Development Of Database Research Articles

Universal Glycosyltransferase Continuous Assay for Uniform Kinetics and Inhibition Database Development and Mechanistic Studies Illustrated on ST3GAL1, C1GALT1, and FUT1.

Chemical systems glycobiology requires experimental and computational tools to make possible big data analytics benefiting genomics and proteomics. The impediment to tool development is that the nature of glycan construction and mutation is not template driven but rests on cooperative glycosyltransferase (GT) catalytic synthesis. What is needed is the collation of kinetics and inhibition data in a standardized form to make possible analytics of glycan and glycoconjugate synthesis, mechanism extraction, and pattern recognition. Currently, kinetics assays in use for GTs are not universal in processing nucleoside phosphate UDP, GDP, and CMP donor-based glycosylation reactions due to limitations in accuracy and large substrate volume requirements. Here we present a universal glycosyltransferase continuous (UGC) assay able to measure the declining concentration of the NADH reporter molecule through fluorescence spectrophotometry and, therefore, determine reaction rate parameters. The development and parametrization of the assay is based on coupling the nucleotide released from GT reactions with pyruvate kinase, via nucleoside diphosphate kinase (NDK) in the case of NDP-based donor reactions. In the case of CMP-based reactions, the coupling is carried out via another kinase, cytidylate kinase in combination with NDK, which phosphorylates CMP to CDP, then CDP to CTP. Following this, we conduct kinetics and inhibition assay studies on the UDP, GDP, and CMP-based glycosylation reactions, specifically C1GAlT1, FUT1, and ST3GAL1, to represent each class of donor, respectively. The accuracy of calculating initial rates using the continuous assay compared to end point (noncontinuous) assays is demonstrated for the three classes of GTs. The previously identified natural product soyasaponin1 inhibitor was used as a model to demonstrate the application of the UGC assay as a standardized inhibition assay for GTs. We show that the dose response of ST3GAL1 to a serial dilution of Soyasaponin1 has time-dependent inhibition. This brings into question previous inhibition findings, arrived at using an end point assay, that have selected a seemingly random time point to measure inhibition. Consequently, using standardized Km values taken from the UGC assay study, ST3GAL1 was shown to be the most responsive enzyme to soyasaponin1 inhibition, followed by FUT1, then C1GALT1 with IC50 values of 37, 52, and 886 μM respectively.

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.

A smoking cessation smartphone app that delivers real-time 'context aware' behavioural support: the Quit Sense feasibility RCT.

During a quit attempt, cues from a smoker's environment are a major cause of brief smoking lapses, which increase the risk of relapse. Quit Sense is a theory-guided Just-In-Time Adaptive Intervention smartphone app, providing smokers with the means to learn about their environmental smoking cues and provides 'in the moment' support to help them manage these during a quit attempt. To undertake a feasibility randomised controlled trial to estimate key parameters to inform a definitive randomised controlled trial of Quit Sense. A parallel, two-arm randomised controlled trial with a qualitative process evaluation and a 'Study Within A Trial' evaluating incentives on attrition. The research team were blind to allocation except for the study statistician, database developers and lead researcher. Participants were not blind to allocation. Online with recruitment, enrolment, randomisation and data collection (excluding manual telephone follow-up) automated through the study website. Smokers (323 screened, 297 eligible, 209 enrolled) recruited via online adverts on Google search, Facebook and Instagram. Participants were allocated to 'usual care' arm (n = 105; text message referral to the National Health Service SmokeFree website) or 'usual care' plus Quit Sense (n = 104), via a text message invitation to install the Quit Sense app. Follow-up at 6 weeks and 6 months post enrolment was undertaken by automated text messages with an online questionnaire link and, for non-responders, by telephone. Definitive trial progression criteria were met if a priori thresholds were included in or lower than the 95% confidence interval of the estimate. Measures included health economic and outcome data completion rates (progression criterion #1 threshold: ≥ 70%), including biochemical validation rates (progression criterion #2 threshold: ≥ 70%), recruitment costs, app installation (progression criterion #3 threshold: ≥ 70%) and engagement rates (progression criterion #4 threshold: ≥ 60%), biochemically verified 6-month abstinence and hypothesised mechanisms of action and participant views of the app (qualitative). Self-reported smoking outcome completion rates were 77% (95% confidence interval 71% to 82%) and health economic data (resource use and quality of life) 70% (95% CI 64% to 77%) at 6 months. Return rate of viable saliva samples for abstinence verification was 39% (95% CI 24% to 54%). The per-participant recruitment cost was £19.20, which included advert (£5.82) and running costs (£13.38). In the Quit Sense arm, 75% (95% CI 67% to 83%; 78/104) installed the app and, of these, 100% set a quit date within the app and 51% engaged with it for more than 1 week. The rate of 6-month biochemically verified sustained abstinence, which we anticipated would be used as a primary outcome in a future study, was 11.5% (12/104) in the Quit Sense arm and 2.9% (3/105) in the usual care arm (estimated effect size: adjusted odds ratio = 4.57, 95% CIs 1.23 to 16.94). There was no evidence of between-arm differences in hypothesised mechanisms of action. Three out of four progression criteria were met. The Study Within A Trial analysis found a £20 versus £10 incentive did not significantly increase follow-up rates though reduced the need for manual follow-up and increased response speed. The process evaluation identified several potential pathways to abstinence for Quit Sense, factors which led to disengagement with the app, and app improvement suggestions. Biochemical validation rates were lower than anticipated and imbalanced between arms. COVID-19-related restrictions likely limited opportunities for Quit Sense to provide location tailored support. The trial design and procedures demonstrated feasibility and evidence was generated supporting the efficacy potential of Quit Sense. Progression to a definitive trial is warranted providing improved biochemical validation rates. This trial is registered as ISRCTN12326962. This award was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (NIHR award ref: 17/92/31) and is published in full in Public Health Research; Vol. 12, No. 4. See the NIHR Funding and Awards website for further award information.

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

Experimental Study on Heterogeneous data storage and exchange abstract storage middleware

Some Internet companies on the market do not have rich experience in database development, and there are certain problems in the face of sudden, urgent and high concurrent requirements like epidemic health code. Hope that through the interface provided by this middleware, the data can be automatically split according to certain rules. Using the advantages of different architecture databases, the data were stored in different architecture databases to achieve data heterogeneity. And it can extract, combine and return data from different databases, abstract the process of database access and access. The decoupling of database and business code is realized to solve the pain point of high-performance product development difficulty.

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.

Artificial Intelligence-Powered Mammography: Navigating the Landscape of Deep Learning for Breast Cancer Detection.

Worldwide, breast cancer (BC) is one of the most commonly diagnosed malignancies in women. Early detection is key to improving survival rates and health outcomes. This literature review focuses on how artificial intelligence (AI), especially deep learning (DL), can enhance the ability of mammography, a key tool in BC detection, to yield more accurate results. Artificial intelligence has shown promise in reducing diagnostic errors and increasing early cancer detection chances. Nevertheless, significant challenges exist, including the requirement for large amounts of high-quality data and concerns over data privacy. Despite these hurdles, AI and DL are advancing the field of radiology, offering better ways to diagnose, detect, and treat diseases. The U.S. Food and Drug Administration (FDA) has approved several AI diagnostic tools. Yet, the full potential of these technologies, especially for more advanced screening methods like digital breast tomosynthesis (DBT), depends on further clinical studies and the development of larger databases. In summary, this review highlights the exciting potential of AI in BC screening. It calls for more research and validation to fully employ the power of AI in clinical practice, ensuring that these technologies can help save lives by improving diagnosis accuracy and efficiency.

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 cases 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.

Differences Between Farmers and Crop Protection Service Providers in the Use of Pesticides

EU-wide statistics on the use of pesticides in agriculture are available on the quantities placed on the market, but data on actual use in practice are limited. In this study, comparative data on pesticide use and resistance management were collected for a region with mixed agriculture (arable and animal husbandry) in Austria. Based on individual pesticide records of 30 farmers and 10 service providers who perform plant protection tasks for 30 farms, the average application rates of pesticides, the change of the active substance group as well as the shortfalls and exceedances of the maximum permissible application rate were compared. Most farmers and service providers usually make a well-considered change in the group of active substances to avoid the development of resistance. Exceedances and underdosing of authorised application rates were found for both groups of people, with farmers tending to deviate more often. The applications amounts were largely in compliance with the law, however, in 3.69% of the 2387 evaluated uses of pesticides, the authorised application rate was exceeded. This excess was found somewhat more frequently among farmers than service providers. The application rate excesses can be attributed to technical overruns due to differences between actually farmed and formally declared area, confusion caused by complex application instructions but also by intention. To avoid inadequate information on authorised application rates and to simplify information gathering, the development of databases and apps that take into account not only the indisputably essential aspect of correct registration, but especially the user and user-friendliness, would be of great benefit and value. Packaging sizes adapted to different requirements and farm sizes would also be advantageous. High priority should be given to the technical training of users and sales personnel. This can contribute to higher professionalism in the use of pesticides in terms of compliance and resistance management, but also to the reduction of pesticides.