Database Techniques Research Articles

Extended multi-stream temporal-attention module for skeleton-based human action recognition (HAR)

Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible. The previous GCN-based models still have a lot of issues: (I) The graph structure is the same for all model layers and input data. GCN model's hierarchical structure and human action recognition input diversity make this a problematic approach; (II) Bone length and orientation are understudied due to their significance and variance in HAR. For this purpose, we introduce an Extended Multi-stream Temporal-attention Adaptive GCN (EMS-TAGCN). By training the network topology of the proposed model either consistently or independently according to the input data, this data-based technique makes graphs more flexible and faster to adapt to a new dataset. A spatial, temporal, and channel attention module helps the adaptive graph convolutional layer focus on joints, frames, and features. Hence, a multi-stream framework representing bones, joints, and their motion enhances recognition accuracy. Our proposed model outperforms the NTU RGBD for CS and CV by 0.6% and 1.4%, respectively, while Kinetics-skeleton Top-1 and Top-5 are 1.4% improved, UCF-101 has improved 2.34% accuracy and HMDB-51 dataset has significantly improved 1.8% accuracy. According to the results, our model has performed better than the other models. Our model consistently outperformed other models, and the results were statistically significant that demonstrating the superiority of our model for the task of HAR and its ability to provide the most reliable and accurate results.

A Machine Learning-Based Algorithm for the Prediction of Eigenfrequencies of Railway Bridges

As part of the development of advanced, data-driven methods for predictive maintenance of railway infrastructure, this paper analyzes and evaluates more realistic predictions of eigenfrequencies of railway bridges, also referred to as natural frequencies, based on a population of already assessed, measured existing bridges using regression techniques. For this purpose, Machine Learning (ML) techniques such as Polynomial Regression (PR), ANN and XGBoost are consistently evaluated and the application of the XGBoost algorithm is identified as the most suitable prediction model for these eigenfrequencies, usable for dynamic train-bridge interactions. The results of the post-processing are incorporated into the safety architecture for bridge verification (risk management). The presented data-based techniques are a steppingstone towards digitalization of structural health monitoring and offer safety and longevity of the railway bridges. Furthermore, the use of these methods can save costs that would be incurred by physical in-situ measurements. The types of bridges analyzed with ML are Filler Beam Bridges (FBE), which outnumber other construction types of bridges in Germany (DB InfraGO AG). This methodology is applicable to any bridge type as long as sufficient data are gathered for training, validation and testing.

Roles of Catalysts and Feedstock in Optimizing the Performance of Heavy Fraction Conversion Processes: Fluid Catalytic Cracking and Ebullated Bed Vacuum Residue Hydrocracking

Petroleum refining has been, is still, and is expected to remain in the next decades the main source of energy required to drive transport for mankind. The demand for automotive and aviation fuels has urged refiners to search for ways to extract more light oil products per barrel of crude oil. The heavy oil conversion processes of ebullated bed vacuum residue hydrocracking (EBVRHC) and fluid catalytic cracking (FCC) can assist refiners in their aim to produce more transportation fuels and feeds for petrochemistry from a ton of petroleum. However, a good understanding of the roles of feed quality and catalyst characteristics is needed to optimize the performance of both heavy oil conversion processes. Three knowledge discovery database techniques—intercriteria and regression analyses, and artificial neural networks—were used to evaluate the performance of commercial FCC and EBVRHC in processing 19 different heavy oils. Seven diverse FCC catalysts were assessed using a cascade and parallel fresh catalyst addition system in an EBVRHC unit. It was found that the vacuum residue conversion in the EBVRHC depended on feed reactivity, which, calculated on the basis of pilot plant tests, varied by 16.4%; the content of vacuum residue (VR) in the mixed EBVRHC unit feed (each 10% fluctuation in VR content leads to an alteration in VR conversion of 1.6%); the reaction temperature (a 1 °C deviation in reaction temperature is associated with a 0.8% shift in VR conversion); and the liquid hourly space velocity (0.01 h-1 change of LHSV leads to 0.85% conversion alteration). The vacuum gas oil conversion in the FCC unit was determined to correlate with feed crackability, which, calculated on the basis of pilot plant tests, varied by 8.2%, and the catalyst ΔCoke (each 0.03% ΔCoke increase reduces FCC conversion by 1%), which was unveiled to depend on FCC feed density and equilibrium FCC micro-activity. The developed correlations can be used to optimize the performance of FCC and EBVRHC units by selecting the appropriate feed slate and catalyst.

Finding Efficient Graph Embeddings and Processing them by a CNN-based Tool

We introduce new tools to support finding efficient graph embedding techniques for graph databases and to process their outputs using deep learning for classification scenarios. Accordingly, we investigate the possibility of creating an ensemble of different graph embedding methods to raise accuracy and present an interconnected neural network-based ensemble to increase the efficiency of the member classification algorithms. We also introduce a new convolutional neural network-based architecture that can be generally proposed to process vectorized graph data provided by various graph embedding methods and compare it with other architectures in the literature to show the competitiveness of our approach. We also exhibit a statistical-based inhomogeneity level estimation procedure to select the optimal embedding for a given graph database efficiently. The efficiency of our framework is exhaustively tested using several publicly available graph datasets and numerous state-of-the-art graph embedding techniques. Our experimental results for classification tasks have proved the competitiveness of our approach by outperforming the state-of-the-art frameworks.

Upscaling Current Data Caching and Prefetching Strategies for Online Databases in Nigeria

This study investigated upscaling current data caching and prefetching strategies for online databases in Nigeria, with a focus on practical implications. The research design adopted for this study was the descriptive survey. The population comprised of all undergraduate’s library students in public tertiary institutions in Ekiti State. A simple random sampling technique was adopted to select 200 library students from Ekiti State University in the study area. The instrument used for data collection was a structured 4 Likert type questionnaire. The questionnaire was distributed to the respondents to find out the effectiveness of caching and prefetching techniques on online databases. The instrument was both face and content validated by two experts from the department of Library studies in Ekiti State University, Ado-Ekiti State. The reliability of the instrument was ensured using Pearson Product Moment Correlation formula which yielded a coefficient of 0.99. The data collected were analyzed using descriptive statistics such as mean and standard deviation. The result showed that the current caching and prefetching techniques employed in online databases are highly effective; the different access patterns have effect on the effectiveness of caching and prefetching techniques in online databases and there are impacts of cache coherence mechanisms on the efficiency of caching and prefetching techniques in online databases. It was therefore recommended that the inclusion of caching and prefetching in curriculum is important across all educational level in Nigeria, with a clear emphasis on the practical implications. In addition, caching and perfecting have come under fire for focusing mostly on computer science.

LLM for Data Management

Machine learning techniques have been verified to be effective in optimizing data management systems and are widely researched in recent years. However, traditional small-sized ML models often struggle to generalize to new scenarios, and have limited context understanding ability (e.g., inputting discrete features only). The emergence of LLMs offers a promising solution to these challenges. LLMs have been trained over a vast number of scenarios and tasks and acquire human-competitive capabilities like context understanding and summarization, which can be highly beneficial for data management tasks (e.g., natural language based data analytics). In this tutorial, we present how to utilize LLMs to optimize data management systems and review new techniques for addressing these technical challenges, including hallucination of LLMs, high cost of interacting with LLMs, and low accuracy for processing complicated tasks. First, we discuss retrieval augmented generation (RAG) techniques to address the hallucination problem. Second, we present vector database techniques to improve the latency. Third, we present LLM agent techniques for processing complicated tasks by generating multi-round pipelines. We also showcase some real-world data management scenarios that can be well optimized by LLMs, including query rewrite, database diagnosis and data analytics. Finally, we summarize some open research challenges.

ADVANCED QUERY OPTIMIZATION IN SQL DATABASES FOR REAL-TIME BIG DATA ANALYTICS

This study investigates the effectiveness of advanced query optimization techniques in SQL databases, focusing on multi-level indexing, query rewriting, and dynamic query execution plans. The research employs a qualitative approach, gathering data from a variety of SQL databases characterized by large datasets typical of big data environments. Through structured interviews, focus groups, and observational methods, insights from database administrators highlight the practical benefits and challenges associated with implementing these techniques. The findings reveal significant improvements in query performance, with multi-level indexing reducing data retrieval times by approximately 40%, query rewriting decreasing execution times by 35%, and dynamic query execution plans enhancing resource utilization efficiency by 25%. These techniques were also praised for their ease of use, adaptability to different data types and query complexities, and overall reliability. This study contributes to the existing body of knowledge by providing a comprehensive analysis of the practical applications and performance enhancements offered by advanced query optimization methods in SQL databases, underscoring their value in managing large-scale, dynamic data environments.

Research on Clustering-Based Fault Diagnosis during ROV Hovering Control

The objective of this study was to perform fault diagnosis (FD) specific to various faults that can occur in the thrusters of remotely operated vehicles (ROVs) during hovering control. Underwater thrusters are predominantly utilized as propulsion systems in the majority of ROVs and are essential components for implementing motions such as trajectory tracking and hovering. Faults in the underwater thrusters can limit the operational capabilities of ROVs, leading to permanent damage. Therefore, this study focused on the FD for faults frequently caused by external factors such as entanglement with floating debris and propeller breakage. For diagnosing faults, a data-based technique that identifies patterns according to data characteristics was utilized. In imitation of the fault situations, data for normal, breakage and entangled conditions were acquired, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) was employed to differentiate between these fault conditions. The proposed methodology was validated by configuring an ROV and conducting experiments in an engineering water tank to verify the performance of the FD.

Therapeutic effects of mobilization in alleviating pain and improving shoulder mobility in adhesive capsulitis – A systematic review

Frozen shoulder (FS) is a painful clinical disease that lasts over three months and affects 3–5% of the general population. The main goal of this review was to show how mobilization techniques can affect FS treatment compared to other physiotherapy options. A systematic review of randomized controlled studies from CINAHL, MEDLINE, AMED, and PubMed published after 2010. Studies that included patients 18 or older with FS, but no other underlying systemic disorders were eligible. The review focused on two primary outcomes: Pain levels and how effectively patients could move their shoulders. The quality of the studies was assessed using the physiotherapy evidence database (PEDro) technique. Six randomized controlled studies with a PEDro score of 5/10 that met the study requirements were included. The pain was reduced in three of the five investigations with P < 0.05, whereas the remaining studies yielded neutral findings in lowering pain in patients with FS. All of the individuals’ range of motion (ROM) was evaluated. With P < 0.05, four of the six trials showed an improvement in shoulder ROM. Bending, straightening, moving arms away from the body, bringing them back in, internal rotation, and external rotation were all assessed and shown to be significant with P ≤ 0.05. Most studies suggested that patients, who have received mobilization treatment, experienced positive improvements. Techniques such as Mulligan mobilization, scapular mobilization, passive mobilization, and angular joint mobilization have proven effective in addressing pain and improving the ROM in individuals with FSs.

EngiWave: Engineering College Recommendation System using Decision Tree and Random Forest Algorithm

Students face numerous challenges when trying to enroll in their desired institution, particularly in engineering programs. The admissions process is complex, often resulting in students being accepted into lower-ranked institutions than they deserve. To address this, the Council Entrance Predictor utilizes historical acceptance data to forecast the most likely institutions for students. Engiwave, another tool, employs similar data to predict potential institutions based on academic performance, background, and admission criteria. These predictions are facilitated by a machine learning model trained on a dataset containing student profiles and university details, including admission outcomes. By leveraging machine learning and database techniques, the system provides students with a prioritized list of engineering institutions where they have the highest probability of admission, aiding them in preparing their applications effectively.

METHODS OF SUPPORTING ACCEPTANCE OF ECONOMICLY BASED DECISIONS IN THE MANAGEMENT OF THE QUALITY SYSTEM OF HIGHER EDUCATION

The aim is to influence the level of preparedness in both IT specialties and economic fields closely tied to IT technologies within higher or secondary educational institutions to meet the needs and requirements of diverse companies. Collaborative efforts between businesses and education can effectively tackle the challenge of cultivating high-quality potential personnel and enhancing students' readiness by establishing a standardized set of business demands.When outlining the requirements for IT specialties, crucial vectors encompass not only work experience and skills but also business qualities and the ability to collaborate in a work collective, aligning with company values. To precisely define the competencies of future employees, we advocate for the Delphi method as one of the best approaches. This method involves expert judgment and comprises multiple rounds of anonymous surveys within an expert group to reach a consensus.By fostering interactions between companies and educational institutions in the study, surveys can collect statistics and utilize educational plans that align with organizational interests. Proposed competencies by experts may convey similar semantic meanings but be articulated with different terminology. Additionally, merging similar competencies into more comprehensive ones is feasible. In the era of rapid IT development, addressing this issue can be accomplished through existing text analysis and comparison techniques in databases. Consequently, the unification of competencies can be established during the learning process.

Enhancing Agriculture Prediction through AI and Parallel Distributed Computing: A Comprehensive Study on the Impact of Weather

This revolutionary study focuses on incorporating Artificial Intelligence (AI) and Parallel Distributed Computing (PDC) in agriculture prediction, with a specific interest in weather patterns. Modern technologies are instrumental in providing solutions to enhance agricultural efficiency and sustainability. Results from this research show that PDCs and AI have the potential to make crop yield predictions more accurate, detect and prevent diseases, and allocate resources efficiently. This paper emphasizes accurate weather forecasts for sustainable farming practices while considering multiple factors involved in decision-making processes according to expert opinions. These hybrid computing approaches are highly effective in generating detailed spatiotemporal information about agricultural systems, improving agricultural predictions' accuracy. Therefore, the study has far-reaching implications for industry players seeking to improve productivity using data-based techniques for sustainability purposes.

An Efficient Steganographic Protocol for WebP Files

In this paper, several ideas of data hiding in WebP images are presented. WebP is a long-known, but not very poplar file format that provides lossy or lossless compression of data, in the form of a still image or an animation. A great number of WebP features are optional, so the structure of the image offers great opportunities for data hiding. The article describes distinct approaches to steganography divided into two categories: format-based and data-based. Among format-based methods, we name simple injection, multi-secret steganography that uses thumbnails, hiding a message in metadata or in a specific data chunk. Data-based methods achieve secret concealment with the use of a transparent, WebP-specific algorithm that embeds bits by choosing proper prediction modes and alteration of the color indexing transform. The capacity of presented techniques varies. It may be unlimited for injection, up to a few hundred megabytes for other format-based algorithms, or be content-dependent in data-based techniques. These methods fit into the container modification branch of steganography. We also present a container selection technique which benefits from available WebP compression parameters. Images generated with the described methods were tested with three applications, including the Firefox web browser, GNU Image Manipulation Program, and ImageMagick. Some of the presented techniques can be combined in order to conceal more than one message in a single carrier.

Optimisation of Process Parameters on Tensile Strength of 3D Printed Polylactic Acid (PLA) Parts: ASTM D638 Type – IV

Purpose: The purpose of this study is to optimize the influence of 3D printing processing parameters on the ultimate tensile strength of 3D printed Polylactic Acid (PLA) parts. The objective is to develop predictive models to help predict and attain optimised mechanical strength integrity of 3D printed parts.
 Design/Methodology/Approach: In the present study, 3D printed PLA samples were modelled and fabricated using carefully selected processing parameters-processing speed, processing temperature and nozzle diameter. Tensile tests were performed by ASTM D638 standard. Two characteristics response optimisation models based on Taguchi Technique and multi-linear regression models were developed to optimise the process parameters and the ultimate tensile strength of the 3D printed samples.
 Findings: Results of this study reveal that ultimate tensile strength is significantly affected by the Nozzle diameter. The ultimate tensile strength of the 3D-printed PLA sample was found to be significantly higher than the strength of the original PLA filament printed.
 Research Implications/Limitations: In this study, only three critical 3D printing processing parameters including, processing speed; processing temperature and nozzle diameter were implemented concurrently.
 Practical implication: The optimisation of process parameters for enhancing the tensile strength of 3D-printed PLA parts holds significant practical consequences, including cost savings, improved performance, sustainability, and innovation. The aforementioned consequences render PLA a more feasible and appealing material option for a diverse array of applications and industries.
 Social implication: Optimisation of PLA printing can promote community engagement by allowing individuals to bring their creative ideas to life, fostering a sense of community and innovation.
 Originality / Value: The unique aspect of this research resides in its particular emphasis on PLA, the utilization of empirical and data-based techniques to optimise process parameters, and its potential to enhance the real-world implementation of 3D printing with PLA across diverse industries.

A Solution to Blockchain Smart Contract Based Parametric Transport and Logistics Insurance

Traditional insurance policies pay the insurer according to the actual loss or damage to a tangible object that is insured. Due to the numerous parties involved, the settlement procedure is usually highly drawn out. Comparatively, parametric insurance protects a policyholder from the occurrence of a particular event by disbursing a predetermined sum dependent on the severity of the event. In the transportation sector, supply chain disruptions caused by delays are precisely the kind of event that parametric insurance may be able to cover. Transportation delays can happen for several reasons. Events like adverse weather, heavy traffic, and poor road conditions can all slow down the logistics supply chain. An occurrence triggers parametric insurance claims. Unfavorable scenarios can be insured, and claims can be automatically processed depending on policy criteria and conditions. The standard centralized database technique can be utilized to build parametric insurance schemes; however, the data is open to manipulation. This paper examines how to create parametric transport insurance using blockchain. It discusses user journey, technology solutions, design and implementation problems, and parametric service insurance. The blueprint for transportation parametric insurance can be used in other fields including Agriculture (Crop Insurance) [22], Disaster Insurance [23], Aviation, Travel, and manufacturing industry.

QTG-Miner aids rapid dissection of the genetic base of tassel branch number in maize

Genetic dissection of agronomic traits is important for crop improvement and global food security. Phenotypic variation of tassel branch number (TBN), a major breeding target, is controlled by many quantitative trait loci (QTLs). The lack of large-scale QTL cloning methodology constrains the systematic dissection of TBN, which hinders modern maize breeding. Here, we devise QTG-Miner, a multi-omics data-based technique for large-scale and rapid cloning of quantitative trait genes (QTGs) in maize. Using QTG-Miner, we clone and verify seven genes underlying seven TBN QTLs. Compared to conventional methods, QTG-Miner performs well for both major- and minor-effect TBN QTLs. Selection analysis indicates that a substantial number of genes and network modules have been subjected to selection during maize improvement. Selection signatures are significantly enriched in multiple biological pathways between female heterotic groups and male heterotic groups. In summary, QTG-Miner provides a large-scale approach for rapid cloning of QTGs in crops and dissects the genetic base of TBN for further maize breeding.

Emotional Regulation and Adolescent Concussion: Overview and Role of Neuroimaging.

Emotional dysregulation symptoms following a concussion are associated with an increased risk for emotional dysregulation disorders (e.g., depression and anxiety), especially in adolescents. However, predicting the emergence or worsening of emotional dysregulation symptoms after concussion and the extent to which this predates the onset of subsequent psychiatric morbidity after injury remains challenging. Although advanced neuroimaging techniques, such as functional magnetic resonance imaging and diffusion magnetic resonance imaging, have been used to detect and monitor concussion-related brain abnormalities in research settings, their clinical utility remains limited. In this narrative review, we have performed a comprehensive search of the available literature regarding emotional regulation, adolescent concussion, and advanced neuroimaging techniques in electronic databases (PubMed, Scopus, and Google Scholar). We highlight clinical evidence showing the heightened susceptibility of adolescents to experiencing emotional dysregulation symptoms following a concussion. Furthermore, we describe and provide empirical support for widely used magnetic resonance imaging modalities (i.e., functional and diffusion imaging), which are utilized to detect abnormalities in circuits responsible for emotional regulation. Additionally, we assess how these abnormalities relate to the emotional dysregulation symptoms often reported by adolescents post-injury. Yet, it remains to be determined if a progression of concussion-related abnormalities exists, especially in brain regions that undergo significant developmental changes during adolescence. We conclude that neuroimaging techniques hold potential as clinically useful tools for predicting and, ultimately, monitoring the treatment response to emotional dysregulation in adolescents following a concussion.

Exploration-exploitation-based trajectory tracking of mobile robots using Gaussian processes and model predictive control

AbstractMobile robots are a key component for the automation of many tasks that either require high precision or are deemed too hazardous for human personnel. One of the typical duties for mobile robots in the industrial sector is to perform trajectory tracking, which involves pursuing a specific path through both space and time. In this paper, an iterative learning-based procedure for highly accurate tracking is proposed. This contribution shows how data-based techniques, namely Gaussian process regression, can be used to tailor a motion model to a specific reoccurring reference. The procedure is capable of explorative behavior meaning that the robot automatically explores states around the prescribed trajectory, enriching the data set for learning and increasing the robustness and practical training accuracy. The trade-off between highly accurate tracking and exploration is done automatically by an optimization-based reference generator using a suitable cost function minimizing the posterior variance of the underlying Gaussian process model. While this study focuses on omnidirectional mobile robots, the scheme can be applied to a wide range of mobile robots. The effectiveness of this approach is validated in meaningful real-world experiments on a custom-built omnidirectional mobile robot where it is shown that explorative behavior can outperform purely exploitative approaches.

Construction of a virtual reality platform for computer-aided navigation Lingnan bone setting technique

ABSTRACTTo establish a standard Traditional Chinese medicine (TCM) bone setting technique, standardize the operation and inherit the TCM bone setting technique. This project was based on the interactive tracking of bone setting techniques with a dedicated position tracker, the motion tracking of bone setting techniques based on RGBD (Red Green Blue Depth) cameras, the digital analysis of bone setting techniques, and the design of the virtual reality platform for bone setting techniques. These key technical researches were combined to construct an interactive bone setting technique. The virtual simulation system can reproduce the implementation process of the expert’s bone setting technique. The user can observe the implementation of the manipulative technique from multiple angles; through human-computer interaction, the whole process of implementation of the bone setting technique can be simulated, and the movement and reduction of the affected bone can be observed at the same time. It can be used as a teaching and training system for assisting bone setting techniques. Students can use the system to carry out repeated self-training, and can instantly compare with the standard techniques of the expert database, breaking the traditional teaching mode of ‘expected and unspeakable’ and avoid directly using patients. Therefore, this research makes it possible to reduce teaching costs, reduce risks, improve teaching quality, and make up for the lack of teaching conditions. It is very positive for the inheritance of the traditional Chinese ‘intangible culture’ of bone setting techniques, and to promote the digitalization and standardization of bone setting techniques.

Penerapan Data Mining Untuk Memprediksi Pembelian Mobil Bekas Menggunakan Algoritma Naïve Bayes

Database can also be interpreted as a data warehouse. The amount of data collected in the database can be processed to generate valuable knowledge for science. One popular and widely used technique for processing databases is data mining. Data mining is the process of extracting knowledge from large and complex data warehouse. Data mining encompasses various algorithm to generate knowledge, one of which is naïve bayes. The dataset used in this research, employing the naïve bayes algorithm, consists of attributes relevant to the purchase of used cars, such year, transmission, mileage, car condition, and brand. This research aims to produce patterns and additional knowledge for participants in the used car business to identify the supporting factors in purchasing used cars.