Indexing Method Research Articles

A model to address the cold-start in peer recommendation by using k-means clustering and sentence embedding

In academia, research collaboration plays a vital role in enhancing the research quality and enriching the academic profile of the authors. Recommending appropriate collaborators from a vast scholarly database, particularly for newcomers, poses a challenging cold-start problem. This study addresses a cold-start problem in peer recommendation, considering a dynamic coauthorship graph as a network structure of academic collaborators. As the coauthorship graph is quite large and complex, an efficient indexing method is essential for speeding up the initial search of similar coauthors. The study introduces an efficient Global Inverted List (GIL) for indexing research areas and active authors in the coauthorship network. An attribute-based search and filtering mechanism is proposed to identify relevant collaborators, followed by the application of k-means clustering and doc2vec metrics to rank and select top recommendations. A cold user is associated with attributes that identify coauthors with similar research interests. For each attribute of the cold user, the model searches the associated authors from the GIL. Further, two filtering approaches are applied to refine the retrieved author list. The first ensures that the authors have a significant presence in the specified research areas, whereas the second one helps avoid recommending authors with only superficial connections to the cold user. The model creates a feature matrix of filtered authors using the publication features of authors. The k-means clustering applied to the feature matrix generates k clusters, among which the model chooses only those with seed nodes i.e. the clusters which are having seed nodes are selected for further process. Selected clusters are ranked using doc2vec metrics, with the top-ranked cluster providing the final recommendation. The model recommends the top L members of the selected cluster, where L is the length of the recommendations provided to the new user. Our extensive experiments show the efficacy of the proposed model.

Left atrial volume in obesity and hypertension: an innovative "ideal human" index allows for weighing their impact - a non-contrast cardiac CT study

Abstract Left atrial (LA) enlargement is a common and early feature of cardiac involvement in the natural history of obesity and arterial hypertension (AH). Mechanisms involved in this process are cumulative, and partly shared, thus separating and balancing their contributions is challenging. In a proportion of patients referred for coronary artery calcium (CAC) scoring, these clinical entities are present. The LA volume (LAV) is relatively easy to quantify from a CT scan. Measured LAV values can be misleading unless constitutional parameters (height (h) or body size area (BSA), are considered. We introduced a new indexing parameter, consisting of height and the difference between numerical values of height and BSA. In the "ideal human", the difference is zero. Any deviation from this value determines the share of body mass regardless of height. Aim We hypothesize that a novel indexation approach allows for earlier detection of the increased LAV in normotensive and AH patients considering the body mass index. Method We retrospectively analyzed CT data from 2535 patients, 1658 w (64±10 ys), and 876 m (aged 6±11ys), with CAC scoring over the last 15 years The LAV was determined by using commercially available software. The raw and indexed (for h, BSA, and the novel parameter [h+ (h-BSA)]) LAV volumes were compared in patients with normal BMI (<25kg/msg), overweight (25-29.9), and obesity (≥30) who with normal blood pressure or with AH. Results The LAV was larger in high BMI categories than in normal BMI. In non-AH subjects, the LAV increased from an average of 71 ml (normal) to 80 ml (overweight) and 90 ml (obese). In AH, the LAV increase showed a similar trend of averages (77, 88, 94 ml, F=2.59,p<0.01). Indexing LAVs were non-concordant, as shown in Figure. Thus, one can recognize that LAV normalization for the new index has brought the differences most evident and persuasive. In normal BMI, indexation ways did not significantly affect the LAV estimation. Whilst the body weight increased, no difference could be recognized for values indexing for BSA (red). The LAV indexation for height (blue) cannot help diagnose its increase in obesity. The distinguishing strength can be seen while using the [h-(h-BSA)] denominator (green). It indicates that in any scenario, body mass gain plays a greater role in the LAV increase than AH does. Conclusions Body mass is a more important factor that is associated with LAV than AH.. Common methods of LAV indexing, especially referred to as BSA, introduce errors leading to values mimicking normality in non-healthy people and should be forbidden. The proposed indexation way can help overcome the observed discrepancies and support early recognition of the consequences of obesity and hypertension.

Cardiac magnetic resonance (CMR) height-based allometric scaling in Southeast Asians; implications in the diagnosis and prognosis of left ventricular hypertrophy (LVH): the REMODEL study

Abstract Background Left ventricular hypertrophy (LVH) is a strong predictor of adverse cardiovascular outcomes (1). Although normalizing LV mass (LVM) to height exponents has been shown to reduce variability from body size, specific recommendations for height exponents are lacking due to the scarcity of normal cohorts to define appropriate height exponents (2). Cardiac magnetic resonance (CMR) is increasingly used in assessing LVM, and the existing body of research is based primarily on the MESA cohort (3). While the MESA cohort is ethnically diverse, the intrinsic effects of ethnicity may not fully be discerned if certain ethnic groups are under-represented. Purpose We aimed to demonstrate the diagnostic and prognostic implications of establishing height exponents specific to sex in a Southeast Asian cohort. Methods Non-overweight/obese Southeast Asian healthy volunteers (n=416) were used to establish appropriate height exponents and normal reference ranges of LVM. The impact of these height exponents was examined in a separate cohort of Southeast Asians with hypertension (n=878). All individuals underwent standardized cardiovascular magnetic resonance imaging (CMR). Primary outcome was a composite of acute coronary syndrome, heart failure hospitalization, stroke, and cardiovascular mortality. Cox regression analysis of various LVH measures were adjusted for 24-hour systolic blood pressure, diabetes, and hyperlipidemia. Results The height exponents for healthy females and males were 1.57 and 2.33, respectively. In patients with hypertension, sex-specific height exponents significantly increased the detection of LVH compared with body surface area (BSA) indexing (47% [n=409/878] versus 27% [n= 236/878], p<0.001). Most individuals re-classified to LVH with sex-specific height exponents were overweight (33.1%, n=58/175) or obese (64.6%, n=113/175). There were 37 primary events over 60 [37-73] months of follow-up. Regardless of indexing method, LVH was independently associated with increased adverse events (sex-specific height exponent hazard ratio (HR): 2.8 [1.25-6.29], P=0.013). The prediction ability of LVH diagnosis by sex-specific height exponents was similar to BSA indexing (HR: 5.43 [2.49-11.8], P<0.001). Conclusions Reference ranges specific to the ethnicity, sex and imaging modality are necessary to establish appropriate height exponents. Although utilizing sex-specific height exponents to index LVM resulted in significantly more LVH reclassification when compared with BSA indexing, this did not translate to a notable improvement in event prediction. Further research into alternative methods of LVM indexing is warranted.Abstract SummaryPredictive Ability of LVH Measures

Detection of biventricular volume increase in overweight and obese by using a novel index of the ideal human - a single center non-contrast-enhanced cardiac CT study

Abstract The bi-ventricular volume (BVV) can be measured from non-contrast-enhanced CT images in patients referred for coronary artery calcium (CAC) scoring. Such determined BVV has been found to correlate with left ventricular mass and to predict increased risk of all-cause and CV mortality in patients with type 2 diabetes mellitus. Currently, no data are available that quantifying the effect of body size on BVV using NCE-CT. We used a new indexing parameter that consisted of two parts: height and the difference between numerical values of height and body surface area (BSA). In the "ideal human", the difference is zero. Any deviation from this value determines the share of body mass regardless of height. Aim We hypothesize that a novel indexation way allows for better discrimination of the increased bi-ventricular volume in overweight and obese patients. Method We retrospectively analyzed the CT data from 2466 patients with CAC scoring over the last 15 years. Among them 1606 women and 860 men with an average age of 64 ± 11 years (range 21-93). The fatless bi-ventricular volume was measured manually using commercial software on a dedicated workstation (Vitrea2). Values of the raw (measured, ml) BVV and BVV normalized for height, BSA, and the novel parameter i.e. [h+ (h-BSA)], were compared in patients taking into account body mass index (BMI) categories of normal weight (25% of patients), overweight (40%) and obese (35%). Results Overall, the measured BVV [ml] was significantly greater in males (414±97) than in females (297±66) (p<0.001). BVV decreased non-linearly from before 40ties to mid-60ties, and then remained nearly stable. In normal-weight males, the measured BVV (370±80ml) was greater than in normal-weight females (279±61ml) (p<0.001). In overweight males, the BVV was 411±89ml; in overweight females, the BVV reached 290±59ml (p<0.001). These differences were non-significantly different from the sample of patients with normal BMI. In obese patients, mean values significantly differed between males and females (442±106 vs 321±71 ml, p<0.001). As the absolute value of the BVV should be interpreted in the context of body habitus, normalization for height, BSA, and the novel index were used. The results of these examinations are given in the figure. Conclusions Commonly used methods of indexing the volumetric parameters of the ventricles, as well as the novel indexing approach showed similar results in patients with normal body weight. The proposed indexation allowed for discriminating the increase in BVV that is easy to overlook in overweight. The use of body surface area for volumetric normalization is misleading as it provides similar values in normal, overweight, and obese patients, and should therefore be avoided. We should consider whether ventricular size standards based on a study population under 50 years of age create reliable conditions for detecting ventricular enlargement at an older age.

Deep learning approaches for image restoration in invasive coronary angiography

Abstract Background Despite being the gold standard for diagnosing coronary artery disease and guiding percutaneous coronary interventions (PCI), image restoration during invasive coronary angiography (ICA) has rarely been explored. Generative adversarial network (GAN), a deep learning model, has demonstrated its effectiveness in image restoration. Our hypothesis was that we could restore ICA images, damaged or deleted to varying degrees, using the inpainting GAN method, and we undertook efforts to test this hypothesis. Methods We extracted 22,659 ICA patch images (128 × 128 size) from 60 patients, focusing on sparse vessels for model training. To assess our inpainting GAN model, we degraded the extracted images to varying degrees: minimal, mild, moderate, severe, and completely blocked. The performance was quantitatively analyzed using metrics such as the peak signal-to-noise ratio (PSNR), structured similarity indexing methods (SSIM), and mean squared error (MSE). Results The inpainting GAN method significantly improved the image quality in all degraded images. For the blocked images, PSNR and SSIM were enhanced by 161.9% and 14.3%, respectively, with a 98% MSE reduction. In severely, moderately, mildly, and minimally damaged images, PSNR and SSIM increased by 14.9% and 14.8%, 15.7% and 14.7%, 19.3% and 15.6%, and 12.7% and 5.3%, respectively, with MSE reductions of 61.5%, 64.1%, 72.7%, and 33.9%, respectively. Conclusions Inpainting GAN can restore ICA images with varying damage levels, including deletions. Our deep-learning model can instantaneously improve ICA image quality, potentially aiding in emergency or chronic total occlusion PCI.Table 2.Quantitative Analysis of ImageFigure 1.Sample Cases Pre and Post-Rest

The current trends in the development of methods and regulatory framework for indexing library information resources

The author examines the development of the basic concepts of indexing information resources using classifications and dictionaries of information retrieval languages. The impact of digital communications and the Internet on the indexing methods in the information environment, including the libraries, is emphasized. The modern library collections comprise various types of resources, both traditional paper publications and digital resources of various types, widely available on the Internet. In these conditions, harmonization of indexing methods with the principles of Internet information retrieval grows in importance. The author discusses the regulatory framework for indexing in the library sector and its relationship with Internet standards within the concept of Semantic Web. The author reviews the main stages of the development of the regulatory framework both on the international level and in this country. The author emphasizes the need to study the experience of other organizations in the field of standardization for further development of indexing technology.

Constructing Efficient Mesh-Based Global Grid Systems with Reduced Distortions

Recent advancements in geospatial technologies have significantly expanded the volume and diversity of geospatial data, unlocking new and innovative applications that require novel Geographic Information Systems (GIS). (Discrete) Global Grid Systems (DGGSs) have emerged as a promising solution to further enhance modern geospatial capabilities. Current DGGSs employ a simple, low-resolution polyhedral approximation of the Earth for efficient operations, but require a projection between the Earth’s surface and the polyhedral faces. Equal-area DGGSs are desirable for their low distortion, but they fall short of this promise due to the inefficiency of equal-area projections. On the other hand, efficiency-first DGGSs need to better address distortion. We introduce a novel mesh-based DGGS (MBD) which generalizes efficient operations over watertight triangular meshes with spherical topology. Unlike traditional approaches that rely on Platonic or Catalan solids, our mesh-based method leverages high-resolution spherical meshes to offer greater flexibility and accuracy. MBD allows high-resolution polyhedra (HRP) to be used as the base polyhedron of a DGGS, significantly reducing distortion. To address the operational challenges, we introduce a new hash encoding method and an efficient barycentric indexing method (BIM). MBD extends Atlas of Connectivity Maps to the BIM to provide efficient spatial and hierarchical traversal. We introduce several new base polyhedra with lower areal and angular distortion, and we experimentally validate their properties and demonstrate their efficiency. Our experimentation shows that we achieve constant-time operations for high-resolution MBD, and we recommend polyhedra to be used as the base polyhedron for low-distortion DGGSs, compact faces, and efficient operations.

Size and Function of the Right Atrium in Healthy Children by Three-Dimensional Echocardiography.

Right atrial volume (RAV) and function have proven prognostic value in the assessment of cardiac disease and may be more accurately assessed using three-dimensional echocardiography (3DE). Normal 3DE reference values for the right atrial (RA) volume and function in healthy children have not yet been published. Furthermore, current methods of indexing cardiac measurements to body surface area (BSA) alone may be insufficient. We aimed to develop reference values for 3DE-derived RAV and function in the pediatric population, to provide a comparison of regression methods using BSA, height, weight, and sex, and to compare 3DE values to two-dimensional echocardiography (2DE) derived volumes. We retrospectively analyzed 187 3DE datasets acquired for a multi-center study on healthy children at two international centers. 3DE volumes were measured with vendor-agnostic software (Tomtec), and 2DE endo-diastolic volume (EDV) using the area-length single-plane approach. Regression curve-fitting of height (Ht), weight (Wt), BSA, and sex were explored to optimize Pearson coefficient and homoscedasticity. The relationship of 3DE RAVs to BSA alone was heteroscedastic, with a residual positive relationship on indexing (R2 = 0.2506). Optimal regression of Ln (Volume) = b0 + x*Ln (Ht) + y*Ln (Wt) + z*Sex was homoscedastic. Total RA emptying fraction (EF) did not vary across age or sex, however active and passive emptying was best fit by a spline curve with a knot at approximately BSA = 1 m2. Strong agreement with 2DE-derived EDV was present (r2 = 0.83). We provide reference values for RA size and function in the pediatric population and provide a comparison of methods for indexing these values.

Hybrid ‘sledom’ [follows] in Area of Interaction of Nouns and Adverbs: Semantics and Grammar

This article presents the results of a study on the process and outcome of the stepwise adverbialization of the instrumental case of nouns in the Russian. The relevance of this work is underscored by the attention to the interaction between grammatical and semantic aspects within lexical units subject to transposition at the level of parts of speech. The aim of the article is to elucidate the specifics of hybrid structures formed as a result of the functional adverbialization of nouns in the instrumental case. The material used consists of contexts from the Russian National Corpus featuring substantive word forms like ‘sledom’, demonstrating various stages of adverbialization. An interdisciplinary approach is employed, utilizing methods of oppositional analysis, indexing, linguistic experimentation, as well as elements of componential and distributional analysis. Using the word form ‘sledom’ as a case study, the primary stages of adverbial transposition of nouns in Russian are identified. The analysis reveals that within the hybrid zone, the word form ‘sledom’ exhibits 55 % correspondence with the features of its original noun (‘sled’) and 60 % correspondence with the characteristics of a core adverb (‘sledom’). Lexicalization of the word form at this stage of adverbialization is not observed; it continues to function within the semantic domain of the original substantive lexeme. The significance of this research lies in its contribution to the development of a general theory of transitivity and syncretism in the grammatical structure of language, as well as the potential application of the proposed methods of oppositional analysis and indexing in studying other types of transpositional transformations within the system of parts of speech in the Russian.

Linkage and Demographic Indexing for Nursing Dynamics in the Integrated Data Service

Objective and ApproachThis project links UK Nursing and Midwifery Council (NMC) Registrant data to the 2021 England and Wales Census (“Census 21”). The Office for National Statistics (ONS) has composed a framework of index tables to facilitate linkage between prominent datasets in the UK. In linking person-level data to Census 21, ONS’ Demographic Index enables onward linkage to other administrative datasets that are indexed alike. NMC Register and Census 21 records have been initially linked using bespoke deterministic and probabilistic methods, with resulting matches being joined to the Demographic Index. ONS’ Demographic Index Matching Service (DIMS) will also be used to link NMC data to the Demographic Index to enable comparisons between methods. ResultsAnalysis of both approaches will compare quality metrics such as match rate, recall and precision and tools will be used to assess bias in matches made through each method. These metrics will provide an insight into how the index matching services have been designed and whether improvements can be made to account for any shortcomings in their performance. ConclusionsHybrid approaches using both bespoke and indexing methods are proving valuable where sole use of the matching services is not suitable. This research will help ONS to refine which methods will yield the highest quality linkage and when. ImplicationsAs ONS pursues an ‘indexing first’ approach to linkage, the outcomes of this and similar projects will influence the future of utilising indexes in varied capacities when creating or maximising the potential of linked data.

A Study of New Indexing Techniques for Multimodal Identification Using Iris, Fingerprint, and Face Biometrics

This paper explores innovative indexing techniques for multimodal biometric identification systems, focusing on iris, fingerprint, and facial recognition technologies. With the proliferation of digital identity verification needs, traditional single-modal biometric systems often fall short in terms of accuracy, speed, and security. Multimodal biometric systems, which integrate multiple biological characteristics, are becoming crucial for enhancing identification performance. This study aims to present a comprehensive analysis of new indexing methods that can significantly improve the efficiency, accuracy, and security of multimodal biometric identification systems.

Enhancing boundary detection of radiofrequency ablation lesions through photoacoustic mapping

Atrial fibrillation (A-fib) is the most common type of heart arrhythmia, typically treated with radiofrequency catheter ablation to isolate the heart from abnormal electrical signals. Monitoring the formation of ablation-induced lesions is crucial for preventing recurrences and complications arising from excessive or insufficient ablation. Existing imaging modalities lack real-time feedback, and their intraoperative usage is in its early stages. A critical need exists for an imaging-based lesion indexing (LSI) method that directly reflects tissue necrosis formation. Previous studies have indicated that spectroscopic photoacoustic (sPA) imaging can differentiate ablated tissues from their non-ablated counterparts based on PA spectrum variation. In this paper, we introduce a method for detecting ablation lesion boundaries using sPA imaging. This approach utilizes ablation LSI, which quantifies the ratio between the signal from ablated tissue and the total tissue signal. We enhance boundary detection accuracy by adapting a regression model-based compensation. Additionally, the method was cross-validated with clinically used intraoperative monitoring parameters. The proposed method was validated with ex vivo porcine cardiac tissues with necrotic lesions created by different ablation durations. The PA-measured lesion size was compared with gross pathology. Statistical analysis demonstrates a strong correlation (R > 0.90) between the PA-detected lesion size and gross pathology. The PA-detected lesion size also exhibits a moderate to strong correlation (R > 0.75) with local impedance changes recorded during procedures. These results suggest that the introduced PA imaging-based LSI has great potential to be incorporated into the clinical workflow, guiding ablation procedures intraoperatively.

Spatial Query Optimization With Learning

Query optimization is a key component in database management systems (DBMS) and distributed data processing platforms. Recent research in the database community incorporated techniques from artificial intelligence to enhance query optimization. Various learning models have been extended and applied to the query optimization tasks, including query execution plan, query rewriting, and cost estimation. The tasks involved in query optimization differ based on the type of data being processed, such as relational data or spatial geometries. This tutorial reviews recent learning-based approaches for spatial query optimization tasks. We go over methods designed specifically for spatial data, as well as solutions proposed for high-dimensional data. Additionally, we present learning-based spatial indexing and spatial partitioning methods, which are also vital components in spatial data processing. We also identify several open research problems in these fields.

Sensitivity analysis-driven machine learning approach for groundwater quality prediction: Insights from integrating ENTROPY and CRITIC methods

Precise estimation of water quality is essential for managing water pollution effectively and improving water management practices. Although, numerous recent investigations have documented inconsistencies in conventional water quality indexing methods owing to inherent subjective nature of its computation. Therefore, this study introduced a novel approach that integrates ENTROPY and CRITIC methods and applied sensitivity analysis-driven machine learning (ML) algorithms for groundwater quality prediction. The aim was to enhance prediction accuracy and robustness while gaining valuable insights into hydrochemical processes influencing groundwater quality. Six ML models, i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN), Linear Regression Model (LRM), Regression Tree (RT), Gaussian Process Regression (GPR), and Boosted Regression Tree (BRT) were applied to forecast water quality across Tamil Nadu, India. This study addresses a significant gap, as systematic ML framework has been lacked to evaluate and predict drinking water quality across the same state, covering 619 monitoring sites. Calibration of the proposed Integrated Water Quality Index (IWQI) models was conducted using 75 % of the dataset (465 training wells) and validated with 25 % (154 monitoring wells), considering 13 physicochemical parameters. Rigorous statistical evaluations employing metrics such as RMSE, MSE, and MAE were performed to identify most effective prediction model, accounting for both uncertainty and predictive performance. This study suggest that ANN model is the most suitable, exhibiting the lowest uncertainty with R2 = 1 and the best predictive performance statistics, including RMSE (training = 0.0264, testing = 0.5638), MSE (training = 0.0007, testing = 0.3179), and MAE (training = 0.0083, testing = 0.0729). Sensitivity analysis identified electrical conductivity (EC) as highest responsive input parameter, while fluoride (F−) showed the lowest sensitivity. The robustness of the ANN model was evident, maintaining accuracy even with perturbations up to 10 %, thereby enabling precise IWQI score forecasting across all monitoring stations.

Revisiting Database Indexing for Parallel and Accelerated Computing: A Comprehensive Study and Novel Approaches

While the importance of indexing strategies for optimizing query performance in database systems is widely acknowledged, the impact of rapidly evolving hardware architectures on indexing techniques has been an underexplored area. As modern computing systems increasingly leverage parallel processing capabilities, multi-core CPUs, and specialized hardware accelerators, traditional indexing approaches may not fully capitalize on these advancements. This comprehensive experimental study investigates the effects of hardware-conscious indexing strategies tailored for contemporary and emerging hardware platforms. Through rigorous experimentation on a real-world database environment using the industry-standard TPC-H benchmark, this research evaluates the performance implications of indexing techniques specifically designed to exploit parallelism, vectorization, and hardware-accelerated operations. By examining approaches such as cache-conscious B-Tree variants, SIMD-optimized hash indexes, and GPU-accelerated spatial indexing, the study provides valuable insights into the potential performance gains and trade-offs associated with these hardware-aware indexing methods. The findings reveal that hardware-conscious indexing strategies can significantly outperform their traditional counterparts, particularly in data-intensive workloads and large-scale database deployments. Our experiments show improvements ranging from 32.4% to 48.6% in query execution time, depending on the specific technique and hardware configuration. However, the study also highlights the complexity of implementing and tuning these techniques, as they often require intricate code optimizations and a deep understanding of the underlying hardware architecture. Additionally, this research explores the potential of machine learning-based indexing approaches, including reinforcement learning for index selection and neural network-based index advisors. While these techniques show promise, with performance improvements of up to 48.6% in certain scenarios, their effectiveness varies across different query types and data distributions. By offering a comprehensive analysis and practical recommendations, this research contributes to the ongoing pursuit of database performance optimization in the era of heterogeneous computing. The findings inform database administrators, developers, and system architects on effective indexing practices tailored for modern hardware, while also paving the way for future research into adaptive indexing techniques that can dynamically leverage hardware capabilities based on workload characteristics and resource availability.

Alzheimer's disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimization

The threat posed by Alzheimer's disease (AD) to human health has grown significantly. However, the precise diagnosis and classification of AD stages remain a challenge. Neuroimaging methods such as structural magnetic resonance imaging (sMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to diagnose and categorize AD. However, feature selection approaches that are frequently used to extract additional data from multimodal imaging are prone to errors. This paper suggests using a static pulse-coupled neural network and a Laplacian pyramid to combine sMRI and FDG-PET data. After that, the fused images are used to train the Mobile Vision Transformer (MViT), optimized with Pareto-Optimal Quantum Dynamic Optimization for Neural Architecture Search, while the fused images are augmented to avoid overfitting and then classify unfused MRI and FDG-PET images obtained from the AD Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets into various stages of AD. The architectural hyperparameters of MViT are optimized using Quantum Dynamic Optimization, which ensures a Pareto-optimal solution. The Peak Signal-to-Noise Ratio (PSNR), the Mean Squared Error (MSE), and the Structured Similarity Indexing Method (SSIM) are used to measure the quality of the fused image. We found that the fused image was consistent in all metrics, having 0.64 SIMM, 35.60 PSNR, and 0.21 MSE for the FDG-PET image. In the classification of AD vs. cognitive normal (CN), AD vs. mild cognitive impairment (MCI), and CN vs. MCI, the precision of the proposed method is 94.73%, 92.98% and 89.36%, respectively. The sensitivity is 90. 70%, 90. 70%, and 90. 91% while the specificity is 100%, 100%, and 85. 71%, respectively, in the ADNI MRI test data.

Enhancing Database Query Performance: Analysis of Indexing Techniques

The need to enhance query performance in databases within the contemporary information environment is crucial for ensuring efficient operations across various domains. This paper is dedicated to an analysis of database indexing techniques aimed at understanding their impact on query performance and efficiency. It meticulously examines various types of indexes, including B-trees, hash tables, and textual indexes, analyzing their advantages and limitations. The investigation into the influence of these techniques on query execution time reveals that performance depends on various factors such as query complexity and data volume. Special attention is given to selecting the optimal index type based on specific database needs and characteristics. The research underscores the importance of considering data volume and database structure in choosing effective indexing methods. Additionally, the advantages and disadvantages of different indexing techniques are scrutinized, taking into account their impact on query execution speed and efficiency. Of the study indicate that the correct selection and utilization of appropriate indexing strategies can significantly enhance the performance of database management systems, providing swift and efficient access to information for users. Ultimately, this work makes a significant contribution to understanding and practically applying indexing techniques to improve query performance in databases.

Employing Constrained Nonnegative Matrix Factorization for Microstructure Segmentation.

Materials characterization using electron backscatter diffraction (EBSD) requires indexing the orientation of the measured region from Kikuchi patterns. The quality of Kikuchi patterns can degrade due to pattern overlaps arising from two or more orientations, in the presence of defects or grain boundaries. In this work, we employ constrained nonnegative matrix factorization to segment a microstructure with small grain misorientations, (<1∘), and predict the amount of pattern overlap. First, we implement the method on mixed simulated patterns-that replicates a pattern overlap scenario, and demonstrate the resolution limit of pattern mixing or factorization resolution using a weight metric. Subsequently, we segment a single-crystal dendritic microstructure and compare the results with high-resolution EBSD. By utilizing weight metrics across a low-angle grain boundary, we demonstrate how very small misorientations/low-angle grain boundaries can be resolved at a pixel level. Our approach constitutes a versatile and robust tool, complementing other fast indexing methods for microstructure characterization.

Stream-aware indexing for distributed inequality join processing

Inequality join is an operator to join data on inequality conditions and it is a fundamental building block for applications. While methods and optimizations exist for efficient inequality join in batch processing, little attention has been given to its streaming version, particularly to large-scale data-intensive applications that run on Distributed Stream Processing Systems (DSPSs). Designing an inequality join in streaming and distributed settings is not an easy task: (i) indexes have to be employed to efficiently support inequality-based comparisons, but the continuous stream of data imposes continuous insertions, updates, and deletions of elements in the indexes—hence a huge overhead for the DSPSs; (ii) oftentimes real data is skewed, which makes indexing even more challenging.To address these challenges, we propose the Stream-Aware inequality join (STA), an indexing method that can reduce redundancy and index update overhead. STA builds a separate in-memory index structure for hotkeys, i.e., the most frequently used keys, which are automatically identified with an efficient data sketch. On the other hand, the cold keys are treated using a linked set of index structures. In this way, STA avoids many superfluous index updates for frequent items. Finally, we implement four state-of-the-art inequality join solutions for a widely employed DSPS (Apache Storm) and compare their performance with STA on four real-world data sets and a synthetic one. The results of our experimental evaluation reveal that our stream-aware approach outperforms existing solutions.

An examination of the criterion-related validity of varying methods of indexing clinically significant change in posttraumatic stress disorder treatment.

Clinicians, patients, and researchers need benchmarks to index individual-level clinically significant change (CSC) to guide decision making and inferences about treatment efficacy. Yet, there is no consensus best practice for determining CSC for posttraumatic stress disorder (PTSD) treatments. We examined criterion-related validity of the most common approach-Jacobson and Truax's (J&T; 1991) procedures for indexing CSC. We generated and compared four methods of calculating the J&T indices of CSC (two sets of sample-specific inputs, putatively norm-referenced benchmarks, and a combination of sample-specific and norm-referenced criteria) with respect to their association with a criterion index of quality of life (QoL). Participants were 91 women Veterans enrolled in a randomized clinical trial for PTSD who completed self-report measures on PTSD symptoms and various domains of QoL and functioning, pre- and posttreatment. For each of the four methods used to calculate CSC, the QoL composite was regressed onto the CSC categories. All methods explained large variance in change in QoL. Across all methods, participants categorized as unchanged had smaller changes in QoL, compared with those who improved or had probable recovery. The norm-referenced benchmarks accounted for the relatively largest amount of variance in QoL, but categorized the fewest patients as having made CSC. The J&T methodology for indexing CSC in PTSD symptoms has criterion-related validity, and a norm-referenced benchmark appears to be the most potent. However, the norm-referenced parameters may be overly specific, potentially leading to an underestimate of improvement. Research is needed to test the generalizability of these results. (PsycInfo Database Record (c) 2024 APA, all rights reserved).