Quantitative Quality Evaluation Research Articles

Water quality assessment and its pollution source analysis from spatial and temporal perspectives in small watershed of Sichuan Province, China.

Rapid socio-economic development has led to many water environmental issues in small watersheds such as non-compliance with water quality standards, complex pollution sources, and difficulties in water environment management. To achieve a quantitative evaluation of water quality, identify pollution sources, and implement refined management in small watersheds, this study collected monthly seven water quality indexes of four monitoring points from 2010 to 2023, and ten water quality indexes of 23 sampling points in the Shiting River and Mianyuan River which are tributaries of the Tuojiang River Basin. Then, water quality evaluation and pollution source analysis were conducted from both temporal and spatial perspectives using the Water Quality Index (WQI) method, the Absolute Principal Component Scores/Multiple Linear Regression (APCS-MLR) method, and the Positive Matrix Factorization (PMF) receptor modeling technique. The results indicated that except for total nitrogen (TN), the concentrations of other water quality indexes exhibited a decreasing trend, and all were divided into two obvious stages before and after 2016. Furthermore, the proportion of water quality grade of Good and above increased from 73.96 to 84.94% from 2010-2015 to 2016-2023, and the water quality grade of Good and above from upstream to downstream dropped from 100 to 23.33%. From the temporal scale, four and five pollution sources were identified in the first and second stages, respectively. The distinct TN pollutant is mainly affected by agricultural non-point sources (NPS), whose impact is enhanced from 17.76 to 78.31%. Total phosphorus (TP) was affected by the phosphorus chemical industry, whose contribution gradually weakened from 50.8 to 24.9%. From a spatial perspective, four and five pollution sources were identified in the upstream and downstream, respectively. Therefore, even though there are some limitations due to the data availability of water monitory and hydrology data, the proposed research framework of this study can be applied to the water environmental management of other similar watersheds.

A Quantitative Evaluation of the Minnesota Long-Stay Resident Quality of Life Survey

A Quantitative Evaluation of the Minnesota Long-Stay Resident Quality of Life Survey

Enhancing automated fiber placement process monitoring and quality inspection: A hybrid thermal vision based framework

Automated Fiber Placement (AFP) has revolutionized composite manufacturing, yet quality assurance remains challenging due to the significant impact of emerging defects on part quality and the current reliance on time-consuming manual inspection protocols. This paper presents a comprehensive hybrid framework that enhances AFP process monitoring and quality inspection by integrating thermal vision with innovative methodologies. Our framework combines model-based and data-driven algorithms across three modules to address key AFP inspection tasks, including in-situ monitoring, dynamic tow identification, defect detection, segmentation, localization, and quantitative lay-up quality evaluation. The setup-independent spatial–temporal analysis algorithm estimates tow boundaries with sub-pixel accuracy. An optimized SVM classifier, trained on an extensive AFP defect database, achieves a defect detection accuracy of 96.4% and an F1-score of 96.43%, meeting industry standards. The active contours-based segmentation and localization module provides critical qualitative traits such as defect shape, size, and location. Moreover, the novel Defect Area Percentage (DAP) metric enables precise quantitative defect impact evaluation at both the course and tow levels. By consolidating qualitative and quantitative outcomes, the system offers real-time high-level feedback for informed decision-making, significantly improving process performance and reducing machine downtimes. This proactive approach advances AFP process monitoring and quality inspection and positions our framework as a promising solution for next-generation composite manufacturing.

A Grid-Wide Comprehensive Evaluation Method of Power Quality Based on Complex Network Theory

To achieve a hierarchical and quantitative evaluation of grid-wide power quality in the distribution network, reflecting the overall power quality level of the distribution network, a comprehensive evaluation method for power quality in a grid-wide system based on complex network theory is proposed. Firstly, based on the propagation characteristics of power quality disturbances, a power quality evaluation index system is constructed. Secondly, to reflect the constraint effect of the local power quality level of nodes on the overall power quality level of the distribution system, corresponding indices such as improved node degree, improved node electrical betweenness, and node self-healing capability are proposed based on complex network theory, and the power quality influence degree of nodes is calculated. Then, the GRA-ANP (Grey Relational Analysis–Analytic Network Process) subjective weight calculation method is improved by introducing grey relational analysis to address the impact of differences in different decision-making results. Based on power quality monitoring data, the entropy weight method is used for objective weighting. To avoid the partiality of a single weight evaluation result, the game equilibrium algorithm is employed to calculate the comprehensive weight of each power quality index. Subsequently, considering the correlation and dependency among indices, the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method is used to obtain the power quality grade of each node. Combining this with the calculation of the power quality influence degree of nodes, the overall power quality grade of the distribution network is determined, achieving a hierarchical and quantitative evaluation of power quality in the entire distribution system. Finally, through a case study analysis of an improved 13-node distribution network, it is verified that the proposed method can fully extract data information and produce comprehensive and accurate power quality assessment results by comparing it with other methods. This provides strong support for the safe and stable operation of the distribution system and the subsequent optimization and management of power quality.

Evaluation of Groundwater Security under Engineering Environment in Geda Township Area,Tibe

Water body is one of the important factors of engineering spatial structure, and also the carrier of spatial activities. The Tibetan Plateau is known as the “third pole in the world”. It has a special geographical location and a large number of projects for engineering infrastructure. Therefore, how to build various major and large-scale projects under the plateau environment needs to be further strengthened to the importance of water bodies. In this paper, through the geological conditions in Geda Township, Tibet, Analysis and performance of water mechanism in the area, through the field discovery, The water direction in the survey area is controlled by the structure and terrain; at the same time, the project water supply guarantee is mainly for the qualitative and quantitative evaluation of water quality and quantity information, the water supply guarantee of the investigation area is mainly divided into surface water supply water guarantee and groundwater water supply guarantee. This paper describes the relevant content of groundwater protection, through the field investigation and field water quality collection, the basic situation in the investigation area is that: the groundwater quality is generally superior to that of the surface water, especially more than Jilin spring water, better water quality and larger amount of water; It is of guiding significance to water quality investigation and geological engineering construction in other areas.

Evaluation of the quality grade of Congou black tea by the fusion of GC-E-Nose, E-tongue, and E-eye

In the present study, the comprehensive quality of Congou black tea (CBT) including aroma, taste, and liquid color was investigated by a combination of gas chromatography electronic nose (GC-E-Nose), electronic tongue (E-tongue), and electronic eye (E-eye). An excellent discrimination of different quality grades of CBT was accomplished through the fusion of GC-E-Nose, E-tongue, and E-eye combined with orthogonal partial least squares discriminant analysis, with parameters of R2Y = 0.803 and Q2 = 0.740. Moreover, the quantitative evaluation of CBT quality was successfully achieved by partial least squares regression analysis, with the absolute error within 1.39 point, and the relative error within 1.62%. Additionally, 12 key variables were screened out by stepwise multiple linear regression analysis, which significantly contributed to the comprehensive quality score of CBT. Our results suggest that the fusion of multiple intelligent sensory technologies offers great potential and practicability in the quality evaluation of black tea.

Automated quantification of photoreceptor outer segments in developing and degenerating retinas on microscopy images across scales

The functionality of photoreceptors, rods, and cones is highly dependent on their outer segments (POS), a cellular compartment containing highly organized membranous structures that generate biochemical signals from incident light. While POS formation and degeneration are qualitatively assessed on microscopy images, reliable methodology for quantitative analyses is still limited. Here, we developed methods to quantify POS (QuaPOS) maturation and quality on retinal sections using automated image analyses. POS formation was examined during the development and in adulthood of wild-type mice via light microscopy (LM) and transmission electron microscopy (TEM). To quantify the number, size, shape, and fluorescence intensity of POS, retinal cryosections were immunostained for the cone POS marker S-opsin. Fluorescence images were used to train the robust classifier QuaPOS-LM based on supervised machine learning for automated image segmentation. Characteristic features of segmentation results were extracted to quantify the maturation of cone POS. Subsequently, this quantification method was applied to characterize POS degeneration in “cone photoreceptor function loss 1” mice. TEM images were used to establish the ultrastructural quantification method QuaPOS-TEM for the alignment of POS membranes. Images were analyzed using a custom-written MATLAB code to extract the orientation of membranes from the image gradient and their alignment (coherency). This analysis was used to quantify the POS morphology of wild-type and two inherited retinal degeneration (“retinal degeneration 19” and “rhodopsin knock-out”) mouse lines. Both automated analysis technologies provided robust characterization and quantification of POS based on LM or TEM images. Automated image segmentation by the classifier QuaPOS-LM and analysis of the orientation of membrane stacks by QuaPOS-TEM using fluorescent or TEM images allowed quantitative evaluation of POS formation and quality. The assessments showed an increase in POS number, volume, and membrane coherency during wild-type postnatal development, while a decrease in all three observables was detected in different retinal degeneration mouse models. All the code used for the presented analysis is open source, including example datasets to reproduce the findings. Hence, the QuaPOS quantification methods are useful for in-depth characterization of POS on retinal sections in developmental studies, for disease modeling, or after therapeutic interventions affecting photoreceptors.

Research on a New Soundscape Evaluation Method Suitable for Scenic Areas

Existing studies have focused mainly on the environmental quality of scenic spots, such as sufficient oxygen content in the air and a high concentration of negative oxygen ions. The perceptions of soundscape in scenic areas are generally good, but there are few reports on the quantitative evaluation of soundscape quality in scenic areas. In this study, we analysed existing methods for evaluating the soundscape of a landscape, evaluated the soundscape comfort of scenic spots, analysed and refined the natural environmental factors affecting the soundscape, and proposed for the first time to use physical environmental indicators such as the air temperature difference, relative humidity, natural illuminance ratio and wind speed as environmental evaluation variables. A quantitative method was used to calculate the soundscape comfort index (SSI) of the landscape. The physical environmental indicators related to famous scenic spots in China, namely, Qingcheng mountain field testing and a subjective soundscape of tourist satisfaction survey, were used to calculate the corresponding soundscape comfort index values, and a quantitative analysis of soundscape comfort and differences in temperature, relative humidity, the illumination ratio, and the correlation between the equivalent sound level A was performed. The measured values of the temperature difference and light ratio were significantly correlated with the soundscape comfort index. The distribution of sound landscape comfort was given by a GIS map, and soundscape comfort was evaluated scientifically. The correlations between soundscape comfort and landscape patch number (PN), landscape patch density (PD), diversity index (Shannon), and landscape shape index (LSI) were quantitatively analysed, which confirmed that the perception of soundscape comfort was affected by landscape space to different degrees. This study has scientific significance and application value for the soundscape evaluation of scenic areas and has significance for soundscape evaluation and design strategies for urban landscapes.

Editorial

بسم الله الرحمن الرحيم In the Name of Allah, Most Gracious, Most Merciful RESEARCH QUALITY AND IMPACT Excellence in research quality and consideration of the practical and social impact as well as the original contributions to the field of knowledge are important determinants in the evaluation of a manuscript for publication. Journals seek to publish manuscripts of high quality to increase their readership, attract high calibre authors, and importantly, increase their impact through more citations, thus getting them to be included in high-ranking index databases. A study by Margherita et al. (2022) specifically examined what represents quality in research practice and what are its characteristics. The authors proposed a multi-dimensional understanding of research quality and found 66 quality attributes that can be grouped into three aspects in the conduct of research: the research design: this relates to the conceptualisation of the research, its aims, methodology and assumptions, and would include attributes such as objectivity, interdisciplinarity, stringent argumentation, etc. the research process: this relates to the execution of the research activities, and would include attributes such as clarity, coherence, rigourousness, thoroughness, originality, conformance to ethics, etc. the research impact: this relates to the influence on academia, practitioners and the society, and would include attributes such as usefulness, novelty, generalizability, dissemination potential, social/political/educational/practical significance, etc. While the above study presents a comprehensive framework for determining research quality, historically, quantitative evaluation of academic research quality (and productivity of authors) has been based on an analysis of the number of publications and their received citations, measured by an author’s H-index. The H-index, short for Hirsch index, was developed by J.E.Hirsch as a quantitative metric to provide ‘an estimate of the importance, significance, and broad impact of a scientist’s cumulative research contributions’ (Hirsch, 2005, p. 16569). It includes an assessment of both the quantity of publications (by taking into account the number of papers published) and an approximation of the quality of those papers (by assessing their impact on the target audience, measured by the number of citations to these articles).

Max Explainability Score–A quantitative metric for explainability evaluation in knowledge graph-based recommendations

Applying machine learning (ML) models within recommender systems (RSs) has proven effective in achieving high recommendation accuracy. However, a prevalent drawback is their inherent lack of explicability. Integrating knowledge graphs (KGs) into RSs enhances interpretability by elucidating the reasoning behind specific recommendations. Explainability evaluation in RSs often relies on subjective metrics assessed using qualitative user feedback. Although they offer a robust foundation for evaluating explainability, they are susceptible to confirmation bias. Quantitative metrics for assessing the explainability of KG-driven RSs are currently unavailable. This study, thus, proposes novel metrics for the quantitative evaluation of explanation quality in KG-based RSs. The proposed metric, Max Explainability Score, is based on four evaluation parameters: the number of rules, the probability of the traversal path, the entropy value and the reward due to the chosen traversal paths. User studies employing these metrics offer enhanced validity and better integration potential with future XAI research.

Formulating a quality model for cloud-native software architectures: conceptual and methodological considerations

Interest in cloud computing is steadily increasing and the range of offerings is evolving due to continuous technological innovation. Hence, cloud-native has been established as a term for building applications in a way that maximally exploits benefits of modern cloud computing concepts. However, cloud-native as a topic is broad and the variety in cloud computing technologies is large. Thus, we identify a need in supporting developers and software architects who want to benefit from cloud-native concepts. We provide this support in the form of a quality model for cloud-native software architectures that explains how architectural characteristics impact different quality aspects. Our focus is on the design time and the aim is that architectural models of applications can be evaluated according to cloud-native characteristics and corresponding quality aspects. In this work we present our approach for formulating and validating the quality model for cloud-native software architectures as well as its current state. This presentation is based on previous work, especially a recently conducted validation survey that focused on the impacts of architectural characteristics on quality aspects. The new contribution of this work is the integrated presentation of our approach in a larger context of conceptual and methodological considerations. Further, revision of the quality model based on a repeated literature search for architectural measures is presented. We provide a more detailed look on the quality model, explaining exemplary product factors and their relevance within the topic of cloud-native. Our results provide a qualitative overview of characteristics associated with cloud native software architectures and lay the foundation for quantitative quality evaluations based on architectural models of applications.

Quantitative surface quality evaluation for 3D-printed concrete with coarse aggregate through 3D scanning

The undulating appearance of 3D-printed concrete (3DPC) is an essential feature that impacts the integrity and stability of the multilayer structure, yet the related evaluation parameters have rarely been proposed. This study proposed a quantitative evaluation and parameters through the on-site 3D scan technology to impel a direct and precise understanding of the surface quality of 3DPC, especially concrete with coarse aggregates. Firstly, 8-layer 3DPC walls are produced at four different printer printing rates and then 3D scanned. After point cloud correction, noise reduction, and 2D processing onto acquired points cloud, the evaluation parameters for evaluating 3DPC surface characteristics are defined and calculated. Results indicate that a higher 3D printer printing rate helps reduce surface fluctuation of 3DPC multilayer structures but disfavor the vertical flatness, of which the linear relationship between the parameters and the printing speed of the printer is explored and discussed. Recommendation of 3DPC straight wall structures controlled to surface defects ratio within 2% and slope within 0.2% is suggested. These conclusions provide precise external surface quality control and treatment basements that contribute to improving the practical application of 3DPC.

Deep Learning in the Phase Extraction of Electronic Speckle Pattern Interferometry

Electronic speckle pattern interferometry (ESPI) is widely used in fields such as materials science, biomedical research, surface morphology analysis, and optical component inspection because of its high measurement accuracy, broad frequency range, and ease of measurement. Phase extraction is a critical stage in ESPI. However, conventional phase extraction methods exhibit problems such as low accuracy, slow processing speed, and poor generalization. With the continuous development of deep learning in image processing, the application of deep learning in phase extraction from electronic speckle interferometry images has become a critical topic of research. This paper reviews the principles and characteristics of ESPI and comprehensively analyzes the phase extraction processes for fringe patterns and wrapped phase maps. The application, advantages, and limitations of deep learning techniques in filtering, fringe skeleton line extraction, and phase unwrapping algorithms are discussed based on the representation of measurement results. Finally, this paper provides a perspective on future trends, such as the construction of physical models for electronic speckle interferometry, improvement and optimization of deep learning models, and quantitative evaluation of phase extraction quality, in this field.

Quantitative Quality Evaluation of the Impact of Indentation in Source Code Using Eye-Tracking

Quantitative Quality Evaluation of the Impact of Indentation in Source Code Using Eye-Tracking

Exploring the optimal model for assessing SOC and TN in Zanthoxylum bungeanum forest on the Loess Plateau using VNIR spectroscopy

Zanthoxylum bungeanum forest is an important economic forest on the Loess Plateau. The rapid assessment of its soil organic carbon (SOC) and total nitrogen (TN) concentrations is critical for the evaluation of soil quality. Soil samples of different forest ages and soil depths were collected for laboratory analyses and spectral measurements, and the visible and near-infrared reflectance (VNIR) spectral data were processed by mathematical transformations, such as first-order derivative (FD) and second-order derivative, multiple scattering correction, and logarithm of the reciprocal. The importance bands of SOC and TN were screened on the basis of competitive adaptive reweighted sampling. In addition, partial least squares regression and support vector machine regression models were constructed, and the applicability of SOC and TN to the models were compared. Results showed that forest age and soil depth remarkably affect SOC and TN concentrations. Among the calibration and validation models, the combination of the SVMR model with Savitzky–Golay smoothing and FD estimated SOC and TN with the highest accuracy, and the validation accuracy of the surface SOC and TN was higher than that of the bottom depths. Meanwhile, the importance bands involved in the modeling were mainly distributed in 400–600 nm and 1000–2400 nm. The combination of the FD transformation and the SVMR model is suitable for determining SOC and TN concentrations in Z. bungeanum forests, and the model has great potential for the quantitative evaluation of soil quality in the Loess Plateau.

GroomGen: A High-Quality Generative Hair Model Using Hierarchical Latent Representations

Despite recent successes in hair acquisition that fits a high-dimensional hair model to a specific input subject, generative hair models, which establish general embedding spaces for encoding, editing, and sampling diverse hairstyles, are way less explored. In this paper, we present GroomGen , the first generative model designed for hair geometry composed of highly-detailed dense strands. Our approach is motivated by two key ideas. First, we construct hair latent spaces covering both individual strands and hairstyles. The latent spaces are compact, expressive, and well-constrained for high-quality and diverse sampling. Second, we adopt a hierarchical hair representation that parameterizes a complete hair model to three levels: single strands, sparse guide hairs, and complete dense hairs. This representation is critical to the compactness of latent spaces, the robustness of training, and the efficiency of inference. Based on this hierarchical latent representation, our proposed pipeline consists of a strand-VAE and a hairstyle-VAE that encode an individual strand and a set of guide hairs to their respective latent spaces, and a hybrid densification step that populates sparse guide hairs to a dense hair model. GroomGen not only enables novel hairstyle sampling and plausible hairstyle interpolation, but also supports interactive editing of complex hairstyles, or can serve as strong data-driven prior for hairstyle reconstruction from images. We demonstrate the superiority of our approach with qualitative examples of diverse sampled hairstyles and quantitative evaluation of generation quality regarding every single component and the entire pipeline.

Unveiling consumers’ nonlinear evaluation of service performances in online food delivery: a quantitative Kano analysis

PurposeThe study proposes an evaluation model that allows quantitative characterization of the effects of service quality on consumer’s satisfaction for online food delivery (OFD) services in a nonlinear manner. As such, the authors endeavor to bridge the research-to-practice gaps whereby the effect magnitudes and nonlinear patterns of service quality have been overlooked in the current literature.Design/methodology/approach The quantitative Kano method is adopted. A Kano questionnaire was first developed by synthesizing and operationalizing existing evidence on OFD service qualities. The questionnaire solicited consumers’ evaluations of 21 OFD service attributes, and it was distributed to an online panel in Singapore. With 580 valid responses, the functions that quantitatively depict effects of each attribute on consumer’s satisfaction were subsequently derived.Findings The results reveal that among Singaporean consumers, food quality, reliability of delivery, responsiveness of customer support, ease-of-use of digital interfaces and promotions are pivotal attributes contributing to above-average satisfaction improvement across all performance levels. Meanwhile, delivery riders’ attitudes and real-time tracking functions emerge as substantial contributors to satisfaction at high-performance levels.Practical implications The findings provide crucial insights for OFD practitioners in Singapore in resource prioritization and service optimization. This study demonstrated the importance of streamlining customer support services and focusing on the utilitarian aspects of OFD services. Moreover, these results can be employed in advanced service improvement procedures, providing a roadmap for future OFD service enhancements.Originality/value This study pioneers the development of a quantitative quality evaluation model in the OFD context. With the established quantitative Kano model, the study addresses the omission of effect magnitudes and nonlinear patterns of service quality. It highlights the transition from a binary “does it affect satisfaction” to a more nuanced “how much does it affect satisfaction” approach, offering a robust understanding of consumer’s satisfaction dynamics.

Quantitative assessment of anterior talofibular ligament quality in chronic lateral ankle instability using magnetic resonance imaging T2* value.

To determine T2* normal reference values for anterior talofibular ligament (ATFL) and to investigate the feasibility of the quantitative ATFL quality evaluation in chronic lateral ankle instability (CLAI) using T2* values. This study enrolled 15 patients with CLAI and 30 healthy volunteers. The entire ATFL T2* values from the MRI T2* mapping were measured. The prediction equation (variables: age, height, and weight) in a multiple linear regression model was used to calculate the T2* normal reference value in the healthy group. T2* ratio was defined as the ratio of the actual T2* value of the patient's ATFL to the normal reference value for each patient. A Telos device was used to measure the talar tilt angle (TTA) from the stress radiograph. T2* values of ATFL in the healthy and CLAI groups were 10.82 ± 1.84 ms and 14.36 ± 4.30 ms, respectively, which are significantly higher in the CLAI group (P < 0.05). The prediction equation of the normal reference T2* value was [14.9 + 0.14 × age (years) - 4.7 × height (m) - 0.03 × weight (kg)] (R2 = 0.65, P < 0.0001). A significant positive correlation was found between the T2* ratio and TTA (r = 0.66, P = 0.007). MRI T2* values in patients with CLAI were higher than those in healthy participants, and the T2* ratio correlated with TTA, suggesting that T2* values are promising for quantitative assessment of ATFL quality preoperatively.

A quantitative evaluation of solvent quality of an environmentally friendly solvent ‘d-Limonene’ for polystyrene

A quantitative evaluation of solvent quality of an environmentally friendly solvent ‘d-Limonene’ for polystyrene

Trabecular Bone Microarchitecture Evaluation in an Osteoporosis Mouse Model.

Bone microstructure refers to the arrangement and quality of bone tissue at the microscopic level. Understanding the bone microstructure of the skeleton is crucial for gaining insight into the pathophysiology of osteoporosis and improving its treatment. However, handling bone samples can be complex due to their hard and dense properties. Secondly, specialized software makes image processing and analysis difficult. In this protocol, we present a cost-effective and easy-to-use solution for trabecular bone microstructure analysis. Detailed steps and precautions are provided. Micro-CT is a non-destructive three-dimensional (3D) imaging technique that provides high-resolution images of trabecular bone structure. It allows for the objective and quantitative evaluation of bone quality, which is why it is widely regarded as the gold standard method for bone quality assessment. However, histomorphometry remains indispensable as it offers crucial cellular-level parameters, bridging the gap between two-dimensional (2D) and 3D assessments of bone specimens. As for the histologic techniques, we chose to decalcify the bone tissue and then perform traditional paraffin embedding. In summary, combining these two methods can provide more comprehensive and accurate information on bone microstructure.