Quality Model Research Articles

An iteratively optimized downscaling method for city-scale air quality forecast emission inventory establishment

Air quality models (AQMs) are pivotal in forecasting air quality and shaping pollution control strategies. Nonetheless, the effectiveness of AQMs is often compromised in many cities due to the absence of accurate local emission inventories. To address this gap, this study presents a novel AQM-ready emission inventory generation technique with iterative optimization ability for city-scale applications in China. An efficient emission processing tool was introduced in this study, which utilizes the High-Resolution Multi-resolution Emission Inventory for China (HR-MEIC) as input. Using environmental observations and a region map, the tool can justify emissions of different regions iteratively. With the iterative optimization method, the model performance can be notably improved even without local emissions. The optimization was realized by splitting model-ready emissions into different regions and adjusting the emissions using scale factors calculated with the modeling results and the observations of each region. This methodology was applied to the Eight Cities in the Chengdu Plain (CP8C), located in the western margin of Sichuan Basin with complex topography and meteorological conditions, southwestern China, monthly throughout 2023. Air quality modeling was carried out using Weather Forecast and Research Model (WRF) and the Community Multiscale Air Quality Model (CMAQ). The results showed that the optimization acquired a good performance after five cycles for PM2.5 and NO2, with correlation coefficients (R values) surging from 0.62 and 0.37 to 0.77 and 0.73, respectively, while their normalized mean bias (NMB) substantially decreased from 22.8 % and 100.4 % to 3.6 % and 3.3 %. The underestimation on O3 concentration was also improved by the optimization, although enhancements in O3 modeling remained modest. This technique provides an easy-to-copy method to generate reasonable AQM-ready emission files with open emission data and observation data, which would be beneficial for the cities' air quality forecast in cities without local emission inventories.

Urban Habitat Quality Enhancement and Optimization under Ecological Network Constraints

The process of urbanization leads to the rapid expansion of construction land and brings a series of ecological and environmental problems. The ecological network, as a linear landscape element, is of great significance to improve the quality of the regional ecological environment. In this study, the Morphological Spatial Pattern Analysis (MSPA) and the model of Minimum Cumulative Resistance (MCR) were used to construct the ecological corridors in the central city of Fuzhou, and the land use pattern under the constraints of the ecological network was simulated and quantified by the patch-level land use simulation (PLUS) tool with the results of the identification of ecological corridors. Meanwhile, with the help of InVEST habitat quality model, the regional habitat quality under different development scenarios was compared. The results show that (1) 19 ecological sources and 35 ecological corridors were identified; (2) under the constraints of ecological corridors, the area of forested land in the study area in 2027 was increased by 1.57% and the area of built-up land was reduced by 0.55% compared with that in 2022; (3) and under the constraints of ecological corridors, the mean value of habitat quality in Fuzhou City improved by 0.0055 and 0.0254 compared with 2022 and 2027 natural evolution scenarios, respectively. The study provides decision-making assistance for the construction of ecological corridors from the perspective of land use planning.

A Generalized approach to the operationalization of Software Quality Models

Comprehensive measures of quality are a research imperative, yet the development of software quality models is a wicked problem. Definitive solutions do not exist and quality is subjective at its most abstract. Definitional measures of quality are contingent on a domain, and even within a domain, the choice of representative characteristics to decompose quality is subjective. Thus, the operationalization of quality models brings even more challenges. A promising approach to quality modeling is the use of hierarchies to represent characteristics, where lower levels of the hierarchy represent concepts closer to real-world observations. Building upon prior hierarchical modeling approaches, we developed the Platform for Investigative software Quality Understanding and Evaluation (PIQUE). PIQUE surmounts several quality modeling challenges because it allows modelers to instantiate abstract hierarchical models in any domain by leveraging organizational tools tailored to their specific contexts. Here, we introduce PIQUE; exemplify its utility with two practical use cases; address challenges associated with parameterizing a PIQUE model; and describe algorithmic techniques that tackle normalization, aggregation, and interpolation of measurements.

Optimization of power system parameters for multi-mode hybrid electric vehicles based on regional demand

The performance of the vehicle power system for multi-mode hybrid electric vehicle (M-MHEV) can be improved through meticulous parameter matching and optimization. This paper developed the powertrain parameter design and dynamic performance calculation program based on the structure design, parameter matching calculation, and powertrain system selection. A HOQM of M-MHEV is formulated to ascertain the weight coefficient of vehicle performance indicators according to different regional requirements and a parameter matching and optimization method for power system, employing the Particle Swarm Optimization (PSO) algorithm, is suggested to assess and harmony the vehicle’s performance. Firstly, a house of quality model of M-MHEV is constructed to ascertain the weight coefficient of vehicle performance indicators derived from different regional demands. Additionally, a method is introduced for optimization of power system parameters of M-MHEV based on regional demand, the PSO algorithm is employed to optimize the characteristic parameters. And sensitivity analysis of characteristic parameters is conducted relying on user needs in different regions. The simulation results show that users in the northern and southern regions have different final weight coefficients for vehicle performance indicators and the parameters not only exert a substantial individual influence but also exhibit an interaction effect on the vehicle performance. Finally, a series of comparative simulations, are carried out with two optimization parameters respectively, the simulation outcomes demonstrate that the solution obtained through optimized design parameters solution could markedly enhance the technical parameters of the vehicle. The efficacy and viability of the proposed method based on user needs in different regions are verified. The conclusion provides a useful reference for differentiated design.

Language teacher expertise research: A theoretical case and research agenda

Abstract Expertise exists among all communities of educational practitioners at all levels and in all national contexts. By identifying expert practitioners, learning from them and valuing their professional competence, researchers can support, promote and build upon sustainable, embodied, holistic models of quality in ways that have direct relevance for the classroom, the curriculum and wider educational goals. Yet, despite its potential as a field of research, there have been relatively few studies involving expert language teachers to date. After a brief historical background, this article makes the case for language teacher expertise research, noting its high ecological validity, its great practical utility, its ability to bridge the research–practice divide and its potentially positive impact on teaching communities. Key methodological considerations are also discussed, including defining expertise, identifying expert teachers and looking beyond the limits of subject-specific pedagogy to understand the whole practitioner in their sociocultural context. The article then proposes a framework for future teacher expertise research that spans diverse methodologies. Six example research tasks from within this framework are proposed, each justified and exemplified, incorporating suggestions for research design that are intended to encourage both experienced and novice researchers to engage with teacher expertise as a promising domain for future investigation.

The importance of source data in river network connectivity modeling: A review

AbstractRiver network connectivity (RC) describes the hydrologic exchange of water, nutrients, sediments, and pollutants between the river channel and other “sites” via heterogenous flowpaths along the river corridor. As water moves downstream it carries these constituents, creating a stream‐to‐ocean continuum of connectivity that regulates global water, carbon, and nutrient cycling. River network connectivity models have developed over many decades, culminating in recent years with network‐scale RC models that explicitly simulate the transport and exchange of water and elements from headwaters to coasts, sometimes requiring models to contain tens of millions of river reaches. These advances provide transformative insights into the aggregate effects of RC on water and material transport across scales from local to global. Yet, recent reviews have pointed to several challenges that need to be overcome to continue advancing network‐scale RC modeling. In service of these goals, I summarize recent network‐scale RC maps and models to identify similarities and differences across the large‐scale RC modeling landscape. Although our computational and upscaling abilities have significantly improved and have revealed new insights, current models are still limited by the quantity, quality, resolution, and lack of standardization of the available in situ databases and source data maps necessary for the modeling. This suggests that we can extend recent advances if we keep improving these source datasets, while continuously revisiting our physics and theory to explain those new data. In doing so, we will continue to expand the role of network‐scale RC models in informing water quality modeling and management into the future.

Application of a reaction-based water quality model to the total dissolved solids concentration of the Pasig River.

With the goal to support effective water resource management, water quality models have gained popularity as tools for evaluating the distributions of pollutants and sediments. This work focuses on the application of the numerical solution of an advection-dispersion-reaction (ADR) water quality model for rivers and streams to a major Philippine waterway, the Pasig River. The water quality constituent is described by a system of reaction and advection-dispersion-reaction equations. The model and method are based on a previously used strategy where Guass-Jordan decomposition is applied to the matrix system and the resulting conservative form of the model is solved numerically using the fully implicit scheme and finite element method. The methodology is demonstrated by a case study in Pasig River involving the concentrations of total dissolved solids (TDS) obtained from the Department of Environment and Natural Resources (DENR) through the Pasig River Unified Monitoring Stations (PRUMS) report. Sensitivity analysis and parameter estimation are also applied to the model to assess which parameters influence the model output the most.

FITT-Q scale a new service quality model in fitness center

A study was conducted to test the measurement of Intern Fitness Center Service Quality involving 435 respondents to confirm the FITT-Q Scale (Fitness Industry Trust Tools-Quality Scale) model. The results of this study strengthened the FITT-Q Scale as an adequate measurement for fitness center service quality as it continued to demonstrate that service quality is a multidimensional construct. The alpha reliability coefficients and construct coefficients found in this study were relatively consistent with previous findings supporting the use of a simpler and less invasive version of fitness center service quality assessment.

Dominant influence of biomass combustion and cross-border transport on nitrogen-containing organic compound levels in the southeastern Tibetan Plateau

Abstract. The Tibetan Plateau (TP) is highly susceptible to climate change, and nitrogen-containing organic compounds (NOCs) in fine particulate matter (PM2.5) represent one of the largest uncertainties with respect to their impact on the climate in high-altitude areas. Previous studies have shown that NOCs play a vital role in the nitrogen budget of PM2.5. However, our understanding of the composition and sources of NOCs in PM2.5, particularly in the TP, is limited. Here, we aim to enhance our understanding of NOCs in the TP region by examining their identification, concentration levels, sources, and origins. We conducted field sampling at a regional background sampling site in Gaomeigu, in the southeastern margin of the TP from 11 March to 13 May 2017, followed by laboratory analysis of the NOCs collected on the filters. The daily mass concentrations of NOCs ranged from 714.4 to 3887.1 ng m−3, with an average of 2119.4 ± 875.0 ng m−3 during the campaign. This average concentration was approximately 40 % higher than that reported at a typical regional site in the North China Plain (NCP), highlighting a more significant presence of NOCs in the Tibetan area. Biomass burning and secondary sources were identified as the major contributors to total NOCs. This was further substantiated by a regional air quality model, which indicated that over 80 % of the aerosol in the southeast of the TP originated from neighboring countries. This study improves our understanding of NOCs' contribution to PM2.5 in the TP and their potential impacts on climate stability in high-altitude areas.

Assessing the psychological pathways of esports events spectators: an application of service quality and its antecedents and consequences

ABSTRACT Research question This research develops and tests a comprehensive structural model to explain the psychological pathway behind esports spectator behaviours, linking perceived service quality to its antecedents (i.e. extrinsic motivation and intrinsic motivation) and consequences (i.e. satisfaction and revisit intention). Research methods Two rounds of data collection were completed for Phase One (N = 485) and Phase Two (N = 217). The measurement items were borrowed from a range of pre-existing scales of traditional sport or esports. Phase One was designed to assess the psychometric properties of the measurement model. Phase Two was purported to test the pathways among the constructs as hypothesised in the structural model. Results and findings In Phase One, the model fit indices showed good model fit, reliability and construct validity. In Phase Two, the SEM model fitness indices showed a marginally acceptable model fit. The path analysis supported 15 out of 19 hypotheses; no significant relationships were found between relatedness and intrinsic motivation, autonomy and extrinsic motivation, intrinsic motivation and physical environment, and intrinsic motivation and interaction. Overall, the structural model showed a moderate to substantial predictive power. Implications The study provides empirical evidence for the proposed relationships among the constructs and expands the realm of the current theories to the context of esports, especially the application of the self-determination theory and the new esports service quality model. It also provides a more comprehensive picture for esports practitioners to understand their spectators and balance their effort in managing elements which promote future attendance.

Water Quality Modeling of Wastewater Discharges for Prediction of Sediment Transport and Deposition in Surface Water

Sediment deposition in surface water bodies can have a range of significant impacts, affecting everything from the aquatic ecosystem to water quality and infrastructure. It can affect the availability of food sources for aquatic organisms; and change the physical structure of habitats, such as riverbeds and lake bottoms. It can equally smother aquatic plants and animals, leading to a decline in biodiversity. A model of the governing equations of particle motion and fluid flow was developed with COMSOL Multiphysics 5.3a. With a coefficient of determination of 0.99, the model result demonstrated good agreement after result validation using experimental data. According to study findings, sediments in the study area, Ele River move more slowly than sediments in the midstream which causes a high deposition of sediments at the river banks. This sediment deposition affects the irrigation system for crop production considering the ability of the sediments to block sprinkler nozzles which limits it from supplying sufficient water for the production of crops.

Prediction and analysis of interior sound quality of high-speed trains based on XGBoost

This paper primarily investigates the objective quantification of subjective perceptions of sound quality in high-speed trains. The study measures and analyzes the sound quality in high-speed trains under high-speed operating conditions and calculates psychoacoustic parameters. A subjective evaluation of the interior sound quality is conducted using the grading scale method. An Extreme Gradient Boosting (XGBoost) algorithm is utilized to construct a predictive model for sound quality in high-speed trains and get high predictive accuracy for sound quality evaluation. Employing AI-based evaluation models to evaluate the sound quality in high-speed trains offers a more effective method for studying the interior sound quality of high-speed trains.

Agronomic and environmental effects of forage-cutting schedule and nitrogen fertilization for bermudagrass (Cynodon dactylon, L.)

Bermudagrass (Cynodon dactylon, L.) is widely used as a forage in ruminant diets owing to its nutritional value and its capacity to grow under various agroecological conditions. For the southeastern United States climate conditions, previous research on bermudagrass recommended the cultivar Tifton 85 for forage production and a monthly frequency for forage-cutting. Even though bermudagrass cultivars are known to positively respond to nitrogen (N) fertilization, the interplay of the forage-cutting and N fertilization rates is not well understood. As a result, there is not sufficient guidance for adequate harvesting schedules and fertilization management to optimize bermudagrass forage production. Hence, this study aims to clarify the interplay of biomass-cutting events and N fertilization rates on forage quality and quantity, and N footprint. The study used experimental data of bermudagrass Tifton 85 forage production under three N fertilization rates (i.e., high = 504 kg N ha−1, medium = 336 kg N ha−1, and low = 168 kg N ha−1), to calibrate and validate Root Zone Water Quality Model 2 (RZWQM2) for biomass weight and biomass N content. For each N fertilization rate, multiyear simulations of four biomass-cutting scenarios were used to investigate the joint effects of harvesting schedules and N fertilization rates on biomass weight, biomass N content, N use efficiency (NUE), and N leaching. Results show statistically significant effects of the biomass-cutting scenarios on biomass weight and biomass N content for both high and medium N fertilization rates. The interplay of biomass-cutting and N fertilization reflected differently on forage quality and quantity, and N footprint. The low N fertilization did not show any statistically significant effect except for the biomass weight. NUE values were higher with both medium and low N fertilization rates compared to the high N fertilization which showed a relatively high N leaching. The outcomes of this study can be used to inform bermudagrass cutting and fertilization options to achieve forage yield goals with an understanding of the potential environmental consequence of N leaching and low NUE.

Modelling pesticide concentrations in Japanese paddy fields using the RICEWQ model

Increasing concerns about plant protection products in surface waters have highlighted the importance of pesticide monitoring and modelling, both nowadays integral components of the pesticide registration process. The Rice Water Quality (RICEWQ) model predicts the fate and transport of pesticides under various paddy environmental conditions. The model has been used widely for regulatory purpose in the U.S., while its use in Europe has been limited to regulatory submissions, despite multiple recommendations to adopt RICEWQ as a high tier assessment model for regulatory purposes. The applicability of the RICEWQ model under Japanese agricultural conditions remains unexplored. This study leverages field experimental data to evaluate the RICEWQ model's capability to simulate daily concentrations of two herbicides, mefenacet and pretilachlor, in paddy water and paddy soil in Japan.The RICEWQ model provided a reasonable level of performance for predicting the fate and transport of the two herbicides by ensuring that the simulated daily water balance corresponded with field observations and with minor pesticide-specific calibration. To further improve the performance of the simulations, we implemented a straightforward calibration framework. Calibrating the RICEWQ model improved the accuracy of all simulations; for both herbicides and in both paddy water and paddy soil compartments, the lowest Nash-Sutcliffe efficiency achieved was 0.725, demonstrating excellent performance. However, the RICEWQ model consistently underestimated the peak concentrations of herbicides in paddy water within the first three days of simulation. Simulation results suggested that approximately 50 % of the total applied herbicide was lost from the paddy system. Increasing the duration of the water holding period after pesticide application or increasing the storage capacity of the rice field via excess water storage depth management has the potential to reduce greatly herbicide loss to below 10 % of the total applied herbicide.

A probabilistic framework for identifying anomalies in urban air quality data.

Just as the value of crude oil is unlocked through refining, the true potential of air quality data is realized through systematic processing, analysis, and application. This refined data is critical for making informed decisions that may protect health and the environment. Perhaps ground-based air quality monitoring data often face quality control issues, notably outliers. The outliers in air quality data are reported as error and event-based. The error-based outliers are due to instrument failure, self-calibration, sensor drift over time, and the event based focused on the sudden change in meteorological conditions. The event-based outliers are meaningful while error-based outliers are noise that needs to be eliminated and replaced post-detection. In this study, we address error-based outlier detection in air quality data, particularly targeting particulate pollutants (PM2.5 and PM10) across various monitoring sites in Delhi. Our research specifically examines data from sites with less than 5% missing values and identifies four distinct types of error-based outliers: extreme values due to measurement errors, consecutive constant readings and low variance due to instrument malfunction, periodic outliers from self-calibration exceptions, and anomalies in the PM2.5/PM10 ratio indicative of issues with the instruments' dryer unit. We developed a robust methodology for outlier detection by fitting a non-linear filter to the data, calculating residuals between observed and predicted values, and then assessing these residuals using a standardized Z-score to determine their probability. Outliers are flagged based on a probability threshold established through sensitivity testing. This approach helps distinguish normal data points from suspicious ones, ensuring the refined quality of data necessary for accurate air quality modeling. This method is essential for improving the reliability of statistical and machine learning models that depend on high-quality environmental data.

Enhancing environmental quality and economic growth through potential effects of energy efficiency and renewable energy in Asian economies

This study examines the potential impacts of energy efficiency and renewable energy on economic growth proxies by gross domestic product and environmental quality proxies by carbon dioxide emissions across eight selected Asian countries from 2000 to 2020. This study contributes by calculating green total factor productivity and carbon total factor productivity based on the famous Solow’s residual via employing a modified extensive growth accounting model that internalized ignored factors such as energy efficiency and renewable energy. The employed panel cointegration techniques confirm that all variables are co-integrated with carbon dioxide emissions and economic growth. The pooled mean group/autoregressive distributed lag model analysis results indicate that energy efficiency is positively associated with both environmental quality and economic growth. Renewable energy hurts economic growth but has a positive effect on environmental quality which suggests the necessity of implementing an effective strategy for renewable energy alongside energy efficiency measures to enhance economic growth and environmental quality in the selected Asian countries. The findings from the fully modified ordinary least squares estimator are consistent with the environmental quality model. The average growth rate of green total factor productivity is positive despite negative contributions from energy efficiency and renewable energy. Similarly, the average growth rate of carbon total factor productivity is negative despite positive contributions from labor and capital. This discrepancy may be attributed to the beneficial effects of labor and capital as input productivity-driven. Embracing renewable energy sources can take significant steps toward improving environmental quality for future generations. Focusing on green technologies that enhance energy efficiency can substantially promote environmental quality and stimulate sustainable economic growth through innovation and climate change integration to achieve Sustainable Development Goals.

A method for evaluating the visual quality of wetland park landscapes: a case study of qianlu lake wetland park in Wuping, China

Wetland parks are an important part of urban ecosystems, characterized by artificial and natural features. While providing outdoor recreational space for urban residents, the aesthetics of the landscape also affect residents’ willingness to visit the wetland park. Previous studies of wetland parks focused on the ecological benefit. However, fewer systematic studies analyzed the aesthetic qualities and influencing elements of wetland parks. In this study, the Wuping Qianlu Lake Wetland Park in Fujian Province, China was used to evaluate the landscape visual quality of the wetland park using the the Scenic Beauty Estimation method (SBE), and the landscape features of the wetland park were obtained through the Semantic Differential (SD) method and semantic segmentation technique. The regression model of landscape visual quality and landscape features in wetland parks was established using SBE values as the dependent variable, and 23 landscape features as independent variables. The SBE values and accessibility were also combined to create four quadrants, which were combined with K-means clustering to diagnose existing problems. The results of the study showed that two landscape features, approach and proportion of water area were weaker among the four types of wetland park landscapes. The SBE values of the man-made landscape type were the most significant. Meanwhile, the high enclosure landscape type had a significant sense of rusticity, accentuating the wildness of the plants. It was less aesthetically pleasing and had the poorest landscape visual quality of the four types. This study provides a systematic method for diagnosing landscape problems in wetland parks, evaluating landscape visual quality assessment, and providing theoretical and technical support for urban wetland park renewal and construction.

Air quality historical correlation model based on time series

Air quality is closely linked to human health and social development, making accurate air quality prediction highly significant. The Air Quality Index (AQI) is inherently a time series. However, most previous studies have overlooked its temporal features and have not thoroughly explored the relationship between pollutant emissions and air quality. To address this issue, this study establishes a historical correlation model for air quality based on a time series model—the Gaussian Hidden Markov Model (GHMM)—using industrial exhaust emissions and historical air quality data. Firstly, a traversal method is used to select the optimal number of hidden states for the GHMM. To optimize the traditional GHMM and reduce error accumulation in the prediction process, the Multi-day Weighted Matching method and the Fixed Training Set Length method are utilized. Both direct and indirect prediction modes are then used to predict the AQI in the Zhangdian District. Experimental results indicate that the improved GHMM with the indirect mode provides higher accuracy and more stable state estimation results (MAE = 13.59, RMSE = 17.59, mean forecasted value = 117.94). Finally, the air quality historical correlation model is integrated with the air quality meteorological correlation model from a previous study, further improving prediction accuracy (MAE = 11.59, RMSE = 14.87, mean forecasted value = 120.88). This study demonstrates that the GHMM’s strong ability to analyze temporal features significantly enhances the accuracy and stability of air quality predictions. The integration of the air quality historical correlation model with the air quality meteorological correlation model from a previous study leverages the strengths of each sub-model in handling different feature groups, leading to even more accurate predictions.

Model of Health-Related Quality of Life in Breast Cancer Patients Using Cross-Sectional Data: The Role of Resilience.

Resilience has been suggested as an important predictor of both physical and mental health-related quality of life in breast cancer patients. However, it is unclear why resilient women handle their diagnosis better, not only mentally, but also physically. The aim of this study was to investigate paths between resilience, physical activity, and mental, physical, and global health-related quality of life in breast cancer patients. Structural equation modeling was conducted to evaluate the proposed structural paths using a sample of 638 women with newly diagnosed breast cancer patients from Sweden. Resilience was directly associated with physical activity and mental health-related quality of life. It was indirectly associated with physical functioning, through mental health-related quality of life and physical activity. Resilience was also indirectly associated with global quality of life, through mental health-related quality of life. Mental health support and encouraging physical activity may be especially relevant to enhance all aspects of health-related quality of life early in the breast cancer process. Results should be replicated longitudinally.

Finance Modeling Approach Using Machine Learning

The integration of machine learning (ML) into financial modeling represents a transformative shift in the finance sector, enhancing analytical capabilities and decision-making processes. This article explores the fundamental concepts of financial modeling and machine learning, illustrating how ML methodologies—both supervised and unsupervised—improve traditional financial models by enabling the analysis of extensive datasets to uncover intricate patterns. By leveraging historical data, ML enhances predictive accuracy in various applications, including risk assessment, algorithmic trading, credit scoring, and fraud detection. The article highlights significant advancements in predictive analytics through techniques such as regression analysis and time series forecasting, which allow financial analysts to navigate market uncertainties more effectively. Moreover, the automation of data processing and feature engineering through ML leads to increased operational efficiency, reducing human error and enhancing real-time decision-making capabilities. However, challenges such as data quality issues, model interpretability, and ethical considerations regarding algorithmic bias necessitate careful management to ensure responsible implementation. The article emphasizes the importance of transparency in ML models to foster trust among stakeholders while meeting regulatory requirements. Future research directions are proposed, focusing on the integration of explainable AI with blockchain technology and the implications of big data analytics on ML models. By addressing these emerging challenges and opportunities, finance professionals can leverage advanced analytical tools to drive innovation and enhance strategic initiatives in an increasingly complex financial landscape. Ultimately, this article underscores the pivotal role of machine learning in reshaping financial modeling practices and its potential to redefine the future of finance.