Quality Model Research Articles

A hybrid Harris Hawks Optimization with Support Vector Regression for air quality forecasting

This paper proposes a hybridized model for air quality forecasting that combines the Support Vector Regression (SVR) method with Harris Hawks Optimization (HHO) called (HHO-SVR). The proposed HHO-SVR model utilizes five datasets from the environmental protection agency’s Downscaler Model (DS) to predict Particulate Matter () levels. In order to assess the efficacy of the suggested HHO-SVR forecasting model, we employ metrics such as Mean Absolute Percentage Error (MAPE), Average, Standard Deviation (SD), Best Fit, Worst Fit, and CPU time. Additionally, we contrast our methodology with recently created models that have been published in the literature, such as the Grey Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Henry Gas Solubility Optimization (HGSO), Barnacles Mating Optimizer (BMO), Whale Optimization Algorithm (WOA), and Manta Ray Foraging Optimization (MRFO). In particular, the proposed HHO-SVR model outperforms other approaches, establishing it as the optimal model based on its superior results.

RIDE QUALITY MEASUREMENT AND USER RATINGS: INITIAL ASSESSMENT OF IMPROVED TECHNIQUES

Improved techniques for assessing ride quality have been developed by the authors that closely simulate actual vehicle ride dynamics and the responses of humans to whole-body vibration. Using numerical models to simulate four generic road-user vehicle classes – passenger cars, heavy commercial vehicles (trucks), off-road recreational and sports utility vehicles (SUVs) and motorcycles – fundamental issues associated with current ride quality measurements are addressed leading to more robust and accurate estimates of pavement rideability. Initial field trials and tests of the proposed ride quality model for a passenger car were conducted. To assess the effectiveness of the model as a ride quality predictor, the overall modelled ride responses were compared with actual measured ride vibrations (objective measures) and road-user ride ratings (subjective measures), all taken on the same sections of pavements. For the objective measures of ride quality portable vibration measuring equipment, comprising accelerometers, event markers and a data logger were assembled, customised and tested. In later versions of the equipment, also described in this paper, GPS tracking was incorporated to provide precise time-based recordings of location and speed. Developed specifically for this project, the equipment is small in size, completely self-contained, and is readily adapted for use in both small and large vehicles (motorcycles, cars, SUVs and trucks). The initial study found there was good agreement between the modelled responses, vibration measurements and subjective ratings. A further larger study is planned, which is discussed in this paper.

Research on the impact of land use and land cover changes on local meteorological conditions and surface ozone in the north China plain from 2001 to 2020

Land use and land cover changes (LULCC) alter local surface attributes, thereby modifying energy balance and material exchanges, ultimately impacting meteorological parameters and air quality. The North China Plain (NCP) has undergone rapid urbanization in recent decades, leading to dramatic changes in land use and land cover. This study utilizes the 2020 land use and land cover data obtained from the MODIS satellite to replace the default 2001 data in the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model. It simulates and analyzes the direct impact of LULCC on meteorological parameters and the indirect impact on surface ozone (O3) concentration through physical and chemical processes in the North China Plain during July in the summer. Six rapidly urbanizing cities were selected to represent the North China Plain. The results show that LULCC significantly increased sensible heat flux and 2-m temperature in rapidly urbanizing areas throughout the diurnal cycle, with more pronounced effects during the daytime, ranging from 6.49 to 23.46 W/m2 and 0.20–0.59 °C, respectively. The 10-m wind speed decreased at night and increased during the day, with changes ranging from − 0.43 to 0.27 m/s at night and − 0.16 to 0.15 m/s during the day. The planetary boundary layer height generally increased, with a larger rise during the daytime, ranging from 23.63 to 84.74 m. Simultaneously, surface O3 concentrations increased during both daytime and nighttime. The daytime increase ranged from 2.89 to 9.82 μg/m3, while the nighttime increase ranged from 1.76 to 7.77 μg/m3. LULCC enhanced meteorological and chemical processes as well as vertical transport, leading to an increase in O3. At the same time, it reduced the increase in O3 through horizontal transport and dry deposition processes. These changes are related to the meteorological variations. The impact on O3 concentrations was not limited to the surface but extended to the top of the planetary boundary layer (approximately 1500 m). Below 500 m, vertical transport increased O3 concentrations, while horizontal transport decreased O3 concentrations. Additionally, the meteorological and chemical processes induced by LULCC showed enhanced effects above the surface, whereas the dry deposition process had a smaller impact on O3 concentrations above the surface. This study reveals the significant impact of urban expansion on regional meteorological parameters and air quality. It optimizes the model’s simulation of regional air quality and provides new insights into understanding the effects of urbanization on meteorological conditions and air quality.

Development of the next-generation air quality prediction system in the Unified Forecast System framework: Enhancing predictability of wildfire air quality impacts

Abstract The National Oceanic and Atmospheric Administration (NOAA) has developed an advanced regional air quality prediction system (AQPS) within the Unified Forecast System (UFS) framework to improve representations of wildfire emissions and their impacts on air quality predictions. This innovative system integrates the Environmental Protection Agency’s (EPA) Community Multiscale Air Quality (CMAQ) model as a column chemistry model with the UFS-based atmospheric model, operating in an online mode. The calculation of wildfire gas and particulate emissions relies on satellite-derived fire products, high-resolution Regional Hourly Advanced Baseline Imager (ABI) and Visible Infrared Imaging Radiometer Suite (VIIRS) Emissions (RAVE). A period in June and July 2023 with Quebec Canadian wildfires, which severely impacted air quality in the United States (US), was chosen as a case study to assess the predictive capability of the UFS-AQM system. The UFS-AQM predictions of fine particulate (PM2.5) and ozone (O3) were evaluated against AirNow observations from June 15 to July 14, 2023. The results indicate a substantial improvement in PM2.5 predictions when compared to the previous operational forecast. Meanwhile, the system demonstrates a strong ability of predicting O3 exceedance events during the dissipation phase of the wildfire. Furthermore, the online system shows more realistic predictions of aerosol optical depth (AOD) as compared to the previous operational forecast and satellite retrieval data. Finally, this study outlines a plan for further advancing a comprehensive regional AQPS at NOAA.

Simulation Model Exchange in Process Industry: Requirements, Solutions, and Open Challenges

AbstractSimulation models are crucial for various applications across the lifecycle of process plants, such as plant engineering. As the industry moves toward modularization, plant components are increasingly supplied by manufacturers who design these modular units, often called package units or process equipment assemblies. Consequently, critical knowledge about their behavior resides with the manufacturers. This paper outlines the requirements for simulation model exchange to share this behavioral knowledge. It reviews solutions and highlights challenges based on literature and case studies. Key elements include metadata, open standards, IP protection, and quality assurance. Solutions, like the asset administration shell (AAS), CAPE‐OPEN, the Functional Mock‐up Interface (FMI), encrypted models, and quality models, are discussed. Remaining gaps are highlighted.

A twofold perspective on the quality of research publications: The use of ICTs and research activity models.

Previous studies have highlighted the inherent subjectivity, complexity, and challenges associated with research quality leading to fragmented findings. We identified determinants of research publication quality in terms of research activities and the use of information and communication technologies by employing an interdisciplinary approach. We conducted web-based surveys among academic scientists and applied machine learning techniques to model behaviors during and after the COVID-19 pandemic. Using model-agnostic explanations, we identified the determinants of research publication quality across 66 activity models. These models reflect the variety of behaviors among academic scientists during and after the COVID-19 pandemic. Our two-fold perspective distinguishes between research activities of academic scientists who increase research publication quality and those who maintain it. Notably, our findings reveal a diversity within activity models in shaping research publication quality. Academic institutions can apply our approach to analyze research staff behavior, stimulate activities, and ensure alignment with institutional objectives, thereby fostering individual and team complementarity.

IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks

The Internet of Things (IoT)-based smart solutions have been developed to predict water quality and they are becoming an increasingly important means of providing efficient solutions through communication technologies. IoT systems are used for enabling connection between various devices based on the ability to gather and collect information. Furthermore, IoT systems are designed to address the environment and the automation industry. The threats associated with aquaponics farming are managed through an IoT-based smart water monitoring framework, which has become increasingly relevant in recent days. Therefore, this approach is crucial for achieving a remarkable improvement in order to increase the productivity rate and yield. The quality of water directly affects the rate of growth, efficiency of feed, and the overall health rate of the fish, plants, and bacteria. Insufficient knowledge about species selection poses a significant challenge in aquaponics farming, as it heavily relies on the water quality parameters. To address the challenges of conventional models, we have developed an effective IoT-based water quality prediction model, more specifically designed for aquaponic fish ponds. The data needed to perform the developed water quality prediction model will be acquired from “a simple dataset of aquaponic fish pond IoT” database. After that, these data are forwarded to the feature extraction phase. The weighted features, DBN (Deep Belief Network) features, and the original features are achieved in the feature extraction stage. The weighted features are obtained using the Revamped Fitness-based Mother Optimization Algorithm (RF-MOA). Subsequently, these extracted features are fed into the Multi-Scale feature fusion-based Convolutional Autoencoder with a Gated Recurrent Unit (MS-CAGRU) network for predicting the water quality. Thus, the water quality predicted data is obtained. The proposed model integrates GRU networks with a convolutional autoencoder to improve water quality prediction by capturing trends and managing temporal dependencies. It enhances accuracy by analysing key parameters and employing techniques to reduce overfitting. The effectiveness of the proposed system is evaluated in comparison to the traditional models using some evaluation measures.

ECB-WQ: A Long-Term Agroecosystem Research (LTAR)-Eastern Corn Belt node field-scale water quality dataset.

The Eastern Corn Belt (ECB) node of the Long-Term Agroecosystem Research (LTAR) network is representative of row crop agricultural production systems in the poorly drained, humid regions of the US Midwest and a significant focus for addressing water quantity and quality concerns affecting Lake Erie and the Gulf of Mexico. The objectives of this paper were to (1) present relevant background information and collection methodology, (2) provide summary analyses of measured data, and (3) provide details for accessing the dataset and discuss potential database applications. The ECB-water quality (ECB-WQ) database is comprised of hydrology and water quality data from three privately owned farms in Northwest Ohio and Northeast Indiana and is available for download through the United States Department of Agriculture Ag Data Commons. The dataset includes information on site characteristics (drainage area and soil type), field management (fertilizer application, planting rate, and yield), and daily discharge and measured nutrient concentrations from surface and subsurface tile drainage outlets. Discharge and water quality vary widely across the ECB and are paramount to developing innovative management strategies that balance crop production goals with environmental targets. Discharge is generally greater from subsurface tile drainage compared to surface runoff. Phosphorus concentrations are typically greater in the surface runoff compared to tile drainage, while nitrogen concentrations are greater in subsurface tile drainage. The ECB-WQ database was developed to better facilitate understanding of water quantity and quality within this unique, systematic, artificially tile-drained region and is critical for understanding implications of field management practices, quantifying environmental and production processes, constraining hydrology/water quality models, and informing future water quality policies.

Predictive Model of Self-Management and Quality of Life for Patients on Hemodialysis Using Information-Motivation-Behavioral Skills Model: A Cross-Sectional Study.

The aim of this study was to develop and validate a predictive model to enhance self-management and quality of life in patients undergoing hemodialysis, using the Information-Motivation-Behavioral (IMB) skills model. A cross-sectional study design was employed, with data collected from 245 participants selected through convenience sampling from seven hemodialysis centers in the Republic of Korea. Data were gathered using a structured questionnaire and analyzed with descriptive statistics, Pearson's correlation coefficients, and path analysis, utilizing Statistical Package for the Social Sciences 27 and Amos 23.0. In patients undergoing hemodialysis, family income, self-management knowledge, attitude toward self-management, and social support were found to influence quality of life indirectly through self-efficacy and self-management. In contrast, comorbidities and duration of hemodialysis were found to have a direct impact on quality of life. The IMB model, incorporating individual-level factors proves to be an effective framework for predicting self-management and quality of life in patients undergoing hemodialysis. Enhancing self-management and quality of life remains a critical issue for this population.

Integrating a multi-dimensional deep convolutional neural network with optimized sample selection for landslide susceptibility assessment

ABSTRACT To address the errors of negative samples in landslide susceptibility modeling and traditional methods in exploring the regularities hidden in the evaluation factors, this paper proposes a stacking one- and three-dimensional Convolutional Neural Network (Stacking-1D-3D-CNN) landslide susceptibility assessment method considering sample optimization selection. First, in order to select negative samples rationally, this paper adopts the Relative Frequency Ratio combined with Certainty Factor Method (RFR-CFM) to determine the negative samples; secondly, the Stacking-1D-3D-CNN proposed is combined with RFR-CFM for the first time for landslide susceptibility assessment. In this work, the negative samples determined by RFR-CFM and the Information Quality Model (IQM) were combined with historical disaster points to form a total modeling sample, and modeled at different ratios. Finally, it is compared with several other models in terms of the landslide hazard susceptibility zoning results, prone zone statistics, and model performance. The findings show that the degree of spatial aggregation of training samples and testing samples has a much greater impact on the accuracy of landslide susceptibility modeling than the impact of their proportions. Furthermore, compared with other models, RFR-CFM-Stacking-1D-3D-CNN has the highest AUC value, precision, recall, F-score, and accuracy, which are 0.95, 0.83, 0.89, 0.85, and 84.76%, respectively, and the lowest RMSE and MAE, 0.39 and 0.15, respectively. This proves the RFR-CFM sample selection method’s rationality and the Stacking-1D-3D-CNN model’s effectiveness.

A Hybrid Distribution Network Model for Cost, Quality and Distribution Optimization in Perishable Goods Supply Chains

The entrenched role of syndicates and intermediaries in the perishable goods sector inflates prices through artificial scarcity and hoarding, creating significant affordability challenges. Addressing these issues is critical amid rising global demand for fresh produce, which is highly vulnerable to quality degradation due to Supply Chain (SC) inefficiencies and inadequate cold storage practices. Consequently, formulating an SC Distribution Network (SCDN) becomes imperative to optimize distribution planning, mitigate quality deterioration, and ensure the sustainability of the SC. This research proposes an advanced SCDN architecture by developing a mixed-integer linear programming (MILP) model tailored for multi-echelon scenarios. The model aims to minimize overall SC costs, reduce cold storage expenses, and preserve the freshness of perishable goods through an efficient and hybrid distribution channel. The proposed model integrates competing objectives by addressing a multi-criteria problem via the weighted sum method (WSM) and is executed using the GUROBI optimizer in Python. Two case studies centred on the distribution of Mango and Jack fruits in Bangladesh validate the model's practicality. The findings highlight the strategic importance of optimal distribution center placement and a dual supply strategy, with plants meeting 63% of mango and 53% of jackfruit demand, reducing reliance on intermediaries by advocating direct shipping to markets while bypassing cold storage. This study further highlights the model's robustness and offers critical managerial insights, facilitating informed decision-making in the complex landscape of perishable product supply chains.

Monitoring the trends of carbon monoxide and tropospheric formaldehyde in Edo State using Sentinel-5P and Google Earth Engine from 2018 to 2023.

This research was carried out to assess the concentrations of carbon monoxide (CO) and formaldehyde (HCHO) in Edo State, Southern Nigeria, using remote sensing data. A secondary data collection method was used for the assessment, and the levels of CO and HCHO were extracted annually from Google Earth Engine using information from Sentinel-5-P satellite data (COPERNISCUS/S5P/NRTI/L3_) and processed using ArcMap, Google Earth Engine, and Microsoft Excel to determine the levels of CO and HCHO in the study area from 2018 to 2023. The geometry of the study location is highlighted, saved and run, and a raster imagery file of the study area is generated after the task has been completed with a 'projection and extent' in the Geographic Tagged Image File Format (.tiff) and downloaded from the Google Drive and saved into folders, imported into the ArcMap for data processing and Excel worksheet for analysis. The raster data were collected annually for each pollutant with the 'filterDate = year-01-01; year-12-31'. Results showed that the annual mean concentrations of CO ranged from '4.67 × 10-2mol/m2' to '5.34 × 10-2mol/m2'. The maximum concentration was found in the year 2018 and the minimum was found in the year 2023, a relatively high concentration of CO may lead to the formation of carboxyhaemoglobin which decreases the capacity of the blood to transport oxygen causing lung cancer, heart problems, respiratory conditions and damage to other organs. While the annual mean concentrations of HCHO ranged from '1.89 × 10-4mol/m2' to '2.18 × 10-4mol/m2', the maximum concentration was found in the year 2021 and the minimum was found in the year 2019, increasing concentration of HCHO may be due to biomass burning and the combustion of methane (CH4 gas), and can cause nasopharyngeal cancer in humans. Based on the result of this study, constant monitoring of the air quality and atmospheric pollutants to ensure early detection of a decrease or increase in the concentration of atmospheric pollutants, implementation of air pollution control policies, spatial data collection, air quality modelling, hotspot identification and source distribution using the geographic information system (GIS), promotion of cleaner technologies, including the use of low-emission vehicles and renewable energy sources, public awareness and education on the impact of atmospheric pollutants and the human contributions to the increasing production of atmospheric pollutants are highly recommended.

Predicting high-quality ecologically suitable areas of Astragalus mongholicus Bunge based on secondary metabolites content using Biomod2 model

Habitat requirements and species’ ecological suitability are essential conditions for species conservation and management. Under the influence of different environmental variables, assessing the habitat quality of medicinal plants is an important issue to ensure the quality of medicinal plants and protect biodiversity. This study explores the impact of environmental variables on the distribution of Astragalus mongholicus Bunge (A. bunge) and the content of effective secondary metabolites (SMs). Combining the Biomod2 model, we constructed a Comprehensive Geospatial Quality Model (CGQM) for A. bunge under current environmental conditions, obtaining a high-quality comprehensive suitable area for A. bunge. The results indicate that the area of the comprehensive high-suitable zone is 4.53 × 105 km2, accounting for 21.91% of the suitable area for A. bunge. It is mainly distributed in the eastern and western parts of Shanxi, the northwest of Shaanxi, the southern part of Ningxia, the southeast of Gansu, the northern part of Hebei, the eastern part of Beijing, the northeastern part of Jilin, the central and southern parts of Inner Mongolia, and a very small part of Xinjiang.

Evaluation of Bayesian Maximum Entropy Data Fusion Approaches to Estimate Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes and to Inform Epidemiological Analyses in the US Gulf States.

The Gulf States are home to industries emitting styrene, benzene, toluene, ethylbenzene, and xylenes (SBTEX). Presently, adverse health effects of ambient SBTEX exposure in highly polluted regions, such as the Gulf States, must be evaluated. Epidemiologists, however, are limited by inadequate estimates of ambient SBTEX. Using Bayesian Maximum Entropy, SBTEX estimation methods of varying resource intensity were evaluated, including simple kriging (least intense), incorporation of observational and emissions data trends (moderately intense), and data fusion of observed and Comprehensive Air quality Model with extensions (CAMx) data (most intense). Generally, as resource intensity increased, so did SBTEX estimation performance, where SBTEX Spearman R values increased by 0.48 on average from the least to most intense methods. Data fusion of observed and CAMx data was identified as the best ambient SBTEX estimation method in the Gulf States. Exposure estimates revealed that Gulf States residences within commuting distance of high industrial activity experienced 1.64 times higher 97.5th percentile daily exposures to SBTEX on average than those living in less industrialized areas, which could contribute to total occupational and ambient exposure disparities. Furthermore, ambient benzene exposure was greater than the acceptable one-in-a-million excess cancer risk threshold for 75% of estimated residence locations in the Gulf States.

Uncertainty analysis of linear and non-linear regression models in the modeling of water quality in the Caspian Sea basin: Application of Monte-Carlo method

Uncertainty analysis of linear and non-linear regression models in the modeling of water quality in the Caspian Sea basin: Application of Monte-Carlo method

The impact of product innovation and service quality on survival recovery of msmes: the mediating role of financial performance

Introduction: The innovation business model explores the development of micro, small, and medium enterprises (MSMEs) in Indonesia between two time periods. In describing the condition of MSMEs, those who regulate MSMEs and policymakers in general are expected to help small and medium-sized enterprises improve service quality and product innovation for performance during the pandemic. Methods: This study employs a quantitative and purposive sampling approach based on a questionnaire disseminated to MSMEs in Indonesia as the research object. Data analysis was conducted using Stata version 17 for multiple linear regression analysis and the classic assumption test with 218 respondents. There are two time periods, namely before 2019–2020 and during the COVID-19 pandemic in 2021–2022. Before and during the pandemic, the innovation business model of service quality and product innovation had a positive influence on MSMEs' performance in Indonesia. Results: This became clear when we compared the quality of service, product innovation, and customer loyalty separately, contrasting the regression results of the circumstances before and during the pandemic and their impact on Indonesian MSMEs. A service quality approach (SERVQUAL) was adopted to measure service quality, which discusses five quality aspects: tangibility, reliability, responsiveness, assurance, and empathy. Conclusions: This approach bears similarities to product innovation and performance variables, whose analyses parallel those of the quality-of-service variable.

TracMyAir smartphone application for modeling exposures to PM2.5 and ozone - Integration with air quality networks and location-activity sensors.

Epidemiologic studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations from central-site monitors or air quality model estimates as exposure surrogates, which can result in exposure errors. We previously developed an exposure model called TracMyAir, which is an iPhone application that determines seven tiers of individual-level exposure metrics for ambient PM2.5 and O3 using outdoor concentrations, home building characteristics, weather, time-activities. The exposure metrics with increasing information needs and complexity include: outdoor concentration (Cout, Tier 1), building infiltration factor (Finf, Tier 2), indoor concentration (Cin, Tier 3), time spent in microenvironments (ME) (TME, Tier 4), personal exposure factor (Fpex, Tier 5), exposure (E, Tier 6), and inhaled dose (D, Tier 7). In this study, we extended TracMyAir with two sets of additional features: (1) time-resolved exposures using smartphone geolocations with a ME classification model (MicroTrac) and official PM2.5 and O3 monitoring network, and (2) exposures based on low-cost outdoor PurpleAir (PA) PM2.5 monitoring network, non-ambient indoor PM2.5 using indoor-outdoor PA monitors, and inhaled dose based on physical activity data from smartphone and smartwatch. To demonstrate the two sets of extended features, we applied TracMyAir to estimate hourly PM2.5 and O3 exposure metrics for two corresponding panel studies with participants living in central North Carolina, USA. For Tier 4, the MicroTrac estimates were compared with 24-h diary data, and correctly classified the ME for 97% of the daily time spent by the participants. Overall, the TracMyAir estimates showed considerable temporal and building-to-building variability of Finf, and Cin (Tiers 2-3), and person-to-person variability of Cout, TME, Fpex, E, and D (Tiers 1, 4-7). Our study demonstrates the capability of extending TracMyAir with air quality monitors, location-activity sensors, and models to determine fine-scale exposures, in support of epidemiologic studies and public health strategies to help reduce exposures to air pollutants.

Estimating two time-varying reaction coefficients in a water quality model from inexact initial and boundary data

Estimating two time-varying reaction coefficients in a water quality model from inexact initial and boundary data

Modeling of the qualitative state of oilseeds from soybean seeds by multifactorial analysis of factor areas

The production of oil products in Ukraine is a leader not only in the European but also in the world market of food and processing industry products, therefore, the priority areas of scientific research in this field are the effective assessment of the quality of the oil obtained. Existing methods for assessing this product by organoleptic characteristics are subjective and do not allow the use of parameters that require more complex measurement technologies; which in general makes it impossible to conduct a comprehensive analysis of the quality of the studied product. The purpose of this study was to develop and implement a mathematical modeling method for numerical analysis of the quality of soybean oil product varieties. Qualitative assessment of the studied oil varieties was carried out using such indicators as the mass fraction of fat, peroxide, and acid number; content of free fatty acids, phosphatides, moisture, and impurities; it allowed to perform a numerical analysis using dimensionless complexes regarding the condition of the studied oil product varieties and compliance with regulatory indicators. When processing experimental data and calculating the developed quality criteria, programming methods, Excel, and COMPASS mathematical environments were used, which allowed the construction of geometric quality models and the necessary assessment criteria. According to the results of the research, it turned out that the characteristics of the studied varieties "OAS Avatar", "ES Mentor", "Sigalia", "ES Mentor" and "Gallek" do not meet the requirements for compliance with the quality thresholds by 160 - 220%, however, their current characteristics, which were selected for assessment, are within the average normative indicators. The proposed mathematical models can be implemented for practical application in assessing the condition of any complex object of research using an unlimited number of characteristics. At the same time, the main requirements for these parameters are their breadth of coverage of the physical, mechanical, and chemical-biological properties of the product and the ability to provide an objective and thorough assessment of its condition.

A Novel Complexity Scoring Model for Non-Functional Requirements in Request for Proposal in Housing Industry Using FURPS Quality and Moscow Priority Model

In this research paper we have attempted to elicit Non-Functional Requirements (NFR) which may or not have been explicitly added to the Request for Proposal (RFP) in housing industry but is important for the success of the program. We have used real time RFPs for the elicitation of NFRs. The sales proposal process, also known as response to RFP process, refers to the methodical steps a vendor takes when developing a proposal in response to a buyer’s RFP which will have details of integrating systems, Software platforms, users, business outcomes, limitations, functional requirements, and may be non-functional requirements (NFR). There is some work done by researchers on assessing opaqueness of NFRs and traceability of NFR. But there has been no work on a complexity scoring model for NFRs which enables a vendor to respond to an RFP with best price and schedule. The paper proposes a novel complexity scoring model for NFRs in RFP in Housing industry by using FURPS (Functionality, Usability, Reliability, Performance, Supportability) quality attribute model in conjunction with MoSCoW(Must Have, Should Have, Could Have, Won’t Have) priority model by using mathematical formula. The working model is broken down in steps for Sales and pre-Sales teams of the Vendor and is ready for adoption.