TikTok has become one of the most widely used platforms, its innovative video format has allowed companies and users to increase their visibility, transforming the way brands communicate their strategies. This systema...
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TikTok has become one of the most widely used platforms, its innovative video format has allowed companies and users to increase their visibility, transforming the way brands communicate their strategies. This systematic literature review (SLR) explored how the TikTok algorithm influences marketing strategies during the period 2021 to 2024. For this purpose, research was conducted based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method. Also, reliable and relevant research databases were consulted, specifically Springer, Science Direct and EBSCO, from which 64 studies aligned with the inclusion and exclusion criteria were extracted, all corresponding to academic articles. After compilation, it was determined that 2024 was the year with the highest number of publications, representing 50% of the total number of articles. Likewise, the country that stood out was China with 28.13% of the related documents. Regarding the research approach, quantitative research predominated, followed by qualitative and mixed research. Finally, the study helped to understand the positive impact of TikTok on marketing, showing how it improves the visibility of brands, as well as identifying trends in consumer preferences, which allows the creation of more accurate strategies that are closer to the public.
This study integrates embedded cognition theory and mindfulness theory to investigate the role of algorithm- based AR (AAR) retail services in seamless shopping experiences in a cross-retail context. After using mixed...
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This study integrates embedded cognition theory and mindfulness theory to investigate the role of algorithm- based AR (AAR) retail services in seamless shopping experiences in a cross-retail context. After using mixed research methods (experimental manipulation and a scenario-based survey), the results reveal that two elements of AAR retail services (human-algorithm interaction and activity-related authenticity) indirectly improve shopping duration and shopping cart amount at the official website. Mindfulness is the psychological mechanism through which consumers may perceive a seamless experience between the AR component and the official website of an online retail service. Even so, retailers and brands should develop AR/website interfaces based on the degree of the technology readiness (TR) of their consumers. This is a pioneer study on the impact of AAR in a cross-retail context.
Accurate prediction of the mechanical properties of strain-hardening cementitious composites (SHCC) is crucial for engineering application. While machine learning (ML) techniques excel in capturing nonlinear relations...
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Accurate prediction of the mechanical properties of strain-hardening cementitious composites (SHCC) is crucial for engineering application. While machine learning (ML) techniques excel in capturing nonlinear relationships compared to traditional regression models, the acquisition of experimental data for SHCC remains challenging, resulting in small datasets that are unfavorable for ML model development. Improved intelligent algorithms have been employed to enhance ML model performance through automatic hyperparameter tuning, offering improved probability of escaping local optima and consequently achieving higher accuracy compared to conventional intelligent algorithms. However, research on optimizing ML model parameters for SHCC using improved intelligent algorithms remains limited, and the interpretability analysis of existing model to SHCC is still limited, especially in the perspective of the combination of three dimension (3D) and two dimension (2D). Moreover, the prediction accuracy for ductility in most studies remains relatively low and the interactive graphical user interface (GUI) design in currently study is not comprehensive. In this study, 434 experimental data from published literature is collected, and an improved sparrow search algorithm (MSSA) is established. The MSSA is based on the sparrow search algorithm and incorporates five improvement strategies, including Levy flights, to optimize decision tree and random forest (RF) models for predicting the compressive strength (CS), tensile strength (TS), and ductility of SHCC. An isolated forest algorithm is used for outlier removal, while shapley additive explanations (SHAP) and partial dependence plots (PDP) are employed to enhance interpretability. Furthermore, a comprehensive GUI, which improves research diversity and system scalability, is developed to facilitate practical application. The results show that the MSSA successfully improves the prediction accuracy in small datasets, with t
Let E = Q(root-d) be an imaginary quadratic field for a square-free positive integer d, and let O be its ring of integers. For every positive integer m, let Im be the free Hermitian lattice over O with an orthonormal ...
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Let E = Q(root-d) be an imaginary quadratic field for a square-free positive integer d, and let O be its ring of integers. For every positive integer m, let Im be the free Hermitian lattice over O with an orthonormal basis, let Sd(1) be the set consisting of all the positive definite integral unary Hermitian lattices over O which can be represented by some Im, and let gd(1) be the smallest positive integer such that all the lattices in Sd(1) can be uniformly represented by Igd(1). In this work, I provide an algorithm to compute the explicit form of Sd(1) and the exact value of gd(1) for every imaginary quadratic field E, which may be viewed as a natural extension of the Pythagoras number in the lattice setting. (c) 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Objective: To quantify and improve the performance of standard rheumatoid arthritis (RA) algorithms in a biobank setting. Methods: This retrospective cohort study within the Mayo Clinic (MC) Biobank and MC Tapestry St...
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Objective: To quantify and improve the performance of standard rheumatoid arthritis (RA) algorithms in a biobank setting. Methods: This retrospective cohort study within the Mayo Clinic (MC) Biobank and MC Tapestry Study identified RA cases by presence of at least two RA codes OR positive anti-cyclic citrullinated peptide antibodies (CCP) plus disease-modifying anti-rheumatic drug (DMARD) prescription as of 7/18/2022. Rheumatology physicians manually verified all RA cases using RA criteria and/or rheumatology physician diagnosis plus DMARD use. All other biobank participants served as non-RA controls. We defined seropositivity as rheumatoid factor and/or anti-CCP positivity. We assessed rules-based and Electronic Medical Records and Genomics (eMERGE) RA algorithms using positive predictive value (PPV). Finally, we developed a novel RA algorithm using a LASSO-based machine learning approach with five-fold cross validation. Results: We identified 1,316 confirmed RA cases (968 MC Biobank, 348 Tapestry, 70 % seropositive) and 82,123 non-RA controls (mean age 65, 61 % female). The PPV of 3 RA codes was 43 %, codes plus DMARD was 54 %, and codes plus DMARD plus seropositivity was 85 %. The PPV of eMERGE was 77 %. Available in the MC Biobank, self-reported RA (PPV 10 %) only minimally improved algorithm performance (PPV from 83 % to 85 %), whereas family history of RA (PPV 3 %) worsened performance. At 90 % PPV, the novel RA algorithm incorporating key variables such as anti-CCP and DMARD use increased sensitivity by 4-11 % compared to eMERGE. Conclusion: Rules-based and eMERGE RA algorithms had worse performance in biobank than administrative settings. Our novel RA algorithm outperformed these standard algorithms.
Industrial linear accelerators often contain many bunches when their pulse widths are extended to microseconds. As they typically operate at low electron energies and high currents, the interactions among bunches cann...
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Industrial linear accelerators often contain many bunches when their pulse widths are extended to microseconds. As they typically operate at low electron energies and high currents, the interactions among bunches cannot be neglected. In this study, an algorithm is introduced for calculating the space charge force of a train with infinite bunches. By utilizing the ring charge model and the particle-in-cell (PIC) method and combining analytical and numerical methods, the proposed algorithm efficiently calculates the space charge force of infinite bunches, enabling the accurate design of accelerator parameters and a comprehensive understanding of the space charge force. This is a significant improvement on existing simulation software such as ASTRA and PARMELA that can only handle a single bunch or a small number of bunches. The PIC algorithm is validated in long drift space transport by comparing it with existing models, such as the infinite-bunch, ASTRA single-bunch, and PARMELA several-bunch algorithms. The space charge force calculation results for the external acceleration field are also verified. The reliability of the proposed algorithm provides a foundation for the design and optimization of industrial accelerators.
In this paper, we will survey the different uses of the term algorithm in contemporary legal practice. We will argue that the concept of algorithm currently exhibits a substantial degree of open texture, co-determined...
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In this paper, we will survey the different uses of the term algorithm in contemporary legal practice. We will argue that the concept of algorithm currently exhibits a substantial degree of open texture, co-determined by the open texture of the concept of algorithm itself and by the open texture inherent to legal discourse. We will substantiate our argument by virtue of a case study, in which we analyze a recent jurisprudential case where the first and second-degree judges have carved-out contrasting notions of algorithm. We will see that, thanks to our analysis of the open texture of the notion of algorithm in legal language, we can make sense of the different decisions taken by the judges as different contextually-determined sharpenings of the concept of algorithm. Finally, we will draw some general conclusions concerning the use of technical terms in legal instruments that address new technologies, such as the EU AI Act.
BackgroundChronic obstructive pulmonary disease (COPD) is a chronic respiratory condition characterized by high morbidity and mortality rates. This study aims to assess the clinical outcomes of COPD patients after imp...
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BackgroundChronic obstructive pulmonary disease (COPD) is a chronic respiratory condition characterized by high morbidity and mortality rates. This study aims to assess the clinical outcomes of COPD patients after implementing an algorithm within the MyTatva *** study involved a sample of 10 COPD patients, evaluating key parameters such as Forced Expiratory Volume in 1 s (FEV1), Forced Vital Capacity, Weight, Body Mass Index (BMI), Fat-Free Mass Index, and Distance Covered during the 6-Minute Walk Test (6MWT) before and after the algorithm's implementation in the MyTatva app. Patient satisfaction was assessed through a CSAT *** the implementation of the MyTatva care plan, significant improvements were observed in several key clinical outcomes for COPD patients. FEV1 increased from a median of 3.24-2.0 L (p = 0.0379), while weight and BMI decreased significantly, with a reduction in weight from a median of 86-70 kg (p = 0.0007) and a corresponding decrease in BMI from 28.43 to 24 kg/m2 (p = 0.0031). The distance covered during the 6MWT also improved from 420 to 568 m (p = 0.0019). The participation of 10 COPD patients in surveys yielded an overall CSAT score of 85%, indicating a high level of satisfaction with the MyTatva *** comprehensive features and functionalities of the MyTatva app, combined with the personalized care plan and real-time feedback mechanisms, have led to substantial clinical improvements in COPD management. These findings highlight the promise of this innovative digital therapeutic approach in addressing chronic respiratory conditions.
Background Identifying patients on dialysis among those with an estimated glomerular filtration rate (eGFR) < 15 mL/min/1.73 m(2) remains challenging. To facilitate clinical research in advanced chronic kidney dise...
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Background Identifying patients on dialysis among those with an estimated glomerular filtration rate (eGFR) < 15 mL/min/1.73 m(2) remains challenging. To facilitate clinical research in advanced chronic kidney disease (CKD) using electronic health records, we aimed to develop algorithms to identify dialysis patients using laboratory data obtained in routine practice. Methods We collected clinical data of patients with an eGFR < 15 mL/min/1.73 m2 from six clinical research core hospitals across Japan: four hospitals for the derivation cohort and two for the validation cohort. The candidate factors for the classification models were identified using logistic regression with stepwise backward selection. To ensure transplant patients were not included in the non-dialysis population, we excluded individuals with the disease code Z94.0. Results We collected data from 1142 patients, with 640 (56%) currently undergoing hemodialysis or peritoneal dialysis (PD), including 426 of 763 patients in the derivation cohort and 214 of 379 patients in the validation cohort. The prescription of PD solutions perfectly identified patients undergoing dialysis. After excluding patients prescribed PD solutions, seven laboratory parameters were included in the algorithm. The areas under the receiver operation characteristic curve were 0.95 and 0.98 and the positive and negative predictive values were 90.9% and 91.4% in the derivation cohort and 96.2% and 94.6% in the validation cohort, respectively. The calibrations were almost linear. Conclusions We identified patients on dialysis among those with an eGFR < 15 ml/min/1.73 m(2). This study paves the way for database research in nephrology, especially for patients with non-dialysis-dependent advanced CKD.
Background: When using electronic health records (EHRs) to conduct population-based studies on inherited bleeding disorders (IBDs), using diagnosis codes alone results in a high number of false positive identification...
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Background: When using electronic health records (EHRs) to conduct population-based studies on inherited bleeding disorders (IBDs), using diagnosis codes alone results in a high number of false positive identifications. Objective: The objective of this study was to develop and validate an algorithm that uses multiple data elements of EHRs to identify pregnant women with IBDs. Methods: The population included pregnant women who had at least one live birth or fetal death (>20 weeks gestation) at our institution from 2016 to 2023. We iteratively developed the algorithm using a composite criteria of encounter diagnosis codes, laboratory and medications data. We assessed the performance of the algorithm for sensitivity and positive predictive value (PPV) using our local registry and manual chart review. Results: Using the source population between 2016 and 2020, the initial algorithm identified 25 pregnant women with IBDs. Eight women with a known diagnosis of an IBD were missed resulting in a sensitivity of 75.8 % and a PPV of 100 %. We revised the algorithm to remove certain IBD diagnosis codes that resulted in contamination and added additional criteria to improve the sensitivity. The revised algorithm had a sensitivity of 97.0 % and a PPV of 91.4 %. The revised algorithm was validated using the source population between 2021 and 2023 and had a sensitivity of 97.1 % and a PPV of 91.7 %. Conclusion: This study demonstrates the utility of an algorithm to better identify pregnant women with specific types of IBD, mainly hemophilia and hemophilia carriers, and von Willebrand disease, within EHRs.
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