In situ digital testing method for quality assessment of soft soil improvement with polyurethane

This study purposes an in situ testing method on quality assessment of soil improvement.Factual drilling data includes the spatial distribution and in situ strength of untreated and treated soil along three different drillholes measured by on-site drilling monitoring method.These factual drilling data can characterize the degree of soil improvement by penetration injection with permeable polyurethane.Result from on-site drilling monitoring shows that the linear zones represent constant drilling speeds shown in the plot of drill bit advancement vs.net drilling time,which indicates the spatial distributions of soil profile.The soil profile at the study site is composed of four layers,which includes fill,untreated silty clay,treated silty clay,and mucky soil.The results of soil profile are verified by the parallel site loggings.The constant drilling speeds profile the coring-resistant strength of drilled soils.By comparing with the untreated silty clay,the constant drilling speeds of the treated silty clay have been decreased by 13.0-62.8%.Two drilling-speed-based indices of 61.2%and 65.6%are proposed to assess the decreased average drilling speed and the increased in situ strength of treated silty clay.Laboratory tests,i.e.uniaxial compressive strength(UCS)test,have been performed with core sample to investigate and characterize in situ strength by comparing that with drilling speeds.Results show that the average predicted strengths of treated silty clay are 2.4-6.9 times higher than the average measured strength of untreated silty clay.The UCS-based indices of 374.5%and 344.2%verified the quality assessment(QA)results by this new in situ method.This method provides a cost-effective tool for quality assessment of soil improvement by utilizing the digital drilling data.

Air Quality Assessment of Ubeji Community near Petroleum-Related Activities

The escalating global concern over air pollution requires rigorous investigations. This study assesses air quality near residential areas affected by petroleum-related activities in Ubeji Community, utilizing Aeroqual handheld mobile multi-gas monitors and air quality multi-meters. Air sampling occurred on three distinct days using multi-gas monitors and meters, covering parameters such as CO, NO2, CH4, NH3, VOCs, Particulate Matter, Temperature, Relative Humidity, and Air Quality Index. Soil and plant samples were collected and analyzed for physicochemical and organic components. Air pollutant concentrations showed significant fluctuations. Carbon monoxide (CO) ranged from 0.00 to 3.22 ppm, NO2 from 0.00 to 0.10 ppm, CH4 from 4.00 to 2083 ppm, NH3 from 371 to 5086 ppm, and VOCs from 414 to 6135 ppm. Soil analysis revealed low total nitrogen, and undetected BTEX levels. Plant samples displayed a pH range of 7.72 to 9.45. CO concentrations, although below WHO limits, indicated potential vehicular and industrial influences. Fluctuations in NO2 and CH4 were linked to traffic, industrial activities, and gas flaring. NH3 levels suggested diverse pollution sources. The result in this study highlights the dynamic nature of air pollution in Ubeji community, emphasizing the urgent need for effective pollution control measures. Although CO concentrations were within limits, continuous monitoring is essential. Elevated NO2 levels gave information on the impact of industrial activities, while high CH4 concentrations may be associated with gas flaring and illegal refining. The study recommends comprehensive measures and collaborative efforts to address these complex issues, safeguarding both the environment and public health. This study shows the potential synergy between air quality sensors and plants for holistic environmental health assessments, offering valuable insights for environmental assessments and remediation endeavours. The findings call for stringent regulations and collaborative efforts to address air pollution in Ubeji community comprehensively.

Perceptual Quality Assessment for Point Clouds:A Survey

A point cloud is considered a promising 3D representation that has achieved wide applications in several fields.However,quality degradation inevitably occurs during its acquisition and generation,communication and transmission,and rendering and display.Therefore,how to accurately perceive the visual quality of point clouds is a meaningful topic.In this survey,we first introduce the point cloud to emphasize the importance of point cloud quality assessment(PCQA).A review of subjective PCQA is followed,including common point cloud distortions,subjective experimental setups and subjective databases.Then we review and compare objective PCQA methods in terms of modelbased and projection-based.Finally,we provide evaluation criteria for objective PCQA methods and compare the performances of various methods across multiple databases.This survey provides an overview of classical methods and recent advances in PCQA.

Bridge the Gap Between Full-Reference and No-Reference:A Totally Full-Reference Induced Blind Image Quality Assessment via Deep Neural Networks

Blind image quality assessment(BIQA)is of fundamental importance in low-level computer vision community.Increasing interest has been drawn in exploiting deep neural networks for BIQA.Despite of the notable success achieved,there is a broad consensus that training deep convolutional neural networks(DCNN)heavily relies on massive annotated data.Unfortunately,BIQA is typically a small sample problem,resulting the generalization ability of BIQA severely restricted.In order to improve the accuracy and generalization ability of BIQA metrics,this work proposed a totally opinion-unaware BIQA in which no subjective annotations are involved in the training stage.Multiple full-reference image quality assessment(FR-IQA)metrics are employed to label the distorted image as a substitution of subjective quality annotation.A deep neural network(DNN)is trained to blindly predict the multiple FR-IQA score in absence of corresponding pristine image.In the end,a selfsupervised FR-IQA score aggregator implemented by adversarial auto-encoder pools the predictions of multiple FR-IQA scores into the final quality predicting score.Even though none of subjective scores are involved in the training stage,experimental results indicate that our proposed full reference induced BIQA framework is as competitive as state-of-the-art BIQA metrics.

Blind Image Quality Assessment by Pairwise Ranking Image Series

Image quality assessment(IQA)is constantly innovating,but there are still three types of stickers that have not been resolved:the“content sticker”-limitation of training set,the“annotation sticker”-subjective instability in opinion scores and the“distortion sticker”-disordered distortion settings.In this paper,a No-Reference Image Quality Assessment(NR IQA)approach is proposed to deal with the problems.For“content sticker”,we introduce the idea of pairwise comparison and generate a largescale ranking set to pre-train the network;For“annotation sticker”,the absolute noise-containing subjective scores are transformed into ranking comparison results,and we design an indirect unsupervised regression based on EigenValue Decomposition(EVD);For“distortion sticker”,we propose a perception-based distortion classification method,which makes the distortion types clear and refined.Experiments have proved that our NR IQA approach Experiments show that the algorithm performs well and has good generalization ability.Furthermore,the proposed perception based distortion classification method would be able to provide insights on how the visual related studies may be developed and to broaden our understanding of human visual system.

Retrospective Analysis of External Quality Assessment Data of Humoral (Except Feces) Morphology in Guangxi from 2010 to 2022

[Objectives] The study aims to improve the ability of inspectors to recognize humoral morphology and provide data reference for the improvement of EQA work of humoral morphology by Guangxi Clinical Laboratory Center. [Methods] A retrospective analysis was made on the EQA of humoral morphology by Guangxi Clinical Laboratory Center from 2010 to 2022, and the number of participating laboratories, the rate of return, sample types and maps of EQA of humoral morphology such as urine sediment, vaginal secretions and so on(except feces), and the reasons for the high error rate of the results of the participating laboratories were analyzed. [Results] The number of laboratories participating in the assessment of humoral morphology increased from 98 in 2010 to 371 in 2022. Except for 2011(90.98%), the rate of return was more than 94%, and the highest rate of return was up to 100%. A total of 119 pictures of EQA of humoral morphology were studied, including 101 urine sediment smears, 13 vaginal secretions smears and 5 other smears. The types of specimens were relatively comprehensive. The distribution of the maps was basically reasonable. The urinary sediment was mainly composed of crystalline salts, followed by tubes, and cells and fungi ranked third. The coverage was relatively complete. The error rate of return results from high to low were bacteria(error rate was equal to 40%), tubes and crystalline salts(error rate was close to 29%), and cells and fungi(error rate was about 14%). The main cases with high error rate of return results are as follows: the difference between different tubes was not clear, and the diversity of crystal morphology was not understood enough;the phase division of cells in the same system was wrong. [Conclusions] The analysis and summary of the reasons for the high error rate of the data and results of Guangxi's EQA of humoral morphology is helpful for laboratory physicians to improve their awareness of morphological examination and identify clearly the error type and blurred concept, and lays a foundation for Guangxi Clinical Laboratory Center to carry out EQA of humoral morphology more specifically.

Image quality assessment based on perceptual grouping

To further explore the human visual system（ HVS）,the perceptual grouping（ PG）, which has been proven to play an important role in the HVS, is adopted to design an effective image quality assessment（ IQA） model. Compared with the existing fixed-window-based models, the proposed one is an adaptive window-like model that introduces the perceptual grouping strategy into the IQA model. It works as follows： first,it preprocesses the images by clustering similar pixels into a group to the greatest extent; then the structural similarity is used to compute the similarity of the superpixels between reference and distorted images; finally, it integrates all the similarity of superpixels of an image to yield a quality score. Experimental results on three databases（ LIVE, IVC and MICT） showthat the proposed method yields good performance in terms of correlation with human judgments of visual quality.

A Comparative Study of Different Methods on Quality Assessment of Soil Environment Polluted by Zinc in Agricultural Production Areas——A Case Study in Shulan City of Jilin Province

[Objective] The aim was to explore evaluated precision on quality of soil environment polluted with zinc in agricultural production areas and to provide references for verification of production area.[Method] In Shulan City in Jilin Province,soils were sampled and analyzed in a laboratory using single-factor pollution index and GIS based spatial interpolation.The quality of environment polluted with zinc was assessed and related methods were compared according to Environment Quality Standard of Green Food Production Area.[Result] Spatial interpolation of zinc in soils based on GIS proved more precise than traditional methods;cokriging method with co-factors was higher in precision than common cokriging;cokriging method with zinc and organic matter was higher in precision than cokriging with zinc alone.[Conclusion] Quality assessment on environment polluted with zinc based on GIS interpolation is more scientific and reasonable than traditional methods.

Projection pursuit cluster model and its application in water quality assessment

One of the difficulties frequently encountered in water quality assessment is that there are many factors and they cannot be assessed according to one factor, all the effect factors associated with water quality must be used. In order to overcome this issues the projection pursuit principle is introduced into water quality assessment, and projection pursuit cluster(PPC) model is developed in this study. The PPC model makes the transition from high dimension to one-dimension. In other words, based on the PPC model, multifactor problem can be converted to one factor problem. The application of PPC model can be divided into four parts: (1) to estimate projection index function Q(); (2) to find the right projection direction ; (3) to calculate projection characteristic value of the i th sample z-i, and (4) to draw comprehensive analysis on the basis of z-i. On the other hand, the empirical formula of cutoff radius R is developed, which is benefit for the model to be used in practice. Finally, a case study of water quality assessment is proposed in this paper. The results showed that the PPC model is reasonable, and it is more objective and less subjective in water quality assessment. It is a new method for multivariate problem comprehensive analysis.

Soil Quality Assessment of Acid Sulfate Paddy Soils with Different Productivities in Guangdong Province,China

Land conversion is considered an effective measure to ensure national food security in China, but little information is available on the quality of low productivity soils, in particular those in acid sulfate soil regions. In our study, acid sulfate paddy soils were divided into soils with high, medium and low levels based on local rice productivity, and 60 soil samples were collected for analysis. Twenty soil variables including physical, chemical and biochemical properties were determined. Those variables that were significantly different between the high, medium and low productivity soils were selected for principal component analysis, and microbial biomass carbon （MBC）, total nitrogen （TN）, available silicon （ASi）, pH and available zinc （AZn） were retained in the minimum data set （MDS）. After scoring the MDS variables, they were integrated to calculate a soil quality index （SQI）, and the high, medium and low productivity paddy soils received mean SQI scores of 0.95, 0.83 and 0.60, respectively. Low productivity paddy soils showed worse soil quality, and a large discrepancy was observed between the low and high productivity paddy soils. Lower MBC, TN, ASi, pH and available K （AK） were considered as the primary limiting factors. Additionally, all the soil samples collected were rich in available P and AZn, but deficient in AK and ASi. The results suggest that soil AK and ASi deficiencies were the main limiting factors for all the studied acid sulfate paddy soil regions. The application of K and Si on a national basis and other sustainable management approaches are suggested to improve rice productivity, especially for low productivity paddy soils. Our results indicated that there is a large potential for increasing productivity and producing more cereals in acid sulfate paddy soil regions.

An ultra-robust fingerprinting method for quality assessment of traditional Chinese medicine using multiple reaction monitoring mass spectrometry

Chromatographic fingerprinting has been perceived as an essential tool for assessing quality and chemical equivalence of traditional Chinese medicine.However,this pattern-oriented approach still has some weak points in terms of chemical coverage and robustness.In this work,we proposed a multiple reaction monitoring(MRM)-based fingerprinting method in which approximately 100 constituents were simultaneously detected for quality assessment.The derivative MRM approach was employed to rapidly design MRM transitions independent of chemical standards,based on which the large-scale fingerprinting method was efficiently established.This approach was exemplified on QiShenYiQi Pill(QSYQ),a traditional Chinese medicine-derived drug product,and its robustness was systematically evaluated by four indices:clustering analysis by principal component analysis,similarity analysis by the congruence coefficient,the number of separated peaks,and the peak area proportion of separated peaks.Compared with conventional ultraviolet-based fingerprints,the MRM fingerprints provided not only better discriminatory capacity for the tested normal/abnormal QSYQ samples,but also higher robustness under different chromatographic conditions(i.e.,flow rate,apparent pH,column temperature,and column).The result also showed for such large-scale fingerprints including a large number of peaks,the angle cosine measure after min-max normalization was more suitable for setting a decision criterion than the unnormalized algorithm.This proof-of-concept application gives evidence that combining MRM technique with proper similarity analysis metrices can provide a highly sensitive,robust and comprehensive analytical approach for quality assessment of traditional Chinese medicine.

An integrated spectroscopic strategy to trace the geographical origins of emblic medicines: Application for the quality assessment of natural medicines

Emblic medicine is a popular natural source in the world due to its outstanding healthcare and therapeutic functions.Our preliminary results indicated that the quality of emblic medicines might have an apparent regional variation.A rapid and effective geographical traceability system has not been designed yet.To trace the geographical origins so that their quality can be controlled,an integrated spectroscopic strategy including spectral pretreatment,outlier diagnosis,feature selection,data fusion,and machine learning algorithm was proposed.A featured data matrix(245
220)was successfully generated,and a carefully adjusted RF machine learning algorithm was utilized to develop the geographical traceability model.The results demonstrate that the proposed strategy is effective and can be generalized.Sensitivity(SEN),specificity(SPE)and accuracy(ACC)of 97.65%,99.85%and 97.63%for the calibrated set,as well as 100.00%predictive efficiency,were obtained using this spectroscopic analysis strategy.Our study has created an integrated analysis process for multiple spectral data,which can achieve a rapid,nondestructive and green quality detection for emblic medicines originating from seventeen geographical origins.

Quality Assessment of FY-4A Lightning Data in Inland China

The Lightning Mapping Imager(LMI)equipped on the FY-4 A(Feng Yun-4 A)geostationary satellite achieves lightning positioning through optical imaging and has the advantages of high temporal resolution,high stability,and continuous observation.In this study,FY-4 A LMI lightning event,group and flash data from April to August 2018 are selected,and their quality are assessed through qualitative and quantitative comparison with the ground-based Advanced Time of Arrival and Direction system(ADTD)lightning observation network data and the American International Space Station(ISS)lightning imaging sensor(LIS)data.The results show that the spatial distributions of FY-4 A lightning are consistent with those of the ground-based ADTD and ISS LIS.The temporal variation in FY-4 A lightning group frequency is consistent with that of ADTD stroke,which reflects that FY-4 A LMI can capture the lightning occurrence in inland China.Quantitative statistics show that the consistency rate of FY-4 A LMI and ISS LIS events is relatively high but their consistency rate is lower in terms of lightning group and flash data.Compared with the lightning observations by the ISS LIS and the ground-based ADTD,FY-4 A LMI reports fewer lightning events in the Tibetan Plateau.The application of Tibetan Plateau lightning data requires further processing and consideration.

Multi-agent Blind Model and Its Application to Regional Eco-environmental Quality Assessment

Through denoting each expert as an agent and viewing a multiple criteria decision-making as a synthesis problem of aggregating experts＇ ratings, a multi-agent blind model （MABM） is developed for regional eco-environmental quality assessment. In this model, the ratings of the evaluated object under an index, given by expert group, are first utilized to construct a series of blind numbers. In general, each index will correspond to different blind numbers. On the basis of aggregating index weights, the rank score in the form of a blind number is obtained for the evaluated object. Then, by means of calculating expected value of the above blind number, its rank score is further converted into a crisp value. By way of comparing the expected value with classification standards, eco-environmental quality of the evaluated sample could he identified successfully in the end. As a case, the MABM is used to evaluate the eco-environmental quality of Chaohu Lake basin. Study result shows that the MABM is a useful model for regional eco-environmental quality assessment.

A NEW IMAGE QUALITY ASSESSMENT BASED ON HVS

This letter proposes a new kind of image quality philosophy—Modulate Quality based on Fixation Points (MQFP) based on Human Visual System (HVS) model. Dissimilar to the former HVS-based quality assessment, the new measure emphasizes particularly on modeling the jumping phenomenon of human sight instead of modeling the visual perception of human. In other words, to model the HVS using fixation points and stay-frequency instead of Contrast Sensitive Function (CSF) etc. which models the visual perception of HVS. The experiment on various frequency-distortion images indicates that the new measure is correlated with the subjective judgment more than the former HVS-based measure and is a robust measure.

Color Image Quality Assessment Based on Structural Similarity

It is well-known that classical quality measures,such as Mean Squared Error(MSE),Weighted Mean Squared Error(WMSE)or Peak Signal-to-Noise Ratio(PSNR),are not always corresponding with visual observations.Structural similarity based image quality assessment was proposed under the assumption that the Human Visual System(HVS)is highly adapted for extracting structural information from an image.While the demand on high color quality increases in the media industry,color loss will make the visual quality different.In this paper,we proposed an improved quality assessment(QA)method by adding color comparison into the structural similarity(SSIM)measurement system for evaluating color image quality.Then we divided the task of similarity measurement into four comparisons:luminance,contrast,structure,and color.Experimental results show that the predicted quality scores of the proposed method are more effective and consistent with visual quality than the classical methods using five different distortion types of color image sets.

Slope excavation quality assessment and excavated volume calculation in hydraulic projects based on laser scanning technology

Slope excavation is one of the most crucial steps in the construction of a hydraulic project. Excavation project quality assessment and excavated volume calculation are critical in construction management. The positioning of excavation projects using traditional instruments is inefficient and may cause error. To improve the efficiency and precision of calculation and assessment, three-dimensional laser scanning technology was used for slope excavation quality assessment. An efficient data acquisition, processing, and management workflow was presented in this study. Based on the quality control indices, including the average gradient, slope toe elevation, and overbreak and underbreak,cross-sectional quality assessment and holistic quality assessment methods were proposed to assess the slope excavation quality with laserscanned data. An algorithm was also presented to calculate the excavated volume with laser-scanned data. A field application and a laboratory experiment were carried out to verify the feasibility of these methods for excavation quality assessment and excavated volume calculation. The results show that the quality assessment indices can be obtained rapidly and accurately with design parameters and scanned data, and the results of holistic quality assessment are consistent with those of cross-sectional quality assessment. In addition, the time consumption in excavation quality assessment with the laser scanning technology can be reduced by 70%e90%, as compared with the traditional method. The excavated volume calculated with the scanned data only slightly differs from measured data, demonstrating the applicability of the excavated volume calculation method presented in this study.

SIMPLE QUALITY ASSESSMENT FOR BINARY IMAGES

Usually image assessment methods could be classified into two categories: subjective as-sessments and objective ones. The latter are judged by the correlation coefficient with subjective quality measurement MOS (Mean Opinion Score). This paper presents an objective quality assessment algorithm special for binary images. In the algorithm, noise energy is measured by Euclidean distance between noises and signals and the structural effects caused by noise are described by Euler number change. The assessment on image quality is calculated quantitatively in terms of PSNR (Peak Signal to Noise Ratio). Our experiments show that the results of the algorithm are highly correlative with subjective MOS and the algorithm is more simple and computational saving than traditional objective assessment methods.

QUALITY ASSESSMENT OF GINSENG BY CHEMICAL FUZZY PATTERN RECOGNITION

A new method of assessing ginseng and screening its bioactive constituents by fuzzy pattern recognition has been proposed.

NEW VISUAL PERCEPTUAL POOLING STRATEGY FOR IMAGE QUALITY ASSESSMENT

Most of Image Quality Assessment (IQA) metrics consist of two processes. In the first process, quality map of image is measured locally. In the second process, the last quality score is converted from the quality map by using the pooling strategy. The first process had been made effective and significant progresses, while the second process was always done in simple ways. In the second process of the pooling strategy, the optimal perceptual pooling weights should be determined and computed according to Human Visual System (HVS). Thus, a reliable spatial pooling mathematical model based on HVS is an important issue worthy of study. In this paper, a new Visual Perceptual Pooling Strategy (VPPS) for IQA is presented based on contrast sensitivity and luminance sensitivity of HVS. Experimental results with the LIVE database show that the visual perceptual weights, obtained by the proposed pooling strategy, can effectively and significantly improve the performances of the IQA metrics with Mean Structural SIMilarity (MSSIM) or Phase Quantization Code (PQC). It is confirmed that the proposed VPPS demonstrates promising results for improving the performances of existing IQA metrics.