Quality Evaluation Index Research Articles

Cultivated Land Quality Assessment and Obstacle Factors Diagnosis in Changtu County, Northeast China

ABSTRACTEffective evaluation of cultivated land quality is crucial for sustainable agricultural management. Existing research often focuses on regional scales and lacks sufficient detailed analysis of spatial distribution and limiting factors at localized scales. This study aims to select the key indicators to evaluate cultivated land quality and analyze their influence on cultivated land quality at the county level. Taking Changtu County as the research area, principal component analysis (PCA) was employed to identify the most effective Minimum Data Set (MDS) for evaluation, including cultivated layer thickness, soil organic matter (SOM), pH, bulk density (BD), and cultivated layer texture. Additionally, an obstacle degree model was used to analyze restricting factors and their degrees of impact. Results showed that the constructed MDS could replace all indicators for cultivated land quality evaluation. Quality was higher in central areas and lower in eastern and western regions. SOM content decreases with the decrease of quality grade, significantly affecting the quality distribution, and pH in the medium category (Grades III and VI) cultivated lands were lower compared to other categories. Low organic matter content and low pH levels were the main obstacles affecting cultivated land quality, with average obstacle degrees of 43.5% and 29.3%, respectively. Low SOM content significantly affected land quality, particularly in the western region, whereas acidic soils in the eastern region influenced quality grade distribution. Thus, strategies for pH control and SOM enhancement are essential for improving cultivated land quality. This study provides valuable insights into sustainable agriculture.

Multiexposed Image-Fusion Strategy Using Mutual Image Translation Learning with Multiscale Surround Switching Maps

The dynamic range of an image represents the difference between its darkest and brightest areas, a crucial concept in digital image processing and computer vision. Despite display technology advancements, replicating the broad dynamic range of the human visual system remains challenging, necessitating high dynamic range (HDR) synthesis, combining multiple low dynamic range images captured at contrasting exposure levels to generate a single HDR image that integrates the optimal exposure regions. Recent deep learning advancements have introduced innovative approaches to HDR generation, with the cycle-consistent generative adversarial network (CycleGAN) gaining attention due to its robustness against domain shifts and ability to preserve content style while enhancing image quality. However, traditional CycleGAN methods often rely on unpaired datasets, limiting their capacity for detail preservation. This study proposes an improved model by incorporating a switching map (SMap) as an additional channel in the CycleGAN generator using paired datasets. The SMap focuses on essential regions, guiding weighted learning to minimize the loss of detail during synthesis. Using translated images to estimate the middle exposure integrates these images into HDR synthesis, reducing unnatural transitions and halo artifacts that could occur at boundaries between various exposures. The multilayered application of the retinex algorithm captures exposure variations, achieving natural and detailed tone mapping. The proposed mutual image translation module extends CycleGAN, demonstrating superior performance in multiexposure fusion and image translation, significantly enhancing HDR image quality. The image quality evaluation indices used are CPBDM, JNBM, LPC-SI, S3, JPEG_2000, and SSEQ, and the proposed model exhibits superior performance compared to existing methods, recording average scores of 0.6196, 15.4142, 0.9642, 0.2838, 80.239, and 25.054, respectively. Therefore, based on qualitative and quantitative results, this study demonstrates the superiority of the proposed model.

Connections and Spatial Network Structure of the Tourism Economy in Beijing–Tianjin–Hebei: A Social Network Perspective

In the context of the coordinated development of Beijing–Tianjin–Hebei, examining the spatial connections and network structure characteristics of the tourism economy holds substantial theoretical and practical significance. This study constructs a tourism economic quality evaluation index system from four dimensions and optimizes the tourism economic gravity model by incorporating time distance and weight factors to dynamically simulate the intensity of tourism economic spatial connection (ITESC). Based on the new perspective of relational data, social network analysis is employed to investigate the spatial correlation network characteristics and group structure changes in the tourism economy within the Beijing–Tianjin–Hebei urban agglomeration (BTHUA). The results demonstrate a significant improvement in overall tourism economic quality from 2013 to 2022, with the average quality increasing from 0.128 in 2013 to 0.157 in 2022. Tourism economic spatial connections are prevalent among cities, exhibiting heterogeneous characteristics, characterized by density in the north and relatively sparse in the south. The network cohesion of ITESC strengthens, and the network structure evolves from a highly centralized to a multi-centralized configuration, with increasingly frequent cooperation and interaction in tourism between cities. Beijing, Tianjin, and Shijiazhuang occupy central positions in the network, exhibiting strong ITESC and a high degree of connectivity, generating a significant “trickle-down effect” on peripheral cities. Consequently, strengthening ITESC between peripheral cities and core areas, as well as enhancing the bidirectional spillover of tourism development between cities, is crucial for the future sustainable and coordinated development of tourism in the BTHUA.

Strengthening crisis resilience in German primary care by using quality indicators: findings of a process evaluation in the RESILARE project

BackgroundIn recent years, health systems worldwide have been confronted with several crises such as natural disasters or the COVID-19 pandemic, that affected lives and health of many people. In light of waves of infections and heat, climate change is considered to be the biggest health threat of the 21st century. Strengthening individual and organizational crisis resilience in healthcare settings thus becomes a crucial factor in maintaining care quality and protecting vulnerable patients during such crises. The RESILARE project therefore aimed to develop and evaluate quality indicators that support primary care practices in preparing for and adapting to crisis-related challenges.MethodsIn a three-phased process, indicator development was based on systematic literature research and qualitative data, a two-stage expert panel process, and pilot testing in a maximum of n = 35 ambulatory practices during an outreach visit. Practice-individual indicator-related status and benchmarking information were provided via feedback reports to complete the audit and feedback program. A mixed-methods process evaluation used semistructured interviews with participating General practitioners and nonphysician health professionals to explore support and challenges for the implementation of the derived set of quality indicators. Two online surveys were conducted to evaluate all indicators and the two-part feedback report. Qualitative data were analyzed inductively using a thematic analysis approach. Survey data were analyzed descriptively.ResultsA total of n = 32 indicators covered four domains: (1) individual resilience, (2) crisis prevention, (3) organizational resilience, and (4) climate resilience. N = 34 practices participated in the piloting and the process evaluation. Participants generally attributed a high relevance to the domains, and considered the indicator set suitable for implementation into existing quality management systems. Planning and implementation of measures that strengthen crisis resilience in practices were triggered or intensified by piloting the indicators and by the two-part feedback report. The identified challenges involved the volume of indicators and practice-individual implementation of renewable energy sources on rented premises. Participants expressed their desire for peer exchange regarding proven concepts for crisis resilience.

Purification Resistance Index: A new water quality assessment method toward drinking water production

Water quality assessment plays a significant role in ensuring the availability of clean and safe water. The Water Quality Index (WQI) model method has been developed to provide a basis for assessing water quality by integrating various water quality parameters. However, existing WQIs do not “actively” consider the difficulty of water treatment from raw water to specific water use scenarios. This study proposes a novel model framework, named as Purification Resistance Index (PRI), quantitatively evaluating not only the exceedance of pollutants but also how difficult they can be removed in the water treatment process. The framework is built based on the conventional drinking water treatment processes, with sub-indices for coagulation-sedimentation (rc), filtration (rf), disinfection (rd), and advanced treatment (ra). The model considers appropriate weights assigned to each sub-index to calculate the purification resistance, resulting in a comprehensive index for water quality evaluation. Case studies on nationwide and citywide water source reservoirs demonstrated the applicability of PRI approach. PRI breakthrough the traditional water quality risk assessment paradigm and extents to engineering region and provide useful tools for water source supervision, drinking water treatment plant planning and updating, operation control, and other purposes. Water authority, water utility and municipal design institute will all benefit. It is open for more localized practices validation and discussion.

Comparative analysis of aroma profiles in walnut, pecan and hickory nuts during the roasting process using E-nose, HS-SPME-GC-MS, and HS-GC-IMS

Walnut, pecan, and hickory nuts are well known for their nutritional value and health benefits, with consumption having increased significantly in recent years. Outstanding aroma is an important indicator for nut quality evaluation. However, the impacts of thermal processing on their aroma formation are not well understood. To address this gap, a comprehensive analysis of the aroma profiles were conducted during the roasting process using both sensory evaluation and advanced analytical equipment, including an electronic nose (E-nose), headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS), and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). The results indicated that thermal processing had a substantial effect on the sensorial characteristics of the nuts, with hickory nuts exhibiting a distinct nutty aroma compared to walnuts. HS-SPME-GC-MS analysis further identified 118 volatile organic compounds (VOCs), encompassing 33 heterocyclic compounds, 18 aldehydes, 11 aromatics, 18 ketones, and other compounds, while HS-GC-IMS analysis detected 63 volatile compounds. Notably, the roasting process led to the reduction in alcohols and acids, while simultaneously caused the increase of esters, ketones, pyrazines, and phenols, all of which are crucial for the development of nut aroma. Especially, some components like 1-Octanol, 2-Pentyl-furan, Hexanal, Acetylpyrazine, Benzophenone, and Octyl ester may contribute to unique odors of pecan and hickory nuts. These findings provide valuable insights into the flavor formation of the three nuts and the optimization of roasting process.

Construction of indicators for evaluating the quality of extended care for children with asthma

Background and aims To construct a set of scientific and effective quality evaluation indicators of continuing care for children with asthma, and to promote the development of continuing care for children with asthma toward a more specialized and standardized direction. Methods Through literature research and semi-structured interview, the item pool of evaluation index was drawn up, and the first draft of expert inquiry questionnaire was formed based on the three-dimensional quality theoretical model of structure-process-result, and the construction of quality evaluation index was completed through two rounds of expert inquiry and analytic hierarchy process. Results The effective recovery rate was 94.44% in the first round and 100.00% in the second round, the expert authority coefficient was 0.852 and 0.863, and the Kendal coordination coefficient was 0.129 and 0.126, respectively (p < 0.01). Finally, the evaluation index of continuing care quality for children with asthma was formed, including 3 first-level indicators, 9 s-level indicators and 36 third-level indicators, and then the weight and combination weight of each index were obtained by AHP. Conclusions The evaluation index of continuing care quality for children with asthma is scientific and specific, which is of great significance to the improvement of continuing care quality for children with asthma.

Application of Computer Simulation Technology in the Development of Tractor Transmission Systems

Tractors are the most important agricultural power machinery. With the development of tractors toward large-scale and electrification, the design of modern tractor transmission systems increasingly relies on computer simulation technology. However, tractor transmission systems, especially power-shift transmissions and CVTs, are highly complex industrial products that involve specialized knowledge from multiple disciplines. Engineers and researchers find it difficult to choose the correct mathematical model and grasp the trend of technological development when applying simulation technology. To address this issue, we conducted a systematic review of the field and carried out the following work: First, the types and basic principles of tractor transmission systems were introduced; Second, the modeling methods and applications of computer simulation technology in the structural optimization, power-shift control, and energy saving of tractor transmission systems were reviewed; Finally, the method of obtaining simulation parameters through system identification was introduced. Although computer simulation technology has been applied in the development of all tractor transmission systems, there are still some issues that need attention, such as the lack of established shift quality evaluation indicators and driving cycles suitable for tractors, which are crucial for the reliability of simulation results but are rarely studied. These issues need to be addressed in future work.

Current status and quality of prognosis prediction models of non-small cell lung cancer constructed using computed tomography (CT)-based radiomics: a systematic review and radiomics quality score 2.0 assessment.

Radiomics extracts specific quantitative data from medical images and explores the characteristics of tumors by analyzing these representations and making predictions. The purpose of this paper is to review computed tomography (CT)-based radiomics articles related to prognostic outcomes in non-small cell lung cancer (NSCLC), assess their scientificity and quality by the latest radiomics quality score (RQS) 2.0 scoring criteria, and provide references for subsequent related studies. CT-based radiomics studies on NSCLC prognosis published from 1 November 2012 to 30 November 2022 in English were screened through the databases of the Cochrane Library, Embase, and PubMed. By excluding criteria such as non-original studies, small sample sizes studies, positron emission tomography (PET)/CT only, and methodological studies only, 17 studies in English were included. The RQS proposed in 2017 is a quality evaluation index specific to radiomics following the PRISMA guidelines, and the latest update of RQS 2.0 has improved the scientificity and completeness of the score. Each checkpoint either belongs to handcrafted radiomics (HCR), deep learning, or both. The 17 included studies covered most treatments for NSCLC, including radiotherapy, chemotherapy, surgery, radiofrequency ablation, immunotherapy, and targeted therapy, and predicted outcomes such as overall survival (OS), progression-free survival (PFS), distant metastases, and disease-free survival (DFS). The median score rate for the included studies was 28%, with a range of 12% to 44%. The quality of studies in HCR is not high, and only 4 studies have been validated with independent cohorts. The value of radiomics studies needs to be increased, such that clinical application will be possible, and the field of radiomics still has much room for growth. To make prediction models more reliable and stable in forecasting the prognosis of NSCLC and advancing the individualized treatment of NSCLC patients, more clinicians must participate in their development and clinical testing.

Development and application of a comprehensive evaluation index system for groundwater quality evolution patterns

In recent years, driven by rapid socio-economic development and intensified human activities, the groundwater quality has exhibited a concerning trend of degradation. The challenge lies in integrating the impacts of both natural and anthropogenic factors to establish a scientific evaluation framework for the evolution of groundwater quality. This study adopts the model of driving forces – pressures – state – impacts – responses (DPSIR) proposed by the European Environment Agency, in conjunction with the Analytic Hierarchy Process (AHP) and Information Entropy Theory (IET), and the Water Quality Index (WQI) evaluation methods, to construct an evaluation index system for groundwater quality evolution that encompasses driving forces, state, and response systems. Initially, twelve indicators relevant to groundwater quality are quantified by screening across three systems, and a functional relationship between the categorization and scoring of each indicator is established. Subsequently, the weights for each system and indicator are obtained through the AHP, and the objective weights of the indicators are determined using the IET. The scores of each indicator are then comprehensively calculated. Finally, based on the defined types of groundwater quality evolution patterns, an integrated assessment of the evolution of groundwater quality over various time periods is conducted. Taking the Shijiazhuang region as a case study and analyzing the hydrochemical data of groundwater from 1985 to 2015, the results indicate a shift in the groundwater quality evolution pattern from one dominated by natural factors to one primarily influenced by human activities (The comprehensive score of the evaluation index system has increased from 1.84 to 3.25). Among these, the application of fertilizers emerges as the most important driving factors affecting groundwater quality. Particularly, nitrate and total hardness (TH) have emerged as the most salient indicators of quality degradation, with a significant escalation in their composite scores. At the outset, nitrate registered a score of 0.408, while TH scored 0.326; yet, these values have sharply ascended to 0.716 and 0.467, respectively, by the advanced stage. The study concludes with a discussion on the accuracy, strengths, limitations, and applicability of the evaluation index system. The establishment of this evaluation framework provides a scientific basis for the management and protection of groundwater resources and serves as a reference for identifying groundwater quality evolution patterns in other regions.

ANALYSIS OF AGRICULTURAL DEVELOPMENT IN GUANGXI UNDER THE BACKGROUND OF RURAL REVITALIZATION

Chinese economy is undergoing a transformation towards high-quality development. As the foundation of the national economy, agriculture must also adapt to the trend of social and economic development, transform its development mode, and transform towards high-quality agricultural development. The implementation of the rural revitalization strategy must be supported by high-quality agriculture; In the context of rural revitalization, we should focus on the new forms and new connotations of high-quality agricultural development. By establishing an agricultural development quality evaluation index system with 13 indicators from 5 dimensions, including economic benefits, comprehensive production capacity, social services, coordinated development, and sustainable development, we can use the comprehensive index evaluation system to analyze the current situation of agricultural development quality in Guangxi and identify the shortcomings and deficiencies that constrain high-quality agricultural development. The research results of comprehensive indicator evaluation and comparative analysis based on factor analysis method indicate that the overall level of agricultural development quality in Guangxi needs to be improved, with problems such as slow development and improvement speed, common shortcomings that restrict development, relative dispersion of agricultural leading enterprises, and insufficient utilization of agglomeration effect advantages. Based on this, it is proposed to promote the development of agriculture in Guangxi towards branding and intensive scale, cultivate diversified agricultural management entities, improve the agricultural production guarantee system, and accelerate the development of agriculture towards green direction.

The Current Status of the Application of Surgical Specialty Nursing Quality Evaluation Indicators

This paper reviews the current status of the application of surgical specialty nursing quality evaluation indicators both domestically and internationally. It covers the related concepts, theoretical foundations, construction methods, construction fields, application research, and existing shortcomings of these indicators. It suggests that scholars and researchers in the nursing field in China should base their work on mature theories, use scientific research methods, and construct a quality indicator system that aligns with China’s surgical development, is practical, unified, suitable for the local context, and beneficial for the development of the surgical nursing discipline.

Olfactory analysis of oolong tea sensory quality using composite nano-colorimetric sensor array

Chinese oolong tea is famous for its rich and diverse aromas, which is an important indicator for sensor quality evaluation. To accurately and rapidly evaluate sensory quality, a novel colorimetric sensor array (CSA) was developed to detect volatile organic compounds (VOCs) in oolong tea. We further explored the binding mechanism between colorimetric dyes that trigger changes in charge transfer and visible color changes. Based on this, we modified and optimized the CSA to improve the sensitivity by 17.1–234.9% and the stability by 8.7–33.3%. The study also assessed the effectiveness of this method by comparing two linear and two non-linear classification models, with the support vector machine (SVM) model achieving the highest accuracy, identifying different flavor intensity and grades with rates of 100% and 95.83%, respectively. These findings sufficiently demonstrated that the novel CSA, integrated with the SVM model, has promising potential for predicting the sensory quality of oolong tea.

Application of soil quality evaluation indices and multivariate statistics to assess soil health: a case study

Application of soil quality evaluation indices and multivariate statistics to assess soil health: a case study

Design and application of a quality evaluation index system for sustainable cultivation of big data professionals in universities based on Bloom cognitive domain classification method

This article aims to design and apply a quality evaluation index system based on Bloom's cognitive domain classification method to evaluate the training quality of big data professionals in universities. With the rapid development of big data technology, the importance of cultivating big data professionals in universities is becoming increasingly prominent. However, the existing training quality evaluation system often lacks systematicity and scientificity, making it difficult to fully reflect the comprehensive abilities of students. This article combines key knowledge of big data in the field of finance and economics with Bloom’s cognitive domain classification method to divide the cognitive process of talent cultivation into six levels: memory, understanding, application, analysis, evaluation, and creation. Through expert interviews and the Delphi method, specific evaluation indicators for each cognitive level were determined, and 18 indicators were extracted. Through data collection and empirical analysis, the performance of students at various cognitive levels was evaluated. The results showed that third year students performed the best on average at all cognitive levels, reflecting their rich learning experience and practical skills. The analysis also shows that there are significant differences in the performance of students at higher-order cognitive levels, with a coefficient of variation of 0.23 for the A1 indicator and 0.84 for the F3 indicator. This indicates that higher-order cognitive activities require higher demands from students, with significant individual differences. Through correlation analysis between various indicators, it was found that the proficiency level of tool usage in the project is strongly correlated with other indicators. Bloom’s cognitive domain classification system can reflect students’ comprehensive abilities and provide a scientific evaluation method for universities. Through continuous improvement and in-depth research, we hope to provide more scientific and comprehensive support for the cultivation of big data talents in universities.

Big data technology for teaching quality monitoring and improvement in higher education - joint K-means clustering algorithm and Apriori algorithm

With the development of big data technology, the field of monitoring and improving teaching quality in universities has ushered in new opportunities and challenges. Big data technology enables the capture and analysis of massive amounts of data generated during the teaching process, providing the possibility for a deeper understanding of teaching activities. However, how to extract useful information from these vast amounts of data and transform it into strategies for teaching improvement is a challenge. The research aims to propose a teaching quality monitoring and improvement method based on big data technology, which combines K-means clustering algorithm and association rule mining algorithm to improve the accuracy of teaching monitoring and the effectiveness of teaching improvement. In order to cope with these challenges, the study proposes a research method of big data technology based on joint K-mean clustering algorithm and association rule mining algorithm. The study first analyzes the teaching quality monitoring and evaluation indexes using the K-mean algorithm. Then the association rule mining algorithm is utilized to mine the data in the teaching quality monitoring indicators with association rules on the basis of the obtained cluster analysis. Finally, on the basis of association rule mining, the study constructs the assessment model of teaching quality monitoring indicators by utilizing the fused method. The outcomes revealed that the average of data analysis accuracy and the average of recall rate of the modeling method were 93.79 % and 91.95 %, respectively. Meanwhile, the evaluation time of the modeling method in the process of teaching quality monitoring data processing was 17.3 s, and the evaluation precision was 93.15 % respectively. Additionally, the process's overall confidence and enhancement are 95.01 % and 86.73 %, respectively, and the modeling method's performance is compared to other approaches. This shown that the approach may significantly boost the precision and effectiveness of monitoring the quality of instruction, as well as offer strong backing for the enhancement of instruction in higher education institutions.

Evaluation of Water Quality Indexes and Heavy Metal Pollution Indexes of Different Industrial Effluents and Karnaphuli River Water in Chattogram, Bangladesh

ABSTRACTThe study evaluated the water quality index (WQI) and heavy metal pollution index (HPI) of different industrial effluent and surface water from various sites of the Karnaphuli River, based on the weighted arithmetic index method. The samples were collected from 20 different industries, including textile dyeing, textile washing, oil processing, chemicals, and tannery, and 17 stations of the Karnaphuli River, from the Kalurghat Bridge to the estuary where the river meets with the Bay of Bengal. This study was carried out in the dry season of the 2021–2022 fiscal year. Six physicochemical parameters, namely pH, biochemical oxygen demand, chemical oxygen demand, total dissolved solids, electrical conductivity, and hardness, were taken for the determination of the WQI, and concentration of heavy metals (Pb, Fe, Cd, Cu, Cr, Mn, and Ni) were considered for the calculation of the HPI. Both indexes were calculated against the Bangladesh Standard (denoted by subscript a) and WHO standards (denoted by subscript b). The water quality indexes WQIa and WQIb for industrial effluent ranged from 11.66 to 176.77 and 44.45 to 9703.99, respectively. Samples namely TD‐5, TD‐7, TD‐8, TD‐9, TD‐10, OS‐1, and C‐1 showed excessively high WQI value. On the other hand, the HPIa and HPIb of industrial effluent varied from 43.6 to 167,785.3 and 0.34 to 18,412.3, respectively. Furthermore, oil processing and chemical effluents showed noticeably higher HPI values than other industrial samples. Both WQI and HPI values indicated a serious pollution load in most experimental samples. No river water sample showed a WQI value below 100, denoting it was unfit for consumption. The water quality indexes WQIa and WQIb for river water samples ranged from 1275.40 to 29,405.77 and 131.20 to 5860.99, respectively. According to the Bangladesh Standard, the river water samples were moderately to highly polluted by metal. The highest HPI value for the river water sample was 7965, denoting extreme metal pollution. The investigation revealed that most industrial effluents and Karnaphuli River water samples contained significant pollution load. Therefore, effective measures must be adopted immediately for future sustainability to cope with this contamination. The results emphasize the immediate requirement for strict regulations and advanced water treatment technologies to reduce contamination risks in the Karnaphuli River. Prompt action by authorities is essential to safeguard the river, prevent ongoing ecological damage, and address metal accumulation in fish affecting the food chain. Public awareness and legislative measures are vital for ecosystem preservation.

Research on emergency decision quality evaluation and optimization basing on public sentiment big data analysis

The increasingly frequent occurrence of emergencies has led to significant losses, both human and economic. It is of the utmost importance to scientifically and effectively formulate emergency decision to prevent and reduce these losses. In this study, an evaluation model is developed for evaluating emergency decision quality based on fuzzy comprehensive evaluation method. The proposed model takes into consideration of the impact of public sentiment appeals on emergency decision in emergencies with the purpose of improving emergency decision quality. The “3·21” major explosion accident in Xiangshui County, Jiangsu Province, China, in March of 2019, is selected as a typical case, and the public sentiment big data is obtained from Weibo. The Latent Dirichlet Allocation (LDA) model and the TF-IDF algorithm are utilized to develop an emergency decision quality evaluation index system, from which public sentiment appeals are extracted and quantified. Furthermore, this study explores the changes of public sentiment attitude towards emergency decision through static and dynamic public sentiment analysis. Based on this, the emergency decision quality for the case event is evaluated by the proposed model. The effectiveness of the proposed approach is demonstrated with in-depth discussion and validation through this real case. The results indicate that the emergency decision quality of the Chinese government in this case is fairly good, yet exhibit room for further improvement. Optimization advice for emergency decision in emergencies is proposed accordingly. Empirical evidence suggests that the model proposed in this study can provide theoretical and methodological support for emergency response and government decision-making in the era of big data.

Prediction of Chinese suitable habitats of Panax notoginseng under climate change based on MaxEnt and chemometric methods

Notoginseng saponin R1; ginsenosides Rg1, Re, Rb1, and Rd; the sum of the five saponins; and underground-part fresh weight (UPFW) of single plants were used as quality evaluation indices for Panax notoginseng (Burk.) F. H. Chen (P. notoginseng). Comprehensive evaluation of P. notoginseng samples from 30 production areas was performed using that MaxEnt model. Spatial pattern changes in suitable P. notoginseng habitats were predicted for current and future periods (2050s, 2070s, and 2090s) using SSP126 and SSP585 models. The results revealed that temperature, precipitation, and solar radiation were important environmental variables. Suitable habitats were located mainly in Yunnan, Guizhou, and Sichuan Provinces. The distribution core of P. notoginseng is predicted to shift southeast in the future. The saponin content decreased from the southeast to the northwest of Yunnan Province, which was contrary to the UPFW trend. This study provides the necessary information for the protection and sustainable utilization of P. notoginseng resources, and a theoretical reference for its application in the quality evaluation of Chinese medicinal products.

Metabolomics analysis of physicochemical properties associated with quality deterioration in insect-infested hawthorn berries

The sliced and dried hawthorn berries are easily infested by insects during storage. This study aimed to determine the effect of insect infestation on the quality of hawthorn berries and assess the change at metabolite level by analyzing physicochemical property and metabolomics profiling. A total of 184 shared differential metabolites were obtained, mainly including flavonoids, fatty acids, carboxylic acids and derivatives, and nitrogenous compounds. Through receiver operating characteristic curve assessment, 9 significant differential markers were screened out to distinguish insect infestation of hawthorn berries. Correlation analysis showed that the color, total organic acids, total phenolics, and total flavonoids were effective indicators for quality evaluation of insect infestation, and uric acid and hippuric acid can serve as biomarkers for the quality deterioration of hawthorn berries during storage. This study demonstrated that insect infestation could decrease the quality of hawthorn berries from macro and micro perspectives.