to derive meaningful insights and facilitation decision-making.