User-friendly System Research Articles

QR-based Canteen Management System

In recent years, the digitization of administrative and operational processes has transformed various sectors in the world, including food service management. This research presents the development and implementation of a QR-based canteen management system, designed to enhance the efficiency of canteen operations by leveraging contemporary technological advancements. The system employs a desktop application built with C# and the .NET framework, enabling users to order and pay for food items using a QR code-based login and cashless payment system. This solution addresses the inefficiencies of traditional pen-and-paper-based canteen management by automating order processing, payment, and sales record maintenance. The primary objective is to streamline canteen operations, reduce service time, and improve customer satisfaction through a user-friendly and reliable system.

Real-Time Hand Gesture Recognition: A Comprehensive Review of Techniques, Applications, and Challenges

Abstract Real-time Hand Gesture Recognition (HGR) has emerged as a vital technology in human-computer interaction, offering intuitive and natural ways for users to interact with computer-vision systems. This comprehensive review explores the advancements, challenges, and future directions in real-time HGR. Various HGR-related technologies have also been investigated, including sensors and vision technologies, which are utilized as a preliminary step in acquiring data in HGR systems. This paper discusses different recognition approaches, from traditional handcrafted feature methods to state-of-the-art deep learning techniques. Learning paradigms have been analyzed such as supervised, unsupervised, transfer, and adaptive learning in the context of HGR. A wide range of applications has been covered, from sign language recognition to healthcare and security systems. Despite significant developments in the computer vision domain, challenges remain in areas such as environmental robustness, gesture complexity, computational efficiency, and user adaptability. Lastly, this paper concludes by highlighting potential solutions and future research directions trying to develop more robust, efficient, and user-friendly real-time HGR systems.

Construction of a Three-Dimensional-Printed Immunosensing Platform Based on Smartphone Photothermal Signal Integration.

The combination of the photothermal effect and immunoassay serves as a potent tool for crafting cost-effective and user-friendly biosensing systems. To ensure efficient light-to-heat conversion, we integrated three-dimensional-printed (3D printed) technology to devise a novel design. This design functions as the structural support for both the cell phone and laser probe, as well as a means for sample handling. The main body features a three-way cavity structure, securing the test sample at a fixed position to maintain consistent light distance and angle, thereby minimizing testing errors. Card slot insert facilitates precise sample positioning to ensure the adequacy of receiving light. The sample holder's wide front and narrow back design enables the accommodation of fixed samples while providing a broad field of view, with intervals therein effectively preventing cascading heat. Our design employs MB@MOF235 (methylene blue adsorbed by iron terephthalate) as the photothermal reagent, successfully enabling the detection of α-fetoprotein (AFP). The detection range spans from 0.01 to 50 ng/mL, with a lower detection limit (LOD) of 0.032 pg/mL. The detection method, combining simplicity, portability, and visualization, offers a reliable reference for furthering precision medicine toward personalized medicine. Meanwhile, to verify the method's accuracy electrochemical testing was conducted to support the proof using the electro-oxidizing activity of MB.

Improving the Usability of Graphical Authentication Systems Using Subject-Based Images

Recognition based graphical authentication systems rely on a system user’s ability to recognize previously chosen images and use those images to gain access into a computing systems, devices or application. Recognition based systems that utilise several types of images have been developed and studied. The déjà vu scheme depends on the use of abstract (meaningless) images. Images in the déjà vu scheme have no exact meaning. This scheme has also been actively studied. This article presents a between user study conducted on two recognition-based schemes using system prototypes. The Abstract Based Graphical Authentication (ABGA) was designed to simulate the déjà vu scheme. The performance of this system was compared with the performance of a novel system developed for this research. The novel system, called Subject Based Graphical Authentication (SBGA), in which the system used images from the various disciplines of the research participants. The researcher conducted a lab-based study with 100 participants, 50 participants for each of the prototypes. The study investigated the impact of using subject based images to enhance the usability and security of recognition-based graphical passwords. Two graphical models were thus developed and presented to the 100 participants, which comprised of staff and students from the departments of Computer Science, Mathematics, and Engineering. In the experiments, participants selected their graphical passwords from sets of images representing their various disciplines. The researcher observed that the memorability rate for graphical passwords consisting of subject based images aligned with participants' disciplines surpassed the rate for passwords with purely abstract images. The results indicated 81.7% successful login rate and 18.3% failure rate for Subject Based Graphical Authentication (SBGA). In contrast, Abstract Based Graphical Authentication (ABGP) exhibited a 51.5% successful login rate and a 48.7% failure rate. The findings provide significant insights for graphical password designers and developers seeking to enhance system memorability. Balancing image familiarity with security considerations can lead to more user-friendly and effective recognition-based authentication systems. This study provides valuable insights into the benefits of incorporating familiar images in recognition-based graphical passwords, showcasing a significant improvement in memorability and user authentication success. The findings underscore the importance of considering the user’s familiarity with portfolio images in the design of graphical authentication systems.

Review on Potential Urban Development Site Selection Using Geospatial-Based Multi-Criteria Decision Analysis (MCDA) Techniques in the Context of Ethiopia

This review paper provides a comprehensive assessment of the opportunities and challenges in leveraging geospatial-based technologies, particularly Multi-Criteria Decision Analysis (MCDA) techniques, to enhance the effectiveness of urban development site selection in Ethiopia. The overarching findings highlight several critical focus areas for improvement. Firstly, the review emphasizes the need to strengthen the availability, quality, and accessibility of geospatial data through the development of robust data infrastructures and innovative data collection methods. Secondly, it recommends adopting a more inclusive and collaborative approach to stakeholder engagement, coupled with targeted capacity-building initiatives to enhance the technical skills and knowledge of urban planners, GIS experts, and local stakeholders. Thirdly, the paper suggests incorporating advanced spatial modeling and simulation techniques to capture the dynamic nature of urban growth patterns, as well as integrating climate change impact assessments and disaster risk analyses into the decision-making framework. Fourthly, it emphasizes the importance of strengthening institutional and organizational frameworks to promote cross-sectoral collaboration, data sharing, and integrated decision-making processes. Finally, the review underscores the significance of designing user-friendly and customizable Spatial Decision Support Systems (SDSS) that cater to the specific needs and constraints of the Ethiopian urban development context. The review concludes that the use of geospatial-based MCDA approaches holds immense potential in addressing Ethiopia's complex urban development challenges, as it enables a comprehensive evaluation of suitability factors and the exploration of alternative urban development strategies.

EasySkinning: Target-oriented skinning by mesh contraction and curve editing

Skinning, a critical process in animation that defines how bones influence the vertices of a 3D character model, significantly impacts the visual effect in animation production. Traditional methods are time-intensive and skill-dependent, whereas automatic techniques lack in flexibility and quality. Our research introduces EasySkinning, a user-friendly system applicable to complex meshes. This method comprises three key components: rigid weight initialization through Voronoi contraction, precise weight editing via curve tools, and smooth weight solving for reconstructing target deformations. EasySkinning begins by contracting the input mesh inwards to the skeletal bones, which improves vertex-to-bone mappings, particularly in intricate mesh areas. We also design intuitive curve-editing tools, allowing users to define more precise bone influential regions. The final stage employs advanced deformation algorithms for smooth weight solving, crucial for achieving desired animations. Through extensive experiments, we demonstrate that EasySkinning not only simplifies the creation of high-quality skinning weights but also consistently outperforms existing automatic and interactive skinning methods.

Novel Strategy for Human Deep Vein Thrombosis Diagnosis Based on Metabolomics and Stacking Machine Learning.

Deep vein thrombosis (DVT) is a serious health issue that often leads to considerable morbidity and mortality. Diagnosis of DVT in a clinical setting, however, presents considerable challenges. The fusion of metabolomics techniques and machine learning methods has led to high diagnostic and prognostic accuracy for various pathological conditions. This study explored the synergistic potential of dual-platform metabolomics (specifically, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS)) to expand the detection of metabolites and improve the precision of DVT diagnosis. Sixty-one differential metabolites were identified in serum from DVT patients: 22 from GC-MS and 39 from LC-MS. Among these, five key metabolites were highlighted by SHapley Additive exPlanations (SHAP)-guided feature engineering and then used to develop a stacking diagnostic model. Additionally, a user-friendly interface application system was developed to streamline and automate the application of the diagnostic model, enhancing its practicality and accessibility for clinical use. This work showed that the integration of dual-platform metabolomics with a stacking machine learning model enables faster and more accurate diagnosis of DVT in clinical environments.

Assessing travel time performance of multimodal transportation systems using fuzzy-analytic hierarchy process: A case study of Bhopal City

Insufficient multimodal transportation infrastructure in Indian cities increases travel time, waiting time, traffic congestion, traffic accidents, travel costs, fast-growing energy consumption, and walking distance. The multimodal transportation system's journey time performance must be analysed to tackle these transportation concerns. Thus, this research provides a fundamental method for assessing multimodal transportation system trip time performance. This study proposes three steps for assessing travel time performance: identifying factors, generating individual indices, and evaluating the Multimodal Transport System Travel-Time Performance Index. The travel-time performance index of Bhopal city was 0.79 after evaluating the results of the individual indices (Access 0.49, In-vehicle 0.39, and Egress 0.38). Consequently, this study will help identify areas for improvement in Bhopal's multimodal transportation system and provide insights for enhancing its efficiency and user satisfaction. The proposed methodology can be applied to other cities facing similar challenges, aiding in the development of more sustainable and user-friendly transportation systems.

A distributed software system for integrating data-intensive imaging methods in a hard X-ray nanoprobe beamline at the SSRF.

The development of hard X-ray nanoprobe techniques has given rise to a number of experimental methods, like nano-XAS, nano-XRD, nano-XRF, ptychography and tomography. Each method has its own unique data processing algorithms. With the increase in data acquisition rate, the large amount of generated data is now a big challenge to these algorithms. In this work, an intuitive, user-friendly software system is introduced to integrate and manage these algorithms; by taking advantage of the loosely coupled, component-based design approach of the system, the data processing speed of the imaging algorithm is enhanced through optimization of the parallelism efficiency. This study provides meaningful solutions to tackle complexity challenges faced in synchrotron data processing.

Manajerial Keuangan dalam Pengelolaan Pajak berlandaskan Sistem Informasi Akuntansi pada Perusahaan Swasta di Semarang

This study analyzes financial management in a private company in Semarang through information management systems in payroll administration. The data were divided into five categories: general, finance, accounting, sales administration, and accounting. The analysis method used was information system analysis with five steps, including monitoring, business processes, task delegation, independent verification, and capture procedures. The business process involves several entities in the application of VAT, Income Tax Article 21, and Income Tax Article 23. The delegation of tasks is considered ineffective because the number of employees is not proportional to the volume of work, which has a negative impact on the company's revenue. Supervision is carried out by the payroll department to ensure data accuracy. Supporting factors for the use of information management systems include information technology, user-friendly systems, top management support, and employee expertise. Conversely, inhibiting factors include lack of funds, declining number of tax employees, and ineffective division of tasks. An independence verification process was also conducted to ensure compliance with the prevailing tax laws in Indonesia.

Challenges, Barriers, and Facilitators in Telemedicine Implementation in India: A Scoping Review.

Telemedicine has revolutionized healthcare access by breaking geographical barriers and facilitating remote consultations. The eSanjeevani platform has been pivotal in India, especially during the COVID-19 pandemic. Despite its benefits, implementing telemedicine faces various challenges and barriers. This scoping review aims to identifythese challenges, barriers, and facilitators in the Indian context. This review follows the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) guidelines. A comprehensive literature search was conducted using PubMed and Google Scholar to identify studies published between January 2019 and January 2024. Studies on telemedicine adoption, barriers, and facilitators in India were included. Data were extracted and synthesized from 26 quantitative, qualitative, and mixed-methods research studies. Individual-level challenges include limited digital literacy, particularly among older adults and those in rural areas, and health literacy issues. Barriers such as limited smartphone access, unreliable internet connectivity, and socio-cultural preferences for in-person consultations were identified. Facilitators at this level include family involvement, training programs, and community outreach. Provider-level challenges involve a lack of training and concerns about care quality, while barriers include insufficient infrastructure and medico-legal concerns. Facilitators include ongoing training, clear guidelines, and user-friendly telehealth systems. System-level challenges include integrating telemedicine into existing infrastructure and ensuring data security. Barriers include inadequate funding and resistance to change, while facilitators include policy support, investment in infrastructure, and collaborative efforts. Telemedicine holds great potential to enhance remote healthcare access in India, but its successful implementation requires addressing significant challenges and barriers. Strengthening digital infrastructure, enhancing digital literacy, standardizing protocols, and developing clear regulatory frameworks are essential. Collaborative efforts and tailored approaches that respect local cultures can further facilitate telemedicine adoption. Continuous research and public awareness campaigns are necessary to ensure telemedicine's sustainable and effective use in India.

ClearScope: a fully integrated light sheet theta microscope for sub-cellular resolution imaging without lateral size constraints.

Three-dimensional (3D) ex vivo imaging of cleared intact brains of animal models and large human and non-human primate postmortem brain specimens is important for understanding the physiological neural network connectivity patterns and the pathological alterations underlying neuropsychiatric and neurological disorders. Light-sheet microscopy has emerged as a highly effective imaging modality for rapid high-resolution imaging of large cleared samples. However, the orthogonal arrangements of illumination and detection optics in light sheet microscopy limits the size of specimen that can be imaged. Recently developed light sheet theta microscopy (LSTM) technology addressed this by utilizing a unique arrangement of two illumination light paths oblique to the detection light path, while allowing perpendicular arrangement of the detection light path relative to the specimen surface. Here, we report development of a next-generation, fully integrated, and user-friendly LSTM system for rapid sub-cellular resolution imaging uniformly throughout a large specimen without constraining the lateral (XY) size. In addition, we provide a seamlessly integrated workflow for image acquisition, data storage, pre- and post-processing, enhancement, and quantitative analysis. We demonstrate the system performance by high-resolution 3D imaging of intact mouse brains and human brain samples, and complete data analysis including digital neuron tracing, vessel reconstruction and design-based stereological analysis in 3D. This technically enhanced and user-friendly LSTM implementation will enable rapid quantitative mapping of molecular and cellular features of interests in diverse types of very large samples.

Research on Financial Product Innovation in Rural Commercial Banks under the Digital Transformation Context: A Case Study of Jiangsu Province

In the era of digitalization and networking, innovation in financial products is crucial for deepening rural financial reform and establishing a financial support system for rural revitalization. However, a comprehensive and user-friendly financial product system based on big data technology has yet to be established. As a result, the needs of farmers are unmet, and small and micro enterprises still face issues of expensive and difficult financing. This study, based on the context of digital transformation and the characteristics of rural commercial banks, analyzes the problems encountered in the innovation and development of financial products from both supply and demand sides, and proposes reasonable solutions.

Comparison of SIMOA and VEUS technologies for serum neurofilament light chain measurement in multiple sclerosis

IntroductionThe gold standard for serum neurofilament light chain (sNfL) determination is the single molecule array (SIMOA), the use of which is limited by availability and cost. The VEUS method is a fully automated, user-friendly diagnostic system requiring no sample preparation, with high reported sensitivity, multiplexing capability, and rapid diagnostics. The aim of this study was to compare the SIMOA and VEUS methods for determining sNfL levels in patients with multiple sclerosis (MS). MethodologyA single-centre cross-sectional study was conducted at the MS Centre of University Hospital Ostrava. Patients were enrolled in the study from January 18 to January 31, 2024. Inclusion criteria were: 1) diagnosis of MS according to the revised 2017 McDonald criteria, 2) age ≥18 years, and 3) signed informed consent. The NF-light V2 diagnostic kit (SIMOA, Quanterix) and the Singleplex Neurology assay kit (VEUDx, EZDiatech) were used to determine sNfL concentrations. The two methods were compared by use of Spearman correlation, Passing–Bablok regression, and Bland–Altman analysis. ResultsA total of 49 patients were included in the study, of whom 39 (79.6 %) were female. The median sNfL concentration was 7.73 (IQR 5.80–9.93) ng/L determined by SIMOA and 1.31 (IQR 1.18–1.65) ng/L by VEUS. We did not find a correlation between SIMOA and VEUS (rs = 0.025, p = 0.866). Passing–Bablok regression demonstrated a systematic and proportional difference between the two methods. A significant disagreement between them was also confirmed by the Bland–Altman plots. On average, sNfL values measured by SIMOA were 3.56 ng/L (95 % CI 0.78 to 6.34) higher than those measured by VEUS. ConclusionOur investigation uncovered noteworthy disparities between the SIMOA and VEUS techniques in determining sNfL levels. Specifically, the VEUS technique systematically produces lower estimates of sNFL levels. This substantial variance emphasizes the importance of carefully evaluating assay methods when quantifying sNfL.

Establishing a Classification System for Predicting Flow-Limiting Dissection After Balloon Angioplasty Using Explainable Machine-Learning Models: A Multicenter Retrospective Cohort Study.

Percutaneous transluminal angioplasty (PTA) is the primary method for treatment in peripheral arterial disease. However, some patients experience flow-limiting dissection (FLD) after PTA. We utilized machine learning and SHapley Additive exPlanations to identify and optimize a classification system to predict FLD after PTA. This was a multi-center, retrospective, cohort study. The cohort comprised 407 patients who underwent treatment of the femoropopliteal (FP) arteries in 3 institutions between January 2021 and June 2023. Preoperative computed tomography angiography images were evaluated to identify FP artery grading, chronic total occlusion (CTO), and vessel calcification (peripheral artery calcium scoring system [PACSS]). After PTA, FLD was identified by angiography. We trained and validated 6 machine-learning models to estimate FLD occurrence after PTA, and the best model was selected. Then, the sum of the Shapley values for each of CTO, FP, and PACSS was calculated for each patient to produce the CTO-FP-PACSS value. The CTO-FP-PACSS classification system was used to classify the patients into classes 1 to 4. Univariate and multivariate analyses were performed to validate the effectiveness of the CTO-FP-PACSS classification system for predicting FLD. Overall, 407 patients were analyzed, comprising 189 patients with FLD and 218 patients without FLD. Differences in sex (71% males vs 54% males, p<0.001), CTO (72% vs 43%, p<0.001), FP (3.26±0.94 vs 2.66±1.06, p<0.001), and PACSS (2.39±1.40 vs 1.74±1.35, p<0.001) were observed between patients with and without FLD, respectively. The random forest model demonstrated the best performance (validation set area under the curve: 0.82). SHapley Additive exPlanations revealed CTO, PACSS, and FP as the 3 most influential FLD predictors, and the univariate and multivariate analyses confirmed CTO-FP-PACSS classification as an independent FLD predictor (multivariate hazard ratio 4.13; p<0.001). The CTO-FP-PACSS classification system accurately predicted FLD after PTA. This user-friendly system may guide surgical decision-making, helping choose between PTA and additional devices to reduce FLD in FP artery treatment. We utilised machine-learning techniques in conjunction with SHapley Additive exPlanations to develop a clinical classification system that predicts the probability of flow-limiting dissection (FLD) after plain old balloon angioplasty. This classification system categorises lesions into Classes 1-4 based on three factors: chronic total occlusion, femoropopliteal grading, and peripheral artery calcium scoring. Each class demonstrated a different probability of developing FLD. This classification system may be valuable for surgeons in their clinical practice, as well as serving as a source of inspiration for other researchers.

The barriers and challenges of using health information technology in medication administration process for pediatrics and neonates: A qualitative study.

Children and neonates are more susceptible to diseases and are a vulnerable group in medication administration (MA). Nurses interact directly with patients, ensuring safety and preventing unintended outcomes. Health Information Technology (HIT) has transformed health care, aiding nurses in decision-making and treatment responses. Despite its benefits, technology presents challenges that must be overcome to facilitate the nursing practice. Therefore, the present study aimed to explore the barriers to HIT use in the process of MA in children and neonates in a developing country. Semi-structured face-to-face interviews were conducted with 22 health care professionals across seven pediatric and neonatal settings. Also, observations were made of these settings for 3 weeks. A qualitative analysis was performed using the conventional content analysis method, recommended by Colaizzi's seven-step approach. The results showed that the most significant barriers to adopting technology in MA process could be classified into two main categories: "inappropriate management approaches" with two sub-categories ("Managers' reluctance to adopt new technology", "lack of adequate budget for hardware resources"), and "resistance to change" with two sub-categories ("A desire to use conventional (traditional) approaches in care","cultural issues and impracticality of providing some specialized technology services"). The findings revealed MA process complexities, which have been insufficiently examined in the current literature. We have highlighted the need for improved "effectiveness of HIT systems in administering medication processes, budget for hardware resources, and managers" interest in using new technology. The present findings can guide the development of more effective and user-friendly HIT systems in pediatric and neonatal care settings.

A Neural Network-Based Scoring System for Predicting Prognosis and Therapy in Breast Cancer.

Breast cancer is a prevalent malignancy affecting women worldwide. Currently, there are no precise molecular biomarkers with immense potential for accurately predicting breast cancer development, which limits clinical management options. Recent evidence has highlighted the importance of metastatic and tumor-infiltrating immune cells in modulating the antitumor therapy response. However, the prognostic value of using these features in combination, and their potential for guiding individualized treatment for breast cancer, remains vague. To address this challenge, we recently developed the metastatic and immunogenomic risk score (MIRS), a comprehensive and user-friendly scoring system that leverages advanced bioinformatics methods to facilitate transcriptomics data analysis. To help users become familiar with the MIRS tool and apply it effectively in analyzing new breast cancer datasets, we describe detailed protocols that require no advanced programming skills. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Calculating a MIRS score from transcriptomics data Basic Protocol 2: Predicting clinical outcomes from MIRS scores Basic Protocol 3: Evaluating treatment responses and guiding therapeutic strategies in breast cancer patients Basic Protocol 4: Guidelines for utilizing the MIRS webtool.

Achieving Inclusive Healthcare through Integrating Education and Research with AI and Personalized Curricula.

Precision medicine promises significant health benefits but faces challenges such as the need for complex data management and analytics, interdisciplinary collaboration, and education of researchers, healthcare professionals, and participants. Addressing these needs requires the integration of computational experts, engineers, designers, and healthcare professionals to develop user-friendly systems and shared terminologies. The widespread adoption of large language models (LLMs) like GPT-4 and Claude 3 highlights the importance of making complex data accessible to non-specialists. The Stanford Data Ocean (SDO) strives to mitigate these challenges through a scalable, cloud-based platform that supports data management for various data types, advanced research, and personalized learning in precision medicine. SDO provides AI tutors and AI-powered data visualization tools to enhance educational and research outcomes and make data analysis accessible for users from diverse educational backgrounds. By extending engagement and cutting-edge research capabilities globally, SDO particularly benefits economically disadvantaged and historically marginalized communities, fostering interdisciplinary biomedical research and bridging the gap between education and practical application in the biomedical field.

An Effective Lung Cancer Diagnosis Model Using the CNN Algorithm

The disease known as lung cancer is a serious condition that may be deadly if it is not diagnosed at an early stage. The diagnosis of lung cancer has to be improved, and there is a need for a cost-effective and user-friendly system that leverages state-of-the-art data science technology. This would help simplify operations, save time and money, and improve diagnosis. This research suggests the use of a convolutional neural network (CNN) architecture for the purpose of categorizing three unique histopathological pictures, namely benign, adenocarcinoma, and squamous cell carcinoma. The purpose of this study is to apply the CNN model to properly classify these three kinds of cancers and to compare the accuracy of the CNN model to the accuracy of other techniques that have been employed in investigations that are comparable to this one. The CNN model was not used in any of the preceding research for the purpose of categorizing these particular histopathological pictures; hence, the relevance of this work cannot be overstated. It is possible to get more positive treatment results by correctly classifying malignant tumors as early as possible. In training, the CNN model obtained an accuracy of 96.11%, and in validation, it earned an accuracy of 97.2%. The suggested method has the potential to improve lung cancer diagnosis in patients by classifying them into subgroups according to the symptoms they exhibit. This approach to machine learning, which makes use of the random forest technique, has the potential to reduce the amount of time, resources, and labor required. Utilizing the CNN model to categorize histopathological pictures may, ultimately, improve the diagnostic accuracy of lung cancer and save lives by allowing early disease identification.

Technology-Enabled Database Education: Challenges andOpportunities

Motivation: User-friendly systems and tools are paramount for facilitating learning of database systems. In many universities around the world, database systems courses are supplemented with the use of industrial-strength relational database management systems (RDBMS). Although there exist a few efforts on mini RDBMS for learners to practice implementing a database kernel, e.g., Minibase [6], unfortunately, there is a lack of learner-friendly technological support to facilitate the learning of various core components of a database systems course, such as database storage and indexing, relational query processing and optimization, and transaction management.