Analytical Potential Research Articles

Energy-Resolved In-Source Collison-Induced Dissociation for Isomer Discrimination.

While mass spectrometry remains a gold-standard tool for analyte detection, characterization, and quantitation, isomer differentiation is often a challenge. Tandem mass spectrometry is a common approach to increase the selectivity of mass spectrometry and energy-resolved measurements can provide further improvements. However, not all mass spectrometers, especially those that are very compact and affordable, are amenable to such experiments. For instance, single-stage mass spectrometers with soft ionization provide no dissociation information and quadrupole ion trap instruments with resonant excitation do not necessarily provide as informative of energy-resolved curves, for instance when extensive sequential dissociation is responsible for much of the "fingerprint". In-source collision-induced dissociation (IS-CID) is one approach to overcoming these barriers to exploit the analytical selectivity of energy-resolved CID without the need for additional instrumentation; this approach could broaden the reach of these selectivity gains to additional user bases (e.g., educational settings, field portable devices). Here, we specifically investigate energy-resolved IS-CID with the goal of (1) comparing between energy-resolved appearance curves measured with true tandem mass spectrometry on a quadrupole time-of-flight instrument and those obtained using IS-CID, (2) evaluating the approach as a means of differentiating isomers/isobar sets, especially those with similar dissociation patterns, and (3) exploring additional analytical considerations relevant to method development and implementation. This proof-of-concept work establishes the analytical potential of this approach, opening doors for future method development for specific applications.

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.

Unveiling the Potential of Learning Analytics in Learning Design: Theoretical Implications for General Education

Unveiling the Potential of Learning Analytics in Learning Design: Theoretical Implications for General Education

From Conceptual Misalignment to Reconceptualizing: Demonstrating the Process of Reconceptualization

Abstract In collecting data, analyzing data, or writing up, researchers can find that the concepts they had decided to use and the available concepts in the literature are mismatched with what they seek to explore and/or explain. This misalignment between concepts and observations can create analytical and theoretical blind spots, foreclosing the opportunity to delve deeper into and articulate the specificities and complexities of what they observe. Researchers experiencing such misalignment of concepts need strategies to help them reconceptualize existing concepts, which we hope to provide here to help researchers develop more nuanced and better-adapted concepts that provide more analytical, theoretical, and empirical leverage. This article suggests a four-step process of reconceptualization, a method for developing and iterating the concepts we employ in designing, conducting, and writing up research. This method of reconceptualization addresses a gap in the existing literature on concepts by providing a new, practical, accessible, and pedagogically oriented solution for this problem of misaligned concepts. We illustrate how to implement reconceptualization by working with the concept of “local” and offer two examples of how we reconceptualized this concept in two projects in Dominica and Moldova. We show how, by reconceptualizing an initial concept, we can move forward in developing new and reconceptualized concepts. Hence, this article also offers two concepts: “local-international” and “internationalized local,” that are more attuned to what we observe and richer in their empirical and analytical potential.

Comparative Analysis of Heart Failure with Preserved Vs Reduced Ejection Fraction: Patient Characteristics, Outcomes, Mortality Prediction, and Machine Learning Model Development in the JoHFR.

Heart failure (HF) is a global health challenge affecting millions, with significant variations in patient characteristics and outcomes based on ejection fraction. This study aimed to differentiate between HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF) with respect to patient characteristics, risk factors, comorbidities, and clinical outcomes, incorporating advanced machine learning models for mortality prediction. The study included 1861 HF patients from 21 centers in Jordan, categorized into HFrEF (EF <40%) and HFpEF (EF ≥ 50%) groups. Data were collected from 2021 to 2023, and machine learning models were employed for mortality prediction. Among the participants, 29.7% had HFpEF and 70.3% HFrEF. Significant differences were noted in demographics and comorbidities, with a higher prevalence of males, younger age, smoking, and familial history of premature ASCVD in the HFrEF group. HFpEF patients were typically older, with higher rates of diabetes, hypertension, and obesity. Machine learning analysis, mainly using the Random Forest Classifier, demonstrated significant predictive capability for mortality with an accuracy of 0.9002 and an AUC of 0.7556. Other models, including Logistic Regression, SVM, and XGBoost, also showed promising results. Length of hospital stay, need for mechanical ventilation, and number of hospital admissions were the top predictors of mortality in our study. The study underscores the heterogeneity in patient profiles between HFrEF and HFpEF. Integrating machine learning models offers valuable insights into mortality risk prediction in HF patients, highlighting the potential of advanced analytics in improving patient care and outcomes.

Class Symbolic Regression: Gotta Fit ’Em All

We introduce “Class Symbolic Regression” (Class SR), the first framework for automatically finding a single analytical functional form that accurately fits multiple data sets—each realization being governed by its own (possibly) unique set of fitting parameters. This hierarchical framework leverages the common constraint that all the members of a single class of physical phenomena follow a common governing law. Our approach extends the capabilities of our earlier Physical Symbolic Optimization (Φ-SO) framework for symbolic regression, which integrates dimensional analysis constraints and deep reinforcement learning for unsupervised symbolic analytical function discovery from data. Additionally, we introduce the first Class SR benchmark, comprising a series of synthetic physical challenges specifically designed to evaluate such algorithms. We demonstrate the efficacy of our novel approach by applying it to these benchmark challenges and showcase its practical utility for astrophysics by successfully extracting an analytic galaxy potential from a set of simulated orbits approximating stellar streams.

Harnessing big data analytics to revolutionize ESG reporting in clean energy initiatives

The integration of Big Data Analytics into Environmental, Social, and Governance (ESG) reporting holds significant potential to revolutionize clean energy initiatives. This paper examines the current challenges in ESG reporting, such as data collection and integration, data quality, regulatory compliance, transparency, and standardization. Big Data Analytics offers advanced techniques, including machine learning, artificial intelligence, predictive analytics, and real-time data processing, which can address these challenges. By leveraging diverse data sources—both structured and unstructured—clean energy projects can achieve more accurate environmental impact assessments, social impact evaluations, and governance analyses. The applications of Big Data Analytics are exemplified through various case studies, such as renewable energy projects, smart grids, and carbon offset programs. The benefits of these applications are vast, including enhanced data accuracy, improved decision-making, increased transparency, better stakeholder engagement, and cost efficiency. However, implementing Big Data Analytics in ESG reporting also presents challenges like data privacy, technological requirements, skills shortage, ethical considerations, and system integration. The future of Big Data Analytics in ESG reporting looks promising with emerging trends like blockchain for data transparency, IoT and sensor technologies, advanced machine learning models, and collaborative platforms. This paper concludes with a call to action for stakeholders to embrace these technologies, highlighting the transformative potential of Big Data Analytics in creating more effective and accountable clean energy initiatives.

Transforming healthcare with data analytics: Predictive models for patient outcomes

Healthcare organizations are increasingly leveraging data analytics to improve patient outcomes and enhance the efficiency of healthcare delivery. Predictive modeling, in particular, has emerged as a powerful tool for forecasting patient outcomes based on various data sources such as electronic health records, wearable devices, and genetic information. This paper provides an overview of the transformative role of data analytics in healthcare, with a specific focus on predictive models for patient outcomes. The introduction discusses the importance of data analytics in healthcare and outlines the purpose of the paper. It highlights the evolution of data analytics in healthcare, types of healthcare data, and challenges in data collection and management. The role of predictive modeling in healthcare is then explored, emphasizing its significance in improving patient outcomes and common techniques used in predictive modeling. The paper discusses various data sources for predictive modeling, including electronic health records, wearable devices, genetic and genomic data, and social determinants of health. It also covers the process of developing predictive models, including data preprocessing, model selection, and validation techniques, as well as ethical considerations. Furthermore, the paper explores the applications of predictive models in healthcare, such as early disease detection, personalized treatment planning, hospital resource optimization, and patient engagement. Case studies and examples illustrate real-world implementations of predictive analytics in healthcare organizations. Finally, the paper addresses challenges and future directions in healthcare data analytics, including data privacy and security concerns, interpretability of predictive models, integration into clinical workflows, and emerging trends. Overall, this paper underscores the transformative potential of data analytics, particularly predictive modeling, in revolutionizing healthcare delivery and improving patient outcomes.

Scanning Probe Microscopy Characterization of Biomolecules enabled by Mass-Selective, Soft-landing Electrospray Ion Beam Deposition.

Scanning probe microscopy (SPM), in particular at low temperature (LT) under ultra-high vacuum (UHV) conditions, offers the possibility of real-space imaging with resolution reaching the atomic level. However, its potential for the analysis of complex biological molecules has been hampered by requirements imposed by sample preparation. Transferring molecules onto surfaces in UHV is typically accomplished by thermal sublimation in vacuum. This approach however is limited by the thermal stability of the molecules, i. e. not possible for biological molecules with low vapour pressure. Bypassing this limitation, electrospray ionisation offers an alternative method to transfer molecules from solution to the gas-phase as intact molecular ions. In soft-landing electrospray ion beam deposition (ESIBD), these molecular ions are subsequently mass-selected and gently landed on surfaces which permits large and thermally fragile molecules to be analyzed by LT-UHV SPM. In this concept, we discuss how ESIBD+SPM prepares samples of complex biological molecules at a surface, offering controls of the molecular structural integrity, three-dimensional shape, and purity. These achievements unlock the analytical potential of SPM which is showcased by imaging proteins, peptides, DNA, glycans, and conjugates of these molecules, revealing details of their connectivity, conformation, and interaction that could not be accessed by any other technique.

Integrating Radiological Diagnostic Tools in Shalya Tantra: Advancements and Clinical Applications

The integration of advanced diagnostic imaging technologies into Ayurvedic surgery marks a significant advancement, blending ancient principles from texts like Sushruta Samhita with modern innovations. This review examines the evolution and clinical applications of diagnostic imaging in Shalya Tantra, emphasizing the synergy between traditional diagnostic methods and contemporary modalities such as ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), artificial intelligence (AI), and augmented reality (AR). These technologies enhance diagnostic accuracy, surgical planning, and therapeutic outcomes across diverse medical specialties within Ayurveda. Case studies illustrate their pivotal role in diagnosing abdominal, pelvic, musculoskeletal, and obstetric conditions, guiding accurate interventions and improving patient care. Future directions explore the potential of AI-driven analytics, virtual simulations, and portable ultrasound devices to further optimize diagnostic capabilities and expand access to advanced imaging in Ayurvedic practice.

AgroSense: An IoT-Based Manual Crops Selection Farming

This capstone project introduces an Intelligent Irri-gation System leveraging IoT technology to enhance agriculture. Using the ESP32 microcontroller and various sensors for soil moisture, water levels, and environmental conditions, the system automates irrigation based on real-time data. It communicates through the Blynk platform, allowing remote monitoring via a mobile app. The project includes a smart algorithm for crop selection and irrigation control, displayed on an LCD and acces- sible through the Blynk app. By considering soil moisture and water availability, the system adapts to different crops like rice, wheat, potato, and corn. The project promotes sustainability by optimizing water usage and encourages efficient crop growth. The integration of a manual crop count for field feedback enhances decision-making. Overall, this system presents a user-friendly and innovative solution for precision agriculture, showcasing the transformative potential of IoT, data analytics, and machine learning in modernizing farming practices.

A Review of Leveraging Big Data Analytics for Sustainable Supply Chain Management

As organizations aim to drive sustainability in their supply chain management practices, big data analytics has emerged as one of the effective approaches. However,data security and privacy challenges remain since big data analytics continue evolving in the supply chain. These challenges need to be addressed by organizations to ensure business performance. This paper conducts systematic literature reviews of the potential of big data analytics in driving sustainability in supply chain management. The findings show that big data analytics enables organizations to make data-driven decisions, optimize business processes, and enhance sustainability through techniques like machine learning, predictive, and prescriptive analysis. Future research directions are suggested, focusing on addressing the challenges with appropriate strategies and exploring the impact of other Industry 4.0 technologies on sustainable supply chain management.

Navigating Health Insurance Economics: A Data-Driven Approach to Enhanced Pricing in the United States

This study delves into the dynamics of healthcare spending in the United States, revealing a considerable financial commitment that hasn't translated into commensurate health outcomes. With healthcare costs reaching $4.3 trillion in 2021, driven by factors such as an aging population and escalating service prices, the nation confronts challenges to its fiscal health. The analysis extends to the intricacies of U.S. health insurance pricing, critiquing the existing system's complexity and opacity while drawing comparisons with approaches in other developed countries. Amid criticisms of profit-centric motives in the private sector, the study prompts discussions on potential reforms, learning from international experiences. Additionally, the study explores the transformative potential of data analytics in health insurance pricing, emphasizing its role in enhancing fairness and competitiveness. However, ethical considerations, including privacy, data security, and potential biases, underscore the need for a delicate balance between leveraging data-driven insights and responsible practices. The results of the study may be of great interest to specialists from the point of view of the further development of the compulsory social health insurance system in the Republic of Kazakhstan.

Leveraging business analytics for competitive advantage: Predictive models and data-driven decision making

This study investigates the transformative potential of predictive analytics in market performance, addressing a significant gap in contemporary business intelligence literature. The primary aim is to evaluate the accuracy, reliability, scalability, and ethical implications of predictive models. Utilizing a qualitative approach, the review incorporates case studies and thematic analysis to capture rich, context-specific insights. Key findings reveal that predictive models significantly enhance market forecasting capabilities, enabling more informed and strategic decision-making processes. The study underscores the robustness of these models across various scenarios, highlighting their efficiency in managing large datasets. Ethical considerations emerge as a crucial theme, emphasizing the need for stringent data privacy measures and transparent practices to build trust and ensure compliance. The conclusions drawn emphasize the substantial competitive advantages that predictive analytics can offer when implemented judiciously, fostering agility and responsiveness in market strategies. However, the study also cautions against potential ethical pitfalls and data privacy issues, advocating for a balanced approach that prioritizes both innovation and integrity. Recommendations include investing in advanced predictive analytics tools while simultaneously strengthening data governance frameworks to enhance predictive accuracy and ensure ethical compliance. Future research should continue exploring the evolving landscape of predictive analytics, particularly focusing on emerging technologies and their potential to reshape market dynamics. Keywords: Predictive Analytics, Market Performance, Business Intelligence, Data Privacy, Qualitative Analysis, Competitive Advantage.

The Use Of Big Data Analytics In Healthcare Organization Management

Introduction. The potential of using big data analytics in the management system of healthcare organizations is of interest to healthcare managers, with a notable emphasis from the research community on the correlation between the incorporation of big data analytics and its advantages for healthcare organizations, particularly in terms of resource allocation and operational efficiency. The purpose of the study was to summarize scientific data characterizing the potential of big data analytics on healthcare organizations' management practices. Materials and Methods. A comprehensive search was conducted across PubMed, eLibrary, CyberLeninka and ScienceDirect databases, as well as the World Health Organization datasets, covering the use of big data analytics in healthcare. In this study, keyword and phrase searching on the following requests was performed: ”big data in healthcare organization management”, “big data analytics in healthcare”, “big data”, “analytics”, “management”, “healthcare”. Results. The study involved an in-depth analysis and consolidation of both general and specific aspects of the potential of big data analytics in the management practices of healthcare organizations. The benefits of big data analytics implementation in personnel management, as well as its efficiency in prognosing the number of referrals to emergency departments and tracking the prescription and intake of medications, were identified.

Self-consistent modelling of the Milky Way structure using live potentials

ABSTRACT To advance our understanding of the evolution of the interstellar medium (ISM) of our Galaxy, numerical models of Milky Way (MW) type galaxies are widely used. However, most models only vaguely resemble the MW (e.g. in total mass), and often use imposed analytic potentials (which cannot evolve dynamically). This poses a problem in asserting their applicability for the interpretation of observations of our own Galaxy. The goal of this work is to identify a numerical model that is not only an MW-type galaxy, but one that can mimic some of the main observed structures of our Galaxy, using dynamically evolving potentials, so that it can be used as a base model to study the ISM cycle in a galaxy like our own. This paper introduces a suite of 15 MW-type galaxy models developed using the arepo numerical code, that are compared to Galactic observations of $^{12}$CO and H i emission via longitude–velocity plots, from where we extract and compare the skeletons of major galactic features and the terminal gas velocities. We found that our best-fitting model to the overall structure, also reproduces some of the more specific observed features of the MW, including a bar with a pattern speed of $30.0 \pm 0.2$ km s$^{-1}$ kpc$^{-1}$, and a bar half-length of $3.2 \pm 0.8$ kpc. Our model shows large streaming motions around spiral arms, and strong radial motions well beyond the inner bar. This model highlights the complex motions of a dynamic MW-type galaxy and has the potential to offer valuable insight into how our Galaxy regulates the ISM and star formation.

ENHANCING FASHION FORECASTING ACCURACY THROUGH CONSUMER DATA ANALYTICS: INSIGHTS FROM CURRENT LITERATURE

The fashion industry is characterized by its fast-paced nature and constant evolution of consumer preferences, making accurate fashion forecasting essential for brands to remain competitive. Traditional forecasting methods, which rely heavily on historical sales data and expert intuition, are increasingly being complemented or replaced by advanced consumer data analytics. This article explores the integration of consumer data analytics into fashion forecasting, drawing insights from recent literature. By examining methodologies such as machine learning, big data analytics, and AI, as well as utilizing diverse data sources including social media, online shopping behaviors, and mobile data, this study highlights the significant improvements in trend prediction accuracy and operational efficiency. Key findings indicate that data-driven approaches provide more precise and real-time insights into consumer preferences, enabling brands to better anticipate market demands and optimize inventory management. The discussion underscores the transformative potential of consumer data analytics in enhancing the overall effectiveness of fashion forecasting.

Toward a systemic functional framework for the multimodal analysis of meaning reconstruction

AbstractIn recent decades, there has been a growing interest in multimodal translation studies. However, there is currently no suitable framework available for analyzing how the original meaning is reconstructed through the interaction of various semiotic resources in the multimodal target text. Under this context, this article proposes a systemic functional framework that integrates the systems of the meaning potential of semiotic resources, including language, sound (human voice), objects, space, and semiotic ensembles of the human body, and a tripartite classification of the relationships between semiotic resources. To exemplify the analytical potential of the framework, this article presents an example of analysis, taking an excerpt from Fences as the object of analysis.

Forecasting HR expenses: A review of predictive analytics in financial planning for HR

This review paper explores the role of predictive analytics in financial planning for Human Resources (HR), focusing on expense forecasting. It examines internal and external factors influencing HR expenses, including workforce size, economic conditions, and regulatory changes. The paper discusses methods and techniques in predictive analytics for HR expense forecasting, such as regression analysis, machine learning algorithms, common data sources, and software tools. Challenges in implementation, including data quality issues and ethical considerations, are addressed alongside future trends and opportunities, such as the integration of AI and real-time analytics. By overcoming challenges and embracing emerging trends, organizations can harness the full potential of predictive analytics to optimize HR expenses and drive organizational success. Keywords: Predictive Analytics, HR Expense Forecasting, Data Sources, Analytical Techniques.

Using multimodal learning analytics as a formative assessment tool: Exploring collaborative dynamics in mathematics teacher education

AbstractBackgroundTraditionally, understanding students' learning dynamics, collaboration, emotions, and their impact on performance has posed challenges in formative assessment. The complexity of monitoring and assessing these factors have often limited the depth and breadth of insights.ObjectivesThis study aims to explore the potential of multimodal learning analytics as a formative assessment tool in math education. The focus is on discerning how collaborative discourse behaviours and emotional indicators interplay with lesson evaluation performance.MethodsUsing undergraduate students' multimodal data, which includes collaboration data, facial behaviour data, and emotional data, the study explored the patterns of collaboration and emotion. Through the lens of multimodal learning analytics, we conducted exploratory data analysis to identify meaningful relationships between specific types of collaborative discourse, facial expressions, and performance indicators. Moreover, the study evaluated a machine learning model's potential to predict target learning outcomes by integrating data from multiple channels.ResultsThe analysis revealed key features from both discourse and emotion data as significant predictors. These findings underscore the potential of a multimodal analytical approach in understanding students' learning process and predicting outcomes.ConclusionsThe study emphasizes the importance and feasibility of a multimodal learning analytic approach in the context of math education. It highlights the academic and practical implications of such an approach, along with its limitations, pointing towards future research directions in this area.