Quality Model Research Articles

CLOUD-QM: a quality model for benchmarking cloud-based enterprise information systems

CLOUD-QM: a quality model for benchmarking cloud-based enterprise information systems

RNA-Seq transcriptome profiling of immature grain wheat is a technique for understanding comparative modeling of baking quality

Improving the baking quality is a primary challenge in the wheat flour production value chain, as baking quality represents a crucial factor in determining its overall value. In the present study, we conducted a comparative RNA-Seq analysis on the high baking quality mutant “O-64.1.10” genotype and its low baking quality wild type "Omid" cultivar to recognize potential genes associated with bread quality. The cDNA libraries were constructed from immature grains that were 15 days post-anthesis, with an average of 16.24 and 18.97 million paired-end short-read sequences in the mutant and wild-type, respectively. A total number of 733 transcripts with differential expression were identified, 585 genes up-regulated and 188 genes down-regulated in the “O-64.1.10” genotype compared to the “Omid”. In addition, the families of HSF, bZIP, C2C2-Dof, B3-ARF, BES1, C3H, GRF, HB-HD-ZIP, PLATZ, MADS-MIKC, GARP-G2-like, NAC, OFP and TUB were appeared as the key transcription factors with specific expression in the “O-64.1.10” genotype. At the same time, pathways related to baking quality were identified through Kyoto Encyclopedia of Genes and Genomes. Collectively, we found that the endoplasmic network, metabolic pathways, secondary metabolite biosynthesis, hormone signaling pathway, B group vitamins, protein pathways, pathways associated with carbohydrate and fat metabolism, as well as the biosynthesis and metabolism of various amino acids, have a great deal of potential to play a significant role in the baking quality. Ultimately, the RNA-seq results were confirmed using quantitative Reverse Transcription PCR for some hub genes such as alpha-gliadin, low molecular weight glutenin subunit and terpene synthase (gibberellin) and as a resource for future study, 127 EST-SSR primers were generated using RNA-seq data.

The impact of Covid‐19 on banking groups’ balance sheets in the euro area

AbstractMost studies have found that the Covid‐19 pandemic did not negatively impact the euro area banking industry's performance at an aggregate level. This study explores whether this finding still holds using the return on assets of 16 banking groups operating in the euro area and considering bank‐specific factors, idiosyncrasies related to different exposures of their portfolios to the business cycle and weaknesses stemming from underlying structural vulnerabilities. The banking groups are classified into clusters using unsupervised learning techniques. This research contributes to the empirical literature on the determinants of banks’ performance by highlighting the importance of banks’ heterogeneity, notably controlling for differential performance due to asset quality, solvency and business model. In addition, this paper shows that the magnitude of return on assets’ exposure to the business cycle varies across banks and that inflation in the euro area matters only for a subset of them. Importantly, the study sheds some light on the possible reasons for the mixed results in the literature regarding the role of non‐interest income, the T1 capital ratio and inflation. Finally, no significant effects of the Covid‐19 variables on banks’ return on assets are found during the first seven quarters of the pandemic.

Modeling polycyclic aromatic hydrocarbons (PAHs) concentrations from wildfires in California

In recent years, wildfires in California have increased in frequency and intensity due to climate change and prolonged drought. The air pollutants released by wildfires cause significant health consequences, among which polycyclic aromatic hydrocarbons (PAHs) are particularly toxic. Estimating PAH emissions from wildfires is challenging due to variability in vegetation types. In this study, we estimate PAH emission rates across California at a high resolution, based on laboratory-measured PAH emission rates from 22 different vegetation types and detailed vegetation mapping. By combining these estimates with biomass burning data from the NCAR Fire Inventory, the Community Multiscale Air Quality Modeling System simulates PAH concentrations for the 2017 fire season. The modeling results compare favorably to measurements from three PAH monitoring sites in California. The peak PAH emissions from wildfire events are up to be 80 times higher in the gas phase and 32 times higher in the particle phase compared to a case without fire emissions. The population-weighted PAH concentrations from the fire case (0.053 µg/m3) are 47 % higher compared to a non-fire case (0.036 µg/m3) in the particle phase and 11 % higher in the gas phase (9.82 ppt compared to 8.83 ppt) during the study period. While highly depended on the meteorological condition, the simulated spatial distribution indicates that gas-phase PAHs are less likely to travel long distances from the fire source and are prone to aging into the particle phase during transport. Consequently, populations are more likely to be exposed to particle-phase PAHs during wildfire events. This finding has important implications for understanding the health impacts of wildfire-induced PAH concentrations, as particle-phase PAHs may have different toxicological effects compared to gas-phase PAHs.

A study of factors affecting the employment quality of contemporary college students through a structural equation model

OBJECTIVES: This paper aims to analyze factors affecting the employment quality of contemporary college students to improve it. METHODS: Data related to the influencing factors and employment quality were collected by means of a questionnaire. After verifying the effectiveness of the data, a structural equation model of employment quality was constructed. FINDINGS: The data collected by the questionnaire were effective. The structural equation model analysis results showed that individual factors, educational factors, and environmental factors were all significantly related to employment quality. NOVELTY: The novelty of this article lies in the use of a structural equation model to quantitatively analyze the relationship between influencing factors and employment quality, providing an effective reference for improving the employment quality of college students.

Implementation of a Far-Field Water Quality Model for the Simulation of Trace Elements in an Eastern Mediterranean Coastal Embayment Receiving High Anthropogenic Pressure

Water quality modeling is a key element for the support of environmental protection and policymaking. The aim of this work is to describe the application of a far-field water quality model for the simulation of marine pollution occurring from heavy metals (cadmium, lead, nickel, copper, and zinc). The highly stressed marine area of the Saronikos Gulf (Aegean Sea, Eastern Mediterranean) was chosen for investigation. Major pollution sources were identified, loads were estimated, and the model was parameterized to reproduce the local seawater conditions. The distribution of the pollutants between the dissolved and particulate phases was examined. The performance of the model set-up was evaluated using field concentration measurements. The described implementation succeeded in reproducing the observed levels of pollution and therefore can be used as a baseline configuration to examine the cumulative impact of future pollution sources; for example, accidental pollution events.

Assessment of Nocturnal NOx Heterogeneous Reaction Mechanisms in the Community Multiscale Air Quality (CMAQ) Model

AbstractNitrogen oxides (NOx) have adverse human health impacts and play a central role in the production of ozone and PM2.5. Nighttime heterogeneous chemistry regulates the nocturnal reservoirs and sinks of NOx such as N2O5 removal and ClNO2 production. However, existing parameterizations of nocturnal NOx heterogeneous chemistry in air quality models do not capture the variability in observations. Here, we implemented for the first time in the Community Multiscale Air Quality (CMAQ) model the Gaston N2O5 uptake (γ(N2O5)) mechanism that accounts for the role of particulate organic matter in regulating N2O5 uptake and the Staudt ClNO2 yield (Φ(ClNO2)) mechanism that includes the role of reactive solutes in suppressing ClNO2 production. With the Gaston and Staudt parameterizations, the coarse mode contributed modestly to N2O5 loss (17.2%) but significantly to ClNO2 production (60.3%), highlighting the impact of coarse mode chemistry. The Gaston γ(N2O5) parameterization in the fine mode increased agreement between modeled N2O5 concentration and observations (RMSEnew = 0.37ppb) compared to the model default (RMSEdefault = 0.43ppb). The Gaston γ(N2O5) parameterization was overall biased low due to underestimates in modeled particle oxygen to carbon ratio (O:C). The Staudt Φ(ClNO2) parameterization resulted in further underestimation (NMBnew = −73.7%) compared to the model default (NMBdefault = −37.9%) because of underestimation of fine mode particle chloride concentration. We expect that the updated parameterizations may more accurately capture the mean state and variability in γ(N2O5) and Φ(ClNO2) under conditions where model particulate O:C and chloride are better represented.

In Situ Functionalisation and Upcycling of Post‐Consumer Textile Blends into 3D Printable Nanocomposite Filaments

AbstractThe linear lifecycle of the textile industry contributes to the enormous waste generation of post‐consumer garments. Recycling or repurposing of post‐consumer garments typically requires separation of the individual components. This study describes a novel and facile chemo‐thermo‐mechanical method for producing extrudable pellets, involving one‐pot, 2,2,6,6‐Tetramethylpiperidine‐1‐oxyl (TEMPO)‐mediated oxidation of post‐consumer polycotton textiles, followed by mild mechanical treatment, all without isolating the constituents of the polycotton blend. The oxidized blend with high cellulose and carboxylate content of 1221 ± 82 mmol COO− per kg of cotton, is pelletised into a masterbatch and further in situ extruded into nanocomposite filaments for 3D printing. The carboxyl groups introduced on the polycotton‐based filters enable cotton fibrillation into nanoscaled fibers during mechanical treatment and extrusion resulting to a variety of functional and high surface‐finish quality models, including filters and fashion accessories. The electrostatic interactions with positively charged species, such as methylene blue (MB), facilitate their adsorption from water while exhibiting promising adsorption capacities. The adsorption of MB follows the Freundlich model and depends on the printed porosity of the filter. A “trash to treasure” concept for textile waste is further corroborated through the use of the developed 3D printing filament into commodity products.

Concentrations of Volatile Methyl Siloxanes in New York City Reflect Emissions from Personal Care and Industrial Use.

Volatile methyl siloxanes (VMS) are a group of organosilicon compounds of interest because of their potential health effects, their ability to form secondary organic aerosols, and their use as tracer compounds. VMS are emitted in the gas-phase from using consumer and personal care products, including deodorants, lotions, and hair conditioners. Because of this emission route, airborne concentrations are expected to increase with population density, although there are few studies in large urban centers. Here, we report summertime concentrations and daily variations of VMS congeners measured in New York City. Median concentrations of the 6 studied congeners, D3 (20 ng m-3), D4 (57 ng m-3), D5 (230 ng m-3), D6 (11 ng m-3), L5 (2.5 ng m-3), and L7 (1.3 ng m-3) are among the highest reported outdoor concentrations in the literature to date. Average congener ratios of D5:D4 and D5:D6 were consistent with previously reported emissions ratios, suggesting that concentrations were dominated by local emissions. Measured concentrations agree with previously published results from a Community Multiscale Air Quality model and support commonly accepted emissions rates for D4, D5, and D6 of 32.8, 135, and 6.1 mg per capita per day. Concentrations of D4, D5, D6, L5, and L7 and total VMS were significantly lower during the day than during the night, consistent with daytime oxidation reactivity. Concentrations of D3 did not show the same diurnal trend but exhibited a strong directional dependence, suggesting that it may be emitted by industrial point sources in the area rather than personal care product use. Concentrations of all congeners had large temporal variations but showed relatively weak relationships with wind speed, temperature, and mixing height.

Projecting non-communicable diseases attributable to air pollution in the climate change era: a systematic review

ObjectivesClimate change is a major global issue with significant consequences, including effects on air quality and human well-being. This review investigated the projection of non-communicable diseases (NCDs) attributable to air...

Quantifying the contributions of meteorology, emissions, and transport to ground-level ozone in the Pearl River Delta, China

Ozone pollution presents a growing air quality threat in urban agglomerations in China. It remains challenge to distinguish the roles of emissions of precursors, chemical production and transportations in shaping the ground-level ozone trends, largely due to complicated interactions among these 3 major processes. This study elucidates the formation factors of ozone pollution and categorizes them into local emissions (anthropogenic and biogenic emissions), transport (precursor transport and direct transport from various regions), and meteorology. Particularly, we attribute meteorology, which affects biogenic emissions and chemical formation as well as transportation, to a perturbation term with fluctuating ranges. The Community Multiscale Air Quality (CMAQ) model was utilized to implement this framework, using the Pearl River Delta region as a case study, to simulate a severe ozone pollution episode in autumn 2019 that affected the entire country. Our findings demonstrate that the average impact of meteorological conditions changed consistently with the variation of ozone pollution levels, indicating that meteorological conditions can exert significant control over the degree of ozone pollution. As the maximum daily 8-hour average (MDA8) ozone concentrations increased from 20 % below to 30 % above the National Ambient Air Quality Standard II, contributions from emissions and precursor transport were enhanced. Concurrently, direct transport within Guangdong province rose from 13.8 % to 22.7 %, underscoring the importance of regional joint prevention and control measures under adverse weather conditions. Regarding biogenic emissions and precursor transport that cannot be directly controlled, we found that their contributions were generally greater in urban areas with high nitrogen oxides (NOx) levels, primarily due to the stronger atmospheric oxidation capacity facilitating ozone formation. Our results indicate that not only local anthropogenic emissions can be controlled in urban areas, but also the impacts of local biogenic emissions and precursor transport can be potentially regulated through reducing atmospheric oxidation capacity.

Emission ensemble approach to improve the development of multi-scale emission inventories

Abstract. Many studies have shown that emission inventories are one of the inputs with the most critical influences on the results of air quality modelling. Comparing emission inventories among themselves is, therefore, essential to build confidence in emission estimates. In this work, we extend the approach of Thunis et al. (2022) to compare emission inventories by building a benchmark that serves as a reference for comparisons. This benchmark is an ensemble that is based on three state-of-the-art EU-wide inventories: CAMS-REG, EMEP and EDGAR. The ensemble-based methodology screens differences between inventories and the ensemble. It excludes differences that are not relevant and identifies among the remaining ones those that need special attention. We applied the ensemble-based screening to both an EU-wide and a local (Poland) inventory. The EU-wide analysis highlighted a large number of inconsistencies. While the origin of some differences between EDGAR and the ensemble can be identified, their magnitude remains to be explained. These differences mostly occur for SO2 (sulfur oxides), PM (particulate matter) and NMVOC (non-methane volatile organic carbon) for the industrial and residential sectors and reach a factor of 10 in some instances. Spatial inconsistencies mostly occur for the industry and other sectors. At the local scale, inconsistencies relate mostly to differences in country sectorial shares that result from different sectors/activities being accounted for in the two types of inventories. This is explained by the fact that some emission sources are omitted in the local inventory due to a lack of appropriate geographically allocated activity data. We identified sectors and pollutants for which discussion between local and EU-wide emission compilers would be needed in order to reduce the magnitude of the observed differences (e.g. in the residential and industrial sectors). The ensemble-based screening proved to be a useful approach to spot inconsistencies by reducing the number of necessary inventory comparisons. With the progressive resolution of inconsistencies and associated inventory improvements, the ensemble will improve. In this sense, we see the ensemble as a useful tool to motivate the community around a single common benchmark and monitor progress towards the improvement of regionally and locally developed emission inventories.

Urban air pollution evaluation in downtown streets of a medium-sized Latin American city using AERMOD dispersion model.

The transport sector is considered the largest contributor of air pollutants in urban areas, mainly on-road vehicles, affecting the environment and human health. Bahía Blanca is a medium-sized Latin American city, with high levels of traffic in the downtown area during peak hours. In this regard, it is necessary to analyze air pollution using an air quality model considering that there are no air pollutant measurements in the central area. Furthermore, this type of study has not been carried out in the region and since the city is expected to grow, it is necessary to evaluate the current situation in order to make effective future decisions. In this sense, the AERMOD model (US-EPA version) and the RLINE source type were used in this work. This study analyzes the variations of pollutant concentrations coming from mobile sources in Bahía Blanca's downtown area, particularly carbon monoxide (CO) and nitrogen oxides (NOx) during the period Jul-2020 to Jun-2022. It is interesting to note the results show the maximum concentration values detected are not directly associated with maximum levels of vehicle flow or emission rates, which highlights the importance of meteorological parameters in the modeling. In addition, alternative scenarios are proposed and analyzed from a sustainable approach. Regarding the scenario analysis, it can be concluded that diesel vehicles have a large influence on NOx emissions. Moreover, restrictions as strict as those proposed for a Low Emission Zone would be less applicable in the city than alternative temporary measures that modify traffic at peak hours.

Exploring Service Accessibility and Quality for Differently-Abled Students: A Qualitative Analysis at a South African University of Technology

This qualitative study investigates the service experiences of differently-abled students within the Financial Aid Department of a prominent South African University of Technology. Employing a semi-structured interview schedule aligned with the Service Quality model (SERVQUAL), five dimensions—tangibles, assurance, empathy, reliability, and responsiveness—are explored. The study unveils the significant challenges students face, including issues related to the location and accessibility of the department, staff approachability, and the reliability of communication systems. The discussion section draws on relevant literature to synthesise the findings, providing comprehensive insights into the intricacies of service delivery for differently-abled students. The study concludes with actionable recommendations for enhancing service quality and inclusivity, addressing the unique needs of this student demographic in the evolving landscape of South African higher education.

Exploring Determinants That Influence the Usage Intention of AI-Based Customer Services in the UAE

Artificial intelligence (AI) is revolutionizing the way customers interact with organizations and companies. There is a lack of research into AI-enabled customer experiences. Hence, this study aims to use the relevant literature to propose a conceptual framework for how the integration of AI in customer service can lead to an improved AI-enabled customer experience. Five propositions drawn from the reviewed literature present the main factors needed to ensure end users' acceptance of AI customer service in the United Arab Emirates (UAE). Our theoretical model extends the trust-commitment theory and service quality model, and incorporates perceived problem-solving ability, to address these factors and thereby guide the successful implementation of AI based customer service projects. The paper will help in understanding the key issues surrounding AI customer service applications that may support successful operations.

Application of SERVQUAL model to measure the customer satisfaction: A study of Digital payment companies in India

In this chapter, we will discuss the service quality concept, its importance for practitioners and academicians. We will relate service quality to other quality concepts such as product and image quality. Also, we will discuss how the concept is being measured and outline various models of service quality and introduce the SERVQUAL model in particular. We will discuss about its attributes and gaps, We will discuss about its attributes and gaps, in what contexts has the model been used and the pros and cons when using this model in measuring service quality. Also, we discuss customer satisfaction and how it is been related service quality. Furthermore, we will introduce our empirical context by highlighting why it is of particular interest to study service quality and customer satisfaction in grocery stores and why we choose to use the SERVQUAL model for our research. These will then lead us to clearly stating our research questions and research purpose in order to have a roadmap for our study.

Diagnosing drivers of PM2.5 simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method

Abstract. Chemical transport models (CTMs) are widely used for air pollution modeling, which suffer from significant biases due to uncertainties in simplified parameterization, meteorological fields, and emission inventories. Accurate diagnosis of simulation biases is critical for the improvement of models, interpretation of results, and management of air quality, especially for the simulation of fine particulate matter (PM2.5). In this study, an efficient method with high speed and a low computational resource requirement based on the tree-based machine learning (ML) method, the light gradient boosting machine (LightGBM), was designed to diagnose CTM simulation biases. The drivers of the Community Multiscale Air Quality (CMAQ) model biases are compared to observations obtained by simulating PM2.5 concentrations from the perspectives of meteorology, chemical composition, and emission sources. The source-oriented CMAQ was used to diagnose the influences of different emission sources on PM2.5 biases. The model can capture the complex relationship between input variables and simulation bias well; meteorology, PM2.5 components, and source sectors can partially explain the simulation bias. The CMAQ model underestimates PM2.5 by −19.25 to −2.66 µg m−3 in 2019, especially in winter and spring and during high-PM2.5 events. Secondary organic components showed the largest contribution to the PM2.5 simulation bias for different regions and seasons (13.8 %–22.6 %) of all components. Relative humidity, cloud cover, and soil surface moisture were the main meteorological factors contributing to PM2.5 bias in the North China Plain, Pearl River Delta, and northwestern China, respectively. Primary and secondary inorganic components from residential sources showed the two largest contributions to this bias (12.05 % and 12.78 %), implying large uncertainties in this sector. The ML-based methods provide valuable complements to traditional-mechanism-based methods for model improvement, with high efficiency and low reliance on prior information.

Global anthropogenic emissions (CAMS-GLOB-ANT) for the Copernicus Atmosphere Monitoring Service simulations of air quality forecasts and reanalyses

Abstract. Anthropogenic emissions are the result of many different economic sectors, including transportation, power generation, industrial, residential and commercial activities, waste treatment and agricultural practices. Air quality models are used to forecast the atmospheric composition, analyze observations and reconstruct the chemical composition of the atmosphere during the previous decades. In order to drive these models, gridded emissions of all compounds need to be provided. This paper describes a new global inventory of emissions called CAMS-GLOB-ANT, developed as part of the Copernicus Atmosphere Monitoring Service (CAMS; https://doi.org/10.24380/eets-qd81, Soulie et al., 2023). The inventory provides monthly averages of the global emissions of 36 compounds, including the main air pollutants and greenhouse gases, at a spatial resolution of 0.1° × 0.1° in latitude and longitude, for 17 emission sectors. The methodology to generate the emissions for the 2000–2023 period is explained, and the datasets are analyzed and compared with publicly available global and regional inventories for selected world regions. Depending on the species and regions, good agreements as well as significant differences are highlighted, which can be further explained through an analysis of different sectors as shown in the figures in the Supplement.

Hybrid modeling of an ultracentrifugation process for separation of full and empty adeno-associated virus particles.

Ultracentrifugation is an attractive method for separating full and empty capsids, exploiting their density difference. Changes of the serotype/capsid, density of loading material, or the genetic information contained in the adeno-associated viruses (AAVs) require the adaptation of the harvesting parameters and the density gradient loaded onto the centrifuge. To streamline these adaptations, a mathematical model could support the design and testing of operating conditions.Here, hybrid models, which combine empirical functions with artificial neural networks, are proposed to describe the separation of full and empty capsids as a function of material and operational parameters, i.e., the harvest model. In addition, critical quality attributes are estimated by a quality model which is operating on top of the harvest model. The performance of these models was evaluated using test data and two additional blind runs. Also, a "what-if" analysis was conducted to investigate whether the models' predictions align with expectations.It is concluded that the models are sufficiently accurate to support the design of operating conditions, though the accuracy and applicability of the models can further be increased by training them on more specific data with higher variability.

Systematic Review of Air Pollution in Morocco: Status, Impacts, and Future Directions

AbstractDespite the recognition of the importance of air pollution in Morocco, current scientific studies are predominantly descriptive and limited. This systematic review aims to provide a comprehensive overview of air pollution in Morocco, delineating the research status and identifying gaps. Searches conducted across PubMed, Google Scholar, and Scopus from 2016 to May 2023 yielded over 3200 articles. From these, 64 original studies are deemed eligible and selected, focusing on air pollution in Moroccan regions or cities. Review articles and those dealing with indoor air quality are excluded, with no restriction on specific pollutant. Research highlights road traffic as the primary contributor to air pollution in Morocco, followed by industrial activities. Exposure to air pollutants is associated with adverse health effects, including mortality, lung disease, respiratory disorders, asthma attacks, and cardiovascular disease. Pollution levels vary among cities, with Casablanca, Kenitra, and Essaouira consistently facing higher pollution levels due to transportation and industrial emissions. Altitude also influences air quality, with higher‐altitude areas generally experiencing lower pollutant concentrations. The review highlights the need for extensive research on air pollution's health and economic effects, improved air quality modeling, and broader pollutant focus beyond just regulated ones.