Analytical Applications Research Articles

Audience Data and Editorial Decision-Making: Evolution and Applications of Web Analytics in Newsrooms

ABSTRACT As web analytics become increasingly common in newsrooms worldwide, it is imperative to understand how they are employed to meet news organizations’ journalistic and business goals. This study examines the evolution and applications of web analytics in newsrooms through a comprehensive analysis of relevant literature. It situates the rise of web analytics in the larger historical, social, and financial context. It explores the strategies news organizations adopt to integrate web analytics in news production. After discussing the impact of analytics on news production, the project juxtaposes the pessimistic ethical concerns—primarily the prediction of market logic uprooting journalistic logic—surrounding the use of web analytics in newsrooms with ground realities. Finally, in view of the analysis, we reflect on the ethical concerns against analytics, the state of editorial autonomy in newsrooms, the implications of newly empowered audience for news ecosystem, and conclude with a practical view of analytics in newsrooms.

Rapid Preparation of a Self-Luminous Cd-Based Metal-Organic Framework Using AIEgen Ligands for High-Performance Electrochemiluminescence.

The design and synthesis of high-efficiency electrochemiluminescence (ECL) emitters hold great promise for a wide range of analytical applications. In this study, we developed a rapid and straightforward strategy to fabricate a self-luminous Cd-based metal-organic framework (Cd-MOF) using individual aggregation-induced emission ligands, specifically 1,1,2,2-tetrakis(4-(pyridin-4-yl)phenyl)ethane (TPPE), within a few seconds. The rigid and directionally enriched metal node of Cd, along with the organic ligands, is formed within the Cd-MOF via coordination, effectively constraining the intramolecular free motions of TPPE and suppressing nonradiative relaxation. Additionally, the unique porous structure combined with the catalytic activity resulting from the incorporation of Cd2+, endow the Cd-MOF with 90-fold ECL enhancement compared to individual TPPE as more chromophores are electro-excited and more coreactants are catalyzed to produce luminescence. The as-made Cd-MOF amplifies the ECL performance by integrating ECL emitters and coreactant accelerators into a single entity, simplifying the sensing process. Leveraging the excellent ECL performance, we constructed a sensitive ECL sensor for hydroquinone based on competitive reactions, with a wide linear range from 200 nM to 1 mM and a satisfying detection limit as low as 80 nM.

The Use and influence of Learning Analytics in Higher Educational Institutions: A Mediation Model of Student Attainment between Student Retention and Student Employability.

Purpose: The goals of this article are to (1) identify factors influencing Learning Analytics in Bengaluru HEIs and (2) examine the mediating role of Student Attainment in the relationship between Student Retention and Student Employability in Higher Educational Institutions (HEIs). Design/methodology/approach – This research assembles and synthesizes data on the application of learning analytics. Learning analytics is the subject of empirical studies, including Public and private higher education institutions in Bengaluru were each sent one of 485 questionnaires. In total, 450 Chancellery, Dean, HOD, and Directors participated and completed the survey. Thirteen questionnaires were eliminated from the analysis because of missing data or outliers discovered during the data screening process. We could sift through 437 surveys, making up the final sample. The study runs SEM analysis using the maximum likelihood method to test the causal relationship between learning analytics factors. The impact of retention as the independent variable (exogenous) on student employability as the dependent variable (endogenous) was tested along with attainment as a mediator between these two variables. Findings: Evidence demonstrates how learning analytics may assist educational institutions in making better use of data for decision-making. The findings revealed that retention significantly indirectly affects Employability through Attainment. Attainment is significantly mediating between Retention and Employability. Since both standardized direct and indirect paths have a p-value below 0.05, thus, it is confirmed that attainment partially mediates the relationship between Retention and Employability of the students. Originality/value: The current study has explored retention, attainment, and employability as the critical factors that must be considered before applying Learning Analytics in Higher Educational Institutions. The research results confirmed that students' employability is significantly influenced by retention and attainment. Educational institutions and academics, particularly those working in open and distance settings, should use the study's findings to stay abreast of this developing subject and provide the groundwork for further research.

Sustainable Manufacturing Engineering: Enhancing Product Quality through Green Process Innovations

The imperative for sustainable practices within manufacturing has become increasingly critical, driven by environmental concerns, regulatory pressures, and the evolving expectations of eco-conscious consumers. This paper investigates the role of sustainable manufacturing engineering in enhancing product quality through the implementation of green process innovations. Specifically, the study explores how sustainable manufacturing methods, including energy-efficient systems, additive manufacturing, zero-waste production, and renewable energy integration, can drive quality improvements while reducing the environmental impact of production processes. The research is grounded in a comprehensive analysis of current green innovations in the manufacturing sector, drawing on case studies of companies that have successfully balanced quality and sustainability. The findings indicate that companies adopting sustainable practices not only reduce waste and lower emissions but also experience notable gains in product durability, customer satisfaction, and brand reputation. For instance, additive manufacturing is shown to improve precision and material efficiency, while zero-waste initiatives lead to a more efficient resource cycle, which in turn supports consistent quality standards. This paper further delves into the challenges of implementing green manufacturing processes without compromising product quality, examining factors such as initial costs, technological readiness, and market adaptation. Visual data in the form of tables and graphs are presented to compare different green process innovations and their respective impacts on quality and sustainability metrics. In addition, case study summaries provide real-world examples of how leading companies have enhanced product quality through sustainable engineering approaches. This study underscores the value of sustainable manufacturing engineering as a dual-benefit strategy that can drive environmental stewardship alongside quality enhancement. The paper recommends that future research focus on the development of new, cost-effective green technologies and the application of advanced data analytics for improved process control in sustainable manufacturing. Through these efforts, the manufacturing sector can continue advancing toward a more sustainable, high-quality production paradigm.

Privacy-Preserving Analytics in HR Tech- Federated Learning and Differential Privacy Techniques for Sensitive Data

This paper explores the application of privacy-preserving analytics in human resources (HR), focusing on the synergistic use of federated learning and differential privacy. As HR departments increasingly leverage data-driven insights, the protection of sensitive employee information becomes paramount. Federated learning enables collaborative model training without centralizing raw data, while differential privacy adds calibrated noise to ensure individual data remains indiscernible. Together, these techniques form a robust framework for safeguarding HR data while enabling advanced analytics. The paper discusses the challenges of handling sensitive HR information, examines the implementation of federated learning and differential privacy, and demonstrates their combined effectiveness in maintaining data utility while ensuring privacy. By adopting these approaches, organizations can derive valuable workforce insights, comply with data protection regulations, and foster employee trust. This research contributes to the growing field of ethical data use in HR, offering a blueprint for balancing analytical capabilities with privacy imperatives in the modern workplace. Keywords—Privacy-preserving analytics, Federated learning, Differential privacy, HR analytics, Data protection, Employee privacy, Decentralized learning, GDPR compliance, CCPA compliance, Sensitive data handling, Data-driven HR, Privacy-utility trade-off.

OpenOPAF: An Open Source Multimodal System for Automated Feedback for Oral Presentations

Providing automated feedback that facilitates the practice and acquisition of oral presentation skills has been one of the notable applications of multimodal learning analytics (MmLA). However, the closedness and general unavailability of existing systems have reduced their potential impact and benefits. This work introduces OpenOPAF, an open-source system designed to provide automated multimodal feedback for oral presentations. By leveraging analytics to assess body language, gaze direction, voice volume, articulation speed, filled pauses, and the use of text in visual aids, it provides real-time, actionable information to presenters. Evaluations conducted on OpenOPAF show that it performs similarly, both technically and pedagogically, to existing closed solutions. This system targets practitioners who wish to use it as-is to provide feedback to novice presenters, developers seeking to adapt it for other learning contexts, and researchers interested in experimenting with new feature extraction algorithms and report mechanisms and studying the acquisition of oral presentation skills. This initiative aims to foster a community-driven approach to democratize access to sophisticated analytics tools for oral presentation skill development.

Application of Mobile Technology-Based Learning Analytics in Educational Assessment

Application of Mobile Technology-Based Learning Analytics in Educational Assessment

Dissociation Constant (Kd) Measurement for Small-Molecule Binding Aptamers: Homogeneous Assay Methods and Critical Evaluations.

Since 1990, numerous aptamers have been isolated and discovered for use in various analytical, biomedical, and environmental applications. This trend continues to date. A critical step in the characterization of aptamer binding is to measure its binding affinity toward both target and non-target molecules. Dissociation constant (Kd) is the most commonly used value in characterizing aptamer binding. In this article, homogenous assays are reviewed for aptamers that can bind small-molecule targets. The reviewed methods include label-free methods, such as isothermal titration calorimetry, intrinsic fluorescence of target molecules, DNA staining dyes, and nuclease digestion assays, and labeled methods, such as the strand displacement reaction. Some methods are not recommended, such as those based on the aggregation of gold nanoparticles and the desorption of fluorophore-labeled DNA from nanomaterials. The difference between the measured apparent Kd and the true Kd of aptamer binding is stressed. In addition, avoiding the titration regime and paying attention to the time required to reach equilibrium are discussed. Finally, it is important to include mutated non-binding sequences as controls.

Transformative Impact of AI and Cloud Technologies: A Comparative Analysis across Healthcare, Retail, and Mobile Financial Services

This article examines the transformative impact of artificial intelligence (AI) and cloud technologies across healthcare, retail, and mobile financial services sectors. Through a comprehensive analysis of industry-specific applications, we explore how these technologies are revolutionizing operational efficiency, customer experiences, and innovation in each domain. In healthcare, we investigate the role of CI/CD automation and scalable cloud infrastructure in improving system reliability and patient care. The retail sector analysis focuses on AI-driven customer insights and supply chain optimization. For mobile financial services, we examine AI applications in fraud detection and real-time analytics. The findings reveal significant improvements in operational efficiency, decision-making processes, and customer satisfaction across all three industries. However, we also identify unique challenges and opportunities specific to each sector. This article contributes to the growing body of literature on digital transformation and provides valuable insights for industry professionals and policymakers navigating the integration of AI and cloud technologies in their respective fields.

Simulation of Space Charge Effects in Fourier Transform Quadrupole Ion Traps (FT-QITs).

We present ion dynamics simulations regarding the effect of space charge on the performance of Fourier transform quadrupole ion traps (FT-QITs) with special attention to signal stability, mass resolving power, and sensitivity. Ion trajectory simulations within an idealized QIT geometry are performed by applying a dedicated application (QITSim) using an in-house developed open simulation framework (IDSimF). Image current detection transients are generated by the application and are subsequently transformed into frequency spectra of ion secular motion. Such frequency spectra are the basis for the calculation of mass spectra in FT-QIT instruments. The simulation results are used to assess the extent of space charge induced effects regarding the analytical performance of FT-QIT systems. The simulation results for two Cl+ and seven Xe+ isotope ions exhibit diverse space charge induced phenomena. Most prominently, complete isotope signal fusion, quantitative individual signal suppression, and severe signal distortions are observed even at low absolute numbers of trapped ions. Furthermore, significant shifts of the secular oscillation frequencies occur, even when the signal shape appears to be mostly unaffected and when the frequency separation for trapped ions with different masses is large. This significantly limits the applicability of FT-QIT systems for high resolution mass spectrometry. An increase of the RF amplitude of the trapping field as well as increasing the extent of ion excitation partly mitigate these adverse space charge effects; nevertheless, the usable operational range of the simulated FT-QIT system for analytical applications remains rather narrow.

Compact Plasma Ionization for Ion Mobility Spectrometry Using a 4.3 MHz Miniature Tesla Coil.

In this study, a low-cost 4.3 MHz plasma ionization source for ion mobility spectrometry (IMS), utilizing a miniaturized Tesla coil, is presented. This compact design, combined with a 3D printed cyclic olefin copolymer (COC) housing, delivers a stable and directed plasma suitable for ionization in IMS applications. The 3D printed housing ensures chemical resistance and low off-gassing, which are crucial for maintaining sample integrity. The Tesla coil produces a consistent sine wave at 4.3 MHz, and when connected to stainless steel screw electrodes it generates a stable plasma capable of ionizing analytes such as limonene, MTBE, nicotine, 2-octanone, and propofol. Measurements were conducted in both positive and negative ion modes. The results demonstrate the Tesla coil's effectiveness as a low-cost and reliable ionization source for IMS, offering comparable performance to traditional Ni63 β-emitters. This advancement in plasma ionization technology could facilitate more accessible and flexible IMS systems for diverse analytical applications. The integration of 3D printing in the development of this ionization source underscores the potential for customized, low-cost analytical instrumentation, promoting innovation in laboratory environments and commercial applications.

Research on the Legal Risks of Digital Currency in the Big Data Environment

With the widespread application and growth of blockchain technology, cloud computing, and big data analytics in the financial industry, digital currencies have emerged. However, relevant regulations specifically targeting digital currencies have not been officially promulgated yet. In the process of promoting digital currencies, legal risks such as unclear boundaries of compensation liability, unclear ownership, potential privacy breaches, and financial regulatory challenges still need to be overcome. Therefore, it is essential to prioritize the prevention and management of legal risks associated with digital currencies in the big data environment, ensuring the smooth circulation and use of digital legal currencies across regions and promoting the internationalization and legalization of China’s digital legal currencies.

Advancements in health information technology and their role in enhancing cancer care: Innovations in early detection, treatment, and data privacy

The rapid advancement of Health Information Technology (HIT) has revolutionized cancer care, introducing new capabilities in early detection, individualized treatment, and data security. This paper reviews the key HIT innovations that have reshaped oncology, including the widespread adoption of Electronic Health Records (EHRs), the use of artificial intelligence (AI) in diagnostic processes, and the application of telemedicine and predictive analytics to improve treatment planning. These technologies have enabled earlier and more accurate diagnoses, expanded access to care, and facilitated data-driven, personalized treatment strategies. In parallel, this review addresses the importance of safeguarding sensitive patient data, focusing on encryption, anonymization techniques, and the regulatory frameworks like HIPAA and GDPR that ensure data privacy. Finally, the paper discusses future directions for HIT, particularly the potential of AI-driven decision support systems, real-time data analytics, and privacy-enhancing algorithms to further advance cancer care, creating a more secure, efficient, and patient-centered healthcare system.

Harnessing AI and Predictive Analytics to Revolutionize Customer Retention Strategies

Artificial Intelligence (AI) and predictive analytics are rapidly transforming how businesses approach customer retention, enabling more proactive and personalized strategies. This research investigates the role of AI-driven predictive analytics in identifying at-risk customers, forecasting churn, and optimizing retention efforts across various industries. By analyzing historical data, machine learning models can accurately predict future customer behavior, enabling businesses to implement targeted retention strategies such as personalized offers, timely engagement, and customized support. This study explores how AI-powered tools enhance customer segmentation, allowing marketers to identify the factors that contribute to customer attrition and loyalty. Moreover, the research delves into the use of predictive analytics in monitoring customer interactions, identifying warning signs of dissatisfaction, and responding with interventions before churn occurs. Ethical concerns, such as the balance between personalization and privacy, will also be addressed, particularly regarding how businesses can maintain consumer trust while leveraging personal data for predictive purposes. Through case studies and industry analysis, this research aims to demonstrate the significant advantages of incorporating AI and predictive analytics into customer retention strategies. It will provide insights into best practices for utilizing these technologies to enhance customer loyalty, improve satisfaction, and ultimately drive sustainable business growth. this research examines the practical applications of AI and predictive analytics across different business sectors, such as e-commerce, telecommunications, and financial services. By integrating AI-powered tools into customer relationship management (CRM) systems, companies can develop more effective retention strategies that are both data-driven and customer-centric. This includes deploying AI algorithms to monitor customer lifetime value (CLV) and predict the likelihood of repeat purchases, helping businesses prioritize high-value customers while minimizing churn rates among at-risk segments.

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.

The Impact of OER’s Continuous Improvement Cycles on Students’ Performance: A Longitudinal Analysis of the RISE Framework

Open educational resources (OER) have been praised for revolutionizing education. However, practitioners and instructors battle keeping OER updated and measuring their impact on students’ performance. Few studies have analyzed the improvement of OER over time in relation to achievement. This longitudinal study uses learning analytics through the open-source Resource Inspection, Selection, and Enhancement (RISE) analysis framework to assess the impact of continuous improvement cycles on students’ outcomes. Panel data (i.e., performance and use) from 190 learning objectives of OER of an introductory sociology course were analyzed using a hierarchical linear model. Results show that more visits to an OER do not improve student achievement, but continuous improvement cycles of targeted OER do. Iterative implementation of the RISE analysis for resource improvement in combination with practitioners’ expertise is key for students’ learning. Given that the RISE classification accounted for 65% of the growth of students’ performance, suggesting a moderate to large effect, we speculate that the RISE analysis could be generalized to other contexts and result in greater student gain. Institutions and practitioners can improve the OER’s impact by introducing learning analytics as a decision-making tool for instructional designers. Yet, user-friendly implementation of learning analytics in a “click-and-go” application is necessary for generalizability and escalation of continuous improvement cycles of OER and tangible improvement of learning outcomes. Finally, in this article, we identify the need for efficient applications of learning analytics that focus more on “learning” and less on analytics.

Brain tumor classification utilizing pixel distribution and spatial dependencies higher-order statistical measurements through explainable ML models

Brain tumors are among the most fatal and devastating diseases, and they often result in a significant reduction in life expectancy. The devising of treatment plans that can extend the lives of affected individuals hinges on an accurate diagnosis of these tumors. Identifying and analyzing large volumes of magnetic resonance imaging (MRI) data manually proves to be both challenging and time-consuming. As a result, there exists a pressing need for a reliable machine-learning approach to accurately diagnose brain tumors, and numerous methods have already been proposed over the last decade. In this paper, a novel, comprehensive approach is proposed for identifying and classifying a given MR brain image as abnormal. Three common brain diseases, namely glioma, meningioma, and pituitary tumor, are chosen as abnormal brains, and the Figshare MRI brain image dataset was collected from the Kaggle and IEEE websites. The proposed method is initiated by employing 1st-order statistics, 2nd-order statistics, and higher-order transformed (DWT) feature extraction to extract features from images. Then missing data is addressed and handled using KNNImputer, followed by the application of the ExtratreesClassifier and PCA feature selection methods to identify the most relevant features and reduce the dimensions of these features. Subsequently, the reduced features are submitted to seven machine learning models, namely RF, GB, CB, SVM, LGBM, DT, and LR. The strategy of k-fold cross-validation is utilized to enhance the performance of those models. Finally, the models are evaluated using XAI approaches, which ensure transparent decision-making processes and provide insights into the model’s predictions. Remarkably, our approach achieves the highest accuracy, precision, recall, F1 score, MCC, Kappa, AUC-ROC, and R2, as well as the lowest loss, among the seven models evaluated, proving its effectiveness and applicability in multiple analytic applications relying on publicly available datasets.

A Comparative Analysis of Machine Learning Algorithms for Big Data Applications in Predictive Analytics

As the volume and complexity of data continue to grow, predictive analytics has emerged as a vital tool for extracting actionable insights from big data, driving decision-making across various domains such as healthcare, finance, and e-commerce. However, selecting an appropriate machine learning algorithm for predictive analytics applications is challenging due to differences in algorithmic performance, computational requirements, and scalability, especially in the context of big data. This paper provides a comprehensive comparative analysis of popular machine learning algorithms utilized in predictive analytics, specifically focusing on their effectiveness and feasibility in big data environments. The study categorizes algorithms based on learning types—supervised, unsupervised, and reinforcement learning—and evaluates their performance across multiple dimensions: prediction accuracy, computational efficiency, scalability, and suitability for real-time analytics. Through a detailed analysis of algorithms, including linear regression, decision trees, support vector machines, neural networks, and clustering techniques, we assess each method’s strengths and limitations in handling large datasets. Additionally, the study introduces a series of metrics, such as accuracy, F1-score, and training time, as benchmarks for assessing the algorithms’ predictive capabilities and computational viability. A hypothetical case study demonstrates the application of these algorithms on a sample big data set, providing insights into their real-world performance across different predictive analytics scenarios. Visual data representations, including comparative tables and performance graphs, offer a clearer perspective on the trade-offs among algorithm choices. The findings highlight that while certain algorithms like random forests and neural networks achieve higher accuracy in prediction tasks, they may also require substantial computational resources, posing limitations for real-time processing in big data applications. This paper concludes with recommendations for selecting machine learning algorithms based on specific predictive analytics objectives, data characteristics, and processing requirements. Furthermore, it discusses the challenges associated with implementing these algorithms in big data contexts and explores potential advancements, such as the integration of deep learning and the use of distributed computing, as promising directions for enhancing predictive analytics performance in future applications.

Lights, Headset, Tablet, Action: Exploring the Use of Hybrid User Interfaces for Immersive Situated Analytics

While augmented reality (AR) headsets provide entirely new ways of seeing and interacting with data, traditional computing devices can play a symbiotic role when used in conjunction with AR as a hybrid user interface. A promising use case for this setup is situated analytics. AR can provide embedded views that are integrated with their physical referents, and a separate device such as a tablet can provide a familiar situated overview of the entire dataset being examined. While prior work has explored similar setups, we sought to understand how people perceive and make use of visualizations presented on both embedded visualizations (in AR) and situated visualizations (on a tablet) to achieve their own goals. To this end, we conducted an exploratory study using a scenario and task familiar to most: adjusting light levels in a smart home based on personal preference and energy usage. In a prototype that simulates AR in virtual reality, embedded visualizations are positioned next to lights distributed across an apartment, and situated visualizations are provided on a handheld tablet. We observed and interviewed 19 participants using the prototype. Participants were easily able to perform the task, though the extent the visualizations were used during the task varied, with some making decisions based on the data and others only on their own preferences. Our findings also suggest the two distinct roles that situated and embedded visualizations can have, and how this clear separation might improve user satisfaction and minimize attention-switching overheads in this hybrid user interface setup. We conclude by discussing the importance of considering the user's needs, goals, and the physical environment for designing and evaluating effective situated analytics applications.

Behavioral trace data in an online learning environment as indicators of learning engagement in university students.

Learning in asynchronous online settings (AOSs) is challenging for university students. However, the construct of learning engagement (LE) represents a possible lever to identify and reduce challenges while learning online, especially, in AOSs. Learning analytics provides a fruitful framework to analyze students' learning processes and LE via trace data. The study, therefore, addresses the questions of whether LE can be modeled with the sub-dimensions of effort, attention, and content interest and by which trace data, derived from behavior within an AOS, these facets of LE are represented in self-reports. Participants were 764 university students attending an AOS. The results of best-subset regression analysis show that a model combining multiple indicators can account for a proportion of the variance in students' LE (highly significant R 2 between 0.04 and 0.13). The identified set of indicators is stable over time supporting the transferability to similar learning contexts. The results of this study can contribute to both research on learning processes in AOSs in higher education and the application of learning analytics in university teaching (e.g., modeling automated feedback).