Objectives This study aimed to develop and preliminarily assess the quality of a Mindfulness Breast Care(MBC)App to reduce body image distress and stigma among breast cancer survivors(BCSs).Methods The development pro...Objectives This study aimed to develop and preliminarily assess the quality of a Mindfulness Breast Care(MBC)App to reduce body image distress and stigma among breast cancer survivors(BCSs).Methods The development process of the MBC App involved:1)establishing a research group;2)determining of the content of the MBC App based on Mindfulness-Based Cognitive Therapy and 3)technical exploitation and maintenance.A mixed-methods study was conducted.We selected ten BCSs by a convenience sampling method.After using the APP for three months,five assessed the quality using the Mobile App Rating Scale:User Version(uMARS)and another five were interviewed for process evaluation.Results The MBC App was developed with three modules:1)Library to provide health education information on body image,stigma,mindfulness,recovery and etc;2)Mindfulness Yoga to offer 12 Hatha yoga videos for daily practice;and 3)Mindfulness Practices to have 12 sessions of mindfulness videoconferences.Based on the uMARS data,the MBC App received high ratings for functionality(4.10±0.34),aesthetics(3.93±0.55),information quality(4.10±0.72),and perceived impact(4.03±0.96),as well as moderate ratings for engagement(3.72±0.94)and subjective quality(3.87±0.77).Participants indicated that the MBC App provided reliable knowledge,information,and emotional support.Recommendations from participants included categorizing knowledge in the Library Module,recording videoconferences of mindfulness practice,and adding discussion sessions in the videoconference.Afterward,we optimized the MBC App to enhance the user experience accordingly.Conclusions The MBC App offers online mindfulness interventions specifically for BCSs in China.The preliminary quality assessment indicates that the MBC App may be a promising tool for delivering mindfulness interventions to BCSs.展开更多
Perceptual quality assessment for point cloud is critical for immersive metaverse experience and is a challenging task.Firstly,because point cloud is formed by unstructured 3D points that makes the topology more compl...Perceptual quality assessment for point cloud is critical for immersive metaverse experience and is a challenging task.Firstly,because point cloud is formed by unstructured 3D points that makes the topology more complex.Secondly,the quality impairment generally involves both geometric attributes and color properties,where the measurement of the geometric distortion becomes more complex.We propose a perceptual point cloud quality assessment model that follows the perceptual features of Human Visual System(HVS)and the intrinsic characteristics of the point cloud.The point cloud is first pre-processed to extract the geometric skeleton keypoints with graph filtering-based re-sampling,and local neighboring regions around the geometric skeleton keypoints are constructed by K-Nearest Neighbors(KNN)clustering.For geometric distortion,the Point Feature Histogram(PFH)is extracted as the feature descriptor,and the Earth Mover’s Distance(EMD)between the PFHs of the corresponding local neighboring regions in the reference and the distorted point clouds is calculated as the geometric quality measurement.For color distortion,the statistical moments between the corresponding local neighboring regions are computed as the color quality measurement.Finally,the global perceptual quality assessment model is obtained as the linear weighting aggregation of the geometric and color quality measurement.The experimental results on extensive datasets show that the proposed method achieves the leading performance as compared to the state-of-the-art methods with less computing time.Meanwhile,the experimental results also demonstrate the robustness of the proposed method across various distortion types.The source codes are available at https://github.com/llsurreal919/Point Cloud Quality Assessment.展开更多
This study explores whether the current external quality assessment(EQA)level and acceptable bias for basic semen analysis in China are clinically useful.We collected data of semen EQA from Andrology laboratories in t...This study explores whether the current external quality assessment(EQA)level and acceptable bias for basic semen analysis in China are clinically useful.We collected data of semen EQA from Andrology laboratories in the Hunan Province(China)in 2022 and searched for data in the published literature from January2000 to December 2023 in China.On the basis of these data,we analyzed the coefficients of variation and acceptable biases of different quality control materials for basic semen analysis through robust statistics.We compared these findings with quality specifications based on biological variation from optimal,desirable,and minimum levels of bias to seek a unified and more suitable semen EQAbias evaluation standard for China's national conditions.Different sources of semen quality control material exhibited considerable variation in acceptable biases among laboratories,ranging from 8.2%to 56.9%.A total of 50.0% of the laboratories met the minimum quality specifications for progressive motility(PR),whereas 100.0%and 75.0%of laboratories met only the minimum quality specifications for sperm concentration and total motility(nonprogressive[NP]+PR),respectively.The Z value for sperm concentration and PR+NP was equivalent to the desirable performance specification,whereas the Z value for PR was equivalent only to the minimum performance specification.This study highlights the feasibility of operating external quality assessment schemes for basic semen analysis using quality specifications based on biological variation.These specifications should be unified among external quality control(EQC)centers based on biological variation.展开更多
The assessment of beach quality is an important prerequisite for beach development and serves as the foundation for coastal zone management and sustainable development.This topic has attracted widespread attention,and...The assessment of beach quality is an important prerequisite for beach development and serves as the foundation for coastal zone management and sustainable development.This topic has attracted widespread attention,and various evaluation systems have been established.Given that beach quality assessment(BQA)involves multidimensional and nonlinear indicators,machine learning methods are well-suited to handling complex data relationships.However,current research utilizing machine learning for BQA often faces challenges such as limited evaluation indicators and difficulties in obtaining relevant data.in this study,a machine learning-based model for beach quality evaluation is proposed to address the limitations of existing evaluation frameworks,particular-ly under conditions of data scarcity.Simulated data were generated,and the analytic hierarchy process was integrated to extract fea-tures from 21 beach evaluation factors.A comparative analysis was conducted using the following four machine learning models:de-cision tree,random forest,XGBoost,and MLP.Results indicate that XGBoost(mean squared error(MSE)=0.1825,weighted F1=0.7513)and MLP(Pearson coefficient=0.6053)outperform traditional models.Furthermore,an ensemble learning model combining XGBoost and MLP was developed,substantially improving predictive performance(reducing MSE to 0.0753,increasing the Pearson coefficient to 0.8002,and achieving an F1 score of 0.783).Validation using real data from Yangkou Beach demonstrated that the model maintained an accuracy of 58%even when 5–10 evaluation factors had randomly missing values.展开更多
Neutron radiographic images(NRIs)typically suffer from multiple distortions,including various types of noise,geometric unsharpness,and white spots.Image quality assessment(IQA)can guide on-site image screening and eve...Neutron radiographic images(NRIs)typically suffer from multiple distortions,including various types of noise,geometric unsharpness,and white spots.Image quality assessment(IQA)can guide on-site image screening and even provide metrics for subsequent image processing.However,existing IQA methods for NRIs cannot effectively evaluate the quality of real NRIs with a specific distortion of white spots,limiting their practical application.In this paper,a novel no-reference IQA method is proposed to comprehensively evaluate the quality of real NRIs with multiple distortions.First,we construct large-scale NRI datasets with more than 20,000 images,including high-quality original NRIs and synthetic NRIs with various distortions.Next,an image quality calibration method based on visual salience and a local quality map is introduced to label the NRI dataset with quality scores.Finally,a lightweight convolutional neural network(CNN)model is designed to learn the abstract relationship between the NRIs and quality scores using the constructed NRI training dataset.Extensive experimental results demonstrate that the proposed method exhibits good consistency with human visual perception when evaluating both real NRIs and processed NRIs using enhancement and restoration algorithms,highlighting its application potential.展开更多
Recent deep neural network(DNN)based blind image quality assessment(BIQA)approaches take mean opinion score(MOS)as ground-truth labels,which would lead to cross-datasets biases and limited generalization ability of th...Recent deep neural network(DNN)based blind image quality assessment(BIQA)approaches take mean opinion score(MOS)as ground-truth labels,which would lead to cross-datasets biases and limited generalization ability of the DNN-based BIQA model.This work validates the natural instability of MOS through investigating the neuropsychological characteristics inside the human visual system during quality perception.By combining persistent homology analysis with electroencephalogram(EEG),the physiologically meaningful features of the brain responses to different distortion levels are extracted.The physiological features indicate that although volunteers view exactly the same image content,their EEG features are quite varied.Based on the physiological results,we advocate treating MOS as noisy labels and optimizing the DNN based BIQA model with earlystop strategies.Experimental results on both innerdataset and cross-dataset demonstrate the superiority of our optimization approach in terms of generalization ability.展开更多
Merged satellite altimeter products are widely used in ocean-related fields.Currently,the altimeter merged products of archiving validation and interpretation of satellite oceanographic(AVISO)data are widely used inte...Merged satellite altimeter products are widely used in ocean-related fields.Currently,the altimeter merged products of archiving validation and interpretation of satellite oceanographic(AVISO)data are widely used internationally.Chinese National Satellite Ocean Application Service also released merged altimeter products(ALT MUL)in 2023.However,there are few studies on the quality assessment of ALT MUL.Based on the data of AVISO merged products,Jason3 satellite,tide gauge and drifter buoy,the quality assessment and effect analysis of ALT MUL merged products were carried out by means of error evaluation index,interpolation along rails,velocity inversion and power spectrum.The result shows that the average sea level anomaly(SLA)of ALT MUL is about 2 cm smaller than that of AVISO.And they are consistent with the large-scale characteristics and spatial distribution.These two SLA products are both in accordance with normal distribution.Results indicate a lesser congruence between ALT MUL and Jason3 satellite compared to AVISO.This difference may be attributed to the fact that AVISO products use Jason3 satellite as crosscalibrated reference satellite during the merged process.Comparing the matching effect of the two merged products with the tide gauge and drifter buoy,ALT MUL merged products are superior to AVISO in general.The energy spectral density was calculated by using Jason3 satellite data along the orbit,and the two merged products were interpolated to the data points along the orbit.The effective resolution of AVISO and ALT MUL merged products was 180 km and 210 km respectively through spectral calculation,indicating that AVISO merged products have higher effective resolution.展开更多
Although the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)model has been widely applied in water quality assessment by numerous studies,several common limitations remain unresolved.Specificall...Although the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)model has been widely applied in water quality assessment by numerous studies,several common limitations remain unresolved.Specifically:1)Subjective elements in methods such as fuzzy theory and the analytic hierarchy process(AHP)may distort evaluation outcomes;2)The utilization of raw sample data is in‐sufficient when constructing evaluation matrices;3)The traditional entropy weight method in TOPSIS merely reflects statistical character‐istics of the final matrix while neglecting richer information embedded in raw datasets.To address these issues,we proximate probability distribution function of various indicators by using cubic spline interpolation and fully exploit information in the existing massive sample data.In this paper,the entropy weight method is enhanced based on the concept mentioned above and integrated with TOPSIS model to construct a novel evaluation model.Furthermore,the experimental analysis using wastewater monitoring data from Guizhou Province,China,verifies its practicality,and its results provide valuable references for local water environmental management.展开更多
Most blind image quality assessment(BIQA)methods require a large amount of time to collect human opinion scores as training labels,which limits their usability in practice.Thus,we present an opinion-unaware BIQA metho...Most blind image quality assessment(BIQA)methods require a large amount of time to collect human opinion scores as training labels,which limits their usability in practice.Thus,we present an opinion-unaware BIQA method based on deep reinforcement learning which is trained without subjective scores,named DRL-IQA.Inspired by the human visual perception process,our model is formulated as a quality reinforced agent,which consists of the dynamic distortion generation part and the quality perception part.By considering the image distortion degradation process as a sequential decision-making process,the dynamic distortion generation part can develop a strategy to add as many different distortions as possible to an image,which enriches the distortion space to alleviate overfitting.A reward function calculated from quality degradation after adding distortion is utilized to continuously optimize the strategy.Furthermore,the quality perception part can extract rich quality features from the quality degradation process without using subjective scores,and accurately predict the state values that represent the image quality.Experimental results reveal that our method achieves competitive quality prediction performance compared to other state-of-the-art BIQA methods.展开更多
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 tra...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.展开更多
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. ...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.展开更多
[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 Shula...[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.展开更多
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 ...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.展开更多
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 regi...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.展开更多
AIM： To prepare high-purity ginseng total saponins from a water decoction of Chinese ginseng root.METHOD： Total saponins were efficiently purified by dynamic anion-cation exchange following the removal of hydrophili...AIM： To prepare high-purity ginseng total saponins from a water decoction of Chinese ginseng root.METHOD： Total saponins were efficiently purified by dynamic anion-cation exchange following the removal of hydrophilic impurities by macroporous resin D101. For quality control, ultrahigh-performance liquid chromatography with a charged aerosol detector （CAD） was applied to quantify marker components. The total saponin content was estimated by a colorimetric method using a vanillin-vitriol system and CAD response. RESULTS： D201, which consisted of a cross-linked polystyrene matrix and -]N＋（CI-13）3 functional groups, was the best of the four anion exchange resins tested. However, no significant difference in cation exchange ability was observed between D001 （strong acid） and D 113 （weak acid）, although they have different functional groups and matrices. After purification in combination with D101, D201, and D 113, the estimated contents of total saponins were 107% and 90% according to the colorimetric method and CAD response, respectively. The total amount of representative ginsenosides Re, Rd, Rgl, and compound K was approximately 22% based on ultrahigh-performance liquid chromatography-CAD quantitative analysis. CONCLUSION： These findings suggest that an ion exchange resin, combined with macroporous adsorption resin separation, is a promising and feasible purification procedure for neutral natural polar components.展开更多
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 i...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.展开更多
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 region...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(245220)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.展开更多
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...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.展开更多
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-enviro...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.展开更多
The growing demand for health management puts forward high requirements for the quality of health knowledge.A content-based method is proposed to address the current demand for high-quality health knowledge,which eval...The growing demand for health management puts forward high requirements for the quality of health knowledge.A content-based method is proposed to address the current demand for high-quality health knowledge,which evaluates the quality including the certainty,accuracy,and operability of different types of knowledge from the perspectives of authority,precision,and information entropy.Herein,the health knowledge of myocardial infarction is used as an example,and knowledge is first classified into different types and then evaluated.This method is applied to knowledge in the existing health management system and it can support knowledge screening and comparison under the cold start condition.Compared with the current evaluation methods based on knowledge use behavior and utility,the new evaluation method provides a reference for evaluation when the knowledge is first used.The screening of high quality knowledge can help the subsequent application of knowledge and improve user’s compliance.Concurrently,the arrangement of myocardial infarction knowledge can also provide a knowledge reference for patients’daily health management.展开更多
基金supported by the National Natural Science Foundation of China(No.71974162 and No.7231101009).
文摘Objectives This study aimed to develop and preliminarily assess the quality of a Mindfulness Breast Care(MBC)App to reduce body image distress and stigma among breast cancer survivors(BCSs).Methods The development process of the MBC App involved:1)establishing a research group;2)determining of the content of the MBC App based on Mindfulness-Based Cognitive Therapy and 3)technical exploitation and maintenance.A mixed-methods study was conducted.We selected ten BCSs by a convenience sampling method.After using the APP for three months,five assessed the quality using the Mobile App Rating Scale:User Version(uMARS)and another five were interviewed for process evaluation.Results The MBC App was developed with three modules:1)Library to provide health education information on body image,stigma,mindfulness,recovery and etc;2)Mindfulness Yoga to offer 12 Hatha yoga videos for daily practice;and 3)Mindfulness Practices to have 12 sessions of mindfulness videoconferences.Based on the uMARS data,the MBC App received high ratings for functionality(4.10±0.34),aesthetics(3.93±0.55),information quality(4.10±0.72),and perceived impact(4.03±0.96),as well as moderate ratings for engagement(3.72±0.94)and subjective quality(3.87±0.77).Participants indicated that the MBC App provided reliable knowledge,information,and emotional support.Recommendations from participants included categorizing knowledge in the Library Module,recording videoconferences of mindfulness practice,and adding discussion sessions in the videoconference.Afterward,we optimized the MBC App to enhance the user experience accordingly.Conclusions The MBC App offers online mindfulness interventions specifically for BCSs in China.The preliminary quality assessment indicates that the MBC App may be a promising tool for delivering mindfulness interventions to BCSs.
基金supported in part by the National Natural Science Foundation of China under Grant(62171257,U22B2001,U19A2052,62020106011,62061015)in part by the Natural Science Foundation of Chongqing under Grant(2023NSCQMSX2930)+1 种基金in part by the Youth Innovation Group Support Program of ICE Discipline of CQUPT under Grant(SCIE-QN-2022-05)in part by the Graduate Scientifc Research and Innovation Project of Chongqing under Grant(CYS22469).
文摘Perceptual quality assessment for point cloud is critical for immersive metaverse experience and is a challenging task.Firstly,because point cloud is formed by unstructured 3D points that makes the topology more complex.Secondly,the quality impairment generally involves both geometric attributes and color properties,where the measurement of the geometric distortion becomes more complex.We propose a perceptual point cloud quality assessment model that follows the perceptual features of Human Visual System(HVS)and the intrinsic characteristics of the point cloud.The point cloud is first pre-processed to extract the geometric skeleton keypoints with graph filtering-based re-sampling,and local neighboring regions around the geometric skeleton keypoints are constructed by K-Nearest Neighbors(KNN)clustering.For geometric distortion,the Point Feature Histogram(PFH)is extracted as the feature descriptor,and the Earth Mover’s Distance(EMD)between the PFHs of the corresponding local neighboring regions in the reference and the distorted point clouds is calculated as the geometric quality measurement.For color distortion,the statistical moments between the corresponding local neighboring regions are computed as the color quality measurement.Finally,the global perceptual quality assessment model is obtained as the linear weighting aggregation of the geometric and color quality measurement.The experimental results on extensive datasets show that the proposed method achieves the leading performance as compared to the state-of-the-art methods with less computing time.Meanwhile,the experimental results also demonstrate the robustness of the proposed method across various distortion types.The source codes are available at https://github.com/llsurreal919/Point Cloud Quality Assessment.
基金supported by the Hunan Province Municipal Natural Science Foundation(2022JJ30018)the Hunan Province Health Commission Science Foundation(B202301037899)to WNL。
文摘This study explores whether the current external quality assessment(EQA)level and acceptable bias for basic semen analysis in China are clinically useful.We collected data of semen EQA from Andrology laboratories in the Hunan Province(China)in 2022 and searched for data in the published literature from January2000 to December 2023 in China.On the basis of these data,we analyzed the coefficients of variation and acceptable biases of different quality control materials for basic semen analysis through robust statistics.We compared these findings with quality specifications based on biological variation from optimal,desirable,and minimum levels of bias to seek a unified and more suitable semen EQAbias evaluation standard for China's national conditions.Different sources of semen quality control material exhibited considerable variation in acceptable biases among laboratories,ranging from 8.2%to 56.9%.A total of 50.0% of the laboratories met the minimum quality specifications for progressive motility(PR),whereas 100.0%and 75.0%of laboratories met only the minimum quality specifications for sperm concentration and total motility(nonprogressive[NP]+PR),respectively.The Z value for sperm concentration and PR+NP was equivalent to the desirable performance specification,whereas the Z value for PR was equivalent only to the minimum performance specification.This study highlights the feasibility of operating external quality assessment schemes for basic semen analysis using quality specifications based on biological variation.These specifications should be unified among external quality control(EQC)centers based on biological variation.
基金supported by the National Natural Science Foundation of China(Nos.82202299,62203060,62403492).
文摘The assessment of beach quality is an important prerequisite for beach development and serves as the foundation for coastal zone management and sustainable development.This topic has attracted widespread attention,and various evaluation systems have been established.Given that beach quality assessment(BQA)involves multidimensional and nonlinear indicators,machine learning methods are well-suited to handling complex data relationships.However,current research utilizing machine learning for BQA often faces challenges such as limited evaluation indicators and difficulties in obtaining relevant data.in this study,a machine learning-based model for beach quality evaluation is proposed to address the limitations of existing evaluation frameworks,particular-ly under conditions of data scarcity.Simulated data were generated,and the analytic hierarchy process was integrated to extract fea-tures from 21 beach evaluation factors.A comparative analysis was conducted using the following four machine learning models:de-cision tree,random forest,XGBoost,and MLP.Results indicate that XGBoost(mean squared error(MSE)=0.1825,weighted F1=0.7513)and MLP(Pearson coefficient=0.6053)outperform traditional models.Furthermore,an ensemble learning model combining XGBoost and MLP was developed,substantially improving predictive performance(reducing MSE to 0.0753,increasing the Pearson coefficient to 0.8002,and achieving an F1 score of 0.783).Validation using real data from Yangkou Beach demonstrated that the model maintained an accuracy of 58%even when 5–10 evaluation factors had randomly missing values.
基金supported by the National Natural Science Foundation of China(Nos.11905028 and 12105040)Scientific Research Project of the Education Department of Jilin Province(No.JJKH20231294KJ)the Youth Growth Technology Project of the Science and Technology Department of Jilin Province(No.20210508027RQ).
文摘Neutron radiographic images(NRIs)typically suffer from multiple distortions,including various types of noise,geometric unsharpness,and white spots.Image quality assessment(IQA)can guide on-site image screening and even provide metrics for subsequent image processing.However,existing IQA methods for NRIs cannot effectively evaluate the quality of real NRIs with a specific distortion of white spots,limiting their practical application.In this paper,a novel no-reference IQA method is proposed to comprehensively evaluate the quality of real NRIs with multiple distortions.First,we construct large-scale NRI datasets with more than 20,000 images,including high-quality original NRIs and synthetic NRIs with various distortions.Next,an image quality calibration method based on visual salience and a local quality map is introduced to label the NRI dataset with quality scores.Finally,a lightweight convolutional neural network(CNN)model is designed to learn the abstract relationship between the NRIs and quality scores using the constructed NRI training dataset.Extensive experimental results demonstrate that the proposed method exhibits good consistency with human visual perception when evaluating both real NRIs and processed NRIs using enhancement and restoration algorithms,highlighting its application potential.
基金supported by the Medium and Long-term Science and Technology Plan for Radio,Television,and Online Audiovisuals(2023AC0200)the Public Welfare Technology Application Research Project of Zhejiang Province,China(No.LGF21F010001).
文摘Recent deep neural network(DNN)based blind image quality assessment(BIQA)approaches take mean opinion score(MOS)as ground-truth labels,which would lead to cross-datasets biases and limited generalization ability of the DNN-based BIQA model.This work validates the natural instability of MOS through investigating the neuropsychological characteristics inside the human visual system during quality perception.By combining persistent homology analysis with electroencephalogram(EEG),the physiologically meaningful features of the brain responses to different distortion levels are extracted.The physiological features indicate that although volunteers view exactly the same image content,their EEG features are quite varied.Based on the physiological results,we advocate treating MOS as noisy labels and optimizing the DNN based BIQA model with earlystop strategies.Experimental results on both innerdataset and cross-dataset demonstrate the superiority of our optimization approach in terms of generalization ability.
基金The National Key R&D Program of China under contract No.2021YFC3101503the National Natural Science Foundation of China under contract Nos 42276205 and 42406195+1 种基金the Hunan Provincial Natural Science Foundation of China under contract No.2023JJ10053the Youth Independent Innovation Science Foundation under contract No.ZK24-54.
文摘Merged satellite altimeter products are widely used in ocean-related fields.Currently,the altimeter merged products of archiving validation and interpretation of satellite oceanographic(AVISO)data are widely used internationally.Chinese National Satellite Ocean Application Service also released merged altimeter products(ALT MUL)in 2023.However,there are few studies on the quality assessment of ALT MUL.Based on the data of AVISO merged products,Jason3 satellite,tide gauge and drifter buoy,the quality assessment and effect analysis of ALT MUL merged products were carried out by means of error evaluation index,interpolation along rails,velocity inversion and power spectrum.The result shows that the average sea level anomaly(SLA)of ALT MUL is about 2 cm smaller than that of AVISO.And they are consistent with the large-scale characteristics and spatial distribution.These two SLA products are both in accordance with normal distribution.Results indicate a lesser congruence between ALT MUL and Jason3 satellite compared to AVISO.This difference may be attributed to the fact that AVISO products use Jason3 satellite as crosscalibrated reference satellite during the merged process.Comparing the matching effect of the two merged products with the tide gauge and drifter buoy,ALT MUL merged products are superior to AVISO in general.The energy spectral density was calculated by using Jason3 satellite data along the orbit,and the two merged products were interpolated to the data points along the orbit.The effective resolution of AVISO and ALT MUL merged products was 180 km and 210 km respectively through spectral calculation,indicating that AVISO merged products have higher effective resolution.
文摘Although the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)model has been widely applied in water quality assessment by numerous studies,several common limitations remain unresolved.Specifically:1)Subjective elements in methods such as fuzzy theory and the analytic hierarchy process(AHP)may distort evaluation outcomes;2)The utilization of raw sample data is in‐sufficient when constructing evaluation matrices;3)The traditional entropy weight method in TOPSIS merely reflects statistical character‐istics of the final matrix while neglecting richer information embedded in raw datasets.To address these issues,we proximate probability distribution function of various indicators by using cubic spline interpolation and fully exploit information in the existing massive sample data.In this paper,the entropy weight method is enhanced based on the concept mentioned above and integrated with TOPSIS model to construct a novel evaluation model.Furthermore,the experimental analysis using wastewater monitoring data from Guizhou Province,China,verifies its practicality,and its results provide valuable references for local water environmental management.
基金supported by the Fundamental Research Funds for the Central Universities.
文摘Most blind image quality assessment(BIQA)methods require a large amount of time to collect human opinion scores as training labels,which limits their usability in practice.Thus,we present an opinion-unaware BIQA method based on deep reinforcement learning which is trained without subjective scores,named DRL-IQA.Inspired by the human visual perception process,our model is formulated as a quality reinforced agent,which consists of the dynamic distortion generation part and the quality perception part.By considering the image distortion degradation process as a sequential decision-making process,the dynamic distortion generation part can develop a strategy to add as many different distortions as possible to an image,which enriches the distortion space to alleviate overfitting.A reward function calculated from quality degradation after adding distortion is utilized to continuously optimize the strategy.Furthermore,the quality perception part can extract rich quality features from the quality degradation process without using subjective scores,and accurately predict the state values that represent the image quality.Experimental results reveal that our method achieves competitive quality prediction performance compared to other state-of-the-art BIQA methods.
文摘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.
基金The National Natural Science Foundation of China(No.81272501)the National Basic Research Program of China(973Program)(No.2011CB707904)Taishan Scholars Program of Shandong Province,China(No.ts20120505)
文摘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.
基金Supported by National 973 Program(2010CB951500)National 863 Program(2006AA-120103)~~
文摘[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.
文摘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.
基金supported by the Special Fund for Agroscientific Research in the Public Interest,China(201003016)the earmarked fund for China Agriculture Research System(CARS-01-31)the National Basic Research Program of China(2013CB127405)
文摘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.
基金supported by the Jiangsu Provincial Natural Science Foundation of China(No.BK2011815)Specialized Research Fund for the Doctoral Program of Higher Education(No.20103237120011)the"Qing Lan"Project from Jiangsu Provincial Framework Teacher Support Scheme
文摘AIM： To prepare high-purity ginseng total saponins from a water decoction of Chinese ginseng root.METHOD： Total saponins were efficiently purified by dynamic anion-cation exchange following the removal of hydrophilic impurities by macroporous resin D101. For quality control, ultrahigh-performance liquid chromatography with a charged aerosol detector （CAD） was applied to quantify marker components. The total saponin content was estimated by a colorimetric method using a vanillin-vitriol system and CAD response. RESULTS： D201, which consisted of a cross-linked polystyrene matrix and -]N＋（CI-13）3 functional groups, was the best of the four anion exchange resins tested. However, no significant difference in cation exchange ability was observed between D001 （strong acid） and D 113 （weak acid）, although they have different functional groups and matrices. After purification in combination with D101, D201, and D 113, the estimated contents of total saponins were 107% and 90% according to the colorimetric method and CAD response, respectively. The total amount of representative ginsenosides Re, Rd, Rgl, and compound K was approximately 22% based on ultrahigh-performance liquid chromatography-CAD quantitative analysis. CONCLUSION： These findings suggest that an ion exchange resin, combined with macroporous adsorption resin separation, is a promising and feasible purification procedure for neutral natural polar components.
基金financially supported by the National Natural Science Foundation of China(Grant No.81803714)the Fundamental Research Funds for the Central Universities(Grant No.2019QNA7041).
文摘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.
基金This work is financially supported by the National Wild Plant Germplasm Resources Infrastructure which is the follow-up work of a project called Standardization and Community for the Collection and Preservation of Important Wild Plant Germplasm Resources(2005DKA21006).
文摘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(245220)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.
基金National Key R&D Program of China(2018YFC1506603)The Second Tibetan Plateau Scientific Expedition and Research(STEP)Program(2019QZKK0105)。
文摘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.
基金Under the auspices of the Natural Science Foundation of Anhui Province (No. 050450303 )
文摘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.
基金the National Natural Science Founda-tion of China(Nos.91646205 and 71421002)the Fundamental Research Funds for the Central Universi-ties of China(No.16JCCS08)。
文摘The growing demand for health management puts forward high requirements for the quality of health knowledge.A content-based method is proposed to address the current demand for high-quality health knowledge,which evaluates the quality including the certainty,accuracy,and operability of different types of knowledge from the perspectives of authority,precision,and information entropy.Herein,the health knowledge of myocardial infarction is used as an example,and knowledge is first classified into different types and then evaluated.This method is applied to knowledge in the existing health management system and it can support knowledge screening and comparison under the cold start condition.Compared with the current evaluation methods based on knowledge use behavior and utility,the new evaluation method provides a reference for evaluation when the knowledge is first used.The screening of high quality knowledge can help the subsequent application of knowledge and improve user’s compliance.Concurrently,the arrangement of myocardial infarction knowledge can also provide a knowledge reference for patients’daily health management.
