Database Handling Research Articles

ROSMOSE: A web-based decision support tool for the design and optimization of industrial and urban energy systems

Energy efficiency is crucial for the sustainable operation of industrial and urban sectors. However, practicing engineers seldom have access to open-source tools that can readily evaluate and compare scenarios. In this work, a novel web-based tool called ROSMOSE is developed and proposed for analyzing the energy efficiency of industrial and urban systems and comparing potential process integration options. This optimization framework computes and graphically represents the minimum energy requirements, the pinch temperatures, and the Grand and integrated Carnot composite curves of process systems. Results are used to design utility systems that meet energy demands, while minimizing a specified objective function, such as total cost or environmental impacts. The Quarto environment, in which ROSMOSE is built upon, allows the integration of various open-source software and tools, such as database handling software, process modeling suites, and optimization solvers, along with interactive data visualization and reporting tools. This paper discusses the application of ROSMOSE for the energy integration and total site optimization of a dairy process consisting of milk treatment, and cream and cheese production to demonstrate the tool features. Other integration options, such as the use of heat pumps or solar panels, and the production of soft drinks or biogas as value-added byproducts, are also proposed and evaluated. As a result, from 75 % to 90 % in fuel savings and up to 80 % CO2 emissions reduction in the dairy plant are identified by optimizing the utility systems integration. Moreover, complete electrification via heat pumps eliminates any fossil fuel needs and it results in 85 % energy savings and up to 96 % CO2 emissions reduction. The activation of renewable electricity sources, such as solar power, made plausible this fully electrified scenario. Finally, optimized waste management strategies led to in-house fuel production and net export of biogas to the grid, but they also reduce cheese yield by prioritizing the biogas production from whey upgrading process. In this way, decision-makers have access to a clear performance comparison for a list of system configurations to draw informed trade-offs. A similar approach could be adopted to aid decision-making for the design and operation of other industrial and urban energy systems using ROSMOSE.

HealthCare education Platform

Our health education platform is designed to provide reliable health information and resources in a user-friendly manner. It features a responsive web design using modern technologies for seamless browsing. The backend utilizes Django or Express.js for robust logic and database handling, with PostgreSQL or MongoDB for efficient data storage. Elasticsearch or Solr powers fast and accurate search functionality. Secure authentication and authorization are ensured through JWT or OAuth. Comments and feedback are managed with moderation tools and notifications. Performance is optimized with monitoring tools, caching, and CDN integration. Scalability is achieved through cloud infrastructure and load balancing. Comprehensive security measures include HTTPS encryption and regular audits. The platform allows users to explore a wide range of health-related content, including articles, blogs, and expert opinions, with personalized content suggestions. By offering a comprehensive and user-friendly platform, we aim to empower individuals to make informed decisions about their health and well-being.

Machine Learning-based Classification of Variable Stars

The classification of variable stars, essential for revealing information on stellar properties and cosmic distances, traditionally relied on statistical methods and limited data. With the emergence of transformative machine learning methodologies and large stellar surveys, we are able to perform more efficient, accurate, and robust handling of expansive databases. We deploy two different machine learning models—random forest and XGBoost—that effectively classify variable stars identified in the most recent phase of the Optical Gravitational Lensing Experiment using four features: (1) time of minimum brightness, (2) I-band amplitude, (3) mean or maximum I-band magnitude, and (4) period. The two models achieve a cross-correlation score of 99.00%, indicating largely shared classifications. The results produced by the models uncover new insights into the precision of supervised learning models in predicting variable stars with the aid of the most up-to-date survey data.

Nanometa Live: a user-friendly application for real-time metagenomic data analysis and pathogen identification.

Nanometa Live presents a user-friendly interface designed for real-time metagenomic data analysis and pathogen identification utilizing Oxford Nanopore Technologies' MinION and Flongle flow cells. It offers an efficient workflow and graphical interface for the visualization and interpretation of metagenomic data as it is being generated. Key features include automated BLAST validation, streamlined handling of custom Kraken2 databases, and a simplified graphical user interface for enhanced user experience. Nanometa Live is particularly notable for its capability to run without constant internet or server access once installed, setting it apart from similar tools. It provides a comprehensive view of taxonomic composition and facilitates the detection of user-defined pathogens or other species of interest, catering to both researchers and clinicians. Nanometa Live has been implemented as a local web application using the Dash framework with Snakemake handling the data processing. The source code is freely accessible on the GitHub repository at https://github.com/FOI-Bioinformatics/nanometa_live and it is easily installable using Bioconda. It includes containerization support via Docker and Singularity, ensuring ease of use, reproducibility, and portability.

Information and communication technology (ICT) competencies of public elementary school teachers in Taal district

This study investigates the levels of information and communication technology (ICT) skills among teachers, focusing on areas such as word processing, spreadsheet management, database handling, and internet navigation. The research utilized a descriptive method and found that while teachers exhibited proficiency in basic ICT tasks, challenges remain, particularly in areas like database management and advanced spreadsheet use. Significant relationships were observed between certain demographic factors and ICT competencies, underscoring the need for continuous ICT training and a robust ICT management plan to enhance teacher competencies and improve educational outcomes. Keywords: ICT companies, Public elementary teachers, Educational technology integration

SealedGRID: Secure and Interoperable Platform for Smart GRID Applications.

Recent advancements in information and communication technologies (ICT) have improved the power grid, leading to what is known as the smart grid, which, as part of a critical economic and social infrastructure, is vulnerable to security threats from the use of ICT and new emerging vulnerabilities and privacy issues. Access control is a fundamental element of a security infrastructure, and security is based on the principles of less privilege, zero-trust, and segregation of duties. This work addresses how access control can be applied without disrupting the power grid’s functioning while also properly maintaining the security, scalability, and interoperability of the smart grid. The authentication in the platform presumes digital certificates using a web of trust. This paper presents the findings of the SealedGRID project, and the steps taken for implementing Attribute-based access control policies specifically customized to the smart grid. The outcome is to develop a novel, hierarchical architecture composed of different licensing entities that manages access to resources within the network infrastructure. They are based on well-drawn policy rules and the security side of these resources is placed through a context awareness module. Together with this technology, the IoT is used with Big Data (facilitating easy handling of large databases). Another goal of this paper is to present implementation and evaluations details of a secure and scalable security platform for the smart grid.

InChI version 1.06: now more than 99.99% reliable

The software for the IUPAC Chemical Identifier, InChI, is extraordinarily reliable. It has been tested on large databases around the world, and has proved itself to be an essential tool in the handling and integration of large chemical databases. InChI version 1.05 was released in January 2017 and version 1.06 in December 2020. In this paper, we report on the current state of the InChI Software, the details of the improvements in the v1.06 release, and the results of a test of the InChI run on PubChem, a database of more than a hundred million molecules. The upgrade introduces significant new features, including support for pseudo-element atoms and an improved description of polymers. We expect that few, if any, applications using the standard InChI will need to change as a result of the changes in version 1.06. Numerical instability was discovered for 0.002% of this database, and a small number of other molecules were discovered for which the algorithm did not run smoothly. On the basis of PubChem data, we can demonstrate that InChI version 1.05 was 99.996% accurate, and InChI version 1.06 represents a step closer to perfection. Finally, we look forward to future releases and extensions for the InChI Chemical identifier.

From DNA sequences to operational reference databases: an opinionated approach using R

Reference databases of sequences that have been taxonomically assigned are a key element for DNA-based identification of organisms. Accurate and complete reference databases are necessary to associate a correct taxonomic name to the sequences obtained in studies using metabarcoding. Today many research projects using DNA metabarcoding include the development of a custom reference database, often derived from large repositories like GenBank. At the same time, many projects are focussing on the development of ready-to-use databases validated by experts and targeting specific markers and taxonomic groups. While mainstream tools such as spreadsheet softwares may be suitable to manage small databases, they quickly become insufficient when the amount of data increases and validation operations become more complex. There is a clear need for providing user‐friendly and powerful tools to manipulate biological sequences and manage reference databases. The R language which is a free software and has already been adopted by many researchers to perform their analyses is highly suitable to develop such tools. In this talk, we will outline the approach we recommend to handle small- to middle-sized reference databases, currently still making the majority of projects. We will advocate that a simple tabular approach where each sequence constitutes an observation may be the most adequate. While such a single table may be less flexible and less optimized than relational databases or more complex data structures, it is easy to maintain and allows the direct use of modern dataframe centric tools. We will specifically present and discuss two R packages that can be used jointly to make reference database development more accessible and more reproducible. First, we will briefly introduce bioseq (Keck 2020) which is dedicated to biological sequence manipulation and analysis. The package implements classes and functions to make analyses of complex datasets including DNA, RNA or protein sequences as simple as possible. The strength of bioseq is to provide standard and more advanced functions to perform low level operations through a simple and consistent programming interface. Then we will present refdb, which has been developed as an environment for semi-automatic and assisted construction of reference databases. The refdb package is a reference database manager offering a set of powerful functions to import, organize, clean, filter, audit and export the data. We will outline how these two packages together can speed up reference database generation and handling, and contribute to standardization and repeatability in metabarcoding studies.

Selective pressure diversity and phenotype-genotype correlation in clinical isolates of mycobacterium tuberculosis

Aims and objectives: Selection pressure, such as drug treatment or host microenvironment, drives the evolution of M. tuberculosis in vivo. The emergence of drug resistance by the accumulation of mutations leading to drug target modifications, drug transport or activation pathways inactivation, is the leading cause of treatment failure. Besides that, secondary mutations involved in bacterial adaptation, fitness-compensation, and resistance level were identified in the genome by microevolution studies. Methods: Whole-genome sequencing data of 2212 clinical isolates of M. tuberculosis with resistance data were downloaded from SRA. Identification of single nucleotide variations (SNVs) was performed using BWA-MEM and C-Sibelia scripts using AL123456.3 as reference. The SNVs database handling and calculations were performed with custom Python scripts. Fisher test analysis was performed as for individual mutations, also for mutation combinations. Pairs of mutations were joined using logical AND function. Thus, n^2/2 records were analyzed for correlation with resistance, where n – total number of mutations in the database. Resistance to the rifampicin, isoniazid, and ethionamide was analyzed. Results: A novel approach for GWAS was developed: we studied the correlation of simultaneous occurrence of mutations pairs and phenotypic resistance for all possible pairs. We identified several SNVs associated with resistance in combinations with other mutations, and its statistical significance was below the threshold when analyzed separately. Conclusions: Fisher exact test for mutation combinations allows for the identification of genetic determinants missed by conventional GWAS approach. Further evaluation of novel resistance determinants using other sets of clinical isolates data from various world regions is needed. Acknowledgements This study was supported by the Russian Foundation for Basic Research (project 20-015-00463).

Finger Print Based Attendance System Using Arduino

At the instant, most of the attendance systems that are getting used in universities still are written a bit of paper. For classes, tutorials, and laboratory sessions the students still have got to sign the signature on the attendance sheet. This method isn't flexible because the danger of losing the attendance data is extremely high. If the attendance sheet is missing, the attendance data are going to be lost. Aside from that, the unethical problems could also be occurring like cheating in the signature. For instance, a student doesn't attend his class but his attendance form has been signed by another student. This technique is proposed to beat these problems. Besides that, since the proposed system also records the time, the lecturer can monitor the punctuality of the students too. “SMART ATTENDANCE SYSTEM” may be a system that will take attendance by using information extracted from the fingerprint Database Handling System. Before this lecturer must use the paper to urge the student’s attendance. There have been tons of problems when using the paper as student attendance like cheating. This system can help the lecturer to scale back the matter like that intentionally automatic attendance using fingerprint.

Mobischool School Management System Using Android Application

Mobiles are used everywhere. It is beneficial to have android application of School Management system. This application includes database handling. Fully functional, flexible application and friendly interface is required for school management application so that it will be easily handled by parents as well as teachers. It is useful to get immediate alerts to the parents about notices [13]. Paper based activities are very costly and difficult to handle [12].

RESTful API Automated Test Case Generation with EvoMaster

RESTful APIs are widespread in industry, especially in enterprise applications developed with a microservice architecture. A RESTful web service will provide data via an API over the network using HTTP, possibly interacting with databases and other web services. Testing a RESTful API poses challenges, because inputs/outputs are sequences of HTTP requests/responses to a remote server. Many approaches in the literature do black-box testing, because often the tested API is a remote service whose code is not available. In this article, we consider testing from the point of view of the developers, who have full access to the code that they are writing. Therefore, we propose a fully automated white-box testing approach, where test cases are automatically generated using an evolutionary algorithm. Tests are rewarded based on code coverage and fault-finding metrics. However, REST is not a protocol but rather a set of guidelines on how to design resources accessed over HTTP endpoints. For example, there are guidelines on how related resources should be structured with hierarchical URIs and how the different HTTP verbs should be used to represent well-defined actions on those resources. Test-case generation for RESTful APIs that only rely on white-box information of the source code might not be able to identify how to create prerequisite resources needed before being able to test some of the REST endpoints. Smart sampling techniques that exploit the knowledge of best practices in RESTful API design are needed to generate tests with predefined structures to speed up the search. We implemented our technique in a tool called E vo M aster , which is open source. Experiments on five open-source, yet non-trivial, RESTful services show that our novel technique automatically found 80 real bugs in those applications. However, obtained code coverage is lower than the one achieved by the manually written test suites already existing in those services. Research directions on how to further improve such an approach are therefore discussed, such as the handling of SQL databases.

Sprawozdanie z Konferencji Naukowej zorganizowanej z okazji Sześćdziesięciolecia Pedagogiki Gdańskiej (1958 – 2018)

Modernisation of the journal's operations and translation into the English language of articles published in Ars Educandi in 2012-2017 were financed with funds from the Ministry of Science and Higher Education as a part of the task Science Promotion Activity (pl. DUN). The task ‘The implementation of the editorial module on the platform of Uniwersyteckie Czasopisma Naukowe – a system facilitating the editing and management of the academic journal Ars Educandi‘ was financed as a part of contract 661/P-DUN/2018 of 12.06.2018 from funds of the Ministry of Science and Higher Education designated for the promotion of science. The task ‘The creation of a modern online version of the academic journal Ars Educandi through the implementation of the publication module on the platform of Uniwersyteckie Czasopisma Naukowe and the handling of international indexing databases’ was financed as a part of contract 661/P-DUN/2018 of 12.06.2018 from funds of the Ministry of Science and Higher Education designated for the promotion of science.

A future-proof architecture for telemedicine using loose-coupled modules and HL7 FHIR

Background and objectivesMost telemedicine solutions are proprietary and disease specific which cause a heterogeneous and silo-oriented system landscape with limited interoperability. Solving the interoperability problem would require a strong focus on data integration and standardization in telemedicine infrastructures. Our objective was to suggest a future-proof architecture, that consisted of small loose-coupled modules to allow flexible integration with new and existing services, and the use of international standards to allow high re-usability of modules, and interoperability in the health IT landscape. MethodsWe identified core features of our future-proof architecture as the following (1) To provide extended functionality the system should be designed as a core with modules. Database handling and implementation of security protocols are modules, to improve flexibility compared to other frameworks. (2) To ensure loosely coupled modules the system should implement an inversion of control mechanism. (3) A focus on ease of implementation requires the system should use HL7 FHIR (Fast Interoperable Health Resources) as the primary standard because it is based on web-technologies. ResultsWe evaluated the feasibility of our architecture by developing an open source implementation of the system called ORDS. ORDS is written in TypeScript, and makes use of the Express Framework and HL7 FHIR DSTU2. The code is distributed on GitHub. All modules have been tested unit wise, but end-to-end testing awaits our first clinical example implementations. ConclusionsOur study showed that highly adaptable and yet interoperable core frameworks for telemedicine can be designed and implemented. Future work includes implementation of a clinical use case and evaluation.

A holistic approach for integration of biological systems and usage in drug discovery

A system can be defined as an organized, interconnected structure consisting of interrelated and interdependent elements (e.g., components, factors, members, parts). These parts and processes are connected by structural and/or behavioral relationships and continually influence one another directly or indirectly to maintain a balance essential for the existence of the system, and for achieving its goal. With increasing inflow of biological data, serious efforts to empathize biological systems as true systems are nowadays almost practicable. Handling high-throughput data places stress mainly on in silico approach comprising database handling, modeling, simulation and analysis, resulting in dramatic progress in system-level analysis. The databases and methods in bioinformatics are now moving in the direction of implementation of integrative dataset systems to represent genes, proteins and metabolic pathways in combination with simulated environment which is dynamic. For understanding the complex biological disorders and normal pathways of system it is significant to integrate the reductionist data which comes from transcriptomics, genomics, proteomics, lipidomics, glycomics, fluxomics and metabolomics. Numerous bioinformatics approaches are being exploited to integrate the molecular information from the biological databases and assist in simulation of metabolic networks. High-throughput experimental data set systems are, however, established on the static representation of the molecular data and existing knowledge. Various biological tools have been developed for understanding the mechanism of several diseases for drug discovery process. Study of dynamic nature of genetic, biochemical and signal transduction pathways can be done by simulating reactions with the help of integrative tools. Rising usage of rational drug designing approach is significant for identification of target in disease polluted network and evaluating ligand interaction for enhanced efficacy. How in-depth investigation of the whole system (a holistic approach) leads to emergence of systems biology is the crux of this review.

Studying Oogenesis in a Non-model Organism Using Transcriptomics: Assembling, Annotating, and Analyzing Your Data.

This chapter provides a guide to processing and analyzing RNA-Seq data in a non-model organism. This approach was implemented for studying oogenesis in the Speckled Wood Butterfly Pararge aegeria. We focus in particular on how to perform a more informative primary annotation of your non-model organism by implementing our multi-BLAST annotation strategy. We also provide a general guide to other essential steps in the next-generation sequencing analysis workflow. Before undertaking these methods, we recommend you familiarize yourself with command line usage and fundamental concepts of database handling. Most of the operations in the primary annotation pipeline can be performed in Galaxy (or equivalent standalone versions of the tools) and through the use of common database operations (e.g. to remove duplicates) but other equivalent programs and/or custom scripts can be implemented for further automation.

An Examination of the Effectiveness of Teaching Data Modelling Concepts

The effective teaching of data modelling concepts is very important; it constitutes the fundament of database planning methods and the handling of databases with the help of database management lan-guages, typically SQL. We examined three courses. The students of two courses prepared for the exam by solving tests, while the students of the third course prepared by solving tasks from a printed exercise book. The number of task for the second course was 2.5 times more than the number of task for the first course. The main purpose of our examination was to determine the effectiveness of the teaching of data modelling concepts, and to decide if there is a significant difference between the results of the three courses. According to our examination, with increasing the number of test tasks and with the use of exercise book, the results became significantly better.

Parametric database approach integration for handling temporal data in GIS

In large spatial, temporal, and spatio-temporal databases, the concept of dimensions – time and space – is central and their efficient processing has always been a great concern. A Geographic Information System (GIS) is widely known for its efficient and sophisticated handling of the spatial databases; however, integration of the time domain introduces additional complexities. Considerable research is ongoing in this area. Though numerous approaches have already been proposed in the literature to address the time-related challenges in GIS community, such as the development of temporal GIS data models, this study contributes to these ongoing efforts through extensions to the Parametric Data Model. Parametric database approaches (Parametric Data Model) have been developed to handle dimensional data such as space, time, belief, etc. In this study, the time dimension is of specific interest and we propose the integration of parametric approaches to GIS to absorb the temporal burden. The temporal processing of the data is done using parametric approaches that address the limitation of GIS to support temporal queries and temporal analysis of phenomena. Using the AutoConViz tool, developed for spatio-temporal data modeling and visualization, results can be directly integrated into standard GIS software programs such as ArcGIS and further processed seamlessly along with and in a similar manner to other attributes.

A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels.

BackgroundIn recent years, computer simulation models have supported development of pandemic influenza preparedness policies. However, U.S. policymakers have raised several concerns about the practical use of these models. In this review paper, we examine the extent to which the current literature already addresses these concerns and identify means of enhancing the current models for higher operational use.MethodsWe surveyed PubMed and other sources for published research literature on simulation models for influenza pandemic preparedness. We identified 23 models published between 1990 and 2010 that consider single-region (e.g., country, province, city) outbreaks and multi-pronged mitigation strategies. We developed a plan for examination of the literature based on the concerns raised by the policymakers.ResultsWhile examining the concerns about the adequacy and validity of data, we found that though the epidemiological data supporting the models appears to be adequate, it should be validated through as many updates as possible during an outbreak. Demographical data must improve its interfaces for access, retrieval, and translation into model parameters. Regarding the concern about credibility and validity of modeling assumptions, we found that the models often simplify reality to reduce computational burden. Such simplifications may be permissible if they do not interfere with the performance assessment of the mitigation strategies. We also agreed with the concern that social behavior is inadequately represented in pandemic influenza models. Our review showed that the models consider only a few social-behavioral aspects including contact rates, withdrawal from work or school due to symptoms appearance or to care for sick relatives, and compliance to social distancing, vaccination, and antiviral prophylaxis. The concern about the degree of accessibility of the models is palpable, since we found three models that are currently accessible by the public while other models are seeking public accessibility. Policymakers would prefer models scalable to any population size that can be downloadable and operable in personal computers. But scaling models to larger populations would often require computational needs that cannot be handled with personal computers and laptops. As a limitation, we state that some existing models could not be included in our review due to their limited available documentation discussing the choice of relevant parameter values.ConclusionsTo adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility.

Highlights on ACM HPDGIS 2011: the Second International ACM SIGSPATIAL Workshop on High Performance and Distributed Geographic Information Systems (HPDGIS) 2011

The ACM SIGSPATIAL HPDGIS 2011 workshop was held in Chicago, Illinois, USA on November 1, 2011 in conjunction with the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information System, and was a great success. High performance computing and distributed systems have become prominent elements in the landscape of communication, computing, and information technologies due to the increasingly computationally and data intensive nature of geospatial problems in numerous fields and significant requirements for service-oriented computing and collaborative problem solving. Efficient handling of massive spatial databases, shared and role-based access to distributed data, and high-end computing services are fundamental to the real-time and near-real-time responses required by many GIS and associated decision support applications. The second international ACM SIGSPATIAL HPDGIS workshop was hence designed to bring together researchers and practitioners to map out the fundamental research areas centered on advances in high performance computing, distributed systems, and their applications to Geographic Information Systems (GIS) and spatial analysis. The primary objective of the workshop series is to provide an overarching forum in which participants discuss the state-of- the-art, share research progress and activities, and establish future directions for research and education in HPDGIS.