Analytical Applications Research Articles

Harnessing Business Analytics For Market Competitiveness: Discovering Pathways To Growth

Business analytics is becoming an essential tool for firms looking to boost market competitiveness and spur growth in a more competitive corporate environment. This study examines how analytics are essential for guiding strategic choices and enhancing operational effectiveness. The significance of the study stems from its capacity to provide light on how companies might use data to their advantage competitively. The main goals of this study are finding the critical elements that affect the effective application of business analytics, investigate how analytics support organizational development, and address the difficulties that businesses encounter in this pursuit. The study seeks to offer a thorough framework that combines theoretical viewpoints with real-world business analytics applications. The gap between the availability of advanced analytics technologies and how well firms use them is the main issue this study attempts to address. The collection includes pertinent case studies and opinions from analytics professionals and leaders in the field. The main conclusions show that to benefit from business analytics fully, real-time data use, technology improvements, and strategic alliances are essential. However, there are still major obstacles to adoption, such as large upfront investment costs and a lack of a data-driven culture. Focusing on particular businesses and possible biases in self-reported data are two of the weaknesses of the study. The theoretical ramifications point to the necessity of more research into frameworks that incorporate analytics into corporate strategy. The results provide useful suggestions for companies looking to improve their analytical skills and gain a competitive edge. Businesses can improve their position for long-term growth in a changing market by comprehending and addressing these trends.

Transforming Insights into Action: The Influence of Business Analytics on Corporate Strategy Development

The revolutionary impact of business analytics on corporate strategy in modern firms is investigated in this research paper. Integrating analytics into strategic planning has become essential for gaining a competitive edge in a time of rapid technological innovation and data abundance. The significance of this study is in its capacity to clarify how data-driven decision-making can improve organizational performance and flexibility in a changing marketplace. The main goal of this study is to investigate the effects of employing business analytics in the development of corporate strategies, with a particular emphasis on how these processes affect decision-making and the overall efficacy of strategic initiatives. By analyzing these dynamics, the study hopes to offer firms looking to use analytics for strategic success practical insights. The scope of the study includes a thorough investigation of how business analytics impacts many aspects of corporate strategy, such as risk management, customer relationship management, and operational efficiency. The main issue discussed is the discrepancy between conventional strategic planning techniques and the requirement for a more data-centric strategy that can adapt to changing market conditions. The idea that better strategic results can result from the efficient application of business analytics forms the basis of the theoretical framework that directs this study. Using secondary data sources including case studies, industry reports, and scholarly literature, a qualitative research methodology is used to accomplish these goals. This methodology makes it easier to conduct a comprehensive analysis of current business analytics methods and knowledge in strategic situations. Organizations that successfully incorporate analytics into their strategic planning procedures report better performance metrics and increased decision-making capabilities, according to the main findings of the analysis. Potential biases in secondary data and the difficulty of extrapolating results across several businesses are drawbacks, though. In terms of theory and practice, this study has consequences. Theorists can better grasp how business analytics may transform strategic frameworks, and practitioners can learn about the most effective ways to put data-driven plans into reality. The findings of this study ultimately highlight how important it is for businesses to develop a strong data culture that values analytics as a fundamental part of their strategic decision-making procedures.

Porous Polymer Sorbents in Micro Solid Phase Extraction: Applications, Advantages, and Challenges.

In recent years, porous polymers have gained significant attention for their application as powerful and selective sorbents in micro solid phase extraction (µSPE). In this review we explore the preparation and utilization of various porous polymer sorbents, highlighting their impact on enhancing µSPE techniques. Molecularly imprinted polymers (MIPs), graphene oxide-modified frameworks, and zeolitic imidazole frameworks (ZIFs) are among the innovative materials discussed. These innovative materials have significantly improved µSPE methods, offering enhanced extraction efficiencies, superior selectivity, and reduced solvent consumption, all of which align with the principles of green chemistry. Key findings of this review include the demonstration that MIPs exhibit excellent target specificity, making them ideal for complex matrices, while graphene oxide frameworks and ZIFs provide increased surface area and stability for diverse analytical applications. Despite these advancements, challenges remain, particularly the high cost of certain innovative materials, limited reusability, and the absence of automation in µSPE workflows. Furthermore, controlling the precise synthesis and functionalization of these sorbents continues to be a limiting factor. To address these issues, future research should focus on developing cost-effectiveness methods, the use of biopolymer or sustainable feedstocks, and scalable synthesis methods; integrating automation into µSPE; and exploring new polymeric materials with enhanced properties. Additionally, novel hybrid materials that combine the strengths of multiple sorbents offer a promising direction for future exploration. We critically analyze the advantages and limitations of each sorbent type, providing a comprehensive overview of their applications in µSPE. This paper also examines the synthesis, characterization, and unique properties of these porous polymers, emphasizing their role in advancing analytical chemistry towards more efficient and environmentally friendly practices. The need for continued development of high-performance, low-cost, and sustainable sorbents is underscored to further enhance the effectiveness of µSPE techniques.

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.

Trace level detection of putrescine and cadaverine in food samples using a novel rhodanine-imidazole dyad and evaluation of its biological properties

Biogenic amines are important indicators of food spoilage and quality. Food safety is significantly influenced by biogenic amines such as Putrescine and Cadaverine, produced by microbes during food spoilage. Herein, a colorimetric probe for detecting Putrescine and Cadaverine based on a chemo-dosimeter strategy has been proposed. The probe L1 irreversibly binds with Putrescine and Cadaverine through an aza-Michael addition reaction in which the dicyanomethyl group of the probe is substituted by the primary amine group from the biogenic amines. This chemical reaction rapidly changes color from colorless to pale green. The probe could detect Putrescine and Cadaverine in trace levels of 52 nM and 18 nM, without much interference from other common biogenic amines. The binding mechanism of probe L1 with biogenic amines was confirmed using 1H NMR, IR, and DFT studies. The detection procedure is made portable and affordable by using a smartphone camera to capture colorimetric changes and convert them into RGB coordinates. Test paper strips coated with the probe were developed to illustrate its real-world analytical application. The potential application of probe L1 in real samples was demonstrated using in-vivo models of Prawn and Beef using test paper strips. Probe L1 showed satisfactory performance for detecting Putrescine and Cadaverine in the vapor phase.

Application of Mobile Technology-Based Learning Analytics in Educational Assessment

Application of Mobile Technology-Based Learning Analytics in Educational Assessment

A novel azo-triazolopyridine-based dual naked-eye chemosensor for the selective detection of CN− and Cu2+ ions

A new chemosensor incorporating a triazolopyridine and an azo chromophore was synthesized and employed as a colorimetric sensor. Visual investigations for detecting anions and cations revealed that the sensor acts as a highly sensitive and selective chromogenic detector for cyanide (CN−) and copper (Cu2+) ions. The observable color change from yellow to deep orange for Cu2+ and from yellow to purple for CN− allows for detecting these ions without the need for advanced equipment, even with the naked eye. The sensor’s limit of detection (LOD) was determined to be 0.02 μM for CN− ions and 1.13 μM for Cu2+ ions. The Job plot indicated that the sensor binds to CN− and Cu2+ with a stoichiometric ratio of 1:1 and 2:1, respectively. The ability to accurately and selectively distinguish Cu2+ and CN− ions makes this sensor valuable for various analytical applications, including environmental monitoring and chemical analysis. Its capacity to produce noticeable and distinct color changes enables the rapid and straightforward detection of these hazardous ions, making it a practical choice for real-world applications. The quantum yield of A-TAP/Cu2+ was 54 % at a λmax (maximum absorbance) of 450 nm, while that of A-TAP/CN− was 62 % at 538 nm, using a reference sensor with a λmax of 380 nm.

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.

AI-Reinforced Wearable Sensors and Intelligent Point-of-Care Tests.

Artificial intelligence (AI) techniques offer great potential to advance point-of-care testing (POCT) and wearable sensors for personalized medicine applications. This review explores the recent advances and the transformative potential of the use of AI in improving wearables and POCT. The integration of AI significantly contributes to empowering these tools and enables continuous monitoring, real-time analysis, and rapid diagnostics, thus enhancing patient outcomes and healthcare efficiency. Wearable sensors powered by AI models offer tremendous opportunities for precise and non-invasive tracking of physiological conditions that are essential for early disease detection and personalized treatments. AI-empowered POCT facilitates rapid, accurate diagnostics, making these medical testing kits accessible and available even in resource-limited settings. This review discusses the key advances in AI applications for data processing, sensor fusion, and multivariate analytics, highlighting case examples that exhibit their impact in different medical scenarios. In addition, the challenges associated with data privacy, regulatory approvals, and technology integrations into the existing healthcare system have been overviewed. The outlook emphasizes the urgent need for continued innovation in AI-driven health technologies to overcome these challenges and to fully achieve the potential of these techniques to revolutionize personalized medicine.

Quality assurance strategies for machine learning applications in big data analytics: an overview

Machine learning (ML) models have gained significant attention in a variety of applications, from computer vision to natural language processing, and are almost always based on big data. There are a growing number of applications and products with built-in machine learning models, and this is the area where software engineering, artificial intelligence and data science meet. The requirement for a system to operate in a real-world environment poses many challenges, such as how to design for wrong predictions the model may make; How to assure safety and security despite possible mistakes; which qualities matter beyond a model’s prediction accuracy; How can we identify and measure important quality requirements, including learning and inference latency, scalability, explainability, fairness, privacy, robustness, and safety. It has become crucial to test thoroughly these models to assess their capabilities and potential errors. Existing software testing methods have been adapted and refined to discover faults in machine learning and deep learning models. This paper covers a taxonomy, a methodologically uniform presentation of all presented solutions to the aforementioned issues, as well as conclusions about possible future development trends. The main contributions of this paper are a classification that closely follows the structure of the ML-pipeline, a precisely defined role of each team member within that pipeline, an overview of trends and challenges in the combination of ML and big data analytics, with uses in the domains of industry and education.

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.