Development Of Database Research Articles

Develop a Program Design and Coding Standard

The use of programming language is increasing in the real world in all domains. So it’s important to follow and maintain coding guidelines & standard and follow the best practice. This article gives an overview of naming & coding standard, use of it, comparison of the main features of different programming languages(C++, Java, Python), data management approaches for local, mobile and cloud solutions for ERP and EHR implementations, the structure of each proposed algorithm and use, Few examples for in-house development, ERP solutions, packaged software implementations, and mobile applications, few proposals, and recommendations for further research. Also explain how to create the corporate standards a sample database and application design and development.

Environmental DNA: The next chapter.

Molecular tools are an indispensable part of ecology and biodiversity sciences and implemented across all biomes. About a decade ago, the use and implementation of environmental DNA (eDNA) to detect biodiversity signals extracted from environmental samples opened new avenues of research. Initial eDNA research focused on understanding population dynamics of target species. Its scope thereafter broadened, uncovering previously unrecorded biodiversity via metabarcoding in both well-studied and understudied ecosystems across all taxonomic groups. The application of eDNA rapidly became an established part of biodiversity research, and a research field by its own. Here, we revisit key expectations made in a land-mark special issue on eDNA in Molecular Ecology in 2012 to frame the development in six key areas: (1) sample collection, (2) primer development, (3) biomonitoring, (4) quantification, (5) behaviour of DNA in the environment and (6) reference database development. We pinpoint the success of eDNA, yet also discuss shortfalls and expectations not met, highlighting areas of research priority and identify the unexpected developments. In parallel, our retrospective couples a screening of the peer-reviewed literature with a survey of eDNA users including academics, end-users and commercial providers, in which we address the priority areas to focus research efforts to advance the field of eDNA. With the rapid and ever-increasing pace of new technical advances, the future of eDNA looks bright, yet successful applications and best practices must become more interdisciplinary to reach its full potential. Our retrospect gives the tools and expectations towards concretely moving the field forward.

Radiogenomics in breast cancer: a review

INTRODUCTION: Early detection of breast cancer is essential in the diagnosis and treatment of this disease. The results of core biopsy, immunohistochemical methods or removed surgical specimens contributed to the identification of molecular markers. Currently, the attention of researchers is attracted by the anatomical and functional features of tumor tissue obtained using imaging methods. The correlation of specific phenotypes based on images (radiomics) with large-scale genomic analysis (genomics) is a new field of research called “radiogenomics” or, more precisely, “image genomics”. This new field examines the relationship between diagnostic methods and gene data, as well as with other clinically relevant information.OBJECTIVE: To analyze current trends in the development of radiogenomics in the study of breast cancer based on the literature data.MATERIALS AND METHODS: The medical literature was searched using information and analytical databases Cochrane, Medline, and Elibrary using the text search queries “radiogenomics of breast cancer”, “mammography and radiogenomics”, “magnetic resonance imaging and radiogenomics”, “ultrasonic radiogenomics”.RESULTS: We found strong evidence that there is a moderate relationship between imaging characteristics and genomic characteristics of breast cancer. However, the results obtained have a number of limiting factors that distort the overall picture.CONCLUSION: Precision medicine can be optimized based on the genotypic and phenotypic characteristics of the tumor. However, the development of this direction requires new research and the development of databases using a multicenter approach.

Development of world famine database for 1840-2019 and risk assessment of its occurrence and severity

Abstract Famine still exists in this world, hindering the achievement of Sustainable Development Goal 2: zero hunger. The history and the mechanisms of famine have been broadly studied; however, few studies have focused on quantitative and long-term analyses of the general characteristics of famine. This study analyzes the factors influencing famine and estimates its risks. We developed a historical famine database and estimated the probability and intensity of famine through regression analysis. Herein, we identified that less food production and underdevelopment are related to famine occurrence, and famine tends to be more intense in less urbanized and dryer areas. By extrapolating the regression with future scenarios, we revealed that famine would be less likely to happen in the future mainly due to GDP growth, although conflict would be the key factor of future famine risk. This study is one of the first steps in the quantitative analysis of long-term famine risk.

Integrating perspectives in developing mycorrhizal trait databases: a call for inclusive and collaborative continental efforts.

Global assessments of mycorrhizal symbiosis present large sampling gaps in rich biodiversity regions. Filling these gaps is necessary to build large-scale, unbiased mycorrhizal databases to obtain reliable analyses and prevent misleading generalizations. Underrepresented regions in mycorrhizal research are mainly in Africa, Asia, and South America. Despite the high biodiversity and endemism in these regions, many groups of organisms remain understudied, especially mycorrhizal fungi. In this Viewpoint, we emphasize the importance of inclusive and collaborative continental efforts in integrating perspectives for comprehensive trait database development and propose a conceptual framework that can help build large mycorrhizal databases in underrepresented regions. Based on the four Vs of big data (volume, variety, veracity, and velocity), we identify the main challenges of constructing a large mycorrhizal dataset and propose solutions for each challenge. We share our collaborative methodology, which involves employing open calls and working groups to engage all mycorrhizal researchers in the region to build a South American Mycorrhizal Database. By fostering interdisciplinary collaborations and embracing a continental-scale approach, we can create robust mycorrhizal trait databases that provide valuable insights into the evolution, ecology, and functioning of mycorrhizal associations, reducing the geographical biases that are so common in large-scale ecological studies.

Matching the elemental fingerprints of fish otoliths with water masses of the St. Lawrence River and its tributaries, Canada

The physicochemical heterogeneity of large river ecosystems generates a mosaic of habitats that support diverse fish communities. Understanding the connectivity between habitats and exploited fish populations is necessary for the sustainable management of fisheries. This study evaluates the spatial concordance between elemental fingerprints of fish otoliths and surface waters in the St. Lawrence River (Canada) and its tributaries. We sampled 16 tributaries and various freshwater habitats of the St. Lawrence River and collected 136 water samples and 930 fish, representing 21 species. We observed a high spatial variability of trace element composition in the water and fish otolith samples. Reclassification using Sr and Ba concentrations of the collected water and otoliths was sufficiently accurate to correctly assign most fish to their capture region. We also observed significant variation in the elemental deposition relationships for Sr and Ba among fish families. The development of this elemental fingerprint reference database is fundamental for understanding the structure of exploited fish populations, validating the connectivity among habitats, and reconstructing habitat use by invasive fish species.

261 Providing Equity in Immunizations and Research

OBJECTIVES/GOALS: Disparities persist among minority and marginalized populations in immunizations and research. The Clinical Research Vehicle Team aimed to bring research to our community’s doorstep, building generalizability of research outcomes. Our mission adapted due to the pandemic to include increasing COVID-19 vaccinations and health education. METHODS/STUDY POPULATION: Community Engagement Events are planned when event organizers submit a request, then the proposal is discussed by the Clinical Research Vehicle (CRV) Team and UF HealthStreet. We go to underserved communities throughout Florida, food distributions, churches, community centers, rural areas, etc. At these events we offer services at no cost, including: COVID-19 and flu vaccines, as well as health screenings. We provide health education to individuals and refer them to UF HealthStreet for community resources, as appropriate. Research events are planned when researchers submit a request for the CRV, then the request is discussed by the CRV Team. The studies covered topics from COVID-19 research to the development of a diverse health database. Our fleet consists of two Community Health Vehicles and the CRV. RESULTS/ANTICIPATED RESULTS: As Community Engagement Events are ongoing, the presented numbers are reflective of the data available at the time of submission. Our impact includes administering 1,606+ COVID vaccines, conducting 1,081+ health screenings, collecting data from 944 screenings, providing 265 Flu vaccines, supporting 267+ community health events, and serving 25+ communities. The Clinical Research Vehicle facilitated 6 IRB-approved studies, administration of monoclonal antibody infusions, extended the capabilities of the Clinical Research Center, and enabled clinical trials aimed for novel treatments for COVID-19 positive participants. DISCUSSION/SIGNIFICANCE: Championing this initiative has shown an impact on vaccination rates and health education in communities. Experience and knowledge gained by our team throughout the pandemic while engaging with community members provided a platform from which to build generalizability of research outcomes and support translational science.

Modelling the Database Development for Waqf Land in District of Melaka Tengah Malaysia

The development of waqaf land database is an agenda that is increasingly given significant attention by the agency or institution around the world as that is the driving force to improving for management dan administation waqaf land. However, waqaf institutions have not yet been seen as institutions that can solve the management and administration problems of waqaf land. This is because there are problems in terms of registration and good procedures including in the State of Malacca. This study will comprehensively look at the waqaf system according to the latest technological developments such as the use of Geographic Information System (GIS) software. The method of interviewing those who are directly involved with waqf institutions, namely Majlis Agama Islam Melaka (MAIM) officials, land administrators at land officers and local authority officials has been conducted. In addition, site visits are also carried out on waqaf land or buildings. The results of the study shows that many data and documents related to waqaf are incomplete and not updated as they should be. MAIM is facing difficulties in developing its waqaf land due to the problem. Finally, this study presents some suggestions that can help strengthen the management and administration of MAIM such as prioritizing the waqaf land database system like the use of Mapinfo software for recording purposes. It can only be achieved if the lack of integration, lack of coordination and waqaf procedures can be overcome, then waqaf land registration can be collected and consolidated.

Development and Application of an Industrial Wastewater Profile Database

Development and Application of an Industrial Wastewater Profile Database

Ways of Import-Substituting Proteins of Collagen Group and Possibilities of Express-Methods of Research

The article provides new data concerning the structure and features of biopolymers that form the basis of by-product collagen-containing raw materials of meat processing industry and the sector of primary treatment of collagen-containing raw materials. In order to resolve the problem of import-substitution, analysis of the structure and features of complicated proteins the author analyzed briefly the economic situation, showed results of investigating certain properties of protein systems and put forward recommendations on the subject development. Theoretical findings can be used for the development of food additive technologies, lipid fraction substitutions, food toppings, food with non-conventional structure and properties, specialized nutrition, including additives of the sport group. According to the author, product groups in demand will include matters, ingredients, food with high biological activity, bio-compatible or modified gels, combined gels, cosmetic preparations, remedy for stimulating growth and development in plants, animals, nano- and bio-structures, polymers with shape memory meant for robotized constructions, etc. Unfortunately, there is no systemized principle for developing the necessary knowledge base for home or overseas products in this segment, however, the author can recommend theoretical and methodological approaches to protein system production, control over their quality, database and academic site development.

Development and Application of a Multidimensional Database for the Detection of Quaternary Ammonium Compounds and Their Phase I Hepatic Metabolites in Humans.

The COVID-19 pandemic has led to significantly increased human exposure to the widely used disinfectants quaternary ammonium compounds (QACs). Xenobiotic metabolism serves a critical role in the clearance of environmental molecules, yet limited data are available on the routes of QAC metabolism or metabolite levels in humans. To address this gap and to advance QAC biomonitoring capabilities, we analyzed 19 commonly used QACs and their phase I metabolites by liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS). In vitro generation of QAC metabolites by human liver microsomes produced a series of oxidized metabolites, with metabolism generally occurring on the alkyl chain group, as supported by MS/MS fragmentation. Discernible trends were observed in the gas-phase IM behavior of QAC metabolites, which, despite their increased mass, displayed smaller collision cross-section (CCS) values than those of their respective parent compounds. We then constructed a multidimensional reference SQLite database consisting of m/z, CCS, retention time (rt), and MS/MS spectra for 19 parent QACs and 81 QAC metabolites. Using this database, we confidently identified 13 parent QACs and 35 metabolites in de-identified human fecal samples. This is the first study to integrate in vitro metabolite biosynthesis with LC-IM-MS/MS for the simultaneous monitoring of parent QACs and their metabolites in humans.

Effect of antitumor therapy on microflora and oral mucosa in patients with malignant neoplasms

The present article investigates the relationship between the microbiota of the oral cavity and oral mucositis induced by systemic antitumor therapy in patients with malignant neoplasms. This article highlights modern ideas about the composition of the normal microbiota of the oral cavity and its changes during chemotherapeutic treatment.
 It has been shown that the normal oral microbiota includes representatives of such genera as Actinomyces, Corynebacterium, Fusobacterium, Leptotrichia, Neisseria, Prevotella, Streptococcus, and Veillonella. Moreover, it is emphasized that despite the existence of modern molecular genetic techniques and development of an oral microbiota database, determining the role of individual taxonomic units in oral homeostasis remains challenging.
 Existing studies of changes in the qualitative and quantitative composition of oral microflora against the background of drug antitumor therapy have demonstrated that treatment is associated with significant changes in the microbiological landscape of the oral cavity. An increase was noted in the number of Gram-negative anaerobic opportunistic flora and a decrease in the representation of protective commensal flora. It has been demonstrated that the components of a bacterial cell can modulate the local reactions of a macroorganism through a system of Toll-like receptors while acting in different directions.
 Several unresolved fundamental issues related to the role of oral microbiota in the pathogenesis of oral mucositis are highlighted.

The national distribution of lymphatic filariasis cases in Malawi using patient mapping and geostatistical modelling.

In 2020 the World Health Organization (WHO) declared that Malawi had successfully eliminated lymphatic filariasis (LF) as a public health problem. Understanding clinical case distributions at a national and sub-national level is important, so essential care packages can be provided to individuals living with LF symptoms. This study aimed to develop a national database and map of LF clinical case numbers across Malawi using geostatistical modelling approaches, programme-identified clinical cases, antigenaemia prevalence and climate information. LF clinical cases identified through programme house-to-house surveys across 90 sub-district administrative boundaries (Traditional Authority (TA)) and antigenaemia prevalence from 57 sampled villages in Malawi were used in a two-step geostatistical modelling process to predict LF clinical cases across all TAs of the country. First, we modelled antigenaemia prevalence in relation to climate covariates to predict nationwide antigenaemia prevalence. Second, we modelled clinical cases for unmapped TAs based on our antigenaemia prevalence spatial estimates. The models estimated 20,938 (95% CrI 18,091 to 24,071) clinical cases in unmapped TAs (70.3%) in addition to the 8,856 (29.7%), programme-identified cases in mapped TAs. In total, the overall national number of LF clinical cases was estimated to be 29,794 (95% CrI 26,957 to 32,927). The antigenaemia prevalence and clinical case mapping and modelling found the highest burden of disease in Chikwawa and Nsanje districts in the Southern Region and Karonga district in the Northern Region of the country. The models presented in this study have facilitated the development of the first national LF clinical case database and map in Malawi, the first endemic country in sub-Saharan Africa. It highlights the value of using existing LF antigenaemia prevalence and clinical case data together with modelling approaches to produce estimates that may be used for the WHO dossier requirements, to help target limited resources and implement long-term health strategies.

Development of a Reverse-Yield Factor Database Disaggregating Japanese Composite Foods into Raw Primary Commodity Ingredients Based on the Standard Tables of Food Composition in Japan.

The reverse-yield factor (RF) database was developed for qualitatively and quantitatively disaggregating Japanese composite foods into raw primary commodity (RPC) ingredients. Representative equations for four types (dried, salted, fermented and mixed foods) were developed to calculate RFs using the food content and composition data for composite foods listed in the Standard Tables of Food Composition in Japan-2020-(STFCJ), published by the Ministry of Education, Culture, Sports, Science and Technology of Japan. Out of 1150 composite foods identified in the STFCJ, RFs for 54 dried, 41 salted, 40 fermented and 818 mixed foods were obtained. RFs for 197 mixed foods could not be calculated because these foods were produced from ingredients with no specified information and/or through complex processing. The content and composition of Japanese composite foods would be interpreted representatively by RFs in the developed database.

Automated license plate authentication framework using multi-view vehicle images

The current framework for detecting Fake License Plates (FLP) in real-time is not robust enough for patrol teams. The objective of this paper is to develop a robust license plate authentication framework, based on the Vehicle Make and Model Recognition (VMMR) and the License Plate Recognition (LPR) algorithms that is implementable at the edge devices. The contributions of this paper are (i) Development of license plate database for 547 Indian cars, (ii) Development of an image dataset with 3173 images of 547 Indian cars in 8 classes, (iii) Development of an ensemble model to recognize vehicle make and model from frontal, rear, and side images, and (iv) Development of a framework to authenticate the license plates with frontal, rear, and side images. The proposed ensemble model is compared with the state-of-the-art networks from the literature. Among the implemented networks for VMMR, the Ensembling model with a size of 303.2 MB achieves the best accuracy of 89% . Due to the limited memory size, Easy OCR is chosen to recognize license plate. The total size of the authentication framework is 308 MB. The performance of the proposed framework is compared with the literature. According to the results, the proposed framework enhances FLP recognition due to the recognition of vehicles from side images. The dataset is made public at https://www.kaggle.com/ganeshmailecture/datasets.

Design and Selection of High Entropy Alloys for Hardmetal Matrix Applications Using a Coupled Machine Learning and Calculation of Phase Diagrams Methodology

This study aims to utilize a combined machine learning (ML) and CALculation of PHAse Diagrams (CALPHAD) methodology to design hardmetal matrix phases for metal‐forming applications that can serve as the basis for carbide reinforcement. The vast compositional space that high entropy alloys (HEAs) occupy offers a promising avenue to satisfy the application design criteria of wear resistance and ductility. To efficiently explore this space, random forest ML models are constructed and trained from publicly available experimental HEA databases to make phase constitution and hardness predictions. Interrogation of the ML models constructed reveals accuracies >78.7% and a mean absolute error of 66.1 HV for phase and hardness predictions respectively. Six promising alloy compositions, extracted from the ML predictions and CALPHAD calculations, are experimentally fabricated and tested. The hardness predictions are found to be systematically under‐ and overpredicted depending on the alloy microstructure. In parallel, the phase classification models are found to lack sensitivity toward additional intermetallic phase formation. Despite the discrepancies identified between ML and experimental results, the fabricated compositions show promise for further experimental evaluation. These discrepancies are believed to be directly associated with the available databases but, importantly, have highlighted several avenues for both ML and database development.

Festivals and Youth: An Educational Cultural Route to Festivals

Festivals have emerged as a compelling subject of study within the realm of the social sciences, with disciplines like popular musicology showcasing festivals as a prominent area of investigation. These cultural events have demonstrated a profound and enduring impact on various generations, solidifying their significance in contemporary history. In the ever-evolving landscape of rapid social change, festivals have evolved into both traditions and heritage. Our research centered on the musical landscape in the city of Cáceres has culminated in the creation of a Festival Route rooted in the pertinent facets of its history and unique sociocultural context. The methodology employed in this study revolves around the development of comprehensive databases that encompass various parameters, principles, and elements. These databases are instrumental in crafting adaptable musical itineraries tailored to the individual participant’s characteristics, including age, educational background, place of origin, and explicit or intrinsic interests. Moreover, they factor into the duration of the route. These databases draw upon the findings of historiographic and ethnographic research, shedding light on the music performed and cherished within the city of Cáceres throughout its rich history. This paper primarily delves into the educational aspects derived from the socio-cultural phenomenon of festivals, specifically in the realms of music pedagogy and value-based education. Our Festival Route is designed with a particular focus on youth engagement, weaving together key locales within the city to create an enriching and educational experience.

Viscosities and Densities of Binary and Ternary Mixtures of Aliphatic and Polyaromatic Hydrocarbons: Pyrene +1-Methylnaphthalene + Dodecane at T = (293.15 to 343.15) K. Experiment and Modeling.

This work presents new experimental viscosity and density data for aromatic and polyaromatic compounds in binary and ternary pyrene, 1-methylnaphthalene, and dodecane mixtures. The lack of experimental viscosity data for these mixtures requires the development of a new database, which is vital for understanding the behavior of mixtures in more complex systems, such as asphaltenes and fuels. The mixtures proposed in this work have been measured over a temperature range of (293.15 to 343.15) K at atmospheric pressure. Several mixture compositions have been studied at these conditions: 1.0, 2.5, 5.0, 7.5, 10.0, 12.5, and 15.0% pyrene mass fraction. The concentration of pyrene correlates with an increase in the viscosity and density values. At the lowest temperature in binary mixtures, the corresponding values reach 4.4217 mPa·s for viscosity and 1.0447 × 103 kg·m-3 for density, respectively. In ternary mixtures, the introduction of dodecane leads to the lowest maximum values of 3.5555 mPa·s for viscosity and 1.0112 × 103 kg·m-3 for density at the same temperature. The experimental data have been employed for the specific modification of viscosity models. These modifications could facilitate the prediction of the viscosity of mixtures that are more complex than those presented in this work. Various viscosity models have been employed, such as Linear, Ratcliff and Khan, modified UNIFAC-Visco, and Krieger-Dougherty. The settings in the models used reliably reproduce the experiment reliably. However, the Ratcliff model agrees excellently with the experiment, having a low standard deviation (2.0%) compared to other models. Furthermore, a model based on the equation of state of Guo is proposed to predict the viscosity values by modifying the specific parameters and adjusting them to the mixtures proposed in this work. The results from this study are compared to previous work, where pyrene, toluene, and heptane mixtures were analyzed. In this case, we find that the decrease of aggregation grade in the present systems is predicted by the model fixed in this work.

AI-Assisted Enhanced Composite Metric-Based Intrusion Detection System for Secured Cyber Internet Security for Next-Generation Wireless Networks

The prevalence of distributed denial of service (DDoS) flooding assaults is one of the most serious risks to cloud computing security. These types of assaults have as their primary objective the exhaustion of the system’s available resources, that is, the target of the attack, in order to make the system in question unavailable to authorized users. Internet thieves often conduct flooding assaults of the kind known as DDoS, focusing primarily on the application and network levels. When the computer infrastructure is multi-mesh-geo distributed, includes multi-parallel services, and a high number of domains, it may be difficult to detect assaults. This is particularly true when a substantial number of domains are present. When there are a big number of independent administrative users using the services, the situation gets more complicated. The purpose of this body of research is to identify signs that may be utilized to detect DDoS flooding assaults; this is its main objective. As a result, throughout the course of our study, we established a composite metric that considers application, system, network, and infrastructure elements as possible indicators of the incidence of DDoS assaults. According to our research, DDoS assaults may be triggered by a combination of variables. Investigations of simulated traffic are being conducted in the cloud. High traffic may be the result of flooding assaults. The composite metric-based intrusion detection system will be the name of a one-of-a-kind intrusion detection system (IDS) that has been agreed upon ICMIDS. This system will use [Formula: see text]-Means clustering and the Genetic Algorithm (GA) to detect whether an effort has been made to flood the cloud environment. CMIDS employs a multi-threshold algorithmic strategy in order to identify malicious traffic occurring on a cloud-based network. Cisco has created this technology. This strategy necessitates a comprehensive investigation of all factors, which is crucial for assuring the continuation of cloud-based computing-based activities. This monitoring system involves the development, administration, and storage of a profile database, denoted as Profile DB. This database is used for recording and using the composite metric for each virtual machine. The results of a series of tests are compared to the ISCX benchmark dataset and statistical settings. The results indicate that ICMIDS has a reasonably high detection rate and the lowest false alarm rate in the majority of situations examined during the series of tests done to validate and verify its efficacy. This was shown by the fact that ICMIDS had the lowest false alarm rate among all examined conditions.

Chemical analysis of polymers used for 3D printing of firearms

3D-printed firearms cause challenges in criminal investigations and forensic analysis because they are difficult to trace. Indeed, in addition to being "ghost guns", they may not produce all the conventional ballistic traces normally used for firearm identification. However, 3D-printed firearms produce other very specific traces, such as polymer traces which come from the polymers used to print the firearm. To date, only a few studies have focused on the analysis of polymer traces. This study therefore aims to characterize polymer traces from 3D-printed firearms, using non-destructive spectroscopic techniques readily available in most forensic laboratories (i.e., FTIR and Raman) and evaluate the potential for association of polymer specimens or traces with their source. To do so, the study was divided into four parts. First, the population study conducted among 3D printing companies and individuals practicing 3D printing has revealed that PLA and PLA+ are the most widely used polymer types in Quebec, Canada. Second, FTIR and Raman spectroscopic analysis of polymer samples collected during the population study has allowed the development of a reference polymer spectral database. The analysis and interpretation of these spectra revealed that polymer filaments present very low intravariability, but very high intervariability, due in part to the different polymer types and the pigments used to color them. The use of chemometric tools with the spectra showed that these two spectroscopic methods were highly discriminating. Third, test firing of 3D-printed firearms has allowed for the simulation of a scene involving this type of firearm and the collection of polymer traces generated. Fourth, the comparison of chemical signatures between polymer filaments and polymer traces has allowed for the evaluation of the potential for chemical association. This study highlights the added value of chemical analysis of 3D-printed firearms polymer traces in a criminal investigation by demonstrating that polymer filaments, the polymer from which a 3D-printed firearm is made, as well as polymer traces generated during firing, can be linked chemically and provide relevant information.