Data Structure Research Articles

3D reconstruction of foot metatarsal bones of women using CT images

Bone morphology is a fundamental factor in human anatomy. However, foot and ankle bones have yet to be adequately evaluated in 3-dimensional. It is essential to present the biometric data of anatomical structures. This study formed 3D models of the metatarsal bones of the feet of young women using image processing techniques to examine biometric measurements and determine morphology on these 3D models. This study investigated bone lengths in the metatarsal bones of women feet in Türkiye. A total of ten young female subjects were included as the test group to measure the lengths of their foot metatarsal bones using CT (Computed Tomography) scans, and 20 feet (left/right) were examined. The parameters that were used for the analyses were detector collimation of 64x0.5 mm, section thickness of 0.5 mm, current of 100 mA, tube voltage of 120 kVp, and pixel spacing of 512x512 pixels with a monochrome resolution providing 16-bit gray levels. CT images were processed, and a 3D metatarsal reconstruction was gathered. Then, the biometric measurements were calculated on this 3D model. For the lengths of the volunteers' right/left foot metatarsal bones, statistically significant differences were calculated using a one-sample t-test. For the female metatarsal bones of the left and right feet, statistically significant differences in length were calculated on 3D models. The mean results of the metatarsal length measurements were MT1(metatarsal): 59.52±1.42 mm, MT2: 70.45±1.82 mm, MT3: 66.25±1.82 mm, MT4: 65.12±1.81 mm and MT5: 63.63±1.81 mm. The level of statistical significance was accepted as p <0.05 for the one-sample t-test conducted for each metatarsal bone. The lengths of the right foot metatarsal bones were different from those of the left foot metatarsal bones in the sample. However, this difference was approximately one-tenth of a millimeter. The shortest bone was MT1, and the longest bone was MT2. These measurements are consistent with the anatomical information in the literature. The 3D models from the CT images and the biometric measurements of the metatarsal bones were found to be reliable and accurate.

Path guiding for wavefront path tracing: A memory efficient approach for GPU path tracers

We propose a path-guiding algorithm to be incorporated into the wavefront style of path tracers (WFPTs). As WFPTs are primarily implemented on graphics processing units (GPUs), the proposed method aims to leverage the capabilities of the GPUs and reduce the hierarchical data structure and memory usage typically required for such techniques. To achieve this, our algorithm only stores the radiant exitance on a single global sparse voxel octree (SVO) data structure. Probability density functions required to guide the rays are generated on-the-fly using this data structure. The proposed approach reduces the scene-related persistent memory requirements compared to other path-guiding techniques while producing similar or better results depending on scene characteristics. To our knowledge, our algorithm is the first one that incorporates path guiding into a WFPT.

New model of college physical education teaching based on the algorithm and data structure of flipped classroom and OBE

Although college physical education (PE) is a compulsory course in college teaching, due to the openness of assessment standards and students studying to pass the final exam, college PE has been undervalued, so this paper aims to explore the new model of college PE teaching. In response, this paper took the air volleyball course as an example and redesigned the teaching of college PE based on the theory of flipped classroom and outcomes-based education (OBE). This paper also proposed a personalised learning system for college sports based on genetic algorithm (GA) and data structure, greatly improving college PE's autonomous learning ability and willingness. In the design of the teaching model, this paper compared and analysed the teaching model combining flipped classroom and OBE with flipped classroom teaching model, OBE teaching model and traditional teaching model. A one-semester investigation was conducted by selecting 40 students who took the air volleyball PE course at Hebei Normal University for Nationalities. The 40 students were divided into four groups, and their learning after one semester was compared. The experimental results showed that, compared with the traditional teaching model group, the outcome-based education and flipped classroom education group's performance of hitting the ball, passing the ball, spiking the ball, and serving the ball increased by 3.8%, 14.3%, 20.8%, and 10.3%, respectively. This suggested that the new college physical education teaching model based on flipped classrooms and OBE has a good teaching effect and can be used as a reference and help for others.

K-dStHash tree for indexing big spatio-temporal datasets

Today’s era is witness of tremendous ever growing spatial, temporal and spatiotemporal data. The huge spatio-temporal data immensely pushes the need for design and development of novel methods tailored for indexing spatio-temporal data. In this research paper, we propose the design of a novel spatio-temporal data indexing method, named as k-dStHash. We have proposed the algorithm k-dStHashInsertion for inserting spatio-temporal objects and an algorithm k-dStHashSrchPlaceTime has been used to search for the objects at given location and time. It is able to handle datasets with duplicate keys which has been ignored in many research works. Though the algorithm k-dStHashInsertion takes 1.3-1.5 times longer time to insert data in k-dStHash data structure as it needs to find a specific location to organize data efficiently, but when it comes to search for required records it is even more than 90 times faster when analyzed in comparison to brute force method. It is generalized enough to organize any kind of k-dimensional data and time-based data also including object finding, fleet management, clustering, leader identification, nearest neighbor, human/animal tracking, path finding and many more.

New energy levels of atomic holmium discovered by laser spectroscopy in the near infrared spectral range

A combination of optogalvanic spectroscopy and laser-induced fluorescence spectroscopy with a continuous wave tunable single mode Ti-Sa laser was applied to find new energy levels of atomic holmium. Free and excited holmium atoms were produced in a liquid nitrogen-cooled hollow-cathode lamp. In this study, we examined 35 spectral lines of Ho I in the near-infrared wavelength range from 698 nm to 833 nm. Altogether, 16 new energy levels have been discovered, 5 levels of even parity and 11 levels of odd parity. Another four energy levels have been discovered but have already been published by another research group. Data on the hyperfine structure for the new levels are presented. Additionally, hyperfine structure data of three known levels of even parity were determined for the first time. The existence of the new energy levels was confirmed by analyzing 60 lines of previously measured Fourier transform spectra to precisely determine the energy of the levels.

Efficient Data Structures & Algorithms Hunter: A Study in Optimization

Abstract: Data Structures and Algorithms is a fundamental course in Computer Science. However, many students find it difficult because it requires abstract thinking. It would be very helpful if there was a visualization tool of data structures such as arrays, queues, stacks, trees, and graphs for students to manipulate. The tool would allow students to see how an element is inserted into or deleted from different data structures, how a tree is traversed in different order (preorder, in-order, post-order, level-order), etc. Moreover, this tool would provide a simple language, by which students can write their own algorithms so that the execution of the algorithm is animated. This project is intended to create an exploration environment, in which students can learn through experimentation. This tool can be used as an effective supplement to the traditional classroom education and textbook for Data Structures and Algorithms courses. In this paper, a software application that features the visualization of commonly used data structures and their associated insertion and deletion operations is introduced. In addition, this software can be used to animate user-defined algorithms.

Web Scraping Application for E-Commerce Website

Abstract: In the rapidly evolving digital world, efficient extraction of data from the web is essential for insights and decision making. This project introduces a versatile web scraping solution by integrating Superagent and Puppeteer. Superagent efficiently handles HTTP requests and static content, while Puppeteer excels in dynamic web page interactions. The combination of their capabilities forms a robust approach for navigating and scraping modern websites. The resulting tool enables seamless extraction of both static and dynamic content, providing researchers, analysts, and businesses with a powerful tool to acquire structured data from diverse web sources, facilitating data-driven decisions and research across multiple domains. The combined power of these libraries offers an end-to-end solution for automating data extraction and integration. It also emphasizes on ethical considerations

Enhancing Business Intelligence with Real-Time Sales Dashboards: Integrating SQL Databases and Power BI

Abstract: In today's fast-paced business environment, the ability to make informed decisions swiftly is crucial for maintaining a competitive edge. This project proposes the integration of SQL databases and Power BI to develop a real-time sales dashboard, aimed at transforming how sales data is visualized and analysed within the organization. By harnessing the robust capabilities of SQL databases for structured data management and Power BI for data visualization and analytics, the project seeks to provide comprehensive insights into sales trends, performance metrics, and customer behaviours. The integration will enable the automatic synchronization of sales data from SQL databases to Power BI, facilitating the creation of interactive and dynamic dashboards. These dashboards will not only display key sales indicators and metrics but also allow users to drill down into specific data points for deeper analysis. The real-time nature of the data presented will empower sales managers and executives to make data-driven decisions rapidly, adjust strategies on the fly, and identify opportunities for growth and improvement with unprecedented speed and accuracy.

Simulated dynamical transitions in a heterogeneous marmoset pFC cluster

Network analysis of the marmoset cortical connectivity data indicates a significant 3D cluster in and around the pre-frontal cortex. A multi-node, heterogeneous neural mass model of this six-node cluster was constructed. Its parameters were informed by available experimental and simulation data so that each neural mass oscillated in a characteristic frequency band. Nodes were connected with directed, weighted links derived from the marmoset structural connectivity data. Heterogeneity arose from the different link weights and model parameters for each node. Stimulation of the cluster with an incident pulse train modulated in the standard frequency bands induced a variety of dynamical state transitions that lasted in the range of 5–10 s, suggestive of timescales relevant to short-term memory. A short gamma burst rapidly reset the beta-induced transition. The theta-induced transition state showed a spontaneous, delayed reset to the resting state. An additional, continuous gamma wave stimulus induced a new beating oscillatory state. Longer or repeated gamma bursts were phase-aligned with the beta oscillation, delivering increasing energy input and causing shorter transition times. The relevance of these results to working memory is yet to be established, but they suggest interesting opportunities.

The characteristics of brain atrophy prior to the onset of Alzheimer’s disease: a longitudinal study

ObjectiveWe aimed to use the onset time of Alzheimer’s disease (AD) as the reference time to longitudinally investigate the atrophic characteristics of brain structures prior to the onset of AD.Materials and methodsA total of 328 participants from the ADNI database with clear onset of AD and structural imaging data were included in our study. The time before the onset of AD (abbreviated as BAD) was calculated. We investigated the longitudinal brain changes in 97 regions using multivariate linear mixed effects regression models.ResultsThe average BAD was −28.15 months, with a range from −156 to 0 months. The 54 brain regions showed significant atrophy prior to the onset of AD, and these regions were mainly distributed in the frontal and temporal lobes. The parietal and occipital lobe exhibited relatively less atrophy than the other brain lobes. Sex, age, and magnetic field strength had greater direct impacts on structural indicators than APOE genotype and education. The analysis of interaction effects revealed that the APOE ε4 mutation carriers exhibited more severe structural changes in specific brain regions as the BAD increased. However, sex, age, and education had minimal regulatory influence on the structural changes associated with BAD.ConclusionLongitudinal analysis, with the onset time point of AD as the reference, can accurately describe the features of structural changes preceding the onset of AD and provide a comprehensive understanding of AD development.

Advancing Fine Branch Biomass Estimation with Lidar and Structural Models.

Lidar is a promising tool for fast and accurate measurements of trees. There are several approaches to estimate aboveground woody biomass using lidar point clouds. One of the most widely used methods involves fitting geometric primitives (e.g. cylinders) to the point cloud, thereby reconstructing both the geometry and topology of the tree. However, current algorithms are not suited for accurate estimation of the volume of finer branches, because of the unreliable point dispersions from e.g. beam footprint compared to the structure diameter. We propose a new method that couples point cloud-based skeletonization and multi-linear statistical modelling based on structural data to make a model (structural model) that accurately estimates the aboveground woody biomass of trees from high-quality lidar point clouds, including finer branches. The structural model was tested at segment, axis, and branch level, and compared to a cylinder fitting algorithm and to the pipe model theory. The model accurately predicted the biomass with 1.6% nRMSE at the segment scale from a k-fold cross-validation. It also gave satisfactory results when up-scaled to the branch level with a significantly lower error (13% nRMSE) and bias (-5%) compared to conventional cylinder fitting to the point cloud (nRMSE: 92%, bias: 82%), or using the pipe model theory (nRMSE: 31%, bias: -27%).The model was then applied to the whole-tree scale and showed that the sampled trees had more than 1.7km of structures on average and that 96% of that length was coming from the twigs (i.e. <5cm diameter). Our results showed that neglecting twigs can lead to a significant underestimation of tree aboveground woody biomass (-21%). The structural model approach is an effective method that allows a more accurate estimation of the volumes of smaller branches from lidar point clouds. This method is versatile but requires manual measurements on branches for calibration. Nevertheless, once the model is calibrated, it can provide unbiased and large-scale estimations of tree structure volumes, making it an excellent choice for accurate 3D reconstruction of trees and estimating standing biomass.

Sex- and cycle-dependent changes in spine density and size in hippocampal CA2 neurons

Sex hormones affect structural and functional plasticity in the rodent hippocampus. However, hormone levels not only differ between males and females, but also fluctuate across the female estrous cycle. While sex- and cycle-dependent differences in dendritic spine density and morphology have been found in the rodent CA1 region, but not in the CA3 or the dentate gyrus, comparable structural data on CA2, i.e. the hippocampal region involved in social recognition memory, is so far lacking. In this study, we, therefore, used wildtype male and female mice in diestrus or proestrus to analyze spines on dendritic segments from identified CA2 neurons. In basal stratum oriens, we found no differences in spine density, but a significant shift towards larger spine head areas in male mice compared to females. Conversely, in apical stratum radiatum diestrus females had a significantly higher spine density, and females in either cycle stage had a significant shift towards larger spine head areas as compared to males, with diestrus females showing the larger shift. Our results provide further evidence for the sexual dimorphism of hippocampal area CA2, and underscore the importance of considering not only the sex, but also the stage of the estrous cycle when interpreting morphological data.

Deep learning algorithm-enabled sediment characterization techniques to determination of water saturation for tight gas carbonate reservoirs in Bohai Bay Basin, China

Understanding water saturation levels in tight gas carbonate reservoirs is vital for optimizing hydrocarbon production and mitigating challenges such as reduced permeability due to water saturation (Sw) and pore throat blockages, given its critical role in managing capillary pressure in water drive mechanisms reservoirs. Traditional sediment characterization methods such as core analysis, are often costly, invasive, and lack comprehensive spatial information. In recent years, several classical machine learning models have been developed to address these shortcomings. Traditional machine learning methods utilized in reservoir characterization encounter various challenges, including the ability to capture intricate relationships, potential overfitting, and handling extensive, multi-dimensional datasets. Moreover, these methods often face difficulties in dealing with temporal dependencies and subtle patterns within geological formations, particularly evident in heterogeneous carbonate reservoirs. Consequently, despite technological advancements, enhancing the reliability, interpretability, and applicability of predictive models remains imperative for effectively characterizing tight gas carbonate reservoirs. This study employs a novel data-driven strategy to prediction of water saturation in tight gas reservoir powered by three recurrent neural network type deep/shallow learning algorithms—Gated Recurrent Unit (GRU), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), K-nearest neighbor (KNN) and Decision tree (DT)—customized to accurately forecast sequential sedimentary structure data. These models, optimized using Adam's optimizer algorithm, demonstrated impressive performance in predicting water saturation levels using conventional petrophysical data. Particularly, the GRU model stood out, achieving remarkable accuracy (an R-squared value of 0.9973) with minimal errors (RMSE of 0.0198) compared to LSTM, RNN, SVM, KNN and, DT algorithms, thus showcasing its proficiency in processing extensive datasets and effectively identifying patterns. By achieving unprecedented accuracy levels, this study not only enhances the understanding of sediment properties and fluid saturation dynamics but also offers practical implications for reservoir management and hydrocarbon exploration in complex geological settings. These insights pave the way for more reliable and efficient decision-making processes, thereby advancing the forefront of reservoir engineering and petroleum geoscience.

Structure-based network analysis predicts pathogenic variants in human proteins associated with inherited retinal disease.

Advances in gene sequencing technologies have accelerated the identification of genetic variants, but better tools are needed to understand which are causal of disease. This would be particularly useful in fields where gene therapy is a potential therapeutic modality for a disease-causing variant such as inherited retinal disease (IRD). Here, we apply structure-based network analysis (SBNA), which has been successfully utilized to identify variant-constrained amino acid residues in viral proteins, to identify residues that may cause IRD if subject to missense mutation. SBNA is based entirely on structural first principles and is not fit to specific outcome data, which makes it distinct from other contemporary missense prediction tools. In 4 well-studied human disease-associated proteins (BRCA1, HRAS, PTEN, and ERK2) with high-quality structural data, we find that SBNA scores correlate strongly with deep mutagenesis data. When applied to 47 IRD genes with available high-quality crystal structure data, SBNA scores reliably identified disease-causing variants according to phenotype definitions from the ClinVar database. Finally, we applied this approach to 63 patients at Massachusetts Eye and Ear (MEE) with IRD but for whom no genetic cause had been identified. Untrained models built using SBNA scores and BLOSUM62 scores for IRD-associated genes successfully predicted the pathogenicity of novel variants (AUC = 0.851), allowing us to identify likely causative disease variants in 40 IRD patients. Model performance was further augmented by incorporating orthogonal data from EVE scores (AUC = 0.927), which are based on evolutionary multiple sequence alignments. In conclusion, SBNA can used to successfully identify variants as causal of disease in human proteins and may help predict variants causative of IRD in an unbiased fashion.

Novel biallelic PISD missense variants cause spondyloepimetaphyseal dysplasia with disproportionate short stature and fragmented mitochondrial morphology.

Biallelic variants in PISD cause a phenotypic spectrum ranging from short stature with spondyloepimetaphyseal dysplasia (SEMD) to a multisystem disorder affecting eyes, ears, bones, and brain. PISD encodes the mitochondrial-localized enzyme phosphatidylserine decarboxylase. The PISD precursor is self-cleaved to generate a heteromeric mature enzyme that converts phosphatidylserine to the phospholipid phosphatidylethanolamine. We describe a 17-year-old male patient, born to unrelated healthy parents, with disproportionate short stature and SEMD, featuring platyspondyly, prominent epiphyses, and metaphyseal dysplasia. Trio genome sequencing revealed compound heterozygous PISD variants c.569C>T; p.(Ser190Leu) and c.799C>T; p.(His267Tyr) in the patient. Investigation of fibroblasts showed similar levels of the PISD precursor protein in both patient and control cells. However, patient cells had a significantly higher proportion of fragmented mitochondria compared to control cells cultured under basal condition and after treatment with 2-deoxyglucose that represses glycolysis and stimulates respiration. Structural data from the PISD orthologue in Escherichia coli suggest that the amino acid substitutions Ser190Leu and His267Tyr likely impair PISD's autoprocessing activity and/or phosphatidylethanolamine biosynthesis. Based on the data, we propose that the novel PISD p.(Ser190Leu) and p.(His267Tyr) variants likely act as hypomorphs and underlie the pure skeletal phenotype in the patient.

Structural acceleration response reconstruction based on BiLSTM network and multi-head attention mechanism

The effectiveness of structural health monitoring often relies on the precise analysis of structural response data, with data completeness directly impacting the performance of monitoring systems. Addressing the common issue of data loss in structural health monitoring systems, this study proposes a Bidirectional Long Short-Term Memory (BiLSTM) network based on a multi-head attention mechanism for the reconstruction of missing structural acceleration responses. Firstly, employing a two-layer BiLSTM network to establish an encoder-decoder framework for extracting spatio-temporal correlation features among sensor data. Subsequently, innovatively embedding a multi-head attention mechanism into the network framework enhances the modeling capability of long-distance input elements and establishes dependencies among different sensors, thereby enabling the network to better capture the global features of input information. The proposed method undergoes validation through a numerical example of a simply supported beam, practical monitoring data on the Hardanger Bridge from the Norwegian University of Science and Technology, and an experimental study of steel frame structure from our laboratory. These validations are compared with alternative response reconstruction models. The experimental results make obvious that the proposed method demonstrates superior accuracy in response reconstruction. Additionally, the suitability of this method for modal identification is validated. Through the utilization of reconstructed responses, the method effectively discerns the structural natural frequencies with accuracy.

Exploration of the Reform of Multi subject Co construction Data Structure Experiment Course under the Mixed Teaching Mode

In response to the high complexity of algorithm courses and the difficulty for students to understand, this paper focuses on the data structure experimental course and proposes a multi subject co construction data structure experimental course reform strategy under the blended teaching mode. The article first explores the problems in the content and methods of practical courses, and then proposes reform measures in teaching modes, teaching systems, and teaching methods. At the same time, on the basis of school enterprise cooperation, design differentiated experimental projects, teach students according to their aptitude, and strive for a student-centered teaching model, collaborative assistance to complete the role exchange between teaching and learning.

Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering

Subspace clustering algorithms have demonstrated remarkable success across diverse fields, including object segmentation, gene clustering, and recommendation systems. However, they often face challenges, such as omitting cluster information and the neglect of higher-order neighbor relationships within the data. To address these issues, a novel subspace clustering method named Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering is proposed. This method seamlessly embeds Markov transition probability information into the self-expression, leveraging a fine-grained neighbor matrix to uncover latent data structures. This matrix preserves crucial high-order local information and complementary details, ensuring a comprehensive understanding of the data. To effectively handle complex nonlinear relationships, the method learns the underlying manifold structure from a cross-order local neighbor graph. Additionally, connectivity constraints are applied to the affinity matrix, enhancing the group structure and further improving the clustering performance. Extensive experiments demonstrate the superiority of this novel method over baseline approaches, validating its effectiveness and practical utility.

Agricultural land use modeling and climate change adaptation: A reinforcement learning approach

AbstractThis paper provides a novel approach to integrate farmers' behavior in spatially explicit agricultural land use modeling to investigate climate change adaptation strategies. More specifically, we develop and apply a computationally efficient machine learning approach based on reinforcement learning to simulate the adoption of agroforestry practices. Using data from an economic experiment with crop farmers in Southeast Germany, our results show that a change in climate, market, and policy conditions shifts the spatial distribution of the uptake of agroforestry systems. Our modeling approach can be used to advance currently used models for ex ante policy analysis by upscaling existing knowledge about farmers behavioral characteristics and combine it with spatially explicit environmental and farm structural data. The approach presents a potential solution for researchers who aim to upscale information, potentially enriching and complementing existing land use modeling approaches.

Human neutralizing antibodies target a conserved lateral patch on H7N9 hemagglutinin head

Avian influenza A virus H7N9 causes severe human infections with >30% fatality. Currently, there is no H7N9-specific prevention or treatment for humans. Here, from a 2013 H7N9 convalescent case in Hong Kong, we isolate four hemagglutinin (HA)-reactive monoclonal antibodies (mAbs), with three directed to the globular head domain (HA1) and one to the stalk domain (HA2). Two clonally related HA1-directed mAbs, H7.HK1 and H7.HK2, potently neutralize H7N9 and protect female mice from lethal H7N9/AH1 challenge. Cryo-EM structures reveal that H7.HK1 and H7.HK2 bind to a β14-centered surface and disrupt the 220-loop that makes hydrophobic contacts with sialic acid on an adjacent protomer, thereby blocking viral entry. Sequence analysis indicates the lateral patch targeted by H7.HK1 and H7.HK2 to be conserved among influenza subtypes. Both H7.HK1 and H7.HK2 retain HA1 binding and neutralization capacity to later H7N9 isolates from 2016–2017, consistent with structural data showing that the antigenic mutations during this timeframe occur at their epitope peripheries. The HA2-directed mAb H7.HK4 lacks neutralizing activity but when used in combination with H7.HK2 moderately augments female mouse protection. Overall, our data reveal antibodies to a conserved lateral HA1 supersite that confer neutralization, and when combined with a HA2-directed non-neutralizing mAb, augment protection.