Development Of Quality Model Research Articles

Design and development of software quality forensics framework and model

The illicit activist tampers the software for malcious purposes. The process of forensics examining and gathering evidence for software accused in forensic laboratories presents challenges in terms of software quality. Therefore, it is crucial to design and develop a framework, model, and dedicated standards for the forensic analysis of software quality. These resources can be employed for investigations, legal proceedings, as well as establishing criteria and standards for both past and present scenarios. The aim of the research work is to design a software-quality forensics framework for forensic investigation to the quality of software. The research primarily concentrates on developing a model for software quality assessment and identifying the pertinent existing standards through the conceptual framework of software quality forensics. The implementation of software quality forensics has improved the efficiency and effectiveness in investigating and managing incidents related to software quality. The proposed framework for software quality forensics allows for the evaluation and assessment of quality issues within specific software systems. To complement the software quality forensic framework, a comparative analysis has been conducted between existing software quality models and the proposed software quality forensic model.

Long-term airborne measurements of pollutants over the United Kingdom to support air quality model development and evaluation

Abstract. The ability of regional air quality models to skilfully represent pollutant distributions throughout the atmospheric column is important to enabling their skilful prediction at the surface. This provides a requirement for model evaluation at elevated altitudes, though observation datasets available for this purpose are limited. This is particularly true of those offering sampling over extended time periods. To address this requirement and support evaluation of regional air quality models such as the UK Met Offices Air Quality in the Unified Model (AQUM), a long-term, quality-assured dataset of the three-dimensional distribution of key pollutants was collected over the southern United Kingdom from July 2019 to April 2022. Measurements were collected using the Met Office Atmospheric Survey Aircraft (MOASA), a Cessna 421 instrumented for this project to measure gaseous nitrogen dioxide, ozone, sulfur dioxide and fine-mode (PM2.5) aerosol. This paper introduces the MOASA measurement platform, flight strategies and instrumentation and is not intended to be an in-depth diagnostic analysis but rather a comprehensive technical reference for future users of these data. The MOASA air quality dataset includes 63 flight sorties (totalling over 150 h of sampling), the data from which are openly available for use. To illustrate potential uses of these upper-air observations for regional-scale model evaluation, example case studies are presented, which include analyses of the spatial scales of measured pollutant variability, a comparison of airborne to ground-based observations over Greater London and initial work to evaluate performance of the AQUM regional air quality model. These case studies show that, for observations of relative humidity, nitrogen dioxide and particle counts, natural pollutant variability is well observed by the aircraft, whereas SO2 variability is limited by instrument precision. Good agreement is seen between observations aloft and those on the ground, particularly for PM2.5. Analysis of odd oxygen suggests titration of ozone is a dominant chemical process throughout the column for the data analysed, although a slight enhancement of ozone aloft is seen. Finally, a preliminary evaluation of AQUM performance for two case studies suggests a large positive model bias for ozone aloft, coincident with a negative model bias for NO2 aloft. In one case, there is evidence that an underprediction in the modelled boundary layer height contributes to the observed biases at elevated altitudes.

Development of service quality model computing ridership of metro rail system using fuzzy system

Gradual increase in the pace of urbanization has brought an increase in demand for rapid transit systems throughout the nation. An increase in traffic congestion on roads has led to the recent advent of the Mass rapid transit system thereby accelerating the commuter's interest to prefer them over the conventional railway system. Over the past few years, rapid transit systems like metros have been trying to maintain a steady count of ridership by attracting new commuters while retaining the existing commuters. Thus, it has become routine for organizations to identify the service parameters that serve as the essence of commuter satisfaction or dissatisfaction by implementing additional necessary changes based on commuter perceptions. The study is focused on the service quality measures adopted by the Chennai metro rail operations which have led them to maintain a steady pace of passengers amidst the pandemic season. The resultant statistical analysis and fuzzy evaluation method are confined to about 74.6% indicating decent fitness of the sample size. The significance value of coach conditioning amenities is 0.002 and the ambiance facility is 0.003, further indicating the need to maintain their service pace. Hospitality facility that accounts for a significance value of about 0.000 needs the most improvement to influence the overall service quality satisfaction of commuters towards improving their ridership statistics.

Model development and calibration of two-dimensional population balance model for twin-screw wet granulation based on particle size distribution and porosity

Population balance models (PBMs) have been widely studied for the simulation of continuous twin-screw wet granulation. Due to the limited data availability and computational efforts, one-dimensional PBM is the most popular application to the pharmaceutical industry. While multi-dimensional PBMs can predict multiple granule quality attributes, model development and calibration has been the big challenges. This paper presents a two-dimensional compartmental PBM which compute granule size and porosity. Granulation experiments were performed for two formulations and two process settings, followed by granule characterizations. In PBM development, consolidation was newly introduced, whereas the aggregation and breakage kernels were based on the previous model. Calibration results proved that the model can simulate granule size and porosity simultaneously for different formulations and process settings. Some cases show porosity increase that needs to be considered in the future. Further experiments and model development can increase the applicability of the two-dimensional PBM for the pharmaceutical industry.

A machine learning framework supporting prospective clinical decisions applied to risk prediction in oncology

We present a general framework for developing a machine learning (ML) tool that supports clinician assessment of patient risk using electronic health record-derived real-world data and apply the framework to a quality improvement use case in an oncology setting to identify patients at risk for a near-term (60 day) emergency department (ED) visit who could potentially be eligible for a home-based acute care program. Framework steps include defining clinical quality improvement goals, model development and validation, bias assessment, retrospective and prospective validation, and deployment in clinical workflow. In the retrospective analysis for the use case, 8% of patient encounters were associated with a high risk (pre-defined as predicted probability ≥20%) for a near-term ED visit by the patient. Positive predictive value (PPV) and negative predictive value (NPV) for future ED events was 26% and 91%, respectively. Odds ratio (OR) of ED visit (high- vs. low-risk) was 3.5 (95% CI: 3.4–3.5). The model appeared to be calibrated across racial, gender, and ethnic groups. In the prospective analysis, 10% of patients were classified as high risk, 76% of whom were confirmed by clinicians as eligible for home-based acute care. PPV and NPV for future ED events was 22% and 95%, respectively. OR of ED visit (high- vs. low-risk) was 5.4 (95% CI: 2.6–11.0). The proposed framework for an ML-based tool that supports clinician assessment of patient risk is a stepwise development approach; we successfully applied the framework to an ED visit risk prediction use case.

Quality Models for Artificial Intelligence Systems: Characteristic-Based Approach, Development and Application

The factors complicating the specification of requirements for artificial intelligence systems (AIS) and their verification for the AIS creation and modernization are analyzed. The harmonization of definitions and building of a hierarchy of AIS characteristics for regulation of the development of techniques and tools for standardization, as well as evaluation and provision of requirements during the creation and implementation of AIS, is extremely important. The study aims to develop and demonstrate the use of quality models for artificial intelligence (AI), AI platform (AIP), and AIS based on the definition and ordering of characteristics. The principles of AI quality model development and its sequence are substantiated. Approaches to formulating definitions of AIS characteristics, methods of representation of dependencies, and hierarchies of characteristics are given. The definitions and harmonization options of hierarchical relations between 46 characteristics of AI and AIP are suggested. The quality models of AI, AIP, and AIS presented in analytical, tabular, and graph forms, are described. The so-called basic models with reduced sets of the most important characteristics are presented. Examples of AIS quality models for UAV video navigation systems and decision support systems for diagnosing diseases are described.

Software Requirements Profile Quality Model

Article opens series of works devoted to profile-oriented software quality assessment. In this article the concept of software requirements profiling for subsequent software requirements profile quality assessment is analyzed and developed. The main result of the article is the development of the software requirements profile quality model. The model describes the following: characteristics and attributes of software requirements and their classification features; characteristics and attributes of software requirements profile and their classification features; semantics and syntax of software requirements. The article is based on analysis and use of the following standards: ISO/IEC 25012:2008, ISO/IEC/IEEE 29148:2018, ISO/IEC/IEEE 29148:2011. Examples of software requirements profile quality model are introduced. The suggested approach is used for the development of terms of reference or draft of a new standard. For example, the model is used for an assessment draft of the new standard “Requirements to computer security of NPP Instrumentation and Control Systems (NPP I&C)” developed by the Ukrainian state regulatory body. As a result of the development of the software requirements profile quality model, a set of propositions for improving the quality of the standard as a branch of the profile for NPP I&C cybersecurity are implemented.

On the intersection between data quality and dynamical modelling of large-scale fMRI signals

Large-scale dynamics of the brain are routinely modelled using systems of nonlinear dynamical equations that describe the evolution of population-level activity, with distinct neural populations often coupled according to an empirically measured structural connectivity matrix. This modelling approach has been used to generate insights into the neural underpinnings of spontaneous brain dynamics, as recorded with techniques such as resting state functional MRI (fMRI). In fMRI, researchers have many degrees of freedom in the way that they can process the data and recent evidence indicates that the choice of pre-processing steps can have a major effect on empirical estimates of functional connectivity. However, the potential influence of such variations on modelling results are seldom considered. Here we show, using three popular whole-brain dynamical models, that different choices during fMRI preprocessing can dramatically affect model fits and interpretations of findings. Critically, we show that the ability of these models to accurately capture patterns in fMRI dynamics is mostly driven by the degree to which they fit global signals rather than interesting sources of coordinated neural dynamics. We show that widespread deflections can arise from simple global synchronisation. We introduce a simple two-parameter model that captures these fluctuations and performs just as well as more complex, multi-parameter biophysical models. From our combined analyses of data and simulations, we describe benchmarks to evaluate model fit and validity. Although most models are not resilient to denoising, we show that relaxing the approximation of homogeneous neural populations by more explicitly modelling inter-regional effective connectivity can improve model accuracy at the expense of increased model complexity. Our results suggest that many complex biophysical models may be fitting relatively trivial properties of the data, and underscore a need for tighter integration between data quality assurance and model development.

Development of best practices in physiologically based pharmacokinetic modeling to support clinical pharmacology regulatory decision-making-A workshop summary.

Development of best practices in physiologically based pharmacokinetic modeling to support clinical pharmacology regulatory decision-making-A workshop summary.

Community Mapping Supports Comprehensive Urban Flood Modeling for Flood Risk Management in a Data-Scarce Environment

In this paper we demonstrate a framework for urban flood modelling with community mapped data, particularly suited for flood risk management in data-scarce environments. The framework comprises three principal stages: data acquisition with survey design and quality assurance, model development and model implementation for flood prediction. We demonstrate that data acquisition based on community mapping can be affordable, comprehensible, quality assured and open source, making it applicable in resource-strained contexts. The framework was demonstrated and validated on a case study in Dar es Salaam, Tanzania. The results obtained show that the community mapped data supports flood modelling on a level of detail that is currently inaccessible in many data-scarce environments. The results obtained also show that the community mapping approach is appropriate for datasets that do not require extensive training, such as flood extent surveys where it is possible to cross-validate the quality of reports given a suitable number and density of data points. More technically advanced features such as dimensions of urban drainage system elements still require trained mappers to create data of sufficient quality. This type of mapping can, however, now be performed in new contexts thanks to the development of smartphones. Future research is suggested to explore how community mapping can become an institutionalized practice to fill in important gaps in data-scarce environments.

Development and application of user review quality model for embedded system

Software industry is poised for robust and sustained growth. Many software products / applications are being developed. It is found that the software so developed must be free from defects / bugs. One major area that requires concentration is requirements analysis. The requirements from various stakeholders must be received and analyzed for their relevance in the final product. Some prioritization analysis is also required to consider some requirements over the others. Kano and AHP are known to help the developers in analyzing relative strengths of various requirements. However, after defining the requirements clearly, the developed product must be free of defects. SWFMEA is a technique to ensure the defect-free development of the software. Software User Review Defect Corrective Model (SURDCM), which has Kano and AHP models for requirements analysis cum prioritization and SWFMEA to detect possible defects and their respective effects, is used for development of a software product. It is expected that with the use of this model, the user involvement is increased to clearly specify the requirements and the possible defects and the effects are analyzed. This model encourages the user participation throughout the development of the product.

Multi-Model Standard for Bitstream-, Pixel-Based and Hybrid Video Quality Assessment of UHD/4K: ITU-T P.1204

The paper presents a series of three new video quality model standards for the assessment of sequences of up to UHD/4K resolution. They were developed in a competition within the International Telecommunication Union (ITU-T), Study Group 12, in collaboration with the Video Quality Experts Group (VQEG), over a period of more than two years. A large video quality test set with a total of 26 individual databases was created, with 13 used for training and 13 for validation and selection of the winning models. For each database, video quality laboratory tests were run with at least 24 subjects each. The 5-point Absolute Category Rating (ACR) scale was used for rating, calculating Mean Opinion Scores (MOS) as ground-truth. To represent today's commonly applied HTTP-based adaptive streaming context, the test sequences comprise a variety of encoding settings, bitrates, resolutions and framerates for the three codecs H.264/AVC, H.265/HEVC and VP9, applied to a wide range of source sequences of around 8 s duration. Processing was carried out with an FFmpeg-based processing chain developed specifically for the competition, and via upload and encoding through exemplary online streaming services. The resulting data represents the largest, lab-test-based dataset used for video quality model development to date, with a total of around 5,000 test sequences. The paper addresses the three models ultimately standardized in the P.1204 Recommendation series, resulting in different model types and for different applications: (i) Rec. P.1204.3, no-reference bitstream-based, with access to encoded bitstream information; (ii) P.1204.4, pixel-based, using information from the reference and the processed video, and (iii) P.1204.5, no-reference hybrid, using both bitstream-and pixel-information without knowledge of the reference. The paper outlines the development process and provides holistic details about the statistical evaluation, test databases, model algorithms and validation results, as well as a performance comparison with state-of-the-art models.

Software Component Quality Model

In Component Based Software Development (CBSD), applications are built from existing components either by assembling or replacing software parts. Reusing components may lead to faster software development and subsequently reduce cost and provide higher product quality. In CBSD, software component models define what components are and how they compose. However, no research has been done to assess the quality of software component models, to assess the characteristics of software component design. This paper proposed a software component quality model specifically to answer the question what characteristics make good component. A Systematic Literature Review (SLR) has been conducted by defining a robust protocol that combines automatic searches from different sources. The finding of the SLR has contributed to the development of quality model for CBSD, i.e. a proposed component quality model with metrics which is specific to software component design.

Evaluation of the Teaching Quality Model and its Relationship with Students’ Academic Performance through Partial Least Squares-Structural Equation Model

Many institutes of higher learning (IHL) globally has implemented student evaluation of teaching (SET) in evaluating teaching quality among lecturer, The implementation of SET not only enhance the standard of teaching and learning and give an impact on students’ academic performance but also as critical decisions such as promotion, and for accreditation and governmental agencies that require such evaluations. Among the crucial components of SET were planning, teaching strategy, students’ participation, coursework assessment, soft skills and course quality. The study withal strives the development of teaching quality model by means of a SET. This study seek to rectify the argument that the teaching quality measured by a SET contributes to students’ academic performance. The teaching quality model and its relationship with students’ academic performance were evaluated by using Partial Least Squares-Structural Equation Model (PLS-SEM) approach as the sample size was too small to utilize Structural Equation Modelling-Analysis of Moment Structure (SEM-AMOS). A purposive sampling was utilized in this study involving 93 undergraduate students of Sultan Idris Education University's (UPSI) Mathematics Education Degree (BEd Maths) program. From the analysis, it revealed that all the relationships in the developed model were significant at p<0.001. The results indicated that the developed model was strengthened by empirical data and in-line with the preceding findings and theoretical framework. A part of teaching quality and students’ academic performance path model, the study also prosperously validated all the indicator variables depicted in SET constructs, these were planning, teaching strategy, students’ participation, coursework assessment, soft skills and course quality by means of structural equation model through PLS-SEM approach. In conclusions, the relationship between teaching quality and students’ academic performance not only be expressed in lower order components in PLS-SEM but also be modeled as a hierarchical component model where the teaching quality measured by a SET contributes to students’ academic performance and were supported by empirical data,

Effective Global Sensitivity Analysis for High-Dimensional Hydrologic and Water Quality Models

AbstractSensitivity analysis (SA) plays a vital role in hydrologic and water quality (H/WQ) model development and reliability assessment. This paper performed the first formal global SA (GSA) evalu...

Development and validation of multi-dimensional wind turbine service quality scale for measuring operations and maintenance service quality in wind energy sector

Service quality and customer satisfaction are very important concepts that organisations must understand in order to remain competitive in business and hence grow. It is very important for any organisation to know how to measure these dimensions from the consumers' perspective in order to better understand their needs and hence satisfy them. Service quality is considered very important because it leads to higher customer satisfaction, profitability, reduced cost, customer loyalty and retention. The aim of the study is to develop and validate a multi-dimensional scale for measuring wind turbine service quality (WTSQ). The instrument consists of 42 items operationalising three dimensions namely functional, technical and corporate quality. The psychometric properties of the questionnaire were assessed through a confirmatory factor analysis and structural equation modelling using the responses from 352 customers of wind turbine industry in India. The study findings indicated that the assessed instrument had three oblige factor structure and also has good reliability and validity. The paper contributes to the development of multi-dimensional service quality model in the wind turbine sector.

Development and validation of multi-dimensional wind turbine service quality scale for measuring operations and maintenance service quality in wind energy sector

Service quality and customer satisfaction are very important concepts that organisations must understand in order to remain competitive in business and hence grow. It is very important for any organisation to know how to measure these dimensions from the consumers' perspective in order to better understand their needs and hence satisfy them. Service quality is considered very important because it leads to higher customer satisfaction, profitability, reduced cost, customer loyalty and retention. The aim of the study is to develop and validate a multi-dimensional scale for measuring wind turbine service quality (WTSQ). The instrument consists of 42 items operationalising three dimensions namely functional, technical and corporate quality. The psychometric properties of the questionnaire were assessed through a confirmatory factor analysis and structural equation modelling using the responses from 352 customers of wind turbine industry in India. The study findings indicated that the assessed instrument had three oblige factor structure and also has good reliability and validity. The paper contributes to the development of multi-dimensional service quality model in the wind turbine sector.

A restaurant quality model based on marketing factors

Previous studies have highlighted the importance of several quality factors in guests’ assessment of restaurant quality. As there is no general consensus on which marketing quality factors really matter in assessing the quality of the dining experience in the previous literature, this paper describes and tests the development of a marketing-oriented Restaurant Quality Model (RQM 7P). In addition, this study tests whether guests’ perceive overall restaurant quality offerings as coherent. Special attention was devoted to a marketing perspective (7P). Following the discussion of the conceptualization and operationalization of the restaurant quality construct, the procedures used in constructing and refi ning a multiple-item scale to measure restaurant quality are described. Evidence of the scale’s reliability, factor structure and validity on the basis of analysing data is presented next. A sample of 1,998 valid questionnaires obtained by domestic guests in different restaurant facilities in Slovenia is analysed. Results show that guests perceive restaurant quality according to three marketing quality dimensions – (1) people, (2) placement, and (3) product (food) and physical evidences, while other marketing factors are not statistically signifi cant in determining restaurant quality. In addition, the results indicate that guests perceive restaurant quality offerings as highly incoherent. From a theoretical point of view, this study contributes by developing a restaurant quality model based on marketing factors. In addition, it is the fi rst study to test the perception of all seven marketing dimensions in the restaurant industry, and it provides insights regarding how guests perceive restaurant offering coherency. This research has thrown up many questions in need of further investigation. Future research should include a cross-national study involving different segments of guests in different types of food and beverage facilities.

The Influence of Organizational Innovation towards Internal Service Quality in MBKS

Internal service quality and organizational innovation are really important in satisfying the internal and external customers’ expectation and needs. Organizational innovation is actually one of the significant factors that can affect the internal service quality of the organization. This paper describes the development of Innovative Internal Service Quality (IISQ) Model in measuring the influence of organizational innovation (based on its three main determinants) towards internal service quality (reliability, responsiveness, tangibles, assurance and empathy) in MBKS while gender acts as the moderating factor. The paper examines three main determinants of organizational innovation (innovation leadership, managerial levers and business processes) which can affect the internal service quality of an organization as well as describes how those three determinants affect the internal service quality. It will help to provide future researchers with literature in studying the influence of organizational innovation towards internal service quality. This paper can contribute in developing an improved model for organizational innovation and internal service quality.

Post-Model Validation of a Deterministic Watershed Model Using Monitoring Data

. Data are often collected during or after hydrological and water quality (H/WQ) model development, thus limiting the ability for direct comparison or use in calibration and validation. In this study, we demonstrate a way to validate the performance of three previously performed watershed simulations by comparing SWAT-simulated loads prior to 2011 with loads estimated from monitoring data collected after the SWAT models were completed. Water quality data, including total suspended solids, total phosphorus, and nitrate-nitrogen concentrations, were collected from the three watersheds from 2011 through 2013. Calibration and validation for WQ was accomplished for the Poteau River watershed using historical monitoring data. However, similar data were not available for the Strawberry and Upper Saline River watersheds, and their subsequent SWAT models were not calibrated or validated for WQ. A combination of regression, residual, and ANOVA analyses was used to post-validate the SWAT models beyond their simulation periods. Results indicated that the SWAT-simulated loads were in general agreement with the loads estimated from monitoring data for the Poteau and Saline River watersheds, but not for the Strawberry River watershed. A comparison of constituent loads modeled using SWAT and estimated from monitoring data for the Strawberry River watershed resulted in statistically different slopes and residuals and dissimilar ellipse orientation, range, and variance for each constituent. Load comparisons for the Saline and Poteau River watersheds using these same techniques resulted in variable slope and residual significance but improved ellipse orientation, range, and variance for each constituent. The approach used in this study was sufficient for analyzing the adequacy of simulated loads and could inform users concerning model performance when an uncalibrated model is used to make policy decisions and allocate resources.