Analytical Power Research Articles

Entrepreneurial growth in digital business ecosystems: an integrated framework blending the knowledge-based view of the firm and business ecosystems

Digitalisation is shaping the contemporary technological context of entrepreneurial activities, where firms grow through interacting with digital ecosystem stakeholders. This study investigates how incumbent firms seek entrepreneurial growth by re-configurating their knowledge bases in digital business ecosystems. We propose and develop a conceptual framework that blends the digital business ecosystem perspective and the knowledge-based view of the firm. Through a longitudinal case study of a Chinese textile manufacturing firm, we identify three pathways for entrepreneurial growth. The results contribute to the entrepreneurship literature by demonstrating how digital technologies foster corporate entrepreneurship in incumbent firms. The proposed framework extends the analytical power of the knowledge-based view by incorporating ecosystem elements into the firm’s internal and external knowledge management. The findings also generate relevant and actionable managerial implications for entrepreneurs, managers, and policymakers that are applicable in the context of digital business ecosystems.

Revolutionizing Healthcare: Unleashing the Power of Data Analytics for Enhanced Clinical Decisions, Operational Efficiency, and Patient Empowerment

Revolutionizing Healthcare: Unleashing the Power of Data Analytics for Enhanced Clinical Decisions, Operational Efficiency, and Patient Empowerment

Run-and-tumble motion in a linear ratchet potential: Analytic solution, power extraction, and first-passage properties.

We explore the properties of run-and-tumble particles moving in a piecewise-linear "ratchet" potential by deriving analytic results for the system's steady-state probability density, current, entropy production rate, extractable power, and thermodynamic efficiency. The ratchet's broken spatial symmetry rectifies the particles' self-propelled motion, resulting in a positive current that peaks at finite values of the diffusion strength, ratchet height, and particle self-propulsion speed. Similar nonmonotonic behavior is also observed for the extractable power and efficiency. We find the optimal apex position for generating maximum current varies with diffusion and that entropy production can have nonmonotonic dependence on diffusion. In particular, for vanishing diffusion, entropy production remains finite when particle self-propulsion is weaker than the ratchet force. Furthermore, power extraction with near-perfect efficiency is achievable in certain parameter regimes due to the simplifications afforded by modeling "dry" active particles. In the final part, we derive mean first-passage times and splitting probabilities for different boundary and initial conditions. This work connects the study of work extraction from active matter with exactly solvable active particle models and will therefore facilitate the design of active engines through these analytic results.

Pricing and investment decisions when facing heterogeneous customers under different supply chain power structures

This paper examines the pricing and joint investment decisions of a two-echelon supply chain with a manufacturer and a retailer under power structures. A manufacturer invests in a specific quality level to produce a quality product. Meanwhile, a retailer invests in the extended warranty service provision as an after-sales service. This study investigates the interplay of these two investments assuming two heterogeneous customer demands exist. Three decision-making analytical power structure models are developed, namely centralized, manufacturer-led decentralized, and retailer-led decentralized models. In both decentralized models, the Stackelberg game determines the optimal price and investment decisions to maximize member's profit. However, independently optimal decision-making with these models does not coordinate the supply chain and its members. Thus, this study proposes wholesale price contracts considering power structures to coordinate effectively and achieve a win–win situation. A numerical analysis has been performed to determine the optimal level for quality investment, extended warranty length, pricing, and supply chain performance. Furthermore, a sensitivity analysis explores key parameter's impact on heterogeneous customer demands and supply chain performance.

Early Detection of Optic Nerve Changes on Optical Coherence Tomography Using Deep Learning for Risk-Stratification of Papilledema and Glaucoma.

The use of artificial intelligence is becoming more prevalence in medicine with numerous successful examples in ophthalmology. However, much of the work has been focused on replicating the works of ophthalmologists. Given the analytical potentials of artificial intelligence, it is plausible that artificial intelligence can detect microfeatures not readily distinguished by humans. In this study, we tested the potential for artificial intelligence to detect early optic coherence tomography changes to predict progression toward papilledema or glaucoma when no significant changes are detected on optical coherence tomography by clinicians. Prediagnostic optical coherence tomography of patients who developed papilledema (n = 93, eyes = 166) and glaucoma (n = 187, eyes = 327) were collected. Given discrepancy in average cup-to-disc ratios of the experimental groups, control groups for papilledema (n = 254, eyes = 379) and glaucoma (n = 441, eyes = 739) are matched by cup-to-disc ratio. Publicly available Visual Geometry Group-19 model is retrained using each experimental group and its respective control group to predict progression to papilledema or glaucoma. Images used for training include retinal nerve fiber layer thickness map, extracted vertical tomogram, ganglion cell thickness map, and ILM-RPE thickness map. Trained model was able to predict progression to papilledema with a precision of 0.714 and a recall of 0.769 when trained with retinal nerve fiber layer thickness map, but not other image types. However, trained model was able to predict progression to glaucoma with a precision of 0.682 and recall of 0.857 when trained with extracted vertical tomogram, but not other image types. Area under precision-recall curve of 0.826 and 0.785 were achieved for papilledema and glaucoma models, respectively. Computational and analytical power of computers have become an invaluable part of our lives and research endeavors. Our proof-of-concept study showed that artificial intelligence (AI) algorithms have the potential to detect early changes on optical coherence tomography for prediction of progression that is not readily observed by clinicians. Further research may help establish possible AI models that can assist with early diagnosis or risk stratification in ophthalmology.

Reference Interval Harmonization: Harnessing the Power of Big Data Analytics to Derive Common Reference Intervals across Populations and Testing Platforms.

Harmonization in laboratory medicine is essential for consistent and accurate clinical decision-making. There is significant and unwarranted variation in reference intervals (RIs) used by laboratories for assays with established analytical traceability. The Canadian Society of Clinical Chemists (CSCC) Working Group on Reference Interval Harmonization (hRI-WG) aims to establish harmonized RIs (hRIs) for laboratory tests and support implementation. Harnessing the power of big data, laboratory results were collected across populations and testing platforms to derive common adult RIs for 16 biochemical markers. A novel comprehensive approach was established, including: (a) analysis of big data from community laboratories across Canada; (b) statistical evaluation of age, sex, and analytical differences; (c) derivation of hRIs using the refineR method; and (d) verification of proposed hRIs across 9 laboratories with different instrumentation using serum and plasma samples collected from healthy Canadian adults. Harmonized RIs were calculated for all assays using the refineR method, except free thyroxine. Derived hRIs met proposed verification criterion across 9 laboratories and 5 manufacturers for alkaline phosphatase, albumin (bromocresol green), chloride, lactate dehydrogenase, magnesium, phosphate, potassium (serum), and total protein (serum). Further investigation is needed for some analytes due to failure to meet verification criteria in one or more laboratories (albumin [bromocresol purple], calcium, total carbon dioxide, total bilirubin, and sodium) or concern regarding excessively wide hRIs (alanine aminotransferase, creatinine, and thyroid stimulating hormone). We report a novel data-driven approach for RI harmonization. Findings support feasibility of RI harmonization for several analytes; however, some presented challenges, highlighting limitations that need to be considered in harmonization and big data analytics.

Data Analytics and Artificial Intelligence

In the realm of modern industry and technology, the amalgamation of data analytics and artificial intelligence (AI) has emerged as a formidable force, propelling significant transformations across various sectors. This partnership between data analytics and AI is not merely reshaping conventional practices but also uncovering valuable insights that were previously hidden from view. The collaboration between these two disciplines is exemplified by data analytics, which involves the systematic examination of extensive datasets to extract meaningful insights through data refinement and interpretation. Dedicated professionals are actively engaged in the pursuit of cutting-edge analytical techniques that empower precise, efficient and sustainable pharmaceutical analysis. They are committed to harnessing the power of data analytics and artificial intelligence to elevate the speed and accuracy of data interpretation, making the drug development process more efficient and reliable. Adaptation to an evolving regulatory landscape is another crucial aspect of the analysts' roles. They diligently work to meet ever-changing standards and ensure that analytical methods are not just innovative but compliant with the necessary regulatory guidelines. A spirit of collaboration and data sharing permeates the community. In this world of pharmaceutical analysis, the dedicated analysts are not just observers of the future. They are the architects of progress and innovation, shaping a future characterized by ground breaking discoveries and enhanced pharmaceutical solutions.

Unleashing the Power of Predictive Analytics to Identify At-Risk Students in Computer Science

Unleashing the Power of Predictive Analytics to Identify At-Risk Students in Computer Science

An automated and portable method for selecting an optimal GPU frequency

Power consumption poses a significant challenge in current and emerging graphics processing unit (GPU) enabled high-performance computing systems. In modern GPUs, dynamic voltage frequency scaling (DVFS) appears to be a reliable control to regulate power consumption and performance. However, the DVFS design space is large - hence, brute-force approaches are infeasible to select the optimal frequency. Furthermore, no single frequency can be universally optimal for applications with varying computational intensities. Thus, the application’s complexity and the availability of a wide range of frequency settings are a challenge in selecting the optimal frequency configuration for a given GPU workload. To that end, this paper proposes a systematic approach that consists of three steps. The feature characterization study identifies the fine-grain GPU utilization metrics that influence the power consumption and execution time of a given workload. To understand the performance, power, and energy consumption behaviors of a workload across GPU’s DVFS design space, we derived analytical power and performance models using the identified fine-grain features. It is shown that the same set of GPU utilization metrics can estimate both the power consumption and execution time while being agnostic of changes to frequency and input sizes. Applying a power control with the single objective of reducing power may cause performance degradation, leading to more energy consumption. A multi-objective approach is proposed to select the optimal GPU DVFS configuration for a workload that reduces power consumption with negligible degradation in performance. The evaluation was conducted using SPEC ACCEL benchmarks and three real applications - NAMD LAMMPS, and LSTM on NVIDIA GV100, GA100, and AMD MI210 GPUs. On average, real applications showed 29.6% energy savings with a performance loss of 5.2% on GA100 and 22.6% energy savings with a performance loss of 4.7% on GV100. Moreover, the proposed models are portable to real applications, GPU architectures, and vendors, and require metric collection at only the default frequency rather than all supported DVFS configurations. Additionally, we conducted a comparison between our models and the GPU assembly instructions (PTX)-based static models. The results revealed a significant reduction in the average error rates, with a decrease from 19.7% to 3.1% for power models and from 29.4% to 5.2% for performance models.

Co-evolution of dust grains and protoplanetary disks

Abstract We propose a new evolutionary process for protoplanetary disks, the co-evolution of dust grains and protoplanetary disks, revealed by dust–gas two-fluid non-ideal magnetohydrodynamics simulations considering the growth of dust grains and associated changes in magnetic resistivity. We found that the dust growth significantly affects disk evolution by changing the coupling between the gas and the magnetic field. Moreover, once the dust grains grow sufficiently large and the adsorption of charged particles on to them becomes negligible, the physical quantities (e.g., density and magnetic field) of the disk are well described by characteristic power laws. In this disk structure, the radial profile of density is steeper and the disk mass is smaller than those of the model ignoring dust growth. We analytically derive these power laws from the basic equations of non-ideal magnetohydrodynamics. The analytical power laws are determined only by observable physical quantities, e.g., central stellar mass and mass accretion rate, and do not include difficult-to-determine parameters, e.g., the viscous parameter α. Therefore, our model is observationally testable and this disk structure is expected to provide a new perspective for future studies on protostar and disk evolution.

Assessment of the predictability of inflow to reservoirs through Bayesian causality

ABSTRACT This research assesses the predictive capacity of Bayesian causality through causal reasoning (CR), which has been successfully applied to the study of reservoir inflows. We combined autoregressive development with a causal modelling approach through a “proof of concept” that assesses the predictive capacity of the approach. The analytical power of CR revealed the logical temporal structure that defines the general behaviour of inflows, which was latent in the historical records. This allowed identifying/quantifying, through a dependence matrix, two temporal runoff fractions, one due to time and the other not. Finally, a predictive model for each temporal fraction was implemented, evaluating its forecasting skills through mean absolute error and root mean square error. This was applied to the reservoirs that supply water to the city of Ávila (Spain), whose watersheds present strong independent temporal behaviour. These results open new possibilities for developing predictive hydrological models within a CR modelling framework.

Big Data and Consumer Behavior: The Power and Pitfalls of Analytics in the Digital Age

The advent of digital technology has transformed the way businesses interact with consumers, providing an abundance of data that can be harnessed to improve the customer experience. Big data analysis offers businesses the tools to gain insight into consumer behavior and preferences, optimize the customer journey, and create personalized experiences. This paper explores the impact of big data analysis on digital consumer behavior, delving into the opportunities, challenges, and ethical considerations that arise in this context.

Tutorial: Power Analyses for Interaction Effects in Cross-Sectional Regressions

Interaction analyses (also termed “moderation” analyses or “moderated multiple regression”) are a form of linear regression analysis designed to test whether the association between two variables changes when conditioned on a third variable. It can be challenging to perform a power analysis for interactions with existing software, particularly when variables are correlated and continuous. Moreover, although power is affected by main effects, their correlation, and variable reliability, it can be unclear how to incorporate these effects into a power analysis. The R package InteractionPoweR and associated Shiny apps allow researchers with minimal or no programming experience to perform analytic and simulation-based power analyses for interactions. At minimum, these analyses require the Pearson’s correlation between variables and sample size, and additional parameters, including reliability and the number of discrete levels that a variable takes (e.g., binary or Likert scale), can optionally be specified. In this tutorial, we demonstrate how to perform power analyses using our package and give examples of how power can be affected by main effects, correlations between main effects, reliability, and variable distributions. We also include a brief discussion of how researchers may select an appropriate interaction effect size when performing a power analysis.

Low-Resolution Raman Enables a Low-Cost, Fully Automated Raman Microscope for Microspectroscopic Analysis

The high cost of Raman micro-spectroscopy limits its application to laboratory settings, while prior low-cost Raman systems are limited to simple bulk analysis. Using the “Low Resolution Raman” concept, we sacrifice the spectral resolution to increase the SNR while maintaining analytic power. With a low-spectral resolution, we eliminate the need for a high quality laser, camera, and optical filters. Combined with a low-cost, automated sample stage we create a fully automated Raman microscope capable of sampling thousands of microscopic objects without user intervention. We demonstrate the performance on classifications of standard microspheres and identifying the fatty acid ratio of fat globules in goat and cow milk, where thousands of spectra are acquired with a single button press. As the cost of the system is >10-fold less than conventional Raman microscopes, it holds great application prospects for truly pervasive Raman spectroscopy.

Leveraging Data Analysis Tools for Enhanced Project Decision Making

In today’s rapidly evolving business environment, leveraging data analysis tools has become essential for enhanced project decision-making. This paper explores how advanced data analysis tools contribute to more informed and strategic project management decisions. By harnessing the power of data analytics, organizations can gain deeper insights into project performance, identify trends, and predict potential risks with greater accuracy. The integration of sophisticated data analysis tools enables project managers to analyse large datasets, uncover hidden patterns, and make data-driven decisions that significantly improve project outcomes. The paper examines various data analysis tools, including statistical software, machine learning algorithms, and visualization platforms, highlighting their role in optimizing decision-making processes. It discusses the benefits of these tools, such as enhanced forecasting capabilities, improved risk management, and the ability to make real-time adjustments based on evolving project dynamics. Additionally, the paper addresses the challenges associated with implementing data analysis tools, such as data quality issues, the need for skilled personnel, and the integration of diverse data sources. Through case studies and practical examples, the paper demonstrates how organizations have successfully utilized data analysis tools to achieve better project results. It emphasizes the importance of a strategic approach to data analysis, where tools are not only employed to process data but are also integrated into the decision-making framework to drive project success. The findings suggest that embracing data analysis tools is crucial for organizations seeking to enhance their project management practices and achieve a competitive edge in the market.

Calculating the power of a planned individual participant data meta-analysis of randomised trials to examine a treatment-covariate interaction with a time-to-event outcome.

Before embarking on an individual participant data meta-analysis (IPDMA) project, researchers should consider the power of their planned IPDMA conditional on the studies promising their IPD and their characteristics. Such power estimates help inform whether the IPDMA project is worth the time and funding investment, before IPD are collected. Here, we suggest how to estimate the power of a planned IPDMA of randomised trials aiming to examine treatment-covariate interactions at the participant-level (i.e., treatment effect modifiers). We focus on a time-to-event (survival) outcome with a binary or continuous covariate, and propose an approximate analytic power calculation that conditions on the actual characteristics of trials, for example, in terms of sample sizes and covariate distributions. The proposed method has five steps: (i) extracting the following aggregate data for each group in each trial-the number of participants and events, the mean and SD for each continuous covariate, and the proportion of participants in each category for each binary covariate; (ii) specifying a minimally important interaction size; (iii) deriving an approximate estimate of Fisher's information matrix for each trial and the corresponding variance of the interaction estimate per trial, based on assuming an exponential survival distribution; (iv) deriving the estimated variance of the summary interaction estimate from the planned IPDMA, under a common-effect assumption, and (v) calculating the power of the IPDMA based on a two-sided Wald test. Stata and R code are provided and a real example provided for illustration. Further evaluation in real examples and simulations is needed.

Relevance and electoralism in the study of political oppositions: the case of Morocco’s radical left

ABSTRACT Relevance is a key concept in the study of political parties. This article argues that the conceptions of relevance used in the study of political oppositions in authoritarian settings need to be clarified and reformulated. More specifically, and in light of current knowledge on electoral authoritarianism, the notion of relevance must go beyond the electoralist conceptions traditionally used when studying democratic contexts. Drawing on the case of Morocco, the article highlights the obscuring effect that such conceptions can have. It then provides an alternative framework, adjusting the concept of relevance to the specificities of the context and the evolving nature of the actors operating within it. The case of al-Nahj al-Dīmoqratī, a Moroccan radical-leftist opposition party, is used to illustrate the analytical power of the framework.

Unleashing the Power of Big Data Analytics: Examining Its Effect on Marketing Efficiency and Effectiveness in the Digital Era

Unleashing the Power of Big Data Analytics: Examining Its Effect on Marketing Efficiency and Effectiveness in the Digital Era

From inflation to reheating and their dynamical stability analysis in Gauss–Bonnet gravity

We investigate the inflation and reheating phenomenology in scalar-Einstein-Gauss–Bonnet theory of gravity where a scalar field non-minimally couples with the Gauss-Bonnet (GB) curvature term. Regarding the inflationary phenomenology, we find – (1) the inflation starts with a quasi de-Sitter phase and has an exit at a finite e-fold, which is indeed consistent with the resolution of horizon and flatness problems, (2) the scalar and tensor perturbations prove to be ghost free and do not suffer from gradient instability, (3) the curvature perturbation amplitude as well as its tilt and the tensor-to-scalar ratio turn out to be simultaneously compatible with the recent Planck data for suitable values of the parameters which also results to the inflationary energy scale ∼1012GeV. After the inflation ends, the scalar field starts to decay to radiation with a constant decay width. For our considered scalar potential and the GB coupling function, the model results to an analytic power law solution of the Hubble parameter and a logarithmic solution of the scalar field during the reheating era, where the exponent of the Hubble parameter determines the effective EoS parameter (weff) during the same. The stability of such reheating dynamics is examined by dynamical analysis which ensures that weff can go beyond unity and reach up-to the maximum value of max(weff)=1.56. The scenario with weff>1 proves to be purely due to the presence of the GB coupling function, which in turn may have important consequences on enhancing the primordial gravitational waves’ amplitude observed today. The inflationary e-fold number (Nf) gets further constrained by the input of the reheating stage, and we critically examine the constraint of Nf coming from both the inflation and reheating phenomenology. We finally construct the complete forms of scalar potential (V(ϕ)) and the GB coupling function (ξ(ϕ)) that smoothly transits from inflation to reheating, and numerically solve the Hubble parameter and the scalar field for such complete forms of V(ϕ) and ξ(ϕ). Such numerical solutions match with analytic ones in the respective regime. After the reheating ends, the GB term gets decouple from the theory and the radiation energy density dominates the energy budget of the universe, which in turn produces the standard radiation era of the universe.

REVEL Framework to Measure Local Linear Explanations for Black-Box Models: Deep Learning Image Classification Case Study

Explainable artificial intelligence is proposed to provide explanations for reasoning performed by artificial intelligence. There is no consensus on how to evaluate the quality of these explanations, since even the definition of explanation itself is not clear in the literature. In particular, for the widely known local linear explanations, there are qualitative proposals for the evaluation of explanations, although they suffer from theoretical inconsistencies. The case of image is even more problematic, where a visual explanation seems to explain a decision while detecting edges is what it really does. There are a large number of metrics in the literature specialized in quantitatively measuring different qualitative aspects, so we should be able to develop metrics capable of measuring in a robust and correct way the desirable aspects of the explanations. Some previous papers have attempted to develop new measures for this purpose. However, these measures suffer from lack of objectivity or lack of mathematical consistency, such as saturation or lack of smoothness. In this paper, we propose a procedure called REVEL to evaluate different aspects concerning the quality of explanations with a theoretically coherent development which do not have the problems of the previous measures. This procedure has several advances in the state of the art: it standardizes the concepts of explanation and develops a series of metrics not only to be able to compare between them but also to obtain absolute information regarding the explanation itself. The experiments have been carried out on four image datasets as benchmark where we show REVEL’s descriptive and analytical power.