Quality Assessment Techniques Research Articles

Non invasive techniques for direct muscle quality assessment after exercise intervention in older adults: a systematic review

BackgroundThe aging process induces neural and morphological changes in the human musculoskeletal system, leading to a decline in muscle mass, strength and quality. These alterations, coupled with shifts in muscle metabolism, underscore the essential role of physical exercise in maintaining and improving muscle quality in older adults. Muscle quality's morphological domain encompasses direct assessments of muscle microscopic and macroscopic aspects of muscle architecture and composition. Various tools exist to estimate muscle quality, each with specific technical requirements. However, due to the heterogeneity in both the studied population and study methodologies, there is a gap in the establishment of reference standards to determine which are the non-invasive and direct tools to assess muscle quality after exercise interventions. Therefore, the purpose of this review is to obtain an overview of the non-invasive tools used to measure muscle quality directly after exercise interventions in healthy older adults, as well as to assess the effects of exercise on muscle quality.Main textTo address the imperative of understanding and optimizing muscle quality in aging individuals, this review provides an overview of non-invasive tools employed to measure muscle quality directly after exercise interventions in healthy older adults, along with an assessment of the effects of exercise on muscle quality.ResultsThirty four studies were included. Several methods of direct muscle quality assessment were identified. Notably, 2 studies harnessed CT, 20 utilized US, 9 employed MRI, 2 opted for TMG, 2 adopted myotonometry, and 1 incorporated BIA, with several studies employing multiple tests. Exploring interventions, 26 studies focus on resistance exercise, 4 on aerobic training, and 5 on concurrent training.ConclusionsThere is significant diversity in the methods of direct assessment of muscle quality, mainly using ultrasound and magnetic resonance imaging; and a consistent positive trend in exercise interventions, indicating their efficacy in improving or preserving muscle quality. However, the lack of standardized assessment criteria poses a challenge given the diversity within the studied population and variations in methodologies.. These data emphasize the need to standardize assessment criteria and underscore the potential benefits of exercise interventions aimed at optimizing muscle quality.

Evaluation of correlation of physicochemical parameters and major ions present in groundwater of Raipur using discretization

Groundwater, vital for human consumption and agriculture, ecosystem support, and industrial activities, requires sustainable management using proper quality assessment techniques. This study examines the relationship between physicochemical parameters and major ions in groundwater samples collected from 44 regions in Raipur, using sensor-based data acquisition alongside traditional methods. Employing K-means clustering for data discretization, correlations between parameters are highlighted. Results show positive associations among EC, TDS, TH, and TA. ArcGIS interpolation maps visualize spatial distribution. Addressing class imbalance, an upsampling technique is utilized. Machine learning algorithms, including Logistic Regression and Random Forest, classify water quality with accuracies of 98.8 % and 98.3 %, respectively. This research, blending traditional and sensor-based methods, emphasizes informed water management.

A Computer Vision-Based Quality Assessment Technique for the automatic control of consumables for analytical laboratories

The rapid growth of the Industry 4.0 paradigm is increasing the pressure to develop effective automated monitoring systems. Artificial Intelligence (AI) is a convenient tool to improve the efficiency of industrial processes while reducing errors and waste. In fact, it allows the use of real-time data to increase the effectiveness of monitoring systems, minimize errors, make the production process more sustainable, and save costs. In this paper, a novel automatic monitoring system is proposed in the context of production process of plastic consumables used in analysis laboratories, with the aim to increase the effectiveness of the control process currently performed by a human operator. In particular, we considered the problem of classifying the presence or absence of a transparent anticoagulant substance inside test tubes. Specifically, a hand-designed deep network model is used and compared with some state-of-the-art models for its ability to categorize different images of vials that can be either filled with the anticoagulant or empty. Collected results indicate that the proposed approach is competitive with state-of-the-art models in terms of accuracy. Furthermore, we increased the complexity of the task by training the models on the ability to discriminate not only the presence or absence of the anticoagulant inside the vial, but also the size of the test tube. The analysis performed in the latter scenario confirms the competitiveness of our approach. Moreover, our model is remarkably superior in terms of its generalization ability and requires significantly fewer resources. These results suggest the possibility of successfully implementing such a model in the production process of a plastic consumables company.

Towards assessing the quality of knowledge graphs via differential testing

Knowledge graphs (KG) can aggregate data and make information resources easier to calculate and understand. With tremendous advancements in knowledge graphs, they have been incorporated into plenty of software systems to assist various tasks. However, while KGs determine the performance of downstream software systems, their quality is often measured by the accuracy of test data. Considering the limitation of accessible high-quality test data, an automated quality assessment technique could fundamentally improve the testing efficiency of KG-driven software systems and save plenty of manual labeling resources.In this paper, we propose an automated approach to quantify the quality of KGs via differential testing. It first constructs multiple Knowledge Graph Embedding Models (KGEM) and conducts head prediction tasks on models. Then, it can produce a differential score that reflects the quality of KGs by comparing the proximity of output results. To validate the effectiveness of this approach, we experiment with four open-sourced knowledge graphs. The experiment results show that our approach is capable of accurately evaluating the quality of KGs and producing reliable results on different datasets. Moreover, we compared our method with existing methods and achieved certain advantages. The potential usefulness of our approach sheds light on the development of various KG-driven software systems.

Advances in cardiac organoid technology: current trends and standardized quality assessment for toxicity testing in preclinical models

Cardiotoxicity is a prominent cause of drug withdrawal, impacting drug development during preclinical and clinical phases, as well as post-market approval. Traditional 2-dimensional cell culture models, despite being resource-rich, suffer from a suboptimal physiological environment. Animal models provide an in vivo setting; however, their use is declining due to interspecies variability and ethical concerns. Cardiac organoids, which are derived from human induced pluripotent stem cells, incorporate human genetic information and contain various cell types within a 3-dimensional environment that mimics the human heart. As a result, they are becoming increasingly popular in the development of preclinical models for drug testing. To effectively use cardiac organoids as preclinical models, a standardized quality assessment is crucial. This review discusses current trends in cardiac organoid generation and examines recent analytical techniques for quality assessment.

Non-Destructive Measurement on Kwini Mango Fruit using Capacitive Sensing Technique at 250kHz to 2MHz for Ripeness Determination

This study aims to evaluate the characteristics and quality of Kwini mango fruit using non-destructive capacitive sensing methods. Unlike other fruits, identifying ripe Kwini mangoes can be challenging due to their green appearance and unfamiliar characteristics to consumers. Despite the growing interest in non-destructive fruit quality assessment techniques, there has not been significant prior exploration of capacitive sensing in the context of Kwini mangoes. Therefore, in this experiment, a parallel plate was used to measure the capacitance of Kwini mango fruit at frequencies ranging from 250 kHz to 2 MHz. Results showed that the moisture content and pH level decreased within 15 days of investigation. This technique was able to predict Kwini maturity with significant precision by applying the capacitance formula. Because of its high R2 value and sensitivity in linear regression, 500kHz was determined to be the most efficient operating frequency for Kwini mango fruits among the frequencies evaluated. In this experiment, the relationship between capacitance and Brix also yielded encouraging findings. The study also includes the development of a GUI application for determining the sweetness and maturity of Kwini mangoes. Overall, this research demonstrates the potential of capacitive sensing technology as a non-destructive approach to measuring Kwini mango fruit ripeness.

Rapid quality assessment and traceability of ginger powder from Northeast India and Indian market based on near infrared spectroscopic fingerprinting.

Ginger (Zingiber officinale Rosc.) varies widely due to varying concentrations of phytochemicals and geographical origin. Rapid non-invasive quality and traceability assessment techniques ensure a sustainable value chain. The objective of this study is the development of suitable machine learning models to estimate the concentration of 6-gingerol and check traceability based on the spectral fingerprints of dried ginger samples collected from Northeast India and the Indian market using near-infrared spectrometry. Samples from the market and Northeast India underwent High Performance Liquid Chromatographic analysis for 6-gingerol content estimation. Near infrared (NIR) Spectrometer acquired spectral data. Quality prediction utilized partial least square regression (PLSR), while fingerprint-based traceability identification employed principal component analysis and t-distributed stochastic neighbor embedding (t-SNE). Model performance was assessed using RMSE and R2 values across selective wavelengths and spectral fingerprints. The standard normal variate pretreated spectral data over the wavelength region of 1,100-1,250 nm and 1,325-1,550 nm showed the optimal calibration model with root mean square error of calibration and R2 C (coefficient of determination for calibration) values of 0.87 and 0.897 respectively. A lower value (0.24) of root mean square error of prediction and a higher value (0.973) of R2 P (coefficient of determination for prediction) indicated the effectiveness of the developed model. t-SNE performed better clustering of samples based on geographical location, which was independent of gingerol content. The developed NIR spectroscopic model for Indian ginger samples predicts the 6-gingerol content and provides geographical traceability-based identification to ensure a sustainable value chain, which can promote efficiency, cost-effectiveness, consumer confidence, sustainable sourcing, traceability, and data-driven decision-making.

Exploring Microbiological Quality Assessment Techniques and Antibiotic Sensitivity: A Comprehensive Review

Microbiological quality assessment and antibiotic sensitivity testing are crucial components of ensuring public health safety, food quality, and effective antimicrobial therapy and provides a comprehensive overview of the methodologies, challenges, and recent advancements in microbiological quality assessment and antibiotic sensitivity testing. It covers various techniques employed in microbiological quality assessment, including culture-based methods, molecular techniques, and rapid detection methods. Additionally, it explores the principles and methodologies of antibiotic sensitivity testing, focusing on disk diffusion, broth microdilution, and automated systems. Furthermore, the article discusses emerging trends and technologies in the field, such as whole-genome sequencing and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and understanding these techniques and their applications is essential for researchers, healthcare professionals, and policymakers to combat microbial infections effectively and safeguard public health.

Towards sustainable lean success: a systematic literature review on critical failure factors

PurposeLean manufacturing (LM) is a leading philosophy used by organizations to become inevitable in the challenging market. However, prior research showed evidence that 90% failure rate of LM within 10 years of implementation, hence this study is to undertake a systematic literature review on the failure of LM implementation.Design/methodology/approachThis study was conducted with the guidance by review protocol, research questions’ formulation, systematic searching based on identification, screening and eligibility using Scopus and Web of Science databases, quality appraisal, data exclusion and analysis.FindingsDerived from 15 sub-themes, five main themes were discovered based on the thematic analysis to answer the research question on “What strategies should manufacturers do to prevent the failure of LM implementation?”, namely (1) leadership, (2) culture, (3) knowledge, (4) resources and (5) suppliers.Research limitations/implicationsWoS and Scopus were employed in this review, despite there are 14 databases with the ability to search for potential articles in them. Mixed-Method Appraisal Tool (MMAT) was used to evaluate the quality control process, however using various quality assessment techniques helps to search for appropriate review objectives.Practical implicationsThe primary emphasis of this study can be used as a reference by the manufacturers to ensure that LM implementation is a success story to gain a competitive advantage.Originality/valueDespite a large body of literature on LM, there has been little attempt to review this study in a systematic approach, discover trends and generate thematic potential on the failure factors of LM, making it difficult for academics to replicate and interpret.

Nondestructive detection of fruit fly (Bactrocera dorsalis) infestation in Alphonso and Totapuri varieties of mango fruits by thermal imaging technique

AbstractThermal imaging is a noncontact and nondestructive sensing technique that provides thermal signatures of objects by detecting the infrared radiations emitted by them above 1 K. The nondestructive detection of internal defects and infestations has vast applications in the Food Industry. In the present experiment, a C3 thermal camera (FLIR systems) having a thermal sensitivity of <0.10°C was used for the detection of fruit fly infestation in mango fruits. The healthy mango fruits of both varieties had a uniform surface temperature with a variation of only ±0.1°C. The maximum temperature difference between the healthy and fruit fly infested regions in fruit fly infested Totapuri mango was found to be 4.56°C in the heated and dark conditions. The maximum difference in the surface temperature of 4.3°C was observed between the healthy and infested regions of the infested Alphonso mango under heated and dark conditions. The difference in temperature between the healthy and infested regions of mangoes can be attributed to the changes in the internal structure, thermal properties, and biochemical properties of the healthy and infested regions of the infested mango fruits. The statistical analysis showed a significant difference (p < 0.05) in surface temperature between the infested and healthy regions of fruit fly infested Alphonso and Totapuri mango fruits. This envisages that nondestructive fruit fly infestation in Alphonso and Totapuri varieties of mango fruits can be detected under active thermography.Practical ApplicationsThe quality of mango fruit can be described in terms of external and internal quality. The external quality parameters such as color, size, and weight are usually considered during the sorting of mangoes. The internal defects such as diseases, physiological disorders, and damage due to pests are also significant in mango processing. The visual inspection methods for detecting pest infestation in fruits are nonreliable, time‐consuming, and subjective. Therefore, there is a need for efficient, reliable, and accurate methods to detect fruit fly infestation in mango fruits. A nondestructive method for the detection of fruit fly infestation has the potential to increase fruit quality and acceptability, thereby benefiting processors, traders, and consumers. Thermal imaging has a distinct advantage over other nondestructive quality assessment techniques as it is noncontact, practically feasible, and economically viable.

Rice Grains Detection, Classification, and Quality Prediction Using Deep Learning

Abstract: Rice, as the most consumed food worldwide, faces a continual demand, necessitating rigorous quality inspection for both local consumption and international trade. Manual quality assessment methods are fraught with issues such as time consumption, high costs, and error susceptibility. This research paper introduces an innovative solution employing Deep Convolutional Neural Networks (CNNs) to automate rice grain detection, classification, and quality prediction from scanned images. The methodology integrates comprehensive image pre-processing and quality assessment techniques, encompassing image acquisition, pre-processing, CNN architecture construction, model training, and evaluation. Notably, it includes noise removal and segmentation to optimize input images. The grain samples undergo image processing using MATLAB, where an algorithm is applied to analyze their features. Classification is then performed based on the color, shape, and size characteristics of the grains. Various image features are extracted such as centroids, mean intensity, chalkiness, and perimeter further aid in quality assessment. Performance evaluation of the trained model is conducted through confusion matrix analysis and computation of classification metrics like accuracy, specificity, sensitivity, precision, recall, and F1-score. This methodology offers a robust framework for automated rice grain classification and quality prediction, thereby significantly enhancing efficiency and reliability in the global rice industry

Schrödinger spectrum and slim CNN architecture-based signal quality estimation for Photoplethysmogram signals

Photoplethysmography (PPG) signal comprises physiological information related to cardiorespiratory health. However, these signals are often contaminated by motion artifacts, resulting in low-quality noise-enriched signals. Therefore, it is crucial to ensure high-quality signals to extract accurate cardiorespiratory information. Although several rule-based and Machine-Learning (ML) - based approaches exist for PPG signal quality estimation but their effectiveness is questionable during evaluation in diverse datasets. This study proposes a lightweight, slim CNN architecture that utilizes a novel Quantum Pattern Recognition (QPR) technique for signal quality assessment. The proposed algorithm is initially validated on the University of Queensland database (clinical) and a noisy PPG database (non-clinical) collected from the ‘Welltory app’. A total of 28366, 5 s signal segments are preprocessed and transformed into image files of 20 × 500 pixels. The image files and heart rate obtained from the PPG signals are treated as input to the 2D slim CNN architecture. The developed model classifies the PPG signal as ‘good’ or ‘bad’ with an accuracy of 98.5%, 99.2% sensitivity, 97.5% specificity, and 99% F1-score. The classified database is further used for cuffless Blood Pressure estimation, and estimation error is reduced ≥ 1 mmHg w.r.t. reference BP signals. Finally, the proposed scheme is validated in STM32 series board for a resource-constrained wearable implementation. The computational load of the algorithm is reduced by network pruning and quantization for edge-device implementation, and it achieves an accuracy of 94.3%. This extensive analysis concludes that a slim architecture, along with a novel spatio–temporal pattern recognition technique, improves the system’s performance.

A State-of-the-Art Review on the Study of the Diffusion Mechanism of Fissure Grouting

China is renowned for its extensive underground engineering projects and the complex geological and hydrological conditions it faces. Grouting treatment technology is widely employed in deep-buried mines and tunnels, where grouting parameters such as materials, pressure, volume, and hole arrangement significantly impact the effectiveness of grouting. This review paper comprehensively examines current research on grouting materials, theories, experiments, and numerical simulations. It summarizes the various factors that must be considered during the grouting process of fissures and explores the diffusion mechanisms of grout under their influence. Furthermore, further research is needed on the mechanisms and treatment methods for poor grouting in rock masses, the distribution patterns of fissures, optimization methods for grouting parameters, and grout quality assessment techniques. Future research should focus on developing more efficient experimental methods with higher accuracy levels while advancing grouting technologies. Establishing comprehensive and accurate rock mass models along with improving monitoring capabilities are also crucial aspects to consider. Therefore, studying the diffusion mechanisms of grout in fissured rock masses is of significant importance for the practical operation of underground engineering projects.

A review of the status, challenges, trends, and prospects of groundwater quality assessment in Nigeria: an evidence-based meta-analysis approach.

With the imminent industrial growth and population increase, Nigeria will continue to experience significant shifts in the quality of water, with a rise in emerging contaminants. This will increase the irregularity and complexity of the water quality information. Therefore, using the PRISMA meta-analysis approach, this review systematically identified the commonly used water quality assessment techniques in Nigeria, the drawback in the application of these techniques as well as the gaps in the area of water quality assessment and monitoring from 2003 to 2023. Recommendations were also made based on the evaluation of a new research direction; through the review of the effectiveness of advanced techniques for monitoring water quality in Nigeria. Sixty-eight published articles were chosen for the meta-analysis while the VOSviewer program was used to perform bibliographic coupling and visualization. The review revealed that the application of machine learning in water quality prediction has not been well explored in Nigeria. This is attributed to limited data availability and poor funding by the government. It was found that southwestern Nigeria has a greater amount of research on groundwater quality monitoring and evaluation than other regions. The variability was explained by variations in the underlying geology, aquifer features; variability in anthropogenic activities, and level of literacy among various geopolitical zones. Further studies should focus on the application of soft-computing and integrated biomonitoring techniques for effective prediction and monitoring of emerging contaminants for improved water quality. Effective collaboration between environmental stakeholders and government agencies is recommended for effective water resource sustainability.

THz Data Analysis and Self-Organizing Map (SOM) for the Quality Assessment of Hazelnuts

In recent years, the use of techniques based on electromagnetic radiation as an investigative tool in the agri-food industry has grown considerably, and between them, the application of imaging and THz spectroscopy has gained significance in the field of food quality control. This study presents the development of an experimental setup operating in transmission mode within the frequency range of 18 to 40 GHz, which was specifically designed for assessing various quality parameters of hazelnuts. The THz measurements were conducted to distinguish between healthy and rotten hazelnut samples. Two different data analysis techniques were employed and compared: a traditional approach based on data matrix manipulation and curve fitting for parameter extrapolation, and the utilization of a Self-Organizing Map (SOM), for which we use a neural network commonly known as the Kohonen neural network, which is recognized for its efficacy in analyzing THz measurement data. The classification of hazelnuts based on their quality was performed using these techniques. The results obtained from the comparative analysis of coding efforts, analysis times, and outcomes shed light on the potential applications of each method. The findings demonstrate that THz spectroscopy is an effective technique for quality assessment in hazelnuts, and this research serves to clarify the suitability of each analysis technique.

Assessing Quality in Interventional Practice: Identifying Meaningful and Actionable Metrics.

Interventional cardiologists should insist on quality assessment techniques that indisputably reflect the merit of care delivered. Only measurable outcomes and metrics that are modifiable should be identified and collected. An evaluation process should be adopted that genuinely appraises clinical practice, incorporating appropriate benchmarks for comparison.

A Literature Survey on Completely Blind Image Quality Evaluator Feature-Enriched

Existing blind image quality assessment (BIQA) technique in which are principally opinion-aware. They learn deterioration models from training photos with associated human subjective scores to predict the sensory activity quality of take a look at pictures. Such opinion-aware methods, however, need a bulky amount of training example with associated human prejudiced achieve moreover of a variety of deformation category. The BIQA representation find out through opinion-aware strategies typically have weak generalization capability, herewith limiting their usability in observe. By comparison, opinion-unaware strategies don't require human prejudiced achieve for training, and then have superior possible designed for good quality simplification ability. Unluckily, to the present point no opinion-unaware BIQA technique has shown systematically better quality prediction accuracy than the opinion-aware strategies. Here, we tend to aim to develop an opinionunaware BIQA technique that may compete with, and maybe exceed, the present opinion-aware methods. By integration the options of ordinary picture information consequent as of multiple cues, we have an affinity to discover a multivariate Gaussian structure of representation patches from a set of perfect natural pictures. using the learned multivariate Gaussian form, a Bhattacharyya-like distance is employed to measure the standard of every image patch, and then a generally value score is find by average pooling. The projected BIQA technique doesn't want any distorted sample images or subjective class achieve for training, yet extensive experiment show its better quality-prediction presentation to the situation of the art opinion-aware BIQA strategies.

Advances in non-destructive techniques for fruit quality assessment: A comprehensive review

Advances in non-destructive techniques for fruit quality assessment: A comprehensive review

Performance Analysis of the Decision Tree Classification Algorithm on the Water Quality and Potability Dataset

Ensuring water potability is paramount for public health and safety. This research aimed to assess the efficacy of the Decision Tree classification algorithm in predicting water potability using the Water Quality and Potability dataset. Employing a 5-fold cross-validation technique, the model showcased a moderate performance with an average accuracy of approximately 54.33%. While the Decision Tree provides a baseline and interpretable mechanism for classification, the results emphasize the need for further exploration using more intricate models or ensemble methods. This study contributes to the broader effort of leveraging machine learning techniques for water quality assessment and provides insights into the potential and limitations of such models in predicting water safety

Software Quality Assessment Technique for the Autonomous Power Plants Automated Control Systems

Software Quality Assessment Technique for the Autonomous Power Plants Automated Control Systems