Data Structure Research Articles

Peripheral arterial pathology and osteoarthritis of the knee: US examination of arterial wall stiffness, thickness, and flow characteristics

BackgroundOsteoarthritis (OA) is a widespread chronic joint disorder characterized by the degeneration of articular cartilage, extensive bone remodeling, ligamentous fibrosis, and synovial inflammation impacting millions. Shared factors like phenotypic similarities, hypofibrinolysis, and inflammation constitute similarities in pathophysiology and clinical manifestations between OA and peripheral vascular disease (PVD). This study investigated peripheral arterial flow characteristics, vascular wall thickness, and stiffness in knee OA to clarify a potential association with early atherosclerosis. MethodsThe OA cohort consisted of 35 knees with a mean age of 69 years. The control cohort consisted of 58 knees with a mean age of 68 years. Subjects underwent arterial ultrasound scanning of the common femoral, superficial femoral, and popliteal arteries. Data measured included peak systolic volumetric flow, intima-media thickness, systolic and diastolic vessel diameter, and simultaneous EKG and flow curves. Structural and functional vascular data were quantified using the incremental Young's modulus, pulse wave velocity (PWV), and distensibility. ResultsSignificantly elevated arterial volumetric flow, measures of arterial stiffness, and intima-media wall thickness were observed in those with OA compared to those without. PWV as calculated by the Bramwell-Hill equation were found to be significantly greater in all three vessels of patients with OA. ConclusionsThis study supports the association between peripheral arterial pathology and knee OA, consistent with shared clinical and phenotypic traits. The observed characteristics of early vascular pathology suggest potential pathophysiologic linkages between OA and PVD. This foundational framework provides avenues for mechanistic studies exploring the relationship between these two disease processes.

Digital Phenotypes of Mobile Keyboard Backspace Rates and Their Associations With Symptoms of Mood Disorder: Algorithm Development and Validation.

Passive sensing through smartphone keyboard data can be used to identify and monitor symptoms of mood disorders with low participant burden. Behavioral phenotyping based on mobile keystroke data can aid in clinical decision-making and provide insights into the individual symptoms of mood disorders. This study aims to derive digital phenotypes based on smartphone keyboard backspace use among 128 community adults across 2948 observations using a Bayesian mixture model. Eligible study participants completed a virtual screening visit where all eligible participants were instructed to download the custom-built BiAffect smartphone keyboard (University of Illinois). The BiAffect keyboard unobtrusively captures keystroke dynamics. All eligible and consenting participants were instructed to use this keyboard exclusively for up to 4 weeks of the study in real life, and participants' compliance was checked at the 2 follow-up visits at week 2 and week 4. As part of the research protocol, every study participant underwent evaluations by a study psychiatrist during each visit. We found that derived phenotypes were associated with not only the diagnoses and severity of depression and mania but also specific individual symptoms. Using a linear mixed-effects model with random intercepts accounting for the nested data structure from daily data, the backspace rates on the continuous scale did not differ between participants in the healthy control and in the mood disorders groups (P=.11). The 3-class model had mean backspace rates of 0.112, 0.180, and 0.268, respectively, with a SD of 0.048. In total, 3 classes, respectively, were estimated to comprise 37.5% (n=47), 54.4% (n=72), and 8.1% (n=9) of the sample. We grouped individuals into Low, Medium, and High backspace rate groups. Individuals with unipolar mood disorder were predominantly in the Medium group (n=54), with some in the Low group (n=27) and a few in the High group (n=6). The Medium group, compared with the Low group, had significantly higher ratings of depression (b=2.32, P=.008). The High group was not associated with ratings of depression with (P=.88) or without (P=.27) adjustment for medication and diagnoses. The High group, compared with the Low group, was associated with both nonzero ratings (b=1.91, P=.02) and higher ratings of mania (b=1.46, P<.001). The High group, compared with the Low group, showed significantly higher odds of elevated mood (P=.03), motor activity (P=.04), and irritability (P<.05). This study demonstrates the promise of mobile typing kinematics in mood disorder research and practice. Monitoring a single mobile typing kinematic feature, that is, backspace rates, through passive sensing imposes a low burden on the participants. Based on real-life keystroke data, our derived digital phenotypes from this single feature can be useful for researchers and practitioners to distinguish between individuals with and those without mood disorder symptoms.

Fruit and Vegetable Image Recognition Based on Multiple Tree Models: Applications of Random Forest, XGBoost and Decision Tree

The primary objective of this study is to evaluate and compare the performance of three machine learning models— Random Forest, XGBoost, and Decision Tree—in the context of fruit and vegetable image classification. This research aims to identify which model best handles the challenges associated with imbalanced datasets and complex data structures. The ultimate goal is to contribute to the development of more efficient and accurate automated systems for agricultural applications, thereby improving productivity and reducing operational costs in the industry. This study utilized a dataset of 3,825 images covering 36 fruit and vegetable classes. Images were resized, normalized, and augmented to enhance diversity. Three models—Random Forest, XGBoost, and Decision Tree—were trained on this dataset. Performance was evaluated using accuracy, precision, recall, and F1-score to assess classification effectiveness and handling of class imbalances. The evaluation revealed that XGBoost outperformed Random Forest and Decision Tree in fruit and vegetable image classification, achieving the highest accuracy of 96.66%. XGBoost demonstrated superior handling of class imbalances and complex data structures, reflected in its precision and recall scores across various classes. Random Forest also performed well, closely following XGBoost, while Decision Tree exhibited more variability in results, indicating potential overfitting in certain classes. In conclusion, this study highlights the effectiveness of ensemble methods, particularly XGBoost, in agricultural image classification tasks. These findings suggest that XGBoost is a robust model for similar applications, offering improved accuracy and reliability

DTAP: Accelerating Strongly-Typed Programs with Data Type-Aware Hardware Prefetching

Queries on linked data structures, such as trees and graphs, often suffer from frequent cache misses and significant performance loss due to dependent and random pointer-chasing memory accesses. In this paper, we propose a software-hardware co-designed solution for accelerating linked data structures implemented in strongly typed languages. The solution incorporates a compiler extension and a hardware prefetcher. The compiler extension extracts type information from the code, annotates each load instruction, and forwards the type information to the hardware prefetcher. The prefetcher leverages the type information to fetch the referred objects and identify the associated pointers in advance. By doing so, the program can find these objects in the cache when it follows the prefetched pointers, thus minimizing cache misses. In the evaluation, the proposed solution achieves an average speedup of 1.37 × over a set of memory-intensive benchmarks.

Fast and accurate short-read alignment with hybrid hash-tree data structure

Rapidly increasing the amount of short-read data generated by NGSs (new-generation sequencers) calls for the development of fast and accurate read alignment programs. The programs based on the hash table (BLAST) and Burrows-Wheeler transform (bwa-mem) are used, and the latter is known to give superior performance. We here present a new algorithm, a hybrid of hash table and suffix tree, which we designed to speed up the alignment of short reads against large reference sequences such as the human genome. The total turnaround time for processing one human genome sample (read depth of 30) is just 31 min with our system while that was more than 25 h with bwa-mem/gatk. The time for the aligner alone is 28 min for our system but around 2 h for bwa-mem. Our new algorithm is 4.4 times faster than bwa-mem while achieving similar accuracy. Variant calling and other downstream analyses after the alignment can be done with open-source tools such as SAMtools and Genome Analysis Toolkit (gatk) packages, as well as our own fast variant caller, which is well parallelized and much faster than gatk.

Isokinetic Force Modeling in Sports Based on Embedded System of Motion Recognition Model

With the gradual deepening of sports research, sports training methods integrating more scientific and technological means can greatly improve athletes’ professional ability. Combined with the isokinetic strength training method, it can effectively alleviate muscle recovery and body flexibility, which requires an analysis of the level of sports body promotion, and a more efficient training program for various sports parameters. In this study, taking strength enhancement as an example, the convolutional neural network is innovatively integrated into isokinetic strength training. Using the motion data obtained by wearable sensors in the training, we trained the neural network model by real-time analysis of data samples through an embedded system. Further, we optimized the network parameters to realize the extraction and recognition of EMG depth features. Compared with the traditional isokinetic strength classification model, this model lays a foundation for further constructing personalized strength training guidance methods. After the simulation test, the difference of the model’s accuracy requirement before and after the pre-training is 4.6%, and the difference of the test set before and after the pre-intensive training is [Formula: see text]1%. It is 16.2% higher than the TCAM algorithm, and the gap with STA-LSTM remains between [Formula: see text]. It is verified that the attention concentration control scheme can not only reduce the error rate in visual recognition tasks, but also improve the complexity of the neural network model. The optimized lightweight data structure is especially suitable for embedded systems with limited computing power. All kinds of test projects verify its rationality and make full use of the control ability of the embedded system and the characteristics of parallel computing and programmability, which has great practical application value for the development of the Internet of Things system network. The research results have made a thorough study of the physiological guidance of existing strength training, the identification and evaluation methods of exercise-induced muscle fatigue, and the current situation of the existing isokinetic equipment.

StructmRNA a BERT based model with dual level and conditional masking for mRNA representation

In this study, we introduce StructmRNA, a new BERT-based model that was designed for the detailed analysis of mRNA sequences and structures. The success of DNABERT in understanding the intricate language of non-coding DNA with bidirectional encoder representations is extended to mRNA with StructmRNA. This new model uses a special dual-level masking technique that covers both sequence and structure, along with conditional masking. This enables StructmRNA to adeptly generate meaningful embeddings for mRNA sequences, even in the absence of explicit structural data, by capitalizing on the intricate sequence-structure correlations learned during extensive pre-training on vast datasets. Compared to well-known models like those in the Stanford OpenVaccine project, StructmRNA performs better in important tasks such as predicting RNA degradation. Thus, StructmRNA can inform better RNA-based treatments by predicting the secondary structures and biological functions of unseen mRNA sequences. The proficiency of this model is further confirmed by rigorous evaluations, revealing its unprecedented ability to generalize across various organisms and conditions, thereby marking a significant advance in the predictive analysis of mRNA for therapeutic design. With this work, we aim to set a new standard for mRNA analysis, contributing to the broader field of genomics and therapeutic development.

Integrating genomic and molecular data to predict antimicrobial minimum inhibitory concentration in Klebsiella pneumoniae

Minimum inhibitory concentration (MIC) denotes the in vitro benchmark indicating the quantity of antibiotic required to inhibit proliferation of specific bacterial strains. Determining MIC values corresponding to the infecting bacterial strain is paramount for tailoring appropriate antibiotic therapy. In the interim between specimen collection and laboratory-derived MIC outcomes, clinicians frequently resort to empirical therapy informed by retrospective analyses. Here introduces two deep learning approaches, a Convolutional Neural Network (CNN)-based model and an Enformer-based model, integrating genomic data of Klebsiella Pneumoniae and molecular structural data of 20 antibiotics to anticipate the MIC value of the bacterium for each antibiotic under consideration. These models demonstrate enhanced raw accuracy over the existing state-of-the-art model, which rely exclusively on genomic data. The CNN-based model achieves a notable 20% increase in raw accuracy while further mirroring the 1-tier accuracy of the state-of-the-art model. Although the Enformer-based model does not quite reach the performance levels of the CNN-based model, it offers an advantage by eliminating the need for arbitrary data processing steps. This streamlining of the data processing pipeline facilitates fast updates and improves the model interpretability. It is expected that these deep learning paradigms can significantly inform and bolster clinician decision-making during the empirical treatment phase.

Geospatial tools and data for health service delivery: opportunities and challenges across the disaster management cycle.

As extreme weather events increase in frequency and intensity, the health system faces significant challenges, not only from shifting patterns of climate-sensitive diseases but also from disruptions to healthcare infrastructure, supply chains and the physical systems essential for delivering care. This necessitates the strategic use of geospatial tools to guide the delivery of healthcare services and make evidence-informed priorities, especially in contexts with scarce human and financial resources. In this article, we highlight several published papers that have been used throughout the phases of the disaster management cycle in relation to health service delivery. We complement the findings from these publications with a rapid scoping review to present the body of knowledge for using spatial methods for health service delivery in the context of disasters. The main aim of this article is to demonstrate the benefits and discuss the challenges associated with the use of geospatial methods throughout the disaster management cycle. Our scoping review identified 48 articles employing geospatial techniques in the disaster management cycle. Most of them focused on geospatial tools employed for preparedness, anticipatory action and mitigation, particularly for targeted health service delivery. We note that while geospatial data analytics are effectively deployed throughout the different phases of disaster management, important challenges remain, such as ensuring timely availability of geospatial data during disasters, developing standardized and structured data formats, securing pre-disaster data for disaster preparedness, addressing gaps in health incidence data, reducing underreporting of cases and overcoming limitations in spatial and temporal coverage and granularity. Overall, existing and novel geospatial methods can bridge specific evidence gaps in all phases of the disaster management cycle. Improvement and 'operationalization' of these methods can provide opportunities for more evidence-informed decision making in responding to health crises during climate change.

Computational Identification and Illustrative Standard for Representation of Unimolecular G-Quadruplex Secondary Structures (CIIS-GQ).

G-quadruplexes refer to a large group of nucleic acid-based structures. In recent years, they have been attracting attention due to their biological roles in the telomeres and promoter regions. These structures show wide diversity in topology, however, development of methods for structural classification of G-quadruplexes has been evaded for a long time. There has been a limited number of studies aiming to bring forth a secondary structure classification method. The situation was even more complex than imagined, since the discovery of bulged and mismatched G-quadruplexes while most of the available tools fail to distinguish these non-canonical G-quadruplex motifs. Moreover, the interpretation of their analysis output still requires expert knowledge. In this study, we propose a new method for identification of unimolecular G-Quadruplexes and classification by secondary structures based on three-dimensional structural data. Briefly, coordinates of guanines are processed to identify tetrads, loops and bulges. Then, we present the secondary structure in the form of a depiction which shows the loop types, bulges, and guanines that participate in each tetrad. Moreover, CIIS-GQ identifies non-guanine nucleotides that joins the G-tetrads and forms multiplets. Finally, the results of our study are compared with DSSR and ElTetrado classification methods, and the advantages of the proposed depiction method for representing secondary structures were discussed. The source code of the method can be accessed via https://github.com/TugayDirek/CIIS-GQ .

Supplemental Material for Worth the Weight: An Examination of Unstructured and Structured Data in Graduate Admissions

Supplemental Material for Worth the Weight: An Examination of Unstructured and Structured Data in Graduate Admissions

Concurrent Access Performance Comparison Between Relational Databases and Graph NoSQL Databases for Complex Algorithms

Databases are a fundamental element of contemporary software applications. The most widely used and recognized type in practice is the relational database, valued for its ability to store and organize data in tabular structures, its emphasis on data consistency and integrity, and its use of a standardized query language, SQL. However, with the rapid increase in both the volume and complexity of data, relational databases have recently encountered challenges in effectively modeling this expanding information. To address performance challenges, new database systems have emerged, offering alternative approaches to data modeling—these are known as NoSQL databases. In this paper, we present an indoor navigation application designed to operate on both a relational database, Microsoft SQL Server, and a graph-based NoSQL database, Neo4j. We describe the algorithms implemented for testing and the performance metrics analyzed to draw our conclusions. The results revealed Neo4j’s strength in managing data with complex relationships but also exposed its limitations in handling concurrent access, where SQL Server demonstrated significantly greater stability.

Neutron skins: A perspective from dispersive optical models

An overview of neutron skin predictions obtained using an empirical nonlocal dispersive optical model (DOM) is presented. The DOM links both scattering and bound-state experimental data through a subtracted dispersion relation which allows for fully consistent, data-informed predictions for nuclei where such data exist. Large skins were predicted for both 48Ca (Rskin48=0.25±0.023 fm in 2017) and 208Pb (Rskin208=0.25±0.05 fm in 2020). Whereas the DOM prediction in 208Pb is within 1σ of the subsequent PREX-2 measurement, the DOM prediction in 48Ca is over 2σ larger than the thin neutron skin resulting from CREX. From the moment it was revealed, the thin skin in 48Ca has puzzled the nuclear-physics community as no adequate theories simultaneously predict both a large skin in 208Pb and a small skin in 48Ca. The DOM is unique in its ability to treat both structure and reaction data on the same footing, providing a unique perspective on this Rskin puzzle. It appears vital that more neutron data be measured in both the scattering and bound-state domain for 48Ca to clarify the situation.

A longitudinal study of the impacts of area-level social cohesion on multimorbidity in Scotland

Abstract Background Multimorbidity is a growing global public health challenge, commonly referring to the co-occurrence of 2+ conditions but with varying measurements. Prior evidence has linked social cohesion with single health conditions; however, its impact on multimorbidity remains unclear. This study aims to examine the association between area-level social cohesion and multimorbidity and to explore whether this association will vary across multimorbidity measurements and levels of urban-rural classification. Methods Using a dataset that links the Scottish Longitudinal Study (5.3% of the Scottish population) with hospitalisation records (2001-2019) and an index that reflects area-level social cohesion (2012), focused on the cohorts of adults aged 40+, we used 3-level logistic regression to account for the clustered data structure of diagnosed chronic conditions over time within individuals within neighbourhoods. Results Areas with the highest social cohesion were observed to have the lowest risks of multimorbidity (p &amp;lt; 0.001), even after adjusting for area-level income deprivation. Varying by multimorbidity measurements, an inverted U-shaped pattern was observed between reduced area-level social cohesion and increased risks of having 2+ co-occurring chronic conditions; while a linear pattern was found for mental-physical multimorbidity. Moderated by levels of urban-rural classification, as compared to rural residents, urban residents were more sensitive to the impact of social cohesion on the co-occurrence of 2+ conditions; however, for mental-physical multimorbidity, no interaction effects between area-level social cohesion and urban-rural classification were found. Conclusions Although varying patterns were seen for different multimorbidity measurements, people living in areas with the highest area-level social cohesion were less likely to be multimorbid. This study offers new insights for developing effective place-based public health interventions. Key messages • This is the first longitudinal study that analysed the association between area-level social cohesion and multimorbidity using a nationally representative dataset based on secondary care data. • Area-level social cohesion affects multimorbidity, but its impact may vary by multimorbidity measurements and by urban-rural classification.

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

This paper presents software advances to easily exploit computer architectures consisting of a multi-core CPU and CPU+GPU to accelerate diverse types of high-performance computing (HPC) applications using a single code implementation. The paper describes and demonstrates the performance of the open-source C++ matrix and array (MATAR) library that uniquely offers: (1) a straightforward syntax for programming productivity, (2) usable data structures for data-oriented programming (DOP) for performance, and (3) a simple interface to the open-source C++ Kokkos library for portability and memory management across CPUs and GPUs. The portability across architectures with a single code implementation is achieved by automatically switching between diverse fine-grained parallelism backends (e.g., CUDA, HIP, OpenMP, pthreads, etc.) at compile time. The MATAR library solves many longstanding challenges associated with easily writing software that can run in parallel on any computer architecture. This work benefits projects seeking to write new C++ codes while also addressing the challenges of quickly making existing Fortran codes performant and portable over modern computer architectures with minimal syntactical changes from Fortran to C++. We demonstrate the feasibility of readily writing new C++ codes and modernizing existing codes with MATAR to be performant, parallel, and portable across diverse computer architectures.

What Matters for an occurrenceID and What Is an occurrenceID That Matters?

In the Darwin Core data standard (Darwin Core Maintenance Group 2023), the concept of dwc:Occurrence (Wieczorek et al. 2012) in ecological data presents a data model construct not commonly found in data management practices used by ecological data collectors. We frequently encounter raw data without an Occurrence table. For example, the concept of Occurrence can be represented as a cell, where the rows represent sampling sites, the columns represent species, and the value of each cell indicates the count of the species at a specific site. A value in a cell in such a matrix can be interpreted as x number of individuals of species y occurred at sampling site z. While data providers tend to track data on tangible individual components (e.g., species, location, sample), generating "Occurrence records" typically requires pivoting and/or joining tables associated to these components. Maintaining a stable and persistent occurrenceID for an Occurrence record created through data transformation is not an easy task. This is especially true for long-term monitoring datasets, where the underlying tables used to generate Occurrence records are continuously updated. Additionally, most ecological data collectors are focused on the primary use of the data, not on the long term integration and accessibility of the data. The Occurrence concept is only required in data exchange format but not needed in ecological data management practices. The disconnect between the practical data management needs of data collectors and the abstractions required for data exchange raises challenges, particularly with an increasing expectation for globally unique and persistent occurrenceIDs. This presentation will explore the difficulties of creating and managing occurrenceIDs for data providers and managers, especially those who manage data using basic systems such as spreadsheets and simple relational databases. Maintaining stability and persistence of identifiers for inherently artificial constructs like Occurrences within the original, component-based data structure can pose significant challenges. We will explore why meaningful identifiers for occurrenceIDs are often preferred by data providers. We will unpack different use cases and delve into how and why occurrenceIDs were constructed for each use case. Through this discussion, we hope to spark a conversation that informs future data modeling efforts and addresses the inherent artificiality of Occurrences.

Per Segment Plane Sweep Line Segment Intersection on the GPU

Polygon overlay operations are used for various purposes such as GIS searches and queries, VLSI and basic geometric operations of intersection, union and difference. There have been recent research articles presenting algorithms using the GPU to perform line segment intersection for geometric operations. We present two parallel algorithms implemented on the GPU that focus on the active list portion of the traditional serial plane sweep algorithm. The first algorithm uses a single block of threads to simulate the active list data structure in hardware; this algorithm is slow due to GPU thread block size limitations and synchronization points, but demonstrates favorable time complexity. The second algorithm uses dynamic parallelism to remove synchronization and scales to utilize available GPU hardware (single GPU). We perform experiments on both synthetic and real world data sets. The presented results show improvement in execution time with respect to recent algorithms, and low memory usage compared to recent algorithms. We achieve speedups of up to 38.8 over the serial sweep line algorithm on real world data.

From Data to Decisions: Analyzing PaRIS for Health Policy Insights

Abstract To inform countries on how well they perform from the perspective of people living with chronic conditions in comparison with other countries, we propose an analytical framework for the data collected in the PaRIS survey, detailing how the data will be analysed in a multilevel approach for cross-country comparison. The data structure of the PaRIS survey represents three levels: countries/health systems, primary care practices and patients. Multilevel analysis is used because of its accuracy in estimating country-level outcomes, its flexibility in modelling relationships, and its opportunities in connecting to relevant policy questions. Country-level outcomes will be estimated to facilitate cross-country comparison and (future) within-country comparison over time. A first indication for areas for cross-country learning and policy making is the distribution of variation over patients, care providers and countries. Characteristics of patients that potentially explain variation in patient-reported outcomes and experiences can be linked to primary care practice and country/health system characteristics. The second indication for areas for cross-country learning and policy making is the variation in the slope of the association between patient and practice characteristics and patient-reported outcomes and experiences. This makes it possible to address policy-relevant questions relating, e.g., to whether some health systems or providers are better able to produce positive outcomes and experiences for (e.g.) older patients, and to the impact of chronic care management on patients with a specific chronic condition. To ensure fair cross-country comparisons, all outcomes will be estimated for an OECD (age and sex standardised) reference population, also including case-mix adjustment where necessary. Analyses will be repeated using a country-specific standard population.

Evidence-Based Programs in France: Insights and Strategies for Implementation Support

Abstract Issue In France, both regional and national health institutions increasingly support and fund the implementation of evidence-based programs (EBP), promoted at the national level by the French Public Health agency. Description of the problem practitioners who implement EBP face several operational questions: scaling issues, implementation in specific and varying contexts, integration and adjustment to daily regional practices. Results/effects In 2023, the French Society for Public Health initiated a collaborative effort to create a platform for discussion among practitioners, researchers, and decision-makers involved in EBP implementation. The first step was to assess EPB implementation experiences, using 3 types of data: an analysis of 6 documented EBP (capitalization works), an online questionnaire answered by 29 stakeholders, and four focus groups with funders, researchers, practitioners, and institutions. Data analysis was based on thematic content analysis and structural data analysis. Lessons Different ways to implement EBP were identified through a typology. The results highlight favorable conditions and levers for EPB implementation in France: • Facilitate access and adoption of EBP by concerned stakeholders, including through clear communication and knowledge sharing; • Strengthen and value support for implementation support (i.e., providing necessary resources and continuous monitoring); • A ‘favorable environment’ is also required (i.e. regional context acceptability and information on program themes or topics); • Breadthen perspectives and promote mutual learning through sharing and comparing implementation experiences across regions; • Value transferability and adaptation studies, which are key to enable EBP adaptations to account for context specificities while maintaining effectiveness. The platform’s next steps are to develop deployment tools and structure an advocacy strategy to mobilize stakeholders around the issue. Key messages • Achieving wide-scale implementation of EBP requires to tackle key implementation issues such as scaling, context adaptation, etc. and to give support to implementing stakeholders. • A collaborative assessment helped identify levers and barriers to EBP implementation in France. Involvement in EBP fosters an evidence-based mindset among practitioners.

Harnessing the Power of Machine Learning and Cube Technologies for Transformative Data Analytics

Machine learning, a cornerstone of artificial intelligence, is increasingly being integrated with traditional cube technologies to enhance data analysis and decision-making capabilities. While cube technologies excel at providing structured data exploration and OLAP capabilities, machine learning algorithms can uncover hidden patterns, predict future trends, and automate complex tasks. This technical article explores the synergistic relationship between machine learning and cube technologies, highlighting how their integration is transforming the landscape of data analytics and empowering organizations to gain deeper insights and make more informed decisions.