Quality Model Research Articles

The value of instream stable water isotope and nitrate concentration data for calibrating a travel time‐based water quality model

AbstractTransit time‐based water quality models using StorAge Selection (SAS) functions are crucial for nitrate (NO3−) management. However, relying solely on instream NO3− concentration for model calibration can result in poor parameter identifiability. This is due to the interaction, or correlation, between transport parameters, such as SAS function parameters, and denitrification rate, which challenges accurate parameters identification and description of catchment‐scale hydrological processes. To tackle this issue, we conducted three Monte‐Carlo experiments for a German mesoscale catchment by calibrating a SAS‐based model with daily instream NO3− concentrations (Experiment 1), monthly instream stable water isotopes (e.g. δ18O) (Experiment 2) and both datasets (Experiment 3). Our findings revealed comparable ranges of SAS transport parameters and median water transit times (TT50) across the experiments. This suggests that, despite their distinct reactive or conservative nature, and sampling strategies, the NO3− and δ18O time series offer similar information for calibration. However, the absolute values of transport parameters and TT50 time series, as well as the degree of parameter interaction differed. Experiment 1 showed greater interaction between certain transport parameters and denitrification rate, leading to greater equifinality. Conversely, Experiment 3 yielded reduced parameters interaction, which enhanced transport parameters identifiability and decreased uncertainty in TT50 time series. Hence, even a modest effort to incorporate only monthly δ18O values in model calibration for highly frequent NO3−, improved the description of hydrological transport. This study showcased the value of combining NO3− and δ18O model results to improve transport parameter identifiability and model robustness, which ultimately enhances NO3− management strategies.

Adaptation of Wind Drag Coefficient Parameterization: Improvement of Hydrodynamic Modeling by a Wave‐Dependent Cd in Large Shallow Lakes

AbstractWind is a critical driving force in hydrodynamic and water quality modeling of large shallow lakes, and is characterized by the wind drag coefficient Cd, representing the momentum transfer at the air‐water interface. Contemporary empirical formulae for Cd estimation were derived over oceans and some of which are solely wind velocity U10 dependent. These formulae were previously found to be inadequate in inland lake models often resulting in the water velocity underestimation. To address this problem, a physical scale experiment was designed, in which Cd was measured using a wind profile and eddy covariance methodology. A new wind‐induced wave‐dependent Cd parameterization was also established and validated in two lake studies. The driving force was modified by the wave‐dependent Cd formula in a hydrodynamic model of the shallow Upper Klamath Lake (UKL), OR, USA. The experimental Cd was negatively correlated to the wind velocity up until the critical U10 = 1.6 m s−1 which was 1.0~3.1 times previous empirical extrapolations at light winds. The variation partitioning results showed that wave parameters contributed to more than 30% of Cd variation combined with wind parameters. The modified wind stress field was spatially heterogeneous and the modeled water velocity was closer to the observations at two sites. Significant main circulation and outer bank circulation were modeled accompanied by higher surface vorticity, compared to the original UKL model. Overall, the wave‐dependent Cd formula provided an improvement of the surface flow field in the UKL model and will improve the management of the lake ecosystems.

Spatiotemporal Interpolation of Water Quality Index and Nitrates using ArcGIS Pro for Surface Water Quality Modeling in the Oum Er-Rabia Watershed

Spatiotemporal Interpolation of Water Quality Index and Nitrates using ArcGIS Pro for Surface Water Quality Modeling in the Oum Er-Rabia Watershed

Development of a UAV-borne sorbent tube sampler and its application on the vertical profile measurement of volatile organic compounds

Vertical detection of volatile organic compounds (VOCs) is essential to expend our understanding of the distribution characteristics of VOCs and improve the predictive ability of existing air quality models. In this work, we report the development of a sorbent tube sampler based on an unmanned aerial vehicle (UAV) platform. Vertical profile measurement of VOCs with a vertical resolution of 25 m was achieved. The sampler consists of five lightweight VOC sorbent tubes and a 5-way solenoid valve, making it available for collecting five atmospheric VOC samples in a single flight with a time response of less than 30 min. The sampler weighed ∼ 1.45 kg and had dimensions of 240 mm × 220 mm × 100 mm with small penetration loss (< 10%) under 4-liter sampling conditions (flow rate of 200 mL/min). Commercialized SUMMA canisters were used as experimental controls to investigate the possible loss of self-made sampler for target compounds in the same sampling process. Comparison experiment on the ground showed that the concentration differences for all VOC species were lower than 0.14 μg/m3, proving the good reliability for VOCs measurements using sorbent tube sampler. The UAV platform also incorporated online instruments for meteorological parameters and O3 measurement. The sampler was successfully applied to characterize the vertical profiles of VOCs up to 100 m in October 2023 in the Huaihe River Basin of China. The UAV platform and the sorbent tube sampler demonstrate good performance and will be a valuable and reliable tool for vertical VOCs measurement.

Street-scale air quality modelling over the West Midlands, United Kingdom: Effect of idealised traffic reduction scenarios

Air pollution is the major environmental risk to human health. Road transport is one of the major sources for air pollution, particularly nitrogen dioxide, in urban areas, and hence traffic control is an important measure in air quality management. A street-scale air quality model, ADMS-Urban, was configured for a case study of the West Midlands, UK to represent a baseline year (2019). Model outputs were evaluated using hourly air pollutant measurement data, and the model demonstrates good performance overall. This modelling tool was then used to explore the effect of five hypothetical traffic reduction scenarios, ranging from 10% to 90% reduction in traffic activity; scenario impacts were analysed over a range of spatial resolutions. The impacts of traffic reduction are highly dependent on spatial resolution (i.e. street scale, electoral ward level and local authority level), which has to be taken into account when formulating policies for managing air quality on local and city-wide scales. There was an almost linear relationship between the predicted annual concentration and traffic reduction for both NO2 and PM2.5. Traffic reduction would principally reduce NO2 concentrations, with even very substantial changes in traffic having more limited effects on reducing PM2.5 concentrations reflecting the importance of regional and non-traffic PM2.5 sources.

MEIAT-CMAQ: A modular emission inventory allocation tool for Community Multiscale Air Quality Model

The Modular Emission Inventory Allocation Tool for Community Multiscale Air Quality Model (MEIAT-CMAQ) refines emission inventories by providing detailed spatial (horizontal and vertical), temporal, and species allocations, enhancing the accuracy of CMAQ performance. Its efficient algorithm and modular design offer flexibility for managing both gridded and tabulated inventories, widely used in various sectors. In addition, the shapefiles with specific shapes supported by MEIAT-CMAQ can address the allocation challenges in transportation emissions. The evaluation of MEIAT-CMAQ, using model-ready inventories before (BASE scenario), and after allocation without (EXPR scenario) or with (EXPR-V scenario) vertical allocation, demonstrates significant improvements in the mean bias (MB) of gaseous pollutants (O₃, NO₂, CO). In both the EXPR and EXPR-V scenarios, the MB for O3 exhibits notable enhancements, with respective improvements of 5.7% and 26.9%. For NO₂, corresponding MB improvements are even more pronounced, reaching 27.6% and 61.7% in the EXPR and EXPR-V scenarios, respectively. Likewise, enhancements are observed in the MBs of CO, demonstrating increases of 8.4% and 45.2% in the EXPR and EXPR-V scenarios, respectively. Moreover, with regard to spatial accuracy, the incorporation of the MEIAT-CMAQ model yields significant improvements. Specifically, in the EXPR scenarios, spatial accuracy for O3 and NO2 demonstrates respective enhancements of 13.5% and 9.5%. Furthermore, the inclusion of vertical allocation leads to additional enhancements in CO, NO₂, and PM2.5, resulting in improvements of 17.6%, 16.6%, and 23.2%, respectively. MEIAT-CMAQ provides an efficient method for transforming coarse-resolution emission inventories into high-resolution files directly useable in the model, offering enhanced flexibility for users to select any period for generating model-ready emission files. This capability provides substantial technical support for automating processes within business departments and significantly improves the performance of high-resolution modeling and forecasting.

Structural equation models of health behaviour, psychological well-being, symptom severity and quality of life in abdominal bloating.

The objective of this study was to investigate the inter-relationship between psychosocial variables and their impact on symptom severity and quality of life (QoL) concerning abdominal bloating. The study adopted a cross-sectional design with purposive sampling. Participants who consented and met the criteria for bloating based on the Rome IV classification completed designated questionnaires. Independent variables comprised health beliefs, intentions, health-promoting behaviors, social support, depression, and anxiety, while dependent variables included bloating severity (general and within 24 h) and QoL. Structural Equation Modeling (SEM) was conducted utilizing Mplus 8.0 to analyze the relationships between these factors. A total of 323 participants, with a mean age of 27.69 years (SD = 11.50), predominantly females (64.7%), volunteered to participate in the study. The final SEM model exhibited good fit based on various indices (CFI = 0.922, SRMR = 0.064, RMSEA (95% CI) = 0.048 (0.041-0.054), p-value = 0.714), with 15 significant path relationships identified. The model explained 12.0% of the variance in severity within 24 h, 6% in general severity, and 53.8% in QoL. The findings underscore the significant influence of health beliefs, intentions, behaviors, social support, depression, and anxiety on symptom severity and QoL in individuals experiencing abdominal bloating.

Factors affecting pedestrian-level ship pollution in port areas: CFD in the service of policy-making

Examining the impact of various factors associated with plume dispersion is essential for quantifying ship pollution in port areas, particularly adjacent to urban environments. This paper concerns the application of Computational Fluid Dynamics (CFD) modelling to study the effect of such factors on air pollution at the passenger port of Marseille, France. Pedestrian-level pollution maps are created for several emission scenarios involving variables such as wind conditions, docking location, emission reduction technology, different emission sources, as well as parameters related to the vessel. Depending on wind direction, docking location and vessel's engine power, different parts of the studied area are exposed to ship pollution. Within a typical examined range, funnel exhaust temperature, funnel height and wind speed deviate up to 78 %, 45 % and 59 % from the baseline scenario, respectively, indicating significant sensitivity of ship pollution to changes in these factors. The lower buoyancy effects of using a scrubber lead to a 17 % larger area exposed to increased ship pollution instead of using Ultra Low Sulphur Fuel Oil (ULSFO). Additionally, it is estimated that an individual vessel may contribute up to six times more to the NOx pollution of a roundabout located 800 m downwind, compared to the local road traffic. The current work can provide valuable information to various stakeholders such as port authorities, decision-makers, or even air quality modelers regarding the impact of shipping emissions on air pollution in port areas, constituting a useful assessment and advisory tool.

Comparative analysis of hydrodynamic flowrate sources as drivers of water quality models for nitrogenous compounds in complex ungauged South African rivers

ABSTRACT Water quality modelling is a critical tool for managing the health of river ecosystems, particularly in regions impacted by point source pollution activities. This study investigates the influence of different hydrodynamic data sources on the performance of two river water quality models, the Basic Model (BM) and the Water Quality Analysis Simulation Programme (WASP) for modelling nitrogenous compounds in a complex river system including wastewater treatment plant effluent discharges. Four diverse hydrodynamic data input types were considered. These included measured station data, altered station data, rainfall-generated flow, and the WRSM/Pitman model estimate. Findings revealed trends, analysis of variance (ANOVA), and t-test analyses consistently demonstrated significant disparities between model predictions and measured data in specific river segments, indicating a need for segment-specific modelling approaches. An increase in Root Mean Square Error (RMSE) and Mean Square Error (MSE) values in certain segments pointed to a decline in model accuracy when confronted with distinct hydrodynamic conditions. Additionally, application of four diverse hydrodynamic data input sources yielded similar performance for BM and WASP against measured data. The research findings indicated a complex interplay between river hydrodynamics and water quality modelling, resulting in a recommendation for tailored modelling strategies that account for unique characteristics of river segments.

Spatiotemporal Coupling between Urbanization and Eco-environment based on Google Earth Engine and Nighttime Light Data: A Case Study of the Yellow River Delta

Based on the cloud platform of Google Earth Engine and ArcGIS, this paper uses daytime optical remote sensing and nighttime light (NTL) remote sensing data to extract the comprehensive nighttime light index (CNLI) and remote sensing ecological index (RSEI) respectively, then constructed and quantitatively analyzed the coupling coordination distance model of urbanization and eco-environmental quality in the study area from 2001 to 2020. The results show that the average value of RSEI fluctuated from 0.446 in 2001 to 0.518 in 2020 from 2001 to 2020. The average value of CNLI from 2001 to 2020 continued to rise from 0.101 in 2001 to 0.238 in 2020. From 2001 to 2020, the average value of the coupling coordination degree showed a downward trend, from 1.356 in 2001 to 1.256 in 2020, and the coupling coordination degree gradually developed in a good direction. The research results can provide data support and theoretical suggestions for promoting the sustainable development of the Yellow River Delta.

Summertime tropospheric ozone source apportionment study in the Madrid region (Spain)

Abstract. The design of emission abatement measures to effectively reduce high ground-level ozone (O3) concentrations in urban areas is very complex. In addition to the strongly non-linear chemistry of this secondary pollutant, precursors can be released by a variety of sources in different regions, and locally produced O3 is mixed with that transported from the regional or continental scales. All of these processes depend also on the specific meteorological conditions and topography of the study area. Consequently, high-resolution comprehensive modeling tools are needed to understand the drivers of photochemical pollution and to assess the potential of local strategies to reduce adverse impacts from high tropospheric O3 levels. In this study, we apply the Integrated Source Apportionment Method (ISAM) implemented in the Community Multiscale Air Quality (CMAQ v5.3.2) model to investigate the origin of summertime O3 in the Madrid region (Spain). Consistent with previous studies, our results confirm that O3 levels are dominated by non-local contributions, representing around 70 % of mean values across the region. Nonetheless, precursors emitted by local sources, mainly road traffic, play a more important role during O3 peaks, with contributions as high as 25 ppb. The potential impact of local measures is higher under unfavorable meteorological conditions associated with regional accumulation patterns. These findings suggest that this modeling system may be used in the future to simulate the potential outcomes of specific emission abatement measures to prevent high-O3 episodes in the Madrid metropolitan area.

Navigating challenges and workarounds: A qualitative study of healthcare and support workers' perceptions on providing care to people seeking sanctuary.

Healthcare and support workers play a pivotal role in delivering quality services and support to people seeking sanctuary who have experienced poor physical and mental health linked to previous trauma, relocation and loss of freedoms. However, they often encounter various challenges in their daily work, ranging from communication barriers to resource constraints. This qualitative study seeks to delve into the perspectives of healthcare and support workers' experience of workarounds, employed to overcome barriers to providing care. This study aims to describe healthcare providers', practitioners' and health and third sector support workers' views on barriers and workarounds to providing care for people seeking sanctuary, to inform policy and practice. A qualitative study was carried out using semi-structured telephone interviews. This study focused on primary, secondary, community and specialist National Health Service (NHS) support services for people seeking sanctuary in Wales, United Kingdom (2018). We interviewed 32 healthcare providers, practitioners and support workers employed by primary care and third sector organisations. Our approach involved obtaining verbal informed consent before digitally recording and transcribing all interviews. To analyse the data, we used the Four Levels of Change for Improving Quality model as a guiding framework for interpretation. Our study findings reveal that certain respondents expressed challenges in meeting the needs of people seeking sanctuary; notably, their experience of delivering care differed by care settings. Specifically, those involved in providing specialist NHS care believed that there was room for improvement. Mainstream primary, secondary and community health practitioners faced limitations due to resource constraints and lacked tailored information to address the unique circumstances and needs of sanctuary seekers. To address these gaps, workarounds emerged at both individual and local levels (team/departmental and organisational level). These included establishing informal communication channels between providers, fostering cross service collaboration to fill gaps and adapting existing services to enhance accessibility. Understanding healthcare providers', practitioners' and support workers' perspectives offers invaluable insights into ways to enhance healthcare delivery to sanctuary seekers. Acknowledging challenges and harnessing innovative workarounds can foster a more effective and compassionate service for this vulnerable population. The HEAR study actively involved public contributors in the design, delivery and dissemination of the research. Two public contributors (S. M. and G. R.) who had personal experience of seeking asylum served as study co-applicants. They played pivotal roles in shaping the research by participating in its development and securing funding. Alongside other co-applicants, S. M. and G. R. formed the Research Management Group, overseeing study delivery. Their contributions extended to strategic decision-making and specific feedback at critical junctures, including participant recruitment, data collection, analysis and reporting. Additionally, S. M. and G. R. were instrumental in recruiting and supporting a team of peer researchers, enhancing respondent participation among people seeking sanctuary. To facilitate effective public involvement, we provided named contacts for support (A. K. and R. F.), research training, honoraria, reimbursement of expenses and accessible information in line with best practice.

Associations between short-term exposure to air pollution and thyroid function in a representative sample of the Korean population

BackgroundDisruption of thyroid function can profoundly affect various organ systems. However, studies on the association between air pollution and thyroid function are relatively scarce and most studies have focused on the long-term effects of air pollution among pregnant women. ObjectivesThis study aimed to explore the associations between short-term exposure to air pollution and thyroid function in the general population. MethodsData from the Korea National Health and Nutrition Examination Survey (2013–2015) were analyzed (n = 5,626). Air pollution concentrations in residential addresses were estimated using Community Multiscale Air Quality models. The moving averages of air pollution over 7 days were set as exposure variables through exploratory analyses. Linear regression and quantile g-computation models were constructed to assess the effects of individual air pollutants and air pollution mixture, respectively. ResultsA 10-ppb increase in NO2 (18.8-μg/m3 increase) and CO (11.5-μg/m3 increase) was associated with 2.43% [95% confidence interval (CI): 0.42, 4.48] and 0.19% (95% CI: 0.01, 0.36) higher thyroid-stimulating hormone (TSH) levels, respectively. A 10-μg/m3 increase in PM2.5 and a 10-ppb increase in O3 (19.6-μg/m3 increment) were associated with 0.87% (95% CI: 1.47, −0.27) and 0.59% (95% CI: 1.18, −0.001) lower free thyroxine (fT4) levels, respectively. A simultaneous quartile increase in PM2.5, NO2, O3, and CO levels was associated with lower fT4 but not TSH levels. ConclusionsAs the subtle changes in thyroid function can affect various organ systems, the present results may have substantial public health implications despite the relatively modest effect sizes. Because this was a cross-sectional study, it is necessary to conduct further experimental or repeated-measures studies to consolidate the current results.

An Explanatory Model of Vascular Access Care Quality: Results of a Cross-Sectional Observational Study.

Using the STROBE statement, an observational, cross-sectional study was conducted on 1576 VA devices. Data were collected using the INCATIV Questionnaire. We performed a multivariate analysis of the questionnaire variables. In total, 50% had a good or very good assessment of the VA condition. This was positively correlated with anatomical location, dressing type, dressing date record, use of needle-free connectors (NFCs), date of last dressing change, presence of phlebitis, visibility of the insertion site and characteristics of the dressing's condition (p < 0.001). The model indicated that the presence of phlebitis was the clearest predictor of a poor VA care assessment (OR = 20.579), followed by no visibility of the insertion site (OR = 14.209). Results also indicated that uncovered VA lumens or no NFCs used were related to a negative quality assessment. By managing and controlling these variables, the likelihood of providing optimal care is ensured. This enables the establishment of a standardised care approach for all nursing professionals and the building of a new quality indicator.

Intelligent quality control of traditional chinese medical tongue diagnosis images based on deep learning.

Computer-aided tongue and face diagnosis technology can make Traditional Chinese Medicine (TCM) more standardized, objective and quantified. However, many tongue images collected by the instrument may not meet the standard in clinical applications, which affects the subsequent quantitative analysis. The common tongue diagnosis instrument cannot determine whether the patient has fully extended the tongue or collected the face. This paper proposes an image quality control algorithm based on deep learning to verify the eligibility of TCM tongue diagnosis images. We firstly gathered enough images and categorized them into five states. Secondly, we preprocessed the training images. Thirdly, we built a ResNet34 model and trained it by the transfer learning method. Finally, we input the test images into the trained model and automatically filter out unqualified images and point out the reasons. Experimental results show that the model's quality control accuracy rate of the test dataset is as high as 97.06%. Our methods have the strong discriminative power of the learned representation. Compared with previous studies, it can guarantee subsequent tongue image processing. Our methods can guarantee the subsequent quantitative analysis of tongue shape, tongue state, tongue spirit, and facial complexion.

Revenue Models of Academic Enterprises – An Empirical Study in Vietnam

Academic publishing is the publishing field associated with scientific works, scientific articles, or scientific documents. Under the impact of the Internet, digital transformation, and open access, academic publishing is also affected. Academic publishers around the world have changed their revenue models to grow but this creates controversial views. In Vietnam, academic publishing is developing in response to internal needs and international integration. Academic publishers and scientific journals in Vietnam are also influenced by digital transformation and open access. Besides the opportunities, the challenges facing the academic publishing sector in Vietnam are research quality and revenue model. Based on the content research method, this writing presented some basic theories about academic publishing, academic publishing revenue models, and some discussions about academic publishing in Vietnam.

Prenatal exposure to air pollutant mixtures and birthweight in the upstate KIDS cohort

BackgroundSingle-pollutant models have linked prenatal PM2.5 exposure to lower birthweight. However, analyzing air pollutant mixtures better captures pollutant interactions and total effects. Unfortunately, strong correlations between pollutants restrict traditional methods. ObjectivesWe explored the association between exposure to a mixture of air pollutants during different gestational age windows of pregnancy and birthweight. MethodsWe included 4,635 mother-infant dyads from a New York State birth cohort born 2008–2010. Air pollution data were sourced from the EPA’s Community Multiscale Air Quality model and matched to the census tract centroid of each maternal home address. Birthweight and gestational age were extracted from vital records. We applied linear regression to study the association between prenatal exposure to PM2.5, PM10, NOX, SO2, and CO and birthweight during six sensitive windows. We then utilized Bayesian kernel machine regression to examine the non-linear effects and interactions within this five-pollutant mixture. Final models adjusted for maternal socio-demographics, infant characteristics, and seasonality. ResultsSingle-pollutant linear regression models indicated that most pollutants were associated with a decrement in birthweight, specifically during the two-week window before birth. An interquartile range increase in PM2.5 exposure (IQR: 3.3 µg/m3) from the median during this window correlated with a 34 g decrement in birthweight (95 % CI: −54, −14), followed by SO2 (IQR: 2.0 ppb; β: −31), PM10 (IQR: 4.6 µg/m3; β: −29), CO (IQR: 60.8 ppb; β: −27), and NOX (IQR: 7.9 ppb; β: −26). Multi-pollutant BKMR models revealed that PM2.5, NOX, and CO exposure were negatively and non-linearly linked with birthweight. As the five-pollutant mixture increased, birthweight decreased until the median level of exposure. DiscussionPrenatal exposure to air pollutants, notably PM2.5, during the final two weeks of pregnancy may negatively impact birthweight. The non-linear relationships between air pollution and birthweight highlight the importance of studying pollutant mixtures and their interactions.

Rapid hydrolysis of NO2 at High Ionic Strengths of Deliquesced Aerosol Particles.

Nitrogen dioxide (NO2) hydrolysis in deliquesced aerosol particles forms nitrous acid and nitrate and thus impacts air quality, climate, and the nitrogen cycle. Traditionally, it is considered to proceed far too slowly in the atmosphere. However, the significance of this process is highly uncertain because kinetic studies have only been made in dilute aqueous solutions but not under high ionic strength conditions of the aerosol particles. Here, we use laboratory experiments, air quality models, and field measurements to examine the effect of the ionic strength on the reaction kinetics of NO2 hydrolysis. We find that high ionic strengths (I) enhance the reaction rate constants (kI) by more than an order of magnitude compared to that at infinite dilution (kI=0), yielding log10(kI/kI=0) = 0.04I or rate enhancement factor = 100.04I. A state-of-the-art air quality model shows that the enhanced NO2 hydrolysis reduces the negative bias in the simulated concentrations of nitrous acid by 28% on average when compared to field observations over the North China Plain. Rapid NO2 hydrolysis also enhances the levels of nitrous acid in other polluted regions such as North India and further promotes atmospheric oxidation capacity. This study highlights the need to evaluate various reaction kinetics of atmospheric aerosols with high ionic strengths.

Advanced discovery mechanisms in model repositories

SummaryAs model‐driven engineering gains traction and poses as the new paradigm for software engineering, it raises a need for efficient approaches and tools to manage, discover, and retrieve relevant modeling artifacts. Hence, industry and academia are conceiving effective ways to store, search, and retrieve heterogeneous model artifacts that employ advanced discovery mechanisms. This paper presents MDEForge‐Search, a novel approach to discovering heterogeneous model artifacts over MDEForge, a distributed cloud‐based model repository. We designed advanced discovery mechanisms that retrieve heterogeneous artifacts within their context (megamodel) and reuse them across model management services. In addition, a domain‐specific approach has been proposed to formulate queries in terms of keywords, search tags, conditional operators, quality model assessment services and a transformation chain discoverer. Finally, the applicability of our approach was assessed in a recommender system modeling framework, which, thanks to the operated integration, can rely on the availability of more than 5000 model artifacts currently persisted in our cloud‐based model repository.

A Robust Intrusion Detection System Empowered by Generative Adversarial Networks

There is a very bleak outlook on cyber security due to the rapid expansion of the Internet and the ever-changing terrain of cyber-attacks. This paper explores the field of intrusion detection through network analysis, with a particular emphasis on applying machine learning (ML) and deep learning (DL) approaches. For every ML/DL technique, a thorough tutorial overview is given together with a review of pertinent research publications. These studies were read, indexed, and summarised according to their thermal or temporal correlations with great care. The paper also provides information on frequently used network datasets in this field, which is relevant given the critical role that data plays in ML/DL techniques. It also discusses the difficulties in using ML/DL for cyber security and provides insightful recommendations for future lines of inquiry. Interestingly, the KDD data set shows up as a reputable industry standard for intrusion detection methods. A lot of work is being done to improve intrusion detection techniques, and both training and evaluating the detection model's quality depend equally on the quality of the data. The KDD data collection is thoroughly analysed in this research, with a special emphasis on four different attribute classes: Basic, Content, Traffic, and Host. We use the Modified Random Forest (MRF) technique to classify these properties.