Quality Classification Research Articles

Typology and classification of water quality in an intermittent river in a semi-arid Mediterranean climate.

The typology and classification of rivers are highly relevant concepts in the field of limnology and freshwater ecology. Water body typology systematically categorizes water bodies based on their natural attributes, while water body classification groups them based on specific criteria or purposes for management, regulatory, or administrative reasons. Both concepts play important roles in understanding and managing water resources effectively. This scientific article focuses on the ZAT River in Morocco as a model for studying low-flow and intermittent rivers. The objective is to develop an accurate model for the typology and classification of small, low-flow rivers into homogeneous classes based on natural and anthropogenic factors. The study also investigates the impact of human activities on altering the uniformity and reference nature of the water body. The typology of water bodies is carried out according to the European methodology specified in The European Commission's Water Framework Directive (WFD) in 2000. The classification of water bodies is conducted by assessing their chemical and biological quality using the weighted index (WI), the Iberian Biological Monitoring Working Group (IBMWP) index, and multivariate statistical methods such as principal component analysis (PCA) for confirming water quality assessment. The results indicate the possibility of dividing the basin into four water bodies. Water bodies show homogeneity in terms of chemical quality when human influence is minimal or during periods of high river flow. However, increased human influence and decreased river flows lead to heterogeneity in chemical quality, indicating an unstable state. This study is the first of its kind in arid and semi-arid intermittent rivers, where such an approach could be suggested to determine their typology and classification.

Investigation of Water Quality of the Karasu River in Bilecik Province in terms of Agricultural Irrigation

Together with increasing people's need for water, water needs to be monitored due to the pressure created by factors such as drought and pollution. In this study, water quality was monitored by determining 10 points on the Karasu River in Bilecik Province and it was aimed to examine the seasonal change in water quality. pH, EC, Na, K, Ca, Mg, CO3, HCO3, Cl, B, SO4, Sodium Adsorption Rate (SAR), Residual Sodium Carbonate (RSC) and %Na parameters were determined in water. And then using these parameters, quality classes were determined with the help of water quality classification systems developed by scientists such as Schofield, Wilcox, Thorne, Doneen and Soifer. The study were temporally diveded into 4 periods. They are period 1, period 2 (Spring), period 3 (Summer) and period 4 (Fall). After all, for the period 3, heavy metal pollution, especially Al, attracts attention. Except for the period 2 of point 7 and period 4 of point 9, the class of all periods and points in terms of sulphate was "Very good (class 1)". According to Schofield (1933 and 1935) systems, point 7 is in particularly bad situation in terms of EC. According to Wilcox (1948), point 7 is not suitable for irrigation in the period 2. At point 7, water pollution in period 2 has been identified as common to most classification systems. According to Christiansen et al. (1977), there is intense pollution in terms of EC and Na2CO3 parameters. According to Soifer (1987), point 2 is the cleanest point. As a result, Karasu river is not suitable for irrigation in terms of Al and B. It was understood that heavy metal pollution did not cause much of a problem in non-industrialized cities such as Bilecik. Besides, it was understood that the most common element among heavy metals was Pb in Karasu River. The SAR and RSC values of all periods, seasons and points were classified as "Very Good-Safe (C1S1)".

Leadership in Energy and Environmental Design Commercial Interior Version 4 (LEED-CI v4) Gold-Certified Office Space Projects: A Pairwise Comparative Analysis between Three Mediterranean Countries

Over the past five years, Leadership in Energy and Environmental Design Commercial Interior version 4 (LEED-CI v4)-certified office projects have been intensively studied in the USA and China, but they have not yet been studied in the Mediterranean region. The purpose of this study was to explore office building certification strategies for LEED-CI v4-certified projects in the Mediterranean region. The study design included pairwise comparative analyses between Spain (number of projects (n) n1 = 14), Türkiye (n2 = 13), and Israel (n3 = 11). Cliff’s δ and exact Wilcoxon–Mann–Whitney tests were used to process ordinal and discrete data, while the natural logarithm of the odds ratio and 2 × 2 Fisher’s exact tests were used to handle dichotomous data. It was found that Spain and Türkiye outperformed Israel in the Location and Transportation (LT) category due to their desire to reduce the use of private transport. Spain and Türkiye were ahead of Israel in the LTc5 “reduced parking footprint” credit (p = 0.008 and 0.0005, respectively). Israel outperformed Spain and Türkiye in the Energy and Atmosphere (EA) category due to the sum of all six EA credits (p = 0.086 and 0.010). Spain overtook Türkiye and Israel in the Materials and Resources (MRs) category due to Spain’s increased use of environmental product declarations. Spain and Türkiye were ahead of Israel in the following four MRs credits: MRc1 “long-term commitment” (p = 0.030), MRc2 “interiors life cycle impact reduction” (p = 0.037), MRc3 “building product disclosure and optimization—environmental product declarations” (p = 0.029), and MRc5 “building product disclosure and optimization—material ingredients” (p = 0.034). Spain, Türkiye, and Israel showed similarly low levels of achievement in the Indoor Environmental Quality (EQ) category (p ≥ 0.405). However, Spain and Türkiye outperformed Israel in the following two credits: EQc1 “enhanced indoor air quality strategies” and EQc2 “low-emitting materials” (p = 0.001 and 0.060, respectively). In parallel, Israel outperformed Türkiye in the EQc3 “construction indoor air quality management plan” (p = 0.026), and Israel outperformed Spain in the EQc8 “quality views” credit (p = 0.066). As a result, a pairwise comparison of the three Mediterranean countries showed that each country has a unique LEED certification strategy. Knowledge of the above green building strategies will be helpful for LEED professionals.

Fruit grading system by reconstructed 3D hyperspectral full-surface images

Conducting hyperspectral imaging of fruit' entire surfaces while simultaneously evaluating their physical properties, such as volume and mass, can provide a richer dataset for comprehensive fruit classification. For this purpose, a fruit grading system using 3D hyperspectral full-surface images is developed, which is based on multi-view imaging of mirrors in the hardware structure design and relies on the virtual volume intersection (VI) algorithm and texture technology in software design. During the design process, a mathematical model for the mirror layout and system geometry parameters is established to determine the system's layout for scanning the entire surface of the sample. In practical applications, a prototype with 28 channels ranging from 400 to 1000 nm is developed for pear samples based on a sub-component control system, a spherical-cap cavity with flashing multi-color LEDs, and multiple side mirrors. The results obtained from this prototype reveal that the predicted volume (R2=96.18%) and mass (R2=98.18%) exhibit a high correlation with measured results and the hyperspectral data between bruised and normal pears is a significant difference. A dataset of pears of three qualities (large, small, and bruised) is prepared for comprehensive classification and resulting in an effective grading (95.33%), which shows that the proposed system is a potential solution for comprehensive fruit quality classification.

A research on inversion of water quality parameters in the mulan river based on GF-1B\C\D remote sensing images

Remote sensing inversion technology can be used for water quality parameter inversion to realize water quality monitoring in large scale space. The current research on water quality parameter inversion is only for a single satellite. In order to make full use of satellite image resources, the remote sensing images of GF-1B\C\D satellite group are taken as the research object. The Mulan River is taken as the research area. The linear regression method is used to construct the regression equations of total phosphorus and ammonia nitrogen, and the inversion model of total phosphorus and ammonia nitrogen is determined according the evaluation parameters. The MSE of the total phosphorus inversion model is 0.049, and the correlation between the inversion value and the measured value is 0.701. The MSE of the ammonia nitrogen inversion model is 0.063, and the correlation between the inversion value and the measured value is 0.813. These data show that the inversion effect is good. The inversion models are applied to the GF-1D satellite remote sensing image on March 15, 2021 to obtain the large-scale spatial distribution maps of total phosphorus concentration and ammonia nitrogen concentration. The water quality classification maps of the the Mulan River in Putian urban area are obtained too, which are convenient for further analysis and evaluation of the water quality.

Association between breakfast consumption, breakfast quality, mental health and quality of life in Turkish adolescents: A high school-based cross-sectional study.

This study aimed to determine the frequency of breakfast consumption and breakfast quality among adolescents and to evaluate the relationships between breakfast consumption, breakfast quality, mental health, and health-related quality of life. This cross-sectional study included 449 students from 17 high schools between December 2022 and May 2023. A face-to-face questionnaire measuring the frequency of breakfast consumption, Mediterranean diet quality index (KIDMED), health-related quality of life (KIDSCREEN), and depression, anxiety and stress scales (DASS-21) was performed. Of the students, 54.1% skipped breakfast 2 or more times a week and 75.9% had poor breakfast quality. There were significant differences in breakfast quality classification according to the frequency of breakfast consumption (p = 0.003). Breakfast consumption ≤1 time/week or 2-5 times/week was associated with depression, anxiety and low adherence to the Mediterranean diet. No relationship was observed between breakfast quality and stress, depression or anxiety (p = 0.620, p = 0.586, p = 0.539, respectively) or between breakfast quality and the KIDSCREEN-27 subscales (p > 0.05). However, those eating poor-quality breakfasts had better results in physical wellbeing (p = 0.022), psychological wellbeing (p = 0.024), autonomy and parent relations (p = 0.017) than breakfast-skippers and also scored lower for depression, stress and anxiety (all p < 0.001). In conclusion, the frequency of breakfast consumption had a stronger association with reduced symptoms of stress, depression and anxiety, as well as improvements in all dimensions of health-related quality of life, compared to the quality of breakfast consumed. Given the association of breakfast consumption with mental health outcomes in adolescents, our findings are of great importance, especially to parents, clinicians and nutritional educators.

Air Quality Changes under the Influence of Cut-Off Lows and Omega Blocking Patterns and Associated Health Risks in a Coastal Mediterranean Region: Case of Bizerte, Northern Tunisia

The study of the daily and diurnal variability of NO, NO2, surface O3 and PM10 was carried out in urban Bizerte (Northern Tunisia), from January to March, 2013. The main results showed three distinct periods depending on multi-scale sources and meteorology. The first period, (≈20% of total cases) influenced by moderately stable conditions, was characterized by a slight excess of NO due to traffic emissions. The second period, (≈ 60% of total cases) associated with moderately unstable conditions, was devoid of any significant impact of sources. The third period, (≈ 20% of total cases) distinguished by noteworthy excess of O3 and PM10, was attributed to a marked effect of succeeding Cut-off lows and Omega blocking patterns. Under these conditions, the scatter plots of NO, NO2, and O3 versus NOx for daytime and nighttime showed good polynomial fit curves with high R2 values for NO and NO2 only. Under both moderately unstable and stable conditions, significant negative correlation coefficients between O3 and NOx were found to be 0.53 (p < 0.05) and 0.50 (p < 0.05) for daytime and nighttime, respectively. Under Cut-off lows and Omega, an absence of any significant correlations between O3 and NOx was noticed. Under Cut-off lows, the good linear regression fits between OX (=O3 + NO2) and NOx illustrated high NOx independent contributions, possibly attributed to the biogenic VOCs effect. The investigation of the air quality index (AQI) for NO2, PM10 and O3 (running 8-h mean) highlighted the significant effect of both O3 and PM10 only under Cut-off lows and Omega which were retraced by poor to very poor air quality categories.

3D Point Cloud Object Detection Method Based on Multi-Scale Dynamic Sparse Voxelization.

Perception plays a crucial role in ensuring the safety and reliability of autonomous driving systems. However, the recognition and localization of small objects in complex scenarios still pose challenges. In this paper, we propose a point cloud object detection method based on dynamic sparse voxelization to enhance the detection performance of small objects. This method employs a specialized point cloud encoding network to learn and generate pseudo-images from point cloud features. The feature extraction part uses sliding windows and transformer-based methods. Furthermore, multi-scale feature fusion is performed to enhance the granularity of small object information. In this experiment, the term "small object" refers to objects such as cyclists and pedestrians, which have fewer pixels compared to vehicles with more pixels, as well as objects of poorer quality in terms of detection. The experimental results demonstrate that, compared to the PointPillars algorithm and other related algorithms on the KITTI public dataset, the proposed algorithm exhibits improved detection accuracy for cyclist and pedestrian target objects. In particular, there is notable improvement in the detection accuracy of objects in the moderate and hard quality categories, with an overall average increase in accuracy of about 5%.

Assessment and analysis of accents in air traffic control speech: a fusion of deep learning and information theory.

Enhancing the generalization and reliability of speech recognition models in the field of air traffic control (ATC) is a challenging task. This is due to the limited storage, difficulty in acquisition, and high labeling costs of ATC speech data, which may result in data sample bias and class imbalance, leading to uncertainty and inaccuracy in speech recognition results. This study investigates a method for assessing the quality of ATC speech based on accents. Different combinations of data quality categories are selected according to the requirements of different model application scenarios to address the aforementioned issues effectively. The impact of accents on the performance of speech recognition models is analyzed, and a fusion feature phoneme recognition model based on prior text information is constructed to identify phonemes of speech uttered by speakers. This model includes an audio encoding module, a prior text encoding module, a feature fusion module, and fully connected layers. The model takes speech and its corresponding prior text as input and outputs a predicted phoneme sequence of the speech. The model recognizes accented speech as phonemes that do not match the transcribed phoneme sequence of the actual speech text and quantitatively evaluates the accents in ATC communication by calculating the differences between the recognized phoneme sequence and the transcribed phoneme sequence of the actual speech text. Additionally, different levels of accents are input into different types of speech recognition models to analyze and compare the recognition accuracy of the models. Experimental results show that, under the same experimental conditions, the highest impact of different levels of accents on speech recognition accuracy in ATC communication is 26.37%. This further demonstrates that accents affect the accuracy of speech recognition models in ATC communication and can be considered as one of the metrics for evaluating the quality of ATC speech.

The Impact of Interaction between Body Posture and Movement Pattern Quality on Injuries in Amateur Athletes.

Background: this study aimed to examine the impact of interaction between body posture and the quality of movement patterns on injury frequencies in amateur athletes. Methods: The study sample consisted of 89 young amateur athletes. Movement pattern quality was assessed by the Functional Movement Screen (FMS), test and body posture in the frontal plane was assessed by the moire method for the parameters Shoulder Slope Angle, Lower Scapula Protrusion Difference, and Pelvic Tilt Angle. Injury data were collected through completion of the Injury History Questionnaire for the past 12 months. Results: Using cluster analysis, participants were allocated into a either category with good (BPg) body posture or poor (BPp), and using FMS cutoff points (14), either a category of good movement pattern quality (MPg) or poor (MPp). Two-way ANOVA was performed, and the Bonferroni post-hoc test revealed a reduction in injuries among participants from the MPg-BPg group compared to the other three groups (p < 0.05). However, no interaction between factors was revealed. No statistically significant differences were observed among the remaining three groups in the case of injury prevalence (p > 0.05). Conclusions: A combination of proper body posture and high-quality movement patterns is associated with a lower frequency of injuries, without direct interaction between chosen factors, which suggests that they impact injury risk independently. Practicing suitable BP and ensuring high-quality MPs should be regarded as a strategy in injury prevention.

Comparison of feature extraction and auto-preprocessing for chili pepper (Capsicum Frutescens) quality classification using machine learning

The low-cost camera for machine vision, such as a webcam, still has a problem with resolution noise. Therefore, it is important to learn strategies to reduce noise from low-cost camera images so that they can be widely used for grading machines in the future. This paper aims to compare three feature extraction methods with auto-preprocessing to classify chili pepper &lt;em&gt;(Capsicum Frutescens)&lt;/em&gt; quality using a machine learning algorithm. Three extraction methods were used, including the color feature, oriented FAST and rotated BRIEF (ORB), and the combination color feature and ORB. A total of 525 image data for quality chili pepper were collected using the webcam. The auto-preprocessing strategy to classify chili peppers can improve the performance of machine-learning algorithms for all data generated by the feature extractor. The performance of the chili paper quality classification model with auto-preprocessing of the variable color feature can improve the performance of machine learning algorithms by up to 64.21%. The performance improvement of the classification model using the ORB feature variable and the auto-preprocessing of up to 4.41%. The performance improvement of the classification model using machine learning algorithms is 11.27% when using the combination color feature and ORB feature and autopreprocessing.

Classification and rating of disintegrated dolomite strata for slope stability analysis

Although disintegrated dolomite, widely distributed across the globe, has conventionally been a focus of research in underground engineering, the issue of slope stability issues in disintegrated dolomite strata is gaining increasing prominence. This is primarily due to their unique properties, including low strength and loose structure. Current methods for evaluating slope stability, such as basic quality (BQ) and slope stability probability classification (SSPC), do not adequately account for the poor integrity and structural fragmentation characteristic of disintegrated dolomite. To address this challenge, an analysis of the applicability of the limit equilibrium method (LEM), BQ, and SSPC methods was conducted on eight disintegrated dolomite slopes located in Baoshan, Southwest China. However, conflicting results were obtained. Therefore, this paper introduces a novel method, SMRDDS, to provide rapid and accurate assessment of disintegrated dolomite slope stability. This method incorporates parameters such as disintegrated grade, joint state, groundwater conditions, and excavation methods. The findings reveal that six slopes exhibit stability, while two are considered partially unstable. Notably, the proposed method demonstrates a closer match with the actual conditions and is more time-efficient compared with the BQ and SSPC methods. However, due to the limited research on disintegrated dolomite slopes, the results of the SMRDDS method tend to be conservative as a safety precaution. In conclusion, the SMRDDS method can quickly evaluate the current situation of disintegrated dolomite slopes in the field. This contributes significantly to disaster risk reduction for disintegrated dolomite slopes.

Engineering characteristics and classification based on JSCE in portal zones of Tunnel A, Yogyakarta - Bawen toll road, Magelang District, Central Java Province

The Yogyakarta - Bawen Twin Tunnels emerge as an environmentally conscious solution for Toll Road Phase 5 (STA 6+300 to STA 27+640), addressing the ecological impact of open-cut excavation, which demands a staggering 1,209,056 m3. Open-cut excavation not only incurs substantial operational costs, encompassing disposal and treatment, but also poses threats like groundwater loss and erosion-induced landslides. The twin tunnels mitigate these concerns by limiting excavation volume to a mere 1/8, averting substantial costs and ecological damage. Challenges persist, particularly in the portal zones of Tunnel A, characterized by a shallow overburden and exposure to weathering. The Japan Society of Civil Engineers (JSCE) Method proves instrumental in assessing ground quality in these zones, considering factors such as competence, unit weight, unconfined compressive strength, depth of cover, elastic wave velocity, boring core condition, and geological characteristics. The analysis reveals a ground quality classification of E for the portal zone in Tunnel A, emphasizing the necessity for a meticulous design approach that incorporates analytical methodologies alongside empirical considerations. Notably, the unconfined compressive strength ranges from 32.89 to 472.88 kPa, and the elastic wave velocity spans 402 to 2000 meters/sec, reinforcing the imperative for a comprehensive and analytically informed design for excavation and support systems.

Jawbone quality classification in dental implant planning and placement studies. A scoping review.

Cone beam computed tomography has become an attractive method for implant planning. However, in most cases, not all the information is taken advantage of and often the radiographic evaluation of bone quality is based on subjective assessment by the individual clinician. Therefore, the aim of this study was to examine classifications of bone tissue characteristics and methods for assessing them in dental implant planning and placement studies. Three databases (Pubmed, Scopus, Web of Science) were searched using specific index terms: "Bone quality, bone quantity, bone density, cone-beam CT and cone-beam computed tomography". Three reviewers selected titles and analyzed abstracts according to inclusion and exclusion criteria. Some descriptions of bone tissue characteristics (bone quality, density, and quantity) used before or during dental implant placement were selected and categorized. The search yielded 442 titles. A total of 32 articles were selected and read in full text. Seventeen articles were considered relevant. Different classification systems were found to evaluate bone tissue characteristics as well as different examination protocols. Thirteen publications included in this review reported on bone quality and quantity using the Lekholm and Zarb classification. However, only four studies implemented and/or proposed modifications of the Lekholm and Zarb system. Four other publications described bone quality according to different classification systems such as Misch, University of California Los Angeles (UCLA), or Trisi and Rao. The assessment methods were often briefly described (or not described at all in one publication). Of the articles analyzed, five presented observer performance, whereas three presented diagnostic accuracy of the assessment method. Currently, there are different classification systems applied to dental implant planning and placement, particularly regarding whether bone quality or quantity affects treatment outcomes. However, most authors have not validated the diagnostic accuracy and reproducibility of the classification used. Therefore, it is necessary to develop a classification system consistent with characteristics of bone tissue, taking into consideration an adequate description of bone tissue assessment methods, their diagnostic accuracy, and observer performance.

Optimization of the algorithms use ensemble and synthetic minority oversampling technique for air quality classification

&lt;p&gt;Rapid economic development, industrialization, and urbanization in Indonesia have caused a large increase in air pollution with negative impacts on the environment and public health. The aim of this research is to use machine learning techniques to categorize air quality and generate an air quality index (AQI) using a dataset that includes six prevalent air pollutants. Next steps are preprocessing and data extraction, K-nearest neighbors (KNN) classification, support vector machine (SVM), and random forest (RF) models are implemented. Furthermore, synthetic minority oversampling technique (SMOTE) is incorporated into the ensemble learning process to improve the results. This research uses K-fold cross validation for improve classification accuracy and reduce overfitting. Research findings show that the application of SMOTE brings a significant increase in model accuracy, effectively solving the problem of imbalanced data sets. These insights provide direction for effective air quality monitoring systems and informed decision making in air pollution management.&lt;/p&gt;

Qingxiangxing Baijiu sensory quality grade classification by 1H NMR and GC combined with multivariate statistical analysis

The traditional uniform artificial sensory evaluation makes it difficult to standardize the classification of different sensory quality grades of Baijiu. In this study, a total of 92 authentic Qingxiangxing Baijiu samples with 3 sensory quality grades were carefully collected. Gas chromatography (GC) was used to determine 46 main flavor components and proton nuclear magnetic resonance (1H NMR) spectroscopy was employed to obtain hydrogen atom characteristic information of organic compounds. The principal component analysis (PCA), k-nearest neighbor (KNN) and linear discriminant analysis (LDA) models were conducted and fully validated by internal leave-one-out cross validation (LOOCV) and external repeated double random cross validation (RDRCV). The sensory quality grades of Qingxiangxing Baijiu were effectively classified by using GC and 1H NMR techniques coupled with PCA/KNN analysis with the averaged accuracy higher than 80%. In addition, synthetic minority oversampling technique (SMOTE) algorithm was successfully used to address the model overfitting problem caused by an unbalanced sample composition. This study demonstrated that 1H NMR and GC combined with multivariate statistical analysis were effective for sensory quality classification of Qingxiangxing Baijiu.

Analyzing Variances in User Story Characteristics: A Comparative Study of Stakeholders with Diverse Domain and Technical Knowledge in Software Requirements Elicitation

Background: In Agile software development, an essential initial stage is eliciting software requirements. This process engages stakeholders to achieve comprehensive results. However, a common issue is the variance in domain and technical knowledge among stakeholders, potentially impacting the quality of software requirements elicitation. Objective: Understanding the characteristics of user stories produced by stakeholders becomes crucial, particularly considering the differences in domain and technical knowledge. This study aims to compare the characteristics of user stories generated by stakeholders with varying backgrounds in domain and technical expertise. Methods: The initial step involves categorizing respondents into distinct stakeholder groups. Three stakeholders are involved in this study, constituting a combination of those with high and low technical and domain knowledge. Subsequently, data collection of user stories is conducted across various case studies. Finally, the acquired user stories are analyzed for further insights. Results: The analysis reveals variations in user stories generated by the three stakeholder categories across the three case studies. Stakeholders with domain knowledge tend to focus on 'what' aspects with task elements and 'why' aspects with hard-goal elements. Meanwhile, technical knowledge crafts user stories with capability elements in the 'what' aspect. Utilizing the QUS framework, it is evident that technical knowledge consistently produces a higher number of high-quality user stories across all quality categories, Conclusion: The contribution offered by this study lies in determining the distinct characteristics of user stories produced by different types of stakeholders, focusing on disparities in domain and technical knowledge. The study highlights the comparison of various characteristics of user story elements, such as hard-goals, soft-goals, tasks, or capabilities, and assesses the quality of user stories based on the user story framework. Additionally, it endorse the importance of process innovation in shaping the requirements gathering process and subsequently influencing the quality of user stories. Keywords: User story, Agile Software Development, Requirements Elicitation, Stakeholder, Domain Knowledge, Process Innovation Background: In Agile software development, an essential initial stage is eliciting software requirements. This process engages stakeholders to achieve comprehensive results. However, a common issue is the variance in domain and technical knowledge among stakeholders, potentially impacting the quality of software requirements elicitation. Objective: Understanding the characteristics of user stories produced by stakeholders becomes crucial, particularly considering the differences in domain and technical knowledge. This study aims to compare the characteristics of user stories generated by stakeholders with varying backgrounds in domain and technical expertise. Methods: The initial step involves categorizing respondents into distinct stakeholder groups. Three stakeholders are involved in this study, constituting a combination of those with high and low technical and domain knowledge. Subsequently, data collection of user stories is conducted across various case studies. Finally, the acquired user stories are analyzed for further insights. Results: The analysis reveals variations in user stories generated by the three stakeholder categories across the three case studies. Stakeholders with domain knowledge tend to focus on 'what' aspects with task elements and 'why' aspects with hard-goal elements. Meanwhile, technical knowledge crafts user stories with capability elements in the 'what' aspect. Utilizing the QUS framework, it is evident that technical knowledge consistently produces a higher number of high-quality user stories across all quality categories, Conclusion: The contribution offered by this study lies in determining the distinct characteristics of user stories produced by different types of stakeholders, focusing on disparities in domain and technical knowledge. The study highlights the comparison of various characteristics of user story elements, such as hard-goals, soft-goals, tasks, or capabilities, and assesses the quality of user stories based on the user story framework. Additionally, it endorse the importance of process innovation in shaping the requirements gathering process and subsequently influencing the quality of user stories. Keywords: User story, Agile Software Development, Requirements Elicitation, Stakeholder, Domain Knowledge, Process Innovation

A study on the development and the application strategy of FT-MIRS-based models for the diagnosis of subclinical mastitis and milk quality classification in buffaloes

Buffalo mastitis detection and buffalo milk quality are of great importance to the world dairy industry. Somatic cell count (SCC) can be employed to assess mammary gland health and milk quality in dairy cows, but SCC detection is costly, and the detection instrument is expensive. The purpose of this study was to develop high and low SCC identification models using Fourier-transform mid-infrared spectrum (FT-MIRS), which could be used to diagnose subclinical mastitis (SCM) in buffalo and to determine the milk quality grade at a low cost. The dataset contained 899 buffalo milk samples collected from two regions in China. Firstly, the samples were divided into positive group above the threshold (SCM or unqualified milk) and negative group below the threshold (healthy or qualified milk) with SCC = 200 × 103 cells/mL (SCM in buffalo), 400 × 103 cells/mL (EU standard for raw cow milk, ES), 500 × 103 cells/mL (Indian standard for raw buffalo milk, IS), and 750 × 103 cells/mL (US standard for raw cow milk, US) as thresholds, respectively. Then, with FT-MIRS as predictive variables, predictive models were developed using Partial Least Squares Discriminant Analysis (PLSDA), Random Forest (RF), and Gradient Boosting Machine (GBM). The main results were as follows: the AUCval of the diagnostic model of SCM in buffalo using SCM criteria as a threshold was 0.84 (PLSDA). The AUCval values of the three buffalo milk quality classification models with ES, IS, and US as thresholds were 0.76 (PLSDA), 0.78 (GBM), and 0.84 (PLSDA), respectively. The predictive models established in this paper had a weak predictive ability for positive samples, and the “stepwise discriminant analysis” was recommended to improve the model application effect: The models were applied to classify samples as positive and negative, and then the samples with higher predictive probability were selected. Finally, the remaining samples were further differentiated using the reference methods of SCC detection. To conclude, FT-MIRS has the potential to predict buffalo mammary gland health status and buffalo milk quality grade, reduce the cost of SCC detection, and improve work efficiency, which will lay the foundation for rapid buffalo milk quality classification by raw milk regulatory authorities and rapid diagnosis of SCM in buffalo by farms.

Effects of Non-Saccharomyces Yeasts and Their Pairwise Combinations in Co-Fermentation with Saccharomyces cerevisiae on the Quality of Chunjian Citrus Wine.

Non-Saccharomyces (NSc) yeasts have great potential in improving wine qualities. In this study, two NSc and two Saccharomyces cerevisiae (Sc) samples were tested on their performance of mono-inoculated and composite culture in the fermentation of Chunjian citrus wine. The cell count, Brix degree, total sugar, total acidity, alcohol level, pH value, color intensity (CI), and tonality were determined to evaluate the contribution of NSc to the quality of citrus wine in the mixed fermentation. Volatile compounds were analyzed by HS-SPME-GC-MS, and sensory evaluation was carried out. During the 9-day fermentation, the mixed-culture wine exhibited a higher cell concentration than the pure culture. After the fermentation, mixed-culture wine specifically decreased the concentrations of unfavorable volatile compounds, such as isobutanol and octanoic acid, and increased favorable volatile compounds, including ethyl octanoate, ethyl decanoate, and phenylethyl acetate. The quality category of the citrus wine was improved compared with the Sc mono-inoculated wines, mainly in regard to aroma, retention, and sweetness. The study shows that the mixed fermentation of NSc and Sc has positive impacts on reducing alcohol level and total acidity and increasing CI. The present work demonstrates that the mixed fermentation of NSc and Sc has enormous beneficial impacts on improving the quality of citrus wine.

Navigating the Usher Syndrome Genetic Landscape: An Evaluation of the Associations between Specific Genes and Quality Categories of Cochlear Implant Outcomes.

Usher syndrome (US) is a clinically and genetically heterogeneous disorder that involves three main features: sensorineural hearing loss, retinitis pigmentosa (RP), and vestibular impairment. With a prevalence of 4-17/100,000, it is the most common cause of deaf-blindness worldwide. Genetic research has provided crucial insights into the complexity of US. Among nine confirmed causative genes, MYO7A and USH2A are major players in US types 1 and 2, respectively, whereas CRLN1 is the sole confirmed gene associated with type 3. Variants in these genes also contribute to isolated forms of hearing loss and RP, indicating intersecting molecular pathways. While hearing loss can be adequately managed with hearing aids or cochlear implants (CIs), approved RP treatment modalities are lacking. Gene replacement and editing, antisense oligonucleotides, and small-molecule drugs hold promise for halting RP progression and restoring vision, enhancing patients' quality of life. Massively parallel sequencing has identified gene variants (e.g., in PCDH15) that influence CI results. Accordingly, preoperative genetic examination appears valuable for predicting CI success. To explore genetic mutations in CI recipients and establish correlations between implant outcomes and involved genes, we comprehensively reviewed the literature to gather data covering a broad spectrum of CI outcomes across all known US-causative genes. Implant outcomes were categorized as excellent or very good, good, poor or fair, and very poor. Our review of 95 cochlear-implant patients with US, along with their CI outcomes, revealed the importance of presurgical genetic testing to elucidate potential challenges and provide tailored counseling to improve auditory outcomes. The multifaceted nature of US demands a comprehensive understanding and innovative interventions. Genetic insights drive therapeutic advancements, offering potential remedies for the retinal component of US. The synergy between genetics and therapeutics holds promise for individuals with US and may enhance their sensory experiences through customized interventions.