Index Structure Research Articles

Assessing the Factor Structure and Measurement Invariance of the Pittsburgh Sleep Quality Index Between Postmenopausal Breast Cancer Survivors and Controls.

Background and Purpose: This study evaluates the Pittsburgh Sleep Quality Index (PSQI) in terms of factor structure and measurement invariance (MI). The sample included postmenopausal breast cancer (BC) survivors (n = 101) and matched healthy controls (n = 60). Methods: Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed on PSQI's seven component scores. MI was tested between groups and across time using Bayes factor (BF). Results: Two factors were identified: sleep efficiency and perceived sleep quality. MI is evidenced between groups (BF < 0.007) and over time (BF > 150). Conclusions: PSQI scores with two subscales are comparable between postmenopausal BC survivors and controls over a 1-year period, providing some validation of PSQI for researching sleep quality in this population.

Research on integrated LiDAR and multi-parameter detection of atmospheric transmittance, turbulence, and wind

An integrated LiDAR system has been reported, which can be used for simultaneous detection of atmospheric transmittance, turbulence, and wind along the same path. Through the integrated design of optics and mechanics, the size and weight of the system were effectively reduced. The comprehensive detection distance of atmospheric transmittance, atmospheric coherence length, and radial wind had also been achieved at least 4 kilometers. Comparative experiments have demonstrated that the detection results of the integrated LiDAR system and the near-surface meteorological observation system have standard deviations of less than 0.03 km−1 for extinction coefficient, 0.2 m/s for wind velocity, and 7.15 × 10−14 m−2/3 for C 2 n (atmospheric refractive index structure parameter), thus verifying the accuracy of the system detection. The reliability of the integrated LiDAR system was validated through six consecutive nights of continuous detection experiments, measuring three parameters simultaneously. Through analysis, the influence laws among atmospheric transmittance, coherence length, and wind were preliminarily explored. Significant variations in wind velocity can induce substantial fluctuations in atmospheric coherence length, and it is positively correlated with atmospheric transmittance. The integrated LiDAR system can provide a variety of reliable real-time detection data for further research on the laser transmission process in the atmosphere.

GTalign: spatial index-driven protein structure alignment, superposition, and search

With protein databases growing rapidly due to advances in structural and computational biology, the ability to accurately align and rapidly search protein structures has become essential for biological research. In response to the challenge posed by vast protein structure repositories, GTalign offers an innovative solution to protein structure alignment and search—an algorithm that achieves optimal superposition at high speeds. Through the design and implementation of spatial structure indexing, GTalign parallelizes all stages of superposition search across residues and protein structure pairs, yielding rapid identification of optimal superpositions. Rigorous evaluation across diverse datasets reveals GTalign as the most accurate among structure aligners while presenting orders of magnitude in speedup at state-of-the-art accuracy. GTalign’s high speed and accuracy make it useful for numerous applications, including functional inference, evolutionary analyses, protein design, and drug discovery, contributing to advancing understanding of protein structure and function.

An Efficient Cross-Modal Privacy-Preserving Image–Text Retrieval Scheme

Preserving the privacy of the ever-increasing multimedia data on the cloud while providing accurate and fast retrieval services has become a hot topic in information security. However, existing relevant schemes still have significant room for improvement in accuracy and speed. Therefore, this paper proposes a privacy-preserving image–text retrieval scheme called PITR. To enhance model performance with minimal parameter training, we freeze all parameters of a multimodal pre-trained model and incorporate trainable modules along with either a general adapter or a specialized adapter, which are used to enhance the model’s ability to perform zero-shot image classification and cross-modal retrieval in general or specialized datasets, respectively. To preserve the privacy of outsourced data on the cloud and the privacy of the user’s retrieval process, we employ asymmetric scalar-product-preserving encryption technology suitable for inner product calculation, and we employ distributed index storage technology and construct a two-level security model. We construct a hierarchical index structure to speed up query matching among massive high-dimensional index vectors. Experimental results demonstrate that our scheme can provide users with secure, accurate, fast cross-modal retrieval service while preserving data privacy.

Finite element method numerical modeling of the crack's impact on the free vibration of 2D functionally graded beams on the Pasternak-Winkler elastic foundation

The study's contribution is to examine, while taking into account pinned-pinned boundary conditions, the free vibration response of multi-cracks Euler-Bernoulli perfect FG beams structure on Pasternak-Winkler elastic foundations. The finite element approach is used to discretize the equations. The material properties are taken into account using a power-law form, and they differ in the thickness and width directions of the beam structure. The 2D FG beam's reduced cross section is used to calculate the stiffness of the cracked structure. On the other hand, the Pasternak-Winkler type foundation has a longitudinal distribution and makes the system stiffer. For convergence research, the numerical results are compared with the outcomes of earlier investigations in terms of dimensionless fundamental frequencies. Case studies were carried out to examine the effects on the natural frequencies of the beams structure of the power law index, multi-cracks, depths, location, and Pasternak-Winkler-elastic foundation. These studies demonstrate the benefits of the two-dimensional FG beam in the presence of cracks over the unidirectional FG and pure metallic beams.

Influence of prosthetic index structures and implant materials on stress distribution in implant restorations: a three-dimensional finite element analysis

BackgroundMechanical complications affect the stability of implant restorations and are a key concern for clinicians, especially with the frequent introduction of new implant designs featuring various structures and materials. This study evaluated the effect of different prosthetic index structure types and implant materials on the stress distribution of implant restorations using both in silico and in vitro methods.MethodsFour finite element analysis (FEA) models of implant restorations were created, incorporating two prosthetic index structures (cross-fit (CF) and torc-fit (TF)) and two implant materials (titanium and titanium-zirconium). A static load was applied to each group. An in vitro study using digital image correlation (DIC) with a research scenario identical to that of the FEA was conducted for validation. The primary strain, sensitivity index, and equivalent von Mises stress were used to evaluate the outcomes.ResultsChanging the implant material from titanium to titanium-zirconium did not significantly affect the stress distribution or maximum stress value of other components, except for the implant itself. In the CF group, implants with a lower elastic modulus increased the stress on the screw. The TF group showed better stress distribution on the abutment and a lower stress value on the screw. The TF group demonstrated similar sensitivity for all components. DIC analysis revealed significant differences between TF-TiZr and CF-Ti in terms of the maximum (P < 0.001) and minimum principal strains (P < 0.05) on the implants and the minimum principal strains on the investment materials in both groups (P < 0.001).ConclusionsChanges in the implant material significantly affected the maximum stress of the implant. The TF group exhibited better structural integrity and reliability.

An incremental algorithm for repairing denial constraint violations

Data repairing algorithms are extensively studied for improving data quality. Denial constraints (DCs) are commonly employed to state quality specifications that data should satisfy and hence facilitate data repairing since DCs are general enough to subsume many other dependencies. Data in practice are usually frequently updated, which motivates the quest for efficient incremental repairing techniques in response to data updates. In this paper, we present the first incremental algorithm for repairing DC violations. Specifically, given a relational instance I consistent with a set Σ of DCs, and a set △I of tuple insertions to I, our aim is to find a set △I′ of tuple insertions such that Σ is satisfied on I+△I′. We first formalize and prove the complexity of the problem of incremental data repairing with DCs. We then present techniques that combine auxiliary indexing structures to efficiently identify DC violations incurred by △Iw.r.t.Σ, and further develop an efficient repairing algorithm to compute △I′ by resolving DC violations. Finally, using both real-life and synthetic datasets, we conduct extensive experiments to demonstrate the effectiveness and efficiency of our approach.

Association of Arterial Stiffness Index and Brain Structure in the UK Biobank: A 10-Year Retrospective Analysis.

Arterial stiffening and changes in brain structure both occur with normal aging and can be exacerbated via acquired health conditions. While cross-sectional associations exist, the longitudinal relationship between arterial stiffness and brain structure remains unclear. In this study, we investigated 1) associations between baseline arterial stiffness index (ASI) and brain structure (global and regional grey matter volumes (GMV), white matter hyperintensities (WMH)) 10-years post-baseline (10.4±0.8 years) and 2) associations between the 10-year change in ASI from baseline and brain structure 10-years post-baseline in 650 healthy middle- to older-aged adults (53.4±7.5 years) from the UK Biobank. We observed significant associations between baseline ASI and GMV (p<0.001) and WMH (p=0.0036) 10-years post-baseline. No significant associations between 10-year change in ASI and brain structure (global GMV p=0.24; WMH volume p=0.87) were observed. There were significant associations of baseline ASI in 2 of 60 regional brain volumes analyzed (right posterior superior temporal gyrus p=0.001; left superior lateral occipital cortex p<0.001). Strong associations with baseline ASI, but not changes in ASI over 10-years, suggest arterial stiffness at the entry point of older adulthood is more impactful on brain structure 10-years later compared to age-related stiffening. Based on these associations, we suggest clinical monitoring and potential intervention for reducing arterial stiffness should occur in midlife to reduce vascular contributions to structural changes in the brain, supporting a healthy trajectory of brain aging. Our findings also support use of ASI as a surrogate for gold standard measures in showing overall relationships between arterial stiffness and brain structure.

DeepSketch: A Query Sketching Interface for Deep Time Series Similarity Search

By empowering domain experts to perform interactive exploration of large time series datasets, sketch-based query interfaces have revitalized interest in the well-studied problem of time series similarity search. In this new interaction paradigm, recent similarity algorithms (e.g., Qetch, Peax, LineNet) that attempt to capture perceptually relevant features have supplanted older, more straightforward distance measures (e.g., Euclidean, DTW). However, the downside of these algorithms is the resulting difficulty in designing corresponding index structures to support efficient similarity search over large datasets, thus necessitating brute-force search. This demo will showcase Deep Time Series Similarity Search (DTS3), our pluggable indexing pipeline for arbitrary distance measures. DTS3 can automatically train a foundation model for any custom, user-supplied distance measure with no strict constraints (e.g., differentiability), thus enabling fast retrieval via an off-the-shelf vector DBMS. Using our DeepSketch web interface, participants can compare DTS3 to the baseline brute-force versions of several similarity algorithms to see that our approach can achieve much lower latency without sacrificing accuracy when searching over large, real-world time series datasets.

Secure and Efficient Multi-keyword Fuzzy Search Over Encrypted Data on Alliance Chain

Background:: Data regulation can effectively resist data privacy leakage and abuse in the process of external sharing of energy big data, but how to securely retrieve vast amounts of regulatory data is a challenge. Objective:: To securely retrieve vast amounts of regulatory data, a secure and efficient searchable encryption scheme that supports multi-keyword fuzzy retrieval on the alliance chain is proposed. Method:: This scheme encrypts and stores the regulatory data on the hyperledger fabric alliance chain. Energy big data files (EBDF) are encrypted and stored on a cloud server. Using the symmetric searchable encryption technology to achieve secure retrieval of regulatory data on the chain and secure access to EBDF. To improve search efficiency, we propose a SDPHashMap index structure and use the special manhatton distance matrix(SMDM) measurement to calculate the similarity of queried keywords and index keywords to locate the trapdoor retrieval hash address. Utilizing the cuckoo filter cluster to test the membership of queried keywords. Results:: This scheme stores EBDF off-chain, effectively relieving the storage and communication pressure of the blockchain, improving search speed, and providing more accurate retrieval services than single keyword fuzzy retrieval. By the simulation-based adversary- challenger game model, the security analysis demonstrates that the proposed scheme has adaptive selected keyword attack semantic security. Conclusion:: Experimental results show that the proposed scheme has high efficiency in trapdoor generation and multi-keyword search stages.

Privacy-preserving top-[formula omitted] spatio-temporal keyword preference query

With the rapid advancement of location-based services and mobile devices, spatial keyword query attracts increasing attention. In this paper, we focus on a new query type known as top-k spatio-temporal keyword preference query. This kind of query considers both the spatial object itself and other spatial objects in the neighborhood to return k spatial objects with the highest score. These k spatial objects satisfy spatial and temporal constraints, while their scores are determined by the keyword similarity of the neighboring spatial objects. We propose a scheme to enable privacy-preserving top-k spatio-temporal keyword preference queries. To effectively represent temporal information, we employ time vectors to denote time periods, allowing us to assess whether the data satisfies temporal constraints based on the inner product of time vectors. Furthermore, we adopt a two-step strategy to execute the query. The first step is to find all Points of Interest (POIs) that meet the spatial and temporal constraints. The second step is to calculate the score of each POI and return the top-k POIs with the highest score. To enhance query efficiency, we build a tree index structure that can achieve sub-linear query complexity. Additionally, we utilize EASPE algorithm to encrypt both the index and the query, ensuring privacy-preserving capabilities. Security analysis proves that our scheme satisfies CQA2-security. At the same time, experimental evaluation validates the query performance of our scheme.

Correction method for a shipborne ultrasonic anemometer in measuring the refractive index structure constant in a marine environment

The atmospheric refractive index structure constant (C n 2) was measured in a coastal region using a shipborne three-dimensional ultrasonic anemometer and a micro-thermometer. The results showed that the C n 2 measurements from both instruments were generally consistent but deviated in some cases, especially as the wind speed increased. Careful examination of the data revealed that this was due to noise introduced by wind-induced high-frequency oscillations of the extended arm carrying the ultrasonic probes, which also resulted in higher energy levels in the high-frequency region of the temperature spectrum. To address these issues, an attempt was made to filter the temperature data using a first-order low-pass filtering (LPF) algorithm. For our case, the corrected temperature spectrum reflects a reasonable distribution of turbulent energy at different frequencies, making it obey the −5/3 power law of Kolmogorov turbulence within the inertial subrange. In addition, the C n 2 values estimated from the corrected temperature data showed excellent agreement with the micro-thermometer measurements. The findings of this study provide experience and guidance for the measurement of optical turbulence in the marine environment.

Superhydrophilic antireflection films with excellent optical and mechanical performance for perovskite solar cells

SiO2-based antireflection (AR) films can obviously improve the transmittance of the glass cover on the solar cells. Nevertheless, it’s still challenging to fabricate SiO2 films in a facile way with great antireflective properties, high hardness and good weather resistance to ensure their long-term use in outdoor environments. To solve this problem, a double-layer broadband SiO2 antireflective film with high transmittance increase and excellent mechanical properties was successfully prepared via an acid-catalyzed sol–gel method using polyethylene glycol (PEG) as pore-forming agent. The porous PEG2000-modified SiO2 films were selected as the outer layer for coating onto the pure SiO2 film to form double-layer SiO2 films with graded refractive index. When the mass ratio of PEG2000 to TEOS was 40 %, the obtained film showed the highest transmittance increase of 3.03 % in the wavelength range of 380–1100 nm and a high hardness of 3H. After applied to the perovskite solar cell, a significant enhancement of 1.19 % in photoelectric conversion efficiency (PCE) was achieved compared to the solar cell covered with bare substrate. Furthermore, the water contact angle (WCA) was also reduced to 6° while that of pure SiO2 film was 51.3°, endowing the double-layer SiO2 film superhydrophilic and anti-fogging performance. After the weather resistance test, the transmittance loss of the film was only 0.38 %. Therefore, this work demonstrated that the use of an appropriate amount of pore-forming agent PEG and the double-layer graded refractive index structure can enhance the transmittance of acid-catalyzed SiO₂ films, while maintaining high hardness and good weathering resistance for perovskite solar cells.

Integrating NoSQL, Hilbert Curve, and R*-Tree to Efficiently Manage Mobile LiDAR Point Cloud Data

The widespread use of Light Detection and Ranging (LiDAR) technology has led to a surge in three-dimensional point cloud data; although, it also poses challenges in terms of data storage and indexing. Efficient storage and management of LiDAR data are prerequisites for data processing and analysis for various LiDAR-based scientific applications. Traditional relational database management systems and centralized file storage struggle to meet the storage, scaling, and specific query requirements of massive point cloud data. However, NoSQL databases, known for their scalability, speed, and cost-effectiveness, provide a viable solution. In this study, a 3D point cloud indexing strategy for mobile LiDAR point cloud data that integrates Hilbert curves, R*-trees, and B+-trees was proposed to support MongoDB-based point cloud storage and querying from the following aspects: (1) partitioning the point cloud using an adaptive space partitioning strategy to improve the I/O efficiency and ensure data locality; (2) encoding partitions using Hilbert curves to construct global indices; (3) constructing local indexes (R*-trees) for each point cloud partition so that MongoDB can natively support indexing of point cloud data; and (4) a MongoDB-oriented storage structure design based on a hierarchical indexing structure. We evaluated the efficacy of chunked point cloud data storage with MongoDB for spatial querying and found that the proposed storage strategy provides higher data encoding, index construction and retrieval speeds, and more scalable storage structures to support efficient point cloud spatial query processing compared to many mainstream point cloud indexing strategies and database systems.

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 Experimental Evaluation of Summarisation-Based Frequent Subgraph Mining for Subgraph Searching

The subgraph searching is a fundamental operation for the analysis and exploration of graphs. Nowadays, molecular databases are nearing close to one hundred million molecules. Since finding all the data graphs in a graph database that contain the query graph using subgraph isomorphism is an NP-complete problem, indexes are built and processed. Further, to assist the formulation of the query by a user, the visual exploratory subgraph query paradigm proposes a graphical user interface and leverages exploration time to reduce query processing time. However, state-of-the-art approaches need to scale better to dynamic graph databases and suffer from efficiency problems. In addition, the existing Summarisation-based frequent subgraph mining for visual exploratory subgraph searching (SuMExplorer) is lacking implementation and evaluation study for handling visual subgraph similarity search and modify operations. In this paper, we present a novel index structure, which aids the subgraph searching using the summarised-based weighted frequent subgraph mining on data graphs. By the structure-preserving, we exploit the indexes to support similarity and modify operations. We conduct extensive performance studies on both real-world and synthetic datasets to evaluate the overall performance of the extended SuMExplorer to the recent visual exploratory FERRARI and traditional subgraph search algorithms (such as the gIndex and the GRAPES-DD). Our results showed that our indexes can query up to 3 times faster in comparison to the FERRARI while reducing the storage footprint by 2 orders of magnitude.

Estimated breeding values of dairy sires for cow colostrum and transfer of passive immunity traits

The objective of this study was to derive the estimated breeding values (EBVs) of Holstein sires for colostrum and passive transfer of immunity traits to a) estimate the genetic association between these new traits and established production, conformation, and functional, and b) explore whether sires can be classified in specific profiles regarding the new traits. For cow colostrum traits, the study included 699 daughters of 67 sires from 6 commercial dairy herds. The number of daughters per sire ranged from 5 to 49. Passive transfer of immunity was measured as the blood serum total protein content in 854 purebred Holstein calves of 61 sires from 8 commercial dairy herds. The number of calves per sire ranged from 5 to 44. Data were statistically analyzed using mixed models. Approximate genetic correlations of the derived sire EBVs for cow colostrum and calf serum total protein with EBVs for several other traits were estimated. Moreover, sires were classified into colostrum and calf serum total protein profile groups. Approximate genetic correlations of cow colostrum and calf serum traits with milk production, conformation, and functional traits were mostly unfavorable. Colostrum total solids and protein contents were negatively correlated with milk yield, fat yield, protein yield, productive life, and livability. The only favorable correlation found was between colostrum total solids/calf serum total protein and daughter pregnancy rate. However, several bulls had favorable profiles in both colostrum/passive transfer of immunity and production traits. Colostrum/passive immunity traits could be included in future genetic improvement programs after careful structure of relevant indexes.

Re-examining the factor structure of the Insomnia Severity Index (ISI) and defining the meaningful within-individual change (MWIC) for subjects with insomnia disorder in two phase III clinical trials of the efficacy of lemborexant

BackgroundThe Insomnia Severity Index (ISI) is a widely used measure of insomnia severity. Various ISI research findings suggest different factor solutions and meaningful within-individual change (MWIC) to detect treatment response in patients with insomnia. This study examined an ISI factor solution and psychometric indices to define MWIC in a robust patient sample from clinical trial settings.MethodsWe endeavored to improve upon previous validation of ISI by examining structural components of confirmatory factor analysis (CFA) models using two large, placebo-controlled clinical trials of lemborexant for insomnia. Using the best-fitting two-factor solution, we evaluated anchor-based, distribution-based and receiver operating characteristic (ROC) curve methods to derive an estimate of the MWIC.ResultsThe model structure for the 7-item scale proposed in other research did not fit the observed data from our two lemborexant clinical trials (N = 1956) as well as a two-factor solution based on 6 items did. Using triangulation of anchor-based, distribution-based, and ROC methods, we determined that a 5-point reduction using 6 items best represented a clinically meaningful improvement in individuals with insomnia in our patient sample.ConclusionsA 6-item two-factor scale had better psychometric properties than the 7-item scale in this patient sample. On the 6-item scale, a reduction of 5 points in the ISI total score represented the MWIC. Generalizability of the proposed MWIC may be limited to patient populations with similar demographic and clinical characteristics.

FurMoLi: A Future Query Technique for Moving Objects Based on a Learned Index

The future query of moving objects involves predicting their future positions based on their current locations and velocities to determine whether they will appear in a specified area. This technique is crucial for positioning and navigation, and its importance in our daily lives has become increasingly evident in recent years. Nonetheless, the growing volume of data renders traditional index structures for moving objects, such as the time-parameterized R-tree (TPR-tree), inefficient due to their substantial storage overhead and high query costs. Recent advancements in learned indexes have demonstrated a capacity to significantly reduce storage overhead and enhance query efficiency. However, most existing research primarily addresses static data, leaving a gap in the context of future queries for moving objects. We propose a novel future query technique for moving objects based on a learned index (FurMoLi for short). FurMoLi encompasses four key stages: firstly, a data partition through clustering based on velocity and position information; secondly, a dimensionality reduction mapping two-dimensional data to one dimension; thirdly, the construction of a learned index utilizing piecewise regression functions; and finally, the execution of a future range query and future KNN query leveraging the established learned index. The experimental results demonstrate that FurMoLi requires 4 orders of magnitude less storage overhead than TPR-tree and 5 orders of magnitude less than B+-tree for moving objects (Bx-tree). Additionally, the future range query time is reduced to just 41.6% of that for TPR-tree and 34.7% of that for Bx-tree. For future KNN queries, FurMoLi’s query time is only 70.1% of that for TPR-tree and 47.4% of that for Bx-tree.

Hierarchical Indexing and Compression Method with AI-Enhanced Restoration for Scientific Data Service

In the process of data services, compressing and indexing data can reduce storage costs, improve query efficiency, and thus enhance the quality of data services. However, different service requirements have diverse demands for data precision. Traditional lossy compression techniques fail to meet the precision requirements of different data due to their fixed compression parameters and schemes. Additionally, error-bounded lossy compression techniques, due to their tightly coupled design, cannot achieve high compression ratios under high precision requirements. To address these issues, this paper proposes a lossy compression technique based on error control. Instead of imposing precision constraints during compression, this method first uses the JPEG compression algorithm for multi-level compression and then manages data through a tree-based index structure to achieve error control. This approach satisfies error control requirements while effectively avoiding tight coupling. Additionally, this paper enhances data restoration effects using a deep learning network and provides a range query processing algorithm for the tree-based index to improve query efficiency. We evaluated our solution using ocean data. Experimental results show that, while maintaining data precision requirements (PSNR of at least 39 dB), our compression ratio can reach 64, which is twice that of the SZ compression algorithm.